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Clinical Biomarkers Overview

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Paving the Road for Clinical Biomarkers

Where Trackless Terrain Once Challenged Biomarker Development, Clearer Paths Are Emerging

http://www.genengnews.com/gen-articles/paving-the-road-for-clinical-biomarkers/5757/

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Fusion detection can be carried out with traditional opposing primer-based library preparation methods, which require target- and fusion-specific primers that define the region to be sequenced. With these methods, primers are needed that flank the target region and the fusion partner, so only known fusions can be detected. An alternative method, ArcherDX’ Anchored Multiplex PCR (AMP), can be used to detect the target of interest, plus any known and unknown fusion partners. This is because AMP uses target-specific unidirectional primers, along with reverse primers, that hybridize to the sequencing adapter that is ligated to each fragment prior to amplification.

 

  • In time, the narrow, tortuous paths followed by pioneers become wider and straighter, whether the pioneers are looking to settle new land or bring new biomarkers to the clinic.

In the case of biomarkers, we’re still at the stage where pioneers need to consult guides and outfitters or, in modern parlance, consultants and technology providers. These hardy souls tend to congregate at events like the Biomarker Conference, which was held recently in San Diego.

At this event, biomarker experts discussed ways to avoid unfortunate detours on the trail from discovery and development to clinical application and regulatory approval. Of particular interest were topics such as the identification of accurate biomarkers, the explication of disease mechanisms, the stratification of patient groups, and the development of standard protocols and assay platforms. In each of these areas, presenters reported progress.

Another crucial subject is the integration of techniques such as next-generation sequencing (NGS). This particular technique has been instrumental in advancing clinical cancer genomics and continues to be the most feasible way of simultaneously interrogating multiple genes for driver mutations.

Enriching nucleic acid libraries for target genes of interest prior to NGS greatly enhances the sensitivity of
detecting mutations, as the enriched regions are sequenced multiple times. This is particularly useful when analyzing clinical samples, which generate low amounts of poor-quality nucleic acids.

However, NGS has been limited in its ability to identify gene fusions and translocations, which underlie oncogenesis in a variety of cancers. “These challenges are largely related to the enrichment chemistry used to produce sequencing libraries,” commented Joshua Stahl, chief scientific officer and general manager, ArcherDX.

Most target-enrichment strategies require prior knowledge of both ends of the target region to be sequenced. Therefore, only gene fusions with known partners can be amplified for downstream NGS assays.

Archer’s Anchored Multiplex PCR (AMP™) technology overcomes this limitation, as it can enrich for novel fusions, while only requiring knowledge of one end of the fusion pair. At the heart of the AMP chemistry are unique Molecular Barcode (MBC) adapters, ligated to the 5′ ends of DNA fragments prior to amplification. The MBCs contain universal primer binding sites for PCR and a molecular barcode for identifying unique molecules. When combined with 3′ gene-specific primers, MBCs enable amplification of target regions with unknown 5′ ends.

“AMP is ideal for identifying gene fusions and other driver mutations from FFPE samples,” asserted Mr. Stahl. “Its robust utility was demonstrated for detection of gene fusions, point mutations, insertions, deletions, and copy number changes from low amounts of clinical formalin-fixed, paraffin-embedded (FFPE) RNA and DNA samples.

“Tagging each molecule of input nucleic acid with a unique molecular barcode allows for de-duplication, error correction, and quantitative analysis, resulting in high sequencing consensus. With its low error rate and low limits of detection, AMP is revolutionizing the field of cancer genomics.”

In a proof-of-concept study, a single-tube 23-plex panel was designed to amplify the kinase domains of ALK, RET, ROS1, and MUSK genes by AMP. This enrichment strategy enabled identification of gene fusions with multiple partners and alternative splicing events in lung cancer, thyroid cancer, and glioblastoma specimens by NGS.

Ignyta, a precision medicine company, adopted Archer’s AMP technology in Trailblaze Pharos™, a multiplex assay employed in their STARTRK-2 trial for identifying actionable NTRK, ROS1, and ALK gene rearrangements in solid tumors that can be treated with the novel kinase inhibitor, entrectinib. “Gene fusions are incredibly important in personalized medicine right now,” stated Mr. Stahl. “Archer’s FusionPlex assays are quickly becoming the new gold standard.”

Reading Cancer Signatures

This image, from the Massachusetts General Hospital Cancer Center, shows multicolor fluorescence in situ hybridization (FISH) analysis of cells from a patient with esophagogastric cancer. Remarkably, the FISH analysis revealed that co-amplification of the MET gene (red signal) and the EGFR gene (green signal) existed simultaneously in the same tumor cells. A chromosome 7 control probe is shown in blue.
“Each year 23,000 kidneys are transplanted, and over 175,000 kidney transplants are functional today,” noted Daniel R. Salomon, M.D., medical program director, Scripps Center for Organ Transplantation, Scripps Research Institute. “However, in just 5 years, 3 out of every 10 patients will be back on dialysis, and in 15 years, at least 75% of all patients will lose their kidney grafts.“Tumor biomarkers are critical for predicting and following patient responses to today’s cancer therapies,” said Darrell Borger, Ph.D., co-director of the Translational Research Laboratory and director of the Biomarker Laboratory, Massachussetts General Hospital (MGH) Cancer Center, Harvard Medical School. “If we understand what drives the malignancy in any given patient, we are able to match existing therapies to the patient’s genotype.”

Over the last decade, the Biomarker/Translational Research Laboratory has focused on developing clinical genotyping and fluorescent in situ hybridization (FISH) assays for rapid personalized genomic testing.

“Initially, we analyzed the most prevalent hotspot mutations, about 160 in 25 cancer genes,” continued Dr. Borger. “However, this approach revealed mutations in only half of our patients. With the advent of NGS, we are able to sequence 190 exons in 39 cancer genes and obtain significantly richer genetic fingerprints, finding genetic aberrations in 92% of our cancer patients.”

Using multiplexed approaches, Dr. Borger’s team within the larger Center for Integrated Diagnostics (CID) program at MGH has established high-throughput genotyping service as an important component of routine care. While only a few susceptible molecular alterations may currently have a corresponding drug, the NGS-driven analysis may supply new information for inclusion of patients into ongoing clinical trials, or bank the result for future research and development.

“A significant impediment to discovery of clinically relevant genomic signatures is our current inability to interconnect the data,” explained Dr. Borger. “On the local level, we are striving to compile the data from clinical observations, including responses to therapy and genotyping. Globally, it is imperative that comprehensive public databases become available to the research community.”

Tumor profiling at MGH have already yielded significant discoveries. Dr. Borger’s lab, in collaboration with oncologists at the MGH Cancer Center, found significant correlations between mutations in the genes encoding the metabolic enzymes isocitrate dehydrogenase (IDH1 and IDH2) and certain types of cancers, such as cholangiocarcinoma and acute myelogenous leukemia (AML).

Historically, cancer signatures largely focus on signaling proteins. Discovery of a correlative metabolic enzyme offered a promise of diagnostics based on metabolic byproducts that may be easily identified in blood. Indeed, the metabolite 2-hydroxyglutarate accumulates to high levels in the tissues of patients carrying IDH1 and IDH2 mutations. They have reported that circulating 2-hydroxyglutarate as measured in the blood correlates with tumor burden, and could serve as an important surrogate marker of treatment response.

Tuning Immunosuppression, Preventing Chronic Rejection

“We believe that this is caused by chronic immune-mediated rejection. Failure of effective immunosuppression reduces functional life of these patients and adds in $9–13 billion in yearly healthcare costs.” Dr. Salomon emphasized that ineffective use of immunosuppressive drugs is partially due to the lack of an objective biomarker which could provide decision support for just-in-time adjustment in therapeutic regimens.

