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Critical role of microRNAs after global myocardial ischemia and reperfusuion

Reporter: Aviva Lev-Ari, PhD, RN

Myocardial ischemia reperfusion injury

Sourced through Scoop.it from: ajbm.net

Critical role of microRNAs after global myocardial ischemia and reperfusuion

crossMark
American Journal of BioMedicine Volume 3, Issue 7, pages 451-467

Published: July 29, 2015


Renzo Grupper,  Ross Edward, Christian Walter, Eva Varnous

Abstract

Myocardial ischemia reperfusion injury is a major problem in heart transplantation and its mechanism is still not fully known. MicroRNAs (miRNAs) are small, single-stranded RNA molecules which play an important role in posttranscriptional regulation of gene expression by inhibiting translation of target mRNAs. The objective of this study is to investigate the mechanistic role of miRNAs on global myocardial ischemia-reperfusion injury in mice. The mice heart was mounted in a Langendorff-type isolated heart perfusion system. The coronary effluent volume was measured at the various time intervals for a total of 120 min. Coronary flow rate (CFR, in ml · min−1 · g−1) was defined as the total volume collected during the reperfusion interval divided by the time, normalized by the heart wet weight (g), which was measured at the beginning of the experiment. The volume fraction of interstitial space (VFITS) in myocardial tissue was determined from H-E-stained sections by using the equation VFITS = (100% × area of interstitial space)/total tissue area. The mitochondrial cross-sectional area was measured. The number of fragmented mitochondria, the number of mitochondria with amorphous matrix densities or granular densities, and the total number of mitochondria studied in each group were counted. Further, The average LV dP/dt during 0–30, 30–60, 60–90, and 90–120 min of reperfusion was calculated from data continuously recorded during the corresponding reperfusion period. In conclusion the present data show that the miRNAs have a great potential as biomarker, therapeutic target, and led to significant new insights into the pathophysiology of global myocardial I/R, graft rejection.

Keywords: Myocardial ischemia reperfusion injury; Heart transplantation; MicroRNAs; Coronary flow rate

SOURCE

https://ajbm.net/critical-role-of-micrornas-after-global-myocardial-ischemia-and-reperfusuion/

See on Scoop.itCardiovascular and vascular imaging

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Global RNA Based Therapeutics Market (Technology, Application, End Users and Geography) – Size, Share, Global Trends, Company Profiles, Demand, Insights, Analysis, Research, Report, Opportunities, Segmentation and Forecast, 2013 – 2020

Reporter: Aviva Lev-Ari, PhD, RN

 

 

SOURCE

http://www.alliedmarketresearch.com/RNA-based-therapeutics-market

CHAPTER 1 INTRODUCTION

1.1 Report Description
1.2 Reason for doing the study
1.3 Key Benefits
1.4 Key Market Segments
1.5 Key Audiences
1.6 Research Methodology

1.6.1 Secondary Research
1.6.2 Primary Research
1.6.3 Analyst tools and models

CHAPTER 2 EXECUTIVE SUMMARY

2.1 CXO perspective
2.2 Market beyond what to expect by 2025

2.2.1 Moderate growth scenario
2.2.2 Rapid growth scenario
2.2.3 Diminishing growth scenario

CHAPTER 3 MARKET OVERVIEW

3.1 Market Definition and Scope
3.2 Historical perspective and current market trends
3.3 SMaRT RNA Technology- A promising Tool To Target Disease Causing RNAs
3.4 Conventional Vs RNA-based therapeutics
3.5 Addressable Market Analysis

3.5.1 DNA based therapeutics
3.5.2 RNA based thearpeutics

3.6 Key Findings

3.6.1 Top Factors Impacting on RNA based therapeutics market
3.6.2 Top Investment Pockets of RNA based therapeutics market
3.6.3 Top winning strategies of RNA based therapeutics market

3.7 Government Regulations

3.7.1 USFDA Perspective
3.7.2 European Union Perspective

3.8 Porter five force model

3.8.1 Bargaining power of buyers (Low)
3.8.2 Bargaining power of suppliers(High)
3.8.3 Threat of new entrants (High)
3.8.4 Threat of substitute products (Moderate)
3.8.5 Intensity of competitive rivalry (Strong)

3.9 Value chain analysis

3.9.1 Primary activities
3.9.2 Support Activities

3.10 Pipeline Review
3.11 Patent Analysis

3.11.1 Patent analysis by geographies(2010-2014)

3.12 Clinical Trials For RNA Based Therapeutics
3.13 Market Dynamics

3.13.1 Drivers

3.13.1.1 Target specificty and selectivity of treatment
3.13.1.2 Strategic Alliances and Funding
3.13.1.3 More intense product focus against platform technology
3.13.1.4 Promising technologies driving the RNA based therapeutics
3.13.1.5 Adoption of virtual drug development models

3.13.2 Restraints

3.13.2.1 Hurdles in drug delivery
3.13.2.2 High cost of research and threat of failure

3.13.3 Opportunities

3.13.3.1 Early commercialization of pipeline therapeutics assists to gain competitive advantage
3.13.3.2 Multifactorial disease targeting

CHAPTER 4 GLOBAL RNA BASED THERAPEUTICS  MARKET BY TECHNOLOGIES, 2013-2020, $MILLION

4.1 Enabling technologies

4.1.1 Microarrays
4.1.2 Labelling
4.1.3 Purification
4.1.4 Linear amplification
4.1.5 qRT-PCR
4.1.6 Inhibition

4.2 Enabled technologies

4.2.1 RNA Interference (RNAi) technologies

4.2.1.1 Market Dynamics

4.2.1.1.1 DRIVERS
4.2.1.1.2 RESTRAINTS
4.2.1.1.3 OPPORTUNITIES

4.2.1.2 Competitive scenario
4.2.1.3 Small interfering RNA (siRNA)
4.2.1.4 MicroRNA (miRNA)
4.2.1.5 Market size & forecast

4.2.2 RNA antisense technologies

4.2.2.1 Market Dynamics

4.2.2.1.1 DRIVERS
4.2.2.1.2 RESTRAINTS
4.2.2.1.3 OPPORTUNITIES

4.2.2.2 Competitive scenario
4.2.2.3 Market size & forecast

CHAPTER 5 GLOBAL RNA BASED THERAPEUTICS MARKET BY APPLICATIONS, 2013-2020, $MILLION

5.1 Cardiovascular

5.1.1 Global Cardiac Disease Incidences

5.1.1.1 Facts for cardiac diseases

5.1.2 Adoption Drivers
5.1.3 Market Size and Forecast

5.2 Kidney Diseases

5.2.1 Global Renal Disease Incidences
5.2.2 Adoption Drivers
5.2.3 Market Size and Forecast

5.3 Oncology

5.3.1 Global Cancer Incidences
5.3.2 Adoption Drivers
5.3.3 Market Size and Forecast

5.4 Infectious diseases

5.4.1 Global Infectious Disease Incidences

5.4.1.1 Tuberculosis
5.4.1.2 HIV and AIDS

5.4.2 Adoption Drivers
5.4.3 Market Size and Forecast

5.5 Metabolic disorders

5.5.1 Global Metabolic Disorders by Type

5.5.1.1 Obesity
5.5.1.2 Diabetes

5.5.2 Adoption Drivers
5.5.3 Market Size and Forecast

5.6 Others
CHAPTER 6 GLOBAL RNA BASED THERAPEUTICS MARKET BY END USERS, 2013-2020, $MILLION

6.1 Research
6.2 Therapeutics
6.3 Diagnosis

CHAPTER 7 GLOBAL RNA BASED THERAPEUTICS MARKET BY GEOGRAPHY, 2013-2020, $MILLION

7.1 North America

7.1.1 United States

7.1.1.1 Pipeline Review
7.1.1.2 Market Trends
7.1.1.3 Competitive scenario
7.1.1.4 Key growth factors and opportunities

7.1.2 Canada

7.1.2.1 Pipeline Review
7.1.2.2 Key market trends
7.1.2.3 Competitive scenario
7.1.2.4 Key growth factors and opportunities

7.2 Europe

7.2.1 Germany

7.2.1.1 Pipeline Review
7.2.1.2 Key market trends
7.2.1.3 Competitive scenario
7.2.1.4 Key growth factors and opportunities

7.2.2 United Kingdom

7.2.2.1 Pipeline Review
7.2.2.2 Key market trends
7.2.2.3 Competitive scenario
7.2.2.4 Key growth factors and opportunities

7.2.3 France
7.2.4 Others

7.3 Asia-Pacific

7.3.1 Pipeline Review
7.3.2 Key market trends
7.3.3 Competitive scenario
7.3.4 Key growth factors and opportunities
7.3.5 Market size and forecast

7.4 LAMEA

7.4.1 Pipeline Review
7.4.2 Key market trends
7.4.3 Competitive scenario
7.4.4 Key growth factors and opportunities
7.4.5 Market size and forecast

CHAPTER 8 COMPANY PROFILES

8.1 Quark Pharmaceuticals, Inc. (USA)

8.1.1 Company overview
8.1.2 Company snapshots
8.1.3 Strategic moves and developments

8.1.3.1 Principal Strategy
8.1.3.2 Secondary Strategy

8.1.4 SWOT Analysis

8.2 Alnylam Pharmaceuticals, Inc. (USA)

8.2.1 Company overview
8.2.2 Company snapshots
8.2.3 Business Performance
8.2.4 Strategic moves and developments

8.2.4.1 Principal Strategy
8.2.4.2 Secondary Strategy

8.2.5 SWOT Analysis

8.3 Dicerna Pharmaceuticals, Inc. (USA)

8.3.1 Company overview
8.3.2 Company snapshots
8.3.3 Business Performance
8.3.4 Strategic moves and developments
8.3.5 SWOT Analysis

8.4 Tekmira Pharmaceuticals Corp. (Canada)

8.4.1 Company overview
8.4.2 Company snapshots
8.4.3 Business Performance
8.4.4 Strategic moves and developments

8.4.4.1 Principal Strategy
8.4.4.2 Secondary Strategy

8.4.5 SWOT Analysis

8.5 Benitec Biopharma Limited (Australia)

8.5.1 Company overview
8.5.2 Company snapshots
8.5.3 Strategic moves and developments

8.5.3.1 Principal Strategy
8.5.3.2 Secondary Strategy

8.5.4 SWOT Analysis

8.6 Cenix BioScience GmbH (Germany)

8.6.1 Company overview
8.6.2 Company snapshots
8.6.3 Strategic moves and developments

8.6.3.1 Principal Strategy
8.6.3.2 Secondary Strategy

8.6.4 SWOT Analysis

8.7 Genzyme Corporation (USA) (A Sanofi Company)

8.7.1 Company overview
8.7.2 Company snapshots
8.7.3 Business performance
8.7.4 SWOT Analysis

8.8 Silence Therapeutics PLC (UK)

8.8.1 Company overview
8.8.2 Company snapshots
8.8.3 Business performance
8.8.4 Strategic moves and developments

8.8.4.1 Principal Strategy
8.8.4.2 Secondary Strategy

8.8.5 SWOT Analysis

8.9 Sirnaomics, Inc. (USA)

8.9.1 Company overview
8.9.2 Company snapshots
8.9.3 Strategic moves and developments

8.9.3.1 Principal Strategy
8.9.3.2 Secondary Strategy

8.9.4 SWOT Analysis

List of Figures

FIG. 1  TOP FACTORS IMPACTING RNA BASED THERAPEUTICS MARKET (2014-2020)
FIG. 2  TOP WINING STRATEGIES FOR RNA BASED THERAPEUTICS MARKET (2011-2013)
FIG. 3  TOP WINING STRATEGIES BY SUBTYPE OF DEVELOPMENT
FIG. 4  PORTER’S FIVE FORCE ANALYSIS OF RNA BASED THERAPEUTICS MARKET
FIG. 5  VALUE CHAIN ANALYSIS OF RNA BASED THERAPEUTICS MARKET
FIG. 6  PIPELINE REVIEW OF RNA BASED THERAPEUTICS
FIG. 7  PATENT ANALYSIS BY GEOGRAPHIES (2011-2014)
FIG. 8  PATENT ANALYSIS BY GEOGRAPHIES BASED ON TECHNOLOGY(2011-2014)
FIG. 9  CLINICAL PHASE STATUS: RNAI THERAPIES
FIG. 10  CLINICAL PHASE STATUS: ANTISENSE THERAPIES
FIG. 11  GLOBAL RENAL DISEASES INCIDENCES: GLOBAL VIEW OF DIALYSIS PATIENTS (2012)
FIG. 12  GLOBAL CANCER INCIDENCES BASED ON COMMON CANCER TYPES (2012)
FIG. 13  NUMBER OF NEW INFECTIOUS DISEASE CASES IN UNITED STATES (2011)
FIG. 14  WHO ESTIMATES OF TUBERCULOSIS INCIDENCE BY WHO REGION (THOUSANDS) (2012)
FIG. 15  PREVALENCE OF OBESITY (2013); AGE 20 YEARS AND OLDER (%)
FIG. 16  TOP TEN COUNTRIES FOR NUMBER OF PEOPLE WITH DIABETES AGED BETWEEN 20-79 YEARS (2013, MILLION)
FIG. 17  SWOT ANALYSIS OF QUARK  PHARMACEUTICALS, INC.
FIG. 18  ALNYLAM’S NET REVENUE FROM RESEARCH COLLABORATORS (2012)
FIG. 19  SWOT ANALYSIS OF ALNYLAM PHARMACEUTICALS, INC.
FIG. 20  OPERATING EXPENSES OF DICERNA PHARMACEUTICALS INC. (2013)
FIG. 21  SWOT ANALYSIS OF DICERNA PHARMACEUTICALS, INC.
FIG. 22  TEKMIRA’S NET REVENUE FROM COLLABORATIONS AND CONTRACTS (2013)
FIG. 23  SWOT ANALYSIS OF TEKMIRA PHARMACEUTICALS CORPORATION
FIG. 24  SWOT ANALYSIS OF BENITEC BIOPHARMA LIMITED
FIG. 25  SWOT ANALYSIS OF CENIX BIOSCIENCE GMBH
FIG. 26  BUSINESS PERFORMANCE SANOFI S.A.  BY GEOGRAPHIES (2013)
FIG. 27  BUSINESS PERFORMANCE OF SANOFI S.A. BY BUSINESS SEGMENTS (2013)
FIG. 28  SWOT ANALYSIS OF SANOFI S.A.
FIG. 29  BUSINESS PERFORMANCE SANOFI S.A.  BY GEOGRAPHIES (2013)
FIG. 30  SWOT ANALYSIS OF SILENCE THERAPEUTICS PLC
FIG. 31  SWOT ANALYSIS OF SIRNAOMICS, INC.

List of Tables

TABLE 1  CONVENTIONAL VS RNA-BASED THERAPEUTICS
TABLE 2  TOP INVESTMENT POCKETS OF RNA BASED THERAPEUTICS MARKET (2013)
TABLE 3  LIST OF EMA DOCUMENTS AND GUIDELINES
TABLE 4  PATENT ANALYSIS BY GEOGRAPHIES(2010-2014)
TABLE 5  CLINICAL TRIALS FOR RNA BASED THERAPEUTICS
TABLE 6  PIPELINE RNA THERAPEUTICS AND THEIR SPECIFIC TARGET
TABLE 7  LIST OF STRATEGIC ALLIANCES FOR THE DEVELOPMENT OF RNA-BASED THERAPEUTICS
TABLE 8  LIST OF COMPANIES USING DIFFERENT RNA TECHNOLOGIES
TABLE 9  GLOBAL RNA BASED THERAPEUTICS MARKET BY TECHNOLOGIES, 2013 – 2020 ($MILLION)
TABLE 10  PRICES OF RNA PURIFICATION KITS
TABLE 11  PRICES OF LINEAR AMPLIFICATION KIT
TABLE 12  PIPELINE RESEARCH STATUS: RNAI THERAPIES
TABLE 13  PRICE CHART FOR SIRNA
TABLE 14  COMPANIES AND THEIR MIRNA TECHNOLOGIES
TABLE 15  PRICE CHART FOR MIRNA
TABLE 16  GLOBAL RNA INTERFERENCE (RNAI) TECHNOLOGY MARKET BY GEOGRAPHY, 2013 – 2020 ($MILLION)
TABLE 17  GLOBAL RNA ANTISENSE TECHNOLOGY MARKET BY GEOGRAPHY, 2013 – 2020 ($MILLION)
TABLE 18  GLOBAL RNA BASED THERAPEUTICS MARKET BY APPLICATION, 2013 – 2020 ($MILLION)
TABLE 19  GLOBAL RNA BASED THERAPEUTICS CARDIOVASCULAR APPLICATIONS MARKET, BY GEOGRA PHY, 2013 – 2020 ($MILLION)
TABLE 20  GLOBAL RNA BASED THERAPEUTICS KIDNEY DISEASES APPLICATIONS MARKET, BY GEOGRAPHY, 2013 – 2020 ($MILLION)
TABLE 21  GLOBAL CANCER INCIDENCE, MORTALITY AND PREVALENCE (2012)
TABLE 22  GLOBAL RNA BASED THERAPEUTICS ONCOLOGY APPLICATIONS MARKET, BY GEOGRAPHY, 2013 – 2020 ($MILLION)
TABLE 23  REGIONAL STATISTICS FOR HIV AND AIDS (2011)
TABLE 24  GLOBAL RNA BASED THERAPEUTICS INFECTIOUS DISEASES APPLICATIONS MARKET, BY GEOGRAPHY, 2013 – 2020 ($MILLION)
TABLE 25  GLOBAL RNA BASED THERAPEUTICS METABOLIC DISORDERS APPLICATIONS MARKET, BY GEOGRAPHY, 2013 – 2020 ($MILLION)
TABLE 26  GLOBAL RNA BASED THERAPEUTICS OTHER  APPLICATIONS MARKET, BY GEOGRAPHY, 2013 – 2020 ($MILLION)
TABLE 27  GLOBAL RNA BASED THERAPEUTICS MARKET REVENUE, BY END USERS, 2013 – 2020 ($MILLION)
TABLE 28  RESEARCH COLLABORATIONS FOR RNA-BASED THERAPEUTICS
TABLE 29  REGION/COUNTRIES AND THEIR RESPECTIVE FUNDING AGENCIES
TABLE 30  GLOBAL RESEARCH MARKET REVENUE, BY GEOGRAPHY, 2013 – 2020 ($MILLION)
TABLE 31  LIST OF RNA BASED THERAPEUTICS : APPROVED AND PHASE IIB & PHASE III CLINICAL TRIALS
TABLE 32  GLOBAL THERAPEUTICS MARKET REVENUE, BY GEOGRAPHY, 2013 – 2020 ($MILLION)
TABLE 33  RNA DIAGNOSTIC COMPANIES AND THEIR CORE PRODUCTS
TABLE 34  GLOBAL DIAGNOSIS MARKET REVENUE, BY GEOGRAPHY, 2013 – 2020 ($MILLION)
TABLE 35  GLOBAL RNA BASED THERAPEUTICS MARKET BY GEOGRAPHY, 2013 – 2020 ($MILLION)
TABLE 36  NORTH AMERICA RNA BASED THERAPEUTICS MARKET BY APPLICATION, 2013 – 2020 ($MILLION)
TABLE 37  UNITED STATES: PIPELINE REVIEW
TABLE 38  EUROPE RNA BASED THERAPEUTICS MARKET BY APPLICATION, 2013 – 2020 ($MILLION)
TABLE 39  ASIA- PACIFIC RNA BASED THERAPEUTICS MARKET BY APPLICATION, 2013 – 2020 ($MILLION)
TABLE 40  LAMEA  RNA BASED THERAPEUTICS MARKET BY APPLICATION, 2013 – 2020 ($MILLION)
TABLE 41  QUARK PHARMACEUTICALS, INC.
TABLE 42  ALNYLAM PHARMACEUTICALS SNAPSHOT
TABLE 43  DICERNA PHARMACEUTICALS, INC.SNAP SHOT
TABLE 44  TEKMIRA PHARMACEUTICALS CORPORATION SNAP SHOT
TABLE 45  BENITEC BIOPHARMA SNAPSHOT
TABLE 46  CENIX BIOSCIENCE SNAPSHOT
TABLE 47  GENZYME CORPORATION SNAPSHOT
TABLE 48  SILENCE THERAPEUTICS PLC SNAPSHOT
TABLE 49  SIRNAOMICS, INC SNAPSHOT

 

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Sirtuins

Writer and Curator: Larry H. Bernstein, MD, FCAP 

7.8  Sirtuins

7.8.1 Function and regulation of the mitochondrial Sirtuin isoform Sirt5 in Mammalia

7.8.2 Substrates and Regulation Mechanisms for the Human Mitochondrial Sirtuins- Sirt3 and Sirt5

7.8.3 The mTORC1 Pathway Stimulates Glutamine Metabolism and Cell Proliferation by Repressing SIRT4

7.8.4  Rab1A and small GTPases Activate mTORC1

7.8.5 PI3K.Akt signaling in osteosarcoma

7.8.6 The mTORC1-S6K1 Pathway Regulates Glutamine Metabolism through the eIF4B-Dependent Control of c-Myc Translation

7.8.7 Localization of mouse mitochondrial SIRT proteins

7.8.8 SIRT4 Has Tumor-Suppressive Activity and Regulates the Cellular Metabolic Response to DNA Damage by Inhibiting Mitochondrial Glutamine Metabolism

7.8.9 Mitochondrial sirtuins and metabolic homeostasis

7.8.10 Mitochondrial sirtuins

7.8.11 Sirtuin regulation of mitochondria: energy production, apoptosis, and signaling

 

7.8.1 Function and regulation of the mitochondrial Sirtuin isoform Sirt5 in Mammalia

Gertz M1Steegborn C.
Biochim Biophys Acta. 2010 Aug; 1804(8):1658-65
http://dx.doi.org:/10.1016/j.bbapap.2009.09.011

Sirtuins are a family of protein deacetylases that catalyze the nicotinamide adenine dinucleotide (NAD(+))-dependent removal of acetyl groups from modified lysine side chains in various proteins. Sirtuins act as metabolic sensors and influence metabolic adaptation but also many other processes such as stress response mechanisms, gene expression, and organismal aging. Mammals have seven Sirtuin isoforms, three of them – Sirt3, Sirt4, and Sirt5 – located to mitochondria, our centers of energy metabolism and apoptosis initiation. In this review, we shortly introduce the mammalian Sirtuin family, with a focus on the mitochondrial isoforms. We then discuss in detail the current knowledge on the mitochondrial isoform Sirt5. Its physiological role in metabolic regulation has recently been confirmed, whereas an additional function in apoptosis regulation remains speculative. We will discuss the biochemical properties of Sirt5 and how they might contribute to its physiological function. Furthermore, we discuss the potential use of Sirt5 as a drug target, structural features of Sirt5 and of an Sirt5/inhibitor complex as well as their differences to other Sirtuins and the current status of modulating Sirt5 activity with pharmacological compounds.

removal of acetyl groups from modified lysine side chain

removal of acetyl groups from modified lysine side chain

http://ars.els-cdn.com/content/image/1-s2.0-S1570963909002593-gr1.sml
removal of acetyl groups from modified lysine side chain

sirtuin structure

sirtuin structure

http://ars.els-cdn.com/content/image/1-s2.0-S1570963909002593-gr2.sml
sirtuin structure

7.8.2 Substrates and Regulation Mechanisms for the Human Mitochondrial Sirtuins- Sirt3 and Sirt5

Schlicker C1Gertz MPapatheodorou PKachholz BBecker CFSteegborn C
J Mol Biol. 2008 Oct 10; 382(3):790-801
http://dx.doi.org/10.1016/j.jmb.2008.07.048

The enzymes of the Sirtuin family of nicotinamide-adenine-dinucleotide-dependent protein deacetylases are emerging key players in nuclear and cytosolic signaling, but also in mitochondrial regulation and aging. Mammalian mitochondria contain three Sirtuins, Sirt3, Sirt4, and Sirt5. Only one substrate is known for Sirt3 as well as for Sirt4, and up to now, no target for Sirt5 has been reported. Here, we describe the identification of novel substrates for the human mitochondrial Sirtuin isoforms Sirt3 and Sirt5. We show that Sirt3 can deacetylate and thereby activate a central metabolic regulator in the mitochondrial matrix, glutamate dehydrogenase. Furthermore, Sirt3 deacetylates and activates isocitrate dehydrogenase 2, an enzyme that promotes regeneration of antioxidants and catalyzes a key regulation point of the citric acid cycle. Sirt3 thus can regulate flux and anapleurosis of this central metabolic cycle. We further find that the N- and C-terminal regions of Sirt3 regulate its activity against glutamate dehydrogenase and a peptide substrate, indicating roles for these regions in substrate recognition and Sirtuin regulation. Sirt5, in contrast to Sirt3, deacetylates none of the mitochondrial matrix proteins tested. Instead, it can deacetylate cytochrome c, a protein of the mitochondrial intermembrane space with a central function in oxidative metabolism, as well as apoptosis initiation. Using a mitochondrial import assay, we find that Sirt5 can indeed be translocated into the mitochondrial intermembrane space, but also into the matrix, indicating that localization might contribute to Sirt5 regulation and substrate selection.

Mitochondria are central organelles in cellular energy metabolism, but also in processes such as apoptosis, cellular senescence, and lifespan regulation.1 and 2 Failures in mitochondrial function and regulation contribute to aging-related diseases, such as atherosclerosis3 and Parkinson’s disease,4 likely by increasing cellular levels of reactive oxygen species and the damage they cause.1 Emerging players in metabolic regulation and cellular signaling are members of the Sirtuin family of homologs of “silent information regulator 2” (Sir2), a yeast protein deacetylase.5 and 6 Sir2 was found to be involved in aging processes and lifespan determination in yeast,7 and 8 and its homologs were subsequently identified as lifespan regulators in various higher organisms.89 and 10 Sirtuins form class III of the protein deacetylase superfamily and hydrolyze one nicotinamide adenine dinucleotide (NAD +) as cosubstrate for each lysine residue they deacetylate.11 and 12 The coupling of deacetylation to NAD + was proposed to link changes in cellular energy levels to deacetylation activity,13 and 14 which would indicate Sirtuins as metabolic sensors. Other known regulation mechanisms for Sirtuin activity are the modulation of the expression levels of their genes6 and the autoinhibitory effect of an N-terminal region on the yeast Sirtuin “homologous to SIR2 protein 2” (Hst2).15

The seven mammalian Sirtuin proteins (Sirt1–Sirt7) have various substrate proteins that mediate functions in genetic, cellular, and mitochondrial regulation.5 and 6 The best-studied mammalian Sir2 homolog, Sirt1, was shown to regulate, among others, transcription factor p53, nuclear factor-kappa B, and peroxisome proliferator-activated receptor gamma coactivator-1-alpha.6 Three human Sirtuin proteins are known to be located in the mitochondria, Sirt3, Sirt4, and Sirt5,161718 and 19 although Sirt3 was reported to change its localization to nuclear when coexpressed with Sirt5.20 The recent identification of the first substrates for mitochondrial Sirtuins—acetyl coenzyme A synthetase 221 and 22 and glutamate dehydrogenase (GDH)16—as targets of Sirtuins 3 and 4, respectively, revealed that these Sirtuins control a regulatory network that has implications for energy metabolism and the mechanisms of caloric restriction (CR) and lifespan determination.23 Sirt3 regulates adaptive thermogenesis and decreases mitochondrial membrane potential and reactive oxygen species production, while increasing cellular respiration.24 Furthermore, Sirt3 is down-regulated in several genetically obese mice,24 and variability in the human SIRT3 gene has been linked to survivorship in the elderly. 25 In contrast to the deacetylases Sirt3 and Sirt5, Sirt4 appears to be an ADP ribosyltransferase. 16 Through this activity, Sirt4 inhibits GDH and thereby down-regulates insulin secretion in response to amino acids. 16 For Sirt5, however, there is no report yet on its physiological function or any physiological substrate. It is dominantly expressed in lymphoblasts and heart muscle cells,17 and 26 and its gene contains multiple repetitive elements that might make it a hotspot for chromosomal breaks. 26 Interestingly, the Sirt5 gene has been located to a chromosomal region known for abnormalities associated with malignant diseases. 26

A proteomics study found 277 acetylation sites in 133 mitochondrial proteins;27 many of them should be substrates for the mitochondrial Sirtuins mediating their various functions, but up to now, only one physiological substrate could be identified for Sirt3,21 and 22 and none could be identified for Sirt5. Our understanding of substrate selection by Sirtuins is incomplete, and knowledge of specific Sirtuin targets would be essential for a better understanding of Sirtuin-mediated processes and Sirtuin-targeted therapy. A first study on several Sirtuins showed varying preferences among acetylated peptides.28 Structural and thermodynamic analysis of peptides bound to the Sirtuin Sir2Tm from Thermatoga maritima indicated that positions − 1 and + 2 relative to the acetylation site play a significant role in substrate binding. 29 However, these studies were conducted with nonphysiological Sirtuin/substrate pairs, and other studies indicated little sequence specificity; instead, the yeast Sirtuin Hst2 was described to display contextual and conformational specificity: Hst2 deacetylated acetyl lysine only in the context of a protein, and it preferentially deacetylated within flexible protein regions. 30 Finally, statistical analysis of a proteomics study on acetylated proteins identified preferences at various positions such as + 1, − 2, and − 3, and deacetylation sites appeared to occur preferentially in helical regions. 27 Thus, our present knowledge of Sirtuin substrates and of factors determining Sirtuin specificity is incomplete and insufficient for sequence-based identification of physiological substrates.

Here, we describe the identification of novel targets for the mitochondrial deacetylases Sirt3 and Sirt5. We show that Sirt3 can deacetylate and thereby activate the enzymes GDH and isocitrate dehydrogenase (ICDH) 2—two key metabolic regulators in the mitochondrial matrix. We find that the N- and C-terminal regions of Sirt3 influence its activity against GDH and a peptide substrate, indicating roles in regulation and substrate recognition for these regions. Furthermore, we find that Sirt5 can deacetylate cytochrome c, a protein of the mitochondrial intermembrane space (IMS) with a central function in oxidative metabolism and apoptosis.

The upstream sequence contributes to the target specificity of Sirt3 and Sirt5

Sirtuins have been reported to have little sequence specificity,30 but other studies indicated a sequence preference dominated by positions − 1 and + 2.29 We tested the importance of the amino acid pattern preceding the acetylation site for recognition by the mitochondrial Sirtuins Sirt3 and Sirt5 through a fluorescence assay. First, the fluorogenic and commercially available modified p53-derived tetrapeptide QPK-acetylK, originally developed for Sirt2 assays but also efficiently used by Sirt3, was tested. Even 60 μg of Sirt5 did not lead to any deacetylation signal, whereas 0.35 μg of Sirt3 efficiently deacetylated the peptide (Fig. 1a). We then tested Sirt3 and Sirt5 on a second modified p53-derived tetrapeptide, RHK-acetylK. Sirt3 (0.5 μg) showed a slightly increased activity against this substrate as compared to QPK-acetylK (Fig. 1b); more importantly, 0.5 μg of Sirt5 showed significant activity against this peptide. These results show that the mitochondrial Sirtuins Sirt3 and, especially, Sirt5 indeed recognize the local target sequence, and target positions further upstream of − 1 seem to be involved in substrate recognition. For identification of novel substrates for the mitochondrial Sirtuins and further characterization of their target recognition mechanisms, we then turned to testing full-length proteins, as the downstream sequence and the larger protein context of the deacetylation site might also contribute to substrate selection.

Sirtuin substrate specificity

Sirtuin substrate specificity

Fig. 1. Testing the substrate specificity of Sirt3 and Sirt5 with peptides. (a) Sirt3, but not Sirt5, deacetylates the fluorogenic peptide QPK-acetylK. (b) Sirt3 efficiently deacetylates the fluorogenic peptide RHK-acetylK, and Sirt5 also significantly deacetylates this substrate.
http://ars.els-cdn.com/content/image/1-s2.0-S0022283608009029-gr1.jpg

Sirt3 deacetylates and activates GDH

In order to identify novel physiological substrates of the mitochondrial Sirtuins, we used proteins isolated in their partly acetylated form from natural sources (i.e., from mammalian mitochondria). These proteins, carrying physiological acetylations, were tested as Sirt3 and Sirt5 substrates in vitro in an ELISA system using an antibody specific for acetylated lysine. In a recent proteomics study, 27 GDH, a central regulator of mitochondrial metabolism, was identified to be acetylated in a feeding-dependent manner. With our ELISA, we found that Sirt3 and Sirt5 can both deacetylate pure GDH isolated from mitochondria, but with very different efficiencies ( Fig. 2a). Sirt3 significantly deacetylated GDH, but even large amounts of Sirt5 decreased the acetylation level of this substrate only slightly. We next tested the effect of GDH deacetylation on its activity. Deacetylation of GDH through incubation with Sirt3 and NAD + before its examination in a GDH activity assay increased its activity by 10%, and a stronger stimulation of GDH activity was seen when larger amounts of Sirt3 were used for deacetylation ( Fig. 2b). GDH is colocalized with Sirt3 in the mitochondrial matrix 1618 and 19 and, thus, likely could be a physiological substrate of this Sirtuin. Indeed, GDH from a Sirt3 knockout mouse was recently shown to be hyperacetylated compared to protein from wild-type mice. 31 Thus, Sirt3 deacetylates GDH in vivo, and our results show that this direct deacetylation of GDH by Sirt3 leads to GDH activation.

sirtuin structure

sirtuin structure

Fig. 2. Sirt3 can deacetylate and thereby activate GDH. (a) Deacetylation of GDH tested in ELISA. Sirt3 efficiently deacetylates GDH, whereas Sirt5 has only a small effect on the acetylation state. (b) GDH activity is increased after deacetylation of the enzyme by Sirt3. The increase in GDH activity depends on the amount of Sirt3 activity used for deacetylation.
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Sirt3 can deacetylate and thereby activate ICDH2

In the proteomics study by Kim et al., the mitochondrial citric acid cycle enzymes fumarase and ICDH2 (a key regulator of this metabolic cycle) were found to be acetylated in a feeding-dependent manner. 27 In our ELISA system, we found that Sirt3 efficiently deacetylated the ICDH2 substrate isolated from mitochondria ( Fig. 3a). Western blot analysis (data not shown) and mass spectrometry confirmed that, indeed, the ICDH2 fraction of the partially purified protein was deacetylated by Sirt3. In contrast, even large amounts of Sirt5 did not significantly decrease the acetylation level of this substrate ( Fig. 3a). As expected, deacetylation of ICDH2 by Sirt3 was dependent on NAD +. Fumarase, in contrast, could not be deacetylated as efficiently as ICDH2 through treatment with either Sirt3 or Sirt5 ( Fig. 3b). The low absolute values over background for the ELISA with fumarase, however, might indicate low acetylation levels of the natively purified protein, and a stronger effect might be attainable when testing fumarase with a higher acetylation level.

Fig. 3. Sirt3 deacetylates ICDH2, but not fumarase. (a) Deacetylation of ICDH2 by Sirt3 and Sirt5 tested in ELISA. Sirt3, but not Sirt5, deacetylates ICDH2 in a NAD +-dependent manner. (b) Fumarase acetylation determined through ELISA cannot be significantly decreased by incubation with recombinant Sirt3 or Sirt5. (c) ICDH2 activity measured in a spectrophotometric assay based on the formation of NADPH. ICDH2 activity (continuous line) is increased after deacetylation of the enzyme by Sirt3 (dashed line). (d) The stimulatory effect of deacetylation on ICDH2 activity depends on the amount of deacetylase activity added during pretreatment. (e) ICDH2 with and without Sirt3 treatment analyzed by mass spectrometry after proteolytic digest. The decrease in the signal at 962.3 Da and the increase in signal at 903.5 Da indicate deacetylation at either K211 or K212.

In order to analyze the potential physiological function of ICDH2 deacetylation, we tested the effect of Sirt3-mediated ICDH2 deacetylation on its activity. Incubation of ICDH2 with Sirt3 and NAD + prior to its analysis in an ICDH activity assay increased its activity (Fig. 3c). The stimulation of ICDH2 activity was further increased when larger amounts of Sirt3 were used for deacetylation (Fig. 3d), and no significant increase in ICDH2 activity was observed when the Sirtuin inhibitor dihydrocoumarin was present during incubation with Sirt3 (data not shown). Sirt3 and ICDH2 are colocalized in the mitochondrial matrix,1619 and 32 and we therefore assume that ICDH2 is likely a physiological substrate for Sirt3, which activates ICDH2 by deacetylation.
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Sirt3 can deacetylate KK motifs in substrate proteins

In order to identify the site of ICDH2 deacetylation upon treatment with Sirt3, we analyzed ICDH2 by mass spectrometry. For analyzing pure ICDH2, we excised its band from an SDS gel before mass spectrometry analysis. In the proteomics study by Kim et al., two acetylation sites were reported for ICDH2: K75 and K241 (numbering of the partial sequence of the unprocessed precursor; SwissProt entry P33198). 27 After digest of ICDH2, we could not detect peptides comprising K75 and, therefore, could not determine its acetylation status, and we only observed the deacetylated form of K241. We identified an additional acetylation site, however, by detecting signals at m/z = 903.5 and m/z = 962.3 for the peptide QYAIQKK (residues 206–212) carrying one and two acetyl groups, respectively ( Fig. 3e; calculated m/z = 903.5 and 962.5). Sirt3 treatment decreased the signal for the double-acetylated form and increased the signal for the single-acetylated form as compared to internal peptides [e.g., m/z = 890.5 (calculated m/z = 890.5) andm/z = 1041.4 (calculated m/z = 1041.5)]. These data indicate that Sirt3 deacetylates either position K211 or K212 of this KK motif located at a surface-exposed end of a helix that flanks the active site of ICDH2. 33Deacetylation of a KK motif by Sirt3 is consistent with the efficient use of the tested peptide substrates (see above) that both carry KK motifs.

Fig. 4. Increased activity of N- and C-terminally truncated Sirt3. (a) Specific activity against a peptide substrate of the longest Sirt3 form after proteolytic processing that covers residues 102–399. N-terminal truncation increases the specific activity dramatically, and an additional C-terminal truncation activates the catalytic core further. (b) Homology model of Sirt3 based on the crystal structure of Sirt2. The part comprising the catalytic core is shown in red. The NAD + and peptide ligands were manually placed into their binding sides based on the crystal structure of their complex with a bacterial Sir2 homolog from T. maritima. Parts removed in N- and C-terminal truncation constructs are shown in cyan and blue, respectively. (c) Level of acetylation of GDH tested in ELISA. The shortest Sirt3 form Sirt3(114–380) deacetylates more efficiently than Sirt3(114–399) and Sirt3(102–399), which show activities comparable to each other.

Sirt5 can deacetylate cytochrome c

Sirt5 can deacetylate cytochrome c

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Sirt5 can deacetylate cytochrome c

The Sirt5 protein that we used for our study comprises residues 34–302, corresponding to the fully active catalytic core determined for Sirt3 (see above). This protein is indeed active against a peptide substrate, but it showed no significant activity against the acetylated mitochondrial matrix proteins tested so far: GDH, ICDH2, and fumarase. We thus picked cytochrome c, a central protein in energy metabolism and apoptosis localized in the mitochondrial IMS, from the list of acetylated mitochondrial proteins 27 for testing as deacetylation substrate. Sirt5 showed deacetylation activity against pure cytochrome c in our ELISA system, whereas Sirt3 had almost no activity against this substrate ( Fig. 5a). Even the more active shortened form of Sirt3(114–380) showed no considerable activity against this substrate.

Fig. 5.  Sirt5 can deacetylate cytochrome c. (a) Deacetylation of cytochrome c tested in ELISA. Sirt5 uses cytochrome c as substrate for deacetylation, whereas Sirt3 treatment leaves the acetylation level of cytochrome c unchanged. (b) Model of the action of the mammalian Sirtuins Sirt3, Sirt4, and Sirt5 in mitochondria. CAC: citric acid cycle. (c) Digest of Sirt5 synthesized in vitro with PK. The protein is fully degraded at proteinase concentrations of 25 μg/ml and above. (d) Import of Sirt5 into isolated yeast mitochondria. Sirt5 reaches an inner mitochondrial compartment in the presence and in the absence of the mitochondrial membrane potential (ΔΨ), whereas Sirt3, as a control for a matrix-targeted protein, is not imported into uncoupled mitochondria. (e) Intramitochondrial localization of Sirt5. Part of the imported Sirt5 is sensitive to PK after swelling (SW) and thus localized in the IMS, but another part of the protein remains protease-resistant and therefore appears to be localized to the matrix. Atp3, a protein localized at the matrix site of the mitochondrial inner membrane, and an IMS-located domain of translocase of inner membrane 23 detected by Western blot analysis served as controls for matrix transport and swelling, respectively. aTim23: anti-Tim23. (f) Scheme of the domain organizations of Sirt3 and Sirt5. Numbers in brackets are residue numbers for boundaries of protein parts. NLS: nuclear localization sequence; MLS: mitochondrial localization sequence; R1, regulatory region 1; R2: regulatory region 2.
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Cytochrome c might be a physiological substrate of Sirt5 if this Sirtuin is localized to the mitochondrial IMS (Fig. 5b). A recent study on overexpressed tagged mouse Sirt5 in COS7 cells 20 indeed indicated that Sirt5, at least from mouse, is localized in the IMS. In order to test whether human Sirt5 can be localized to the IMS, we performed import experiments with human Sirt3 and Sirt5 using isolated yeast mitochondria as a model system. 3 Sirt3 and Sirt5 proteins were incubated with mitochondria, followed by PK treatment for degradation of nonimported protein ( Fig. 5d). In a parallel reaction, mitochondria were uncoupled prior to the import reaction by addition of valinomycin (− ΔΨ). Sirt3, a protein known to be located in the mitochondrial matrix, 19 was only efficiently imported in the presence of a membrane potential. Dependence on the mitochondrial potential is a hallmark of matrix import, 38 and the results thus show that Sirt3 is imported into the correct compartment in our experimental system. Sirt5, in contrast, reaches an inner-mitochondrial compartment both in the presence and in the absence of the membrane potential, suggesting that Sirt5 may accumulate in the IMS.

In order to further test the localization of Sirt5, we removed the outer mitochondrial membrane after the import reaction by osmotic swelling, followed by PK digest of then accessible proteins (Fig. 5e). Rupture of the outer membrane was confirmed by monitoring the accessibility of an IMS-exposed domain of endogenous translocase of inner membrane 23 (detected by Western blot analysis). Part of the imported Sirt5 was degraded by PK, indicating its localization in the IMS.

Sirtuins are involved in central physiological regulation mechanisms, many of them with relevance to metabolic regulation and aging processes.5 and 6 Therefore, the seven mammalian Sirtuin isoforms are emerging targets for the treatment of metabolic disorders and aging-related diseases.39 For most Sirtuin effects, however, the specific signaling mechanisms and molecular targets are not yet known. We have identified novel potential targets for Sirtuins in mitochondria, the major metabolic centers in cells. We found that Sirt3 can deacetylate and thereby activate ICDH2, a key regulation point for flux throughout the citric acid cycle. Interestingly, the ICDH isoform regulated by Sirt3 forms NADPH instead of the NADH used for ATP synthesis. This activity is assumed to be important for the NADPH-dependent regeneration of antioxidants,40 and its stimulation by Sirt3 should thus help to slow oxidative damage and cellular aging processes. Furthermore, Sirt3 deacetylates GDH in vitro (this study) and in vivo31 and we find that this modification also stimulates GDH activity that promotes glucose and ATP synthesis by enabling amino acids to be used as fuels for citric acid cycle and gluconeogenesis. 41 Consistently, Sirt3 was reported to increase respiration, 24 which is needed for ATP synthesis but also for conversion of amino acids into glucose and urea. 41 The enzyme previously identified to be activated by Sirt3, acetyl coenzyme A synthetase 2, 21 and 22 also fuels the citric acid cycle independently of glycolysis by activating free acetate (Fig. 5b). Interestingly, a shift away from liver glycolysis is one of the metabolic changes observed under CR, a feeding regimen with 20–40% fewer calories than consumed ad libitum that is found to extend the lifespan of a variety of organisms. 6 CR was previously reported to increase GDH activity in the liver, 42where Sirt3 is highly expressed, 17 and Sirt3 activity is known to be increased by CR. 6 and 24 It thus appears that Sirt3 mediates some of the effects of CR and lifespan regulation, consistent with its implication in survivorship in the elderly 25 and 43 and the prominent role of Sirtuins in CR found for various organisms,6 and 44 and it also appears that GDH activation likely contributes to the Sirt3-dependent effects.

Little is known about additional factors regulating the activity and specificity of Sirtuin enzymes. Their requirement for NAD + indicates that the NAD +/NADH ratio should regulate Sirtuins,13 and 14 but even changes to ratios observed under extreme conditions such as CR appear to influence Sirtuin activity only slightly.35 Furthermore, NAD + levels would influence all Sirtuins similarly, but a more specific tuning of individual Sirtuin activities appears necessary in order to orchestrate the many effects mediated by Sirtuins (see, e.g., discussion above).6 and 45 A deeper insight into the regulation of Sirtuin enzymes would also be required for the development of more specific Sirtuin inhibitors—a prerequisite for Sirtuin-targeted therapy.39 The regulatory parts flanking the catalytic cores might be interesting target sites (Fig. 5f). N-terminal extensions between ∼ 30 and 120 residues are present in all human Sirtuins but show little conservation, indicating that they might respond to various regulators. Our results indicate that the corresponding N-terminal region in Sirt3 also blocks productive binding for small peptides (Fig. 4a), but enables access for entire protein substrates (Fig. 4c). The C-terminal truncated part in our experiments (Sirt3 residues 380–399) is formed by α14 (secondary structure numbering for Sirt236) whose end corresponds to the N-terminus of Hst2 α13 that partly occupies the NAD +binding site.15 In Sirt3, however, the C-terminal truncation alone lowers activity only slightly, and we assume that it has no regulatory function on its own but might instead assist the N-terminal autoinhibitory region. This module of the N-terminus and the C-terminus (Figs. 4b and 5f) appears to contribute to the substrate specificity of the enzyme, and ligands binding to it might enable or block rearrangements opening up the active site and thereby regulate the enzyme’s activity. Alternatively, the flanking parts might be removed by proteolytic processing or alternative splicing, thereby changing Sirtuin activity and specificity.

7.8.3 The mTORC1 Pathway Stimulates Glutamine Metabolism and Cell Proliferation by Repressing SIRT4

Csibi A1Fendt SMLi CPoulogiannis GChoo AYChapski DJ, et al.
Cell. 2013 May 9; 153(4):840-54.
http://dx.doi.org:/10.1016/j.cell.2013.04.023

Proliferating mammalian cells use glutamine as a source of nitrogen and as a key anaplerotic source to provide metabolites to the tricarboxylic acid cycle (TCA) for biosynthesis. Recently, mTORC1 activation has been correlated with increased nutrient uptake and metabolism, but no molecular connection to glutaminolysis has been reported. Here, we show that mTORC1 promotes glutamine anaplerosis by activating glutamate dehydrogenase (GDH). This regulation requires transcriptional repression of SIRT4, the mitochondrial-localized sirtuin that inhibits GDH. Mechanistically, mTORC1 represses SIRT4 by promoting the proteasome-mediated destabilization of cAMP response element binding-2 (CREB2). Thus, a relationship between mTORC1, SIRT4 and cancer is suggested by our findings. Indeed, SIRT4 expression is reduced in human cancer, and its overexpression reduces cell proliferation, transformation and tumor development. Finally, our data indicate that targeting nutrient metabolism in energy-addicted cancers with high mTORC1 signaling may be an effective therapeutic approach.

Proliferating mammalian cells use glutamine as a source of nitrogen and as a key anaplerotic source to provide metabolites to the tricarboxylic acid cycle (TCA) for biosynthesis. Recently, mTORC1 activation has been correlated with increased nutrient uptake and metabolism, but no molecular connection to glutaminolysis has been reported. Here, we show that mTORC1 promotes glutamine anaplerosis by activating glutamate dehydrogenase (GDH). This regulation requires transcriptional repression of SIRT4, the mitochondrial-localized sirtuin that inhibits GDH. Mechanistically, mTORC1 represses SIRT4 by promoting the proteasome-mediated destabilization of cAMP response element binding-2 (CREB2). Thus, a relationship between mTORC1, SIRT4 and cancer is suggested by our findings. Indeed, SIRT4 expression is reduced in human cancer, and its overexpression reduces cell proliferation, transformation and tumor development. Finally, our data indicate that targeting nutrient metabolism in energy-addicted cancers with high mTORC1 signaling may be an effective therapeutic approach.

Nutrient availability plays a pivotal role in the decision of a cell to commit to cell proliferation. In conditions of sufficient nutrient sources and growth factors (GFs), the cell generates enough energy and acquires or synthesizes essential building blocks at a sufficient rate to meet the demands of proliferation. Conversely, when nutrients are scarce, the cell responds by halting the biosynthetic machinery and by stimulating catabolic processes such as fatty acid oxidation and autophagy to provide energy maintenance (Vander Heiden et al., 2009). Essential to the decision process between anabolism and catabolism is the highly conserved, atypical Serine/Threonine kinase mammalian Target of Rapamycin Complex 1 (mTORC1), whose activity is deregulated in many cancers (Menon and Manning, 2008). This complex, which consists of mTOR, Raptor, and mLST8, is activated by amino acids (aa), GFs (insulin/IGF-1) and cellular energy to drive nutrient uptake and subsequently proliferation (Yecies and Manning, 2011). The molecular details of these nutrient-sensing processes are not yet fully elucidated, but it has been shown that aa activate the Rag GTPases to regulate mTORC1 localization to the lysosomes (Kim et al., 2008Sancak et al., 2008); and GFs signal through the PI3K-Akt or the extracellular signal-regulated kinase (ERK)-ribosomal protein S6 kinase (RSK) pathways to activate mTORC1 by releasing the Ras homolog enriched in brain (RHEB) GTPase from repression by the tumor suppressors, tuberous sclerosis 1 (TSC1)– TSC2 (Inoki et al., 2002Manning et al., 2002Roux et al., 2004). Finally, low energy conditions inhibit mTORC1 by activating AMPK and by repressing the assembly of the TTT-RUVBL1/2 complex. (Inoki et al., 2003Gwinn et al., 2008Kim et al., 2013).

Glutamine, the most abundant amino acid in the body plays an important role in cellular proliferation. It is catabolized to α-ketoglutarate (αKG), an intermediate of the tricarboxylic acid (TCA) cycle through two deamination reactions in a process termed glutamine anaplerosis (DeBerardinis et al., 2007). The first reaction requires glutaminase (GLS) to generate glutamate, and the second occurs by the action of either glutamate dehydrogenase (GDH) or transaminases. Incorporation of αKG into the TCA cycle is the major anaplerotic step critical for the production of biomass building blocks including nucleotides, lipids and aa (Wise and Thompson, 2010). Recent studies have demonstrated that glutamine is also an important signaling molecule. Accordingly, it positively regulates the mTORC1 pathway by facilitating the uptake of leucine (Nicklin et al., 2009) and by promoting mTORC1 assembly and lysosomal localization (Duran et al., 2012;Kim et al., 2013).

Commonly occurring oncogenic signals directly stimulate nutrient metabolism, resulting in nutrient addiction. Oncogenic levels of Myc have been linked to increased glutamine uptake and metabolism through a coordinated transcriptional program (Wise et al., 2008Gao et al., 2009). Hence, it is not surprising that cancer cells are addicted to glutamine (Wise and Thompson, 2010). Thus, considering the prevalence of mTORC1 activation in cancer and the requirement of nutrients for cell proliferation, understanding how mTORC1 activation regulates nutrient levels and metabolism is critical. Activation of the mTORC1 pathway promotes the utilization of glucose, another nutrient absolutely required for cell growth. However, no study has yet investigated if and how the mTORC1 pathway regulates glutamine uptake and metabolism. Here, we discover a novel role of the mTORC1 pathway in the stimulation of glutamine anaplerosis by promoting the activity of GDH. Mechanistically, mTORC1 represses the transcription of SIRT4, an inhibitor of GDH. SIRT4 is a mitochondrial-localized member of the sirtuin family of NAD-dependent enzymes known to play key roles in metabolism, stress response and longevity (Haigis and Guarente, 2006). We demonstrate that the mTORC1 pathway negatively controls SIRT4 by promoting the proteasome-mediated degradation of cAMP-responsive element-binding (CREB) 2. We reveal that SIRT4 levels are decreased in a variety of cancers, and when expressed, SIRT4 delays tumor development in a Tsc2−/− mouse embryonic fibroblasts (MEFs) xenograft model. Thus, our findings provide new insights into how mTORC1 regulates glutamine anaplerosis, contributing therefore to the metabolic reprogramming of cancer cells, an essential hallmark to support their excessive needs for proliferation.