“Our research aims to provide that objective measure to clinicians,” explained Dr. Salomon.

To date, kidney transplant biopsies remain the gold standard, even though they are not suitable for continuous monitoring and have both costs and risks. Dr. Salomon’s team developed a minimally invasive diagnostic approach based on unbiased whole-genome expression profiling of blood samples. Using Affymetrix Human Genome U133 Plus 2.0 Gene Chips, the team analyzed 275 bloodsamples of kidney transplant patients with biopsy-proved acute rejection, acute dysfunction without rejection and transplant excellent phenotype.

The data was passed through several machine-learning algorithms to identify a group of about 250 classifiers that predict subacute or acute rejection with 80% accuracy. This signature is locked while the team continues to expand the core dataset aiming to reach a thousand samples by the end of this year.

“As opposed to classical approaches to biomarker discoveries limited to just a few classifiers, our methodology provides for the first use of unbiased whole-genome profiling in the identification of multiple molecular predictors,” declared Dr. Salomon. “We can use this molecular diagnostic strategy to reveal a subacute rejection prior to significant tissue injury leading to transplant dysfunction. Continuous monitoring would inform physicians on the balance between over-suppression and effective/optimal therapy.”

Dr. Salomon is a chief scientific advisor for Transplant Genomics (TGI), a start-up company created to translate the blood-based molecular diagnostics into clinical tests. In late 2016, TGI will begin providing its TruGraf blood tests for kidney transplant recipients for use by four to six U.S. transplant centers through an early-access program (EAP).

Additional tests designed to be used serially to diagnose and treat subclinical episodes of rejection including biopsy gene profiling are in the final stages of development. Validation and will be made available through the EAP in the upcoming months.

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BioAgilytix’ MultiMuscle Analysis is a process that can split sample analysis into multiple parallel tracks to minimize antibody cross-reactivity and allow for use of the best-fit platform or kit for each biomarker analysis. The process may require only one tube of sample with only one F/T cycle.

Focusing on Large Molecules 

BioAgilytix, a specialized bioanalytical laboratory, is a global leader in large molecule bioanalysis. The company’s business encompasses pharmacokinetic/pharmacodynamic (PK/PD) studies of large biomolecules, in addition to immunogenicity, biomarkers, and cell-based assays. In less than 10 years,BioAgilytix has grown from a start-up to an international powerhouse with over 100 employees—more than half possessing advanced scientific degrees—because of its team’s expertise in the complexities of large molecule drug development.

“In contrast to small molecule analysis, which has become more of a commodity due to its semiautomated and process-oriented nature, large molecule analysis is inherently challenging,” said Afshin Safavi, Ph.D., founder and chief science officer of BioAgilytix. “In large molecule bioanalysis, we rely heavily on analytical reagents, such as antibodies and recombinant proteins, which are known to show considerable variability from lot to lot.

BioAgilytix’ MultiMuscle Analysis is a process that can split sample analysis into multiple parallel tracks to minimize antibody cross-reactivity and allow for use of the best-fit platform or kit for each biomarker analysis. The process may require only one tube of sample with only one F/T cycle.

“Therefore, designing an effective analytical process for large biomolecules requires scientific personnel with years of experience. It also requires careful management of critical reagents, and a deep understanding of the capabilities and limitations of the platforms selected for use.”

Dr. Safavi explains that the biomarker field has been trending away from a gunshot approach traditionally favored by large pharma to more focused analyses of a few key biomarkers.

“Unlike several years ago, most biotech and pharma companies now perform careful due diligence and literature research before approaching us, to narrow down their investigation to just a handful of biomarkers,” he explained. Limited samples may drive the desire to multiplex as many biomarkers as possible, but a multiplex approach may often result in low quality data due to reagent cross-reactivity.

A recent process innovation developed by BioAgilytix, called MultiMuscle Analysis™, uses a customized parallel process to drastically reduce analytical process time and increase data quality. MultiMuscle Analysis splits the sample analysis into multiple parallel tracks, each performed on specialized equipment by scientists experienced in that particular platform.

“Say, for example, a customer requests measurements of 10 biomarkers,” ventured Dr. Safavi. “If we know some of the antibodies may cross-react, then we may, for example, end up with one heptaplex and three as uniplexes, all done in parallel.”

Using this approach, BioAgilytix is able to perform large biomarker analyses on a very large number of samples in near real-time. “We now receive samples from over 20 countries,” Dr. Safavi stated. “We have used the MultiMuscle approach successfully over and over.”

Feature ArticlesMore » May 1, 2016 (Vol. 36, No. 9)

Paving the Road for Clinical Biomarkers

Where Trackless Terrain Once Challenged Biomarker Development, Clearer Paths Are Emerging

Kate Marusina, Ph.D.

Focusing on Large Molecules

BioAgilytix’ MultiMuscle Analysis is a process that can split sample analysis into multiple parallel tracks to minimize antibody cross-reactivity and allow for use of the best-fit platform or kit for each biomarker analysis. The process may require only one tube of sample with only one F/T cycle.

BioAgilytix, a specialized bioanalytical laboratory, is a global leader in large molecule bioanalysis. The company’s business encompasses pharmacokinetic/pharmacodynamic (PK/PD) studies of large biomolecules, in addition to immunogenicity, biomarkers, and cell-based assays. In less than 10 years, BioAgilytix has grown from a start-up to an international powerhouse with over 100 employees—more than half possessing advanced scientific degrees—because of its team’s expertise in the complexities of large molecule drug development.

“In contrast to small molecule analysis, which has become more of a commodity due to its semiautomated and process-oriented nature, large molecule analysis is inherently challenging,” said Afshin Safavi, Ph.D., founder and chief science officer of BioAgilytix. “In large molecule bioanalysis, we rely heavily on analytical reagents, such as antibodies and recombinant proteins, which are known to show considerable variability from lot to lot.

“Therefore, designing an effective analytical process for large biomolecules requires scientific personnel with years of experience. It also requires careful management of critical reagents, and a deep understanding of the capabilities and limitations of the platforms selected for use.”

Dr. Safavi explains that the biomarker field has been trending away from a gunshot approach traditionally favored by large pharma to more focused analyses of a few key biomarkers.

“Unlike several years ago, most biotech and pharma companies now perform careful due diligence and literature research before approaching us, to narrow down their investigation to just a handful of biomarkers,” he explained. Limited samples may drive the desire to multiplex as many biomarkers as possible, but a multiplex approach may often result in low quality data due to reagent cross-reactivity.

A recent process innovation developed by BioAgilytix, called MultiMuscle Analysis™, uses a customized parallel process to drastically reduce analytical process time and increase data quality. MultiMuscle Analysis splits the sample analysis into multiple parallel tracks, each performed on specialized equipment by scientists experienced in that particular platform.

“Say, for example, a customer requests measurements of 10 biomarkers,” ventured Dr. Safavi. “If we know some of the antibodies may cross-react, then we may, for example, end up with one heptaplex and three as uniplexes, all done in parallel.”

Using this approach, BioAgilytix is able to perform large biomarker analyses on a very large number of samples in near real-time. “We now receive samples from over 20 countries,” Dr. Safavi stated. “We have used the MultiMuscle approach successfully over and over.”

Predicting Clotting or Hemorrhaging

Venous thromboembolism (VTE) is a disease that includes both deep vein thrombosis (DVT) and pulmonary embolism (PE). It is a common, lethal disorder, symptoms of which are often overlooked. VTE is the third most common cardiovascular illness after acute coronary syndrome and stroke.

Venous thrombi, composed predominately of red blood cells bound together by fibrin, form in sites of vessel damage and areas of stagnant blood flow. Once VTE is diagnosed, anticoagulation therapy is indicated.

A novel anticoagulant that reversibly and directly inhibits factor Xa, a key factor in the coagulation system, has been developed by Daiichi Sankyo. “Once on the path of development of an anticoagulant, we recognized the lack of a rapid and sensitive coagulation test that would not be affected by blood traces of anticoagulant therapies,” said Michele Mercuri, M.D., Ph.D., the company’s senior vice president. “An improved diagnostic test would speed up recognition and treatment of thrombosis, and would aid in development of reversing agents that reduce the effect of anticoagulant therapies when needed.”

When Daiichi Sankyo entered in collaboration with Perosphere to develop a novel broad-spectrum reversing agent, the company also supported development of a point-of-care coagulometer (still under development), a hand-held device designed for broad-spectrum monitoring of the activity of anticoagulants and their corresponding reversing agents, across drug classes. A single test requires only 10 µL of fresh or citrated whole blood from a venous draw or finger stick. It optically measures clotting starting with Factor XII activation to fibrin assembly.

Dr. Mercuri explains that none of the existing tests are able to predict whether a patient is at risk for either clotting or hemorrhaging. “Together with Prof. Zahi Fayad’s Team from the Icahn School of Medicine at Mt Sinai, we used magnetic resonance imaging with the gadolinium-based contrast reagent to detect the venous thrombi and follow their dissolution with edoxaban treatment,” reported Dr. Mercuri.

This study, the edoxaban Thrombus Reduction Imaging Study (eTRIS), was focused on developing and validating a magnetic resonance venography (MRV) image acquisition and analysis protocol for the quantification of thrombus volume in deep vein thrombosis. The multicenter study demonstrated excellent reproducibility of analysis of quantifying thrombus volume.

 

Sequence and Epigenetic Factors Determine Overall DNA Structure

Researchers at Ulsan National Institute of Science and Technology (UNIST) in South Korea found that DNA molecules directly interact with one another in ways that are dependent on the sequence of the DNA and epigenetic factors.

The researchers found evidence for sequence-dependent attractive interactions between double-stranded DNA molecules that neither involve intermolecular strand exchange nor are mediated by DNA-binding proteins.

“DNA molecules tend to repel each other in water, but in the presence of special types of cations, they can attract each other just like nuclei pulling each other by sharing electrons in between,” explained lead study author Hajin Kim, Ph.D., assistant professor of biophysics at UNIST. “Our study suggests that the attractive force strongly depends on the nucleic acid sequence and also the epigenetic modifications.”

The investigators used atomic-level simulations to measure forces between double-stranded DNA helices, proposing that the distribution of methyl groups on DNA were the key to regulating this sequence-dependent attraction.

The findings from this study were published recently in Nature Communications through an article entitled “Direct evidence for sequence-dependent attraction between double-stranded DNA controlled by methylation.”

The researchers surmised that direct DNA-DNA interactions could play a central role in how chromosomes are organized and packaged, determining the ultimate fate of many cell types.

Dr. Kim concluded by stating that “in our lab, we try to unravel the mysteries within human cells based on the principles of physics and the mechanisms of biology—seeking for ways to prevent chronic illnesses and diseases associated with aging.”

Searches Related to Direct evidence for sequence-dependent attraction between double-stranded DNA controlled by methylation

 

Direct evidence for sequence-dependent attraction between double-stranded DNA controlled by methylation

Jejoong Yoo, Hajin Kim, Aleksei Aksimentiev  & Taekjip Ha

Nature Communications 22 Mar 2016; 7(11045)    http://dx.doi.org:/10.1038/ncomms11045

Although proteins mediate highly ordered DNA organization in vivo, theoretical studies suggest that homologous DNA duplexes can preferentially associate with one another even in the absence of proteins. Here we combine molecular dynamics simulations with single-molecule fluorescence resonance energy transfer experiments to examine the interactions between duplex DNA in the presence of spermine, a biological polycation. We find that AT-rich DNA duplexes associate more strongly than GC-rich duplexes, regardless of the sequence homology. Methyl groups of thymine acts as a steric block, relocating spermine from major grooves to interhelical regions, thereby increasing DNA–DNA attraction. Indeed, methylation of cytosines makes attraction between GC-rich DNA as strong as that between AT-rich DNA. Recent genome-wide chromosome organization studies showed that remote contact frequencies are higher for AT-rich and methylated DNA, suggesting that direct DNA–DNA interactions that we report here may play a role in the chromosome organization and gene regulation.

Formation of a DNA double helix occurs through Watson–Crick pairing mediated by the complementary hydrogen bond patterns of the two DNA strands and base stacking. Interactions between double-stranded (ds)DNA molecules in typical experimental conditions containing mono- and divalent cations are repulsive1, but can turn attractive in the presence of high-valence cations2. Theoretical studies have identified the ion–ion correlation effect as a possible microscopic mechanism of the DNA condensation phenomena345. Theoretical investigations have also suggested that sequence-specific attractive forces might exist between two homologous fragments of dsDNA6, and this ‘homology recognition’ hypothesis was supported by in vitro atomic force microscopy7 and in vivo point mutation assays8. However, the systems used in these measurements were too complex to rule out other possible causes such as Watson–Crick strand exchange between partially melted DNA or protein-mediated association of DNA.

Here we present direct evidence for sequence-dependent attractive interactions between dsDNA molecules that neither involve intermolecular strand exchange nor are mediated by proteins. Further, we find that the sequence-dependent attraction is controlled not by homology—contradictory to the ‘homology recognition’ hypothesis6—but by a methylation pattern. Unlike the previous in vitro study that used monovalent (Na+) or divalent (Mg2+) cations7, we presumed that for the sequence-dependent attractive interactions to operate polyamines would have to be present. Polyamine is a biological polycation present at a millimolar concentration in most eukaryotic cells and essential for cell growth and proliferation910. Polyamines are also known to condense DNA in a concentration-dependent manner211. In this study, we use spermine4+(Sm4+) that contains four positively charged amine groups per molecule.

 

Methylation determines the strength of DNA–DNA attraction

Analysis of the MD simulations revealed the molecular mechanism of the polyamine-mediated sequence-dependent attraction (Fig. 2). In the case of the AT-rich fragments, the bulky methyl group of thymine base blocks Sm4+ binding to the N7 nitrogen atom of adenine, which is the cation-binding hotspot2122. As a result, Sm4+ is not found in the major grooves of the AT-rich duplexes and resides mostly near the DNA backbone (Fig. 2a,d). Such relocated Sm4+ molecules bridge the two DNA duplexes better, accounting for the stronger attraction16232425. In contrast, significant amount of Sm4+ is adsorbed to the major groove of the GC-rich helices that lacks cation-blocking methyl group (Fig. 2b,e).

Figure 2: Molecular mechanism of polyamine-mediated DNA sequence recognition.

(ac) Representative configurations of Sm4+ molecules at the DNA–DNA distance of 28 Å for the (AT)10–(AT)10 (a), (GC)10–(GC)10 (b) and (GmC)10–(GmC)10 (c) DNA pairs. The backbone and bases of DNA are shown as ribbon and molecular bond, respectively; Sm4+ molecules are shown as molecular bonds. Spheres indicate the location of the N7 atoms and the methyl groups. (df) The average distributions of cations for the three sequence pairs featured in ac. Top: density of Sm4+ nitrogen atoms (d=28 Å) averaged over the corresponding MD trajectory and the z axis. White circles (20 Å in diameter) indicate the location of the DNA helices. Bottom: the average density of Sm4+ nitrogen (blue), DNA phosphate (black) and sodium (red) atoms projected onto the DNA–DNA distance axis (x axis). The plot was obtained by averaging the corresponding heat map data over y=[−10, 10] Å. See Supplementary Figs 4 and 5 for the cation distributions at d=30, 32, 34 and 36 Å.