The mTORC1 pathway regulates glutamine metabolism via GDH

The activation of the mTORC1 pathway has recently been linked to glutamine addiction of cancer cells (Choo et al., 2010), yet it remains to be resolved if mTORC1 serves as a regulator of glutamine anaplerosis. To investigate this possibility, we first determined the effect of mTORC1 activity on glutamine uptake. We measured glutamine uptake rates in Tsc2 wild-type (WT) and Tsc2−/− MEFs. We found that Tsc2−/− MEFs consumed significantly more glutamine (Figure 1A), showing that mTORC1 activation stimulates the uptake of this nutrient. In addition, re-expression of Tsc2 in Tsc2−/− cells reduced glutamine uptake (Figure S1A). Similarly, mTORC1 inhibition with rapamycin resulted in decreased glutamine uptake in MEFs (Figure 1A). The decreased on glutamine uptake was significantly reduced after 6h of rapamycin treatment when compared to control (data not shown). To further confirm the role of mTORC1 on glutamine uptake, we used human embryonic kidney (HEK) 293T cells stably expressing either WT-RHEB or a constitutively active mutant (S16H) of RHEB. Increased mTORC1 signaling, as evidenced by sustained phosphorylation of S6K1 and its target rpS6, was observed in RHEB-expressing cells (Figure S1B). The activation of the mTORC1 pathway nicely correlated with an increase in glutamine consumption, therefore confirming that changes in mTORC1 signaling are reflected in cellular glutamine uptake (Figure S1B). To determine whether the modulation of glutamine uptake by the mTORC1 pathway occurs in cancer cells, we examined glutamine uptake rates in conditions of mTORC1 inhibition in human epithelial tumor cell lines, including the colon carcinoma DLD1, and the prostate cancer DU145. Rapamycin treatment resulted in decreased proliferation (data not shown) and yielded a decreased glutamine uptake in both cell lines (Figure 1B & data not shown). Glutamine is the major nitrogen donor for the majority of ammonia production in cells (Figure 1C) (Shanware et al., 2011). Consistent with decreased glutamine uptake, we found that ammonia levels were also diminished after rapamycin treatment (Figure S1C).

Figure 1  The mTORC1 pathway regulates glutamine metabolism via glutamate dehydrogenase

We next examined the fate of glutamine in conditions of mTORC1 inhibition, using gas chromatography/mass spectrometry (GC/MS) analysis to monitor the incorporation of uniformly labeled [U-13C5]-Glutamine into TCA cycle intermediates. Direct glutamine contribution to I̧KG (m+5), succinate (m+4), malate (m+4) and citrate (m+4) was decreased in rapamycin treated cells (Figure S1D) indicating that rapamycin impaired glutamine oxidation and subsequent carbon contribution into the TCA cycle.

To test whether glutamine uptake or glutamine conversion is limiting, we measured the intracellular levels of glutamine and glutamate in DLD1 cells. Increased levels of glutamine and/or glutamate will show that the catalyzing enzyme activity is limiting and not glutamine transport itself (Fendt et al., 2010). Rapamycin treatment resulted in increased intracellular levels of both glutamine and glutamate, showing that glutamate to αKG conversion is the critical limiting reaction (Figures 1D & 1E). To further confirm the implication of the glutamate catalyzing reaction we also measured αKG levels. If glutamate conversion is indeed critical we expect no alteration in αKG levels. This is expected because αKG is downstream of the potentially limiting glutamate conversion step, and it has been shown that product metabolite concentrations of limiting metabolic enzymes stay unaltered, while the substrate metabolite concentrations change to keep metabolic homeostasis (Fendt et al., 2010). We found that αKG levels were unaltered after rapamycin treatment, corroborating that the limiting enzymatic step is glutamate conversion (Figure 1F). To further confirm the limitation in glutamate-to-αKG conversion, we measured flux through this reaction. Strikingly, this flux was significantly reduced during rapamycin treatment (Figure 1G). Additionally, the inhibition of mTORC1 resulted in increased glutamate secretion (Figure 1H), thus confirming that the glutamate-to-αKG conversion step is a major bottleneck in the glutamine pathway during rapamycin treatment.

Glutamate conversion can be conducted by GDH (Figure 1C), suggesting that the mTORC1 pathway potentially regulates this enzyme. In agreement, rapamycin treatment resulted in decreased GDH activity in DLD1 cells (Figure 1I). To exclude that transaminases play a role in the mTORC1-induced regulation of glutamine metabolism, we used amin ooxyacetate (AOA) at a concentration shown to effectively inhibit the two predominant transaminases, alanine aminotransferase (ALT) and aspartate aminotransferase (AST) (Figure 1C) (Wise et al., 2008), or rapamycin in the presence of α-15N-labeled glutamine. Subsequently, we measured 15N-labeling patterns and metabolite levels of alanine, an amino acid that is predominately produced by a transaminase-catalyzed reaction (Possemato et al., 2011). We found that AOA dramatically decreased 15N contribution and metabolite levels of alanine, while rapamycin only mildly affected the 15N contribution to this amino acid and showed no effect on alanine levels compared to the control condition (Figures 1J & S1E). In conclusion, these data demonstrate that GDH, not transaminases, plays a major role in the regulation of glutamine metabolism downstream of mTORC1.

mTORC1 controls GDH activity by repressing SIRT4

As our results show that mTORC1 regulates glutamate dehydrogenase, we sought to identify the molecular mechanism. SIRT4 is a negative regulator of GDH activity through ADP-ribosylation (Haigis et al., 2006), thus suggesting that mTORC1 potentially controls this step of glutamine metabolism via SIRT4. To test this possibility, we first assessed the ADP-ribosylation status of GDH by introducing biotin-labeled NAD followed by immunoprecipitation using avidin-coated beads. Rapamycin treatment led to an increase in the mono-ADP-ribosylation status of GDH, similar to that observed in cells stably expressing SIRT4 (Figure 2A). Importantly, we found that the knockdown of SIRT4 abrogated the rapamycin-induced decrease in the activity of GDH (Figures 2B & S2A). Strikingly, SIRT4 protein levels were increased upon mTORC1 inhibition in MEFs (Figures 2C). This regulation was confirmed in both DLD1 and DU145 cells (Figures 2D). Remarkably, rapamycin potently increased SIRT4 levels after 6h of treatment (Figure S2B), correlating with reduced glutamine consumption at the same time point (data not shown). In contrast, SIRT4 levels were not influenced by the treatment of MEFs with U0216, an inhibitor of MEK1/2 in the MAPK pathway (Figure S2C). All other mTOR catalytic inhibitors tested in Tsc2−/− MEFs also resulted in increased SIRT4 protein levels (Figure S2D). To evaluate a potential regulation of SIRT4 by mTORC2, we performed RNA interference (RNAi) experiments of either raptor or the mTORC2 component, rictor, in Tsc2−/− MEFs. The knockdown of raptor, but not rictor, was sufficient to increase SIRT4 protein levels, confirming the role of the mTORC1 pathway in the regulation of SIRT4 (Figure 2E). To investigate whether mTORC1 regulation of SIRT4 occurs in tumor samples, a TSC-xenograft model was used. We injected a TSC2−/− rat leiomyoma cell line; ELT3 cells, expressing either an empty vector (V3) or TSC2 (T3), in the flank of nude mice. SIRT4 levels were dramatically increased in TSC2-expressing tumors compared to empty vector samples (Figure S2E). In addition, we assessed the levels of SIRT4 in both ELT3 xenograft tumors and in mouse Tsc2+/− liver tumors after rapamycin treatment. As expected, these tumor samples exhibited robust elevation of SIRT4 after rapamycin treatment (Figures 2F & S2F). Thus, these data demonstrate that the mTORC1 pathway represses SIRT4 in several tumor systems.

Figure 2  mTORC1 controls glutamate dehydrogenase activity by repressing SIRT4

CREB2 regulates the transcription of SIRT4 in an mTORC1-dependent fashion

We next asked whether the mTORC1-dependent regulation of SIRT4 occurred at the mRNA level. Quantitative RT-PCR results show that rapamycin treatment significantly increased the expression of SIRT4mRNA in Tsc2−/− MEFs (Figure 3A). SIRT4 mRNA levels were dramatically reduced in Tsc2−/− MEFs compared to their WT counterpart (Figure 3B). Similar results were obtained from transcriptional profiling analysis of the SIRT4 gene from a previously published dataset (GSE21755) (Figure 3C) (Duvel et al. 2010). Altogether, our data demonstrate that mTORC1 negatively regulates the transcription of SIRT4. To determine whether CREB2 is involved in the mTORC1-dependent regulation of SIRT4, we performed RNAi experiments. The silencing of CREB2 abolished the rapamycin-induced expression of SIRT4 (Figures 3E & S3A). The knockdown of CREB1 did not affect the upregulation of SIRT4 upon mTORC1 inhibition, thus demonstrating the specificity of CREB2 to induce SIRT4 (Figure S3B), and the knockdown of CREB2 significantly abrogated the rapamycin-induced increase in the activity of the SIRT4 promoter.

Figure 3  SIRT4 is regulated at the mRNA level in an mTORC1-dependent fashion

mTORC1 regulates the stability of CREB2

We next investigated whether the mTORC1 pathway regulates CREB2. Although we did not observe major changes in Creb2 mRNA in normal growth conditions (Figure S4A), mTORC1 inhibition resulted in accumulation of CREB2 protein levels by 2h of rapamycin treatment (Figure 4A). U0126 failed to cause the accumulation of CREB2 (Figure S4B). In contrast, CREB1 protein levels were not affected after 24h rapamycin treatment (Figure S4C). As observed for SIRT4, mTOR catalytic inhibitors, and the specific knockdown of mTOR, resulted in upregulation of CREB2 protein levels (Figures S4D & S4E). CREB2 is upregulated in diverse cell types as a response to a variety of stresses, including hypoxia, DNA damage, and withdrawal of GFs, glucose, and aa (Cherasse et al., 2007Rouschop et al., 2010Yamaguchi et al., 2008;Whitney et al., 2009). Interestingly, mTORC1 is negatively regulated by all of these environmental inputs (Zoncu et al., 2011). Since mTORC1 signaling in Tsc2−/− MEFs is insensitive to serum deprivation, we assessed the role of aa withdrawal and re-stimulation on CREB2 levels. As shown in Fig. 4B, CREB2 accumulated upon aa deprivation, and was decreased following aa re-addition. This phenomenon required the action of the proteasome as MG132 efficiently blocked CREB2 degradation following aa re-addition. Importantly, we found that mTORC1 inhibition abrogated the aa-induced decrease of CREB2 (Figure 4B).

Figure 4  mTORC1 regulates the stability of CREB2

mTORC1 activation promotes the binding of CREB2 to βTrCP and modulates CREB2 ubiquitination

Next, we attempted to identify the E3 ubiquitin ligase that might be responsible for CREB2 turnover. Consistent with a recent study, we found CREB2 to bind the E3 ligase, βTrCP (Frank et al., 2010). However, other related E3 ligases including Fbxw2, Fbxw7a, and Fbxw9 did not bind to CREB2 (data not shown). The interaction of CREB2 with Flag-βTrCP1 was enhanced in the presence of insulin, and was abolished by rapamycin pretreatment (Figure 4D). Importantly, insulin treatment promoted the ubiquitination of CREB2 in an mTORC1-dependent fashion (Figure 4E). Altogether, our results support the notion that the mTORC1 pathway regulates the targeting of CREB2 for proteasome-mediated degradation. βTrCP binds substrates via phosphorylated residues in conserved degradation motifs (degrons), typically including the consensus sequence DpSGX(n)pS or similar variants. We found an evolutionary conserved putative βTrCP binding site (DSGXXXS) in CREB2 (Figure 4F). Interestingly, we noted a downward mobility shift in CREB2 protein with mTORC1 inhibition, consistent with a possible decrease in the phosphorylation of CREB2. (Figure 4A). Frank et al. (2010) showed that phosphorylation of the first serine in the degron motif corresponding to Ser218 is required for the CREB2/βTrCP interaction, and this modification acts as a priming site for a gradient of phosphorylation events on five proline-directed residues codons (T212, S223, S230, S234, and S247) that is required for CREB2 degradation during the cell cycle progression (Frank et al., 2010). Consistent with these observations, we found that the mutation of the five residues to alanine (5A mutant) resulted in strong stabilization of CREB2, comparable to the serine-to-alanine mutation on the priming Ser218 phosphorylation site (Figure S4G).

SIRT4 represses bioenergetics and cell proliferation

We observed that glutamine utilization is repressed by rapamycin treatment (Figure 1) and SIRT4 is induced by mTORC1 inhibition (Figure 2). Thus, we tested whether SIRT4 itself directly regulates cellular glutamine uptake. The stable expression of SIRT4 resulted in the repression of glutamine uptake in Tsc2−/− MEFs and DLD1 cells (Figures 5A & 5B). Glucose uptake was not affected by SIRT4 expression (data not shown). Because glutamine can be an important nutrient for energy production, we examined ATP levels in SIRT4 expressing cells. Consistent with reduced glutamine consumption, the expression of SIRT4 in Tsc2−/− cells resulted in decreased ATP/ADP ratio compared to control cells (Figure 5C). Cells produce ATP via glycolysis and oxidative phosphorylation (OXPHOS). To test the contribution of mitochondrial metabolism versus glycolysis to ATP, we measured the ATP/ADP ratio after the treatment with oligomycin, an inhibitor of ATP synthesis from OXPHOS. Importantly, the difference of the ATP/ADP ratio between control and SIRT4 expressing cells was abrogated by oligomycin (Figure 5C), further demonstrating that SIRT4 may repress the ability of cells to generate energy from mitochondrial glutamine catabolism. Mitochondrial glutamine catabolism is essential for energy production and viability in the absence of glucose (Yang et al., 2009Choo et al., 2010). Thus, we examined the effect of SIRT4 on the survival of Tsc2−/− MEFs during glucose deprivation. Control cells remained viable following 48h of glucose deprivation. Conversely, SIRT4 expressing cells showed a dramatic increase in cell death under glucose-free conditions, which was rescued by the addition of the cell permeable dimethyl-I̧KG (DM-I̧KG) (Figure 5D). Conversely, the expression of SIRT4 did not affect the viability of glucose-deprived Tsc2 WT MEFs (Figure S5A). Glucose deprivation also induced death of the human DU145 cancer cell line stably expressing SIRT4 (data not shown).

Figure 5  SIRT4 represses bioenergetics and proliferation

Glutamine is an essential metabolite for proliferating cells, and many cancer cells exhibit a high rate of glutamine consumption (DeBerardinis et al., 2007). Thus, decreased glutamine uptake in DLD1 and DU145 cancer cells expressing SIRT4 might result in decreased proliferation. Indeed, these cells grew significantly slower than did control cells. Remarkably, DM-I̧KG completely abrogated the decreased proliferation of SIRT4 expressing cells (Figure 5E & 5F), suggesting that repressed glutamine metabolism drove the reduced proliferation of cells expressing SIRT4. The expression of SIRT4 also slowed the proliferation of Tsc2−/− MEFs but did not affect Tsc2 WT MEFs (Figures S5B & S5C). Finally, to rule out that the effect on proliferation was due to aberrant localization and to off-target effects of the overexpressed protein, we examined the localization of HA-SIRT4. We found that SIRT4 is co-localized with the MitoTracker, a mitochondrial-selective marker (Figure S5D). Taken together, these data demonstrate that SIRT4 is a critical negative regulator of mitochondrial glutamine metabolism and cell proliferation.

SIRT4 represses TSC-tumor development

Recent studies have demonstrated a major role of glutamine metabolism in driving oncogenic transformation of many cell lines (Gao et al., 2009Wang et al., 2011). Since SIRT4 expression represses glutamine uptake and cell proliferation (Figure 5), we hypothesized that it could affect tumorigenesis. To test this idea, we assessed the role of SIRT4 in cell transformation by using an anchorage-independent growth assay. SIRT4 expression reduced the ability of Tsc2−/−p53−/− MEFs to grow in soft agar. However, the expression of SIRT4 in Tsc2+/+p53−/− did not impair their colony formation properties (Figure 6A). Tumor incidence in mice injected with Tsc2+/+p53−/− MEFs was not affected by SIRT4 (data not shown). Conversely, in the Tsc2−/−p53−/− cohort, SIRT4 reduced tumor incidence by 20 days at median (Figure 6B). SIRT4 expression inTsc2−/−p53−/− MEFs resulted in reduction of Ki-67 positivity by 60% (Figure 6E), consistent with the finding that SIRT4 inhibits the proliferation of these cells in vitro (Figure S5B). Finally, we performed a comprehensive meta-analysis of SIRT4 expression in human tumors and found significantly lower expression levels of SIRT4, relative to normal tissue, in bladder, breast, colon, gastric, ovarian and thyroid carcinomas (Figure 6F). Interestingly, loss of SIRT4 expression showed a strong association with shorter time to metastasis in patients with breast cancer (Figures 6G & 6H). Altogether, these data strongly suggest that SIRT4 delays tumorigenesis regulated by the mTORC1 pathway.

Figure 6
SIRT4 suppresses TSC-tumor development

The pharmacologic inhibition of glutamine anaplerosis synergizes with glycolytic inhibition to induce the specific death of mTORC1 hyperactive cells

The activation of mTORC1 leads to glucose and glutamine addiction as a result of increased uptake and metabolism of these nutrients (Choo et al., 2010Duvel et al., 2010 & Figure 1). These observations suggest that targeting this addiction offers an interesting therapeutic approach for mTORC1-driven tumors. The alkylating agent, mechlorethamine (Mechlo), incites cell toxicity in part by the inhibition of the GAPDH step of glycolysis via poly-ADP ribose polymerase (PARP)-dependent cellular consumption of cytoplasmic NAD+. The ultimate consequence is glycolytic inhibition, thus mimicking glucose deprivation (Zong et al., 2004). Treatment of Tsc2−/− MEFs with Mechlo decreased both NAD levels and lactate production (Figure 7A and data not shown). The decrease in NAD+ levels was rescued by addition of DPQ (Figure 7A), a PARP inhibitor (Zong et al., 2004). We next tested the ability of glutamine inhibition to determine the sensitivity of Tsc2−/− MEFs to Mechlo. As shown in Figure 7B, the treatment with EGCG, a GDH inhibitor (Figure 1G), potently synergized with Mechlo to kill Tsc2−/− MEFs with the greatest effect observed at 30μM (Figure 7B). As a result, this combination dramatically increased the cleavage of PARP, an apoptotic marker (Figure 7E). Similarly, glutamine deprivation sensitized Tsc2−/− MEFs to Mechlo (data not shown). The RNAi-mediated knockdown of GDH also synergized with Mechlo to induce death of Tsc2−/− MEFs (Figure 7D). Importantly, at these concentrations the combination did not induce death of a Tsc2-rescued cell line (Figure 7C).

Figure 7 The combination of glutamine metabolism inhibitors with glycolytic inhibition is an effective therapy to kill Tsc2−/− and PTEN−/− cells

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3684628/bin/nihms-474527-f0007.gif

Because the metabolic properties of cells with activated mTORC1 by Tsc2– deficiency can be efficiently targeted, we also examined other cell types in which mTORC1 is hyperactive by the loss of PTEN. We found that the combination of Mechlo and EGCG was also effective to induce specific toxicity of PTEN−/− MEFs, while PTEN+/+ MEFs were not affected (Figures S7A & S7B). In addition, the PTEN-deficient human prostate adenocarcinoma cell line, LNCaP, was also sensitive to treatment with Mechlo and EGCG (Figure 7F). This effect was specifically due to lack of TCA cycle replenishment as pyruvate supplementation completely reversed the synergistic effect (Figure 7F). The combination of Mechlo with the GLS1 inhibitor, BPTES (Figure 1G), also resulted in decreased viability of Tsc2−/− cells but not of Tsc2-reexpressing cells (Figures S7C & S7D). Again, death in Tsc2−/− cells was rescued with pyruvate or OAA (Figure S7E). To further investigate if the potent cell death in Tsc2−/− was restricted to Mechlo, we used 2-DG, a glycolytic inhibitor. The combination of 2-DG with either EGCG or BPTES resulted in enhanced cell death of Tsc2−/− MEFs compared to single agent treatments (Figure S7F). This effect was also specific to Tsc2−/− cells, since this combination was less toxic in Tsc2-reexpressing MEFs (Figure S7G). Taken together, our results demonstrate that the combination treatments aimed at inhibiting glycolysis and glutaminolysis potently synergize to kill cells with hyperactive mTORC1 signaling.

Here, we define a novel mTORC1-regulated pathway that controls glutamine-dependent anaplerosis and energy metabolism (Figure 7G). We discovered that the mTORC1 pathway regulates glutamine metabolism by promoting the activity of GDH (Figures 1​-3).3). We show that this regulation occurs by repressing the expression of SIRT4, an inhibitor of GDH (Figures 2 & 3). Molecularly, this is the result of mTORC1-dependent proteasome-mediated degradation of the SIRT4 transcriptional regulator, CREB2 (Figure 4). Interestingly, the modulation of CREB2 levels correlates with increased sensitivity to glutamine deprivation (Ye et al., 2010Qing et al., 2012), fitting with our model of glutamine addiction as a result of mTORC1 activation (Choo et al., 2010). Our data suggest that mTORC1 promotes the binding of the E3 ligase, βTrCP, to CREB2 (Figure 4D), promoting CREB2 degradation by the proteasome (Figure 4E). A previous study has demonstrated that five residues in CREB2 located next to the βTrCP degron are required for its stability (Frank et al., 2010). Accordingly, the mutation of these residues to alanine resulted in stabilization of CREB2 and SIRT4 following insulin and aa-dependent mTORC1 activation (Figure 4G). Future work is aimed at determining if mTORC1 and/or downstream kinases are directly responsible for the multisite phosphorylation of CREB2.

The identification of CREB2 as an mTORC1-regulated transcription factor increases the repertoire of transcriptional regulators modulated by this pathway including HIF1α (glycolysis), Myc (glycolysis) and SREBP1 (lipid biosynthesis) (Duvel et al., 2010Yecies and Manning, 2011). The oncogene Myc has also been linked to the regulation of glutamine metabolism by increasing the expression of the surface transporters ASCT2 and SN2, and the enzyme GLS. Thus, enhanced activity of Myc correlates with increased glutamine uptake and glutamate production (Wise et al., 2008Gao et al., 2009). Our findings describe a new level of control to this metabolic node as shown by the modulation of the glutamate-to-αKG flux (Figure 2). This regulation is particularly relevant as some cancer cells produce more than 50% of their ATP by oxidizing glutamine-derived αKG in the mitochondria (Reitzer et al JBC, 1979). Therefore, these studies support the notion that Myc and CREB2/SIRT4 cooperate to regulate the metabolism of glutamine to αKG.

7.8.4  Rab1A and small GTPases Activate mTORC1

7.8.4.1 Rab1A Is an mTORC1 Activator and a Colorectal Oncogene

Thomas JD1Zhang YJ2Wei YH3Cho JH3Morris LE3Wang HY4Zheng XF5.
Cancer Cell. 2014 Nov 10; 26(5):754-69.
http://dx.doi.org:/10.1016/j.ccell.2014.09.008.

Highlights

  • Rab1A mediates amino acid signaling to activate mTORC1 independently of Rag
  • Rab1A regulates mTORC1-Rheb interaction on the Golgi apparatus
  • Rab1A is an oncogene that is frequently overexpressed in human cancer
  • Hyperactive amino acid signaling is a common driver for cancer

Amino acid (AA) is a potent mitogen that controls growth and metabolism. Here we describe the identification of Rab1 as a conserved regulator of AA signaling to mTORC1. AA stimulates Rab1A GTP binding and interaction with mTORC1 and Rheb-mTORC1 interaction in the Golgi. Rab1A overexpression promotes mTORC1 signaling and oncogenic growth in an AA- and mTORC1-dependent manner. Conversely, Rab1A knockdown selectively attenuates oncogenic growth of Rab1-overexpressing cancer cells. Moreover, Rab1A is overexpressed in colorectal cancer (CRC), which is correlated with elevated mTORC1 signaling, tumor invasion, progression, and poor prognosis. Our results demonstrate that Rab1 is an mTORC1 activator and an oncogene and that hyperactive AA signaling through Rab1A overexpression drives oncogenesis and renders cancer cells prone to mTORC1-targeted therapy.

7.8.4.2 Regulation of TOR by small GTPases

Raúl V Durán1 and Michael N Halla,1
EMBO Rep. 2012 Feb; 13(2): 121–128.
http://dx.doi.org/10.1038%2Fembor.2011.257

TOR is a conserved serine/threonine kinase that responds to nutrients, growth factors, the bioenergetic status of the cell and cellular stress to control growth, metabolism and ageing. A diverse group of small GTPases including Rheb, Rag, Rac1, RalA and Ryh1 play a variety of roles in the regulation of TOR. For example, while Rheb binds to and activates TOR directly, Rag and Rac1 regulate its localization and RalA activates it indirectly through the production of phosphatidic acid. Here, we review recent findings on the regulation of TOR by small GTPases.

The growth-controlling TOR signalling pathway is structurally and functionally conserved from unicellular eukaryotes to humans. TOR, an atypical serine/threonine kinase, was originally discovered inSaccharomyces cerevisiae as the target of rapamycin (Heitman et al, 1991). It was later described in many other organisms including the protozoan Trypanosoma brucei, the yeast Schizosaccharomyces pombe, photosynthetic organisms such as Arabidopsis thaliana and Chlamydomonas reinhardtii, and in metazoans such as Caenorhabditis elegansDrosophila melanogaster and mammals. TOR integrates various stimuli to control growth, metabolism and ageing (Avruch et al, 2009Kim & Guan, 2011Soulard et al, 2009;Wullschleger et al, 2006Zoncu et al, 2011a). In mammals, mTOR is activated by nutrients, growth factors and cellular energy, and is inhibited by stress. Thus, the molecular regulation of TOR is complex and diverse. Among the increasing number of TOR regulators, small GTPases are currently garnering much attention. Small GTPases (20–25 kDa) are either in an inactive GDP-bound form or an active GTP-bound form (Bos et al, 2007). GDP–GTP exchange is regulated by GEFs, which mediate the replacement of GDP by GTP, and by GAPs, which stimulate the intrinsic GTPase activity of a cognate GTPase to convert GTP into GDP (Fig 1). Upon activation, small GTPases interact with effector proteins, thereby stimulating downstream signalling pathways. Small GTPases constitute a superfamily that comprises several subfamilies, such as the Rho, Ras, Rab, Ran and Arf families. Rheb, Rag, RalA, Rac1 and Ryh1, all members of the small GTPase superfamily, play a role in the concerted regulation of TOR by different stimuli. This review summarizes recent advances in the understanding of TOR regulation by these small GTPases.

Regulation of small GTPases by GEFs and GAPs

Regulation of small GTPases by GEFs and GAPs

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3271343/bin/embor2011257f1.gif

Figure 1 Regulation of small GTPases by GEFs and GAPs. A guanine nucleotide exchange factor (GEF) replaces GDP with GTP to activate the signalling function of the GTPase. Conversely, a GTPase-activating protein (GAP) stimulates hydrolysis of GTP into GDP

The TOR complexes

TOR is found in two functionally and structurally distinct multiprotein complexes, named TORC1 and TORC2 (Avruch et al, 2009Kim & Guan, 2011Soulard et al, 2009Wullschleger et al, 2006Zoncu et al, 2011a). TORC1 regulates several cellular processes including protein synthesis, ribosome biogenesis, nutrient uptake and autophagy. TORC2, in turn, regulates actin cytoskeleton organization, cell survival, lipid synthesis and probably other processes. TORC1 and TORC2 are rapamycin-sensitive and rapamycin-insensitive, respectively, although in some organisms, for example A. thaliana and T. brucei, this rule does not apply (Barquilla et al, 2008Mahfouz et al, 2006). Nevertheless, long-term treatment with rapamycin can also indirectly inhibit TORC2 in mammalian cell lines (Sarbassov et al, 2006). Furthermore, there is accumulating evidence that not all TORC1 readouts are rapamycin-sensitive (Choo & Blenis, 2009Dowling et al, 2010Peterson et al, 2011).

Upstream of TOR

Four main inputs regulate mTORC1: nutrients, growth factors, the bioenergetic status of the cell and oxygen availability. It is well established that growth factors activate mTORC1 through the PI3K–AKT pathway. Once activated, AKT phosphorylates and inhibits the heterodimeric complex TSC1–TSC2, a GAP for Rheb and thus an inhibitor of mTORC1 (Avruch et al, 2009). The TSC1–TSC2 heterodimer is a ‘reception centre’ for various stimuli that are then transduced to mTORC1, including growth factor signals transduced through the AKT and ERK pathways, hypoxia through HIF1 and REDD1, and energy status through AMPK (Wullschleger et al, 2006). In addition to the small GTPases Rheb and Rag (see below), PA also binds to and activates mTORC1 (Fang et al, 2001). Pharmacological or genetic inhibition of PA production, through the inhibition of PLD, impairs activation of mTORC1 by nutrients and growth factors (Fang et al, 2001). Moreover, elevated PLD activity leads to rapamycin resistance in human breast cancer cells (Chen et al, 2003), further supporting a role for PA as an mTORC1 regulator. As discussed below, the small GTPase RalA participates in the mechanism by which PA activates mTORC1 (Maehama et al, 2008Xu et al, 2011).

In the case of nutrients, amino acids in particular, several elements mediate the activation of TORC1. As discussed below, the Rag GTPases are necessary to activate TORC1 in response to amino acids (Binda et al, 2009Kim et al, 2008Sancak et al, 2008). In mammals, it has also been proposed that amino acids stimulate an increase in intracellular calcium concentration, which in turn activates mTORC1 through the class III PI3K Vps34 (Gulati et al, 2008).

Downstream of TOR

TORC1 regulates growth-related processes such as transcription, ribosome biogenesis, protein synthesis, nutrient transport and autophagy (Wullschleger et al, 2006). In mammals, the best-characterized substrates of mTORC1 are S6K and 4E-BP1, through which mTORC1 stimulates protein synthesis. mTORC1 activates S6K, which is a positive regulator of protein synthesis, and inhibits 4E-BP1, which is a negative regulator of protein synthesis. Upon phosphorylation by mTORC1, 4E-BP1 releases eIF4E. Once released from 4E-BP1, eIF4E interacts with the eIF4G subunit of the eIF4F complex, allowing initiation of translation. In mammals, 4E-BP1 participates mainly in the regulation of cell proliferation and metabolism (Dowling et al, 2010). In S. cerevisiae, the main substrate of TORC1 is the S6K orthologue Sch9 (Urban et al, 2007). Sch9 is required for the activation of ribosome biogenesis and translation initiation stimulated by TORC1. Furthermore, it participates in TORC1-dependent inhibition of G0 phase entry.

Regulation of TOR by Rheb

The small GTPase Rheb was first identified in 1994 in a screen for genes induced in neurons in response to synaptic activity (Yamagata et al, 1994), and was first described to interact with the Raf1 kinase (Yee & Worley, 1997). A later report showed that loss of Rhb1, the Rheb orthologue in S. pombe, causes a starvation-like growth arrest (Mach et al, 2000). In 2003, several independent groups working with mammalian cells in vitro and Drosophila in vivo demonstrated that Rheb is the target of the TSC1–TSC2 GAP and a TORC1 activator (Avruch et al, 2009).

Interestingly, the Rheb–mTOR interaction both in vivo and in vitro does not depend on GTP loading of Rheb. This is unusual for GTPases as GTP loading usually regulates effector binding. However, GTP loading of Rheb is crucial for the activation of mTOR kinase activity (Sancak et al, 2007). Conversely, mTOR becomes inactive after association with a nucleotide-deficient Rheb (Long et al, 2005a; Fig 2). Similar results were obtained in S. pombe, making use of mutations that hyperactivate Rheb by increasing its overall GTP : GDP binding ratio (Urano et al, 2005). In contrast to the situation in mammals, interaction of Rheb with SpTOR2 in fission yeast is detected only with a hyperactive Rheb mutant. This suggests that, in S. pombe, Rheb binds to SpTOR2 in a GTP-dependent manner.

Rheb activates TORC1

Rheb activates TORC1

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3271343/bin/embor2011257f2.gif

Figure 2 Rheb activates TORC1 both directly and indirectly. GTP-bound Rheb interacts directly with TORC1 to activate TORC1 kinase. GTP-bound Rheb also activates RalA, which activates PLD to increase production of PA. PA in turn interacts with TORC1

In addition to the direct interaction between mTOR and Rheb, activation of PA production by Rheb is an additional mechanism by which Rheb might regulate mTORC1. Rheb binds to and activates PLD in a GTP-dependent manner (Sun et al, 2008). PLD produces PA, which binds directly to and upregulates mTORC1. This finding reveals cross-talk between the TSC–Rheb and the PA pathways in the regulation of mTORC1 signalling. A recent study by Yoon and colleagues further demonstrated the role of PLD in mTORC1 regulation (Yoon et al, 2011). They showed that amino acids activate PLD through translocation of PLD to the lysosomal compartment. This translocation is positively regulated by human Vps34 and is necessary for the activation of mTORC1 by amino acids. These authors propose the existence of a Vps34–PLD1 pathway that activates mTORC1 in parallel to the Rag pathway (Yoon et al, 2011).

Although Rheb is required for the activation of mTORC1 by amino acids, Rheb itself does not participate in amino acid sensing, and GTP-loading of Rheb is not affected by amino acid depletion (Long et al, 2005b). Furthermore, amino acid depletion inhibits mTORC1 even in TSC2−/− fibroblasts (Roccio et al, 2006). Nevertheless, interaction of mTORC1 with Rheb depends on amino acid availability (Long et al, 2005b). As discussed below, the current model proposes that amino acids mediate translocation of mTORC1 to the lysosomal surface where mTORC1 interacts with and is activated by GTP-loaded Rheb (Sancak et al, 2008).

Regulation of TOR by Rag

Rag GTPases have unique features among the Ras GTPase subfamily members: they form heterodimers and lack a membrane-targeting sequence (Nakashima et al, 1999Sekiguchi et al, 2001). Gtr1 in S. cerevisiaewas the first member of this GTPase subfamily to be identified (Bun-Ya et al, 1992). The mammalian RagA and RagB GTPases were later described as Gtr1 orthologues (Hirose et al, 1998). Gtr2 in yeast (Nakashima et al, 1999) and its mammalian orthologues RagC and RagD (Sekiguchi et al, 2001) were subsequently discovered due to their ability to form heterodimers with Gtr1 in yeast and RagA and RagB in mammals, respectively. The crystal structure of the Gtr1–Gtr2 complex has been determined recently (Gong et al, 2011). Gtr1 and Gtr2 have similar structures, organized in two domains: an amino-terminal GTPase domain (designated as the G domain) and a carboxy-terminal domain. The Gtr1–Gtr2 heterodimer presents a pseudo-twofold symmetry resembling a horseshoe. The crystal structure reveals that Gtr1–Gtr2 dimerization results from extensive contacts between the C-terminal domains of both proteins, while the G domains do not contact each other (Gong et al, 2011).

Rag proteins mediate the activation of TORC1 in response to amino acids.

Rag proteins mediate the activation of TORC1 in response to amino acids.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3271343/bin/embor2011257f3.gif

Figure 3 Rag proteins mediate the activation of TORC1 in response to amino acids. The RagA/B–RagC/D heterodimer is anchored to the MP1–p14–p18 complex on the surface of the lysosome.

Overexpressed Rheb is mislocalized throughout the cell, and therefore interaction of mTORC1 with Rheb does not require amino-acid-induced translocation of mTORC1 to the lysosome. The model is further supported by observations in Drosophila showing that expression of a constitutively active mutant of RagA significantly increases the size of individual cells, whereas expression of a dominant negative mutant of RagA reduces cell size (Kim et al, 2008). Moreover, Rag plays a role in TORC1-mediated inhibition of autophagy both in Drosophila (Kim et al, 2008) and in human cells (Narita et al, 2011).

mTOR and small GTPases are therapeutic targets in the treatment of cancer (Berndt et al, 2011Dazert & Hall, 2011). Aberrant activation of GTPases, including Ras, Rho, Rab or Ran GTPases, promotes cell transformation and cancer (Agola et al, 2011Ly et al, 2010Pylayeva-Gupta et al, 2011), in some cases by acting in the mTOR pathway. Targeting GTPases by using farnesyltransferase inhibitors or geranylgeranyltransferase inhibitors affects signal transduction pathways, cell cycle progression, proliferation and cell survival. Both types of inhibitor are currently under investigation for cancer therapy, although only a small subset of patients responds to these inhibitors (Berndt et al, 2011). A better understanding of the relationship between GTPases and mTOR is essential for the design of combined therapies.

From a mechanistic point of view, research on TOR in different systems is continually adding new insight on the role of TOR in cell biology. However, what is lacking is an integration of the various proposed regulators of TOR, in particular small GTPases (see Sidebar A).

Sidebar A | In need of answers

  1. How are amino acids sensed by the cell?
  2. What is the mechanism by which amino acids regulate the GTP-loading of Rag proteins? What are the GEF and GAP for the Rag proteins?
  3. Is there a GEF that regulates the GTP-loading of Rheb?
  4. What is the molecular mechanism by which Rheb activates TORC1?
  5. How is the dual effect of Rac1 being both upstream and downstream from TOR regulated?
  6. How are the diverse GTPases that impinge on TOR integrated?

7.8.5 PI3K.Akt signaling in osteosarcoma

Zhang J1Yu XH2Yan YG1Wang C1Wang WJ3.
Clin Chim Acta. 2015 Apr 15; 444:182-192.
http://dx.doi.org:/10.1016/j.cca.2014.12.041

Highlights

  • Activation of the PI3K/Akt signaling regulates various cellular functions.
  • The PI3K/Akt signaling may play a key role in the progression of osteosarcoma.
  • Targeting the PI3K/Akt signaling has therapeutic potential for osteosarcoma.

Osteosarcoma (OS) is the most common nonhematologic bone malignancy in children and adolescents. Despite the advances of adjuvant chemotherapy and significant improvement of survival, the prognosis remains generally poor. As such, the search for more effective anti-OS agents is urgent. The phosphatidylinositol 3-kinase (PI3K)/Akt pathway is thought to be one of the most important oncogenic pathways in human cancer. An increasing body of evidence has shown that this pathway is frequently hyperactivated in OS and contributes to disease initiation and development, including tumorigenesis, proliferation, invasion, cell cycle progression, inhibition of apoptosis, angiogenesis, metastasis and chemoresistance. Inhibition of this pathway through small molecule compounds represents an attractive potential therapeutic approach for OS. The aim of this review is to summarize the roles of the PI3K/Akt pathway in the development and progression of OS, and to highlight the therapeutic potential of targeting this signaling pathway. Knowledge obtained from the application of these compounds will help in further understanding the pathogenesis of OS and designing subsequent treatment strategies.

PK.Akt signaling

PK.Akt signaling

http://ars.els-cdn.com/content/image/1-s2.0-S0009898115001059-gr1.sml

PI3K/Akt signaling

PI3K.Akt signaling pathway

PI3K.Akt signaling pathway

http://ars.els-cdn.com/content/image/1-s2.0-S0009898115001059-gr2.sml

PI3K/Akt signaling pathway

PK.Akt therapeutic target

PK.Akt therapeutic target

http://ars.els-cdn.com/content/image/1-s2.0-S0009898115001059-gr3.sml

PK/Akt therapeutic target

7.8.6 The mTORC1-S6K1 Pathway Regulates Glutamine Metabolism through the eIF4B-Dependent Control of c-Myc Translation

Csibi A1Lee G1Yoon SO1Tong H2,…, Fendt SM4Roberts TM2Blenis J5.
Curr Biol. 2014 Oct 6; 24(19):2274-80.
http://dx.doi.org:/10.1016/j.cub.2014.08.007

Growth-promoting signaling molecules, including the mammalian target of rapamycin complex 1 (mTORC1), drive the metabolic reprogramming of cancer cells required to support their biosynthetic needs for rapid growth and proliferation. Glutamine is catabolyzed to α-ketoglutarate (αKG), a tricarboxylic acid (TCA) cycle intermediate, through two deamination reactions, the first requiring glutaminase (GLS) to generate glutamate and the second occurring via glutamate dehydrogenase (GDH) or transaminases. Activation of the mTORC1 pathway has been shown previously to promote the anaplerotic entry of glutamine to the TCA cycle via GDH. Moreover, mTORC1 activation also stimulates the uptake of glutamine, but the mechanism is unknown. It is generally thought that rates of glutamine utilization are limited by mitochondrial uptake via GLS, suggesting that, in addition to GDH, mTORC1 could regulate GLS. Here we demonstrate that mTORC1 positively regulates GLS and glutamine flux through this enzyme. We show that mTORC1 controls GLS levels through the S6K1-dependent regulation of c-Myc (Myc). Molecularly, S6K1 enhances Myc translation efficiency by modulating the phosphorylation of eukaryotic initiation factor eIF4B, which is critical to unwind its structured 5′ untranslated region (5’UTR). Finally, our data show that the pharmacological inhibition of GLS is a promising target in pancreatic cancers expressing low levels of PTEN.

Highlights

  • The mTORC1 pathway positively regulates GLS and glutamine flux
  • mTORC1 controls the translation efficiency of Myc mRNA
  • S6K1 regulates Myc translation through eIF4B phosphorylation
  • Inhibition of GLS decreases the growth of pancreatic cancer cells

Figure 1. The mTORC1 Pathway Regulates GLS1 (A–C and E) GLS protein levels in whole cell lysates from Tsc2 WT and Tsc22/2 MEFs treated with rapamycin (Rapa) for 8 hr (A); HEK293T cells stably expressing Rheb WT, the mutant S16H Rheb, or EV and treated with rapamycin for 24 hr (B); Tsc22/2 MEFs treated with rapamycin at the indicated time points (C); and Tsc2 WT and Tsc22/2 MEFs treated with the indicated compounds for 8 hr (E). The concentrations of the compounds were as follows: rapamycin, 20 ng/ml; LY294002 (LY), 20 mM; and BEZ235, 10 mM. (D) Time course of glutamine consumption in Tsc22/2 MEFs incubated with or without 20ng/ml rapamycin for 24 hr. Each time data point is an average of triplicate experiments. (F) Intracellular glutamine levels in Tsc22/2 MEFs treated with rapamycin for 24 hr. (G) Glutamineflux inTsc22/2 MEFs expressing an EV or re-expressingTSC2 treated with theindicated compounds for 24hr.The concentrations of the compounds were as follows: rapamycin 20 ng/ml; LY294002, 20 mM; BEZ235, 10 mM; BPTES, 10 mM; and 6-diazo-5-oxo-l-norleucine, 1mM. The mean is shown. Error bars represent the SEM from at least three biological replicates. Numbers below the immunoblot image represent quantification normalized to the loading control. See also Figure S1.

Figure2. The mTORC1 Pathway Regulates GLS1 via Myc GLS and Myc protein levels in whole cell lysates from BxPC3 cells transfected with a nontargeting control (NTC) siRNA or four independent siRNAs against Myc for 72 hr (A), Tsc2 WT and Tsc22/2 MEFs treated with rapamycin (20 ng/ml) for 8 hr (B), and Tsc22/2 MEFs stably expressing Myc or EV and treated with rapamycin (20 ng/ml) for 24 hr (C).

Figure 3. The mTORC1 Substrate S6K1 Controls GLS through Myc mRNA Translation (A) Normalized luciferase light units of Tsc22/2 MEFs stably expressing a Myc-responsive firefly luciferase construct (Myc-Luc) or vector control (pCignal Lenti-TRE Reporter). Myc transcriptional activity was measured after treatment with rapamycin (20 ng/ml) or PF4708671 (10 mM) for 8 hr. (B) GLS and Myc protein levels in whole cell lysates from HEK293T cells expressing HA-S6K1-CA (F5A-R3A-T389E) or EV treated with rapamycin (20 ng/ml) for 24 hr. HA, hemagglutinin. (CandD) Intracellular glutamine levels of Tsc22/2 MEFs stably expressing S6K-CA(F5A/R5A/T389E, mutating either the three arginines or all residues within the RSPRR motif to alanines shows the same effect; [10]) or empty vector and treated with rapamycin (20 ng/ml) or DMSO for 48 hr (C) or transfected with NTC siRNA or siRNA against both S6K1/2 (D). 24 hr posttransfection, cells transfected with NTC siRNA were treated with PF4708671 (10 mM) or DMSO for 48 hr. (E) Glutamine consumption of Tsc22/2 MEFs transfected with NTC siRNA or siRNA against both S6K1/2. 72 hr posttransfection, media were collected, and levels of glutamine in the media were determined. (F) Normalized luciferase light units of Tsc2WTMEFs transfected with thepDL-N reporter construct containing the 50 UTR of Myc under the control of Renilla luciferase. Firefly luciferase was used as an internal control. 48hr posttransfection, cells were treated with rapamycin (20ng/ml) or PF4708671 (10mM) for 8h. (G) Relative levels of Myc, Gls, and Actin mRNA in each polysomal gradient fraction. mRNA levels were measured by quantitative PCR and normalized to the 5S rRNA level. HEK293T cells were treated with rapamycin (20 ng/ml) for 24 hr, and polysomes were fractionated on sucrose density gradients. The values are averaged from two independent experiments performed in duplicate, and the error bars denote SEM (n = 4). (Hand I) GLS and Myc protein levels in whole cell lysates from Tsc22/2 MEFs transfected with NTC siRNA or two independent siRNAs against eIF4B for 72hr (H) and Tsc22/2 MEFs stably expressing eIF4B WT, mutant S422D, or EV) and treated with rapamycin for 24 hr (I). The mean is shown. Error bars represent the SEM from at least three biological replicates. The asterisk denotes a nonspecific band. The numbers below the immunoblot image represent quantification normalized to the loading control. See also Figures S2 and S3.

Figure 4. Inhibition of GLS Reduces the Growth of Pancreatic Cancer Cells (A) GLS and Myc protein levels in whole cell lysates from BxPC3, MIAPaCa-2, or AsPC-1 cells treated with rapamycin (20 ng/ml) or BEZ235 (1 mM) for 24 hr. (B) Glutamine consumption of BxPC3 or AsPC-1 cells 48 hr after plating. (Cand D) Soft agar assays with BxPC3 or AsPC-1 cells treated with BPTES (10 mM), the combination of BPTES (10 mM) + OAA (2 mM) (C) and BxPC3 or AsPC-1 cells treated with BPTES, and the combination of BPTES (10 mM) + NAC (10 mM) (D). NS, not significant. The mean is shown. Error bars represent the SEM from at least three biological replicates.

7.8.7 Localization of mouse mitochondrial SIRT proteins

Nakamura Y1Ogura MTanaka DInagaki N.
Biochem Biophys Res Commun. 2008 Feb 1; 366(1):174-9
http://www.ncbi.nlm.nih.gov/pubmed/18054327#

Yeast silent information regulator 2 (SIR2) is involved in extension of yeast longevity by calorie restriction, and SIRT3, SIRT4, and SIRT5 are mammalian homologs of SIR2 localized in mitochondria. We have investigated the localization of these three SIRT proteins of mouse. SIRT3, SIRT4, and SIRT5 proteins were localized in different compartments of the mitochondria. When SIRT3 and SIRT5 were co-expressed in the cell, localization of SIRT3 protein changed from mitochondria to nucleus. These results suggest that the SIRT3, SIRT4, and SIRT5 proteins exert distinct functions in mitochondria. In addition, the SIRT3 protein might function in nucleus

Fig. 1. Localization of SIRT3, SIRT4, and SIRT5 in mitochondria. (A) Confocal microscopy. SIRT3-myc (upper panels), SIRT4-myc (middle panels), and SIRT5-FLAG (lower panels) were expressed in COS7 cells and immunostained with anti-myc antibody or anti-FLAG antibody. Mitochondria and nuclei were stained by MitoTracker Red and DAPI, respectively, and fluorescent images were obtained using a confocal microscope. (B) Fractionation of post-nuclear supernatant. SIRT3-myc, SIRT4-myc, and SIRT5-FLAG proteins each was expressed in COS7 cells, and the obtained PNS was fractionated into mitochondria-enriched precipitate (P1), microsome-enriched precipitate (P2), and supernatant (S) fractions. The three fractions were separated by SDS–PAGE and then analyzed by Western blotting using anti-myc antibody for SIRT3-myc and SIRT4-myc or anti-FLAG antibody for SIRT5-FLAG. Hsp60, calnexin, and GAPDH were used as endogenous markers for mitochondria, microsome, and cytosol, respectively. (C) Alkaline treatment of mitochondria. Mitochondria prepared from the COS7 cells expressing each of the SIRT3-myc, SIRT4-myc, and SIRT5-FLAG proteins were treated with Na2CO3. The reaction mixture was centrifuged to separate the precipitate and supernatant fractions, containing membrane-integrated proteins and soluble proteins, respectively. The two fractions were analyzed by Western blotting. Cytochrome c (cytc) and hsp60 were used as endogenous protein markers for mitochondrial soluble protein. (D) Submitochondrial fractionation. The mitochondria from COS7 cells expressing one of three SIRT proteins were treated with either H2O (hypotonic) or TX-100, and then treated with trypsin. The reaction mixtures were analyzed by Western blotting. Cytochrome c and hsp60 were used as endogenous markers for mitochondrial intermembrane space protein and matrix protein, respectively.

Fig. 2. Localization of SIRT3 when co-expressed with SIRT5. (A) Confocal microscopic analysis of COS7 cells expressing two of the three mitochondrial SIRT proteins. SIRT3-myc and SIRT5-FLAG (upper panels), SIRT3-myc and SIRT4-FLAG (middle panels), and SIRT4-myc and SIRT5-FLAG (lower panels) were co-expressed in COS7 cells, and immunostained using antibodies against myc tag and FLAG tag. Nuclei were stained by DAPI. (B) Subcellular fractionation of PNS. PNS of COS7 cells co-expressing SIRT3-myc and SIRT5-FLAG was fractionated into mitochondria-enriched precipitate (P1), microsome-enriched precipitate (P2), and supernatant (S) fractions, and these fractions along with whole cell lysate were analyzed by Western blotting. (C) Subcellular fractionation using digitonin. COS7 cells expressing either SIRT3-myc (left) or SIRT5-FLAG (middle) or both (right) were solubilized by digitonin, and the obtained lysate was centrifuged and fractionated into nuclear-enriched insoluble (INS), and soluble (SOL) fractions. Hsp60 and laminA/C were used as endogenous markers for mitochondria protein and nucleus protein, respectively.

Because the segment containing amino acid residues 66– 88 potentially forms a basic amphiphilic a-helical structure, it could serve as a MTS. To examine the role of this segment, SIRT3 mutant SIRT3mt, in which the four amino acid residues 72–75 were replaced by four alanine residues, was constructed (Fig. 3A). When SIRT3mt alone was expressed in COS7 cells, SIRT3mt protein was not detected in mitochondria but was widely distributed in the cell in confocal microscopic analysis (Fig. 3B, upper panels). In addition, when SIRT3mt and SIRT5 were co-expressed, the distribution of SIRT3mt protein was not changed compared to that expressed alone (Fig. 3B, lower panels). In fractionation of PNS, SIRT3mt protein was fractionated into S fraction both when SIRT3mt was expressed alone and when SIRT3mt and SIRT5 were co-expressed. SIRT5 protein was localized in mitochondria when SIRT3mt and SIRT5 were co-expressed (Fig. 3C). These results indicate that the MTS is necessary not only for targeting SIRT3 to mitochondria in the absence of SIRT5 but also for targeting SIRT3 to nucleus in the presence of SIRT5.