Genome-wide investigations of chromosome conformations using the Hi–C technique revealed that AT-rich loci form tight clusters in human nucleus2728. Gene or chromosome inactivation is often accompanied by increased methylation of DNA29 and compaction of facultative heterochromatin regions30. The consistency between those phenomena and our findings suggest the possibility that the polyamine-mediated sequence-dependent DNA–DNA interaction might play a role in chromosome folding and epigenetic regulation of gene expression.

 

Phenotypic and Biomarker-based Drug Discovery

Organizers: Michael Foley (Tri-Institutional Therapeutics Discovery Institute), Ralph Garippa (Memorial Sloan-Kettering Cancer Center), David Mark (F. Hoffmann-La Roche), Lorenz Mayr (Astra Zeneca), John Moffat (Genentech), Marco Prunotto (F. Hoffmann-La Roche), and Sonya Dougal (The New York Academy of Sciences)Presented by the Biochemical Pharmacology Discussion Group

Reported by Robert Frawley | Posted January 12, 2016

Overview

There are two major methods for designing pharmaceutical drugs. In traditional drug discovery (TDD), or empiric design, researchers target a particular domain or protein after working to understand its mechanisms and molecular biology. In phenotypic drug discovery (PDD), many different compounds are tested on a system until one results in an observable phenotype of success, and the compounds’ mechanisms of action are not considered. The Phenotypic and Biomarker-based Drug Discovery symposium, presented by the Academy’s Biochemical Pharmacology Discussion Group on October 27, 2015, featured current work in PDD and highlighted the need to bridge commercial and academic research to improve phenotypic drug design.

Phenotypic drug discovery—screening of thousands of substances for functional cellular outputs such as gene expression, growth arrest, and cancer cell death—has led to the development of more commercial drugs than TDD, the more common method of discovery. Indeed, as Jonathan A. Lee of Eli Lilly noted, spending on TDD is out of sync with the rate of new drugs reaching approval; the number of new drugs per billion dollars spent dropped sharply in the last few decades. He argued that the need for functionally validated drugs could be met through a renewed focus on PDD.

Bruce A. Posner started the morning session with a discussion of a phenotypic screen conducted at the University of Texas Southwestern Medical Center which identified two chemical scaffolds that are effective in killing non-small cell lung cancer (NSCLC) cells but are harmless to the non-cancer cells tested. In further studies, the group showed that an optimized analog of one scaffold arrested tumor growth in a mouse xenograft model of NSCLC. Both chemical scaffolds appear to work through a novel mechanism targeting stearoyl-CoA desaturase (SCD), which is known to be important in unsaturated fatty acid synthesis. These compounds were found to be specific, effective, and potent in NSCLC cell lines that express elevated levels of Cyp4F11 and/or related Cyp family members. The group also showed that these scaffolds function as prodrugs that are activated only in cancer cells expressing these Cyp isoforms and that the Cyps produce metabolites of the prodrug that bring about cancer-specific cell toxicity. The group is working to improve these scaffolds and to develop a putative biomarker based on Cyp expression.

The Broad Institute’s LINCS (Library of Network-based Cellular Signatures) database is designed to keep track of small-molecule therapeutics, collecting data on cellular responses to “perturbagens” (drugs, factors, and others stimuli). Data are generated using the L1000 assay, which assesses the expression of 1000 genes known to explain 80% of genetic variation in assayed cell lines. Aravind Subramanian explained that the technique can identify the majority of drug effects for a fraction of the cost of RNA sequencing. Although it examines only a subset of molecules and relies on measuring genetic responses, the technique can help predict the likelihood that new compounds will elicit desired effects.

Martin Main of AstraZeneca described phenotypic drug discovery at AstraZeneca. The company’s model for discovery is to check phenotypic markers at every step, as drugs are moved from cell lines to patients. Main’s team identified a molecule that enhances the regenerative function of cardiac myocytes after infarction. Using cells from several donors, the team validated a promising compound that increases proliferation of cardiac myocytes and drives epicardium-derived progenitor cells to assume a myocyte lineage. In another discovery, the team used islet β-cell regeneration as the phenotype, discovering a compound the researchers believe will reach clinical trials for type 2 diabetes.

Andras J. Bauer of Boehringer Ingelheim discussed a method to increase predictive strength in compound selection before phenotypic screening. By cataloging the structures of known target–reference compound binding pairs, the team can compare those structures to untested compounds, and then assess only the most promising compounds. The THICK (Target Hypothesis Information from Curated Knowledge bases) database gives interaction-probability scores to untested compounds on the basis of structure. Bauer also described a method to verify target–compound interaction without labeling the molecules, in which phenotypic results were verified with mass spectrometry.

In the afternoon session, Myles Fennell of Memorial Sloan-Kettering Cancer Center described his work testing small interfering RNA (siRNA) libraries to find siRNAs that alter macropinocytosis (MP), cell-surface ruffling that is seen in prostate cancer cells. The surface phenotype allows TMR-dextran uptake, which the researchers measured in the screen. MP is driven by RAS (a commonly affected gene family in cancers) and the pathways are already popular drug targets. The researchers tested two libraries of siRNAs, which block translation of specific proteins, using TMR as a marker to report MP severity, as well as sensitive single-cell assays to determine siRNA efficacy. The team identified promising target sequences and used a data-analysis pipeline called KNIME to define several hits, which the researchers are pursuing in therapeutic development.

http://www.nyas.org/image.axd?id=0b4496f6-28fb-435c-bd11-06b4d31fc0ad&t=635863102714400000

TMR-dextran is able to work into cells undergoing macropinocytosis and thus these cells can be separated by phenotype as seen in the controls above. (Image courtesy of Myles Fennell)

Giulio Superti-Furga of the Austrian Academy of Sciences is a proponent of understanding the mechanisms of action (MOA) of candidate drugs. He began by explaining that the genome is an incomplete indicator of disease; epigenetics, altered protein function, metabolism, and other factors are also important. He then introduced pharmacoscopy and the “thermal shiftome” as methods to phenotypically screen compounds. Pharmacoscopy uses high-power automated microscopy to describe how compounds affect cell populations by using specific stains for different cell types; a computer then counts the cells expressing each stain, yielding results similar to those obtained via fluorescence-activated cell sorting but generated through an automated process. The thermal shiftome catalogs changes in thermal stability after protein binding in known reactions and is used to characterize the stability of new reactions. Superti-Furga offered a perspective that tempered the enthusiasm for pure PDD and advocated a mechanistic approach to drug discovery.

Michael R. Jackson, at one of the largest academic screening facilities, the Sanford Burnham Prebys Medical Discovery Institute, led a reexamination of drug screens performed by pharmaceutical companies. His team conducted millions of assays and accumulated a large data library with few new hits. However, the researchers were able to closely characterize the chemistry of one hit, an undisclosed interaction, and Jackson’s group is proceeding to develop a drug to modulate nuclear receptor signaling. The researchers also have a procedure that can screen for the differentiation of human induced pluripotent stem cells (iPSCs) into neurons for potential neuro-regenerative therapies. They developed high-throughput morphology, endpoint-measurement, and proliferation assays that generate tightly clustered, repeatable data. The team has produced consistent results screening 10 immune modulators and various cytokines to assess the reactivity and stability of the cells, providing reliable compound characterization. This success in human cells shows that a disease-relevant patient-derived screening platform to characterize differentiation and immune response is possible with robust assays.

In the next set of talks, Friedrich Metzger and Susanne Swalley described the parallel work of Hoffmann-La Roche and Novartis, respectively, toward treating spinal muscular atrophy (SMA). A devastating disease that leads to loss of motor function and affects motor nerve cells in the spinal cord, SMA presents a unique drug development opportunity. The condition is caused by the loss of function of a single gene product called survival of motor neuron (SMN1). Humans encode an unstable gene product, called SMN2, which is nearly homologous to SMN1.

Metzger explained that the inactive SMN2 variant is largely the same as active SMN1 but, missing exon 7, cannot compensate in its absence. The group from Hoffmann-La Roche aimed to stabilize SMN2 by promoting the inclusion of exon 7. The researchers conducted a phenotypic screen seeking a compound that could change the splicing in patient fibroblasts in vitro and produce a stable, functional SMN2 protein including exon 7. In studies with an SMN2Δ7 mouse model (lacking exon 7), mice drugged with the compound experienced full phenotypic rescue. The compound has been shown to induce alternative splicing of SMN2 to include exon 7 in healthy human volunteers; it was well tolerated and is moving to human patient trials.

Swalley discussed the target identification and MOA of the Novartis compound. After a screening process similar to Roche’s, Novartis moved its compound into animal models while also beginning parallel experimentation to find out why it worked. The group found that U1-snRNP, a spliceosome component required for the splicing process, is bound at two essential nucleotides by the compound. In the SMN2Δ7 mice, the compound improved survival and rescued full SMN2 protein expression. The Novartis compound stabilizes the appropriate spliceosome components to produce SMN2 with exon 7 intact. This novel mechanism demonstrates that a sequence-selective small molecule therapy can alter splicing activity to treat SMA. Together these talks demonstrated the power of PDD and the importance of validating drug mechanisms.

The final talk of the day was given by Hoffmann-La Roche’s Jitao David Zhang, who suggested that pathway reporter genes, which are only modulated when a specific signaling pathway is activated or inhibited, can be used as phenotypic readouts. It is known that gene expression data can predict cell phenotype. Using transcriptomics as a surrogate for downstream phenotypes, for example by using expression data from a gene subset to predict outcomes, would save time and effort. In an iPSC cardiomyocyte model of diabetic stress, machine learning (guided by pathway information) characterizes the response of the iPSCs to a library of compounds, highlighting compounds and pathways worthy of further investigation. This new platform for molecular phenotyping using pathway reporter genes, sequencing, and early analysis speeds compound characterization.

Use the tabs above to find multimedia from this event.

Presentations available from:
Andras J. Bauer, PhD, PharmD (Boehringer Ingelheim)
Myles Fennell, PhD (Memorial Sloan-Kettering Cancer Center)
Jonathan A. Lee, PhD (Eli Lilly)
Martin Main, PhD (AstraZeneca)
Yao Shen, PhD (Columbia University)
Susanne Swalley, PhD (Novartis Institutes for BioMedical Research)
Jitao David Zhang, PhD (F. Hoffmann-La Roche)

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Blood test uses DNA strands of dying cells

Curators:  Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

LPBI

 

Hadassah-Developed Blood Test Detects Multiple Sclerosis, Cancer & Brain Damage

http://www.hadassah.org/news-stories/blood-test-detects-neurodegenerative-disease.html

A new blood test that uses the DNA strands of dying cells to detect diabetes, cancer, traumatic brain injury, and neurodegenerative disease has been developed by researchers at Hadassah Medical Organization (HMO) and The Hebrew University.

In a study involving 320 patients, the researchers were able to infer cell death in specific tissues by looking at the unique chemical modifications (called methylation patterns) of circulating DNA that these dying cells release. Previously, it had not been possible to measure cell death in specific human tissues non-invasively.

The findings are reported in the March 14, 2016 online edition of Proceedings of National Academy of Sciences USA, in an article entitled “Identification of tissue specific cell death using methylation patterns of circulating DNA.”  Prof. Benjamin Glaser, head of Endocrinology at Hadassah, and Dr. Ruth Shemer and Prof. Yuval Dor from The Hebrew University of Jerusalem led an international team in performing the groundbreaking research.

Cell death is a central feature in health and disease. It can signify the early stages of pathology (e.g. a developing tumor or the beginning of an autoimmune or neurodegenerative disease); it can illuminate whether a disease has progressed and whether a particular treatment, such as chemotherapy, is working; and it can alert physicians to unintended toxic effects of treatment or the early rejection of a transplant.

As the researchers relate: “The approach can be adapted to identify cfDNA (cell-free circulating DNA) derived from any cell type in the body, offering a minimally invasive window for diagnosing and monitoring a broad spectrum of human pathologies as well as providing a better understanding of normal tissue dynamics.”

“In the long run,” notes Prof. Glaser, “we envision a new type of blood test aimed at the sensitive detection of tissue damage, even without a-priori suspicion of disease in a specific organ. We believe that such a tool will have broad utility in diagnostic medicine and in the study of human biology.”

The research was performed by Hebrew University students Roni Lehmann-Werman, Daniel Neiman, Hai Zemmour, Joshua Moss and Judith Magenheim, aided by clinicians and scientists from Hadassah Medical Center, Sheba Medical Center, and from institutions in Germany, Sweden, the USA and Canada, who provided precious blood samples from patients.

Scientists have known for decades that dying cells release fragmented DNA into the blood; however, since the DNA sequence of all cells in the body is identical, it had not been possible to determine the tissue of origin of the circulating DNA.  Knowing that the DNA of each cell type carries a unique methylation and that methylation patterns of DNA account for the identity of cells, the researchers were able to use patterns of methylated DNA sequences as biomarkers to detect the origin of the DNA and to identify a specific pathology. For example, they were able to detect evidence of pancreatic beta-cell death in the blood of patients with new-onset type 1 diabetes, oligodendrocyte cell death in patients with relapsing multiple sclerosis, brain cell death in patients after traumatic or ischemic brain damage, and exocrine pancreatic tissue cell death in patients with pancreatic cancer or pancreatitis.

Support for the research came from the Juvenile Diabetes Research Foundation, the Human Islet Research Network of the National Institutes of Health, the Sir Zalman Cowen Universities Fund, the DFG (a Trilateral German-Israel-Palestine program), and the Soyka pancreatic cancer fund.

 Identification of tissue-specific cell death using methylation patterns of circulating DNA.
Minimally invasive detection of cell death could prove an invaluable resource in many physiologic and pathologic situations. Cell-free circulating DNA (cfDNA) released from dying cells is emerging as a diagnostic tool for monitoring cancer dynamics and graft failure. However, existing methods rely on differences in DNA sequences in source tissues, so that cell death cannot be identified in tissues with a normal genome. We developed a method of detecting tissue-specific cell death in humans based on tissue-specific methylation patterns in cfDNA. We interrogated tissue-specific methylome databases to identify cell type-specific DNA methylation signatures and developed a method to detect these signatures in mixed DNA samples. We isolated cfDNA from plasma or serum of donors, treated the cfDNA with bisulfite, PCR-amplified the cfDNA, and sequenced it to quantify cfDNA carrying the methylation markers of the cell type of interest. Pancreatic β-cell DNA was identified in the circulation of patients with recently diagnosed type-1 diabetes and islet-graft recipients; oligodendrocyte DNA was identified in patients with relapsing multiple sclerosis; neuronal/glial DNA was identified in patients after traumatic brain injury or cardiac arrest; and exocrine pancreas DNA was identified in patients with pancreatic cancer or pancreatitis. This proof-of-concept study demonstrates that the tissue origins of cfDNA and thus the rate of death of specific cell types can be determined in humans. The approach can be adapted to identify cfDNA derived from any cell type in the body, offering a minimally invasive window for diagnosing and monitoring a broad spectrum of human pathologies as well as providing a better understanding of normal tissue dynamics.