Fig. 3. Effect of disruption of putative mitochondrial targeting signal of SIRT3. (A) Alanine replacement of putative MTS of SIRT3. Four residues of the putative MTS of SIRT3 (amino acid residues 72–75) were replaced with four alanine residues. In the SIRT3mt sequence, amino acid residues identical with wild-type SIRT3 protein are indicated with dots. (B) Confocal microscopy. Immunofluorescent images of COS7 cells expressing SIRT3mt-myc alone (upper panels) or both SIRT3mt-myc and SIRT5-FLAG (lower panels) are shown. Mitochondria and nuclei were stained by MitoTracker Red and DAPI, respectively. (C) Subcellular fractionation of PNS. PNSs of COS7 cells expressing SIRT3mt-myc alone (an upper panel) or co-expressing SIRT3mt-myc and SIRT5-FLAG (middle and lower panels) were centrifuged and fractionated into mitochondria-enriched precipitate (P1), microsome-enriched precipitate (P2), and supernatant (S) fractions. The fractions were analyzed by Western blotting.

Fig. 4. Effect of disruption of putative nuclear localization signal of SIRT3. (A) Comparison of the amino acid sequences of putative NLS of SIRT3, SIRT3nu, and SV40 large T antigen. Three basic amino acid residues of the putative NLS of SIRT3 (amino acid residues 214–216) were replaced with three alanine residues. In the SIRT3nu sequence, amino acid residues identical with wild-type SIRT3 protein are indicated with dots. The classical NLS of SV40 large T antigen also is shown (SV40). (B) Confocal microscopy. Immunofluorescent images of COS7 cells expressing SIRT3nu-myc alone (upper panels) or both SIRT3nu-myc and SIRT5-FLAG (lower panels) are shown. Mitochondria and nuclei were stained by MitoTracker Red and DAPI, respectively. (C) Subcellular fractionation of PNS. PNSs of the COS7 cells expressing SIRT3nu-myc alone (an upper panel) or co-expressing SIRT3numyc and SIRT5-FLAG (middle and lower panels) were fractionated into mitochondria-enriched precipitate (P1), microsome-enriched precipitate (P2), and supernatant (S) fractions. The fractions were analyzed by Western blotting.

The sequence containing amino acid sequence 213-219 of the SIRT3 closely resembles the putative protein classical NLS of the SV40 T antigen (Fig. 4A). To examine whether this sequence functions as a NLS, the mutant SIRT3 protein SIRT3nu, in which the three basic amino acid residues (214–216) in the putative NLS of SIRT3 were replaced by three alanine residues (Fig. 4A), was constructed. When SIRT3nu alone was expressed in COS7 cells, it was localized in mitochondria (Fig. 4B, upper panels). In the cells co-expressing SIRT3nu and SIRT5, a shift of SIRT3nu protein to the nucleus was not observed, and SIRT3nu protein and a part of SIRT5 protein were scattered widely in the cell in confocal microscopic analysis (Fig. 4B, lower panels). In fractionation of PNS, all of the SIRT3nu protein and nearly half of the SIRT5 protein were shifted from P1 fraction to S fraction by co-expression (Figs. 1B and 4C). These results suggest that the segment containing amino acid residues 213–219 of SIRT3 plays an important role in the localization shift of SIRT3 protein to nucleus when co-expressed with SIRT5. Furthermore, SIRT5 may well hamper SIRT3nu localization in mitochondria through interaction with SIRT3nu. However, further study is required to elucidate the mechanism of the localization shift of SIRT3 protein. Interestingly, recent study has reported that human prohibitin 2 (PHB2), known as a repressor of estrogen receptor (ER) activity, is localized in the mitochondrial inner membrane, and translocates to the nucleus in the presence of ER and estradiol [18]. Although the mechanism of regulation of the expression level of SIRT5 remains unknown, SIRT3 might play a role in communication between nucleus and mitochondria in a SIRT5-dependent manner. The function of mitochondrial SIRT proteins is still not well known. In the present study, we determined the exact localization of mouse SIRT3, SIRT4, and SIRT5 proteins in mitochondria. In addition, we demonstrated that SIRT3 can be present in nucleus in the presence of SIRT5. It has been reported that SIRT3 deacetylates proteins that are not localized in mitochondria in vitro such as histone-4 peptide and tubulin [14]. Thus, if SIRT3 is present in nucleus in vivo, SIRT3 protein might well deacetylate nuclear proteins. These results provide useful information for the investigation of the function of these proteins.

References

[1] J.C. Tanny, G.J. Dowd, J. Huang, H. Hilz, D. Moazed, An enzymatic activity in the yeast Sir2 protein that is essential for gene silencing, Cell 99 (1999) 735–745.
[2] S. Imai, C.M. Armstrong, M. Kaeberlein, L. Guarente, Transcriptional silencing and longevity protein Sir2 is an NAD-dependent histone deacetylase, Nature 403 (2000) 795–800.
[3] M. Gotta, S. Strahl-Bolsinger, H. Renauld, T. Laroche, B.K. Kennedy, M. Grunstein, S.M. Gasser, Localization of Sir2p: the nucleolus as a compartment for silent information regulators, EMBO J. 16 (1997) 3243–3255.
[4] I. Muller, M. Zimmermann, D. Becker, M. Flomer, Calendar life span versus budding life span of Saccharomyces cerevisiae, Mech. Aging Dev. 12 (1980) 47–52.
[5] S.J. Lin, M. Kaeberlein, A.A. Andalis, L.A. Sturtz, P.A. Defossez, V.C. Culotta, G.R. Fink, L. Guarente, Calorie restriction extends Saccharomyces cerevisiae lifespan by increasing respiration, Nature 418 (2002) 344–348.
[6] S.J. Lin, P.A. Defossez, L. Guarente, Requirement of NAD and SIR2 for life-span extension by calorie restriction in Saccharomyces cerevisiae, Science 289 (2000) 2126–2128.

7.8.8 SIRT4 Has Tumor-Suppressive Activity and Regulates the Cellular Metabolic Response to DNA Damage by Inhibiting Mitochondrial Glutamine Metabolism

Jeong SM1Xiao CFinley LWLahusen TSouza ALPierce KLi YH, et al.
Cancer Cell. 2013 Apr 15; 23(4):450-63.
http://www.ncbi.nlm.nih.gov/pubmed/23562301#
http://dx.doi.org:/10.1016/j.ccr.2013.02.024

DNA damage elicits a cellular signaling response that initiates cell cycle arrest and DNA repair. Here we find that DNA damage triggers a critical block in glutamine metabolism, which is required for proper DNA damage responses. This block requires the mitochondrial SIRT4, which is induced by numerous genotoxic agents and represses the metabolism of glutamine into TCA cycle. SIRT4 loss leads to both increased glutamine-dependent proliferation and stress-induced genomic instability, resulting in tumorigenic phenotypes. Moreover, SIRT4 knockout mice spontaneously develop lung tumors. Our data uncover SIRT4 as an important component of the DNA damage response pathway that orchestrates a metabolic block in glutamine metabolism, cell cycle arrest and tumor suppression.

DNA damage initiates a tightly coordinated signaling response to maintain genomic integrity by promoting cell cycle arrest and DNA repair. Upon DNA damage, ataxia telangiectasia mutated (ATM) and ataxia telangiectasia and RAD3-related protein (ATR) are activated and induce phosphorylation of Chk1, Chk2 and γ-H2AX to trigger cell cycle arrest and to initiate assembly of DNA damage repair machinery (Abraham, 2001Ciccia and Elledge, 2010Su, 2006). Cell cycle arrest is a critical outcome of the DNA damage response (DDR) and defects in the DDR often lead to increased incorporation of mutations into newly synthesized DNA, the accumulation of chromosomal instability and tumor development (Abbas and Dutta, 2009Deng, 2006Negrini et al., 2010).

The cellular metabolic response to DNA damage is not well elucidated. Recently, it has been shown that DNA damage causes cells to upregulate the pentose phosphate pathway (PPP) to generate nucleotide precursors needed for DNA repair (Cosentino et al., 2011). Intriguingly, a related metabolic switch to increase anabolic glucose metabolism has been observed for tumor cells and is an important component of rapid generation of biomass for cell growth and proliferation (Jones and Thompson, 2009Koppenol et al., 2011). Hence, cells exposed to genotoxic stress face a metabolic challenge; they must be able to upregulate nucleotide biosynthesis to facilitate DNA repair, while at the same time limiting proliferation and inducing cell cycle arrest to limit the accumulation of damaged DNA. The molecular events that regulate this specific metabolic program in response to DNA damage are still unclear.

Sirtuins are a highly conserved family of NAD+-dependent deacetylases, deacylases, and ADP-ribosyltransferases that play various roles in metabolism, stress response and longevity (Finkel et al., 2009;Haigis and Guarente, 2006). In this study, we studied the role of SIRT4, a mitochondria-localized sirtuin, in cellular metabolic response to DNA damage and tumorigenesis.

DNA damage represses glutamine metabolism

To investigate how cells might balance needs for continued nucleotide synthesis, while also preparing for cell cycle arrest, we assessed the metabolic response to DNA damage by monitoring changes in the cellular consumption of two important fuels, glucose and glutamine, after DNA-damage. Strikingly, treatment of primary mouse embryonic fibroblasts (MEFs) with camptothecin (CPT), a topoisomerase 1 inhibitor that causes double-stranded DNA breaks (DSBs), resulted in a pronounced reduction in glutamine consumption (Figure 1A). Glutamine metabolism in mammalian cells is complex and contributes to a number of metabolic pathways. Glutamine is the primary nitrogen donor for protein and nucleotide synthesis, which are essential for cell proliferation (Wise and Thompson, 2010). Additionally, glutamine provides mitochondrial anaplerosis. Glutamine can be metabolized via glutaminase (GLS) to glutamate and NH4+, and further converted to the tricarboxylic acid (TCA) cycle intermediate α-ketoglutarate via glutamate dehydrogenase (GDH) or aminotransferases. This metabolism of glutamine provides an important entry point of carbon to fuel the TCA cycle (Jones and Thompson, 2009), and accounts for the majority of ammonia production in cells (Yang et al., 2009). CPT-induced reduction of glutamine consumption was accompanied by a reduction in ammonia secretion from cells (Figure 1B). Notably, under these conditions, we observed no obvious decrease in glucose uptake and lactate production (Figures 1C and 1D), consistent with previous studies showing that intact glucose utilization through the PPP is important for a normal DNA damage response (Cosentino et al., 2011). Preservation of glucose uptake also suggests that repression of glutamine consumption may be a specific metabolic response to genotoxic stress and not reflective of a non-specific metabolic crisis.

Figure 1 Glutamine metabolism is repressed by genotoxic stress

To examine the metabolic response to other forms of genotoxic stress, we monitored the metabolic response to ultra-violet (UV) exposure in primary MEFs. Similar to CPT treatment, UV exposure reduced glutamine uptake, without significant changes in glucose consumption (Figures 1E and 1F). Similarly two human cell lines, HepG2 and HEK293T, also demonstrated marked reductions in glutamine uptake in response to DNA damaging agents without comparable changes in glucose uptake (Figures 1G and 1HFigures S1A and S1B). Taken together, these results suggest that a variety of primary and tumor cell lines (from mouse or human) respond to genotoxic stress by down-regulating glutamine metabolism.

To examine in more detail the changes in cellular glutamine metabolism after genotoxic stress, we performed a global metabolomic analysis with transformed MEFs before and after DNA damage. As previously reported, we observed that PPP intermediates were increased in response to DNA damage (Figures 1I and 1J). Remarkably, we observed a decrease in measured TCA cycle intermediates after UV exposure (Figures 1I and 1K). Moreover, we found that HepG2 cells showed a similar metabolomic shift in response to DNA damage (Figure S1D). We did not observe a clear, coordinated repression of nucleotides or glutamine-derived amino acids after exposure to DNA damage (Figure S1C).

To determine whether reduction in TCA cycle metabolites was the consequence of reduced glutamine metabolism, we performed a time-course tracer study to monitor the incorporation of [U-13C5]glutamine into TCA cycle intermediates at 0, 2 and 4 hr after UV treatment. We observed that after UV exposure, cells reduced contribution of glutamine to TCA cycle intermediates in a time-dependent manner (Figure 1L). Moreover, the vast majority of the labeled fumarate and malate contained four carbon atoms derived from [U-13 C5]glutamine (Figure S1F, M+3 versus M+4), indicating that most glutamine was used in the non-reductive direction towards succinate, fumarate and malate production. We were able to observe little contribution of glutamine flux into nucleotides or glutathione in control or UV-treated cells at these time points (data not shown), suggesting that the mitochondrial metabolism of glutamine accounts for the majority of glutamine consumption in these cells. Taken together, the metabolic flux analysis demonstrates that DNA damage results in a reduction of mitochondrial glutamine anaplerosis, thus limiting the critical refueling of carbons into the TCA cycle.

To assess the functional relevance of decreased glutamine metabolism after DNA damage, we deprived cells of glucose, thereby shifting cellular dependence to glutamine to maintain viability (Choo et al., 2011Dang, 2010). If DNA damage represses glutamine usage, we reasoned that cells would be more sensitive to glucose deprivation. Indeed, following 72 hr of glucose deprivation, cell death in primary MEFs was significantly elevated at 10 hr after UV exposure (Figure S1E). However, cells cultured with glucose remained viable in these conditions. Thus, these data demonstrate that genotoxic stress limits glutamine entry into the central mitochondrial metabolism of the TCA cycle.

SIRT4 is induced in response to genotoxic stress

Because sirtuins regulate both cellular metabolism and stress responses (Finkel et al., 2009Schwer and Verdin, 2008), we examined whether sirtuins were involved in the metabolic adaptation to DNA damage. We first examined the expression of sirtuins in the response to DNA damage. Specifically, we probed SIRT1, which is involved in stress responses (Haigis and Guarente, 2006), as well as mitochondrial sirtuins (SIRT3–5), which have been shown to regulate amino acid metabolism (Haigis et al., 2006Hallows et al., 2011Nakagawa et al., 2009). Remarkably, SIRT4 mRNA levels were induced by nearly 15-fold at 15 hr after CPT treatment and 5-fold after etoposide (ETS), a topoisomerase 2 inhibitor, in HEK293T cells (Figure 2A). Interestingly, the induction of SIRT4 was significantly higher than the induction of SIRT1 and mitochondrial SIRT3 (~2-fold), sirtuins known to be induced by DNA damage and regulate cellular responses to DNA damage (Sundaresan et al., 2008Vaziri et al., 2001Wang et al., 2006). Moreover, overall mitochondrial mass was increased by only 10% in comparison with control cells (Figure S2A), indicating that the induction of SIRT4 is not an indirect consequence of mitochondrial biogenesis. These data hint that SIRT4 may have an important, previously undetermined role in the DDR.

Figure 2 SIRT4 is induced by DNA damage stimuli

To test the induction of SIRT4 in the general genotoxic stress response, we treated cells with other types of DNA damage, including UV and gamma-irradiation (IR). SIRT4 mRNA levels were also increased by these genotoxic agents (Figures S2B and S2C) and low doses of CPT and UV treatment also induced SIRT4expression (Figures S2D and S2E). We observed similar results with MEFs (Figures 2B and 2DFigure S2F) and HepG2 cells (Figure S2G). DNA damaging agents elevated SIRT4 in p53-inactive HEK293T cells (Figures 2A and 2C) and in p53-null PC3 human prostate cancer cells (Figure S2H), suggesting that SIRT4can be induced in a p53-independent manner.

To examine whether the induction of SIRT4 occurred as a result of cell cycle arrest, we measured SIRT4levels after the treatment of nocodazole, which inhibits microtubule polymerization to block mitosis. While treatment with nocodazole completely inhibited cell proliferation (data not shown), SIRT4 expression was not elevated (Figure S2I). In addition, we analyzed SIRT4 expression in distinct stages of the cell cycle in HepG2 cells synchronized with thymidine block (Figure S2J, Left). SIRT4 mRNA levels were measured at different times after release and were not elevated during G1 or G2/M phases (Figure S2J, Right), suggesting thatSIRT4 is not induced as a general consequence of cell cycle arrest. Next, we re-examined the localization of SIRT4 after DNA damage. SIRT4 localizes to the mitochondria of human and mouse cells under basal, unstressed conditions (Ahuja et al., 2007Haigis et al., 2006). Following CPT treatment, SIRT4 colocalized with MitoTracker, a mitochondrial-selective marker, indicating that SIRT4 retains its mitochondrial localization after exposure to DNA damage (Figure S2K). Taken together, our findings demonstrate that SIRT4 is induced by multiple forms of DNA damage in numerous cell types, perhaps to coordinate the mitochondrial response to genotoxic stress.

SIRT4 represses glutamine anaplerosis

We observed that glutamine anaplerosis is repressed by genotoxic stress (Figure 1) and SIRT4 is induced by DNA damage (Figure 2). Additionally, previous studies reported that SIRT4 represses glutamine anaplerosis (Haigis et al., 2006). We next tested whether SIRT4 directly regulates cellular glutamine metabolism and contribution of glutamine to the TCA cycle. Like DNA damage, SIRT4 overexpression (SIRT4-OE) in HepG2, HeLa or HEK293T cells resulted in the repression of glutamine consumption (Figure 3AFigures S3A–C). Conversely, SIRT4 knockout (KO) MEFs consumed more glutamine than did wild-type (WT) cells (Figure 3B).

Figure 3 SIRT4 represses mitochondrial glutamine metabolism in response to DNA damage

Mitochondrial glutamine catabolism refuels the TCA cycle and is essential for viability in the absence of glucose (Choo et al., 2011Yang et al., 2009). Thus, we examined the effect of SIRT4 on cell survival during glucose deprivation. Overexpression of SIRT4 in HEK293T or HeLa cells increased cell death in glucose-free media compared to control cells (Figure 3CFigure S3D). Importantly, this cell death was completely rescued by the addition of pyruvate or cell permeable dimethyl α-ketoglutarate (DM-KG), demonstrating that SIRT4 overexpression reduced the ability of cells to utilize glutamine for mitochondrial energy production. Moreover, cell death was equally maximized in the absence of glucose and presence of the mitochondrial ATPase inhibitor oligomycin (Figure 3C). These findings are in line with the model that SIRT4 induction with DNA damage limits glutamine metabolism and utilization by the TCA cycle

We next utilized a metabolomic approach to interrogate glutamine usage in the absence of SIRT4. SIRT4 KO MEFs demonstrated elevated levels of TCA cycle intermediates (Figure 3J, WT versus KO), whereas intermediates of glycolysis were comparable with WT cells (data not shown). Nucleotides and other metabolites downstream of glutamine metabolism were not coordinately regulated by SIRT4 loss (Figure S3E and data not shown). Next, we analyzed glutamine flux in WT and SIRT4 KO MEFs in medium containing [U-13C5]glutamine for 2 or 4 hours and measured isotopic enrichment of TCA cycle intermediates. Loss of SIRT4 promoted a higher rate of incorporation of 13C-labeled metabolites derived from [U-13C5]glutamine in all TCA cycle intermediates measured (Figure 3D). These data provide direct evidence that SIRT4 loss drives increased entry of glutamine-derived carbon into the TCA cycle.

Next, we examined the mechanisms involved in this repression of glutamine anaplerosis. GLS is the first required enzyme for mitochondrial glutamine metabolism (Curthoys and Watford, 1995) and its inhibition limits glutamine flux into the TCA cycle (Wang et al., 2010; Le et al., 2012; Yuneva et al., 2012). Treatment with bis-2-(5-phenylacetoamido-1,2,4-thiadiazol-2-yl)ethyl sulfide (BPTES) (Robinson et al., 2007), an inhibitor of GLS1, repressed glutamine uptake and completely rescued the increased glutamine consumption of SIRT4 KO cells (Figure 3E). Moreover, SIRT4 overexpression no longer inhibited glutamine uptake when GLS1 was reduced by using short hairpin RNAs (shRNAs) (Figures 3F and 3G), demonstrating that SIRT4 regulates mitochondrial glutamine metabolism. SIRT4 is a negative regulator of GDH activity (Haigis et al., 2006) and SIRT4 KO MEFs exhibited increased GDH activity in comparison with WT MEFs (Figure S3F). To test whether SIRT4 regulates mitochondrial glutamine metabolism via inhibiting GDH activity, we measured glutamine uptake in WT and SIRT4 KO cells in the presence of EGCG, a GDH inhibitor (Choo et al., 2011Li et al., 2006). The treatment of EGCG partially rescued the increased glutamine uptake of KO cells (Figure S3G), suggesting that GDH contributes to the role of SIRT4 in glutamine metabolism.

SIRT4 represses mitochondrial glutamine metabolism after DNA damage

SIRT4 regulates cell cycle progression and genomic fidelity in response to DNA damage

Figure 4 SIRT4 is involved in cellular DNA damage responses

SIRT4 represses tumor proliferation

Figure 5 SIRT4 has tumor suppressive function

(A and B) Growth curves of WT and SIRT4 KO MEFs (n = 3) cultured in standard media (A) or media supplemented with BPTES (10 μM) (B). Data are means ±SD.

(C and D) Growth curves of Vector and SIRT4-OE HeLa cells (n = 3) cultured in standard media (C) or media supplemented with BPTES (10 μM) (D). Data are means ±SD.

(E) Focus formation assays with transformed WT and SIRT4 KO MEFs (left). Cells were cultured with normal medium or medium without glucose or glutamine for 10 days and stained with crystal violet. The number of colonies was counted (right) (n =3 samples of each condition). n.d., not determined.

(F) Focus formation assays with transformed KO MEFs reconstituted with SIRT4 or a catalytic mutant of SIRT4 (n = 3). Cells were cultured for 8 days and stained with crystal violet.

(G) Contact inhibited cell growth of transformed WT and SIRT4 KO MEFs cultured in the presence of DMSO or BPTES (10 μM) for 14 days (left). The number of colonies was counted (right). Data are means ±SEM. n.s., not significant. *p < 0.05, **p < 0.005. See also Figure S5.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3650305/bin/nihms451579f5.jpg

SIRT4 represses tumor formation in vivo

To investigate SIRT4 function in human cancers, we examined changes in SIRT4 expression. SIRT4 mRNA level was reduced in several human cancers, such as small cell lung carcinoma (Garber et al., 2001), gastric cancer (Wang et al., 2012), bladder carcinoma (Blaveri et al., 2005), breast cancer (TCGA) and leukemia (Choi et al., 2007) (Figure 6A). Of note, lower SIRT4 expression associated with shorter time to death in lung tumor patients (Shedden et al., 2008) (Figure 6B). Overall the expression data is consistent with the model that SIRT4 may play a tumor suppressive role in human cancers.

Figure 6 SIRT4 is a mitochondrial tumor suppressor

SIRT4 regulates glutamine metabolism in lung tissue

To test further the biological relevance of this pathway in lung, we examined whether SIRT4 is induced in vivo after exposure to DNA damaging IR treatment. Remarkably, Sirt4 was significantly induced in lung tissue after IR exposure (Figure 7A). We next examined whether IR repressed glutamine metabolism in vivo, as observed in cell culture by examining GDH activity in lung tissue from WT and SIRT4 KO mice with or without IR exposure. GDH activity was elevated in lung tissue extracts from SIRT4 KO mice compared with WT lung tissue (Figure 7B). Importantly, GDH activity was significantly decreased in lung tissue from WT mice after IR exposure, whereas not in lung tissue from KO mice (Figure 7C). Thus, these findings recapitulate our cellular studies and are in line with the model that SIRT4 induction with DNA damage limits mitochondrial glutamine metabolism and utilization.

SIRT4 inhibits mitochondria glutamine metabolism in vivo

SIRT4 inhibits mitochondria glutamine metabolism in vivo

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3650305/bin/nihms451579f7.gif

Figure 7 SIRT4 inhibits mitochondria glutamine metabolism in vivo

To assess whether the functions of SIRT4 can be reproduced in these lung tumors, cells derived from SIRT4 KO lung tumors were reconstituted with wild type SIRT4 (Figure S7A). As expected, SIRT4 reconstitution reduced glutamine uptake, but not glucose uptake (Figures 7D and 7E) and repressed proliferation (Figure S7B) of lung tumor cells.

Here, we report that SIRT4 has an important role in cellular metabolic response to DNA damage by regulating mitochondrial glutamine metabolism with important implication for the DDR and tumorigenesis. First, we discovered that DNA damage represses cellular glutamine metabolism (Figure 1). Next, we found that SIRT4 is induced by genotoxic stress (Figure 2) and is required for the repression of mitochondrial glutamine metabolism (Figure 3). This metabolic response contributes to the control of cell cycle progression and the maintenance of genomic integrity in response to DNA damage (Figure 4). Loss of SIRT4 increased glutamine-dependent tumor cell proliferation and tumorigenesis (Figure 5). In mice, SIRT4 loss resulted in spontaneous tumor development (Figure 6). We demonstrate that SIRT4 is induced in normal lung tissue in response to DNA damage where it represses GDH activity. Finally, the glutamine metabolism-genomic fidelity axis is recapitulated in lung tumor cells derived from SIRT4 KO mice via SIRT4 reconstitution (Figure 7). Our studies therefore uncover SIRT4 as a important regulator of cellular metabolic response to DNA damage that coordinates repression of glutamine metabolism, genomic stability and tumor suppression.

The DDR is a highly orchestrated and well-studied signaling response that detects and repairs DNA damage. Upon sensing DNA damage, the ATM/ATR protein kinases are activated to phosphorylate target proteins, leading to cell cycle arrest, DNA repair, transcriptional regulation and initiation of apoptosis (Ciccia and Elledge, 2010Su, 2006). Dysregulation of this pathway is frequently observed in many tumors. Emerging evidence has suggested that cell metabolism also plays key roles downstream of the DDR-induced pathways.

 

7.8.9 Mitochondrial sirtuins and metabolic homeostasis

Pirinen E1Lo Sasso GAuwerx J.
Best Pract Res Clin Endocrinol Metab. 2012 Dec; 26(6):759-70. http://dx.doi.org:/10.1016/j.beem.2012.05.001

The maintenance of metabolic homeostasis requires the well-orchestrated network of several pathways of glucose, lipid and amino acid metabolism. Mitochondria integrate these pathways and serve not only as the prime site of cellular energy harvesting but also as the producer of many key metabolic intermediates. The sirtuins are a family of NAD+-dependent enzymes, which have a crucial role in the cellular adaptation to metabolic stress. The mitochondrial sirtuins SIRT3, SIRT4 and SIRT5 together with the nuclear SIRT1 regulate several aspects of mitochondrial physiology by controlling posttranslational modifications of mitochondrial protein and transcription of mitochondrial genes. Here we discuss current knowledge how mitochondrial sirtuins and SIRT1 govern mitochondrial processes involved in different metabolic pathways.

Mitochondria are organelles composed of a matrix enclosed by a double (inner and outer) membrane (1). Major cellular functions, such as nutrient oxidation, nitrogen metabolism, and especially ATP production, take place in the mitochondria. ATP production occurs in a process referred to as oxidative phosphorylation (OXPHOS), which involves electron transport through a chain of protein complexes (I-IV), located in the inner mitochondrial membrane. These complexes carry electrons from electron donors (e.g. NADH) to electron acceptors (e.g. oxygen), generating a chemiosmotic gradient between the mitochondrial intermembrane space and matrix. The energy stored in this gradient is then used by ATP synthase to produce ATP (1). One well-known side effect of the OXPHOS process is the production of reactive oxygen species (ROS) that can generate oxidative damage in biological macromolecules (1). However, to neutralize the harmful effects of ROS, cells have several antioxidant enzymes, including superoxide dismutase, catalase, and peroxidases (1). The sirtuin silent information regulator 2 (Sir2), the founding member of the sirtuin protein family, was identified in 1984 (2). Sir2 was subsequently characterized as important in yeast replicative aging (3) and shown to posses NAD+-dependent histone deacetylase activity (4), suggesting it could play a role as an energy sensor. A family of conserved Sir2-related proteins was subsequently identified. Given their involvement in basic cellular processes and their potential contribution to the pathogenesis of several diseases (5), the sirtuins became a widely studied protein family.

In mammals the sirtuin family consists of seven proteins (SIRT1-SIRT7), which show different functions, structure, and localization. SIRT1 is mostly localized in the nucleus but, under specific physiological conditions, it shuttles to the cytosol (6). Similar to SIRT1, also SIRT6 (7) and SIRT7 (8) are localized in the nucleus. On the contrary, SIRT2 is mainly present in the cytosol and shuttles into the nucleus during G2/M cell cycle transition (9). Finally, SIRT3, SIRT4, and SIRT5, are mitochondrial proteins (10).

The main enzymatic activity catalyzed by the sirtuins is NAD+-dependent deacetylation, as known for the progenitor Sir2 (4,11). Along with histones also many transcription factors and enzymes were identified as targets for deacetylation by the sirtuins. Remarkably, mammalian sirtuins show additional interesting enzymatic activities. SIRT4 has an important ADP-ribosyltransferase activity (12), while SIRT6 can both deacetylate and ADP-ribosylate proteins (13,14). Moreover, SIRT5 was recently shown to demalonylate and desuccinylate proteins (15,16), in particular the urea cycle enzyme carbamoyl phosphate synthetase 1 (CPS1) (16). The (patho-)physiological context in which the seven mammalian sirtuins exert their functions, as well as their biochemical characteristics, are extensively discussed in the literature (17,18) and will not be addressed in this review; here we will focus on the emerging roles of the mitochondrial sirtuins, and their involvement in metabolism. Moreover, SIRT1 will be discussed as an important enzyme that indirectly affects mitochondrial physiology.

Sirtuins are regulated at different levels. Their subcellular localization, but also transcriptional regulation, post-translational modifications, and substrate availability, all impact on sirtuin activity. Moreover, nutrients and other molecules could affect directly or indirectly sirtuin activity. As sirtuins are NAD+-dependent enzymes, the availability of NAD+ is perhaps one of the most important mechanisms to regulate their activity. Changes in NAD+ levels occur as the result of modification in both its synthesis or consumption (19). Increase in NAD+ amounts during metabolic stress, as prolonged fasting or caloric restriction (CR) (2022), is well documented and tightly connected with sirtuin activation (4,19). Furthermore, the depletion and or inhibition of poly-ADP-ribose polymerase (PARP) 1 (23) or cADP-ribose synthase 38 (24), two NAD+consuming enzymes, increase SIRT1 action.

Analysis of the SIRT1 promoter region identified several transcription factors involved in up- or down-regulation of SIRT1 expression. FOXO1 (25), peroxisome proliferator-activated receptors (PPAR) α/β (26,27), and cAMP response element-binding (28) induce SIRT1 transcription, while PPARγ (29), hypermethylated in cancer 1 (30), PARP2 (31), and carbohydrate response element-binding protein (28) repress SIRT1 transcription. Of note, SIRT1 is also under the negative control of miRNAs, like miR34a (32) and miR199a (33). Furthermore, the SIRT1 protein contains several phosphorylation sites that are targeted by several kinases (34,35), which may tag the SIRT1 protein so that it only exerts activity towards specific targets (36,37). The beneficial effects driven by the SIRT1 activation – discussed below- led the development of small molecules modulators of SIRT1. Of note, resveratrol, a natural plant polyphenol, was shown to increase SIRT1 activity (38), most likely indirectly (22,39,40), inducing lifespan in a range of species ranging from yeast (38) to high-fat diet fed mice (41). The beneficial effect of SIRT1 activation by resveratrol on lifespan, may involve enhanced mitochondrial function and metabolic control documented both in mice (42) and humans (43). Subsequently, several powerful synthetic SIRT1 agonists have been identified (e.g. SRT1720 (44)), which, analogously to resveratrol, improve mitochondrial function and metabolic diseases (45). The precise mechanism of action of these compounds is still under debate; in fact, it may well be that part of their action is mediated by AMP-activated protein kinase (AMPK) activation (21,22,46), as resveratrol was shown to inhibit ATP synthesis by directly inhibiting ATP synthase in the mitochondrial respiratory chain (47), leading to an energy stress with subsequent activation of AMPK. However, at least in β-cells, resveratrol-mediated SIRT1 activation and AMPK activation seem to regulate glucose response in the opposite direction, pointing to the existence of alternative molecular targets (48).

Another hypothesis to explain the pleitropic effects of resveratrol suggests it inhibits cAMP-degrading phosphodiesterase 4 (PDE4), resulting in the cAMP-dependent activation of exchange proteins activated by cyclic AMP (Epac1) (40). The consequent Epac1-mediated increase of intracellular Ca2+ levels may then activate of CamKKβ-AMPK pathway (40), which ultimately will result in an increase in NAD+ levels and SIRT1 activation (21). Interestingly, also PDE4 inhibitors reproduce some of the metabolic benefits of resveratrol representing yet another putative way to activate SIRT1.

The regulation of the activity of the mitochondrial sirtuins is at present poorly understood. SIRT3 expression is induced in white adipose (WAT) and brown adipose tissues upon CR (49), while it is down-regulated in the liver of high-fat fed mice (50). SIRT3 activity changes also in the muscle after fasting (51) and chronic contraction (52). All these processes are associated with increase (20,53) or decrease (50) in NAD+ levels. From a transcriptional point of view, SIRT3 gene expression in brown adipocytes seems under the control of peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α) -estrogen-related receptor α (ERRα) axis, and this effect is crucial for full brown adipocyte differentiation (54,55). SIRT4 expression is reported to be reduced during CR (12), while the impact of resveratrol on SIRT4 is still under debate (56). Finally, upon ethanol exposure, SIRT5 gene expression was shown to be decreased together with the NAD+levels (57), probably explaining the protein hyperacetylation caused by alcohol exposure (58).

Metabolic homeostasis

The maintenance of metabolic homeostasis is critical for the survival of all species to sustain body structure and function. Metabolic homeostasis is achieved through complicated interactions between metabolic pathways that govern glucose, lipid and amino acid metabolism. Mitochondria are organelles, which integrate these metabolic pathways by serving a physical site for the production and recycling of metabolic intermediates.

Glucose metabolism

Overview

Glucose homeostasis is regulated through various complex processes including hepatic glucose output, glucose uptake, glucose utilization and storage. The main hormones regulating glucose homeostasis are insulin and glucagon, and the balance between these hormones determines glucose homeostasis. Insulin promotes glucose uptake in peripheral tissues (muscle and WAT), glycolysis and storage of glucose as glycogen in the fed state, while glucagon stimulates hepatic glucose production during fasting. Sirtuins influence many aspects of glucose homeostasis in several tissues such as muscle, WAT, liver and pancreas.

Gluconeogenesis

The body’s ability to synthesise glucose is vital in order to provide an uninterrupted supply of glucose to the brain and survive during starvation. Gluconeogenesis is a cytosolic process, in which glucose is formed from non-carbohydrate sources, such as amino acids, lactate, the glycerol portion of fats and tricarboxylic acid (59) cycle intermediates, during energy demand. This process, which occurs mainly in liver and kidney, shares some enzymes with glycolysis but it employs phosphoenolpyruvate carboxykinase, fructose-1,6-bisphosphatase and glucose-6-phosphatase to control the flow of metabolites towards glucose production. These three enzymes are stimulated by glucagon, epinephrine and glucocorticoids, whereas their activity is suppressed by insulin.

The role of mitochondrial sirtuins in the control of gluconeogenesis is not well established. SIRT3 is suggested to induce fasting-dependent hepatic glucose production from amino acids by deacetylating and activating the mitochondrial conversion of glutamate into the TCA cycle intermediate α-ketoglutarate, via the enzyme glutamate dehydrogenase (GDH) (Fig. 1A) (60,61). As SIRT3−/− mice do not display changes in GDH activity (62), the mechanism requires further clarification. In contrast to SIRT3, SIRT4 inhibits GDH via ADP-ribosylation under basal dietary conditions (Fig. 1A-B) (12). Conversely, SIRT4 activity is suppressed during CR resulting in activation of GDH, which fuels the TCA cycle and possibly also gluconeogenesis (12). Therefore, mitochondrial sirtuins may function to support gluconeogenesis during energy limitation, but further research is required to understand the exact roles of mitochondrial sirtuins in gluconeogenesis.

Summary of mitochondrial sirtuins’ role in mitochondrial pathways

Summary of mitochondrial sirtuins’ role in mitochondrial pathways

Figure 1 Summary of mitochondrial sirtuins’ role in mitochondrial pathways

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Glucose utilization

 Lipid metabolism

Urea metabolism

The recent discoveries in the biology of mitochondria have shed light on the metabolic regulatory roles of the sirtuin family. To maintain proper metabolic homeostasis, sirtuins sense cellular NAD+ levels, which reflect the nutritional status of the cells, and translate this information to adapt the activity of mitochondrial processes via posttranslational modifications and transcriptional regulation. SIRT1 and SIRT3 function to stimulate proper energy production via FAO and SIRT3 also protects from oxidative stress and ammonia accumulation during nutrient deprivation. SIRT4 seems to play role in the regulation of gluconeogenesis, insulin secretion and fatty acid utilization during times of energy limitation, while SIRT5 detoxifies excess ammonia that can accumulate during fasting. However, we are only at the beginning of our understanding of the roles of the mitochondrial sirtuins, SIRT3, SIRT4 and SIRT5 in complex metabolic processes. In the coming years, further research should identify and verify novel sirtuin targets in vivo and in vitro. We need also to elucidate the regulation and tissue-specific functions of these mitochondrial sirtuins, as well as to understand the potential crosstalk and synchrony between the different sirtuins in different subcellular compartments. Ultimately, the understanding of mitochondrial sirtuin functions may open new possibilities, not only for treatment of cancer and metabolic diseases characterized by mitochondrial dysfunction, but also for disease prevention and health maintenance.

7.8.10 Mitochondrial sirtuins

Huang JY1Hirschey MDShimazu THo LVerdin E.
Biochim Biophys Acta. 2010 Aug; 1804(8):1645-51. http://dx.doi.org:/10.1016/j.bbapap.2009.12.021

Sirtuins have emerged as important proteins in aging, stress resistance and metabolic regulation. Three sirtuins, SIRT3, 4 and 5, are located within the mitochondrial matrix. SIRT3 and SIRT5 are NAD(+)-dependent deacetylases that remove acetyl groups from acetyllysine-modified proteins and yield 2′-O-acetyl-ADP-ribose and nicotinamide. SIRT4 can transfer the ADP-ribose group from NAD(+) onto acceptor proteins. Recent findings reveal that a large fraction of mitochondrial proteins are acetylated and that mitochondrial protein acetylation is modulated by nutritional status. This and the identification of targets for SIRT3, 4 and 5 support the model that mitochondrial sirtuins are metabolic sensors that modulate the activity of metabolic enzymes via protein deacetylation or mono-ADP-ribosylation. Here, we review and discuss recent progress in the study of mitochondrial sirtuins and their targets.

mitochondrial sirtuins

mitochondrial sirtuins

http://www.sciencedirect.com/science/article/pii/S1570963909003902

mitochondrial sirtuins
Fig.1 .NAD+ -dependent deacetylation of sirtuins. The two step catalytic reaction mechanism. In this diagram ADPR = acetyl-ADP-ribose, NAM = nicotinamide, 1-O-AADPR = 1-O-acetyl ADP-ribose and βNAD = beta nicotinamide adenine dinucleotide.

Table 1 Shows subcellular localization, substrates and functions of different types of sirtuins.

Fig.2. Sirt3 regulated pathways in mitochondrial metabolism. Schematic diagram demonstrating the different roles of Sirt3 in the regulation of the main metabolic pathways of mitochondria.In this diagram LCAD = long-chain acyl-CoA dehydrogenase, ACeS2 = acetyl coenzyme synthetase 2, Mn SOD = manganese superoxide dismutase, CypD = cyclophilin D, ICDH2 = isocitrate dehydrogenase 2, OTC = ornithine transcarbomylase,TCA = tricaboxylic acid, ROS = reactive oxygen species, mPTP = membrane permeability transition pore, I–V = respiratory chain complex I–V

Fig. 3.(A) Schematic diagram showing different roles of Sirt4 in the regulation of various metabolic pathways. The diagram shows the Sirt4 regulated decrease in insulin level and the increase in availability of ATP inside mitochondria via upregulation of insulin degrading enzyme (IDE) and adenine translocator (ANT). The diagram also shows the Sirt4 regulated decrease in the efficiency of fatty acid oxidation and tricarboxylic acid cycle (TCA) via inhibition of glutamate dehydrogenase (GDH) and malonyl CoA decarboxylase (MCoAD). (B) Schematic diagram indicating the different roles of Sirt5 in regulation of various metabolic pathways. Sirt5 regulates urea production, fatty acid oxidation, tricarboxylic acid cycle (TCA), glycolysis, reactive oxygen species (ROS) metabolism, purine metabolism via regulating carbamoyl phosphate synthetase (CPS), hydroxyl-coenzyme A dehydrogenase (HADH), pyruvate dehydrogenase (PDH), pyruvate kinase (PK), succinate dehydrogenase(SDH) andurate oxidase (UO) respectively

Conclusion and future perspectives

Sirtuins are highly conserved NAD+-dependent protein deacetylases or ADP ribosyl transferases involved in many cellular processes including genome stability, cell survival, oxidative stress responses, metabolism, and aging. Mitochondrial sirtuins, Sirt3, Sirt4 and Sirt5 are important energy sensors and thus can be regarded as master regulators of mitochondrial metabolism. But it is still not known whether specific sirtuins can only function within particular metabolic pathways or two or more sirtuins could affect the same pathways. One of the mitochondrial sirtuins, Sirt3 is a major mitochondrial deacetylase that plays a pivotal role in the acetylation based regulation of numerous mitochondrial proteins. However, the question how mitochondrial proteins become acetylated is still unsolved and the identity of mitochondrial acetyltransferases is mysterious. Although the predominant function of the sirtuins is NAD+ dependent lysine deacetylation, but along with this major function another less characterized activity of these sirtuins includes ADP ribosylation which is mainly done by Sirt4. Moreover, in the case when the mitochondrial sirtuins exhibit both deacetylase and ADP ribosyl transferase activity, the conditions that determine the relative contribution of both of these activities in same or different metabolic pathways require further investigation. Sirt5 another mitochondrial sirtuin, was a puzzle until the recent finding as it possesses unique demalonylase and desuccinylase activities. However, most of the malonylated or succinylated proteins are important metabolic enzymes but as the significance of lysine malonylation and succinylation is still unknown thus it would be interesting to know how lysine malonylation and succinylation alter the functions of various metabolic enzymes. The mitochondrial sirtuins Sirt3, Sirt4 and Sirt5 serve as critical junctions and are required to exert many of the beneficial effect in mitochondrial metabolism. The emerging multidimensional role of mitochondrial sirtuins in regulation of mitochondrial metabolism and bioenergetics may have far-reaching consequences for many diseases associated with mitochondrial dysfunctions. However it is very important to fully elucidate the functions of mitochondrial sirtuins in different tissues to achieve the goal of therapeutic intervention in different metabolic diseases. Although several proteomic studies have provided detailed information that how mitochondrial sirtuin driven modification takes place on various targets in response to different environmental conditions, still the role of sirtuins in mitochondrial physiology and human diseases requires further exploration. Hopefully the progress in the field of sirtuin biology will soon provide insight into the therapeutic applications for targeting mitochondrial sirtuins by bioactive compounds to treat various human age-related diseases.

References

Ahn B.H.,et al.,2008. A role for the mitochondrial deacetylase Sirt3 in regulating energy homeostasis. Proc. Natl. Acad. Sci. U. S. A. 105 (38), 14447–14452. http://dx.doi.org/10.1073/pnas.0803790105.

Ahuja N.,et al., 2007. Regulation of insulin secretion by SIRT4, a mitochondrial ADP ribosyltransferase. J. Biol. Chem. 282 (46), 33583–33592. http://dx.doi.org/10.1074/jbc.M705488200.

Allison, S.J., Milner, J., 2007. SIRT3 is pro-apoptotic and participates in distinct basal apoptotic pathways. Cell Cycle 6, 2669–2677. http://dx.doi.org/10.4161/cc.6.21.4866.

Ashraf, N., et al., 2006. Altered sirtuin expression is associated with node-positive breast cancer. Br. J. Cancer 95, 1056–1061. http://dx.doi.org/10.1038/sj.bjc.6603384.

Bao, J.,et al.,2010. SIRT3 is regulated by nutrient excess and modulates hepatic susceptibility to lipotoxicity. Free Radic. Biol. Med. 49, 1230–1237.

Beal, M.F., 2005. Less stress, longer life. Nat. Med. 11 (6), 598–599. http://dx.doi.org/10.1038/nm0605-598.

Bell, E.L., Guarente,L., 2011. The SirT3 divining rod points to oxidative stress. Mol.Cell 42 (5), 561–568. http://dx.doi.org/10.1016/j.molcel.2011.05.008
(Review).

Bell,E.L., Emerling,B.M., Ricoult,S.J.H., Guarente,L., 2011. SirT3 suppresses hypoxia inducible factor 1α and tumor growth by inhibiting mitochondrial ROS production. Oncogene 30, 2986–2996. http://dx.doi.org/10.1038/onc.2011.37.

Bellizzi,D.,Rose,G.,Cavalcante,P.,Covello,G.,et al., 2005. A novel VNTR enhancer within the SIRT3 gene, a human homologue of SIR2, is associated with survival at oldest ages. Genomics 85, 258–263.
http://dx.doi.org/10.1016/j.ygeno.2004.11.003.

7.8.11 Sirtuin regulation of mitochondria: energy production, apoptosis, and signaling

Verdin E1Hirschey MDFinley LWHaigis MC.
Trends Biochem Sci. 2010 Dec; 35(12):669-75.
http://dx.doi.org:/10.1016/j.tibs.2010.07.003

Sirtuins are a highly conserved family of proteins whose activity can prolong the lifespan of model organisms such as yeast, worms and flies. Mammals contain seven sirtuins (SIRT1-7) that modulate distinct metabolic and stress response pathways. Three sirtuins, SIRT3, SIRT4 and SIRT5, are located in the mitochondria, dynamic organelles that function as the primary site of oxidative metabolism and play crucial roles in apoptosis and intracellular signaling. Recent findings have shed light on how the mitochondrial sirtuins function in the control of basic mitochondrial biology, including energy production, metabolism, apoptosis and intracellular signaling.

Mitochondria play critical roles in energy production, metabolism, apoptosis, and intracellular signaling [13]. These highly dynamic organelles have the ability to change their function, morphology and number in response to physiological conditions and stressors such as diet, exercise, temperature, and hormones [4]. Proper mitochondrial function is crucial for maintenance of metabolic homeostasis and activation of appropriate stress responses. Not surprisingly, changes in mitochondrial number and activity are implicated in aging and age-related diseases, including diabetes, neurodegenerative diseases, and cancer [1]. Despite the important link between mitochondrial dysfunction and human diseases, in most cases, the molecular causes for dysfunction have not been identified and remain poorly understood.

One of the principal bioenergetic functions of mitochondria is to generate ATP through the process of oxidative phosphorylation (OXPHOS), which occurs in the inner-mitochondrial membrane. Mitochondria are unique bi-membrane organelles that contain their own circular genome (mtDNA) encoding 13 protein subunits involved in electron transport. The remainder of the estimated 1000-1500 mitochondrial proteins are encoded by the nuclear genome and imported into mitochondria from the cytoplasm [56]. These imported proteins can be found either in the matrix, associated with inner or outer mitochondrial membranes or in the inner membrane space (Figure 1). Dozens of nuclear-encoded protein subunits form complexes with the mtDNA-encoded subunits to form electron transport complexes I-IV and ATP synthase, again highlighting the need for precise coordination between these two genomes. The transcriptional coactivator PGC-1α, a master regulator of mitochondrial biogenesis and function, is responsive to a variety of metabolic stresses, ensuring that the number and capacity of mitochondria keeps pace with the energetic demands of tissues [7].

Network of mitochondrial sirtuins

Network of mitochondrial sirtuins

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Network of mitochondrial sirtuins. Mitochondria can metabolize fuels, such as fatty acids, amino acids, and pyruvate, derived from glucose. Electrons pass through electron transport complexes (I-IV; red) generating a proton gradient, which is used to drive ATP synthase (AS; red) to generate ATP. SIRT3 (gold) binds complexes I and II, regulating cellular energy levels in the cell [4355]. Moreover, SIRT3 binds and deacetylates acetyl-CoA synthetase 2 (AceCS2) [3940] and glutamate dehydrogenase (GDH) [3347], thereby activating their enzymatic activities. SIRT3 also binds and activates long-chain acyl-CoA dehydrogenase (LCAD) [46]. SIRT4 (light purple) binds and represses GDH activity via ADP-ribosylation [21]. In the rate-limiting step of the urea cycle, SIRT5 (light blue) deacetylates and activates carbamoyl phosphate synthetase 1 (CPS1) [4849].

As high-energy electrons derived from glucose, amino acids or fatty acids fuels are passed through a series of protein complexes (I-IV), their energy is used to pump protons from the mitochondrial matrix through the inner membrane into the inner-membrane space, generating a proton gradient known as the mitochondrial membrane potential (Dψm) (Figure 1). Ultimately, the electrons reduce oxygen to form water, and the protons flow down their gradient through ATP synthase, driving the formation of ATP from ADP. Protons can also flow through uncoupling proteins (UCPs), dissipating their potential energy as heat. Reactive oxygen species (ROS) are a normal side-product of the respiration process [18]. In addition, an increase in Dψm, whether caused by impaired OXPHOS or by an overabundance of nutrients relative to ADP, will result in aberrant electron migration in the electron transport chain and elevated ROS production [1]. ROS react with lipids, protein and DNA, generating oxidative damage. Consequently, cells have evolved robust mechanisms to guard against an increase in oxidative stress accompanying ROS production [9].

Mitochondria are the primary site of ROS production within the cell, and increased oxidative stress is proposed to be one of the causes of mammalian aging [1210]. Major mitochondrial age-related changes are observed in multiple tissues and include decreased Dψm, increased ROS production and an increase in oxidative damage to mtDNA, proteins, and lipids [1114]. As a result, mitochondrial bioenergetic changes that occur with aging have been extensively reviewed [1517].

Silent information regulator (SIR) 2 protein and its orthologs in other species, termed sirtuins, promote an increased lifespan in model organisms such as yeast, worms and flies. Mammals contain seven sirtuins (SIRT1–7) that are characterized by an evolutionary conserved sirtuin core domain [1819]. This domain contains the catalytic activity and invariant amino acid residues involved in binding NAD+, a metabolic co-substrate. All sirtuins exhibit two major enzymatic activities in vitro: NAD+-dependent protein deacetylase activity and ADP-ribosyltransferase activity. Except for SIRT4, well-defined acetylated substrates have been identified for the other sirtuins. So far, only ADP-ribosyltransferase activity has been described for SIRT4 [2021]. Thus, these enzymes couple their biochemical and biological functions to an organism’s energetic state via their dependency on NAD+. A decade of research, largely focused on SIRT1, has revealed that mammalian sirtuins regulate metabolism and cellular survival. In brief, SIRT1–7 target distinct acetylated protein substrates and are localized in distinct subcellular compartments. SIRT1, SIRT6 and SIRT7 are found in nucleus, SIRT2 is primarily cytosolic and SIRT3, 4 and 5 are found in the mitochondria. The mitochondrial-only localization of SIRT3 is controversial and other groups have reported non-mitochondrial localization of this sirtuin [2223]. The biology and biochemistry of the seven mammalian sirtuins have been extensively discussed in the literature [2426] and is not the topic of this review. Instead, we focus on the mitochondrial sirtuins, their substrates, and their impact on mitochondrial biology.

The mitochondrial sirtuins, SIRT3–5 [212729], participate in the regulation of ATP production, metabolism, apoptosis and cell signaling. Unlike SIRT1, a 100 kDa protein, the mitochondrial sirtuins are small, ranging from 30–40 kDa. Thus, their amino acid sequence consists mostly of an N-terminal mitochondrial targeting sequence and the sirtuin core domain, with small flanking regions. Whereas, SIRT3 and SIRT5 function as NAD+-dependent deacetylases on well defined substrates, SIRT4 has no identified acetylated substrate and only shows ADP-ribosyltransferase activity. It is likely, however, that SIRT4 possesses substrate-specific NAD+-dependent deacetylase activity, as has been demonstrated for SIRT6 [30,31]. The three-dimensional structures for the core domains of human SIRT3 and human SIRT5 have been solved and reveal remarkable structural conservation with other sirtuins, such as the ancestral yeast protein and human SIRT2 (Figure 2) [3234]. Given its sequence conservation with the other sirtuins [18], it is likely that SIRT4 adopts a similar three-dimensional conformation.