While impressively organ specific, they did not specifically prove that the DNA was from an actual dying cell. For example, you would need to see if Troponin levels were elevated when assuming the DNA is from injured myocardium. Also, for brain, though impractical , you’d want to see a brain biopsy or imaging for the brain related cases. The experiment of spiking with DNA was clever though. Also, what is the turnaround time for this test in practical use?

Larry HB

Very good comment. I was reluctant to put this up, but it was of interest and published in PNAS.  Perhaps I can find more information.  Troponin levels would be good for 48 hours, longer than CK and comparable to LD.  What about Nat peptides?

Glutamine and cancer: cell biology, physiology, and clinical opportunities

Christopher T. Hensley,1 Ajla T. Wasti,1,2 

J Clin Invest 2013   https://www.jci.org/articles/view/69600

Glutamine is an abundant and versatile nutrient that participates in energy formation, redox homeostasis, macromolecular synthesis, and signaling in cancer cells. These characteristics make glutamine metabolism an appealing target for new clinical strategies to detect, monitor, and treat cancer. Here we review the metabolic functions of glutamine as a super nutrient and the surprising roles of glutamine in supporting the biological hallmarks of malignancy. We also review recent efforts in imaging and therapeutics to exploit tumor cell glutamine dependence, discuss some of the challenges in this arena, and suggest a disease-focused paradigm to deploy these emerging approaches.

It has been nearly a century since the discovery that tumors display metabolic activities that distinguish them from differentiated, non-proliferating tissues and presumably contribute to their supraphysiological survival and growth (1). Interest in cancer metabolism was boosted by discoveries that oncogenes and tumor suppressors could regulate nutrient metabolism, and that mutations in some metabolic enzymes participate in the development of malignancy (2, 3). The persistent appeal of cancer metabolism as a line of investigation lies both in its ability to uncover fundamental aspects of malignancy and in the translational potential of exploiting cancer metabolism to improve the way we diagnose, monitor, and treat cancer. Furthermore, an improved understanding of how altered metabolism contributes to cancer has a high potential for synergy with translational efforts. For example, the demonstration that asparagine is a conditionally essential nutrient in rapidly growing cancer cells paved the way for L-asparaginase therapy in leukemia. Additionally, the avidity of some tumors for glucose uptake led to the development of 18fluoro-2-deoxyglucose imaging by PET; this in turn stimulated hundreds of studies on the biological underpinnings of tumor glucose metabolism.

There continue to be large gaps in understanding which metabolic pathways are altered in cancer, whether these alterations benefit the tumor in a substantive way, and how this information could be used in clinical oncology. In this Review, we consider glutamine, a highly versatile nutrient whose metabolism has implications for tumor cell biology, metabolic imaging, and perhaps novel therapeutics.

Glutamine in intermediary metabolism

Glutamine metabolism has been reviewed extensively and is briefly outlined here (4, 5). The importance of glutamine as a nutrient in cancer derives from its abilities to donate its nitrogen and carbon into an array of growth-promoting pathways (Figure 1). At concentrations of 0.6–0.9 mmol/l, glutamine is the most abundant amino acid in plasma (6). Although most tissues can synthesize glutamine, during periods of rapid growth or other stresses, demand outpaces supply, and glutamine becomes conditionally essential (7). This requirement for glutamine is particularly true in cancer cells, many of which display oncogene-dependent addictions to glutamine in culture (8). Glutamine catabolism begins with its conversion to glutamate in reactions that either donate the amide nitrogen to biosynthetic pathways or release it as ammonia. The latter reactions are catalyzed by the glutaminases (GLSs), of which several isozymes are encoded by human genes GLS and GLS2 (9). Classical studies revealed that GLS isozymes, particularly those encoded by GLS, are expressed in experimental tumors in rats and mice, where their enzyme activity correlates with growth rate and malignancy. Silencing GLS expression or inhibiting GLS activity is sufficient to delay tumor growth in a number of models (1013). The role of GLS2 in cancer appears to be context specific and regulated by factors that are still incompletely characterized. In some tissues, GLS2 is a p53 target gene and seems to function in tumor suppression (14). On the other hand, GLS2 expression is enhanced in some neuroblastomas, where it contributes to cell survival (15). These observations, coupled with the demonstration that c-Myc stimulates GLS expression (12, 16), position at least some of the GLS isozymes as pro-oncogenic.

Glutamine metabolism as a target for diagnostic imaging and therapy in cancFigure 1Glutamine metabolism as a target for diagnostic imaging and therapy in cancer. Glutamine is imported via SLC1A5 and other transporters, then enters a complex metabolic network by which its carbon and nitrogen are supplied to pathways that promote cell survival and growth. Enzymes discussed in the text are shown in green, and inhibitors that target various aspects of glutamine metabolism are shown in red. Green arrows denote reductive carboxylation. 18F-labeled analogs of glutamine are also under development as PET probes for localization of tumor tissue. AcCoA, acetyl-CoA; DON, 6-diazo-5-oxo-L-norleucine; GSH, glutathione; NEAA, nonessential amino acids; ME, malic enzyme; OAA, oxaloacetate; TA, transaminase; 968, compound 968; α-KG, α-ketoglutarate.

Glutamate, the product of the GLS reaction, is a precursor of glutathione, the major cellular antioxidant. It is also the source of amino groups for nonessential amino acids like alanine, aspartate, serine, and glycine, all of which are required for macromolecular synthesis. In glutamine-consuming cells, glutamate is also the major source of α-ketoglutarate, a TCA cycle intermediate and substrate for dioxygenases that modify proteins and DNA. These dioxygenases include prolyl hydroxylases, histone demethylases, and 5-methylcytosine hydroxylases. Their requirement for α-ketoglutarate, although likely accounting for only a small fraction of total α-ketoglutarate utilization, makes this metabolite an essential component of cell signaling and epigenetic networks.

Conversion of glutamate to α-ketoglutarate occurs either through oxidative deamination by glutamate dehydrogenase (GDH) in the mitochondrion or by transamination to produce nonessential amino acids in either the cytosol or the mitochondrion. During avid glucose metabolism, the transamination pathway predominates (17). When glucose is scarce, GDH becomes a major pathway to supply glutamine carbon to the TCA cycle, and is required for cell survival (17, 18). Metabolism of glutamine-derived α-ketoglutarate in the TCA cycle serves several purposes: it generates reducing equivalents for the electron transport chain (ETC) and oxidative phosphorylation, becoming a major source of energy (19); and it is an important anaplerotic nutrient, feeding net production of oxaloacetate to offset export of intermediates from the cycle to supply anabolism (20). Glutamine oxidation also supports redox homeostasis by supplying carbon to malic enzyme, some isoforms of which produce NADPH (Figure 1). In KRAS-driven pancreatic adenocarcinoma cells, a pathway involving glutamine-dependent NADPH production is essential for redox balance and growth (21). In these cells, glutamine is used to produce aspartate in the mitochondria. This aspartate is then trafficked to the cytosol, where it is deaminated to produce oxaloacetate and then malate, the substrate for malic enzyme.

Recent work has uncovered an unexpected role for glutamine in cells with reduced mitochondrial function. Despite glutamine’s conventional role as a respiratory substrate, several studies demonstrated a persistence of glutamine dependence in cells with permanent mitochondrial dysfunction from mutations in the ETC or TCA cycle, or transient impairment secondary to hypoxia (2225). Under these conditions, glutamine-derived α-ketoglutarate is reductively carboxylated by NADPH-dependent isoforms of isocitrate dehydrogenase to produce isocitrate, citrate, and other TCA cycle intermediates (Figure 1). These conditions broaden glutamine’s utility as a carbon source because it becomes not only a major source of oxaloacetate, but also generates acetyl-CoA in what amounts to a striking rewiring of TCA cycle metabolism.

Glutamine promotes hallmarks of malignancy

Deregulated energetics. One hallmark of cancer cells is aberrant bioenergetics (26). Glutamine’s involvement in the pathways outlined above contributes to a phenotype conducive to energy formation, survival, and growth. In addition to its role in mitochondrial metabolism, glutamine also suppresses expression of thioredoxin-interacting protein, a negative regulator of glucose uptake (27). Thus, glutamine contributes to both of the energy-forming pathways in cancer cells: oxidative phosphorylation and glycolysis. Glutamine also modulates hallmarks not traditionally thought to be metabolic, as outlined below. These interactions highlight the complex interplay between glutamine metabolism and many aspects of cell biology.

Sustaining proliferative signaling. Pathological cancer cell growth relies on maintenance of proliferative signaling pathways with increased autonomy relative to non-malignant cells. Several lines of evidence argue that glutamine reinforces activity of these pathways. In some cancer cells, excess glutamine is exported in exchange for leucine and other essential amino acids. This exchange facilitates activation of the serine/threonine kinase mTOR, a major positive regulator of cell growth (28). In addition, glutamine-derived nitrogen is a component of amino sugars, known as hexosamines, that are used to glycosylate growth factor receptors and promote their localization to the cell surface. Disruption of hexosamine synthesis reduces the ability to initiate signaling pathways downstream of growth factors (29).

Enabling replicative immortality. Some aspects of glutamine metabolism oppose senescence and promote replicative immortality in cultured cells. In IMR90 lung fibroblasts, silencing either of two NADPH-generating isoforms of malic enzyme (ME1, ME2) rapidly induced senescence, while malic enzyme overexpression suppressed senescence (30). Both malic enzyme isoforms are repressed at the transcriptional level by p53 and contribute to enhanced levels of glutamine consumption and NADPH production in p53-deficient cells. The ability of p53-replete cells to resist senescence required the expression of ME1 and ME2, and silencing either enzyme reduced the growth of TP53+/+ and, to a lesser degree, TP53–/– tumors (30). These observations position malic enzymes as potential therapeutic targets.

Resisting cell death. Although many cancer cells require glutamine for survival, cells with enhanced expression of Myc oncoproteins are particularly sensitive to glutamine deprivation (8, 12, 16). In these cells, glutamine deprivation induces depletion of TCA cycle intermediates, depression of ATP levels, delayed growth, diminished glutathione pools, and apoptosis. Myc drives glutamine uptake and catabolism by activating the expression of genes involved in glutamine metabolism, including GLSand SLC1A5, which encodes the Na+-dependent amino acid transporter ASCT2 (12, 16). SilencingGLS mimicked some of the effects of glutamine deprivation, including growth suppression in Myc-expressing cells and tumors (10, 12). MYCN amplification occurs in 20%–25% of neuroblastomas and is correlated with poor outcome (31). In cells with high N-Myc levels, glutamine deprivation triggered an ATF4-dependent induction of apoptosis that could be prevented by restoring downstream metabolites oxaloacetate and α-ketoglutarate (15). In this model, pharmacological activation of ATF4, inhibition of glutamine metabolic enzymes, or combinations of these treatments mimicked the effects of glutamine deprivation in cells and suppressed growth of MYCN-amplified subcutaneous and transgenic tumors in mice.

The PKC isoform PKC-ζ also regulates glutamine metabolism. Loss of PKC-ζ enhances glutamine utilization and enables cells to survive glucose deprivation (32). This effect requires flux of carbon and nitrogen from glutamine into serine. PKC-ζ reduces the expression of phosphoglycerate dehydrogenase, an enzyme required for glutamine-dependent serine biosynthesis, and also phosphorylates and inactivates this enzyme. Thus, PKC-ζ loss, which promotes intestinal tumorigenesis in mice, enables cells to alter glutamine metabolism in response to nutrient stress.

Invasion and metastasis. Loss of the epithelial cell-cell adhesion molecule E-cadherin is a component of the epithelial-mesenchymal transition, and is sufficient to induce migration, invasion, and tumor progression (33, 34). Addiction to glutamine may oppose this process because glutamine favors stabilization of tight junctions in some cells (35). Furthermore, the selection of breast cancer cells with the ability to grow without glutamine yielded highly adaptable subpopulations with enhanced mesenchymal marker expression and improved capacity for anchorage-independent growth, therapeutic resistance, and metastasis in vivo (36). It is unknown whether this result reflects a primary role for glutamine in suppressing these markers of aggressiveness in breast cancer, or whether prolonged glutamine deprivation selects for cells with enhanced fitness across a number of phenotypes.

Organ-specific glutamine metabolism in health and disease

As a major player in carbon and nitrogen transport, glutamine metabolism displays complex inter-organ dynamics, with some organs functioning as net producers and others as consumers (Figure 2). Organ-specific glutamine metabolism has frequently been studied in humans and animal models by measuring the arteriovenous difference in plasma glutamine abundance. In healthy subjects, the plasma glutamine pool is largely the result of release from skeletal muscle (3739). In rats, the lungs are comparable to muscle in terms of glutamine production (40, 41), and human lungs also have the capacity for marked glutamine release, although such release is most prominent in times of stress (42, 43). Stress-induced release from the lung is regulated by an induction of glutamine synthase expression as a consequence of glucocorticoid signaling and other mechanisms (44, 45). Although this results in a small arteriovenous difference, the overall release of glutamine is significant because of the large pulmonary perfusion. In rats and humans, adipose tissue is a minor but potentially important source of glutamine (46, 47). The liver has the capacity to synthesize or catabolize glutamine, with these activities subject both to regional heterogeneity among hepatocytes and regulatory effects of systemic acidosis and hyperammonemia. However, the liver does not appear to be a major contributor to the plasma glutamine pool in healthy rats and humans (39, 48, 49).

Model for inter-organ glutamine metabolism in health and cancer.Figure 2Model for inter-organ glutamine metabolism in health and cancer. Organs that release glutamine into the bloodstream are shown in green, and those that consume glutamine are in red; the shade denotes magnitude of consumption/release. For some organs (liver, kidneys), evidence from model systems and/or human studies suggests that there is a change in net glutamine flux during tumorigenesis.

Glutamine consumption occurs largely in the gut and kidney. The organs of the gastrointestinal tract drained by the portal vein, particularly the small intestine, are major consumers of plasma glutamine in both rats and humans (37, 38, 49, 50). Enterocytes oxidize more than half of glutamine carbon to CO2, accounting for a third of the respiration of these cells in fasting animals (51). The kidney consumes net quantities of glutamine to maintain acid-base balance (37, 38, 52, 53). During acidosis, the kidneys substantially increase their uptake of glutamine, cleaving it by GLS to produce ammonia, which is excreted along with organic acids to maintain physiologic pH (52, 54). Glutamine is also a major metabolic substrate in lymphocytes and macrophages, at least during mitogenic stimulation of primary cells in culture (5557).

Importantly, cancer seems to cause major changes in inter-organ glutamine trafficking (Figure 2). Currently, much work in this area is derived from studies in methylcholanthrene-induced fibrosarcoma in the rat, a model of an aggressively growing, glutamine-consuming tumor. In this model, fibrosarcoma induces skeletal muscle expression of glutamine synthetase and greatly increases the release of glutamine into the circulation. As the tumor increases in size, intramuscular glutamine pools are depleted in association with loss of lean muscle mass, mimicking the cachectic phenotype of humans in advanced stages of cancer (52). Simultaneously, both the liver and the kidneys become net glutamine exporters, although the hepatic effect may be diminished as the tumor size becomes very large (48, 49, 52). Glutamine utilization by organs supplied by the portal vein is diminished in cancer (48). In addition to its function as a nutrient for the tumor itself, and possibly for cancer-associated immune cells, glutamine provides additional, indirect metabolic benefits to both the tumor and the host. For example, glutamine was used as a gluconeogenic substrate in cachectic mice with large orthotopic gliomas, providing a significant source of carbon in the plasma glucose pool (58). This glucose was taken up and metabolized by the tumor to produce lactate and to supply the TCA cycle.