Figure 2 Structure and alignment of sirtuins

Role of mitochondrial sirtuins in metabolism and energy production

The NAD+ dependence of sirtuins provided the first clue that these enzymes function as metabolic sensors. For instance, sirtuin activity can increase when NAD+ levels are abundant, such as times of nutrient deprivation. In line with this model, mass spectrometry studies have revealed that metabolic proteins, such as tricarboxylic acid (TCA) cycle enzymes, fatty acid oxidation enzymes and subunits of oxidative phosphorylation complexes are acetylated in response to metabolic stress [3537].

Fatty acid oxidation

Consistent with the hypothesis that nutrient stress alters sirtuin activity, a recent report identified significant metabolic abnormalities in Sirt3-/- mice during fasting [38]. In this study, hepatic SIRT3 protein expression increased during fasting, suggesting that both its levels and enzymatic activity are elevated during nutrient deprivation. SIRT3 activates hepatic lipid catabolism via deacetylation of long-chain acyl-CoA dehydrogenase (LCAD), a central enzyme in the fatty acid oxidation pathway. Sirt3-/- mice have diminished fatty acid oxidation, develop fatty liver, have low ATP production, and show a defect in thermogenesis and hypoglycemia during a cold test [38].

Surprisingly, many of the phenotypes observed in Sirt3-/- mice were also observed in mice lacking acetyl-CoA synthetase 2 (AceCS2), a previously identified substrate of SIRT3 [3940]. For example, fasting ATP levels were reduced by 50% in skeletal muscle of AceCS2-/- mice, in comparison to wild type (WT) mice. As a result, fasted AceCS2-/- mice were hypothermic and had reduced capacity for exercise. By converting acetate into acetyl CoA, AceCS2 provides an alternate energy source during times of metabolic challenges, such as thermogenesis or fasting. Interestingly, Acadl-deficient mice (Acadl encodes LCAD) also show cold intolerance, reduced ATP, and hypoglycemia under fasting conditions [41]. These overlapping phenotypes between Sirt3-/-AceCS2-/- and Acadl-/- mice indicate that the regulation of LCAD and AceCS2 acetylation by SIRT3 represents an important adaptive signal during the fasting response (Figure 2).

Electron transport chain

Of all mitochondrial proteins, oxidative phosphorylation complexes are among the most heavily acetylated. One study reported that 511 lysine residues in complexes I-IV and ATP synthase are modified by acetylation [37], hinting that a mitochondrial sirtuin might deacetylate these residues. Indeed, SIRT3 interacts with and deacetylates complex I subunits (including NDUFA9) [42], succinate dehydrogenase (complex II) [43]. SIRT3 has also been shown to bind ATP synthase in a proteomic analysis [44]. SIRT3 also regulates mitochondrial translation, a process which can impact electron transport [45]. Mice lacking SIRT3 demonstrate reduced ATP levels in many tissues [42 46]; however, additional work is required to determine if reduced ATP levels in Sirt3-/- mice is a direct result of OX PHOS hyperacetylation or an indirect effect, via decreased fatty acid oxidation, or a combination of both effects.

Less is known about the roles of SIRT4 and SIRT5 in electron transport. SIRT4 binds adenine nucleotide translocator (ANT), which transports ATP into the cytosol and ADP into the mitochondrial matrix, thereby providing a substrate for ATP synthase [20]. SIRT5 physically interacts with cytochrome C. The biological significance of these interactions, however, remains unknown [21].

TCA cycle

Enzymes for the TCA cycle (also called the Kreb’s cycle) are located in the mitochondrial matrix; this compartmentalization provides a way for cells to utilize metabolites from carbohydrates, fats and proteins. Numerous TCA cycle enzymes are modified by acetylation, although the functional consequences of acetylation have been examined for only a few of these proteins. SIRT3 interacts with several TCA cycle enzymes, including succinate dehydrogenase (SDH, see above [43]) and isocitrate dehydrogenase 2 (ICDH2) [33]. ICDH2 catalyzes the irreversible oxidative decarboxylation of isocitrate to form alpha-ketoglutarate and CO2, while converting NAD+ to NADH. Although the biological significance of these interactions is not yet known, it seems possible that SIRT3 might regulate flux through the TCA cycle.

Role of mitochondrial sirtuins in signaling

During cellular stress or damage, mitochondria release a variety of signals to the cytosol and the nucleus to alert the cell of changes in mitochondrial function. In response, the nucleus generates transcriptional changes to activate a stress response or repair the damage. For example, mitochondrial biogenesis requires a sophisticated transcriptional program capable of responding to the energetic demands of the cell by coordinating expression of both nuclear and mitochondrial encoded genes [4]. Unlike anterograde transcriptional control of mitochondria from nuclear transcription regulators such as PGC-1α, the retrograde signaling pathway, from the mitochondria to the nucleus is poorly understood in mammals. Although there is no evidence directly linking sirtuins to a mammalian retrograde signaling pathway, changes in mitochondrial sirtuin activity could influence signals transmitted from the mitochondria. Interestingly, the nuclear sirtuin SIRT1 deacetylates and activates PGC-1α, a key factor in the transcriptional regulation of genes involved in fatty acid oxidation and oxidative phosphorylation (Figure 3) [5051]. Thus, mitochondrial and nuclear sirtuins might exist in a signaling communication loop to control metabolism.

mitochondria-at-nexus-of-cellular-signaling-nihms239607f3

mitochondria-at-nexus-of-cellular-signaling-nihms239607f3

http://www.ncbi.nlm.nih.gov/pmc/articles/instance/2992946/bin/nihms239607f3.gif

Mitochondria at nexus of cellular signaling. Mitochondria and mitochondrial sirtuins play a central role in intra- and extra-cellular signaling. Circulating fatty acids and acetate provide whole body energy homeostasis. The mitochondrial metabolites NAD+, NADH, ATP, Ca2+, ROS, ketone bodies, and acetyl-CoA participate in intracellular signaling.

Numerous signaling pathways are activated by changes in mitochondrial release of metabolites and molecules, such as Ca2+, ATP, NAD+, NADH, nitric oxide (NO), and ROS (Figure 3). Of these, Ca2+ is the best studied as a mitochondrial messenger. Mitochondria are important regulators of Ca2+ storage and homeostasis, and mitochondrial Ca2+ uptake is directly tied to the membrane potential of the organelle. Membrane potential serves as a gauge of mitochondrial function: disruption of OXPHOS, interruption in the supply or catabolism of nutrients or loss of structural integrity generally result in a fall in membrane potential, and, in turn, decreased mitochondrial Ca2+ uptake. Subsequent increases in cytosolic free Ca2+ will activate calcineurin and several Ca2+-dependent kinases [52] and affect a wide variety of transcription factors to produce appropriate cell-specific transcriptional responses [53]. Through regulation of nutrient oxidation and electron transport or yet to be identified target(s), mitochondrial sirtuins could influence mAlthough the effect of sirtuins on intracellular calcium signaling has not been studied directly, sirtuin effects on ATP production have been shown. ANT facilitates the exchange of mitochondrial ATP with cytosolic ADP. As a result the cytosolic ATP:ADP ratio reflects changes in mitochondrial energy production. A fall in ATP production activates AMP-activated protein kinase (AMPK), which directly stimulates mitochondrial energy production, inhibits protein synthesis through regulation of mammalian target of rapamycin (mTOR), and influences mitochondrial transcriptional programs [54]. SIRT3 regulates ATP levels in a variety of tissues, suggesting that its activity could have an important role in ATP-mediated retrograde signaling [46,55]. Indeed, recent studies have shown that SIRT3 regulates AMPK activation [5658]. Furthermore, SIRT4 interacts with ANT [20], raising the possibility that SIRT4 activity also influences the ATP:ADP ratio or membrane potential and modulates important mitochondrial signals.

NAD+ and NADH levels are intimately connected with mitochondrial energy production and regulate mitochondrial sirtuin activity. Unlike NAD+, however, NADH is not a sirtuin co-substrate. Indeed, changes in the NAD+:NADH ratio can change the redox state of the cell and alter the activity of enzymes such as poly-ADP-ribose polymerases and sirtuins, with subsequent effects on signaling cascades and gene expression [5961]. Changes in mitochondrial sirtuin activity could change the balance of these metabolites within the mitochondria. For example, fatty acid oxidation reduces NAD+ to NADH, which is oxidized back to NAD+ by OXPHOS. However, it is unclear whether changes in NAD+/NADH can be transmitted outside the organelle. The inner mitochondrial membrane is impermeable to NAD+ and NADH; however, the mitochondrial malate-aspartate shuttle could transfer reducing equivalents across the mitochondrial membranes.

Mitochondrial sirtuin control of apoptosis

Apoptosis is a cellular process of programmed cell death. Mitochondria play an important role in apoptosis by the activation of mitochondrial outer membrane permeabilization, which represents the irrevocable point of no return in committing a cell to death. Outer membrane permeabilization leads to the release of caspase-activating molecules, caspase-independent death effectors, and disruption of ATP production. Despite the central role for mitochondria in the control of apoptosis, surprisingly little is known about how mitochondrial sirtuins participate in apoptotic programs. SIRT3 plays a pro-apoptotic role in both BCL2-53- and JNK-regulated apoptosis [63]. Additionally, cells lacking SIRT3 show decreased stress-induced apoptosis, lending further support for a pro-apoptotic role for SIRT3 [62]. Furthermore, recent work points to a tumor suppressive role for SIRT3: SIRT3 levels are decreased in human breast cancers and Sirt3 null mice develop mammary tumors after 12 months [62]. The mechanism for the tumor suppressive function of SIRT3 is incompletely understood, but involves repression of ROS and protection against DNA damage [62]. In conflicting studies, SIRT3 has been shown to be anti-apoptotic. For example, in the cellular response to DNA damage when mitochondrial NAD+ levels fall below critical levels, SIRT3 and SIRT4 display anti-apoptotic activity, protecting cells from death [64]. SIRT3 has also been shown to be cardioprotective, in part by activation of ROS clearance genes [65]. In future studies, it will be important to elucidate the balance achieved by SIRT3 between stress resistance (anti-apoptosis) and tumor suppression (pro-apoptosis). Additionally, the role of SIRT4 and SIRT5 in regulating metabolism suggests that these mitochondrial sirtuins could also contribute to apoptosis in tumor suppressive or stress resistant manners.

Concluding remarks

An elegant coordination of metabolism by mitochondrial sirtuins is emerging where SIRT3, SIRT4 and SIRT5 serve at critical junctions in mitochondrial metabolism by acting as switches to facilitate energy production during nutrient adaptation and stress. Rather than satisfy, these studies lead to more questions. How important are changes in global mitochondrial acetylation to mitochondrial biology and is acetylation status a readout for sirtuin activity? What are other substrates for SIRT4 and SIRT5? What molecular factors dictate substrate specificity for mitochondrial sirtuins? Moreover, further studies will provide insight into the therapeutic applications for targeting mitochondrial sirtuins to treat human diseases. It is clear that many discoveries have yet to be made in this exciting area of biology.

Body of review in energetic metabolic pathways in malignant T cells

Antigen stimulation of T cell receptor (TCR) signaling to nuclear factor (NF)-B is required for T cell proliferation and differentiation of effector cells.
The TCR-to-NF-B pathway is generally viewed as a linear sequence of events in which TCR engagement triggers a cytoplasmic cascade of protein-protein interactions and post-translational modifications, ultimately culminating in the nuclear translocation of NF-B.
Activation of effect or T cells leads to increased glucose uptake, glycolysis, and lipid synthesis to support growth and proliferation.
Activated T cells were identified with CD7, CD5, CD3, CD2, CD4, CD8 and CD45RO. Simultaneously, the expression of CD95 and its ligand causes apoptotic cells death by paracrine or autocrine mechanism, and during inflammation, IL1-β and interferon-1α. The receptor glucose, Glut 1, is expressed at a low level in naive T cells, and rapidly induced by Myc following T cell receptor (TCR) activation. Glut1 trafficking is also highly regulated, with Glut1 protein remaining in intracellular vesicles until T cell activation.

Dr. Aurel,
Targu Jiu

  1. sjwilliamspa

    Wouldn’t then the preferred target be mTORC instead of Sirtuins if mTORC represses Sirtuin activity?

  2. The answer may not be so simple, perhaps a conundrum.

    In conflicting studies, SIRT3 has been shown to be anti-apoptotic. For example, in the cellular response to DNA damage when mitochondrial NAD+ levels fall below critical levels, SIRT3 and SIRT4 display anti-apoptotic activity, protecting cells from death [64].

    For anti-cancer activity, apoptosis is a desired effect. This reminds me of the problem 15 years ago with the drug that would be effective against sepsis, the best paper of the year in NEJM. It failed.

    We tend to not appeciate the intricacies of biological interactions and fail to see bypass reactions. Pleotropy comes up again and again. The seminal work from Britton Chances lab on the NAD+/NADH ratio have been overlooked.

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Pathway Specific Targeting in Anticancer Therapies

Writer and Curator: Larry H. Bernstein, MD, FCAP 

 

7.7 Pathway specific targeting in anticancer therapies

7.7.1 Structural basis for the allosteric inhibitory mechanism of human kidney-type glutaminase (KGA) and its regulation by Raf-Mek-Erk signaling in cancer cell metabolism

7.7.2 Sonic hedgehog (Shh) signaling promotes tumorigenicity and stemness via activation of epithelial-to-mesenchymal transition (EMT) in bladder cancer.

7.7.3 Differential activation of NF-κB signaling is associated with platinum and taxane resistance in MyD88 deficient epithelial ovarian cancer cells

7.7.4 Activation of apoptosis by caspase-3-dependent specific RelB cleavage in anticancer agent-treated cancer cells

7.7.5 Identification of Liver Cancer Progenitors Whose Malignant Progression Depends on Autocrine IL-6 Signaling

7.7.6 Acetylation Stabilizes ATP-Citrate Lyase to Promote Lipid Biosynthesis and Tumor Growth

7.7.7 Monoacylglycerol Lipase Regulates a Fatty Acid Network that Promotes Cancer Pathogenesis

7.7.8 Pirin regulates epithelial to mesenchymal transition and down-regulates EAF/U19 signaling in prostate cancer cells

7.7.9 O-GlcNAcylation at promoters, nutrient sensors, and transcriptional regulation

 

7.7.1 Structural basis for the allosteric inhibitory mechanism of human kidney-type glutaminase (KGA) and its regulation by Raf-Mek-Erk signaling in cancer cell metabolism

Thangavelua, CQ Pana, …, BC Lowa, and J. Sivaramana
Proc Nat Acad Sci 2012; 109(20):7705–7710
http://dx.doi.org:/10.1073/pnas.1116573109

Besides thriving on altered glucose metabolism, cancer cells undergo glutaminolysis to meet their energy demands. As the first enzyme in catalyzing glutaminolysis, human kidney-type glutaminase isoform (KGA) is becoming an attractive target for small molecules such as BPTES [bis-2-(5 phenylacetamido-1, 2, 4-thiadiazol-2-yl) ethyl sulfide], although the regulatory mechanism of KGA remains unknown. On the basis of crystal structures, we reveal that BPTES binds to an allosteric pocket at the dimer interface of KGA, triggering a dramatic conformational change of the key loop (Glu312-Pro329) near the catalytic site and rendering it inactive. The binding mode of BPTES on the hydrophobic pocket explains its specificity to KGA. Interestingly, KGA activity in cells is stimulated by EGF, and KGA associates with all three kinase components of the Raf-1/Mek2/Erk signaling module. However, the enhanced activity is abrogated by kinase-dead, dominant negative mutants of Raf-1 (Raf-1-K375M) and Mek2 (Mek2-K101A), protein phosphatase PP2A, and Mek-inhibitor U0126, indicative of phosphorylation-dependent regulation. Furthermore, treating cells that coexpressed Mek2-K101A and KGA with suboptimal level of BPTES leads to synergistic inhibition on cell proliferation. Consequently, mutating the crucial hydrophobic residues at this key loop abrogates KGA activity and cell proliferation, despite the binding of constitutive active Mek2-S222/226D. These studies therefore offer insights into (i) allosteric inhibition of KGA by BPTES, revealing the dynamic nature of KGA’s active and inhibitory sites, and (ii) cross-talk and regulation of KGA activities by EGF-mediated Raf-Mek-Erk signaling. These findings will help in the design of better inhibitors and strategies for the treatment of cancers addicted with glutamine metabolism.

The Warburg effect in cancer biology describes the tendency of cancer cells to take up more glucose than most normal cells, despite the availability of oxygen (12). In addition to altered glucose metabolism, glutaminolysis (catabolism of glutamine to ATP and lactate) is another hallmark of cancer cells (23). In glutaminolysis, mitochondrial glutaminase catalyzes the conversion of glutamine to glutamate (4), which is further catabolized in the Krebs cycle for the production of ATP, nucleotides, certain amino acids, lipids, and glutathione (25).

Humans express two glutaminase isoforms: KGA (kidney-type) and LGA (liver-type) from two closely related genes (6). Although KGA is important for promoting growth, nothing is known about the precise mechanism of its activation or inhibition and how its functions are regulated under physiological or pathophysiological conditions. Inhibition of rat KGA activity by antisense mRNA results in decreased growth and tumorigenicity of Ehrlich ascites tumor cells (7), reduced level of glutathione, and induced apoptosis (8), whereas Myc, an oncogenic transcription factor, stimulates KGA expression and glutamine metabolism (5). Interestingly, direct suppression of miR23a and miR23b (9) or activation of TGF-β (10) enhances KGA expression. Similarly, Rho GTPase that controls cytoskeleton and cell division also up-regulates KGA expression in an NF-κB–dependent manner (11). In addition, KGA is a substrate for the ubiquitin ligase anaphase-promoting complex/cyclosome (APC/C)-Cdh1, linking glutaminolysis to cell cycle progression (12). In comparison, function and regulation of LGA is not well studied, although it was recently shown to be linked to p53 pathway (1314). Although intense efforts are being made to develop a specific KGA inhibitor such as BPTES [bis-2-(5-phenylacetamido-1, 2, 4-thiadiazol-2-yl) ethyl sulfide] (15), its mechanism of inhibition and selectivity is not yet understood. Equally important is to understand how KGA function is regulated in normal and cancer cells so that a better treatment strategy can be considered.

The previous crystal structures of microbial (Mglu) and Escherichia coli glutaminases show a conserved catalytic domain of KGA (1617). However, detailed structural information and regulation are not available for human glutaminases especially the KGA, and this has hindered our strategies to develop inhibitors. Here we report the crystal structure of the catalytic domain of human apo KGA and its complexes with substrate (L-glutamine), product (L-glutamate), BPTES, and its derived inhibitors. Further, Raf-Mek-Erk module is identified as the regulator of KGA activity. Although BPTES is not recognized in the active site, its binding confers a drastic conformational change of a key loop (Glu312-Pro329), which is essential in stabilizing the catalytic pocket. Significantly, EGF activates KGA activity, which can be abolished by the kinase-dead, dominant negative mutants of Mek2 (Mek2-K101A) or its upstream activator Raf-1 (Raf-1-K375M), which are the kinase components of the growth-promoting Raf-Mek2-Erk signaling node. Furthermore, coexpression of phosphatase PP2A and treatment with Mek-specific inhibitor or alkaline phosphatase all abolished enhanced KGA activity inside the cells and in vitro, indicating that stimulation of KGA is phosphorylation dependent. Our results therefore provide mechanistic insights into KGA inhibition by BPTES and its regulation by EGF-mediated Raf-Mek-Erk module in cell growth and possibly cancer manifestation.

Structures of cKGA and Its Complexes with L-Glutamine and L-Glutamate.
The human KGA consists of 669 amino acids. We refer to Ile221-Leu533 as the catalytic domain of KGA (cKGA) (Fig. 1A). The crystal structures of the apo cKGA and in complex with L-glutamine or L-glutamate were determined (Table S1). The structure of cKGA has two domains with the active site located at the interface. Domain I comprises (Ile221-Pro281 and Cys424 -Leu533) of a five-stranded anti-parallel β-sheet (β2↓β1↑β5↓β4↑β3↓) surrounded by six α-helices and several loops. The domain II (Phe282-Thr423) mainly consists of seven α-helices. L-Glutamine/L-glutamate is bound in the active site cleft (Fig. 1B and Fig. S1B). Overall the active site is highly basic, and the bound ligand makes several hydrogen-bonding contacts to Gln285, Ser286, Asn335, Glu381, Asn388, Tyr414, Tyr466, and Val484 (Fig. 1C and Fig. S1C), and these residues are highly conserved among KGA homologs (Fig. S1D). Notably, the putative serine-lysine catalytic dyad (286-SCVK-289), corresponding to the SXXK motif of class D β-lactamase (17), is located in close proximity to the bound ligand. In the apo structure, two water molecules were located in the active site, one of them being displaced by glutamine in the substrate complex. The substrate side chain is within hydrogen-bonding distance (2.9 Å) to the active site Ser286. Other key residues involved in catalysis, such as Lys289, Tyr414, and Tyr466, are in the vicinity of the active site. Lys289 is within hydrogen-bonding distance to Ser286 (3.1 Å) and acts as a general base for the nucleophilic attack by accepting the proton from Ser286. Tyr466, which is close to Ser286 and in hydrogen-bonding contact (3.2 Å) with glutamine, is involved in proton transfer during catalysis. Moreover, the carbonyl oxygen of the glutamine is hydrogen-bonded with the main chain amino groups of Ser286 and Val484, forming the oxyanion hole. Thus, we propose that in addition to the putative catalytic dyad (Ser286 XX Lys289), Tyr466 could play an important role in the catalysis (Fig. 1Cand Fig. S2).

structure of the cKGA-L-glutamine complex

structure of the cKGA-L-glutamine complex

http://www.pnas.org/content/109/20/7705/F1.medium.gif

Fig. 1.  Schematic view and structure of the cKGA-L-glutamine complex. (A) Human KGA domains and signature motifs (refer to Fig. S1A for details). (B) Structure of the of cKGA and bound substrate (L-glutamine) is shown as a cyan stick. (C) Fourier 2Fo-Fc electron density map (contoured at 1 σ) for L-glutamine, that makes hydrogen bonds with active site residues are shown.

Allosteric Binding Pocket for BPTES. The chemical structure of BPTES has an internal symmetry, with two exactly equivalent parts including a thiadiazole, amide, and a phenyl group (Fig. S3A), and it equally interacts with each monomer. The thiadiazole group and the aliphatic linker are well buried in a hydrophobic cluster that consists of Leu321, Phe322, Leu323, and Tyr394 from both monomers, which forms the allosteric pocket (Fig. 2 B–E). The side chain of Phe322 is found at the bottom of the allosteric pocket. The phenyl-acetamido moiety of BPTES is partially exposed on the loop (Asn324-Glu325), where it interacts with Phe318, Asn324, and the aliphatic part of the Glu325 side chain. On the basis of our observations we synthesized a series of BPTES-derived inhibitors (compounds2–5) (Fig. S3 AF and SI Results) and solved their cocrystal structure of compounds 2–4. Similar to BPTES, compounds 24 all resides within the hydrophobic cluster of the allosteric pocket (Fig. S3 CF).

Fig. 2. Structure of cKGA: BPTES complex and the allosteric binding mode of BPTES.

Allosteric Binding of BPTES Triggers Major Conformational Change in the Key Loop Near the Active Site.  The overall structure of these inhibitor complexes superimposes well with apo cKGA. However, a major conformational change at the Glu312 to Pro329 loop was observed in the BPTES complex (Fig. 2F). The most conformational changes of the backbone atoms that moved away from the active site region are found at the center of the loop (Leu316-Lys320). The backbone of the residues Phe318 and Asn319 is moved ≈9 Å and ≈7 Å, respectively, compared with the apo structure, whereas the side chain of these residues moved ≈14 Å and ≈12 Å, respectively. This loop rearrangement in turn brings Phe318 closer to the phenyl group of the inhibitor and forms the inhibitor binding pocket, whereas in the apo structure the same loop region (Leu316-Lys320) was found to be adjacent to the active site and forms a closed conformation of the active site.

Binding of BPTES Stabilizes the Inactive Tetramers of cKGA.  To understand the role of oligomerization in KGA function, dimers and tetramers of cKGA were generated using the symmetry-related monomers (Fig. 2 A–E and Fig. S4 D and E). The dimer interface in the cKGA: BPTES complex is formed by residues from the helix Asp386-Lys398 of both monomers and involves hydrogen bonding, salt bridges, and hydrophobic interactions (Phe389, Ala390, Tyr393, and Tyr394), besides two sulfate ions located in the interface (Fig. 2E). The dimers are further stabilized by binding of BPTES, where it binds to loop residues (Glu312-Pro329) and Tyr394 from both monomers (Fig. 2 D and E). Similarly, residues from Lys311-Asn319 loop and Arg454, His461, Gln471, and Asn529-Leu533 are involved in the interface with neighboring monomers to form the tetramer in the BPTES complex.

BPTES Induces Allosteric Conformational Changes That Destabilize Catalytic Function of KGA

Fig. 3A shows that 293T cells overexpressing KGA produced higher level of glutamate compared with the vector control cells. Most significantly, all of these mutants, except Phe322Ala, greatly diminished the KGA activity.

Fig. 3. Mutations at allosteric loop and BPTES binding pocket abrogate KGA activity and BPTES sensitivity.

Raf-Mek-Erk Signaling Module Regulates KGA Activity. Because KGA supports cell growth and proliferation, we first validated that treatment of cells with BPTES indeed inhibits KGA activity and cell proliferation (Fig. S5 A–D and SI Results). Next, as cells respond to various physiological stimuli to regulate their metabolism, with many of the metabolic enzymes being the primary targets of modulation (18), we examined whether KGA activity can be regulated by physiological stimuli, in particular EGF, which is important for cell growth and proliferation. Cells overexpressing KGA were made quiescent and then stimulated with EGF for various time points. Fig. 4A shows that the basal KGA activity remained unchanged 30 min after EGF stimulation, but the activity was substantially enhanced after 1 h and then gradually returned to the basal level after 4 h. Because EGF activates the Raf-Mek-Erk signaling module (19), treatment of cells with Mek-specific inhibitor U0126 could block the enhanced KGA activity with parallel inhibition of Erk phosphorylation (Fig. 4A). Interestingly, such Mek-induced KGA activity is specific to EGF and lysophosphatidic acid (LPA) but not with other growth factors, such as PDGF, TGF-β, and basic FGF (bFGF), despite activation of Mek-Erk by bFGF (Fig. S6A).

The results show that KGA could interact equally well with the wild-type or mutant forms of Raf-1 and Mek2 (Fig. 4C). Importantly, endogenous Raf-1 or Erk1/2, including the phosphorylated Erk1/2 (Fig. 4 C and D), could be detected in the KGA complex. Taken together, these results indicate that the activity of KGA is directly regulated by Raf-Mek-Erk downstream of EGF receptor. To further show that Mek2-enhanced KGA activity requires both the kinase activity of Mek2 and the core residues for KGA catalysis, wild-type or triple mutant (Leu321Ala/Phe322Ala/Leu323Ala) of KGA was coexpressed with dominant negative Mek2-KA or the constitutive active Mek2-SD and their KGA activities measured. The result shows that the presence of Mek2-KA blocks KGA activity, whereas the triple mutant still remains inert even in the presence of the constitutively active Mek2 (Fig. 4E), and despite Mek2 binding to the KGA triple mutant (Fig. S7B). Consequently, expressing triple mutant did not support cell proliferation as well as the wild-type control (Fig. S7C).

Fig. 4. EGFR-Raf-Mek-Erk signaling stimulates KGA activity.

When cells expressing both KGA and Mek2-K101A were treated with subthreshold levels of BPTES, there was a synergistic reduction in cell proliferation (Fig. S6C and SI Results). Lastly, to determine whether regulation of KGA by Raf-Mek-Erk depends on its phosphorylation status, cells were transfected with KGA with or without the protein phosphatase PP2A and assayed for the KGA activity. PP2A is a ubiquitous and conserved serine/threonine phosphatase with broad substrate specificity. The results indicate that KGA activity was reduced down to the basal level in the presence of PP2A (Fig. 5A). Coimmunoprecipitation study also revealed that KGA interacts with PP2A (Fig. 5B), suggesting a negative feedback regulation by this protein phosphatase. Furthermore, treatment of immunoprecipitated and purified KGA with calf-intestine alkaline phosphatase (CIAP) almost completely abolished the KGA activity in vitro (Fig. S6D). Taken together, these results indicate that KGA activity is regulated by Raf-Mek2, and KGA activation by EGF could be part of the EGF-stimulated Raf-Mek-Erk signaling program in controlling cell growth and proliferation (Fig. 5C).

KGA activity is regulated by phosphorylation

KGA activity is regulated by phosphorylation

http://www.pnas.org/content/109/20/7705/F5.medium.gif

Fig. 5. KGA activity is regulated by phosphorylation. (C) Schematic model depicting the synergistic cross-talk between KGA-mediated glutaminolysis and EGF-activated Raf-Mek-Erk signaling. Exogenous glutamine can be transported across the membrane and converted to glutamate by glutaminase (KGA), thus feeding the metabolite to the ATP-producing tricarboxylic acid (TCA) cycle. This process can be stimulated by EGF receptor-mediated Raf-Mek-Erk signaling via their phosphorylation-dependent pathway, as evidenced by the inhibition of KGA activity by the kinase-dead and dominant negative mutants of Raf-1 (Raf-1-K375M) and Mek2 (Mek2-K101A), protein phosphatase PP2A, and Mek-specific inhibitor U0126. Consequently, inhibiting KGA with BPTES and blocking Raf-Mek pathway with Mek2-K101A provide a synergistic inhibition on cell proliferation.

Small-molecule inhibitors that target glutaminase activity in cancer cells are under development. Earlier efforts targeting glutaminase using glutamine analogs have been unsuccessful owing to their toxicities (2). BPTES has attracted much attention as a selective, nontoxic inhibitor of KGA (15), and preclinical testing of BPTES toward human cancers has just begun (20). BPTES selectively suppresses the growth of glioma cells (21) and inhibits the growth of lymphoma tumor growth in animal model studies (22). Wang et al. (11) reported a small molecule that targets glutaminase activity and oncogenic transformation. Despite extensive studies, nothing is known about the structural and molecular basis for KGA inhibitory mechanisms and how their function is regulated during normal and cancer cell metabolism. Such limited information impedes our effort in producing better generations of inhibitors for better treatment regimens.

Comparison of the complex structures with apo cKGA structure, which has well-defined electron density for the key loop, we provide the atomic view of an allosteric binding pocket for BPTES and elucidate the inhibitory mechanism of KGA by BPTES. The key residues of the loop (Glu312-Pro329) undergo major conformational changes upon binding of BPTES. In addition, structure-based mutagenesis studies suggest that this loop is essential for stabilizing the active site. Therefore, by binding in an allosteric pocket, BPTES inhibits the enzymatic activity of KGA through (i) triggering a major conformational change on the key residues that would normally be involved in stabilizing the active sites and regulating its enzymatic activity; and (ii) forming a stable inactive tetrameric KGA form. Our findings are further supported by two very recent reports on KGA isoform (GAC) (2324), although these studies lack full details owing to limitation of their electron density maps. BPTES is specific to KGA but not to LGA (15). Sequence comparison of KGA with LGA (Fig. S8A) reveals two unique residues on KGA, Phe318 and Phe322, which upon mutation to LGA counterparts, become resistant to BPTES. Thus, our study provides the molecular basis of BPTES specificity.

7.7.2 Sonic hedgehog (Shh) signaling promotes tumorigenicity and stemness via activation of epithelial-to-mesenchymal transition (EMT) in bladder cancer.

Islam SS, Mokhtari RB, Noman AS, …, van der Kwast T, Yeger H, Farhat WA.
Molec Carcinogenesis mar 2015; 54(5). http://dx.doi.org:/10.1002/mc.22300

shh sonic hedgehog signaling pathway nri2151-f1

shh sonic hedgehog signaling pathway nri2151-f1

Activation of the sonic hedgehog (Shh) signaling pathway controls tumorigenesis in a variety of cancers. Here, we show a role for Shh signaling in the promotion of epithelial-to-mesenchymal transition (EMT), tumorigenicity, and stemness in the bladder cancer. EMT induction was assessed by the decreased expression of E-cadherin and ZO-1 and increased expression of N-cadherin. The induced EMT was associated with increased cell motility, invasiveness, and clonogenicity. These progression relevant behaviors were attenuated by treatment with Hh inhibitors cyclopamine and GDC-0449, and after knockdown by Shh-siRNA, and led to reversal of the EMT phenotype. The results with HTB-9 were confirmed using a second bladder cancer cell line, BFTC905 (DM). In a xenograft mouse model TGF-β1 treated HTB-9 cells exhibited enhanced tumor growth. Although normal bladder epithelial cells could also undergo EMT and upregulate Shh with TGF-β1 they did not exhibit tumorigenicity. The TGF-β1 treated HTB-9 xenografts showed strong evidence for a switch to a more stem cell like phenotype, with functional activation of CD133, Sox2, Nanog, and Oct4. The bladder cancer specific stem cell markers CK5 and CK14 were upregulated in the TGF-β1 treated xenograft tumor samples, while CD44 remained unchanged in both treated and untreated tumors. Immunohistochemical analysis of 22 primary human bladder tumors indicated that Shh expression was positively correlated with tumor grade and stage. Elevated expression of Ki-67, Shh, Gli2, and N-cadherin were observed in the high grade and stage human bladder tumor samples, and conversely, the downregulation of these genes were observed in the low grade and stage tumor samples. Collectively, this study indicates that TGF-β1-induced Shh may regulate EMT and tumorigenicity in bladder cancer. Our studies reveal that the TGF-β1 induction of EMT and Shh is cell type context dependent. Thus, targeting the Shh pathway could be clinically beneficial in the ability to reverse the EMT phenotype of tumor cells and potentially inhibit bladder cancer progression and metastasis

Sonic_hedgehog_pathway

Sonic_hedgehog_pathway

7.7.3 Differential activation of NF-κB signaling is associated with platinum and taxane resistance in MyD88 deficient epithelial ovarian cancer cells

Gaikwad SM, Thakur B, Sakpal A, Singh RK, Ray P.
Int J Biochem Cell Biol. 2015 Apr; 61:90-102
http://dx.doi.org:/10.1016/j.biocel.2015.02.001

Development of chemoresistance is a major impediment to successful treatment of patients suffering from epithelial ovarian carcinoma (EOC). Among various molecular factors, presence of MyD88, a component of TLR-4/MyD88 mediated NF-κB signaling in EOC tumors is reported to cause intrinsic paclitaxel resistance and poor survival. However, 50-60% of EOC patients do not express MyD88 and one-third of these patients finally relapses and dies due to disease burden. The status and role of NF-κB signaling in this chemoresistant MyD88(negative) population has not been investigated so far. Using isogenic cellular matrices of cisplatin, paclitaxel and platinum-taxol resistant MyD88(negative) A2780 ovarian cancer cells expressing a NF-κB reporter sensor, we showed that enhanced NF-κB activity was required for cisplatin but not for paclitaxel resistance. Immunofluorescence and gel mobility shift assay demonstrated enhanced nuclear localization of NF-κB and subsequent binding to NF-κB response element in cisplatin resistant cells. The enhanced NF-κB activity was measurable from in vivo tumor xenografts by dual bioluminescence imaging. In contrast, paclitaxel and the platinum-taxol resistant cells showed down regulation in NF-κB activity. Intriguingly, silencing of MyD88 in cisplatin resistant and MyD88(positive) TOV21G and SKOV3 cells showed enhanced NF-κB activity after cisplatin but not after paclitaxel or platinum-taxol treatments. Our data thus suggest that NF-κB signaling is important for maintenance of cisplatin resistance but not for taxol or platinum-taxol resistance in absence of an active TLR-4/MyD88 receptor mediated cell survival pathway in epithelial ovarian carcinoma.

7.7.4 Activation of apoptosis by caspase-3-dependent specific RelB cleavage in anticancer agent-treated cancer cells

Kuboki MIto ASimizu SUmezawa K.
Biochem Biophys Res Commun. 2015 Jan 16; 456(3):810-4
http://dx.doi.org:/10.1016/j.bbrc.2014.12.024

Activation of caspase 3 and caspase-dependent apoptosis  nrmicro2071-f1

Activation of caspase 3 and caspase-dependent apoptosis nrmicro2071-f1

Highlights

  • We have prepared RelB mutants that are resistant to caspase 3-induced scission.
  • Vinblastine induced caspase 3-dependent site-specific RelB cleavage in cancer cells.
  • Cancer cells expressing cleavage-resistant RelB showed less sensitivity to vinblastine.
  • Caspase 3-induced RelB cleavage may provide positive feedback mechanism in apoptosis.

DTCM-glutarimide (DTCM-G) is a newly found anti-inflammatory agent. In the course of experiments with lymphoma cells, we found that DTCM-G induced specific RelB cleavage. Anticancer agent vinblastine also induced the specific RelB cleavage in human fibrosarcoma HT1080 cells. The site-directed mutagenesis analysis revealed that the Asp205 site in RelB was specifically cleaved possibly by caspase-3 in vinblastine-treated HT1080 cells. Moreover, the cells stably overexpressing RelB Asp205Ala were resistant to vinblastine-induced apoptosis. Thus, the specific Asp205 cleavage of RelB by caspase-3 would be involved in the apoptosis induction by anticancer agents, which would provide the positive feedback mechanism.

apoptotic-caspases-control-microglia-activation-cdd2011107f3

apoptotic-caspases-control-microglia-activation-cdd2011107f3

 

 

7.7.5 Identification of Liver Cancer Progenitors Whose Malignant Progression Depends on Autocrine IL-6 Signaling

He GDhar DNakagawa HFont-Burgada JOgata HJiang Y, et al.
Cell. 2013 Oct 10; 155(2):384-96
http://dx.doi.org/10.1016%2Fj.cell.2013.09.031

Il-6 signaling in cancer cells

Il-6 signaling in cancer cells

Hepatocellular carcinoma (HCC) is a slowly developing malignancy postulated to evolve from pre-malignant lesions in chronically damaged livers. However, it was never established that premalignant lesions actually contain tumor progenitors that give rise to cancer. Here, we describe isolation and characterization of HCC progenitor cells (HcPCs) from different mouse HCC models. Unlike fully malignant HCC, HcPCs give rise to cancer only when introduced into a liver undergoing chronic damage and compensatory proliferation. Although HcPCs exhibit a similar transcriptomic profile to bipotential hepatobiliary progenitors, the latter do not give rise to tumors. Cells resembling HcPCs reside within dysplastic lesions that appear several months before HCC nodules. Unlike early hepatocarcinogenesis, which depends on paracrine IL-6 production by inflammatory cells, due to upregulation of LIN28 expression, HcPCs had acquired autocrine IL-6 signaling that stimulates their in vivo growth and malignant progression. This may be a general mechanism that drives other IL-6-producing malignancies.

Clonal evolution and selective pressure may cause some descendants of the initial progenitor to cross the bridge of no return and form a premalignant lesion. Cancer genome sequencing indicates that most cancers require at least five genetic changes to evolve (Wood et al., 2007). It has been difficult to isolate and propagate cancer progenitors prior to detection of tumor masses. Further, it is not clear whether cancer progenitors are the precursors for the  cancer stem cells (CSCs)isolated from cancers. An answer to these critical questions depends on identification and isolation of cancer progenitors, which may also enable definition of molecular markers and signaling pathways suitable for early detection and treatment.

Hepatocellular carcinoma (HCC), the end product of chronic liver diseases, requires several decades to evolve (El-Serag, 2011). It is the third most deadly and fifth most common cancer worldwide, and in the United States its incidence has doubled in the past two decades. Furthermore, 8% of the world’s population are chronically infected with hepatitis B or C viruses (HBV and HCV) and are at a high risk of new HCC development (El-Serag, 2011). Up to 5% of HCV patients will develop HCC in their lifetime, and the yearly HCC incidence in patients with cirrhosis is 3%–5%. These tumors may arise from premalignant lesions, ranging from dysplastic foci to dysplastic hepatocyte nodules that are often seen in damaged and cirrhotic livers and are more proliferative than the surrounding parenchyma (Hytiroglou et al., 2007). There is no effective treatment for HCC and, upon diagnosis, most patients with advanced disease have a remaining lifespan of 4–6 months. Premalignant lesions, called foci of altered hepatocytes (FAH), were described in chemically induced HCC models (Pitot, 1990), but it was questioned whether these lesions harbor tumor progenitors or result from compensatory proliferation (Sell and Leffert, 2008). The aim of this study was to determine whether HCC progenitor cells (HcPCs) exist and if so, to isolate these cells and identify some of the signaling networks that are involved in their maintenance and progression.

We now describe HcPC isolation from mice treated with the procarcinogen diethyl nitrosamine (DEN), which induces poorly differentiated HCC nodules within 8 to 9 months (Verna et al., 1996). The use of a chemical carcinogen is justified because the finding of up to 121 mutations per HCC genome suggests that carcinogens may be responsible for human HCC induction (Guichard et al., 2012). Furthermore, 20%–30% of HCC, especially in HBV-infected individuals, evolve in noncirrhotic livers (El-Serag, 2011). Nonetheless, we also isolated HcPCs fromTak1Δhep mice, which develop spontaneous HCC as a result of progressive liver damage, inflammation, and fibrosis caused by ablation of TAK1 (Inokuchi et al., 2010). Although the etiology of each model is distinct, both contain HcPCs that express marker genes and signaling pathways previously identified in human HCC stem cells (Marquardt and Thorgeirsson, 2010) long before visible tumors are detected. Furthermore, DEN-induced premalignant lesions and HcPCs exhibit autocrine IL-6 production that is critical for tumorigenic progression. Circulating IL-6 is a risk indicator in several human pathologies and is strongly correlated with adverse prognosis in HCC and cholangiocarcinoma (Porta et al., 2008Soresi et al., 2006). IL-6 produced by in-vitro-induced CSCs was suggested to be important for their maintenance (Iliopoulos et al., 2009). Little is known about the source of IL-6 in HCC.

DEN-Induced Collagenase-Resistant Aggregates of HCC Progenitors

A single intraperitoneal (i.p.) injection of DEN into 15-day-old BL/6 mice induces HCC nodules first detected 8 to 9 months later. However, hepatocytes prepared from macroscopically normal livers 3 months after DEN administration already contain cells that progress to HCC when transplanted into the permissive liver environment of MUP-uPA mice (He et al., 2010), which express urokinase plasminogen activator (uPA) from a mouse liver-specific major urinary protein (MUP) promoter and undergo chronic liver damage and compensatory proliferation (Rhim et al., 1994). HCC markers such as α fetoprotein (AFP), glypican 3 (Gpc3), and Ly6D, whose expression in mouse liver cancer was reported (Meyer et al., 2003), were upregulated in aggregates from DEN-treated livers, but not in nonaggregated hepatocytes or aggregates from control livers (Figure S1A). Using 70 μm and 40 μm sieves, we separated aggregated from nonaggregated hepatocytes (Figure 1A) and tested their tumorigenic potential by transplantation into MUP-uPA mice (Figure 1B). To facilitate transplantation, the aggregates were mechanically dispersed and suspended in Dulbecco’s modified Eagle’s medium (DMEM). Five months after intrasplenic (i.s.) injection of 104 viable cells, mice receiving cells from aggregates developed about 18 liver tumors per mouse, whereas mice receiving nonaggregated hepatocytes developed less than 1 tumor each (Figure 1B). The tumors exhibited typical trabecular HCC morphology and contained cells that abundantly express AFP (Figure S1B).

Only liver tumors were formed by the transplanted cells. Other organs, including the spleen into which the cells were injected, remained tumor free (Figure 1B), suggesting that HcPCs progress to cancer only in the proper microenvironment. Indeed, no tumors appeared after HcPC transplantation into normal BL/6 mice. But, if BL/6 mice were first treated with retrorsine (a chemical that permanently inhibits hepatocyte proliferation [Laconi et al., 1998]), intrasplenically transplanted with HcPC-containing aggregates, and challenged with CCl4 to induce liver injury and compensatory proliferation (Guo et al., 2002), HCCs readily appeared (Figure 1C). CCl4 omission prevented tumor development. Notably, MUP-uPA or CCl4-treated livers are fragile, rendering direct intrahepatic transplantation difficult. CCl4-induced liver damage, especially within a male liver, generates a microenvironment that drives HcPC proliferation and malignant progression. To examine this point, we transplanted GFP-labeled HcPC-containing aggregates into retrorsine-treated BL/6 mice and examined their ability to proliferate with or without subsequent CCl4 treatment. Indeed, the GFP+ cells formed clusters that grew in size only in CCl4-treated host livers (Figure S1E). Omission of CC14 prevented their expansion.

Because CD44 is expressed by HCC stem cells (Yang et al., 2008Zhu et al., 2010), we dispersed the aggregates and separated CD44+ from CD44 cells and transplanted both into MUP-uPA mice. Whereas as few as 103 CD44+ cells gave rise to HCCs in 100% of recipients, no tumors were detected after transplantation of CD44 cells (Figure 1E). Remarkably, 50% of recipients developed at least one HCC after receiving as few as 102 CD44+ cells.

HcPC-Containing Aggregates in Tak1Δhep Mice

We applied the same HcPC isolation protocol to Tak1Δhep mice, which develop HCC of different etiology from DEN-induced HCC. Importantly, Tak1Δhep mice develop HCC as a consequence of chronic liver injury and fibrosis without carcinogen or toxicant exposure (Inokuchi et al., 2010). Indeed, whole-tumor exome sequencing revealed that DEN-induced HCC contained about 24 mutations per 106 bases (Mb) sequenced, with B-RafV637E being the most recurrent, whereas 1.4 mutations per Mb were detected inTak1Δhep HCC’s exome (Table S1). By contrast, Tak1Δhep HCC exhibited gene copy number changes. HCC developed in 75% of MUP-uPA mice that received dispersed Tak1Δhep aggregates, but no tumors appeared in mice receiving nonaggregated Tak1Δhep or totalTak1f/f hepatocytes (Figure 2B). bile duct ligation (BDL) or feeding with 3,5-dicarbethoxy-1,4-dihydrocollidine (DDC), treatments that cause cholestatic liver injuries and oval cell expansion (Dorrell et al., 2011), did increase the number of small hepatocytic cell aggregates (Figure S2A). Nonetheless, no tumors were observed 5 months after injection of such aggregates into MUP-uPA mice (Figure S2B). Thus, not all hepatocytic aggregates contain HcPCs, and HcPCs only appear under tumorigenic conditions.

The HcPC Transcriptome Is Similar to that of HCC and Oval Cells

To determine the relationship between DEN-induced HcPCs, normal hepatocytes, and fully transformed HCC cells, we analyzed the transcriptomes of aggregated and nonaggregated hepatocytes from male littermates 5 months after DEN administration, HCC epithelial cells from DEN-induced tumors, and normal hepatocytes from age- and gender-matched littermate controls. Clustering analysis distinguished the HCC samples from other samples and revealed that the aggregated hepatocyte samples did not cluster with each other but rather with nonaggregated hepatocytes derived from the same mouse (Figure S3A). 57% (583/1,020) of genes differentially expressed in aggregated relative to nonaggregated hepatocytes are also differentially expressed in HCC relative to normal hepatocytes (Figure 3B, top), a value that is highly significant (p < 7.13 × 10−243). More specifically, 85% (494/583) of these genes are overexpressed in both HCC and HcPC-containing aggregates (Figure 3B, bottom table). Thus, hepatocyte aggregates isolated 5 months after DEN injection contain cells that are related in their gene expression profile to HCC cells isolated from fully developed tumor nodules.

Figure 3 Aggregated Hepatocytes Exhibit an Altered Transcriptome Similar to that of HCC Cells

We examined which biological processes or cellular compartments were significantly overrepresented in the induced or repressed genes in both pairwise comparisons (Gene Ontology Analysis). As expected, processes and compartments that were enriched in aggregated hepatocytes relative to nonaggregated hepatocytes were almost identical to those that were enriched in HCC relative to normal hepatocytes (Figure 3C). Several human HCC markers, including AFP, Gpc3 and H19, were upregulated in aggregated hepatocytes (Figures 3D and 3E). Aggregated hepatocytes also expressed more Tetraspanin 8 (Tspan8), a cell-surface glycoprotein that complexes with integrins and is overexpressed in human carcinomas (Zöller, 2009). Another cell-surface molecule highly expressed in aggregated cells is Ly6D (Figures 3D and 3E). Immunofluorescence (IF) analysis revealed that Ly6D was undetectable in normal liver but was elevated in FAH and ubiquitously expressed in most HCC cells (Figure S3C). A fluorescent-labeled Ly6D antibody injected into HCC-bearing mice specifically stained tumor nodules (Figure S3D). Other cell-surface molecules that were upregulated in aggregated cells included syndecan 3 (Sdc3), integrin α 9 (Itga9), claudin 5 (Cldn5), and cadherin 5 (Cdh5) (Figure 3D). Aggregated hepatocytes also exhibited elevated expression of extracellular matrix proteins (TIF3 and Reln1) and a serine protease inhibitor (Spink3). Elevated expression of such proteins may explain aggregate formation. Aggregated hepatocytes also expressed progenitor cell markers, including the epithelial cell adhesion molecule (EpCAM) (Figure 3E) and Dlk1 (Figure 3D). We compared the HcPC and HCC (Figure 3A) to the transcriptome of DDC-induced oval cells (Shin et al., 2011). This analysis revealed a striking similarity between the HCC, HcPC, and the oval cell transcriptomes (Figure S3B). Despite these similarities, some genes that were upregulated in HcPC-containing aggregates and HCC were not upregulated in oval cells. Such genes may account for the tumorigenic properties of HcPC and HCC.

Figure 4  DEN-Induced HcPC Aggregates Express Pathways and Markers Characteristic of HCC and Hepatobiliary Stem Cells

We examined the aggregates for signaling pathways and transcription factors involved in hepatocarcinogenesis. Many aggregated cells were positive for phosphorylated c-Jun and STAT3 (Figure 4A), transcription factors involved in DEN-induced hepatocarcinogenesis (Eferl et al., 2003He et al., 2010). Sox9, a transcription factor that marks hepatobiliary progenitors (Dorrell et al., 2011), was also expressed by many of the aggregated cells, which were also positive for phosphorylated c-Met (Figure 4A), a receptor tyrosine kinase that is activated by hepatocyte growth factor (HGF) and is essential for liver development (Bladt et al., 1995) and hepatocarcinogenesis (Wang et al., 2001). Few of the nonaggregated hepatocytes exhibited activation of these signaling pathways. Despite different etiology, HcPC-containing aggregates from Tak1Δhep mice exhibit upregulation of many of the same markers and pathways that are upregulated in DEN-induced HcPC-containing aggregates. Flow cytometry confirmed enrichment of CD44+ cells as well as CD44+/CD90+ and CD44+/EpCAM+ double-positive cells in the HcPC-containing aggregates from either DEN-treated or Tak1Δhep livers (Figure S4B).

HcPC-Containing Aggregates Originate from Premalignant Dysplastic Lesions

FAH are dysplastic lesions occurring in rodent livers exposed to hepatic carcinogens (Su et al., 1990). Similar lesions are present in premalignant human livers (Su et al., 1997). Yet, it is still debated whether FAH correspond to premalignant lesions or are a reaction to liver injury that does not lead to cancer (Sell and Leffert, 2008). In DEN-treated males, FAH were detected as early as 3 months after DEN administration (Figure 5A), concomitant with the time at which HcPC-containing aggregates were detected. In females, FAH development was delayed. FAH contained cells positive for the same progenitor cell markers and activated signaling pathways present in HcPC-containing aggregates, including AFP, CD44, and EpCAM (Figure 5C). FAH also contained cells positive for activated STAT3, c-Jun, and PCNA (Figure 5C).

HcPCs Exhibit Autocrine IL-6 Expression Necessary for HCC Progression

In situ hybridization (ISH) and immunohistochemistry (IHC) revealed that DEN-induced FAH contained IL-6-expressing cells (Figures 6A, 6B, and S5), and freshly isolated DEN-induced aggregates contained more IL-6 messenger RNA (mRNA) than nonaggregated hepatocytes (Figure 6C). We examined several factors that control IL-6 expression and found that LIN28A and B were significantly upregulated in HcPCs and HCC (Figures 6D and 6E). LIN28-expressing cells were also detected within FAH (Figure 6F). As reported (Iliopoulos et al., 2009), knockdown of LIN28B in cultured HcPC or HCC cell lines decreased IL-6 expression (Figure 6G). LIN28 exerts its effects through downregulation of the microRNA (miRNA) Let-7 (Iliopoulos et al., 2009).

Figure 6  Liver Premalignant Lesions and HcPCs Exhibit Elevated IL-6 and LIN28 Expression

Figure 7  HCC Growth Depends on Autocrine IL-6 Production

The isolation and characterization of cells that can give rise to HCC only after transplantation into an appropriate host liver undergoing chronic injury demonstrates that cancer arises from progenitor cells that are yet to become fully malignant. Importantly, unlike fully malignant HCC cells, the HcPCs we isolated cannot form s.c. tumors or even liver tumors when introduced into a nondamaged liver. Liver damage induced by uPA expression or CCl4 treatment provides HcPCs with the proper cytokine and growth factor milieu needed for their proliferation. Although HcPCs produce IL-6, they may also depend on other cytokines such as TNF, which is produced by macrophages that are recruited to the damaged liver. In addition, uPA expression and CCl4 treatment may enhance HcPC growth and progression through their fibrogenic effect on hepatic stellate cells. Although HCC and other cancers have been suspected to arise from premalignant/dysplastic lesions (Hruban et al., 2007Hytiroglou et al., 2007), a direct demonstration that such lesions progress into malignant tumors has been lacking. Based on expression of common markers—EpCAM, CD44, AFP, activated STAT3, and IL-6—that are not expressed in normal hepatocytes, we postulate that HcPCs originate from FAH or dysplastic foci, which are first observed in male mice within 3 months of DEN exposure.

7.7.6 Acetylation Stabilizes ATP-Citrate Lyase to Promote Lipid Biosynthesis and Tumor Growth

Lin R1Tao RGao XLi TZhou XGuan KLXiong YLei QY.
Mol Cell. 2013 Aug 22; 51(4):506-18
http://dx.doi.org:/10.1016/j.molcel.2013.07.002

Increased fatty acid synthesis is required to meet the demand for membrane expansion of rapidly growing cells. ATP-citrate lyase (ACLY) is upregulated or activated in several types of cancer, and inhibition of ACLY arrests proliferation of cancer cells. Here we show that ACLY is acetylated at lysine residues 540, 546, and 554 (3K). Acetylation at these three lysine residues is stimulated by P300/calcium-binding protein (CBP)-associated factor (PCAF) acetyltransferase under high glucose and increases ACLY stability by blocking its ubiquitylation and degradation. Conversely, the protein deacetylase sirtuin 2 (SIRT2) deacetylates and destabilizes ACLY. Substitution of 3K abolishes ACLY ubiquitylation and promotes de novo lipid synthesis, cell proliferation, and tumor growth. Importantly, 3K acetylation of ACLY is increased in human lung cancers. Our study reveals a crosstalk between acetylation and ubiquitylation by competing for the same lysine residues in the regulation of fatty acid synthesis and cell growth in response to glucose.

Fatty acid synthesis occurs at low rates in most nondividing cells of normal tissues that primarily uptake lipids from circulation. In contrast, increased lipogenesis, especially de novo lipid synthesis, is a key characteristic of cancer cells. Many studies have demonstrated that in cancer cells, fatty acids are preferred to be derived from de novo synthesis instead of extracellular lipid supply (Medes et al., 1953Menendez and Lupu, 2007;Ookhtens et al., 1984Sabine et al., 1967). Fatty acids are key building blocks for membrane biogenesis, and glucose serves as a major carbon source for de novo fatty acid synthesis (Kuhajda, 2000McAndrew, 1986;Swinnen et al., 2006). In rapidly proliferating cells, citrate generated by the tricarboxylic acid (TCA) cycle, either from glucose by glycolysis or glutamine by anaplerosis, is preferentially exported from mitochondria to cytosol and then cleaved by ATP citrate lyase (ACLY) (Icard et al., 2012) to produce cytosolic acetyl coenzyme A (acetyl-CoA), which is the building block for de novo lipid synthesis. As such, ACLY couples energy metabolism with fatty acids synthesis and plays a critical role in supporting cell growth. The function of ACLY in cell growth is supported by the observation that inhibition of ACLY by chemical inhibitors or RNAi dramatically suppresses tumor cell proliferation and induces differentiation in vitro and in vivo (Bauer et al., 2005Hatzivassiliou et al., 2005). In addition, ACLY activity may link metabolic status to histone acetylation by providing acetyl-CoA and, therefore, gene expression (Wellen et al., 2009).

While ACLY is transcriptionally regulated by sterol regulatory element-binding protein 1 (SREBP-1) (Kim et al., 2010), ACLY activity is regulated by the phosphatidylinositol 3-kinase (PI3K)/Akt pathway (Berwick et al., 2002Migita et al., 2008Pierce et al., 1982). Akt can directly phosphorylate and activate ACLY (Bauer et al., 2005Berwick et al., 2002Migita et al., 2008Potapova et al., 2000). Covalent lysine acetylation has recently been found to play a broad and critical role in the regulation of multiple metabolic enzymes (Choudhary et al., 2009Zhao et al., 2010). In this study, we demonstrate that ACLY protein is acetylated on multiple lysine residues in response to high glucose. Acetylation of ACLY blocks its ubiquitinylation and degradation, thus leading to ACLY accumulation and increased fatty acid synthesis. Our observations reveal a crosstalk between protein acetylation and ubiquitylation in the regulation of fatty acid synthesis and cell growth.

Acetylation of ACLY at Lysines 540, 546, and 554

Recent mass spectrometry-based proteomic analyses have potentially identified a large number of acetylated proteins, including ACLY (Figure S1A available online; Choudhary et al., 2009Zhao et al., 2010). We detected the acetylation level of ectopically expressed ACLY followed by western blot using pan-specific anti-acetylated lysine antibody. ACLY was indeed acetylated, and its acetylation was increased by nearly 3-fold after treatment with nicotinamide (NAM), an inhibitor of the SIRT family deacetylases, and trichostatin A (TSA), an inhibitor of histone deacetylase (HDAC) class I and class II (Figure 1A). Experiments with endogenous ACLY also showed that TSA and NAM treatment enhanced ACLY acetylation (Figure 1B).

Figure 1  ACLY Is Acetylated at Lysines 540, 546, and 554

Ten putative acetylation sites were identified by mass spectrometry analyses (Table S1). We singly mutated each lysine to either a glutamine (Q) or an arginine (R) and found that no single mutation resulted in a significant reduction of ACLY acetylation (data not shown), indicating that ACLY may be acetylated at multiple lysine residues. Three lysine residues, K540, K546, and K554, received high scores in the acetylation proteomic screen and are evolutionarily conserved from C. elegans to mammals (Figure S1A). We generated triple Q and R mutants of K540, K546, and K554 (3KQ and 3KR) and found that both 3KQ and 3KR mutations resulted in a significant (~60%) decrease in ACLY acetylation (Figure 1C), indicating that 3K are the major acetylation sites of ACLY.  Further, we found that the acetylation of endogenous ACLY is clearly increased after treatment of cells with NAM and TSA (Figure 1D). These results demonstrate that ACLY is acetylated at K540, K546, and K554.

Glucose Promotes ACLY Acetylation to Stabilize ACLY

In mammalian cells, glucose is the main carbon source for de novo lipid synthesis. We found that ACLY levels increased with increasing glucose concentration, which also correlated with increased ACLY 3K acetylation (Figure 1E). Furthermore, to confirm whether the glucose level affects ACLY protein stability in vivo, we intraperitoneally injected glucose in BALB/c mice and found that high glucose resulted in a significant increase of ACLY protein levels (Figure 1F).

To determine whether ACLY acetylation affects its protein levels, we treated HeLa and Chang liver cells with NAM and TSA and found an increase in ACLY protein levels (Figure S1G, upper panel). ACLY mRNA levels were not significantly changed by the treatment of NAM and TSA (Figure S1G, lower panel), indicating that this upregulation of ACLY is mostly achieved at the posttranscriptional level. Indeed, ACLY protein was also accumulated in cells treated with the proteasome inhibitor MG132, indicating that ACLY stability could be regulated by the ubiquitin-proteasome pathway (Figure 1G). Blocking deacetylase activity stabilized ACLY (Figure S1H). The stabilization of ACLY induced by high glucose was associated with an increase of ACLY acetylation at K540, K546, and K554. Together, these data support a notion that high glucose induces both ACLY acetylation and protein stabilization and prompted us to ask whether acetylation directly regulates ACLY stability. We then generated ACLYWT, ACLY3KQ, and ACLY3KRstable cells after knocking down the endogenous ACLY. We found that the ACLY3KR or ACLY3KQmutant was more stable than the ACLYWT (Figures 1I and S1I). Collectively, our results suggest that glucose induces acetylation at K540, 546, and 554 to stabilize ACLY.

Acetylation Stabilizes ACLY by Inhibiting Ubiquitylation

To determine the mechanism underlying the acetylation and ACLY protein stability, we first examined ACLY ubiquitylation and found that it was actively ubiquitylated (Figure 2A). Previous proteomic analyses have identified K546 in ACLY as a ubiquitylation site (Wagner et al., 2011). In order to identify the ubiquitylation sites, we tested the ubiquitylation levels of double mutants 540R–546R and 546–554R (Figure S2A). We found that the ubiquitylation of the 540R-546R and 546R-554R mutants is partially decreased, while mutation of K540, K546, and K554 (3KR), which changes all three putative acetylation lysine residues of ACLY to arginine residues, dramatically reduced the ACLY ubiquitylation level (Figures 2B and S2A), indicating that 3K lysines might also be the ubiquitylation target residues. Moreover, inhibition of deacetylases by NAM and TSA decreased ubiquitylation of WT but not 3KQ or 3KR mutant ACLY (Figure 2C). These results implicate an antagonizing role of the acetylation towards the ubiquitylation of ACLY at these three lysine residues.

Figure 2  Acetylation Protects ACLY from Proteasome Degradation by Inhibiting Ubiquitylation

We found that ACLY acetylation was only detected in the nonubiquitylated, but not the ubiquitylated (high-molecular-weight), ACLY species. This result indicates that ACLY acetylation and ubiquitylation are mutually exclusive and is consistent with the model that K540, K546, and K554 are the sites of both ubiquitylation and acetylation. Therefore, acetylation of these lysines would block ubiquitylation.

We also found that glucose upregulates ACLY acetylation at 3K and decreases its ubiquitylation (Figure S2B). High glucose (25 mM) effectively decreased ACLY ubiquitylation, while inhibition of deacetylases clearly diminished its ubiquitylation (Figure 2E). We conclude that acetylation and ubiquitylation occur mutually exclusively at K540, K546, and K554 and that high-glucose-induced acetylation at these three sites blocks ACLY ubiquitylation and degradation.

UBR4 Targets ACLY for Degradation

UBR4 was identified as a putative ACLY-interacting protein by affinity purification coupled with mass spectrometry analysis (data not shown). To address if UBR4 is a potential ACLY E3 ligase, we determined the interaction between ACLY and UBR4 and found that ACLY interacted with the E3 ligase domain of UBR4; this interaction was enhanced by MG132 treatment (Figure 3A). UBR4 knockdown in A549 cells resulted in an increase of endogenous ACLY protein level (Figure 3C). Moreover, UBR4 knockdown significantly stabilized ACLY (Figure 3D) and decreased ACLY ubiquitylation (Figure 3E). Taken together, these results indicate that UBR4 is an ACLY E3 ligase that responds to glucose regulation.

Figure 3  UBR4 Is the E3 Ligase of ACLY

PCAF Acetylates ACLY

PCAF knockdown significantly reduced acetylation of 3K, indicating that PCAF is a potential 3K acetyltransferase in vivo (Figure 4C, upper panel). Furthermore, PCAF knockdown decreased the steady-state level of endogenous ACLY, but not ACLY mRNA (Figure 4C, middle and lower panels). Moreover, we found that PCAF knockdown destabilized ACLY (Figure 4D). In addition, overexpression of PCAF decreases ACLY ubiquitylation (Figure 4E), while PCAF inhibition increases the interaction between UBR4 E3 ligase domain and wild-type ACLY, but not 3KR (Figure 4F). Together, our results indicate that PCAF increases ACLY protein level, possibly via acetylating ACLY at 3K.

Figure 4  PCAF Is the Acetylase of ACLY

SIRT2 Deacetylates ACLY

Figure 5  SIRT2 Decreases ACLY Acetylation and Increases Its Protein Levels In Vivo

Acetylation of ACLY Promotes Cell Proliferation and De Novo Lipid Synthesis

The protein levels of ACLY 3KQ and 3KR were accumulated to a level higher than the wild-type cells upon extended culture in low-glucose medium (Figure S6A, right panel), indicating a growth advantage conferred by ACLY stabilization resulting from the disruption of both acetylation and ubiquitylation at K540, K546, and K554. Cellular acetyl-CoA assay showed that cells expressing 3KQ or 3KR mutant ACLY produce more acetyl-CoA than cells expressing the wild-type ACLY under low glucose (Figures 6B and S6B), further supporting the conclusion that 3KQ or 3KR mutation stabilizes ACLY.

Figure 6  Acetylation of ACLY at 3K Promotes Lipogenesis and Tumor Cell Proliferation

ACLY is a key enzyme in de novo lipid synthesis. Silencing ACLY inhibited the proliferation of multiple cancer cell lines, and this inhibition can be partially rescued by adding extra fatty acids or cholesterol into the culture media (Zaidi et al., 2012). This prompted us to measure extracellular lipid incorporation in A549 cells after knockdown and ectopic expression of ACLY. We found that when cultured in low glucose (2.5 mM), cells expressing wild-type ACLY uptake significantly more phospholipids compared to cells expressing 3KQ or 3KR mutant ACLY (Figures 6C, 6D, and S6D). When cultured in the presence of high glucose (25 mM), however, cells expressing either the wild-type, 3KQ, or 3KR mutant ACLY all have reduced, but similar, uptake of extracellular phospholipids (Figures 6C, 6D, and S6D). The above results are consistent with a model that acetylation of ACLY induced by high glucose increases its stability and stimulates de novo lipid synthesis.

3K Acetylation of ACLY Is Increased in Lung Cancer

ACLY is reported to be upregulated in human lung cancer (Migita et al., 2008). Many small chemicals targeting ACLY have been designed for cancer treatment (Zu et al., 2012). The finding that 3KQ or 3KR mutant increased the ability of ACLY to support A549 lung cancer cell proliferation prompted us to examine 3K acetylation in human lung cancers. We collected a total of 54 pairs of primary human lung cancer samples with adjacent normal lung tissues and performed immunoblotting for ACLY protein levels. This analysis revealed that, when compared to the matched normal lung tissues, 29 pairs showed a significant increase of total ACLY protein using b-actin as a loading control (Figures 7A and S7A). The tumor sample analyses demonstrate that ACLY protein levels are elevated in lung cancers, and 3K acetylation positively correlates with the elevated ACLY protein. These data also indicate that ACLY with 3K acetylation may be potential biomarker for lung cancer diagnosis.

Figure 7
  Acetylation of ACLY at 3K Is Upregulated in Human Lung Carcinoma

Dysregulation of cellular metabolism is a hallmark of cancer (Hanahan and Weinberg, 2011Vander Heiden et al., 2009). Besides elevated glycolysis, increased lipogenesis, especially de novo lipid synthesis, also plays an important role in tumor growth. Because most carbon sources for fatty acid synthesis are from glucose in mammalian cells (Wellen et al., 2009), the channeling of carbon into de novo lipid synthesis as building blocks for tumor cell growth is primarily linked to acetyl-CoA production by ACLY. Moreover, the ACLY-catalyzed reaction consumes ATP. Therefore, as the key cellular energy and carbon source, one may expect a role for glucose in ACLY regulation. In the present study, we have uncovered a mechanism of ACLY regulation by glucose that increases ACLY protein level to meet the enhanced demand of lipogenesis in growing cells, such as tumor cells (Figure 7C). Glucose increases ACLY protein levels by stimulating its acetylation.

Upregulation of ACLY is common in many cancers (Kuhajda, 2000Milgraum et al., 1997Swinnen et al., 2004Yahagi et al., 2005). This is in part due to the transcriptional activation by SREBP-1 resulting from the activation of the PI3K/AKT pathway in cancers (Kim et al., 2010Nadler et al., 2001Wang and Dey, 2006). In this study, we report a mechanism of ACLY regulation at the posttranscriptional level. We propose that acetylation modulated by glucose status plays a crucial role in coordinating the intracellular level of ACLY, hence fatty acid synthesis, and glucose availability. When glucose is sufficient, lipogenesis is enhanced. This can be achieved, at least in part, by the glucose-induced stabilization of ACLY. High glucose increases ACLY acetylation, which inhibits its ubiquitylation and degradation, leading to the accumulation of ACLY and enhanced lipogenesis. In contrast, when glucose is limited, ACLY is not acetylated and thus can be ubiquitylated, leading to ACLY degradation and reduced lipogenesis. Moreover, our data indicate that acetylation and ubiquitylation in ACLY may compete with each other by targeting the same lysine residues at K540, K546, and K554. Consistently, previous proteomic analyses have identified K546 in ACLY as a ubiquitylation site (Wagner et al., 2011). Similar models of different modifications on the same lysine residues have been reported in the regulation of other proteins (Grönroos et al., 2002Li et al., 20022012). We propose that acetylation and ubiquitylation have opposing effects in the regulation of ACLY by competitively modifying the same lysine residues. The acetylation-mimetic 3KQ and the acetylation-deficient 3KR mutants behaved indistinguishably in most biochemical and functional assays, mainly due to the fact that these mutations disrupt lysine ubiquitylation that primarily occurs on these three residues.

ACLY is increased in lung cancer tissues compared to adjacent tissues. Consistently, ACLY acetylation at 3K is also significantly increased in lung cancer tissues. These observations not only confirm ACLY acetylation in vivo, but also suggest that ACLY 3K acetylation may play a role in lung cancer development. Our study reveals a mechanism of ACLY regulation in response to glucose signals.

 

7.7.7 Monoacylglycerol Lipase Regulates a Fatty Acid Network that Promotes Cancer Pathogenesis

Nomura DK1Long JZNiessen SHoover HSNg SWCravatt BF.
Cell. 2010 Jan 8; 140(1):49-61
http://dx.doi.org/10.1016.2Fj.cell.2009.11.027

Highlights

  • Monoacylglycerol lipase (MAGL) is elevated in aggressive human cancer cells
  • Loss of MAGL lowers fatty acid levels in cancer cells and impairs pathogenicity
  • MAGL controls a signaling network enriched in protumorigenic lipids
  • A high-fat diet can restore the growth of tumors lacking MAGL in vivo
monoacylglycerol-lipase-magl-is-highly-expressed-in-aggressive-human-cancer-cells-and-primary-tumors

monoacylglycerol-lipase-magl-is-highly-expressed-in-aggressive-human-cancer-cells-and-primary-tumors

http://www.cell.com/cms/attachment/1082768/7977146/fx1.jpg

Tumor cells display progressive changes in metabolism that correlate with malignancy, including development of a lipogenic phenotype. How stored fats are liberated and remodeled to support cancer pathogenesis, however, remains unknown. Here, we show that the enzyme monoacylglycerol lipase (MAGL) is highly expressed in aggressive human cancer cells and primary tumors, where it regulates a fatty acid network enriched in oncogenic signaling lipids that promotes migration, invasion, survival, and in vivo tumor growth. Overexpression of MAGL in nonaggressive cancer cells recapitulates this fatty acid network and increases their pathogenicity—phenotypes that are reversed by an MAGL inhibitor. Impairments in MAGL-dependent tumor growth are rescued by a high-fat diet, indicating that exogenous sources of fatty acids can contribute to malignancy in cancers lacking MAGL activity. Together, these findings reveal how cancer cells can co-opt a lipolytic enzyme to translate their lipogenic state into an array of protumorigenic signals.

We show that the enzyme monoacylglycerol lipase (MAGL) is highly expressed in aggressive human cancer cells and primary tumors, where it regulates a fatty acid network enriched in oncogenic signaling lipids that promotes migration, invasion, survival, and in vivo tumor growth. Overexpression of MAGL in non-aggressive cancer cells recapitulates this fatty acid network and increases their pathogenicity — phenotypes that are reversed by an MAGL inhibitor. Interestingly, impairments in MAGL-dependent tumor growth are rescued by a high-fat diet, indicating that exogenous sources of fatty acids can contribute to malignancy in cancers lacking MAGL activity. Together, these findings reveal how cancer cells can co-opt a lipolytic enzyme to translate their lipogenic state into an array of pro-tumorigenic signals.

The conversion of cells from a normal to cancerous state is accompanied by reprogramming of metabolic pathways (Deberardinis et al., 2008Jones and Thompson, 2009Kroemer and Pouyssegur, 2008), including those that regulate glycolysis (Christofk et al., 2008Gatenby and Gillies, 2004), glutamine-dependent anaplerosis (DeBerardinis et al., 2008DeBerardinis et al., 2007Wise et al., 2008), and the production of lipids (DeBerardinis et al., 2008Menendez and Lupu, 2007). Despite a growing appreciation that dysregulated metabolism is a defining feature of cancer, it remains unclear, in many instances, how such biochemical changes occur and whether they play crucial roles in disease progression and malignancy.

Among dysregulated metabolic pathways, heightened de novo lipid biosynthesis, or the development a “lipogenic” phenotype (Menendez and Lupu, 2007), has been posited to play a major role in cancer. For instance, elevated levels of fatty acid synthase (FAS), the enzyme responsible for fatty acid biosynthesis from acetate and malonyl CoA, are correlated with poor prognosis in breast cancer patients, and inhibition of FAS results in decreased cell proliferation, loss of cell viability, and decreased tumor growth in vivo (Kuhajda et al., 2000Menendez and Lupu, 2007Zhou et al., 2007). FAS may support cancer growth, at least in part, by providing metabolic substrates for energy production (via fatty acid oxidation) (Buzzai et al., 2005Buzzai et al., 2007Liu, 2006). Many other features of lipid biochemistry, however, are also critical for supporting the malignancy of cancer cells, including:

Prominent examples of lipid messengers that contribute to cancer include:

Here, we use functional proteomic methods to discover a lipolytic enzyme, monoacylglycerol lipase (MAGL), that is highly elevated in aggressive cancer cells from multiple tissues of origin. We show that MAGL, through hydrolysis of monoacylglycerols (MAGs), controls free fatty acid (FFA) levels in cancer cells. The resulting MAGL-FFA pathway feeds into a diverse lipid network enriched in pro-tumorigenic signaling molecules and promotes migration, survival, and in vivo tumor growth. Aggressive cancer cells thus pair lipogenesis with high lipolytic activity to generate an array of pro-tumorigenic signals that support their malignant behavior.

Activity-Based Proteomic Analysis of Hydrolytic Enzymes in Human Cancer Cells

To identify enzyme activities that contribute to cancer pathogenesis, we conducted a functional proteomic analysis of a panel of aggressive and non-aggressive human cancer cell lines from multiple tumors of origin, including melanoma [aggressive (C8161, MUM2B), non-aggressive (MUM2C)], ovarian [aggressive (SKOV3), non-aggressive (OVCAR3)], and breast [aggressive (231MFP), non-aggressive (MCF7)] cancer. Aggressive cancer lines were confirmed to display much greater in vitro migration and in vivo tumor-growth rates compared to their non-aggressive counterparts (Figure S1), as previously shown (Jessani et al., 2004;Jessani et al., 2002Seftor et al., 2002Welch et al., 1991). Proteomes from these cancer lines were screened by activity-based protein profiling (ABPP) using serine hydrolase-directed fluorophosphonate (FP) activity-based probes (Jessani et al., 2002Patricelli et al., 2001). Serine hydrolases are one of the largest and most diverse enzyme classes in the human proteome (representing ~ 1–1.5% of all human proteins) and play important roles in many biochemical processes of potential relevance to cancer, such as proteolysis (McMahon and Kwaan, 2008Puustinen et al., 2009), signal transduction (Puustinen et al., 2009), and lipid metabolism (Menendez and Lupu, 2007Zechner et al., 2005). The goal of this study was to identify hydrolytic enzyme activities that were consistently altered in aggressive versus non-aggressive cancer lines, working under the hypothesis that these conserved enzymatic changes would have a high probability of contributing to the pathogenic state of cancer cells.

Among the more than 50 serine hydrolases detected in this analysis (Tables S13), two enzymes, KIAA1363 and MAGL, were found to be consistently elevated in aggressive cancer cells relative to their non-aggressive counterparts, as judged by spectral counting (Jessani et al., 2005Liu et al., 2004). We confirmed elevations in KIAA1363 and MAGL in aggressive cancer cells by gel-based ABPP, where proteomes are treated with a rhodamine-tagged FP probe and resolved by 1D-SDS-PAGE and in-gel fluorescence scanning (Figure 1A). In both cases, two forms of each enzyme were detected (Figure 1A), due to differential glycoslyation for KIAA1363 (Jessani et al., 2002), and possibly alternative splicing for MAGL (Karlsson et al., 2001). We have previously shown that KIAA1363 plays a role in regulating ether lipid signaling pathways in aggressive cancer cells (Chiang et al., 2006). On the other hand, very little was known about the function of MAGL in cancer.

Figure 1  MAGL is elevated in aggressive cancer cells, where the enzyme regulates monoacylgycerol (MAG) and free fatty acid (FFA) levels

The heightened activity of MAGL in aggressive cancer cells was confirmed using the substrate C20:4 MAG (Figure 1B). Since several enzymes have been shown to display MAG hydrolytic activity (Blankman et al., 2007), we confirmed the contribution that MAGL makes to this process in cancer cells using the potent and selective MAGL inhibitor JZL184 (Long et al., 2009a).

MAGL Regulates Free Fatty Acid Levels in Aggressive Cancer Cells

MAGL is perhaps best recognized for its role in degrading the endogenous cannabinoid 2-arachidonoylglycerol (2-AG, C20:4 MAG), as well as other MAGs, in brain and peripheral tissues (Dinh et al., 2002Long et al., 2009aLong et al., 2009bNomura et al., 2008). Consistent with this established function, blockade of MAGL by JZL184 (1 μM, 4 hr) produced significant elevations in the levels of several MAGs, including 2-AG, in each of the aggressive cancer cell lines (Figure 1C and Figure S2). Interestingly, however, MAGL inhibition also caused significant reductions in the levels of FFAs in aggressive cancer cells (Figure 1D and Figure S2). This surprising finding contrasts with the function of MAGL in normal tissues, where the enzyme does not, in general, control the levels of FFAs (Long et al., 2009aLong et al., 2009b;Nomura et al., 2008).

Metabolic labeling studies using the non-natural C17:0-MAG confirmed that MAGs are converted to LPC and LPE by aggressive cancer cells, and that this metabolic transformation is significantly enhanced by treatment with JZL184 (Figure S1). Finally, JZL184 treatment did not affect the levels of MAGs and FFAs in non-aggressive cancer lines (Figure 1C, D), consistent with the negligible expression of MAGL in these cells (Figure 1A, B).

We next stably knocked down MAGL expression by RNA interference technology using two independent shRNA probes (shMAGL1, shMAGL2), both of which reduced MAGL activity by 70–80% in aggressive cancer lines (Figure 2A, D and Figure S2). Other serine hydrolase activities were unaffected by shMAGL probes (Figure 2A, D and Figures S2), confirming the specificity of these reagents. Both shMAGL probes caused significant elevations in MAGs and corresponding reductions in FFAs in aggressive melanoma (Figure 2B, C), ovarian (Figure 2E, F), and breast cancer cells (Figure S2).

Figure 2  Stable shRNA-mediated knockdown of MAGL lowers FFA levels in aggressive cancer cells.

Together, these data demonstrate that both acute (pharmacological) and stable (shRNA) blockade of MAGL cause elevations in MAGs and reductions in FFAs in aggressive cancer cells. These intriguing findings indicate that MAGL is the principal regulator of FFA levels in aggressive cancer cells. Finally, we confirmed that MAGL activity (Figure 3A, B) and FFA levels (Figure 3C) are also elevated in high-grade primary human ovarian tumors compared to benign or low-grade tumors. Thus, heightened expression of the MAGL-FFA pathway is a prominent feature of both aggressive human cancer cell lines and primary tumors.

Figure 3  High-grade primary human ovarian tumors possess elevated MAGL activity and FFAs compared to benign tumors.

Disruption of MAGL Expression and Activity Impairs Cancer Pathogenicity

shMAGL cancer lines were next examined for alterations in pathogenicity using a set of in vitro and in vivo assays. shMAGL-melanoma (C8161), ovarian (SKOV3), and breast (231MFP) cancer cells exhibited significantly reduced in vitro migration (Figure 4A, F and Figure S2), invasion (Figure 4B, G and Figure S2), and cell survival under serum-starvation conditions (Figure 4C, H and Figure S2). Acute pharmacological blockade of MAGL by JZL184 also decreased cancer cell migration (Figure S2), but not survival, possibly indicating that maximal impairments in cancer aggressiveness require sustained inhibition of MAGL.

Figure 4  shRNA-mediated knockdown and pharmacological inhibition of MAGL impair cancer aggressiveness.

MAGL Overexpression Increases FFAs and the Aggressiveness of Cancer Cells

Stable MAGL-overexpressing (MAGL-OE) and control [expressing an empty vector or a catalytically inactive version of MAGL, where the serine nucleophile was mutated to alanine (S122A)] variants of MUM2C and OVCAR3 cells were generated by retroviral infection and evaluated for their respective MAGL activities by ABPP and C20:4 MAG substrate assays. Both assays confirmed that MAGL-OE cells possess greater than 10-fold elevations in MAGL activity compared to control cells (Figure 5A and Figure S4). MAGL-OE cells also showed significant reductions in MAGs (Figure 5B andFigure S4) and elevated FFAs (Figure 5C and Figure S4). This altered metabolic profile was accompanied by increased migration (Figure 5D and Figure S4), invasion (Figure 5E and Figure S4), and survival (Figure S4) in MAGL-OE cells. None of these effects were observed in cancer cells expressing the S122A MAGL mutant, indicating that they require MAGL activity.  MAGL-OE MUM2C cells also showed enhanced tumor growth in vivo compared to control cells (Figure 5F). Notably, the increased tumor growth rate of MAGL-OE MUM2C cells nearly matched that of aggressive C8161 cells (Figure S4). These data indicate that the ectopic expression of MAGL in non-aggressive cancer cells is sufficient to elevate their FFA levels and promote pathogenicity both in vitro and in vivo.

Figure 5 Ectopic expression of MAGL elevates FFA levels and enhances the in vitro and in vivo pathogenicity of MUM2C melanoma cells.

Metabolic Rescue of Impaired Pathogenicity in MAGL-Disrupted Cancer Cells

MAGL could support the aggressiveness of cancer cells by either reducing the levels of its MAG substrates, elevating the levels of its FFA products, or both. Among MAGs, the principal signaling molecule is the endocannabinoid 2-AG, which activates the CB1 and CB2 receptors (Ahn et al., 2008Mackie and Stella, 2006). The endocannabinoid system has been implicated previously in cancer progression and, depending on the specific study, shown to promote (Sarnataro et al., 2006Zhao et al., 2005) or suppress (Endsley et al., 2007Wang et al., 2008) cancer pathogenesis. Neither a CB1 or CB2 antagonist rescued the migratory defects of shMAGL cancer cells (Figure S5). CB1 and CB2 antagonists also did not affect the levels of MAGs or FFAs in cancer cells (Figure S5).

We then determined whether increased FFA delivery could rectify the tumor growth defect observed for shMAGL cells in vivo. Immune-deficient mice were fed either a normal chow or high-fat diet throughout the duration of a xenograft tumor growth experiment. Notably, the impaired tumor growth rate of shMAGL-C8161 cells was completely rescued in mice fed a high-fat diet. In contrast, shControl-C8161 cells showed equivalent tumor growth rates on a normal versus high-fat diet. The recovery in tumor growth for shMAGL-C8161 cells in the high-fat diet group correlated with significantly increases levels of FFAs in excised tumors (Figure 6D). Collectively, these results indicate that MAGL supports the pathogenic properties of cancer cells by maintaining tonically elevated levels of FFAs.

Figure 6  Recovery of the pathogenic properties of shMAGL cancer cells by treatment with exogenous fatty acids.

MAGL Regulates a Fatty Acid Network Enriched in Pro-Tumorigenic Signals

Studies revealed that neither

  • the MAGL-FFA pathway might serve as a means to regenerate NAD+ (via continual fatty acyl glyceride/FFA recycling) to fuel glycolysis, or
  • increased lipolysis could be to generate FFA substrates for β-oxidation, which may serve as an important energy source for cancer cells (Buzzai et al., 2005), or
  • CPT1 blockade (reduced expression of CPT1 in aggressive cancer cells (data not shown) has been reported previously (Deberardinis et al., 2006))

providing evidence against a role for β-oxidation as a downstream mediator of the pathogenic effects of the MAGL-fatty acid pathway.

Considering that FFAs are fundamental building blocks for the production and remodeling of membrane structures and signaling molecules, perturbations in MAGL might be expected to affect several lipid-dependent biochemical networks important for malignancy. To test this hypothesis, we performed lipidomic analyses of cancer cell models with altered MAGL activity, including comparisons of:

  1. MAGL-OE versus control cancer cells (OVCAR3, MUM2C), and
  2. shMAGL versus shControl cancer cells (SKOV3, C8161).

Complementing these global profiles, we also conducted targeted measurements of specific bioactive lipids (e.g., prostaglandins) that are too low in abundance for detection by standard lipidomic methods. The resulting data sets were then mined to identify a common signature of lipid metabolites regulated by MAGL, which we defined as metabolites that were significantly increased or reduced in MAGL–OE cells and showed the opposite change in shMAGL cells relative to their respective control groups (Figure 7A, B and Table S4).

Figure 7  MAGL regulates a lipid network enriched in pro-tumorigenic signaling molecules.

Most of the lipids in the MAGL-fatty acid network, including several lysophospholipids (LPC, LPA, LPE), ether lipids (MAGE, alkyl LPE), phosphatidic acid (PA), and prostaglandin E2 (PGE2), displayed similar profiles to FFAs, being consistently elevated and reduced in MAGL-OE and shMAGL cells, respectively. Only MAGs were found to show the opposite profile (elevated and reduced in shMAGL and MAGL-OE cells, respectively). Interestingly, virtually this entire lipidomic signature was also observed in aggressive cancer cells when compared to their non-aggressive counterparts (e.g., C8161 versus MUM2C and SKOV3 versus OVCAR3, respectively; Table S4). These findings demonstrate that MAGL regulates a lipid network in aggressive cancer cells that consists of not only FFAs and MAGs, but also a host of secondary lipid metabolites. Increases (rather than decreases) in LPCs and LPEs were observed in JZL184-treated cells (Figure S1 and Table S4). These data indicate that acute and chronic blockade of MAGL generate distinct metabolomic effects in cancer cells, likely reflecting the differential outcomes of short- versus long-term depletion of FFAs.

Within the MAGL-fatty acid network are several pro-tumorigenic lipid messengers, including LPA and PGE2, that have been reported to promote the aggressiveness of cancer cells (Gupta et al., 2007Mills and Moolenaar, 2003). Metabolic labeling studies confirmed that aggressive cancer cells can convert both MAGs and FFAs (Figure S1) to LPA and PGE2 and, for MAGs, this conversion was blocked by JZL184 (Figure S1). Interestingly, treatment with either LPA or PGE2 (100 nM, 4 hr) rescued the impaired migration of shMAGL cancer cells at concentrations that did not affect the migration of shControl cells (Figure 7E).

Heightened lipogenesis is an established early hallmark of dysregulated metabolism and pathogenicity in cancer (Menendez and Lupu, 2007). Cancer lipogenesis appears to be driven principally by FAS, which is elevated in most transformed cells and important for survival and proliferation (De Schrijver et al., 2003;Kuhajda et al., 2000Vazquez-Martin et al., 2008). It is not yet clear how FAS supports cancer growth, but most of the proposed mechanisms invoke pro-tumorigenic functions for the enzyme s fatty acid products and their lipid derivatives (Menendez and Lupu, 2007). This creates a conundrum, since the fatty acid molecules produced by FAS are thought to be rapidly incorporated into neutral- and phospho-lipids, pointing to the need for complementary lipolytic pathways in cancer cells to release stored fatty acids for metabolic and signaling purposes (Prentki and Madiraju, 2008Przybytkowski et al., 2007). Consistent with this hypothesis, we found that acute treatment with the FAS inhibitor C75 (40 μM, 4 h) did not reduce FFA levels in cancer cells (data not shown). Furthermore, aggressive and non-aggressive cancer cells exhibited similar levels of FAS (data not shown), indicating that lipogenesis in the absence of paired lipolysis may be insufficient to confer high levels of malignancy.

Here we show that aggressive cancer cells do indeed acquire the ability to liberate FFAs from neutral lipid stores as a consequence of heightened expression of MAGL. MAGL and its FFA products were found to be elevated in aggressive human cancer cell lines from multiple tissues of origin, as well as in high-grade primary human ovarian tumors. These data suggest that the MAGL-FFA pathway may be a conserved feature of advanced forms of many types of cancer. Further evidence in support of this premise originates from gene expression profiling studies, which have identified increased levels of MAGL in primary human ductal breast tumors compared to less malignant medullary breast tumors (Gjerstorff et al., 2006). The key role that MAGL plays in regulating FFA levels in aggressive cancer cells contrasts with the function of this enzyme in normal tissues, where it mainly controls the levels of MAGs, but not FFAs (Long et al., 2009b). These data thus provide a striking example of the co-opting of an enzyme by cancer cells to serve a distinct metabolic purpose that supports their pathogenic behavior.

Taken together, our results indicate that MAGL serves as key metabolic hub in aggressive cancer cells, where the enzyme regulates a fatty acid network that feeds into a number of pro-tumorigenic signaling pathways.

 

7.7.8 Pirin regulates epithelial to mesenchymal transition and down-regulates EAF/U19 signaling in prostate cancer cells

7.7.8.1  Pirin regulates epithelial to mesenchymal transition independently of Bcl3-Slug signaling

Komai K1Niwa Y1Sasazawa Y1Simizu S2.
FEBS Lett. 2015 Mar 12; 589(6):738-43
http://dx.doi.org:/10.1016/j.febslet.2015.01.040

Highlights

  • Pirin decreases E-cadherin expression and induces EMT.
  • The induction of EMT by Pirin is achieved through a Bcl3 independent pathway.
  • Pirin may be a novel target for cancer therapy.

Epithelial to mesenchymal transition (EMT) is an important mechanism for the initial step of metastasis. Proteomic analysis indicates that Pirin is involved in metastasis. However, there are no reports demonstrating its direct contribution. Here we investigated the involvement of Pirin in EMT. In HeLa cells, Pirin suppressed E-cadherin expression and regulated the expression of other EMT markers. Furthermore, cells expressing Pirin exhibited a spindle-like morphology, which is reminiscent of EMT. A Pirin mutant defective for Bcl3 binding decreased E-cadherin expression similar to wild-type, suggesting that Pirin regulates E-cadherin independently of Bcl3-Slug signaling. These data provide direct evidence that Pirin contributes to cancer metastasis.

Pirin regulates the expression of E-cadherin and EMT markers

In melanoma, Pirin enhances NF-jB activity and increases Slug expression by binding Bcl3 [31], and it may also be involved in adenoid cystic tumor metastasis [23]. Since Slug suppresses E-cadherin transcription and is recognized as a major EMT inducer, we hypothesized that Pirin may regulate EMT through inducing Slug expression. To investigate whether Pirin regulates EMT, we measured E-cadherin expression following Pirin knockdown. As shown in Fig. 1A and B, E-cadherin expression was significantly increased following Pirin knockdown indicating that it may promote EMT. To confirm this, we established Pirin-expressing HeLa cells (Fig. 1C), which inhibited the expression of E-cadherin (Fig. 1D). Additionally, the expression of Occludin, an epithelial marker, was decreased, and several mesenchymal markers, including Fibronectin, N-cadherin, and Vimentin, were increased by Pirin expression (Fig. 1D). These data suggest that Pirin promotes EMT.

Pirin induces EMT-associated cell morphological changes

As mentioned above, cells undergo morphological changes during EMT. Therefore, we next analyzed whether Pirin expression affects cell morphology. Quantitative analysis of morphological changes was based on cell circularity, {4p(area)/(perimeter)2}100, which decreases during EMT-associated morphological changes [34–36]. Indeed, TGF-b or TNF-a exposure induced EMTassociated cell morphological changes in HeLa cells (data not shown). Employing this parameter of circularity, we compared the morphology of our established HeLa/Pirin-GFP cells with control HeLa/GFP cells. Although the control HeLa/GFP cells displayed a cobblestone-like morphology, HeLa/Pirin-GFP cells were elongated in shape (Fig. 2A). Indeed, compared with control cells, the circularity of HeLa/Pirin-GFP cells was significantly decreased (Fig. 2B). To confirm that these observations were dependent on Pirin expression, HeLa/Pirin-GFP cells were treated with an siRNA targeting Pirin. HeLa/Pirin-GFP cells recovered a cobblestone-like morphology (Fig. 2C) and circularity (Fig. 2D) when treated with Pirin siRNA indicating that Pirin expression induces EMT.

Pirin induces cell migration

During EMT cells acquire migratory capabilities. Therefore, we analyzed whether Pirin affects cell migration. HeLa cells were treated with an siRNA targeting Pirin and migration was assessed using a wound healing assay. Although Pirin knockdown had no effect on cell proliferation (data not shown), wound repair was inhibited in Pirin-depleted HeLa cells (Fig. 3A and B) suggesting that Pirin promoted cell migration. Furthermore, camptothecin treatment of HeLa/GFP cells caused decreased cell viability in a dose-dependent manner, whereas HeLa/Pirin-GFP cells were more resistantto drugtreatment (datanot shown).These results suggest that Pirin induces EMT-like phenotypes, such as cell migration and anticancer drug resistance.
Pirin regulates EMT independently of Bcl3-Slug signaling

To investigate whether Pirin controls E-cadherin expression at the transcriptional level, we measured E-cadherin promoter activity with a reporter assay. Indeed, the luciferase reporter analysis indicated that Pirin inhibited E-cadherin promoter activity (Fig. 4A and B). To determine if Bcl3 is involved in Pirin-induced EMT, we tested whether a Pirin mutant defective in Bcl3 binding could inhibit E-cadherin expression. We generated a mutation in the metal-binding cavity of Pirin(E103A) and confirmed that it disrupted Bcl3 binding. In vitro GST pull-down analysis using recombinant Pirin and Bcl3/ARD demonstrated that the Pirin mutant was defective for Bcl3 binding compared to wild-type (Fig. 5A). Interestingly, expression of both wild-type Pirin and the mutant defective in Bcl3 binding reduced E-cadherin gene and protein expression (Fig. 5B and C). Taken together these results indicate that Pirin decreases E-cadherin expression without binding Bcl3, and suggest that Pirin regulates EMT independently of Bcl3-Slug signaling.

Discussion

A characteristic feature of EMT is the disruption of epithelial cell–cell contact, which is achieved by reduced E-cadherin expression. Therefore, revealing the regulatory pathways controlling E-cadherin expression may elucidate the mechanisms of EMT. Several transcription factors regulate E-cadherin transcription. For instance,Snail,Slug,Twist,and Zebact as mastertranscriptional regulators that bind the consensus E-box sequence in the E-cadherin gene promoter and decrease the transcriptional activity [38]. Since Pirin regulates the transcription of Slug [31], we hypothesized that Pirin may also regulate EMT. In this study we demonstrated that Pirin decreases E-cadherin expression, and induces EMT and cancer malignant phenotypes. Since EMT is an initial step of metastasis, Pirin may contribute to cancer progression. We next examined whether the regulation of EMT by Pirin is attributed to Bcl3 binding and the induction of Slug. To this end, we generated a Pirin mutant (E103A) defective for Bcl3 binding (Fig. 5A). Single Fe2+ ion chelating is coordinated by His56, His58, His101, and Glu103 of Pirin, and the N-terminal domain containing these residues is highly conserved between mammals, plants, fungi, and prokaryotic organisms [15,27]. Therefore, it has been predicted that this N-terminal domain containing the metal-binding cavity is important for Pirin function [20,26,31]. Indeed, TPh A inserts into the metal-binding cavity and inhibits binding to Bcl3 suggesting that the interaction occurs with the metal-binding cavity of Pirin [31]. In contrast, Hai Pang suggests that a Pirin–Bcl3– (p50)2 complex forms between acidic regions of the N-terminal Pirin domain at residues 77–82, 97–103 and 124–128 with a basic patch of Bcl3 [27]. In this study, we mutated Glutamic acid 103, a residue common between Hai Pang’s model and Pirin’s metalbinding cavity. Pull-down analysis indicated that an E103A mutant is defectiveinfor Bcl3binding(Fig.5A). Thisis the firstexperimental demonstration showing that Glu103 of Pirin is important Bcl3 binding. However, expression of the E103A mutant suppressed Ecadherin gene expression similarly to wild-type Pirin (Fig. 5B and C). Although the Bcl3–(p50)2 complex participates in oncogene addiction in cervical cells [39,40], expression of Pirin in HeLa cells did not increase Slug expression (data not shown). Therefore, we concludethatPirindecreasesE-cadherinexpressionindependently of Bcl3-Slug signaling. To understand how Pirin suppresses E-cadherin gene expression, we analyzed E-cadherin promoter activity (Fig. 4). Since Pirin decreased the activity of the E-cadherin promoter (995+1), we constructed a series of promoter deletion mutants (795+1, 565+1, 365+1, 175+1) to identify a region important for Pirin-mediated regulation. Expression of Pirin decreased the transcriptional activity of all constructs (Supplementary Fig. S1A), suggesting that Pirin may suppress E-cadherin expression through element(s) in region 175+1. Yan-Nan Liu and colleagues proposed that this region contains four Sp1-binding sites and two E-boxes that regulate E-cadherin expression.

Fig. 1. Pirin regulates E-cadherin gene expression. (A, B) HeLa cells were transfected with siRNA targeting Pirin (siPirin#1 or #2) or control siRNA (siCTRL). Forty-eight hours after transfection, cDNA was used for PCR using primer sets specific against Pirin, E-cadherin and GAPDH (A). Forty-eight hours after transfection, HeLa cells were lysed and the lysates were analyzed by Western blot with the indicated antibodies (B). (C) Lysates from HeLa/Pirin-GFP and HeLa/GFP cells were analyzed by Western blot with the indicated antibodies. (D) cDNA from HeLa/GFP or HeLa/Pirin-GFP cells was used for PCR to determine the effect of Pirin on the expression of EMT marker genes.

Fig. 2. Pirin induces cell morphological changes associated with EMT. (A) Phase contrast and fluorescence microscopic images were taken of HeLa/GFP and HeLa/Pirin-GFP cells. (B) Cell circularity was defined as form factor, {4p(area)/(perimeter)2}100 [%], and calculated using Image J software. A random selection of 100 cells from each condition was measured. (C, D) Phase contrast and fluorescence microscopic images were taken of siRNA-treated HeLa/GFP and HeLa/Pirin-GFP cells. Each cell line was transfected with siPirin#2 or siCTRL. Cells were observed by microscopy 48 h after transfection (C) and circularity was measured (D). Data shown are means ± s.d. ⁄P <0.05, bars 100lm.

Fig. 3. Pirin knockdown suppresses cell migration. (A, B) HeLa cells were transfected with siPirin#2 or siCTRL. An artificial wound was created with a tip 24h after transfection and cells were cultured for an additional 12 h. For quantification, the cells were photographed after 12h of incubation (A) and the area covered by cells was measured using Image J and normalized to control cells (B).

Fig. 4. Pirin regulates E-cadherin promoter activity.(A). HeLacells were transfected with siPirin#2 or siGFP (control) and cultured for 24 h. The E-cadherin promoter construct (995+1) and phRL-TK vectorwere transfected and cellswere cultured for an additional 24 h. Luciferase activities were measured and normalized to Renilla luciferase activity. (B) HeLa cells were transfected with the promoter construct (995+1), phRL-TK vector, and a Pirin expression vector. After 24 h, luciferase activities were measured and normalized to Renilla luciferase activity. Data are the mean ± s.d. ⁄P < 0.05.

Fig. 5. Pirin decreases E-cadherin expression in a Bcl3-independent manner. (A) Purified His6-Pirin and His6-Pirin(E103A) were incubated with Glutathione-Sepharose beads conjugated to GST or GST-Bcl3/ARD. The samples were analyzed by Western blot. (B, C) HeLa cells were transfected with vectors encoding GFP, Pirin-GFP, or Pirin(E103A)GFP. Cells were lysed 48 h after transfection and lysates were analyzed by Western blot (B). RNA collected at 48h was used for RT-PCR with the specified primer sets for each gene (C).

7.7.8.2 1324 PIRIN DOWN-REGULATES THE EAF2/U19 SIGNALING AND RETARDS THE GROWTH INHIBITION INDUCED BY EAF2/U19 IN PROSTATE CANCER CELLS

Zhongjie Qiao, Dan Wang, Zhou Wang
The Journal of Urology Apr 2013; 189(4), Supplement: e541
http://dx.doi.org/10.1016/j.juro.2013.02.2678
EAF2/U19, as the tumor suppressor, has been reported to induce apoptosis of LNCaP cells and suppress AT6.1 xenograft prostate tumor growth in vivo, and its expression level is down-regulated in advanced human prostate cancer. EAF2/U19 is also a putative transcription factor with a transactivation domain and capability of sequence-specific DNA binding. Identification and characterization of the binding partners and regulators of EAF2/U19 is essential to understand its function in regulating apoptosis/survival of prostate cancer cells.

7.7.8.3 Pirin Inhibits Cellular Senescence in Melanocytic Cells

Cellular senescence has been widely recognized as a tumor suppressing mechanism that acts as a barrier to cancer development after oncogenic stimuli. A prominent in vivo model of the senescence barrier is represented by nevi, which are composed of melanocytes that, after an initial phase of proliferation induced by activated oncogenes (most commonly BRAF), are blocked in a state of cellular senescence. Transformation to melanoma occurs when genes involved in controlling senescence are mutated or silenced and cells reacquire the capacity to proliferate. Pirin (PIR) is a highly conserved nuclear protein that likely functions as a transcriptional regulator whose expression levels are altered in different types of tumors. We analyzed the expression pattern of PIR in adult human tissues and found that it is expressed in melanocytes and has a complex pattern of regulation in nevi and melanoma: it is rarely detected in mature nevi, but is expressed at high levels in a subset of melanomas. Loss of function and overexpression experiments in normal and transformed melanocytic cells revealed that PIR is involved in the negative control of cellular senescence and that its expression is necessary to overcome the senescence barrier. Our results suggest that PIR may have a relevant role in melanoma progression

Cellular senescence is a physiological process through which normal somatic cells lose their ability to divide and enter an irreversible state of cell cycle arrest, although they remain viable and metabolically active.1,2The specific molecular circuitry underlying the onset of cellular senescence is dependent on the type of stimulus and on the cellular context. A central role is held by the activation of the tumor suppressor proteins p53 and retinoblastoma susceptibility protein (pRB),3–5 which act by interfering with the transcriptional program of the cell and ultimately arresting cell cycle progression.

In the last decade, senescence has been recognized as a major barrier against the development of tumors in mammals.6–8 One of the most prominent in vivo examples is represented by nevi, in which cells proliferate after oncogene activation and then become senescent. Melanoma is a highly aggressive form of neoplasm often observed to derive from nevi, and the transition implies suppression of the mechanisms that sustain the onset and maintenance of senescence.9 In fact, many of the melanoma-associated tumor suppressor genes identified to date are themselves involved in control of senescence, including BRAF (encoding serine/threonine-protein kinase B-raf), CKD4 (cyclin-dependent kinase 4), and CDKN2A (encoding cyclin-dependent kinase inhibitor 2A isoforms p16INK4a and p19ARF).3,10

Nevi frequently harbor oncogenic mutations of the tyrosine kinase BRAF gene, particularly V600E,11 andBRAFV600E is also found in approximately 70% of cutaneous melanomas.12 Expression of BRAFV600E in human melanocytes leads to oncogene-induced senescence,8 which can be considered as a mechanism that protects from malignant progression. In time, some cells may eventually escape senescence, probably through the acquisition of additional genetic abnormalities, thus favoring transformation to melanoma.13

Pirin (PIR) is a highly conserved nuclear protein belonging to the Cupin superfamily14 whose function is, to date, poorly characterized. It has been described as a putative transcriptional regulator on the basis of its physical association with the nuclear I/CCAAT box transcription factor NFI/CTF115 and with the B-cell lymphoma protein, BCL-3, a regulator of NF-κB/Rel activity. A recent report shows that PIR controls melanoma cell migration through the transcriptional regulation of snail homolog 2, SNAI2 (previously SLUG).16 Other reports described quercetinase enzymatic activity,17 and regulation of apoptosis18,19 and stress response, unveiling a high degree of cell-type and species specificity in PIR function.

There is evidence of variations in PIR expression levels in different types of malignancies, but a systematic analysis of PIR expression in human tumors has been lacking. We analyzed PIR expression pattern in a collection of normal and neoplastic human tissues and found that it is expressed in scattered melanocytes, virtually absent in more mature regions of nevi, and present at high levels in a subset of melanomas. Functional studies performed in normal and transformed melanocytic cells revealed that PIR ablation results in cellular senescence, and that PIR levels decrease in response to senescence stimuli. Our results suggest that PIR may be a relevant player in the negative control of cellular senescence in PIR-expressing melanomas.

PIR overexpression in melanoma

Figure 3  PIR overexpression in PIR melanoma cells has no effect on proliferation.
PIR Expression Is Down-Regulated by BRAF Activation and Camptothecin Treatment

BRAF mutations are frequent in nevi, and are directly linked to the induction of oncogene-induced senescence. Variations in PIR expression levels were therefore investigated in an experimental model of senescence induced by oncogenic BRAF. Human diploid fibroblasts (TIG3–hTERT) expressing a conditional form of constitutively activated BRAF fused to the ligand-binding domain of the estrogen receptor (ER) rapidly undergo oncogene-induced senescence on treatment with 4-hydroxytamoxifen (OHT).28,29 PIR protein and mRNA levels were measured in TIG3-BRAF-ER cells at different time points of treatment with 800 nmol/L OHT. PIR expression was significantly repressed both at the mRNA and at the protein level after BRAF activation (Figure 6A), and remained at low levels after 120 hours, suggesting that a significant reduction of PIR expression is associated with the establishment of oncogene-induced senescence in different cell types.

7.7.9 O-GlcNAcylation at promoters, nutrient sensors, and transcriptional regulation

Brian A. Lewis
Biochim et Biophys Acta (BBA) – Gene Regulatory Mechanisms Nov 2013; 1829(11): 1202–1206
http://dx.doi.org/10.1016/j.bbagrm.2013.09.003

Highlights

  • This review article discusses recent advances in the links between O-GlcNAc and transcriptional regulation.
  • Discusses several systems to illustrate O-GlcNAc dynamics: Tet proteins, MLL complexes, circadian clock proteins and RNA pol II.
  • Suggests that promoters are nutrient sensors.

Post-translational modifications play important roles in transcriptional regulation. Among the less understood PTMs is O-GlcNAcylation. Nevertheless, O-GlcNAcylation in the nucleus is found on hundreds of transcription factors and coactivators and is often found in a mutually exclusive ying–yang relationship with phosphorylation. O-GlcNAcylation also links cellular metabolism directly to the proteome, serving as a conduit of metabolic information to the nucleus. This review serves as a brief introduction to O-GlcNAcylation, emphasizing its important thematic roles in transcriptional regulation, and highlights several recent and important additions to the literature that illustrate the connections between O-GlcNAc and transcription.

links between O-GlcNAc and transcriptional regulation.

links between O-GlcNAc and transcriptional regulation.

http://ars.els-cdn.com/content/image/1-s2.0-S1874939913001351-gr1.sml
links between O-GlcNAc and transcriptional regulation.

systems to illustrate O-GlcNAc dynamics

systems to illustrate O-GlcNAc dynamics

http://ars.els-cdn.com/content/image/1-s2.0-S1874939913001351-gr2.sml
systems to illustrate O-GlcNAc dynamics

7.7.10 O-GlcNAcylation in cellular functions and human diseases

Yang YR1Suh PG2.
Adv Biol Regul. 2014 Jan; 54:68-73
http://dx.doi.org:/10.1016/j.jbior.2013.09.007

O-GlcNAcylation is dynamic and a ubiquitous post-translational modification. O-GlcNAcylated proteins influence fundamental functions of proteins such as protein-protein interactions, altering protein stability, and changing protein activity. Thus, aberrant regulation of O-GlcNAcylation contributes to the etiology of chronic diseases of aging, including cancer, cardiovascular disease, metabolic disorders, and Alzheimer’s disease. Diverse cellular signaling systems are involved in pathogenesis of these diseases. O-GlcNAcylated proteins occur in many different tissues and cellular compartments and affect specific cell signaling. This review focuses on the O-GlcNAcylation in basic cellular functions and human diseases.

O-GlcNAcylated proteins influence protein phosphorylation and protein-protein interactions

O-GlcNAcylated proteins influence protein phosphorylation and protein-protein interactions

http://ars.els-cdn.com/content/image/1-s2.0-S2212492613000717-gr2.sml
O-GlcNAcylated proteins influence protein phosphorylation and protein-protein interactions

aberrant regulation of O-GlcNAcylation in disease

aberrant regulation of O-GlcNAcylation in disease

http://ars.els-cdn.com/content/image/1-s2.0-S2212492613000717-gr3.sml
aberrant regulation of O-GlcNAcylation in disease

 Comment:

Body of review in energetic metabolic pathways in malignant T cells

Antigen stimulation of T cell receptor (TCR) signaling to nuclear factor (NF)-B is required for T cell proliferation and differentiation of effector cells.
The TCR-to-NF-B pathway is generally viewed as a linear sequence of events in which TCR engagement triggers a cytoplasmic cascade of protein-protein interactions and post-translational modifications, ultimately culminating in the nuclear translocation of NF-B.
Activation of effect or T cells leads to increased glucose uptake, glycolysis, and lipid synthesis to support growth and proliferation.
Activated T cells were identified with CD7, CD5, CD3, CD2, CD4, CD8 and CD45RO. Simultaneously, the expression of CD95 and its ligand causes apoptotic cells death by paracrine or autocrine mechanism, and during inflammation, IL1-β and interferon-1α. The receptor glucose, Glut 1, is expressed at a low level in naive T cells, and rapidly induced by Myc following T cell receptor (TCR) activation. Glut1 trafficking is also highly regulated, with Glut1 protein remaining in intracellular vesicles until T cell activation.

Dr. Aurel,
Targu Jiu

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The Reconstruction of Life Processes requires both Genomics and Metabolomics to explain Phenotypes and Phylogenetics

Writer and Curator: Larry H. Bernstein, MD, FCAP 

 

phylogenetics

phylogenetics

http://upload.wikimedia.org/wikipedia/commons/thumb/1/12/CollapsedtreeLabels-simplified.svg/200px-CollapsedtreeLabels-simplified.svg.png

 

This discussion that completes and is an epicrisis (summary and critical evaluation) of the series of discussions that preceded it.

  1. Innervation of Heart and Heart Rate
  2. Action of hormones on the circulation
  3. Allogeneic Transfusion Reactions
  4. Graft-versus Host reaction
  5. Unique problems of perinatal period
  6. High altitude sickness
  7. Deep water adaptation
  8. Heart-Lung-and Kidney
  9. Acute Lung Injury

The concept inherent in this series is that the genetic code is an imprint that is translated into a message.  It is much the same as a blueprint, or a darkroom photographic image that has to be converted to a print. It is biologically an innovation of evolutionary nature because it establishes a simple and reproducible standard for the transcription of the message through the transcription of the message using strings of nucleotides (oligonucleotides) that systematically transfer the message through ribonucleotides that communicate in the cytoplasm with the cytoskeleton based endoplasmic reticulum (ER), composing a primary amino acid sequence.  This process is a quite simple and convenient method of biological activity.  However, the simplicity ends at this step.  The metabolic components of the cell are organelles consisting of lipoprotein membranes and a cytosol which have particularly aligned active proteins, as in the inner membrane of the mitochondrion, or as in the liposome or phagosome, or the structure of the  ER, each of which is critical for energy transduction and respiration, in particular, for the mitochondria, cellular remodeling or cell death, with respect to the phagosome, and construction of proteins with respect to the ER, and anaerobic glycolysis and the hexose monophosphate shunt in the cytoplasmic domain.  All of this refers to structure and function, not to leave out the membrane assigned transport of inorganic, and organic ions (electrolytes and metabolites).

I have identified a specific role of the ER, the organelles, and cellular transactions within and between cells that is orchestrated.  But what I have outlined is a somewhat limited and rigid model that does not reach into the dynamics of cellular transactions.  The DNA has expression that may be old, no longer used messages, and this is perhaps only part of a significant portion of “dark matter”.  There is also nuclear DNA that is enmeshed with protein, mRNA that is a copy of DNA, and mDNA  is copied to ribosomal RNA (rRNA).  There is also rDNA. The classic model is DNA to RNA to protein.  However, there is also noncoding RNA, which plays an important role in regulation of transcription.

This has been discussed in other articles.  But the important point is that proteins have secondary structure through disulfide bonds, which is determined by position of sulfur amino acids, and by van der Waal forces, attraction and repulsion. They have tertiary structure, which is critical for 3-D structure.  When like subunits associate, or dissimilar oligomers, then you have heterodimers and oligomers.  These constructs that have emerged over time interact with metabolites within the cell, and also have an important interaction with the extracellular environment.

When you take this into consideration then a more complete picture emerges. The primitive cell or the multicellular organism lives in an environment that has the following characteristics – air composition, water and salinity, natural habitat, temperature, exposure to radiation, availability of nutrients, and exposure to chemical toxins or to predators.  In addition, there is a time dimension that proceeds from embryonic stage to birth in mammals, a rapid growth phase, a tapering, and a decline.  The time span is determined by body size, fluidity of adaptation, and environmental factors.  This is covered in great detail in this work.  The last two pieces are in the writing stage that completes the series. Much content has already be presented in previous articles.

The function of the heart, kidneys and metabolism of stressful conditions have already been extensively covered in http://pharmaceuticalintelligence.com  in the following and more:

The Amazing Structure and Adaptive Functioning of the Kidneys: Nitric Oxide – Part I

http://pharmaceuticalintelligence.com/2012/11/26/the-amazing-structure-and-adaptive-functioning-of-the-kidneys/

Nitric Oxide and iNOS have Key Roles in Kidney Diseases – Part II

http://pharmaceuticalintelligence.com/2012/11/26/nitric-oxide-and-inos-have-key-roles-in-kidney-diseases/

The pathological role of IL-18Rα in renal ischemia/reperfusion injury – Nature.com

http://pharmaceuticalintelligence.com/2014/10/24/the-pathological-role-of-il-18r%CE%B1-in-renal-ischemiareperfusion-injury-nature-com/

Summary, Metabolic Pathways

http://pharmaceuticalintelligence.com/2014/10/23/summary-metabolic-pathways/

 

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The Challenge of Antimicrobial Resistance

Writer and Curator: Larry H. Bernstein, MD, FCAP

 

Antibiotic resistance has become a major challenge of our time.  Common microorganisms that inhabit the skin, mouth and nares, and fecal organisms are transmitted in the hospital setting. Handwashing procedures have had limited benefit. Operating rooms are ventilated and environmentally engineered to minimize transmission intraoperatively. The patient may be immune-compromized. The organisms that are encountered have genetically adapted to the most effective antibiotics at our disposal. even with some risk of secondary toxicity in some cases.

What is Drug Resistance?

Antimicrobial resistance is the ability of microbes, such as bacteria, viruses,
parasites, or fungi, to grow in the presence of a chemical (drug) that would
normally kill it or limit its growth.

Diagram showing the difference between non-resistant bacteria and drug
resistant bacteria.

Drug Resistance difference between non-resistant bacteria and drug resistant bacteria

Drug Resistance difference between non-resistant bacteria and drug resistant bacteria

Credit: NIAID

http://www.niaid.nih.gov/SiteCollectionImages/topics/
antimicrobialresistance/1whatIsDrugResistance.gif

Diagram showing the difference between non-resistant bacteria and drug
resistant bacteria. Non-resistant bacteria multiply, and upon drug treatment,
the bacteria die. Drug resistant bacteria multiply as well, but upon drug
treatment, the bacteria continue to spread.

Many infectious diseases are increasingly difficult to treat because of
antimicrobial-resistant organisms, including HIV infection, staphylococcal
infection, tuberculosis, influenza, gonorrhea, candida infection, and malaria.

Between 5 and 10 percent of all hospital patients develop an infection. About
90,000 of these patients die each year as a result of their infection, up from
13,300 patient deaths in 1992.

According to the Centers for Disease Control and Prevention (April 2011),
antibiotic resistance in the United States costs an estimated $20 billion a year
in excess health care costs, $35 million in other societal costs and more than 8
million additional days that people spend in the hospital.

World Health Organization – 2014 Report
WHO/HSE/PED/AIP/2014.2
http://www.who.int.org/

Antimicrobial resistance (AMR) is an increasingly serious threat to
global public health. AMR develops when a microorganism (bacteria,
fungus, virus or parasite) no longer responds to a drug to which it
was originally sensitive. This means that standard treatments no
longer work; infections are harder or impossible to control; the risk
of the spread of infection to others is increased; illness and hospital
stays are prolonged, with added economic and social costs; and the
risk of death is greater—in some cases, twice that of patients who
have infections caused by non-resistant bacteria. The problem is so
serious that it threatens the achievements of modern medicine. A
post-antibiotic era—in which common infections and minor
injuries can kill—is a very real possibility for the 21st century.

WHO is developing a global action plan for AMR that
will include:
• development of tools and standards for harmonized
surveillance of ABR in humans, and for integrated
surveillance in food-producing animals and the
food chain;
• elaboration of strategies for population-based
surveillance of AMR and its health and economic
impact; and
• collaboration between AMR surveillance networks
and centres to create or strengthen coordinated
regional and global surveillance.
AMR is a global health security threat that requires
action across government sectors and society as a
whole. Surveillance that generates reliable data is the
essential foundation of global strategies and public
health actions to contain AMR.

Resistance to Antibiotics: Are We in the Post-Antibiotic Era?
Alfonso J. Alanis
Archives of Medical Research 36 (2005) 697–705
http://dx.doi.org:/10.1016/j.arcmed.2005.06.009

Serious infections caused by bacteria that have become resistant
to commonly used antibiotics have become a major global healthcare
problem in the 21st century. They not only are more severe and
require longer and more complex treatments, but they are also
significantly more expensive to diagnose and to treat. Antibiotic
resistance, initially a problem of the hospital setting associated
with an increased number of hospital acquired infections usually
in critically ill and immunosuppressed patients, has now extended
into the community causing severe infections difficult to diagnose
and treat. The molecular mechanisms by which bacteria have
become resistant to antibiotics are diverse and complex. Bacteria
have developed resistance to all different classes of antibiotics
discovered to date. The most frequent type of resistance is
acquired and transmitted horizontally via the conjugation
of a plasmid. In recent times new mechanisms of resistance
have resulted in the simultaneous development of resistance
to several antibiotic classes creating very dangerous multidrug
-resistant (MDR) bacterial strains, some also known as
‘‘superbugs’’. The indiscriminate and inappropriate use of
antibiotics in outpatient clinics, hospitalized patients and
in the food industry is the single largest factor leading to
antibiotic resistance. In recent years, the number of new
antibiotics licensed for human use in different parts of the
world has been lower than in the recent past. In addition,
there has been less innovation in the field of antimicrobial
discovery research and development. The pharmaceutical
industry, large academic institutions or the government are
not investing the necessary resources to produce the next
generation of newer safe and effective antimicrobial drugs.
In many cases, large pharmaceutical companies have terminated
their anti-infective research programs altogether due to economic
reasons. The potential negative consequences of all these events
are relevant because they put society at risk for the spread of
potentially serious MDR bacterial infections.

Structural and biological studies on bacterial nitric oxide synthase
inhibitors
JK Holden,  H Li, Q Jing, S Kang, J Richo, RB Silverman, TL Poulos

Significance: Nitric oxide (NO) produced by bacterial nitric oxide
synthase has recently been shown to protect the Gram-positive
pathogens Bacillus anthracis and Staphylococcus aureus from
antibiotics and oxidative stress. Using Bacillus subtilis as a model
system, we identified two NOS inhibitors that work in conjunction
with an antibiotic to kill B. subtilis. Moreover, comparison of inhibitor-bound crystal structures between the bacterial NOS and mammalian
NOS revealed an unprecedented mode of binding to the bacterial NOS
that can be further exploited for future structure-based drug design.
Overall, this work is an important advance in developing inhibitors
against gram-positive pathogens.

Summary: Nitric oxide (NO) produced by bacterial NOS functions as a
cytoprotective agent against oxidative stress in Staphylococcus aureus,
Bacillus anthracis, and Bacillus subtilis. The screening of several NOS-selective inhibitors uncovered two inhibitors with potential antimicrobial
properties. These two compounds impede the growth of B. subtilis under
oxidative stress, and crystal structures show that each compound exhibits
a unique binding mode. Both compounds serve as excellent leads for the
future development of antimicrobials against bacterial NOS-containing
bacteria.  http://dx.doi.org/10.1073/pnas.1314080110

Speciation of clinically significant coagulase negative Staphylococci
and their antibiotic resistant patterns in a tertiary care hospital
PR Vysakh, S Kandasamy and RM Prabhavathi
Int.J.Curr.Microbiol.App.Sci (2015) 4(1): 704-709
http://www.ijcmas.com

Human skin and mucus membrane has Coagulase Negative Staphylococci
(CoNS) as the indigenous flora. CoNS had become an important agent for
nosocomial infections accounting for about 9%. These infections are
difficult to treat because of the risk factors and the multiple drug resistance
nature of these organisms. The study was undertaken to identify the
prevalence of clinical isolates of CoNS, their speciation and to determine
the antibiotic sensitivity/resistant patterns of CoNS. A total of 490 isolates
were collected from different samples and subjected to biochemical
characterization and antimicrobial screening using conventional
microbiological methods. 165 isolates were identified as CoNS. 23% of
CoNS were isolated from blood, 30% from post-operative wound infections,
23% from pus, 18% from urine, 3% from body fluids (CSF, ascitic fluid etc)
and 3% from CVP tips. The antibiotic sensitivity revealed 81% resistance
to Penicillin,32% resistance to Cefoxitin, 27% resistance to Cefazolin,
55% resistance to Erythromycin, 22% to Clindamycin and 35% to
Cotrimoxazole and with no resistance to Vancomycin, Linezolid and
Ciprofloxacin. The increased recognition of CoNS and emergence of
drug resistance among them demonstrates the need to consider them
as a potent pathogen and to devise laboratory procedure to identify
and to determine the prevalence and antibiotic resistant patterns of CoNS.

Resistance to rifampicin: a review
Beth P Goldstein
The Journal of Antibiotics (2014) 67, 625–630
http:://dx.doi.org:/10.1038/ja.2014.107

Resistance to rifampicin (RIF) is a broad subject covering not just the
mechanism of clinical resistance, nearly always due to a genetic change
in the b subunit of bacterial RNA polymerase (RNAP), but also how
studies of resistant polymerases have helped us understand the structure
of the enzyme, the intricacies of the transcription process and its role
in complex physiological pathways. This review can only scratch the
surface of these phenomena. The identification, in strains of
Escherichia coli, of the positions within b of the mutations determining
resistance is discussed in some detail, as are mutations in organisms
that are therapeutic targets of RIF, in particular Mycobacterium
tuberculosis. Interestingly, changes in the same three codons of
the consensus sequence occur repeatedly in unrelated RIF-resistant
(RIFr) clinical isolates of several different single mutation
predominates in mycobacteria. The utilization of our knowledge of
these mutations to develop rapid screening tests for detecting resistance
is briefly discussed. Cross-resistance among rifamycins has been a topic
of controversy; current thinking is that there is no difference in the
susceptibility of RNAP mutants to RIF, rifapentine and rifabutin.
Also summarized are intrinsic RIF resistance and other resistance
mechanisms.

Multi-drug resistance, inappropriate initial antibiotic therapy and
mortality in Gram negative severe sepsis and septic shock: A
retrospective cohort study
MD Zilberberg, AF Shorr, ST Micek, C Vazquez-Guillamet, MH Kollef
Critical Care 2014, 18:596 http://dx.doi.org:/10.1186/s13054-014-0596-8
http://ccforum.com/content/18/6/596

Introduction
The impact of in vitro resistance on initially appropriate antibiotic therapy
(IAAT) remains unclear. We elucidated the relationship between non-IAAT
and mortality, and between IAAT and multi-drug resistance (MDR) in
sepsis due to Gram-negative bacteremia (GNS).
Methods
We conducted a single-center retrospective cohort study of adult intensive
care unit patients with bacteremia and severe sepsis/septic shock caused by
a gram-negative (GN) organism. We identified the following MDR pathogens:
MDR P. aeruginosa, extended spectrum beta lactamase and carbapenemase-
producing organisms. IAAT was defined as exposure within 24 hours of
infection onset to antibiotics active against identified pathogens based on
in vitro susceptibility testing. We derived logistic regression models to
examine a) predictors of hospital mortality and b) impact of MDR on
non-IAAT. Proportions are presented for categorical variables, and
median values with interquartile ranges (IQR) for continuous
variables.

Results
Out of 1,064 patients with GNS, 351 (29.2%) did not survive
hospitalization. Non-survivors were older (66.5 (55, 73.5)
versus 63 (53, 72) years, P =0.036), sicker (Acute Physiology and
Chronic Health Evaluation II (19 (15, 25) versus 16 (12, 19),
P <0.001), and more likely to be on pressors (odds ratio (OR) 2.79,
95% confidence interval (CI) 2.12 to 3.68), mechanically ventilated
(OR 3.06, 95% CI 2.29 to 4.10) have MDR (10.0% versus 4.0%,
P <0.001) and receive non-IAAT (43.4% versus 14.6%, P <0.001).
In a logistic regression model, non-IAAT was an independent
predictor of hospital mortality (adjusted OR 3.87, 95% CI 2.77 to
5.41). In a separate model, MDR was strongly associated with
the receipt of non-IAAT (adjusted OR 13.05, 95% CI 7.00 to 24.31).
Conclusions
MDR, an important determinant of non-IAAT, is associated with
a three-fold increase in the risk of hospital mortality. Given the
paucity of therapies to cover GN MDRs, prevention and
development of new agents are critical.

Phenotypic and molecular characteristics of methicillin-resistant
Staphylococcus aureus isolates from Ekiti State, Nigeria
OA Olowe, OO Kukoyi, SS Taiwo, O Ojurongbe, OO Opaleye, et al.
Infection and Drug Resistance 2013:6 87–92
http://dx.doi.org/10.2147/IDR.S48809

Introduction: The characteristics and antimicrobial resistance profiles
of Staphylococcus aureus differs according to geographical regions and
in relation to antibiotic usage. The aim of this study was to determine
the biochemical characteristics of the prevalent S. aureus from Ekiti State,
Nigeria, and to evaluate three commonly used disk diffusion methods
(cefoxitin, oxacillin, and methicillin) for the detection of methicillin
resistance in comparison with mecA gene detection by polymerase chain
reaction.
Materials and methods: A total of 208 isolates of S. aureus recovered
from clinical specimens were included in this study. Standard
microbiological procedures were employed in isolating the strains.
Susceptibility of each isolate to methicillin (5 μg), oxacillin (1 μg),
and cefoxitin (30 μg) was carried out using the modified Kirby–Bauer/
Clinical and Laboratory Standard Institute disk diffusion technique.
They were also tested against panels of antibiotics including vancomycin.
The conventional polymerase chain reaction method was used to detect
the presence of the mecA gene.
Results: Phenotypic resistance to methicillin, oxacillin, and cefoxitin
were 32.7%, 40.3%, and 46.5%, respectively. The mecA gene was detected
in 40 isolates, giving a methicillin-resistant S. aureus (MRSA) prevalence
of 19.2%. The S. aureus isolates were resistant to penicillin (82.7%) and
tetracycline (65.4%), but largely susceptible to erythromycin (78.8%
sensitive), pefloxacin (82.7%), and gentamicin (88.5%). When compared
to the mecA gene as the gold standard for MRSA detection, methicillin,
oxacillin, and cefoxitin gave sensitivity rates of 70%, 80%, and 100%,
and specificity rates of 76.2%, 69.1%, and 78.5% respectively.
Conclusion: When compared with previous studies employing mecA
polymerase chain reaction for MRSA detection, the prevalence of 19.2%
reported in Ekiti State, Nigeria in this study is an indication of gradual rise
in the prevalence of MRSA in Nigeria. A cefoxitin (30 μg) disk diffusion test
is recommended above methicillin and oxacillin for the phenotypic detection
of MRSA in clinical laboratories.

Direct sequencing for rapid detection of multidrug resistant Mycobacterium
tuberculosis strains in Morocco
F Zakham, I Chaoui, AH Echchaoui, F Chetioui, M Driss Elmessaoudi, et al.
Infection and Drug Resistance 2013:6 207–213
http://dx.doi.org/10.2147/IDR.S47724

Background: Tuberculosis (TB) is a major public health problem with high
mortality and morbidity rates, especially in low-income countries.
Disturbingly, the emergence of multidrug resistant (MDR) and extensively
drug resistant (XDR) TB cases has worsened the situation, raising concerns
of a future epidemic of virtually untreatable TB. Indeed, the rapid diagnosis
of MDR TB is a critical issue for TB management. This study is an attempt to
establish a rapid diagnosis of MDR TB by sequencing the target fragments of
the rpoB gene which linked to resistance against rifampicin and the katG gene
and inhA promoter region, which are associated with resistance to isoniazid.
Methods: For this purpose, 133 sputum samples of TB patients from Morocco
were enrolled in this study. One hundred samples were collected from new
cases, and the remaining 33 were from previously treated patients (drug
relapse or failure, chronic cases) and did not respond to anti-TB drugs after
a sufficient duration of treatment. All samples were subjected to rpoB, katG
and pinhA mutation analysis by polymerase chain reaction and DNA sequencing.
Results: Molecular analysis showed that seven strains were isoniazid-
monoresistant and 17 were rifampicin-monoresistant. MDR TB strains were
identified in nine cases (6.8%). Among them, eight were traditionally
diagnosed as critical cases, comprising four chronic and four drug-relapse
cases. The last strain was isolated from a new case. The most recorded
mutation in the rpoB gene was the substitution TCG . TTG at codon 531
(Ser531 Leu), accounting for 46.15%. Significantly, the only mutation found
in the katG gene was at codon 315 (AGC to ACC) with a Ser315Thr amino acid
change. Only one sample harbored mutation in the inhA promoter region
and was a point mutation at the −15p position (C . T). Conclusion: The
polymerase chain reaction sequencing approach is an accurate and rapid
method for detection of drug-resistant TB in clinical specimens, and could
be of great interest in the management of TB in critical cases to adjust the
treatment regimen and limit the emergence of MDR and XDR strains.

Limiting and controlling carbapenem-resistant Klebsiella pneumoniae
L Saidel-Odes, A Borer.
Infection and Drug Resistance 2014:7 9–14
http://dx.doi.org/10.2147/IDR.S44358

Carbapenem-resistant Klebsiella pneumoniae (CRKP) is resistant to
almost all antimicrobial agents, is associated with substantial morbidity
and mortality, and poses a serious threat to public health. The ongoing
worldwide spread of this pathogen emphasizes the need for immediate
intervention. This article reviews the global spread and risk factors for
CRKP colonization/infection, and provides an overview of the strategy
to combat CRKP dissemination.

Staphylococcus aureus – antimicrobial resistance and the immuno-
compromised child
J Chase McNeil
Infection and Drug Resistance 2014:7 117–127
http://dx.doi.org/10.2147/IDR.S39639

Children with immunocompromising conditions represent a unique
group for the acquisition of antimicrobial resistant infections due to
their frequent encounters with the health care system, need for empiric
antimicrobials, and immune dysfunction. These infections are further
complicated in that there is a relative paucity of literature on the clinical
features and management of Staphylococcus aureus infections in
immunocompromised children. The available literature on the clinical
features, antimicrobial susceptibility, and management of S. aureus
infections in immunocompromised children is reviewed. S. aureus
infections in children with human immunodeficiency virus (HIV) are
associated with higher HIV viral loads and a greater degree of CD4 T-cell
suppression. In addition, staphylococcal infections in children with HIV
often exhibit a multidrug resistant phenotype. Children with cancer have
a high rate of S. aureus bacteremia and associated complications. Increased
tolerance to antiseptics among staphylococcal isolates from pediatric
oncology patients is an emerging area of research. The incidence of S. aureus
infections among pediatric solid organ transplant recipients varies
considerably by the organ transplanted; in general however, staphylococci
figure prominently among infections in the early post-transplant period.
Staphylococcal infections are also prominent pathogens among children
with a number of immunodeficiencies, notably chronic granulomatous
disease. Significant gaps in knowledge exist regarding the epidemiology
and management of S. aureus infection in these vulnerable children.

selected Staphylococcus aureus mechanisms for immune evasion.

selected Staphylococcus aureus mechanisms for immune evasion.

Figure 1 A schematic depiction of selected Staphylococcus aureus
mechanisms for immune evasion.
Notes: Cna interacts with C1q preventing formation of the C1qrs complex.
ClfA and SdrE each promote Factor I mediated conversion of C3b to iC3b.
Protein A is depicted binding to the Fc region of IgG preventing immunoglobulin
opsonization.
Abbreviations: ClfA, staphylococcal clumping factor A; Cna, collagen adhesin;
IgG, immunoglobulin G; PVL, Panton–Valentine leukocidin; SdrE, S. aureus
surface protein.

The Future of Antibiotics and Resistance
B Spellberg, JG Bartlett, and DN Gilbert
N Engl J Med Jan 24, 2013; 368(4): 299-302
http://dx.doi.org:/ 10.1056/NEJMp1215093

In its recent annual report on global risks, the World Economic
Forum (WEF) concluded that “arguably the greatest
risk . . . to human health comes in the form of antibiotic-resistant
bacteria. We live in a bacterial world where we will never be able
to stay ahead of the mutation curve. A test of our resilience is
how far behind the curve we allow ourselves to fall.”

The WEF report underscores the facts that antibiotic resistance
and the collapse of the antibiotic research and-development
pipeline continue to worsen despite our ongoing efforts on
current fronts. If we’re to develop countermeasures that
have lasting effects, new ideas that complement traditional
approaches will be needed.

Resistance is primarily the result of bacterial adaptation to eons
of antibiotic exposure. What are the fundamental implications of
this reality? First, in addition to antibiotics’ curative power, their
use naturally selects for preexisting resistant populations of bacteria
in nature. Second, it is not just “inappropriate” antibiotic use
that selects for resistance. Rather, the speed with which resistance
spreads is driven by microbial exposure to all antibiotics, whether
appropriately prescribed or not. Thus, even if all inappropriate
antibiotic use were eliminated, antibiotic-resistant infections
would still occur (albeit at lower frequency). Third, after billions
of years of evolution, microbes have most likely invented
antibiotics against every biochemical target that can be attacked
— and, of necessity, developed resistance mechanisms
to protect all those biochemical targets.

Remarkably, resistance was found even to synthetic antibiotics
that did not exist on earth until the 20th century. These results
underscore a critical reality: antibiotic resistance already exists,
widely disseminated in nature, to drugs we have not yet invented.

Table **

Interventions to Address the Antibiotic-Resistance Crisis.*

Intervention Status                                                   Preventing infection
and resistance

“Self-cleaning” hospital rooms;                                Some commercially available
automated disinfectant application                         but require clinical validation;
through misting, vapor, radiation, etc.                    more needed

Novel drug-delivery systems to replace                  Basic science and
IV catheters; regenerative-tissue technology        conceptual stages
to replace prosthetics; superior, noninvasive
ventilation strategies

Improvement of population health and                 Implementation
health care systems to reduce admissions             research stage
to hospitals and skilled nursing facilities

Niche vaccines to prevent resistant                        Basic and clinical
bacterial infections                                                    development stage

Refilling antibiotic pipeline by aligning
economic and regulatory approaches

Models in place, expansion needed in number    Government or nonprofit grants
and scope; new nonprofit corporations                 and contracts to defray R&D costs
needed                                                                          and establish nonprofits
to develop antibiotics

Institution of novel approval pathways                 Proposed, legislative
(e.g., Limited Population Antibiotic                        and regulatory
Drug proposal)                                                            action needed

Preserving available antibiotics,
slowing resistance

Public reporting of antibiotic-use data as a         Policy action needed to
basis for benchmarking and reimbursement      develop and implement

Development of and reimbursement for            Basic and applied research
rapid diagnostic and biomarker tests to              and policy action and
enable appropriate use of antibiotics                   policy action needed

Elimination of use of antibiotics to                       Legislation proposed
promote livestock growth

New waste-treatment strategies;                       One strategy approaching
targeted chemical or biologic                              clinical trials
degradation of antibiotics in waste

Studies to define shortest effective                    Some trials completed
courses of antibiotics for infections

Developing microbe-attacking                            Preclinical, proof-of-
treatments with diminished                                principle stage
potential to drive resistance

Immune-based therapies, such
as infusion of monoclonal antibodies
and white cells that kill microbes

Antibiotics or biologic agents that
don’t kill bacteria but alter their ability
to trigger inflammation or cause disease

Developing treatments attacking host             Preclinical, proof-of-principle stage
targets rather than microbial targets to
avoid selective pressure driving resistance

Direct moderation of host inflammation
in response to infection (e.g., cytokine
agonists or antagonists, PAMP receptor
agonists)

Sequestration of host nutrients to
prevent microbial access to nutrients

Probiotics that compete with microbial
growth

* IV denotes intravenous, PAMP pathogen-associated molecular
pattern, and R&D research and development

Antibiotic-Resistant Bugs Appear to Use Universal Ribosome-Stalling Mechanism

GEN News  Jan 26, 2015
http://www.genengnews.com/gen-news-highlights/antibiotic-resistant-bugs-
appear-to-use-universal-ribosome-stalling-mechanism/81250847/

Researchers at St. Louis University say they have discovered new information
about how antibiotics like azithromycin stop staph infections, and why staph
sometimes becomes resistant to drugs. The team, led by Mee-Ngan F. Yap, Ph.D.,
believe their evidence suggests a universal, evolutionary mechanism by which
the bacteria elude this kind of drug, offering scientists a way to improve the
effectiveness of antibiotics to which bacteria have become resistant.  Their
study (“Sequence selectivity of macrolide-induced translational attenuation”)
was published in PNAS.

Staphylococcus aureus  is a strain of bacteria that frequently has become
resistant to antibiotics, a development that has been challenging for doctors
and dangerous for patients with severe infections. Dr. Yap and her research
team studied staph that had been treated with the antibiotic azithromycin and
learned two things: One, it turns out that the antibiotic isn’t as effective as was
previously thought. And two, the process that the bacteria use to evade the
antibiotic appears to be an evolutionary mechanism that the bacteria developed
in order to delay genetic replication when beneficial.

Genomic epidemiology of a protracted hospital outbreak caused by multidrug-
resistant Acinetobacter baumannii in Birmingham, England
MR Halachev, J Z-M Chan, CI Constantinidou, N Cumley, C Bradley, et al.
Genome Medicine 2014, 6:70 http://genomemedicine.com/content/6/11/70

Background: Multidrug-resistant Acinetobacter baumannii commonly causes
hospital outbreaks. However, within an outbreak, it can be difficult to identify
the routes of cross-infection rapidly and accurately enough to inform infection
control. Here, we describe a protracted hospital outbreak of multidrug-resistant
A. baumannii, in which whole-genome sequencing (WGS) was used to obtain
a high-resolution view of the relationships between isolates.
Methods: To delineate and investigate the outbreak, we attempted to genome-
sequence 114 isolates that had been assigned to the A. baumannii complex
by the Vitek2 system and obtained informative draft genome sequences from
102 of them. Genomes were mapped against an outbreak reference sequence
to identify single nucleotide variants (SNVs).
Results: We found that the pulsotype 27 outbreak strain was distinct from all
other genome-sequenced strains. Seventy-four isolates from 49 patients
could be assigned to the pulsotype 27 outbreak on the basis of genomic
similarity, while WGS allowed 18 isolates to be ruled out of the outbreak.
Among the pulsotype 27 outbreak isolates, we identified 31 SNVs and seven
major genotypic clusters. In two patients, we documented within-host diversity,
including mixtures of unrelated strains and within-strain clouds of SNV diversity.
By combining WGS and epidemiological data, we reconstructed potential
transmission events that linked all but 10 of the patients and confirmed links
between clinical and environmental isolates. Identification of a contaminated
bed and a burns theatre as sources of transmission led to enhanced
environmental decontamination procedures.
Conclusions: WGS is now poised to make an impact on hospital infection
prevention and control, delivering cost-effective identification of routes of
infection within a clinically relevant timeframe and allowing infection control
teams to track, and even prevent, the spread of drug-resistant hospital pathogens.

Discovery of β-lactam-resistant variants in diverse pneumococcal populations
Regine Hakenbeck
Genome Medicine 2014, 6:72  http://genomemedicine.com/content/6/9/72

Understanding of antibiotic resistance in Streptococcus pneumoniae has been
hindered by the low frequency of recombination events in bacteria and thus the
presence of large linked haplotype blocks, which preclude identification of
causative variants. A recent study combining a large number of genomes of
resistant phenotypes has given an insight into the evolving resistance to
β-lactams, providing the first large-scale identification of candidate variants
underlying resistance.

Additional sources:

A Simple Method for Assessment of MDR Bacteria for Over-Expressed
Efflux Pumps
M Martins, MP McCusker, M Viveiros, I Couto, S Fanning, .., L Amaral
The Open Microbiology Journal, 2013, 7, 1-5

Identification of Efflux Pump-mediated Multidrug-resistant
Bacteria by the Ethidium Bromide-agar Cartwheel Method
M MARTINS, M VIVEIROS, I COUTO,, SS COSTA, .., L AMARAL
in vivo 25: 171-178 (2011)

Efflux Pumps that Bestow Multi-Drug Resistance of Pathogenic
Gram negative Bacteria
Amaral L, Spengler G, Martins A and Molnar J
Biochem Pharmacol 2013; 2(3):119
http://dx.doi.org/10.4172/2167-0501.1000119

graphical abstract

graphical abstract

An Instrument-free Method for the Demonstration
of Efflux Pump Activity of Bacteria
M MARTINS, B SANTOS, A MARTINS, M VIVEIROS, I COUTO,
A CRUZ, THE MANAGEMENT COMMITTEE MEMBERS
OF COST B16 OF THE EUROPEAN COMMISSION/
EUROPEAN SCIENCE FOUNDATION,…, J MOLNAR, S FANNING
and LEONARD AMARAL
in vivo 20: 657-664 (2006)

Potential Therapy of Multidrug-resistant and Extremely
Drug-resistant Tuberculosis with Thioridazine
LEONARD AMARAL and JOSEPH MOLNAR
in vivo 26: 231-236 (2012)

Inhibitors of efflux pumps of Gram-negative bacteria
inhibit Quorum Sensing
Leonard Amaral, Joseph Molnar
Open Journal of Pharmacology, 2012, 2-2

An Overview of Clinical Microbiology, Classification,
and Antimicrobial Resistance
Larry H. Bernstein
http://pharmaceuticalintelligence.com/2015/01/17/an-overview-
of-clinical-microbiology-classification-and-antimicrobial-resistance/

New protein detonates bacteria from within

By Tim Sandle     in Science

Tel Aviv – By sequencing the DNA of bacteria resistant to viral toxins, scientists have identified novel proteins capable of stymieing growth in pathogenic, antibiotic-resistant bacteria.

Today’s arsenal of antibiotics is ineffective against some emerging strains of antibiotic-resistant pathogens. Novel inhibitors of bacterial growth therefore need to be found. One way is looking into the viruses that infect bacteria.

Key to the new initiative is the concept of fighting bacteria from within, rather than using an external chemical to batter through the bacterial cell wall. the basis of the new weapon is viral. In order to select an appropriate viral protein, researchers undertook a comprehensive screening exercise in order to identify proteins in viruses that are known to infect bacteria (bacteriophages). Bacteriophages occur abundantly in the biosphere, with different virions, genomes and lifestyles. The review was so comprehensive that it took almost three years to complete.

The screening was achieved through the use of high-throughput DNA sequencing. This is the process of determining the precise order of nucleotides within a DNA molecule. By using this advanced genetic method, the scientists identified mutations in bacterial genes that resisted the toxicity of growth inhibitors produced by bacterial viruses. Through this, a new, tiny protein was found. The protein is termed “growth inhibitor gene product (Gp) 0.6”.

Later testing found that the protein specifically targets and inhibits the activity of a protein essential to bacterial cells. The bacterial protein affected has the function of holding the microbe’s cell wall together. Without this protein functioning correctly, the cell bursts open from within and the bacterium dies.

For the next wave of research, the Israeli science group are looking further at bacterial viruses with the aim of finding compounds that facilitate improved treatment of antibiotic-resistant bacteria.
Read more: http://www.digitaljournal.com/science/new-protein-detonates-bacteria-from-within/article/424747#ixzz3QJN0uo1d

Revealing bacterial targets of growth inhibitors encoded by bacteriophage T7

Shahar Molshanski-Mora, Ido Yosefa, Ruth Kiroa, Rotem Edgara, Miriam Manora, Michael Gershovitsb, Mia Lasersonb, Tal Pupkob, and Udi Qimrona,1

Author Affiliations

Edited* by Sankar Adhya, National Institutes of Health, National Cancer Institute, Bethesda, MD, and approved November 24, 2014 (received for review July 13, 2014)

Significance

Antibiotic resistance of pathogens is a growing threat to human health, requiring immediate action. Identifying new gene products of bacterial viruses and their bacterial targets may provide potent tools for fighting antibiotic-resistant strains. We show that a significant number of phage proteins are inhibitory to their bacterial host. DNA sequencing was used to map the targets of these proteins. One particular target was a key cytoskeleton protein whose function is impaired following the phage protein’s expression, resulting in bacterial death. Strikingly, in over 70 y of extensive research into the tested bacteriophage, this inhibition had never been characterized. We believe that the presented approach may be broadened to identify novel, clinically relevant bacteriophage growth inhibitors and to characterize their targets.

Abstract

Today’s arsenal of antibiotics is ineffective against some emerging strains of antibiotic-resistant pathogens. Novel inhibitors of bacterial growth therefore need to be found. The target of such bacterial-growth inhibitors must be identified, and one way to achieve this is by locating mutations that suppress their inhibitory effect. Here, we identified five growth inhibitors encoded by T7 bacteriophage. High-throughput sequencing of genomic DNA of resistant bacterial mutants evolving against three of these inhibitors revealed unique mutations in three specific genes. We found that a nonessential host gene, ppiB, is required for growth inhibition by one bacteriophage inhibitor and another nonessential gene, pcnB, is required for growth inhibition by a different inhibitor. Notably, we found a previously unidentified growth inhibitor, gene product (Gp) 0.6, that interacts with the essential cytoskeleton protein MreB and inhibits its function. We further identified mutations in two distinct regions in the mreB gene that overcome this inhibition. Bacterial two-hybrid assay and accumulation of Gp0.6 only in MreB-expressing bacteria confirmed interaction of MreB and Gp0.6. Expression of Gp0.6 resulted in lemon-shaped bacteria followed by cell lysis, as previously reported for MreB inhibitors. The described approach may be extended for the identification of new growth inhibitors and their targets across bacterial species and in higher organisms.

New funding to fight antibiotic resistance SPECIAL

By Tim Sandle

This week the White House stated that it will double the amount of federal funding put aside to combat and preventing antibiotic resistance. The sum stands at greater than $1.2 billion.

Read more: http://www.digitaljournal.com/life/health/new-funding-to-fight-antibiotic-resistance/article/424745#ixzz3QJSBRxLU

U.S. Senator Sherrod Brown has been campaigning across the U.S. about the risks related to antibiotic-resistant infections for several years. Such infections affect more than two million U.S. citizens each year. The issue is not only of importance in one country for the growing menace of antibiotic resistance is, arguably, the single biggest threat faced by the world’s population. Moreover, emerging antimicrobial resistance and the growing shortage of effective antibiotic drugs is widely regarded as a crisis that jeopardizes patient safety and public health.

Senator Brown has welcomed the increased spending, although he also feels that more action is required. “To combat antibiotic resistance, it’s important that we leverage the best in medical expertise, stewardship, and technological innovation,” Brown has told Digital Journal.

He went on to add: “This unprecedented proposal underscores the importance of taking a comprehensive, wide-ranging approach to tackle this issue. I look forward to continuing to work with federal agencies, research institutions, and health care providers to combat this threat to America’s health.”

In 2014, Brown proposed the Strategies to Address Antimicrobial Resistance (STAAR) Act. The aim of this legislation was to boost the federal response to antibiotic resistance through promoting prevention and control. Other measures included: tracking drug-resistant bacteria; supporting enhanced research efforts; and improving the development, use, and stewardship of antibiotics. The Act would have provided an opportunity to bring multiple federal and non-governmental partners together to protect the public health from these drug-resistant bugs.

The Act, reported by Digital Journal, did not get through, despite the recent announcement of increased federal spending. Senator Brown argues that more preventative measures are needed. For this reason he plans to reintroduce similar legislation this year.

The STAAR Act would:

Promote prevention through public health partnerships at the U.S. Centers for Disease Control and Prevention (CDC) and local health departments;

Track resistant bacteria by making data collection better and requiring better reporting;

Improve the use of antibiotics by educating health care facilities on appropriate antibiotic use;

Enhance leadership and accountability in antibiotic resistance by reauthorizing a task force and coordinating agency efforts;

Support research by directing the National Institutes of Health (NIH) to work with other agencies and experts to create a strategic plan to address the problem.

Read more: http://www.digitaljournal.com/life/health/new-funding-to-fight-antibiotic-resistance/article/424745#ixzz3QJSliTXy

Senator takes on antibiotic resistant organisms SPECIAL

By Tim Sandle     Apr 16, 2014 in Science

Washington – With so-called “super bugs” on the rise, U.S. Sen. Sherrod Brown (D-OH) has introduced a bill aimed at slowing down the rate of antibiotic resistant microorganisms.

Read more: http://www.digitaljournal.com/science/senator-takes-on-antibiotic-resistant-organisms/article/381328#ixzz3QJT1jbOk

Senator Brown has introduced the Strategies to Address Antimicrobial Resistance (STAAR) Act. This is legislation aimed at combating antimicrobial resistance. In presenting the Act, Brown called for greater Federal attention to the growth of antibiotic-resistant infections, which affect more than two million Americans each year.

Brown is aiming for the STAAR Act to provide an opportunity to bring multiple federal and non-governmental partners together to protect the public health from these drug-resistant bugs.

Senator Brown contacted Digital Journal to explain more. In explaining the basis to the Act, Brown said: “Each year more than 23,000 Americans die from bacterial infections that are resistant to antibiotics.”

Antimicrobial resistance describes the ability of a microorganism to resist the action of antimicrobial drugs. In some instances some microorganisms are naturally resistant to particular antimicrobial agents; in other instances, the genes of non-disease-causing bacteria can be transferred to pathogenic bacteria, leading to patterns of clinically significant antibiotic resistance. Since the 1990s antibiotic resistance has been of concern for scientists and health policy makers.

Looking at the reasons for this, Brown explained that: “Antibiotics and other antimicrobial drugs have been a victim of their own success. We have used these drugs so widely and for so long that the microbes they are designed to kill have adapted to them, making the drugs less effective.”

Considering this in the context of his Act, Brown added: “We need a comprehensive strategy to address antimicrobial resistance. That is why I am introducing the STAAR Act, which would revitalize efforts to combat super bugs.”

Emerging antimicrobial resistance and the growing shortage of effective antibiotic drugs is widely regarded as a crisis that jeopardizes patient safety and public health. Once confined to hospitals, drug-resistant microbes, such as multi-drug-resistant Staphylococcus aureus (MRSA), are now striking down healthy, non-hospitalized citizens. This includes both the young and old, adults and children. These infections are painful, difficult to treat, and have become a silent epidemic in communities and hospitals across the U.S. (according to CDC).

Brown hopes that the STAAR Act will help strengthen the federal response to antimicrobial resistance by placing more of an emphasis on federal antimicrobial resistance surveillance, prevention and control, and research efforts.

In addition the Senator hopes that the Act will strengthen coordination within both Department of Health and Human Services (HHS) agencies as well as across other federal departments that are important to addressing antimicrobial resistance and enable opportunities to address this issue globally.

By providing for a more comprehensive and coordinated approach to the antimicrobial resistance crisis, it would seem that the STAAR Act represents a critical first step toward resolving what has become a major public health crisis.

Read more: http://www.digitaljournal.com/science/senator-takes-on-antibiotic-resistant-organisms/article/381328#ixzz3QJTWUxTB

H.R. 2285 (113th): Strategies to Address Antimicrobial Resistance Act

Introduced:
Jun 6, 2013 (113th Congress, 2013–2015)

Status:
Died (Referred to Committee) in a previous session of Congress

See Instead:
S. 2236 (same title)

Referred to Committee — Apr 10, 2014

  • Vaccination -how is vaccination important in preventing resistance?
  • Bioterrorism – what are the risks of resistance associated with bioterrorism
  • Antibacterials – do they cause resistance?
  • Food & Farming – why are antimicrobials used in farming?

Read Full Post »

The Life and Work of Allan Wilson

Curator: Larry H. Bernstein, MD, FCAP

 

Allan Charles Wilson (18 October 1934 – 21 July 1991) was a Professor of Biochemistry at the University of California, Berkeley, a pioneer in the use of molecular approaches to understand evolutionary change and reconstruct phylogenies, and a revolutionary contributor to the study of human evolution. He was one of the most controversial figures in post-war biology; his work attracted a great deal of attention both from within and outside the academic world. He is the only New Zealander to have won the MacArthur Fellowship.

He is best known for experimental demonstration of the concept of the molecular clock (with his doctoral student Vincent Sarich), which was theoretically postulated by Linus Pauling and Emile Zuckerkandl, revolutionary insights into the nature of the molecular anthropology of higher primates and human evolution, called Mitochondrial Eve hypothesis (with his doctoral students Rebecca L. Cann and Mark Stoneking).

Allan Wilson was born in Ngaruawahia, New Zealand, and raised on his family’s rural dairy farm at Helvetia, Pukekohe, about twenty miles south of Auckland. At his local Sunday School, the vicar’s wife was impressed by young Allan’s interest in evolution and encouraged Allan’s mother to enroll him at the elite King’s College secondary school in Auckland. There he excelled in mathematics, chemistry, and sports.

Wilson already had an interest in evolution and biochemistry, but intended to be the first in his family to attend university by pursuing studies in agriculture and animal science. Wilson met Professor Campbell Percy McMeekan, a New Zealand pioneer in animal science, who suggested that Wilson attend the University of Otago in southern New Zealand to further his study in biochemistry rather than veterinary science. Wilson gained a BSc from the University of Otago in 1955, majoring in both zoology and biochemistry.

The bird physiologist Donald S. Farner met Wilson as an undergraduate at Otago and invited him to Washington State University at Pullman as his graduate student. Wilson obliged and completed a master’s degree in zoology at WSU under Farner in 1957, where he worked on the effects of photoperiod on the physiology of birds.

Wilson then moved to the University of California, Berkeley, to pursue his doctoral research. At the time the family thought Allan would only be gone two years. Instead, Wilson remained in the United States, gaining his PhD at Berkeley in 1961 under the direction of biochemist Arthur Pardee for work on the regulation of flavin biosynthesis in bacteria. From 1961 to 1964, Wilson studied as a post-doc under biochemist Nathan O. Kaplan at Brandeis University in Waltham, Massachusetts. In Kaplan’s lab, working with lactate and malate dehydrogenases, Wilson was first introduced to the nascent field of molecular evolution. Nate Kaplan was one of the very earliest pioneers to address phylogenetic problems with evidence from protein molecules, an approach that Wilson later famously applied to human evolution and primate relationships. After Brandeis, Wilson returned to Berkeley where he set up his own lab in the Biochemistry department, remaining there for the rest of his life.

Wilson joined the UC Berkeley faculty of biochemistry in 1964, and was promoted to full professor in 1972. His first major scientific contribution was published as Immunological Time-Scale For Hominid Evolution in the journal Science in December 1967. With his student Vincent Sarich, he showed that evolutionary relationships of the human species with other primates, in particular the Great Apes (chimpanzees, gorillas, and orangutans), could be inferred from molecular evidence obtained from living species, rather than solely from fossils of extinct creatures.

Their microcomplement fixation method (see complement system) measured the strength of the immune reaction between an antigen (serum albumin) from one species and an antibody raised against the same antigen in another species. The strength of the antibody-antigen reaction was known to be stronger between more closely related species: their innovation was to measure it quantitatively among many species pairs as an “immunological distance”. When these distances were plotted against the divergence times of species pair with well-established evolutionary histories, the data showed that the molecular difference increased linearly with time, in what was termed a “molecular clock”. Given this calibration curve, the time of divergence between species pairs with unknown or uncertain fossil histories could be inferred. Most controversially, their data suggested that divergence times between humans, chimpanzees, and gorillas were on the order of 3~5 million years, far less than the estimates of 9~30 million years accepted by conventional paleoanthropologists from fossil hominids such as Ramapithecus. This ‘recent origin’ theory of human/ape divergence remained controversial until the discovery of the “Lucy” fossils in 1974.

Wilson and another PhD student Mary-Claire King subsequently compared several lines of genetic evidence (immunology, amino acid differences, and protein electrophoresis) on the divergence of humans and chimpanzees, and showed that all methods agreed that the two species were >99% similar.[4][19] Given the large organismal differences between the two species in the absence of large genetic differences, King and Wilson argued that it was not structural gene differences that were responsible for species differences, but gene regulation of those differences, that is, the timing and manner in which near-identical gene products are assembled during embryology and development. In combination with the “molecular clock” hypothesis, this contrasted sharply with the accepted view that larger or smaller organismal differences were due to large or smaller rates of genetic divergence.

In the early 1980s, Wilson further refined traditional anthropological thinking with his work with PhD students Rebecca Cann and Mark Stoneking on the so-called “Mitochondrial Eve” hypothesis.[20] In his efforts to identify informative genetic markers for tracking human evolutionary history, he focused on mitochondrial DNA (mtDNA) — genes that are found in mitochondria in the cytoplasm of the cell outside the nucleus. Because of its location in the cytoplasm, mtDNA is passed exclusively from mother to child, the father making no contribution, and in the absence of genetic recombination defines female lineages over evolutionary timescales. Because it also mutates rapidly, it is possible to measure the small genetic differences between individual within species by restriction endonuclease gene mapping. Wilson, Cann, and Stoneking measured differences among many individuals from different human continental groups, and found that humans from Africa showed the greatest inter-individual differences, consistent with an African origin of the human species (the so-called “Out of Africa” hypothesis). The data further indicated that all living humans shared a common maternal ancestor, who lived in Africa only a few hundreds of thousands of years ago.

This common ancestor became widely known in the media and popular culture as the Mitochondrial Eve. This had the unfortunate and erroneous implication that only a single female lived at that time, when in fact the occurrence of a coalescent ancestor is a necessary consequence of population genetic theory, and the Mitochondrial Eve would have been only one of many humans (male and female) alive at that time.[2][3] This finding was, like his earlier results, not readily accepted by anthropologists. Conventional hypothesis was that various human continental groups had evolved from diverse ancestors, over several million of years since divergence from chimpanzees. The mtDNA data, however, strongly suggested that all humans descended from a common, quite recent, African mother.

Wilson became ill with leukemia, and after a bone marrow transplant, died on Sunday, 21 July 1991, at the Fred Hutchinson Memorial Cancer Research Center in Seattle. He had been scheduled to give the keynote address at an international conference the same day. He was 56, at the height of his scientific recognition and powers.

Wilson’s success can be attributed to his strong interest and depth of knowledge in biochemistry and evolutionary biology, his insistence of quantification of evolutionary phenomena, and has early recognition of new molecular techniques that could shed light on questions of evolutionary biology. After development of quantitative immunological methods, his lab was the first to recognize restriction endonuclease mapping analysis as a quantitative evolutionary genetic method, which led to his early use of DNA sequencing, and the then-nascent technique of PCR to obtain large DNA sets for genetic analysis of populations. He trained scores of undergraduate, graduate (34 people, 17 each of men and women, received their doctoral degrees in his lab), and post-doctoral students in molecular evolutionary biology, including sabbatical visitors from six continents. His lab published more than 300 technical papers, and was recognized as a mecca for those wishing to enter the field of molecular evolution in the 1970s and 1980s.

The Allan Wilson Centre for Molecular Ecology and Evolution was established in 2002 in his honour to advance knowledge of the evolution and ecology of New Zealand and Pacific plant and animal life, and human history in the Pacific. The Centre is under the Massey University, at Palmerston North, New Zealand, and is a national collaboration involving the University of Auckland, Victoria University of Wellington, the University of Otago, University of Canterbury and the New Zealand Institute for Plant and Food Research.

A 41-minutes documentary film of his life entitled Allan Wilson, Evolutionary: Biochemist, Biologist, Giant of Molecular Biology was released by Films Media Group in 2008.

 

Allan Charles Wilson. 18 October 1934 — 21 July 1991

Rebecca L. Cann

Department of Cell and Molecular Biology, University of Hawaii at Manoa, Biomedical Sciences Building T514, 1960 East–West Rd, Honolulu, HI 96822, USA

Abstract

Allan Charles Wilson was born on 18 October 1934 at Ngaruawahia, New Zealand. He died in Seattle, Washington, on 21 July 1991 while undergoing treatment for leukemia.  Allan was known as a pioneering and highly innovative biochemist, helping to define the field of molecular evolution and establish the use of a molecular clock to measure evolutionary change between living species. The molecular clock, a method of measuring the timescale of evolutionary change between two organisms on the basis of the number of mutations that they have accumulated since last sharing a common genetic ancestor, was an idea initially championed by Émile Zuckerkandl and Linus Pauling (Zuckerkandl & Pauling 1962), on the basis of their observations that the number of changes in an amino acid sequence was roughly linear with time in the aligned hemoglobin proteins of animals. Although it is now not unusual to see the words ‘molecular evolution’ and ‘molecular phylogeny’ together, when Allan formed his own biochemistry laboratory in 1964 at the University of California, Berkeley, many scientists in the field of evolutionary biology considered these ideas complete heresy. Allan’s death at the relatively young age of 56 years left behind his wife, Leona (deceased in 2009), a daughter, Ruth (b. 1961), and a son, David (b. 1964), as well his as mother, Eunice (deceased in 2002), a younger brother, Gary Wilson, and a sister, Colleen Macmillan, along with numerous nieces, nephews and cousins in New Zealand, Australia and the USA. In this short span of time, he trained more than 55 doctoral students and helped launch the careers of numerous postdoctoral fellows.

Allan Charles Wilson, Biochemistry; Molecular Biology: Berkeley

1934-1991

Professor

The sudden death of Allan Wilson, of leukemia, on 21 July 1991, at the age of 56, and at the height of his powers, robbed the Berkeley campus and the international scientific community of one of its most active and respected leaders.

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The Union of Biomarkers and Drug Development

The Union of Biomarkers and Drug Development

Author and Curator: Larry H. Bernstein, MD, FCAP

There has been consolidation going on for over a decade in both thr pharmaceutical and in the diagnostics industry, and at the same time the page is being rewritten for health care delivery.  I shall try to work through a clear picture of these not coincidental events.

Key notables:

  1. A growing segment of the US population is reaching Medicare age
  2. There is also a large underserved population in both metropolitan and nonurban areas and a fragmentation of the middle class after a growth slowdown in the economy since the 2008 deep recession.
  3. The deep recession affecting worldwide economies was only buffered by availability of oil or natural gas.
  4. In addition, there was a self-destructive strategy to cut spending on national scales that withdrew the support that would bolster support for infrastrucrue renewl.
  5. There has been a dramatic success in the clinical diagnostics industry, with a long history of being viewed as a loss leader, and this has been recently followed by the pharmaceutical industry faced with inability to introduce new products, leading to more competition in off-patent medications.
  6. The introduction of the Accountable Care Act has opened the opportunities for improved care, despite political opposition, and has probably sustained opportunity in the healthcare market.

Let’s take a look at this three headed serpent. – Pharma, Diagnostics, New Entity
?  The patient  ?
?  Insurance    ?
?  Physician    ?

Part I.   The Concept

When Illumina Buys Roche: The Dawning Of The Era Of Diagnostics Dominance

Robert J. Easton, Alain J. Gilbert, Olivier Lesueur, Rachel Laing, and Mark Ratner
http://PharmaMedtechBI.com    | IN VIVO: The Business & Medicine Report Jul/Aug 2014; 32(7).

  • With current technology and resources, a well-funded IVD company can create and pursue a strategy of information gathering and informatics application to create medical knowledge, enabling it to assume the risk and manage certain segments of patients
  • We see the first step in the process as the emergence of new specialty therapy companies coming from an IVD legacy, most likely focused in cancer, infection, or critical care

When Illumina Inc. acquired the regulatory consulting firm Myraqa, a specialist in in vitro diagnostics (IVD), in July, the press release announcement characterized the deal as one that would bolster illumina’s in-house capabilities for clinical readiness and help prepare for its next growth phase in regulated markets. That’s not surprising given the US Food and Drug Administration’s (FDA) approval a year and a half ago of its MiSeq next-generation sequencer for clinical use. But the deal could also suggest illumina is beginning to move along the path toward taking on clinical risk – that is, eventually

  • advising physicians and patients, which would mean facing regulators directly

Such a move – by illumina, another life sciences tools firm, or an information specialist from the high-tech universe – is inevitable given

  • the emerging power of diagnostics and traditional health care players’ reluctance to themselves take on such risk.

Alternatively, we believe that a well-funded diagnostics company could establish this position. either way, such a champion would establish dominion over and earn higher valuation than less-aggressive players who

  • only supply compartmentalized drug and device solutions.

Diagnostics companies have long been dogged by a fundamental issue:

  1. they are viewed and valued more along the lines of a commodity business than as firms that deliver a unique product or service
  2. diagnostics companies are in position to do just that today because they are now advantaged by having access to more data points.
  3. if they were to cobble together the right capabilities, diagnostics companies would have the ability to turn information into true medical knowledge

Example: PathGEN PathChip

nucleic-acid-based platform detects 296 viruses, bacteria, fungi & parasites

http://ow.ly/d/2GvQhttp://ow.ly/DSORV

This puts the diagnostics player in an unfamiliar realm where it can ask the question of what value they offer compared with a therapeutic. The key is that diagnostics can now offer unique information and potentially unique tools to capture that information. In order to do so, it has to create information from the data it generates, and then to supply that knowledge to users who will value and act on that knowledge. Complex genomic tests, as much as physical examination, may be the first meaningful touch point for physicians’ classification of disease.

Even if lab tests are more expensive, it is a cheaper means for deciding what to do first for a patient than the trial and error of prescribing medication without adequate information. Information is gaining in value as the amount of treatment data available on genomically characterizable subpopulations increases. In such a circumstance
it is the ability to perform that advisory function that will add tremendous value above what any test provides, the leverage of being able to apply a proprietary diagnostics platform – and importantly, the data it generates. It is the ability to perform that advisory function that will add tremendous value above what any test provides.

Integrated Diagnostics Inc. and Biodesix Inc. with mass spectrometry has the tools for unraveling disease processes, and numerous players are quite visibly in or are getting into the business of providing medical knowledge and clinical decision support in pursuit of a huge payout for those who actually solve important disease mysteries. Of course one has to ask whether MS/MS is sufficient for the assigned task, and also whether the technology is ready for the kind of workload experienced in a clinical service compared to a research vehicle.  My impression (as a reviewer) is that it is not now the time to take this seriously.

Roche has not realized its intent with Ventana: failing to deliver on the promise of boosting Roche’s pipeline, which was a significant factor in the high price Roche paid. The combined company was to be “uniquely positioned to further expand Ventana’s business globally and together develop more cost-efficient, differentiated, and targeted medicines.  On the other hand,  Biodesix decided to use Veristrat to look back and analyze important trial data to try to ascertain which patients would benefit from ficlatuzumab (subset). The predictive effect for the otherwise unimpressive trial results was observed in both progression-free survival and overall survival endpoints, and encouraged the companies to conduct a proof-of-concept study of ficlatuzumab in combination with Tarceva in advanced Non Small Cell Lung Cancer Patients (NSCLC) selected using the Veristrat test.

A second phase of IVD evolution will be far more challenging to pharma, when the most accomplished companies begin to assemble and integrate much broader data
sets, thereby gaining knowledge sufficient to actually manage patients and dictate therapy, including drug selection. No individual physician has or will have access to all of this information on thousands of patients, combined with the informatics to tease out from trillions of data points the optimal personalized medical approach. When the IVD-origin knowledge integrator amasses enough data and understanding to guide therapy decisions in large categories, particularly drug choices, it will become more valuable than any of the drug suppliers.

This is an apparent reversal of fortune. The pharmaceutical industry has been considered the valued provider, while the IVD manufacturer has been the low valued cousin. Now, it is by an ability to make kore accurate the drug administration that the IVD company can control the drug bill, to the detriment of drug developers, by finding algorithms that generate equal-to-innovative-drug outcomes using generics for most of the patients, thereby limiting the margins of drug suppliers and the upsides for new drug discovery/development.

It is here that there appears to be a misunderstanding of the whole picture of the development of the healthcare industry.  The pharmaceutical industry had a high value added only insofar it could replace market leaders for treatment before or at the time of patent expiration, which largely depended either introducing a new class of drug, or by relieving the current drug in its class of undesired toxicities or “side effects”.  Otherwise, the drug armamentarium was time limited to the expiration date. In other words, the value was dependent on a window of no competition.  In addition, as the regulation of healthcare costs were tightening under managed care, the introduction of new products that were deemed to be only marginally better, could be substitued by “off-patent” drug products.

The other misunderstanding is related to the IVD sector.  Laboratory tests in the 1950’s were manual, and they could be done by “technicians” who might not have completed a specialized training in clinical laboratory sciences.  The first sign of progress was the introduction of continuous flow chemistry, with a sampling probe, tubing to bring the reacting reagents into a photocell, and the timing of the reaction controlled by a coiled glass tubing before introducing the colored product into a uv-visible photometer.  In perhaps a decade, the Technicon SMA 12 and 6 instruments were introduced that could do up to 18 tests from a single sample.

Part 2. Emergence of an IVD Clinical Automated Diagnostics Industry

Why tests are ordered

  1. Screening
  2. Diagnosis
  3. Monitoring

Historical Perspective

Case in Point 1:  Outstanding Contributions in Clinical Chemistry. 1991. Arthur Karmen.

Dr. Karmen was born in New York City in 1930. He graduated from the Bronx High School of Science in 1946 and earned an A.B. and M.D. in 1950 and 1954, respectively, from New York University. In 1952, while a medical student working on a summer project at Memorial-Sloan Kettering, he used paper chromatography of amino acids to demonstrate the presence of glutamic-oxaloacetic and glutaniic-pyruvic ransaminases (aspartate and alanine aminotransferases) in serum and blood. In 1954, he devised the spectrophotometric method for measuring aspartate aminotransferase in serum, which, with minor modifications, is still used for diagnostic testing today. When developing this assay, he studied the reaction of NADH with serum and demonstrated the presence of lactate and malate dehydrogenases, both of which were also later used in diagnosis. Using the spectrophotometric method, he found that aspartate aminotransferase increased in the period immediately after an acute myocardial infarction and did the pilot studies that showed its diagnostic utility in heart and liver diseases.  This became as important as the EKG. It was replaced in cardiology usage by the MB isoenzyme of creatine kinase, which was driven by Burton Sobel’s work on infarct size, and later by the troponins.

Case in point 2: Arterial Blood Gases.  Van Slyke. National Academy of Sciences.

The test is used to determine the pH of the blood, the partial pressure of carbon dioxide and oxygen, and the bicarbonate level. Many blood gas analyzers will also report concentrations of lactate, hemoglobin, several electrolytes, oxyhemoglobin, carboxyhemoglobin and methemoglobin. ABG testing is mainly used in pulmonology and critical care medicine to determine gas exchange which reflect gas exchange across the alveolar-capillary membrane.

DONALD DEXTER VAN SLYKE died on May 4, 1971, after a long and productive career that spanned three generations of biochemists and physicians. He left behind not only a bibliography of 317 journal publications and 5 books, but also more than 100 persons who had worked with him and distinguished themselves in biochemistry and academic medicine. His doctoral thesis, with Gomberg at University of Michigan was published in the Journal of the American Chemical Society in 1907.  Van Slyke received an invitation from Dr. Simon Flexner, Director of the Rockefeller Institute, to come to New York for an interview. In 1911 he spent a year in Berlin with Emil Fischer, who was then the leading chemist of the scientific world. He was particularly impressed by Fischer’s performing all laboratory operations quantitatively —a procedure Van followed throughout his life. Prior to going to Berlin, he published the  classic nitrous acid method for the quantitative determination of primary aliphatic amino groups,  the first of the many gasometric procedures devised by Van Slyke, and made possible the determination of amino acids. It was the primary method used to study amino acid

composition of proteins for years before chromatography. Thus, his first seven postdoctoral years were centered around the development of better methodology for protein composition and amino acid metabolism.

With his colleague G. M. Meyer, he first demonstrated that amino acids, liberated during digestion in the intestine, are absorbed into the bloodstream, that they are removed by the tissues, and that the liver alone possesses the ability to convert the amino acid nitrogen into urea.  From the study of the kinetics of urease action, Van Slyke and Cullen developed equations that depended upon two reactions: (1) the combination of enzyme and substrate in stoichiometric proportions and (2) the reaction of the combination into the end products. Published in 1914, this formulation, involving two velocity constants, was similar to that arrived at contemporaneously by Michaelis and Menten in Germany in 1913.

He transferred to the Rockefeller Institute’s Hospital in 2013, under Dr. Rufus Cole, where “Men who were studying disease clinically had the right to go as deeply into its fundamental nature as their training allowed, and in the Rockefeller Institute’s Hospital every man who was caring for patients should also be engaged in more fundamental study”.  The study of diabetes was already under way by Dr. F. M. Allen, but patients inevitably died of acidosis.  Van Slyke reasoned that if incomplete oxidation of fatty acids in the body led to the accumulation of acetoacetic and beta-hydroxybutyric acids in the blood, then a reaction would result between these acids and the bicarbonate ions that would lead to a lower than-normal bicarbonate concentration in blood plasma. The problem thus became one of devising an analytical method that would permit the quantitative determination of bicarbonate concentration in small amounts of blood plasma.  He ingeniously devised a volumetric glass apparatus that was easy to use and required less than ten minutes for the determination of the total carbon dioxide in one cubic centimeter of plasma.  It also was soon found to be an excellent apparatus by which to determine blood oxygen concentrations, thus leading to measurements of the percentage saturation of blood hemoglobin with oxygen. This found extensive application in the study of respiratory diseases, such as pneumonia and tuberculosis. It also led to the quantitative study of cyanosis and a monograph on the subject by C. Lundsgaard and Van Slyke.

In all, Van Slyke and his colleagues published twenty-one papers under the general title “Studies of Acidosis,” beginning in 1917 and ending in 1934. They included not only chemical manifestations of acidosis, but Van Slyke, in No. 17 of the series (1921), elaborated and expanded the subject to describe in chemical terms the normal and abnormal variations in the acid-base balance of the blood. This was a landmark in understanding acid-base balance pathology.  Within seven years after Van moved to the Hospital, he had published a total of fifty-three papers, thirty-three of them coauthored with clinical colleagues.

In 1920, Van Slyke and his colleagues undertook a comprehensive investigation of gas and electrolyte equilibria in blood. McLean and Henderson at Harvard had made preliminary studies of blood as a physico-chemical system, but realized that Van Slyke and his colleagues at the Rockefeller Hospital had superior techniques and the facilities necessary for such an undertaking. A collaboration thereupon began between the two laboratories, which resulted in rapid progress toward an exact physico-chemical description of the role of hemoglobin in the transport of oxygen and carbon dioxide, of the distribution of diffusible ions and water between erythrocytes and plasma,
and of factors such as degree of oxygenation of hemoglobin and hydrogen ion concentration that modified these distributions. In this Van Slyke revised his volumetric gas analysis apparatus into a manometric method.  The manometric apparatus proved to give results that were from five to ten times more accurate.

A series of papers on the CO2 titration curves of oxy- and deoxyhemoglobin, of oxygenated and reduced whole blood, and of blood subjected to different degrees of oxygenation and on the distribution of diffusible ions in blood resulted.  These developed equations that predicted the change in distribution of water and diffusible ions between blood plasma and blood cells when there was a change in pH of the oxygenated blood. A significant contribution of Van Slyke and his colleagues was the application of the Gibbs-Donnan Law to the blood—regarded as a two-phase system, in which one phase (the erythrocytes) contained a high concentration of nondiffusible negative ions, i.e., those associated with hemoglobin, and cations, which were not freely exchaThe importance of Vanngeable between cells and plasma. By changing the pH through varying the CO2 tension, the concentration of negative hemoglobin charges changed in a predictable amount. This, in turn, changed the distribution of diffusible anions such as Cl” and HCO3″ in order to restore the Gibbs-Donnan equilibrium. Redistribution of water occurred to restore osmotic equilibrium. The experimental results confirmed the predictions of the equations.

As a spin-off from the physico-chemical study of the blood, Van undertook, in 1922, to put the concept of buffer value of weak electrolytes on a mathematically exact basis.
This proved to be useful in determining buffer values of mixed, polyvalent, and amphoteric electrolytes, and put the understanding of buffering on a quantitative basis. A
monograph in Medicine entitled “Observation on the Courses of Different Types of Bright’s Disease, and on the Resultant Changes in Renal Anatomy,” was a landmark that
related the changes occurring at different stages of renal deterioration to the quantitative changes taking place in kidney function. During this period, Van Slyke and R. M. Archibald identified glutamine as the source of urinary ammonia. During World War II, Van and his colleagues documented the effect of shock on renal function and, with R. A. Phillips, developed a simple method, based on specific gravity, suitable for use in the field.

Over 100 of Van’s 300 publications were devoted to methodology. The importance of Van Slyke’s contribution to clinical chemical methodology cannot be overestimated.
These included the blood organic constituents (carbohydrates, fats, proteins, amino acids, urea, nonprotein nitrogen, and phospholipids) and the inorganic constituents (total cations, calcium, chlorides, phosphate, and the gases carbon dioxide, carbon monoxide, and nitrogen). It was said that a Van Slyke manometric apparatus was almost all the special equipment needed to perform most of the clinical chemical analyses customarily performed prior to the introduction of photocolorimeters and spectrophotometers for such determinations.

The progress made in the medical sciences in genetics, immunology, endocrinology, and antibiotics during the second half of the twentieth century obscures at times the progress that was made in basic and necessary biochemical knowledge during the first half. Methods capable of giving accurate quantitative chemical information on biological material had to be painstakingly devised; basic questions on chemical behavior and metabolism had to be answered; and, finally, those factors that adversely modified the normal chemical reactions in the body so that abnormal conditions arise that we characterize as disease states had to be identified.

Viewed in retrospect, he combined in one scientific lifetime (1) basic contributions to the chemistry of body constituents and their chemical behavior in the body, (2) a chemical understanding of physiological functions of certain organ systems (notably the respiratory and renal), and (3) how such information could be exploited in the
understanding and treatment of disease. That outstanding additions to knowledge in all three categories were possible was in large measure due to his sound and broadly based chemical preparation, his ingenuity in devising means of accurate measurements of chemical constituents, and the opportunity given him at the Hospital of the Rockefeller Institute to study disease in company with physicians.

In addition, he found time to work collaboratively with Dr. John P. Peters of Yale on the classic, two-volume Quantitative Clinical Chemistry. In 1922, John P. Peters, who had just gone to Yale from Van Slyke’s laboratory as an Associate Professor of Medicine, was asked by a publisher to write a modest handbook for clinicians describing useful chemical methods and discussing their application to clinical problems. It was originally to be called “Quantitative Chemistry in Clinical Medicine.” He soon found that it was going to be a bigger job than he could handle alone and asked Van Slyke to join him in writing it. Van agreed, and the two men proceeded to draw up an outline and divide up the writing of the first drafts of the chapters between them. They also agreed to exchange each chapter until it met the satisfaction of both.At the time it was published in 1931, it contained practically all that could be stated with confidence about those aspects of disease that could be and had been studied by chemical means. It was widely accepted throughout the medical world as the “Bible” of quantitative clinical chemistry, and to this day some of the chapters have not become outdated.

History of Laboratory Medicine at Yale University.

The roots of the Department of Laboratory Medicine at Yale can be traced back to John Peters, the head of what he called the “Chemical Division” of the Department of Internal Medicine, subsequently known as the Section of Metabolism, who co-authored with Donald Van Slyke the landmark 1931 textbook Quantitative Clinical Chemistry (2.3); and to Pauline Hald, research collaborator of Dr. Peters who subsequently served as Director of Clinical Chemistry at Yale-New Haven Hospital for many years. In 1947, Miss Hald reported the very first flame photometric measurements of sodium and potassium in serum (4). This study helped to lay the foundation for modern studies of metabolism and their application to clinical care.

The Laboratory Medicine program at Yale had its inception in 1958 as a section of Internal Medicine under the leadership of David Seligson. In 1965, Laboratory Medicine achieved autonomous section status and in 1971, became a full-fledged academic department. Dr. Seligson, who served as the first Chair, pioneered modern automation and computerized data processing in the clinical laboratory. In particular, he demonstrated the feasibility of discrete sample handling for automation that is now the basis of virtually all automated chemistry analyzers. In addition, Seligson and Zetner demonstrated the first clinical use of atomic absorption spectrophotometry. He was one of the founding members of the major Laboratory Medicine academic society, the Academy of Clinical Laboratory Physicians and Scientists.

Davenport fig 10.jpg

Case in Point 3.  Nathan Gochman.  Developer of Automated Chemistries.

Nathan Gochman, PhD, has over 40 years of experience in the clinical diagnostics industry. This includes academic teaching and research, and 30 years in the pharmaceutical and in vitro diagnostics industry. He has managed R & D, technical marketing and technical support departments. As a leader in the industry he was President of the American Association for Clinical Chemistry (AACC) and the National Committee for Clinical Laboratory Standards (NCCLS, now CLSI). He is currently a Consultant to investment firms and IVD companies.

Nathan Gochman

Nathan Gochman

The clinical laboratory has become so productive, particularly in chemistry and immunology, and the labor, instrument and reagent costs are well determined, that today a physician’s medical decisions are 80% determined by the clinical laboratory.  Medical information systems have lagged far behind.  Why is that?  Because the decision for a MIS has historical been based on billing capture.  Moreover, the historical use of chemical profiles were quite good at validating healthy dtatus in an outpatient population, but the profiles became restricted under Diagnostic Related Groups.    Thus, it came to be that the diagnostics was considered a “commodity”.  In order to be competitive, a laboratory had to provide “high complexity” tests that were drawn in by a large volume of “moderate complexity”tests.

Part 3. Biomarkers in Medical Practice

Case in Point 1.

A Solid Prognostic Biomarker

HDL-C: Target of Therapy or Fuggedaboutit?

Steven E. Nissen, MD, MACC, Peter Libby, MD

DisclosuresNovember 06, 2014

Steven E. Nissen, MD, MACC: I am Steve Nissen, chairman of the Department of Cardiovascular Medicine at the Cleveland Clinic. I am here with Dr Peter Libby, chief of cardiology at the Brigham and Women’s Hospital and professor of medicine at Harvard Medical School. We are going to discuss high-density lipoprotein cholesterol (HDL-C), a topic that has been very controversial recently. Peter, HDL-C has been a pretty good biomarker. The question is whether it is a good target.

Peter Libby, MD: Since the early days in Berkley, when they were doing ultracentrifugation, and when it was reinforced and put on the map by the Framingham Study,[1] we have known that HDL-C is an extremely good biomarker of prospective cardiovascular risk with an inverse relationship with all kinds of cardiovascular events. That is as solid a finding as you can get in observational epidemiology. It is a very reliable prospective marker. It’s natural that the pharmaceutical industry and those of us who are interested in risk reduction would focus on HDL-C as a target. That is where the controversies come in.

Dr Nissen: It has been difficult. My view is that the trials that have attempted to modulate HDL-C or the drugs they used have been flawed. Although the results have not been promising, the jury is yet out. Torcetrapib, the cholesteryl ester transfer protein (CETP) inhibitor developed by Pfizer, had anoff-target toxicity.[2] Niacin is not very effective, and there are a lot of downsides to the drug. That has been an issue, but people are still working on this. We have done some studies. We did our ApoA-1 Milano infusion study[3]about a decade ago, which showed very promising results with respect to shrinking plaques in coronary arteries. I remain open to the possibility that the right drug in the right trial will work.

Dr Libby: What do you do with the genetic data that have come out in the past couple of years? Sekar Kathiresan masterminded and organized an enormous collaboration[4] in which they looked, with contemporary genetics, at whether HDL had the genetic markers of being a causal risk factor. They came up empty-handed.

Dr Nissen: I am cautious about interpreting those data, like I am cautious about interpreting animal studies of atherosclerosis. We have both lived through this problem in which something works extremely well in animals but doesn’t work in humans, or it doesn’t work in animals but it works in humans. The genetic studies don’t seal the fate of HDL. I have an open mind about this. Drugs are complex. They work by complex mechanisms. It is my belief that what we have to do is test these hypotheses in well-designed clinical trials, which are rigorously performed with drugs that are clean—unlike torcetrapib—and don’t have off-target toxicities.

An Unmet Need: High Lp(a) Levels

Dr Nissen: I’m going to push back on that and make a couple of points. The HPS2-THRIVE study was flawed. They studied the wrong people. It was not a good study, and AIM-HIGH[8] was underpowered. I am not putting people on niacin. What do you do with a patient whose Lp(a) is 200 mg/dL?

Dr Libby: I’m waiting for the results of the PCSK9 and anacetrapib studies. You can tell me about evacetrapib.[9]Reducing Lp(a) is an unmet medical need. We both care for kindreds with high Lp(a) levels and premature coronary artery disease. We have no idea what to do with them other than to treat them with statins and lower their LDL-C levels.

Dr Nissen: I have taken a more cautious approach with respect to taking people off of niacin. If I have patients who are doing well and tolerating it (depending on why it was started), I am discontinuing niacin in some people. I am starting very few people on the drug, but I worry about the quality of the trial.

Dr Libby: So you are of the “don’t start don’t stop” school?

Dr Nissen: Yes. It’s difficult when the trial is fatally flawed. There were 11,000 patients from China in this study. I have known for years that if you give niacin to people of Asiatic ethnic descent, they have terrible flushing and they won’t continue the drug. One question is, what was the adherence? The adverse events would have been tolerable had there been efficacy. The concern here is that this study was destined to fail because they studied a low LDL/high HDL population, a group of people for whom niacin just isn’t used.

Triglycerides and HDL: Do We Have It Backwards?

Dr Libby: What about the recent genetic[10] and epidemiologic data that support triglycerides, and apolipoprotein C3 in particular as a causal risk factor? Have we been misled through all of the generations in whom we have been adjusting triglycerides for HDL-C and saying that triglycerides are not a causal risk factor because once we adjust for HDL, the risk goes away? Do you think we got it backwards?

Dr Nissen: The tricky factor here is that because of this intimate inverse relationship between triglycerides and HDL, we may be talking about the same phenomenon. That is one of the reasons that I am not certain we are not going to be able to find a therapy. What if you had a therapy that lowered triglycerides and raised HDL-C? Could that work? Could that combination be favorable? I want answers from rigorous, well-designed clinical trials that ask the right questions in the right populations. I am disappointed, just as I have been disappointed by the fibrate trials.[11,12] There is a class of drugs that raises HDL-C a little and lowers triglycerides a lot.

Dr Nissen: But the gemfibrozil studies (VA-HIT[13] and Helsinki Heart[14]) showed benefit.

The Dyslipidemia Bar Has Been Raised

Dr Libby: Those studies were from the pre-statin era. We both were involved in trials in which patients were on high-dose statins at baseline. Do you think that this is too high a bar?

Dr Nissen: The bar has been raised, and for the pharmaceutical industry, the studies that we need to find out whether lowering triglycerides or raising HDL is beneficial are going to be large. We are doing a study with evacetrapib. It has 12,000 patients. It’s fully enrolled. Evacetrapib is a very clean-looking drug. It doesn’t have such a long biological half-life as anacetrapib, so I am very encouraged that it won’t have that baggage of being around for 2-4 years. We’ve got a couple of shots on goal here. Don’t forget that we have multiple ongoing studies of HDL-C infusion therapies that are still under development. Those have some promise too. The jury is still out.

Dr Libby: We agree on the need to do rigorous, large-scale endpoint trials. Do the biomarker studies, but don’t wait to start the endpoint trial because that’s the proof in the pudding.

Dr Nissen: Exactly. We have had a little controversy about HDL-C. We often agree, but not always, and we may have a different perspective. Thanks for joining me in this interesting discussion of what will continue to be a controversial topic for the next several years until we get the results of the current ongoing trials.

Case in Point 2.

NSTEMI? Honesty in Coding and Communication?

Melissa Walton-Shirley

November 07, 2014

The complaint at ER triage: Weakness, fatigue, near syncope of several days’ duration, vomiting, and decreased sensorium.

The findings: O2sat: 88% on room air. BP: 88 systolic. Telemetry: Sinus tachycardia 120 bpm. Blood sugar: 500 mg/dL. Chest X ray: atelectasis. Urinalysis: pyuria. ECG: T-wave-inversion anterior leads. Echocardiography: normal left ventricular ejection fraction (LVEF) and wall motion. Troponin I: 0.3 ng/mL. CT angiography: negative for pulmonary embolism (PE). White blood cell count: 20K with left shift. Blood cultures: positive for Gram-negative rods.

The treatment: Intravenous fluids and IV levofloxacin—changed to ciprofloxacin.

The communication at discharge: “You had a severe urinary-tract infection and grew bacteria in your bloodstream. Also, you’ve had a slight heart attack. See your cardiologist immediately upon discharge-no more than 5 days from now.”

The diagnoses coded at discharge: Urosepsis and non-ST segment elevation MI (NSTEMI) 410.1.

One year earlier: This moderately obese patient was referred to our practice for a preoperative risk assessment. The surgery planned was a technically simple procedure, but due to the need for precise instrumentation, general endotracheal anesthesia (GETA) was being considered. The patient was diabetic, overweight, and short of air. A stress exam was equivocal for CAD due to poor exercise tolerance and suboptimal imaging. Upon further discussion, symptoms were progressive; therefore, cardiac cath was recommended, revealing angiographically normal coronaries and a predictably elevated left ventricular end diastolic pressure (LVEDP) in the mid-20s range. The patient was given a diagnosis of diastolic dysfunction, a prescription for better hypertension control, and in-depth discussion on exercise and the Mediterranean and DASH diets for weight loss. Symptoms improved with a low dose of diuretic. The surgery was completed without difficulty. Upon follow-up visit, the patient felt well, had lost a few pounds, and blood pressure was well controlled.

Five days after ER workup: While out of town, the patient developed profound weakness and went to the ER as described above. Fast forward to our office visit in the designated time frame of “no longer than 5 days’ postdischarge,” where the patient and family asked me about the “slight heart attack” that literally came on the heels of a normal coronary angiogram.

But the patient really didn’t have a “heart attack,” did they? The cardiologist aptly stated that it was likely nonspecific troponin I leak in his progress notes. Yet the hospitalist framed the diagnosis of NSTEMI as item number 2 in the final diagnoses.

The motivations on behalf of personnel who code charts are largely innocent and likely a direct result of the lack of understanding of the coding system on behalf of us as healthcare providers. I have a feeling, though, that hospitals aren’t anxious to correct this misperception, due to an opportunity for increased reimbursement. I contacted a director of a coding department for a large hospital who prefers to remain anonymous. She explained that NSTEMI ICD9 code 410.1 falls in DRG 282 with a weight of .7562. The diagnosis of “demand ischemia,” code 411.89, a slightly less inappropriate code for a nonspecific troponin I leak, falls in DRG 311 with a weight of .5662. To determine reimbursement, one must multiply the weight by the average hospital Medicare base rate of $5370. Keep in mind that each hospital’s base rate and corresponding payment will vary. The difference in reimbursement for a large hospital bill between these two choices for coding is substantial, at over $1000 difference ($4060 vs $3040).

Although hospitals that are already reeling from shrinking revenues will make more money on the front end by coding the troponin leak incorrectly as an NSTEMI, when multiple unnecessary tests are generated to follow up on a nondiagnostic troponin leak, the amount of available Centers for Medicare & Medicaid Services (CMS) reimbursement pie shrinks in the long run. Furthermore, this inappropriate categorization generates extreme concern on behalf of patients and family members that is often never laid to rest. The emotional toll of a “heart-attack” diagnosis has an impact on work fitness, quality of life, cost of medication, and the cost of future testing. If the patient lived for another 100 years, they will likely still list a “heart attack” in their medical history.

As a cardiologist, I resent the loose utilization of one of “my” heart-attack codes when it wasn’t that at all. At discharge, we need to develop a better way of communicating what exactly did happen. Equally important, we need to communicate what exactly didn’t happen as well.

Case in Point 3.

Blood Markers Predict CKD Heart Failure 

Published: Oct 3, 2014 | Updated: Oct 3, 2014

Elevated levels of high-sensitivity troponin T (hsTnT) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) strongly predicted heart failure in patients with chronic kidney disease followed for a median of close to 6 years, researchers reported.

Compared with patients with the lowest blood levels of hsTnT, those with the highest had a nearly five-fold higher risk for developing heart failure and the risk was 10-fold higher in patients with the highest NT-proBNP levels compared with those with the lowest levels of the protein, researcher Nisha Bansal, MD, of the University of Washington in Seattle, and colleagues wrote online in the Journal of the American Society of Nephrology.

A separate study, published online in theJournal of the American Medical Association earlier in the week, also examined the comorbid conditions of heart and kidney disease, finding no benefit to the practice of treating cardiac surgery patients who developed acute kidney injury with infusions of the antihypertensive drug fenoldopam.

The study, reported by researcher Giovanni Landoni, MD, of the IRCCS San Raffaele Scientific Institute, Milan, Italy, and colleagues, was stopped early “for futility,” according to the authors, and the incidence of hypotension during drug infusion was significantly higher in patients infused with fenoldopam than placebo (26% vs. 15%; P=0.001).

Blood Markers Predict CKD Heart Failure

The study in patients with mild to moderate chronic kidney disease (CKD) was conducted to determine if blood markers could help identify patients at high risk for developing heart failure.

Heart failure is the most common cardiovascular complication among people with renal disease, occurring in about a quarter of CKD patients.

The two markers, hsTnT and NT-proBNP, are associated with overworked cardiac myocytes and have been shown to predict heart failure in the general population.

However, Bansal and colleagues noted, the markers have not been widely used in diagnosing heart failure among patients with CKD due to concerns that reduced renal excretion may raise levels of these markers, and therefore do not reflect an actual increase in heart muscle strain.

To better understand the importance of elevated concentrations of hsTnT and NT-proBNP in CKD patients, the researchers examined their association with incident heart failure events in 3,483 participants in the ongoing observational Chronic Renal Insufficiency Cohort (CRIC) study.

All participants were recruited from June 2003 to August 2008, and all were free of heart failure at baseline. The researchers used Cox regression to examine the association of baseline levels of hsTnT and NT-proBNP with incident heart failure after adjustment for demographic influences, traditional cardiovascular risk factors, makers of kidney disease, pertinent medication use, and mineral metabolism markers.

At baseline, hsTnT levels ranged from ≤5.0 to 378.7 pg/mL and NT-proBNP levels ranged from ≤5 to 35,000 pg/mL. Compared with patients who had undetectable hsTnT, those in the highest quartile (>26.5 ng/mL) had a significantly higher rate of heart failure (hazard ratio 4.77; 95% CI 2.49-9.14).

Compared with those in the lowest NT-proBNP quintile (<47.6 ng/mL), patients in the highest quintile (>433.0 ng/mL) experienced an almost 10-fold increase in heart failure risk (HR 9.57; 95% CI 4.40-20.83).

The researchers noted that these associations remained robust after adjustment for potential confounders and for the other biomarker, suggesting that while hsTnT and NT-proBNP are complementary, they may be indicative of distinct biological pathways for heart failure.

Even Modest Increases in NP-proBNP Linked to Heart Failure

The findings are consistent with an earlier analysis that included 8,000 patients with albuminuria in the Prevention of REnal and Vascular ENd-stage Disease (PREVEND) study, which showed that hsTnT was associated with incident cardiovascular events, even after adjustment for eGFR and severity of albuminuria.

“Among participants in the CRIC study, those with the highest quartile of detectable hsTnT had a twofold higher odds of left ventricular hypertrophy compared with those in the lowest quartile,” Bansal and colleagues wrote, adding that the findings were similar after excluding participants with any cardiovascular disease at baseline.

Even modest elevations in NT-proBNP were associated with significantly increased rates of heart failure, including in subgroups stratified by eGFR, proteinuria, and diabetic status.

“NT-proBNP regulates blood pressure and body fluid volume by its natriuretic and diuretic actions, arterial dilation, and inhibition of the renin-aldosterone-angiotensin system and increased levels of this marker likely reflect myocardial stress induced by subclinical changes in volume or pressure, even in persons without clinical disease,” the researchers wrote.

The researchers concluded that further studies are needed to develop and validate risk prediction tools for clinical heart failure in patients with CKD, and to determine the potential role of these two biomarkers in a heart failure risk prediction and prevention strategy.

Fenoldopam ‘Widely Promoted’ in AKI Cardiac Surgery Setting

The JAMA study examined whether the selective dopamine receptor D agonist fenoldopam mesylate can reduce the need for dialysis in cardiac surgery patients who develop acute kidney injury (AKI).

Fenoldopam induces vasodilation of the renal, mesenteric, peripheral, and coronary arteries, and, unlike dopamine, it has no significant affinity for D2 receptors, meaning that it theoretically induces greater vasodilation in the renal medulla than in the cortex, the researchers wrote.

“Because of these hemodynamic effects, fenoldopam has been widely promoted for the prevention and therapy of AKI in the United States and many other countries with apparent favorable results in cardiac surgery and other settings,” Landoni and colleagues wrote.

The drug was approved in 1997 by the FDA for the indication of in-hospital, short-term management of severe hypertension. It has not been approved for renal indications, but is commonly used off-label in cardiac surgery patients who develop AKI.

Although a meta analysis of randomized trials, conducted by the researchers, indicated a reduction in the incidence and progression of AKI associated with the treatment, Landoni and colleagues wrote that the absence of a definitive trial “leaves clinicians uncertain as to whether fenoldopam should be prescribed after cardiac surgery to prevent deterioration in renal function.”

To address this uncertainty, the researchers conducted a prospective, randomized, parallel-group trial in 667 patients treated at 19 hospitals in Italy from March 2008 to April 2013.

All patients had been admitted to ICUs after cardiac surgery with early acute kidney injury (≥50% increase of serum creatinine level from baseline or low output of urine for ≥6 hours). A total of 338 received fenoldopam by continuous intravenous infusion for a total of 96 hours or until ICU discharge, while 329 patients received saline infusions.

The primary end point was the rate of renal replacement therapy, and secondary end points included mortality (intensive care unit and 30-day mortality) and the rate of hypotension during study drug infusion.

Study Showed No Benefit, Was Stopped Early

Yale Lampoon – AA Liebow.   1954

Not As a Doctor
[Fourth Year]

These lyrics, sung by John Cole, Jack Gariepy and Ed Ransenhofer to music borrowed from Gilbert and Sullivan’s The Mikado, lampooned Averill Liebow, M.D., a pathologist noted for his demands on students. (CPC stands for clinical pathology conference.)

If you want to know what this is,
it’s a medical CPC
Where we give the house staff
the biz, for there’s no one so
wise as we!
We pathologists show them how,
Although it is too late now.
Our art is a sacred cow!

American physician, born 1911, Stryj in Galicia, Austria (now in Ukraine); died 1978.

Averill Abraham Liebow, born in Austria, was the “founding father” of pulmonary pathology in the United States. He started his career as a pathologist at Yale, where he remained for many years. In 1968 he moved to the University of California School of Medicine, San Diego, where he taught for 7 years as Professor and Chairman, Department of Pathology.

His studies include many classic studies of lung diseases. Best known of these is his famous classification of interstitial lung disease. He also published papers on sclerosing pneumocytoma, pulmonary alveolar proteinosis, meningothelial-like nodules, pulmonary hypertension, pulmonary veno-occlusive disease, lymphomatoid granulomatosis, pulmonary Langerhans cell histiocytosis, pulmonary epithelioid hemangioendothelioma and pulmonary hyalinizing granuloma .

As a Lieutenant Colonel in the US Army Medical Corps, He was a member of the Atomic Bomb Casualty Commission who studied the effects of the atomic bomb in Hiroshima and Nagasaki.

We thank Sanjay Mukhopadhyay, M.D., for information submitted.

As a resident at UCSD, Dr. Liebow held “Organ Recitals” every morning, including Mother’s day.  The organs had to be presented in specified order… heart, lung, and so forth.  On one occasion, we needed a heart for purification of human lactate dehydrogenase for a medical student project, so I presented the lung out of order.  Dr. Liebow asked where the heart was, and I told the group it was noprmal and I froze it for enzyme purification (smiles).  In the future show it to me first. He was generous to those who showed interest.  As I was also doing research in Nathan Kaplan’s laboratory, he made special arrangements for me to mentor Deborah Peters, the daughter of a pulmonary physician, and granddaughter of the Peters who collaborated with Van Slyke.  I mentored many students with great reward since then.  He could look at a slide and tell you what the x-ray looked like.  I didn’t encounter that again until he sent me to the Armed Forces Institute of Pathology, Washington, DC during the Vietnam War and Watergate, and I worked in Orthopedic Pathology with Lent C. Johnson.  He would not review a case without the x-ray, and he taught the radiologists.

Part 3

My Cancer Genome from Vanderbilt University: Matching Tumor Mutations to Therapies & Clinical Trials

Reporter: Aviva Lev-Ari, PhD, RN

My Cancer Genome from Vanderbilt University: Matching Tumor Mutations to Therapies & Clinical Trials


GenomOncology and Vanderbilt-Ingram Cancer Center (VICC) today announced a partnership for the exclusive commercial development of a decision support tool based on My Cancer Genome™, an online precision cancer medicine knowledge resource for physicians, patients, caregivers and researchers.

Through this collaboration, GenomOncology and VICC will enhance My Cancer Genome through the development of a new genomics content management tool. The MyCancerGenome.org website will remain free and open to the public. In addition, GenomOncology will develop a decision support tool based on My Cancer Genome™ data that will enable automated interpretation of mutations in the genome of a patient’s tumor, providing actionable results in hours versus days.

Vanderbilt-Ingram Cancer Center (VICC) launched My Cancer Genome™ in January 2011 as an integral part of their Personalized Cancer Medicine Initiative that helps physicians and researchers track the latest developments in precision cancer medicine and connect with clinical research trials. This web-based information tool is designed to quickly educate clinicians on the rapidly expanding list of genetic mutations that impact cancers and enable the research of treatment options based on specific mutations. For more information on My Cancer Genome™visit www.mycancergenome.org/about/what-is-my-cancer-genome.

Therapies based on the specific genetic alterations that underlie a patient’s cancer not only result in better outcomes but often have less adverse reactions

Up front fee

Nominal fee covers installation support, configuring the Workbench to your specification, designing and developing custom report(s) and training your team.

Per sample fee

GenomOncology is paid on signed-out clinical reports. This philosophy aligns GenomOncology with your Laboratory as we are incentivized to offer world-class support and solutions to differentiate your clinical NGS program. There is no annual license fee.

Part 4

Clinical Trial Services: Foundation Medicine & EmergingMed to Partner

Reporter: Aviva Lev-Ari, PhD, RN

Clinical Trial Services: Foundation Medicine & EmergingMed to Partner


Foundation Medicine and EmergingMed said today that they will partner to offer clinical trial navigation services for health care providers and their patients who have received one of Foundation Medicine’s tumor genomic profiling tests.

The firms will provide concierge services to help physicians

  • identify appropriate clinical trials for patients
  • based on the results of FoundationOne or FoundationOne Heme.

“By providing clinical trial navigation services, we aim to facilitate

  • timely and accurate clinical trial information and enrollment support services for physicians and patients,
  • enabling greater access to treatment options based on the unique genomic profile of a patient’s cancer

Currently, there are over 800 candidate therapies that target genomic alterations in clinical trials,

  • but “patients and physicians must identify and act on relevant options
  • when the patient’s clinical profile is aligned with the often short enrollment window for each trial.

These investigational therapies are an opportunity to engage patients with cancer whose cancer has progressed or returned following standard treatment in a most favorable second option after relapse.  The new service is unique in notifying when new clinical trials emerge that match a patient’s genomic and clinical profile.

Google signs on to Foundation Medicine cancer Dx by offering tests to employees

By Emily Wasserman

Diagnostics luminary Foundation Medicine ($FMI) is generating some upward momentum, fueled by growing revenues and the success of its clinical tests. Tech giant Google ($GOOG) has taken note and is signing onto the company’s cancer diagnostics by offering them to employees.

Foundation Medicine CEO Michael Pellini said during the company’s Q3 earnings call that Google will start covering its DNA tests for employees and their family members suffering from cancer as part of its health benefits portfolio, Reuters reports.

Both sides stand to benefit from the deal, as Google looks to keep a leg up on Silicon Valley competitors and Foundation Medicine expands its cancer diagnostics platform. Last month, Apple ($AAPL) and Facebook ($FB) announced that they would begin covering the cost of egg freezing for female employees. A diagnostics partnership and attractive health benefits could work wonders for Google’s employee retention rates and bottom line.

In the meantime, Cambridge, MA-based Foundation Medicine is charging full speed ahead with its cancer diagnostics platform after filing for an IPO in September 2013. The company chalked up 6,428 clinical tests during Q3 2014, an eye-popping 149% increase year over year, and brought in total revenue for the quarter of $16.4 million–a 100% leap from last year. Foundation Medicine credits the promising numbers in part to new diagnostic partnerships and extended coverage for its tests.

In January, the company teamed up with Novartis ($NVS) to help the drugmaker evaluate potential candidates for its cancer therapies. In April, Foundation Medicine announced that it would develop a companion diagnostic test for a Clovis Oncology ($CLVS) drug under development to treat patients with ovarian cancer, building on an ongoing collaboration between the two companies.

Foundation Medicine also has its sights set on China’s growing diagnostics market, inking a deal in October with WuXi PharmaTech ($WX) that allows the company to perform lab testing for its FoundationOne assay at WuXi’s Shanghai-based Genome Center.

a nod to the deal with Google during a corporate earnings call on Wednesday, according to a person who listened in. Pellini said Google employees were made aware of this new benefit last week.

Foundation Medicine teams with MD Anderson for new trial of cancer Dx

Second study to see if targeted therapy can change patient outcomes

August 15, 2014 | By   FierceDiagnostics

Foundation Medicine ($FMI) is teaming up with the MD Anderson Cancer Center in Texas for a new trial of the the Cambridge, MA-based company’s molecular diagnostic cancer test that targets therapies matched to individual patients.

The study is called IMPACT2 (Initiative for Molecular Profiling and Advanced Cancer Therapy) and is designed to build on results from the the first IMPACT study that found

  • 40% of the 1,144 patients enrolled had an identifiable genomic alteration.

The company said that

  • by matching specific gene alterations to therapies,
  • 27% of patients in the first study responded versus
  • 5% with an unmatched treatment, and
  • “progression-free survival” was longer in the matched group.

The FoundationOne molecular diagnostic test

  • combines genetic sequencing and data gathering
  • to help oncologists choose the best treatment for individual patients.

Costing $5,800 per test, FoundationOne’s technology can uncover a large number of genetic alterations for 200 cancer-related genes,

  • blending genomic sequencing, information and clinical practice.

“Based on the IMPACT1 data, a validated, comprehensive profiling approach has already been adopted by many academic and community-based oncology practices,” Vincent Miller, chief medical officer of Foundation Medicine, said in a release. “This study has the potential to yield sufficient evidence necessary to support broader adoption across most newly diagnosed metastatic tumors.”

The company got a boost last month when the New York State Department of Health approved Foundation Medicine’s two initial cancer tests: the FoundationOne test and FoundationOne Heme, which creates a genetic profile for blood cancers. Typically,

  • diagnostics companies struggle to win insurance approval for their tests
  • even after they gain a regulatory approval, leaving revenue growth relatively flat.

However, Foundation Medicine reported earlier this week its Q2 revenue reached $14.5 million compared to $5.9 million for the same period a year ago. Still,

  1. net losses continue to soar as the company ramps up
  2. its commercial and business development operation,
  • hitting $13.7 million versus a $10.1 million deficit in the second quarter of 2013.

Oncology

There has been a remarkable transformation in our understanding of

  • the molecular genetic basis of cancer and its treatment during the past decade or so.

In depth genetic and genomic analysis of cancers has revealed that

  • each cancer type can be sub-classified into many groups based on the genetic profiles and
  • this information can be used to develop new targeted therapies and treatment options for cancer patients.

This panel will explore the technologies that are facilitating our understanding of cancer, and

  • how this information is being used in novel approaches for clinical development and treatment.
Oncology _ Reprted by Dr. Aviva Lev-Ari, Founder, Leaders in Pharmaceutical Intelligence

Opening Speaker & Moderator:

Lynda Chin, M.D.
Department Chair, Department of Genomic Medicine
MD Anderson Cancer Center

  • Who pays for PM?
  • potential of Big data, analytics, Expert systems, so not each MD needs to see all cases, Profile disease to get same treatment
  • business model: IP, Discovery, sharing, ownership — yet accelerate therapy
  • security of healthcare data
  • segmentation of patient population
  • management of data and tracking innovations
  • platforms to be shared for innovations
  • study to be longitudinal,
  • How do we reconcile course of disease with PM
  • phinotyping the disease vs a Patient in wait for cure/treatment

Panelists:

Roy Herbst, M.D., Ph.D.
Ensign Professor of Medicine and Professor of Pharmacology;
Chief of Medical Oncology, Yale Cancer Center and Smilow Cancer Hospital

Development new drugs to match patient, disease and drug – finding the right patient for the right Clinical Trial

  • match patient to drugs
  • partnerships: out of 100 screened patients, 10 had the gene, 5 were able to attend the trial — without the biomarker — all 100 patients would participate for the WRONG drug for them (except the 5)
  • patients wants to participate in trials next to home NOT to have to travel — now it is in the protocol
  • Annotated Databases – clinical Trial informed consent – adaptive design of Clinical Trial vs protocol
  • even Academic MD can’t read the reports on Genomics
  • patients are treated in the community — more training to MDs
  • Five companies collaborating – comparison og 6 drugs in the same class
  • if drug exist and you have the patient — you must apply PM

Summary and Perspective:

The current changes in Biotechnology have been reviewed with an open question about the relationship of In Vitro Diagnostics to Biopharmaceuticals switching, with the potential, particularly in cancer and infectious diseases, to added value in targeted therapy by matching patients to the best potential treatment for a favorable outcome.

This reviewer does not see the movement of the major diagnostics leaders entering into the domain of direct patient care, even though there are signals in that direction.  The Roche example is perhaps the most interesting because Roche already became the elephant in the room after the introduction of Valium,  subsequently bought out Boehringer Mannheim Diagnostics to gain entry into the IVD market, and established a huge presence in Molecular Diagnostics early.  If it did anything to gain a foothold in the treatment realm, it would more likely forge a relationship with Foundation Medicine.  Abbott Laboratories more than a decade ago was overextended, and it had become the leader in IVD as a result of the specialty tests, but it fell into difficulties with quality control of its products in the high volume testing market, and acceeded to Olympus, Roche, and in the mid volume market to Beckman and Siemens.  Of course, Dupont and Kodak, pioneering companies in IVD, both left the market.

The biggest challenge in the long run is identified by the ability to eliminate many treatments that would be failures for a large number of patients. That has already met the proof of concept.  However, when you look at the size of the subgroups, we are not anywhere near a large scale endeavor.  In addition, there is a lot that has to be worked out that is not related to genomic expression by the “classic” model, but has to take into account the emrging knowledge and greater understanding of regulation of cell metabolism, not only in cancer, but also in chronic inflammatory diseases.

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AGENDA for microRNA Conference by CHI, March 16-17, 2015, Cambridge, MA

Reporter: Aviva Lev-Ari, PhD, RN

 

 

BIDMC’s Distinguished RNA Scientist, Dr. Frank Slack, to Deliver Presentation at Cambridge Healthtech Institute’s eleventh annual microRNA Conference. 

Dr. Slack, Director of Beth Israel Deaconess Medical Center’s new Institute for RNA Medicine, has led his team of molecular biologists in pioneering various aspects of the microRNA field and continues to make important contributions to this aspect of post-transcriptional control of gene regulation in stem cell development, cancer and aging.

Recent research has shown microRNAs to have tremendous potential as non-invasive biomarkers for the diagnosis and prognosis of disease, monitoring of treatment, and patient stratification. Cambridge Healthtech Institute’s Eleventh Annual microRNA as Biomarkers and Diagnostics will cover the latest developments in the use of microRNA in the early detection of disease for more effective treatment, monitoring tumor growth and disease progression, issues associated with microRNA measurement, and the potential for personalized medicine based on microRNA profile.

————————————————————————

Exosomal RNA/DNA in Cancer

  • The Biology and Functional Contribution of Exosomes in Cancer Progression and Metastasis

Raghu Kalluri, M.D., Ph.D., Professor and Chair, Cancer Biology, University of Texas MD Anderson Cancer Center

  • Exosomic microRNAs Affect the Biology of the Tumor Microenvironment

Muller Fabbri, M.D., Ph.D., Assistant Professor, Pediatrics and Molecular Microbiology & Immunology, University of Southern California Keck School of Medicine, Children’s Hospital Los Angeles

microRNA Biomarkers in Drug Development 

  • Identification of Urinary microRNA Biomarkers of Glomerular Injury

Rounak Nassirpour, Ph.D., Principal Scientist, Investigative Pathology Laboratory, Drug Safety R&D, Pfizer

  • MiRNAs as Circulating Biomarkers for Neurodegenerative Disorders

Pavan Kumar, Ph.D., Senior Scientist, Biomarkers and Personalized Medicine, Eisai

  • MicroRNAs as Biomarkers in Clinical Fluids

Martin Beaulieu, Ph.D., Director, MicroMarkers™, Regulus Therapeutics

  • MiRNAs as Biomarkers for Cancer Development and Drug Resistance

Bing-Hua Jiang, Ph.D., Professor, Pathology, Anatomy and Cell Biology, Thomas Jefferson University

  • Universal Screening Test Based on Analysis of Circulating Organ-Enriched microRNAs

Kira Sheinerman, Ph.D., CEO, DiamiR Biosciences Corp.

microRNAs in Personalized Medicine 

  • MiRNAs in Personalized Medicine: Hurdles and Possibilities

Omar Laterza, Ph.D., Director, Clinical Development Laboratory, Merck

  • Circulating miRNAs in Autoimmune Disorders – Opportunities as Biomarkers for Patient Stratification and Challenges

Hungyun Lin, Ph.D., Principal Scientist, Drug Safety R&D, Pfizer

  • MiRNA Biomarkers for Colorectal Cancers

Ajay Goel, Ph.D., Director, Epigenetics and Cancer Prevention, Baylor Research Institute

microRNA in Disease Diagnostics 

  • Circulating miRNA Markers for Disease Diagnosis

Xuemei Zhao, Ph.D., Principal Scientist, Molecular Biomarkers and Diagnostics Laboratory, Merck

  • MicroRNA Biofluid Profiles as Diagnostic Biomarkers in Epilepsy

David Henshall, Ph.D., Professor, Physiology & Medical Physics, Royal College of Surgeons in Ireland, Dublin, Ireland

  • Circulating microRNAs Show Promising Clinical Implications for Type 2 Diabetes

Elena Flowers, Ph.D., Assistant Professor, Physiological Nursing, University of California, San Francisco

Roundtable Discussions

  • Topic 1: microRNA Normalization Strategy

Moderator: Christos Argyropoulos, M.D., Ph.D., University of New Mexico School of Medicine

  • Topic 2: Quantitation Issues

Moderator: John Chevillet, Ph.D., Institute for Systems Biology

  • Topic 3: Biomarker Biology: Delineating between Biomarkers that are the Drivers vs. Passengers in Disease Initiation, Progression and Maintenance

Moderator: Andrea Kasinski, Ph.D., Purdue University

microRNA as Predictive Cancer Biomarkers

Talk Title to be Announced

Frank J. Slack, Ph.D., Director, Institute for RNA Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School

  • Tumor-Associated Circulating microRNAs as Biomarkers of Pancreatic Cancer

Subrata Sen, Ph.D., Professor and Deputy Chair, Translational Molecular Pathology, University of Texas MD Anderson Cancer Center

  • MiRNAs as Predictors of Recurrence and Death in Melanoma

Iman Osman, M.D., Associate Director, NYU Cancer Institute & Professor, Oncology, and Director, NYU Interdisciplinary, Melanoma Coop Group, New York University Langone Medical Center

  • MicroRNAs that Control the PI3K Survival Pathway

Andrei Thomas-Tikhonenko, Ph.D., Professor, Pathology and Laboratory Medicine, University of Pennsylvania

microRNAs in Cancer Pathways 

  • Epigenetic Regulation of miRNAs in a Human T Cell Leukemia

Sundararajan Jayaraman, Ph.D., Clinical Associate Professor, Surgery, University of Illinois at Chicago

  • A Combinatorial microRNA Therapeutics Approach to Eradicating Non-Small Cell Lung Cancer

Andrea Kasinski, Ph.D., Assistant Professor, Biological Sciences, Purdue University

  • MicroRNA as Therapy – MRX34, a First-in-Human, First-in-Class microRNA Therapy in Advanced Cancer and Heme Malignancies

David S. Hong, M.D., Associate Professor and Chair, Investigational Cancer Therapeutics, University of Texas MD Anderson Cancer Center

Sponsored presentation opportunities are available.  For details, contact Carolyn Benton

 

CONFERENCE TRACKS

 

MARCH 16-17

Systems Biology, Evidence Synthesis and in silico Discovery Approaches to microRNA Biomarkers 

Executive ThinkTank: Development of Exosome-Based Diagnostics 

 

MARCH 17-18

RNA-Seq: A Fundamental Guide to the Field 

Circulating Nucleic Acid Biomarkers for Development of Non-Invasive Prenatal Tests

Separate registration is required.

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From: microRNA as Biomarkers & Diagnostics <jaimeh@healthtech.com>

Date: Mon, 10 Nov 2014 10:30:00 -0500
To: <avivalev-ari@alum.berkeley.edu>
Subject: BIDMC’s Dr. Frank Slack to Deliver microRNA Research Updates

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Summary and Perspectives: Impairments in Pathological States: Endocrine Disorders, Stress Hypermetabolism and Cancer

Summary and Perspectives: Impairments in Pathological States: Endocrine Disorders, Stress Hypermetabolism and Cancer

Author and Curator: Larry H. Bernstein, MD, FCAP

Article ID #160: Summary and Perspectives: Impairments in Pathological States: Endocrine Disorders, Stress Hypermetabolism and Cancer. Published on 11/9/2014

WordCloud Image Produced by Adam Tubman

This summary is the last of a series on the impact of transcriptomics, proteomics, and metabolomics on disease investigation, and the sorting and integration of genomic signatures and metabolic signatures to explain phenotypic relationships in variability and individuality of response to disease expression and how this leads to  pharmaceutical discovery and personalized medicine.  We have unquestionably better tools at our disposal than has ever existed in the history of mankind, and an enormous knowledge-base that has to be accessed.  I shall conclude here these discussions with the powerful contribution to and current knowledge pertaining to biochemistry, metabolism, protein-interactions, signaling, and the application of the -OMICS to diseases and drug discovery at this time.

The Ever-Transcendent Cell

Deriving physiologic first principles By John S. Torday | The Scientist Nov 1, 2014
http://www.the-scientist.com/?articles.view/articleNo/41282/title/The-Ever-Transcendent-Cell/

Both the developmental and phylogenetic histories of an organism describe the evolution of physiology—the complex of metabolic pathways that govern the function of an organism as a whole. The necessity of establishing and maintaining homeostatic mechanisms began at the cellular level, with the very first cells, and homeostasis provides the underlying selection pressure fueling evolution.

While the events leading to the formation of the first functioning cell are debatable, a critical one was certainly the formation of simple lipid-enclosed vesicles, which provided a protected space for the evolution of metabolic pathways. Protocells evolved from a common ancestor that experienced environmental stresses early in the history of cellular development, such as acidic ocean conditions and low atmospheric oxygen levels, which shaped the evolution of metabolism.

The reduction of evolution to cell biology may answer the perennially unresolved question of why organisms return to their unicellular origins during the life cycle.

As primitive protocells evolved to form prokaryotes and, much later, eukaryotes, changes to the cell membrane occurred that were critical to the maintenance of chemiosmosis, the generation of bioenergy through the partitioning of ions. The incorporation of cholesterol into the plasma membrane surrounding primitive eukaryotic cells marked the beginning of their differentiation from prokaryotes. Cholesterol imparted more fluidity to eukaryotic cell membranes, enhancing functionality by increasing motility and endocytosis. Membrane deformability also allowed for increased gas exchange.

Acidification of the oceans by atmospheric carbon dioxide generated high intracellular calcium ion concentrations in primitive aquatic eukaryotes, which had to be lowered to prevent toxic effects, namely the aggregation of nucleotides, proteins, and lipids. The early cells achieved this by the evolution of calcium channels composed of cholesterol embedded within the cell’s plasma membrane, and of internal membranes, such as that of the endoplasmic reticulum, peroxisomes, and other cytoplasmic organelles, which hosted intracellular chemiosmosis and helped regulate calcium.

As eukaryotes thrived, they experienced increasingly competitive pressure for metabolic efficiency. Engulfed bacteria, assimilated as mitochondria, provided more bioenergy. As the evolution of eukaryotic organisms progressed, metabolic cooperation evolved, perhaps to enable competition with biofilm-forming, quorum-sensing prokaryotes. The subsequent appearance of multicellular eukaryotes expressing cellular growth factors and their respective receptors facilitated cell-cell signaling, forming the basis for an explosion of multicellular eukaryote evolution, culminating in the metazoans.

Casting a cellular perspective on evolution highlights the integration of genotype and phenotype. Starting from the protocell membrane, the functional homolog for all complex metazoan organs, it offers a way of experimentally determining the role of genes that fostered evolution based on the ontogeny and phylogeny of cellular processes that can be traced back, in some cases, to our last universal common ancestor.  ….

As eukaryotes thrived, they experienced increasingly competitive pressure for metabolic efficiency. Engulfed bacteria, assimilated as mitochondria, provided more bioenergy. As the evolution of eukaryotic organisms progressed, metabolic cooperation evolved, perhaps to enable competition with biofilm-forming, quorum-sensing prokaryotes. The subsequent appearance of multicellular eukaryotes expressing cellular growth factors and their respective receptors facilitated cell-cell signaling, forming the basis for an explosion of multicellular eukaryote evolution, culminating in the metazoans.

Casting a cellular perspective on evolution highlights the integration of genotype and phenotype. Starting from the protocell membrane, the functional homolog for all complex metazoan organs, it offers a way of experimentally determining the role of genes that fostered evolution based on the ontogeny and phylogeny of cellular processes that can be traced back, in some cases, to our last universal common ancestor.

Given that the unicellular toolkit is complete with all the traits necessary for forming multicellular organisms (Science, 301:361-63, 2003), it is distinctly possible that metazoans are merely permutations of the unicellular body plan. That scenario would clarify a lot of puzzling biology: molecular commonalities between the skin, lung, gut, and brain that affect physiology and pathophysiology exist because the cell membranes of unicellular organisms perform the equivalents of these tissue functions, and the existence of pleiotropy—one gene affecting many phenotypes—may be a consequence of the common unicellular source for all complex biologic traits.  …

The cell-molecular homeostatic model for evolution and stability addresses how the external environment generates homeostasis developmentally at the cellular level. It also determines homeostatic set points in adaptation to the environment through specific effectors, such as growth factors and their receptors, second messengers, inflammatory mediators, crossover mutations, and gene duplications. This is a highly mechanistic, heritable, plastic process that lends itself to understanding evolution at the cellular, tissue, organ, system, and population levels, mediated by physiologically linked mechanisms throughout, without having to invoke random, chance mechanisms to bridge different scales of evolutionary change. In other words, it is an integrated mechanism that can often be traced all the way back to its unicellular origins.

The switch from swim bladder to lung as vertebrates moved from water to land is proof of principle that stress-induced evolution in metazoans can be understood from changes at the cellular level.

http://www.the-scientist.com/Nov2014/TE_21.jpg

A MECHANISTIC BASIS FOR LUNG DEVELOPMENT: Stress from periodic atmospheric hypoxia (1) during vertebrate adaptation to land enhances positive selection of the stretch-regulated parathyroid hormone-related protein (PTHrP) in the pituitary and adrenal glands. In the pituitary (2), PTHrP signaling upregulates the release of adrenocorticotropic hormone (ACTH) (3), which stimulates the release of glucocorticoids (GC) by the adrenal gland (4). In the adrenal gland, PTHrP signaling also stimulates glucocorticoid production of adrenaline (5), which in turn affects the secretion of lung surfactant, the distension of alveoli, and the perfusion of alveolar capillaries (6). PTHrP signaling integrates the inflation and deflation of the alveoli with surfactant production and capillary perfusion.  THE SCIENTIST STAFF

From a cell-cell signaling perspective, two critical duplications in genes coding for cell-surface receptors occurred during this period of water-to-land transition—in the stretch-regulated parathyroid hormone-related protein (PTHrP) receptor gene and the β adrenergic (βA) receptor gene. These gene duplications can be disassembled by following their effects on vertebrate physiology backwards over phylogeny. PTHrP signaling is necessary for traits specifically relevant to land adaptation: calcification of bone, skin barrier formation, and the inflation and distention of lung alveoli. Microvascular shear stress in PTHrP-expressing organs such as bone, skin, kidney, and lung would have favored duplication of the PTHrP receptor, since sheer stress generates radical oxygen species (ROS) known to have this effect and PTHrP is a potent vasodilator, acting as an epistatic balancing selection for this constraint.

Positive selection for PTHrP signaling also evolved in the pituitary and adrenal cortex (see figure on this page), stimulating the secretion of ACTH and corticoids, respectively, in response to the stress of land adaptation. This cascade amplified adrenaline production by the adrenal medulla, since corticoids passing through it enzymatically stimulate adrenaline synthesis. Positive selection for this functional trait may have resulted from hypoxic stress that arose during global episodes of atmospheric hypoxia over geologic time. Since hypoxia is the most potent physiologic stressor, such transient oxygen deficiencies would have been acutely alleviated by increasing adrenaline levels, which would have stimulated alveolar surfactant production, increasing gas exchange by facilitating the distension of the alveoli. Over time, increased alveolar distension would have generated more alveoli by stimulating PTHrP secretion, impelling evolution of the alveolar bed of the lung.

This scenario similarly explains βA receptor gene duplication, since increased density of the βA receptor within the alveolar walls was necessary for relieving another constraint during the evolution of the lung in adaptation to land: the bottleneck created by the existence of a common mechanism for blood pressure control in both the lung alveoli and the systemic blood pressure. The pulmonary vasculature was constrained by its ability to withstand the swings in pressure caused by the systemic perfusion necessary to sustain all the other vital organs. PTHrP is a potent vasodilator, subserving the blood pressure constraint, but eventually the βA receptors evolved to coordinate blood pressure in both the lung and the periphery.

Gut Microbiome Heritability

Analyzing data from a large twin study, researchers have homed in on how host genetics can shape the gut microbiome.
By Tracy Vence | The Scientist Nov 6, 2014

Previous research suggested host genetic variation can influence microbial phenotype, but an analysis of data from a large twin study published in Cell today (November 6) solidifies the connection between human genotype and the composition of the gut microbiome. Studying more than 1,000 fecal samples from 416 monozygotic and dizygotic twin pairs, Cornell University’s Ruth Ley and her colleagues have homed in on one bacterial taxon, the family Christensenellaceae, as the most highly heritable group of microbes in the human gut. The researchers also found that Christensenellaceae—which was first described just two years ago—is central to a network of co-occurring heritable microbes that is associated with lean body mass index (BMI).  …

Of particular interest was the family Christensenellaceae, which was the most heritable taxon among those identified in the team’s analysis of fecal samples obtained from the TwinsUK study population.

While microbiologists had previously detected 16S rRNA sequences belonging to Christensenellaceae in the human microbiome, the family wasn’t named until 2012. “People hadn’t looked into it, partly because it didn’t have a name . . . it sort of flew under the radar,” said Ley.

Ley and her colleagues discovered that Christensenellaceae appears to be the hub in a network of co-occurring heritable taxa, which—among TwinsUK participants—was associated with low BMI. The researchers also found that Christensenellaceae had been found at greater abundance in low-BMI twins in older studies.

To interrogate the effects of Christensenellaceae on host metabolic phenotype, the Ley’s team introduced lean and obese human fecal samples into germ-free mice. They found animals that received lean fecal samples containing more Christensenellaceae showed reduced weight gain compared with their counterparts. And treatment of mice that had obesity-associated microbiomes with one member of the Christensenellaceae family, Christensenella minuta, led to reduced weight gain.   …

Ley and her colleagues are now focusing on the host alleles underlying the heritability of the gut microbiome. “We’re running a genome-wide association analysis to try to find genes—particular variants of genes—that might associate with higher levels of these highly heritable microbiota.  . . . Hopefully that will point us to possible reasons they’re heritable,” she said. “The genes will guide us toward understanding how these relationships are maintained between host genotype and microbiome composition.”

J.K. Goodrich et al., “Human genetics shape the gut microbiome,” Cell,  http://dx.doi.org:/10.1016/j.cell.2014.09.053, 2014.

Light-Operated Drugs

Scientists create a photosensitive pharmaceutical to target a glutamate receptor.
By Ruth Williams | The Scentist Nov 1, 2014
http://www.the-scientist.com/?articles.view/articleNo/41279/title/Light-Operated-Drugs/

light operated drugs MO1

light operated drugs MO1

http://www.the-scientist.com/Nov2014/MO1.jpg

The desire for temporal and spatial control of medications to minimize side effects and maximize benefits has inspired the development of light-controllable drugs, or optopharmacology. Early versions of such drugs have manipulated ion channels or protein-protein interactions, “but never, to my knowledge, G protein–coupled receptors [GPCRs], which are one of the most important pharmacological targets,” says Pau Gorostiza of the Institute for Bioengineering of Catalonia, in Barcelona.

Gorostiza has taken the first step toward filling that gap, creating a photosensitive inhibitor of the metabotropic glutamate 5 (mGlu5) receptor—a GPCR expressed in neurons and implicated in a number of neurological and psychiatric disorders. The new mGlu5 inhibitor—called alloswitch-1—is based on a known mGlu receptor inhibitor, but the simple addition of a light-responsive appendage, as had been done for other photosensitive drugs, wasn’t an option. The binding site on mGlu5 is “extremely tight,” explains Gorostiza, and would not accommodate a differently shaped molecule. Instead, alloswitch-1 has an intrinsic light-responsive element.

In a human cell line, the drug was active under dim light conditions, switched off by exposure to violet light, and switched back on by green light. When Gorostiza’s team administered alloswitch-1 to tadpoles, switching between violet and green light made the animals stop and start swimming, respectively.

The fact that alloswitch-1 is constitutively active and switched off by light is not ideal, says Gorostiza. “If you are thinking of therapy, then in principle you would prefer the opposite,” an “on” switch. Indeed, tweaks are required before alloswitch-1 could be a useful drug or research tool, says Stefan Herlitze, who studies ion channels at Ruhr-Universität Bochum in Germany. But, he adds, “as a proof of principle it is great.” (Nat Chem Biol, http://dx.doi.org:/10.1038/nchembio.1612, 2014)

Enhanced Enhancers

The recent discovery of super-enhancers may offer new drug targets for a range of diseases.
By Eric Olson | The Scientist Nov 1, 2014
http://www.the-scientist.com/?articles.view/articleNo/41281/title/Enhanced-Enhancers/

To understand disease processes, scientists often focus on unraveling how gene expression in disease-associated cells is altered. Increases or decreases in transcription—as dictated by a regulatory stretch of DNA called an enhancer, which serves as a binding site for transcription factors and associated proteins—can produce an aberrant composition of proteins, metabolites, and signaling molecules that drives pathologic states. Identifying the root causes of these changes may lead to new therapeutic approaches for many different diseases.

Although few therapies for human diseases aim to alter gene expression, the outstanding examples—including antiestrogens for hormone-positive breast cancer, antiandrogens for prostate cancer, and PPAR-γ agonists for type 2 diabetes—demonstrate the benefits that can be achieved through targeting gene-control mechanisms.  Now, thanks to recent papers from laboratories at MIT, Harvard, and the National Institutes of Health, researchers have a new, much bigger transcriptional target: large DNA regions known as super-enhancers or stretch-enhancers. Already, work on super-enhancers is providing insights into how gene-expression programs are established and maintained, and how they may go awry in disease.  Such research promises to open new avenues for discovering medicines for diseases where novel approaches are sorely needed.

Super-enhancers cover stretches of DNA that are 10- to 100-fold longer and about 10-fold less abundant in the genome than typical enhancer regions (Cell, 153:307-19, 2013). They also appear to bind a large percentage of the transcriptional machinery compared to typical enhancers, allowing them to better establish and enforce cell-type specific transcriptional programs (Cell, 153:320-34, 2013).

Super-enhancers are closely associated with genes that dictate cell identity, including those for cell-type–specific master regulatory transcription factors. This observation led to the intriguing hypothesis that cells with a pathologic identity, such as cancer cells, have an altered gene expression program driven by the loss, gain, or altered function of super-enhancers.

Sure enough, by mapping the genome-wide location of super-enhancers in several cancer cell lines and from patients’ tumor cells, we and others have demonstrated that genes located near super-enhancers are involved in processes that underlie tumorigenesis, such as cell proliferation, signaling, and apoptosis.

Super-enhancers cover stretches of DNA that are 10- to 100-fold longer and about 10-fold less abundant in the genome than typical enhancer regions.

Genome-wide association studies (GWAS) have found that disease- and trait-associated genetic variants often occur in greater numbers in super-enhancers (compared to typical enhancers) in cell types involved in the disease or trait of interest (Cell, 155:934-47, 2013). For example, an enrichment of fasting glucose–associated single nucleotide polymorphisms (SNPs) was found in the stretch-enhancers of pancreatic islet cells (PNAS, 110:17921-26, 2013). Given that some 90 percent of reported disease-associated SNPs are located in noncoding regions, super-enhancer maps may be extremely valuable in assigning functional significance to GWAS variants and identifying target pathways.

Because only 1 to 2 percent of active genes are physically linked to a super-enhancer, mapping the locations of super-enhancers can be used to pinpoint the small number of genes that may drive the biology of that cell. Differential super-enhancer maps that compare normal cells to diseased cells can be used to unravel the gene-control circuitry and identify new molecular targets, in much the same way that somatic mutations in tumor cells can point to oncogenic drivers in cancer. This approach is especially attractive in diseases for which an incomplete understanding of the pathogenic mechanisms has been a barrier to discovering effective new therapies.

Another therapeutic approach could be to disrupt the formation or function of super-enhancers by interfering with their associated protein components. This strategy could make it possible to downregulate multiple disease-associated genes through a single molecular intervention. A group of Boston-area researchers recently published support for this concept when they described inhibited expression of cancer-specific genes, leading to a decrease in cancer cell growth, by using a small molecule inhibitor to knock down a super-enhancer component called BRD4 (Cancer Cell, 24:777-90, 2013).  More recently, another group showed that expression of the RUNX1 transcription factor, involved in a form of T-cell leukemia, can be diminished by treating cells with an inhibitor of a transcriptional kinase that is present at the RUNX1 super-enhancer (Nature, 511:616-20, 2014).

Fungal effector Ecp6 outcompetes host immune receptor for chitin binding through intrachain LysM dimerization 
Andrea Sánchez-Vallet, et al.   eLife 2013;2:e00790 http://elifesciences.org/content/2/e00790#sthash.LnqVMJ9p.dpuf

LysM effector

LysM effector

http://img.scoop.it/ZniCRKQSvJOG18fHbb4p0Tl72eJkfbmt4t8yenImKBVvK0kTmF0xjctABnaLJIm9

While host immune receptors

  • detect pathogen-associated molecular patterns to activate immunity,
  • pathogens attempt to deregulate host immunity through secreted effectors.

Fungi employ LysM effectors to prevent

  • recognition of cell wall-derived chitin by host immune receptors

Structural analysis of the LysM effector Ecp6 of

  • the fungal tomato pathogen Cladosporium fulvum reveals
  • a novel mechanism for chitin binding,
  • mediated by intrachain LysM dimerization,

leading to a chitin-binding groove that is deeply buried in the effector protein.

This composite binding site involves

  • two of the three LysMs of Ecp6 and
  • mediates chitin binding with ultra-high (pM) affinity.

The remaining singular LysM domain of Ecp6 binds chitin with

  • low micromolar affinity but can nevertheless still perturb chitin-triggered immunity.

Conceivably, the perturbation by this LysM domain is not established through chitin sequestration but possibly through interference with the host immune receptor complex.

Mutated Genes in Schizophrenia Map to Brain Networks
From www.nih.gov –  Sep 3, 2013

Previous studies have shown that many people with schizophrenia have de novo, or new, genetic mutations. These misspellings in a gene’s DNA sequence

  • occur spontaneously and so aren’t shared by their close relatives.

Dr. Mary-Claire King of the University of Washington in Seattle and colleagues set out to

  • identify spontaneous genetic mutations in people with schizophrenia and
  • to assess where and when in the brain these misspelled genes are turned on, or expressed.

The study was funded in part by NIH’s National Institute of Mental Health (NIMH). The results were published in the August 1, 2013, issue of Cell.

The researchers sequenced the exomes (protein-coding DNA regions) of 399 people—105 with schizophrenia plus their unaffected parents and siblings. Gene variations
that were found in a person with schizophrenia but not in either parent were considered spontaneous.

The likelihood of having a spontaneous mutation was associated with

  • the age of the father in both affected and unaffected siblings.

Significantly more mutations were found in people

  • whose fathers were 33-45 years at the time of conception compared to 19-28 years.

Among people with schizophrenia, the scientists identified

  • 54 genes with spontaneous mutations
  • predicted to cause damage to the function of the protein they encode.

The researchers used newly available database resources that show

  • where in the brain and when during development genes are expressed.

The genes form an interconnected expression network with many more connections than

  • that of the genes with spontaneous damaging mutations in unaffected siblings.

The spontaneously mutated genes in people with schizophrenia

  • were expressed in the prefrontal cortex, a region in the front of the brain.

The genes are known to be involved in important pathways in brain development. Fifty of these genes were active

  • mainly during the period of fetal development.

“Processes critical for the brain’s development can be revealed by the mutations that disrupt them,” King says. “Mutations can lead to loss of integrity of a whole pathway,
not just of a single gene.”

These findings support the concept that schizophrenia may result, in part, from

  • disruptions in development in the prefrontal cortex during fetal development.

James E. Darnell’s “Reflections”

A brief history of the discovery of RNA and its role in transcription — peppered with career advice
By Joseph P. Tiano

James Darnell begins his Journal of Biological Chemistry “Reflections” article by saying, “graduate students these days

  • have to swim in a sea virtually turgid with the daily avalanche of new information and
  • may be momentarily too overwhelmed to listen to the aging.

I firmly believe how we learned what we know can provide useful guidance for how and what a newcomer will learn.” Considering his remarkable discoveries in

  • RNA processing and eukaryotic transcriptional regulation

spanning 60 years of research, Darnell’s advice should be cherished. In his second year at medical school at Washington University School of Medicine in St. Louis, while
studying streptococcal disease in Robert J. Glaser’s laboratory, Darnell realized he “loved doing the experiments” and had his first “career advancement event.”
He and technician Barbara Pesch discovered that in vivo penicillin treatment killed streptococci only in the exponential growth phase and not in the stationary phase. These
results were published in the Journal of Clinical Investigation and earned Darnell an interview with Harry Eagle at the National Institutes of Health.

Darnell arrived at the NIH in 1956, shortly after Eagle  shifted his research interest to developing his minimal essential cell culture medium, still used. Eagle, then studying cell metabolism, suggested that Darnell take up a side project on poliovirus replication in mammalian cells in collaboration with Robert I. DeMars. DeMars’ Ph.D.
adviser was also James  Watson’s mentor, so Darnell met Watson, who invited him to give a talk at Harvard University, which led to an assistant professor position
at the MIT under Salvador Luria. A take-home message is to embrace side projects, because you never know where they may lead: this project helped to shape
his career.

Darnell arrived in Boston in 1961. Following the discovery of DNA’s structure in 1953, the world of molecular biology was turning to RNA in an effort to understand how
proteins are made. Darnell’s background in virology (it was discovered in 1960 that viruses used RNA to replicate) was ideal for the aim of his first independent lab:
exploring mRNA in animal cells grown in culture. While at MIT, he developed a new technique for purifying RNA along with making other observations

  • suggesting that nonribosomal cytoplasmic RNA may be involved in protein synthesis.

When Darnell moved to Albert Einstein College of Medicine for full professorship in 1964,  it was hypothesized that heterogenous nuclear RNA was a precursor to mRNA.
At Einstein, Darnell discovered RNA processing of pre-tRNAs and demonstrated for the first time

  • that a specific nuclear RNA could represent a possible specific mRNA precursor.

In 1967 Darnell took a position at Columbia University, and it was there that he discovered (simultaneously with two other labs) that

  • mRNA contained a polyadenosine tail.

The three groups all published their results together in the Proceedings of the National Academy of Sciences in 1971. Shortly afterward, Darnell made his final career move
four short miles down the street to Rockefeller University in 1974.

Over the next 35-plus years at Rockefeller, Darnell never strayed from his original research question: How do mammalian cells make and control the making of different
mRNAs? His work was instrumental in the collaborative discovery of

  • splicing in the late 1970s and
  • in identifying and cloning many transcriptional activators.

Perhaps his greatest contribution during this time, with the help of Ernest Knight, was

  • the discovery and cloning of the signal transducers and activators of transcription (STAT) proteins.

And with George Stark, Andy Wilks and John Krowlewski, he described

  • cytokine signaling via the JAK-STAT pathway.

Darnell closes his “Reflections” with perhaps his best advice: Do not get too wrapped up in your own work, because “we are all needed and we are all in this together.”

Darnell Reflections - James_Darnell

Darnell Reflections – James_Darnell

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Recent findings on presenilins and signal peptide peptidase

By Dinu-Valantin Bălănescu

γ-secretase and SPP

γ-secretase and SPP

Fig. 1 from the minireview shows a schematic depiction of γ-secretase and SPP

http://www.asbmb.org/assets/0/366/418/428/85528/85529/85530/c2de032a-daad-41e5-ba19-87a17bd26362.png

GxGD proteases are a family of intramembranous enzymes capable of hydrolyzing

  • the transmembrane domain of some integral membrane proteins.

The GxGD family is one of the three families of

  • intramembrane-cleaving proteases discovered so far (along with the rhomboid and site-2 protease) and
  • includes the γ-secretase and the signal peptide peptidase.

Although only recently discovered, a number of functions in human pathology and in numerous other biological processes

  • have been attributed to γ-secretase and SPP.

Taisuke Tomita and Takeshi Iwatsubo of the University of Tokyo highlighted the latest findings on the structure and function of γ-secretase and SPP
in a recent minireview in The Journal of Biological Chemistry.

  • γ-secretase is involved in cleaving the amyloid-β precursor protein, thus producing amyloid-β peptide,

the main component of senile plaques in Alzheimer’s disease patients’ brains. The complete structure of mammalian γ-secretase is not yet known; however,
Tomita and Iwatsubo note that biochemical analyses have revealed it to be a multisubunit protein complex.

  • Its catalytic subunit is presenilin, an aspartyl protease.

In vitro and in vivo functional and chemical biology analyses have revealed that

  • presenilin is a modulator and mandatory component of the γ-secretase–mediated cleavage of APP.

Genetic studies have identified three other components required for γ-secretase activity:

  1. nicastrin,
  2. anterior pharynx defective 1 and
  3. presenilin enhancer 2.

By coexpression of presenilin with the other three components, the authors managed to

  • reconstitute γ-secretase activity.

Tomita and Iwatsubo determined using the substituted cysteine accessibility method and by topological analyses, that

  • the catalytic aspartates are located at the center of the nine transmembrane domains of presenilin,
  • by revealing the exact location of the enzyme’s catalytic site.

The minireview also describes in detail the formerly enigmatic mechanism of γ-secretase mediated cleavage.

SPP, an enzyme that cleaves remnant signal peptides in the membrane

  • during the biogenesis of membrane proteins and
  • signal peptides from major histocompatibility complex type I,
  • also is involved in the maturation of proteins of the hepatitis C virus and GB virus B.

Bioinformatics methods have revealed in fruit flies and mammals four SPP-like proteins,

  • two of which are involved in immunological processes.

By using γ-secretase inhibitors and modulators, it has been confirmed

  • that SPP shares a similar GxGD active site and proteolytic activity with γ-secretase.

Upon purification of the human SPP protein with the baculovirus/Sf9 cell system,

  • single-particle analysis revealed further structural and functional details.

HLA targeting efficiency correlates with human T-cell response magnitude and with mortality from influenza A infection

From www.pnas.org –  Sep 3, 2013 4:24 PM

Experimental and computational evidence suggests that

  • HLAs preferentially bind conserved regions of viral proteins, a concept we term “targeting efficiency,” and that
  • this preference may provide improved clearance of infection in several viral systems.

To test this hypothesis, T-cell responses to A/H1N1 (2009) were measured from peripheral blood mononuclear cells obtained from a household cohort study
performed during the 2009–2010 influenza season. We found that HLA targeting efficiency scores significantly correlated with

  • IFN-γ enzyme-linked immunosorbent spot responses (P = 0.042, multiple regression).

A further population-based analysis found that the carriage frequencies of the alleles with the lowest targeting efficiencies, A*24,

  • were associated with pH1N1 mortality (r = 0.37, P = 0.031) and
  • are common in certain indigenous populations in which increased pH1N1 morbidity has been reported.

HLA efficiency scores and HLA use are associated with CD8 T-cell magnitude in humans after influenza infection.
The computational tools used in this study may be useful predictors of potential morbidity and

  • identify immunologic differences of new variant influenza strains
  • more accurately than evolutionary sequence comparisons.

Population-based studies of the relative frequency of these alleles in severe vs. mild influenza cases

  • might advance clinical practices for severe H1N1 infections among genetically susceptible populations.

Metabolomics in drug target discovery

J D Rabinowitz et al.

Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ.
Cold Spring Harbor Symposia on Quantitative Biology 11/2011; 76:235-46.
http://dx.doi.org:/10.1101/sqb.2011.76.010694 

Most diseases result in metabolic changes. In many cases, these changes play a causative role in disease progression. By identifying pathological metabolic changes,

  • metabolomics can point to potential new sites for therapeutic intervention.

Particularly promising enzymatic targets are those that

  • carry increased flux in the disease state.

Definitive assessment of flux requires the use of isotope tracers. Here we present techniques for

  • finding new drug targets using metabolomics and isotope tracers.

The utility of these methods is exemplified in the study of three different viral pathogens. For influenza A and herpes simplex virus,

  • metabolomic analysis of infected versus mock-infected cells revealed
  • dramatic concentration changes around the current antiviral target enzymes.

Similar analysis of human-cytomegalovirus-infected cells, however, found the greatest changes

  • in a region of metabolism unrelated to the current antiviral target.

Instead, it pointed to the tricarboxylic acid (TCA) cycle and

  • its efflux to feed fatty acid biosynthesis as a potential preferred target.

Isotope tracer studies revealed that cytomegalovirus greatly increases flux through

  • the key fatty acid metabolic enzyme acetyl-coenzyme A carboxylase.
  • Inhibition of this enzyme blocks human cytomegalovirus replication.

Examples where metabolomics has contributed to identification of anticancer drug targets are also discussed. Eventual proof of the value of

  • metabolomics as a drug target discovery strategy will be
  • successful clinical development of therapeutics hitting these new targets.

 Related References

Use of metabolic pathway flux information in targeted cancer drug design. Drug Discovery Today: Therapeutic Strategies 1:435-443, 2004.

Detection of resistance to imatinib by metabolic profiling: clinical and drug development implications. Am J Pharmacogenomics. 2005;5(5):293-302. Review. PMID: 16196499

Medicinal chemistry, metabolic profiling and drug target discovery: a role for metabolic profiling in reverse pharmacology and chemical genetics.
Mini Rev Med Chem.  2005 Jan;5(1):13-20. Review. PMID: 15638788 [PubMed – indexed for MEDLINE] Related citations

Development of Tracer-Based Metabolomics and its Implications for the Pharmaceutical Industry. Int J Pharm Med 2007; 21 (3): 217-224.

Use of metabolic pathway flux information in anticancer drug design. Ernst Schering Found Symp Proc. 2007;(4):189-203. Review. PMID: 18811058

Pharmacological targeting of glucagon and glucagon-like peptide 1 receptors has different effects on energy state and glucose homeostasis in diet-induced obese mice. J Pharmacol Exp Ther. 2011 Jul;338(1):70-81. http://dx.doi.org:/10.1124/jpet.111.179986. PMID: 21471191

Single valproic acid treatment inhibits glycogen and RNA ribose turnover while disrupting glucose-derived cholesterol synthesis in liver as revealed by the
[U-C(6)]-d-glucose tracer in mice. Metabolomics. 2009 Sep;5(3):336-345. PMID: 19718458

Metabolic Pathways as Targets for Drug Screening, Metabolomics, Dr Ute Roessner (Ed.), ISBN: 978-953-51-0046-1, InTech, Available from: http://www.intechopen.com/books/metabolomics/metabolic-pathways-as-targets-for-drug-screening

Iron regulates glucose homeostasis in liver and muscle via AMP-activated protein kinase in mice. FASEB J. 2013 Jul;27(7):2845-54.
http://dx.doi.org:/10.1096/fj.12-216929. PMID: 23515442

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Drug Discov. Today 19 (2014), 171–182     http://dx.doi.org:/10.1016/j.drudis.2013.07.014

Highlights

  • We now have metabolic network models; the metabolome is represented by their nodes.
  • Metabolite levels are sensitive to changes in enzyme activities.
  • Drugs hitchhike on metabolite transporters to get into and out of cells.
  • The consensus network Recon2 represents the present state of the art, and has predictive power.
  • Constraint-based modelling relates network structure to metabolic fluxes.

Metabolism represents the ‘sharp end’ of systems biology, because changes in metabolite concentrations are

  • necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs.

To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of

  • the human metabolic network that include the important transporters.

Small molecule ‘drug’ transporters are in fact metabolite transporters, because

  • drugs bear structural similarities to metabolites known from the network reconstructions and
  • from measurements of the metabolome.

Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia:

(i) the effects of inborn errors of metabolism;

(ii) which metabolites are exometabolites, and

(iii) how metabolism varies between tissues and cellular compartments.

However, even these qualitative network models are not yet complete. As our understanding improves

  • so do we recognise more clearly the need for a systems (poly)pharmacology.

Introduction – a systems biology approach to drug discovery

It is clearly not news that the productivity of the pharmaceutical industry has declined significantly during recent years

  • following an ‘inverse Moore’s Law’, Eroom’s Law, or
  • that many commentators, consider that the main cause of this is
  • because of an excessive focus on individual molecular target discovery rather than a more sensible strategy
  • based on a systems-level approach (Fig. 1).
drug discovery science

drug discovery science

Figure 1.

The change in drug discovery strategy from ‘classical’ function-first approaches (in which the assay of drug function was at the tissue or organism level),
with mechanistic studies potentially coming later, to more-recent target-based approaches where initial assays usually involve assessing the interactions
of drugs with specified (and often cloned, recombinant) proteins in vitro. In the latter cases, effects in vivo are assessed later, with concomitantly high levels of attrition.

Arguably the two chief hallmarks of the systems biology approach are:

(i) that we seek to make mathematical models of our systems iteratively or in parallel with well-designed ‘wet’ experiments, and
(ii) that we do not necessarily start with a hypothesis but measure as many things as possible (the ’omes) and

  • let the data tell us the hypothesis that best fits and describes them.

Although metabolism was once seen as something of a Cinderella subject,

  • there are fundamental reasons to do with the organisation of biochemical networks as
  • to why the metabol(om)ic level – now in fact seen as the ‘apogee’ of the ’omics trilogy –
  •  is indeed likely to be far more discriminating than are
  • changes in the transcriptome or proteome.

The next two subsections deal with these points and Fig. 2 summarises the paper in the form of a Mind Map.

metabolomics and systems pharmacology

metabolomics and systems pharmacology

http://ars.els-cdn.com/content/image/1-s2.0-S1359644613002481-gr2.jpg

Metabolic Disease Drug Discovery— “Hitting the Target” Is Easier Said Than Done

David E. Moller, et al.   http://dx.doi.org:/10.1016/j.cmet.2011.10.012

Despite the advent of new drug classes, the global epidemic of cardiometabolic disease has not abated. Continuing

  • unmet medical needs remain a major driver for new research.

Drug discovery approaches in this field have mirrored industry trends, leading to a recent

  • increase in the number of molecules entering development.

However, worrisome trends and newer hurdles are also apparent. The history of two newer drug classes—

  1. glucagon-like peptide-1 receptor agonists and
  2. dipeptidyl peptidase-4 inhibitors—

illustrates both progress and challenges. Future success requires that researchers learn from these experiences and

  • continue to explore and apply new technology platforms and research paradigms.

The global epidemic of obesity and diabetes continues to progress relentlessly. The International Diabetes Federation predicts an even greater diabetes burden (>430 million people afflicted) by 2030, which will disproportionately affect developing nations (International Diabetes Federation, 2011). Yet

  • existing drug classes for diabetes, obesity, and comorbid cardiovascular (CV) conditions have substantial limitations.

Currently available prescription drugs for treatment of hyperglycemia in patients with type 2 diabetes (Table 1) have notable shortcomings. In general,

Therefore, clinicians must often use combination therapy, adding additional agents over time. Ultimately many patients will need to use insulin—a therapeutic class first introduced in 1922. Most existing agents also have

  • issues around safety and tolerability as well as dosing convenience (which can impact patient compliance).

Pharmacometabolomics, also known as pharmacometabonomics, is a field which stems from metabolomics,

  • the quantification and analysis of metabolites produced by the body.

It refers to the direct measurement of metabolites in an individual’s bodily fluids, in order to

  • predict or evaluate the metabolism of pharmaceutical compounds, and
  • to better understand the pharmacokinetic profile of a drug.

Alternatively, pharmacometabolomics can be applied to measure metabolite levels

  • following the administration of a pharmaceutical compound, in order to
  • monitor the effects of the compound on certain metabolic pathways(pharmacodynamics).

This provides detailed mapping of drug effects on metabolism and

  • the pathways that are implicated in mechanism of variation of response to treatment.

In addition, the metabolic profile of an individual at baseline (metabotype) provides information about

  • how individuals respond to treatment and highlights heterogeneity within a disease state.

All three approaches require the quantification of metabolites found

relationship between -OMICS

relationship between -OMICS

http://upload.wikimedia.org/wikipedia/commons/thumb/e/eb/OMICS.png/350px-OMICS.png

Pharmacometabolomics is thought to provide information that

Looking at the characteristics of an individual down through these different levels of detail, there is an

  • increasingly more accurate prediction of a person’s ability to respond to a pharmaceutical compound.
  1. the genome, made up of 25 000 genes, can indicate possible errors in drug metabolism;
  2. the transcriptome, made up of 85,000 transcripts, can provide information about which genes important in metabolism are being actively transcribed;
  3. and the proteome, >10,000,000 members, depicts which proteins are active in the body to carry out these functions.

Pharmacometabolomics complements the omics with

  • direct measurement of the products of all of these reactions, but with perhaps a relatively
  • smaller number of members: that was initially projected to be approximately 2200 metabolites,

but could be a larger number when gut derived metabolites and xenobiotics are added to the list. Overall, the goal of pharmacometabolomics is

  • to more closely predict or assess the response of an individual to a pharmaceutical compound,
  • permitting continued treatment with the right drug or dosage
  • depending on the variations in their metabolism and ability to respond to treatment.

Pharmacometabolomic analyses, through the use of a metabolomics approach,

  • can provide a comprehensive and detailed metabolic profile or “metabolic fingerprint” for an individual patient.

Such metabolic profiles can provide a complete overview of individual metabolite or pathway alterations,

This approach can then be applied to the prediction of response to a pharmaceutical compound

  • by patients with a particular metabolic profile.

Pharmacometabolomic analyses of drug response are

Pharmacogenetics focuses on the identification of genetic variations (e.g. single-nucleotide polymorphisms)

  • within patients that may contribute to altered drug responses and overall outcome of a certain treatment.

The results of pharmacometabolomics analyses can act to “inform” or “direct”

  • pharmacogenetic analyses by correlating aberrant metabolite concentrations or metabolic pathways to potential alterations at the genetic level.

This concept has been established with two seminal publications from studies of antidepressants serotonin reuptake inhibitors

  • where metabolic signatures were able to define a pathway implicated in response to the antidepressant and
  • that lead to identification of genetic variants within a key gene
  • within the highlighted pathway as being implicated in variation in response.

These genetic variants were not identified through genetic analysis alone and hence

  • illustrated how metabolomics can guide and inform genetic data.

en.wikipedia.org/wiki/Pharmacometabolomics

Benznidazole Biotransformation and Multiple Targets in Trypanosoma cruzi Revealed by Metabolomics

Andrea Trochine, Darren J. Creek, Paula Faral-Tello, Michael P. Barrett, Carlos Robello
Published: May 22, 2014   http://dx.doi.org:/10.1371/journal.pntd.0002844

The first line treatment for Chagas disease, a neglected tropical disease caused by the protozoan parasite Trypanosoma cruzi,

  • involves administration of benznidazole (Bzn).

Bzn is a 2-nitroimidazole pro-drug which requires nitroreduction to become active. We used a

  • non-targeted MS-based metabolomics approach to study the metabolic response of T. cruzi to Bzn.

Parasites treated with Bzn were minimally altered compared to untreated trypanosomes, although the redox active thiols

  1. trypanothione,
  2. homotrypanothione and
  3. cysteine

were significantly diminished in abundance post-treatment. In addition, multiple Bzn-derived metabolites were detected after treatment.

These metabolites included reduction products, fragments and covalent adducts of reduced Bzn

  • linked to each of the major low molecular weight thiols:
  1. trypanothione,
  2. glutathione,
  3. g-glutamylcysteine,
  4. glutathionylspermidine,
  5. cysteine and
  6. ovothiol A.

Bzn products known to be generated in vitro by the unusual trypanosomal nitroreductase, TcNTRI,

  • were found within the parasites,
  • but low molecular weight adducts of glyoxal, a proposed toxic end-product of NTRI Bzn metabolism, were not detected.

Our data is indicative of a major role of the

  • thiol binding capacity of Bzn reduction products
  • in the mechanism of Bzn toxicity against T. cruzi.

 

 

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