It will be valuable to extend work in human inter-organ glutamine trafficking, both in healthy subjects and in cancer patients. Such studies will likely produce a better understanding of the pathophysiology of cancer cachexia, a major source of morbidity and mortality. Research in this area should also aid in the anticipation of organ-specific toxicities of drugs designed to interfere with glutamine metabolism. Alterations of glutamine handling in cancer may induce a different spectrum of toxicities compared with healthy subjects.

Tumors differ according to their need for glutamine

One important consideration is that not all cancer cells need an exogenous supply of glutamine. A panel of lung cancer cell lines displayed significant variability in their response to glutamine deprivation, with some cells possessing almost complete independence (59). Breast cancer cells also demonstrate systematic differences in glutamine dependence, with basal-type cells tending to be glutamine dependent and luminal-type cells tending to be glutamine independent (60). Resistance to glutamine deprivation is associated with the ability to synthesize glutamine de novo and/or to engage alternative pathways of anaplerosis (10, 60).

Tumors also display variable levels of glutamine metabolism in vivo. A study of orthotopic gliomas revealed that genetically diverse, human-derived tumors took up glutamine in the mouse brain but did not catabolize it (58). Rather, the tumors synthesized glutamine de novo and used pyruvate carboxylation for anaplerosis. Cells derived from these tumors did not require glutamine to survive or proliferate when cultured ex vivo. Glutamine synthesis from glucose was also a prominent feature of primary gliomas in human subjects infused with 13C-glucose at the time of surgical resection (61). Furthermore, an analysis of glutamine metabolism in lung and liver tumors revealed that both the tissue of origin and the oncogene influence whether the tumor produces or consumes glutamine (62). MET-induced hepatic tumors produced glutamine, whereas Myc-induced liver tumors catabolized it. In the lung, however, Myc expression was associated with glutamine accumulation.

This variability makes it imperative to develop ways to predict which tumors have the highest likelihood of responding to inhibitors of glutamine metabolism. Methods to image or otherwise quantify glutamine metabolism in vivo would be useful in this regard (63). Infusions of pre-surgical subjects with isotopically labeled glutamine, followed by extraction of metabolites from the tumor and analysis of 13C enrichment, can be used to detect both glutamine uptake and catabolism (58, 62). However, this approach requires a specimen of the tumor to be obtained. Approaches for glutamine-based imaging, which avoid this problem, include a number of glutamine analogs compatible with PET. Although glutamine could in principle be imaged using the radioisotopes 11C, 13N, or 18F, the relatively long half-life of the latter increases its appeal. In mice, 18F-(2S, 4R)4-fluoroglutamine is avidly taken up by tumors derived from highly glutaminolytic cells, and by glutamine-consuming organs including the intestine, kidney, liver, and pancreas (64). Labeled analogs of glutamate are also taken up by some tumors (65, 66). One of these, (4S)-4-(3-[18F] fluoropropyl)-L-glutamate (18F-FSPG, also called BAY 94-9392), was evaluated in small clinical trials involving patients with several types of cancer (65, 67). This analog enters the cell through the cystine/glutamate exchange transporter (xCtransport system), which is linked to glutathione biosynthesis (68). The analog was well tolerated, with high tumor detection rates and good tumor-to-background ratios in hepatocellular carcinoma and lung cancer.

PET approaches detect analog uptake and retention but cannot provide information about downstream metabolism. Analysis of hyperpolarized nuclei can provide a real-time view of enzyme-catalyzed reactions. This technique involves redistribution of the populations of energy levels of a nucleus (e.g., 13C, 15N), resulting in a gain in magnetic resonance signal that can temporarily exceed 10,000-fold (69). This gain in signal enables rapid detection of both the labeled molecule and its downstream metabolites. Glutamine has been hyperpolarized on 15N and 13C (70, 71). In the latter case, the conversion of hyperpolarized glutamine to glutamate could be detected in intact hepatoma cells (70). If these analogs are translated to clinical studies, they might provide a dynamic view of the proximal reactions of glutaminolysis in vivo.

Pharmacological strategies to inhibit glutamine metabolism in cancer

Efforts to inhibit glutamine metabolism using amino acid analogs have an extensive history, including evaluation in clinical trials. Acivicin, 6-diazo-5-oxo-L-norleucine, and azaserine, three of the most widely studied analogs (Figure 1), all demonstrated variable degrees of gastrointestinal toxicity, myelosuppression, and neurotoxicity (72). Because these agents non-selectively target glutamine-consuming processes, recent interest has focused on developing methods directed at specific nodes of glutamine metabolism. First, ASCT2, the Na+-dependent neutral amino acid transporter encoded by SLC1A5, is broadly expressed in lung cancer cell lines and accounts for a majority of glutamine transport in those cells (Figure 1). It has been shown that γ-L-glutamyl-p-nitroanilide (GPNA) inhibits this transporter and limits lung cancer cell growth (73). Additional interest in GPNA lies in its ability to enhance the uptake of drugs imported via the monocarboxylate transporter MCT1. Suppressing glutamine uptake with GPNA enhances MCT1 stability and stimulates uptake of the glycolytic inhibitor 3-bromopyruvate (3-BrPyr) (74, 75). Because enforced MCT1 overexpression is sufficient to sensitize tumor xenografts to 3-BrPyr (76), GPNA may have a place in 3-BrPyr–based therapeutic regimens.

Two inhibitors of GLS isoforms have been characterized in recent years (Figure 1). Compound 968, an inhibitor of the GLS-encoded splice isoform GAC, inhibits the transformation of fibroblasts by oncogenic RhoGTPases and delays the growth of GLS-expressing lymphoma xenografts (13). Bis-2-(5-phenylacetamido-1,2,4-thiadiazol-2-yl)ethyl sulfide (BPTES) also potently inhibits GLS isoforms encoded by GLS (77). BPTES impairs ATP levels and growth rates of P493 lymphoma cells under both normoxic and hypoxic conditions and suppresses the growth of P493-derived xenografts (78).

Evidence also supports a role for targeting the flux from glutamate to α-ketoglutarate, although no potent, specific inhibitors yet exist to inhibit these enzymes in intact cells. Aminooxyacetate (AOA) inhibits aminotransferases non-specifically, but milliomolar doses are typically used to achieve this effect in cultured cells (Figure 1). Nevertheless, AOA has demonstrated efficacy in both breast adenocarcinoma xenografts and autochthonous neuroblastomas in mice (15, 79). Epigallocatechin gallate (EGCG), a green tea polyphenol, has numerous pharmacological effects, one of which is to inhibit GDH (80). The effects of EGCG on GDH have been used to kill glutamine-addicted cancer cells during glucose deprivation or glycolytic inhibition (17, 18) and to suppress growth of neuroblastoma xenografts (15).

A paradigm to exploit glutamine metabolism in cancer

Recent advances in glutamine-based imaging, coupled with the successful application of glutamine metabolic inhibitors in mouse models of cancer, make it possible to conceive of treatment plans that feature consideration of tumor glutamine utilization. A key challenge will be predicting which tumors are most likely to respond to inhibitors of glutamine metabolism. Neuroblastoma is used here as an example of a tumor in which evidence supports the utility of strategies that would involve both glutamine-based imaging and therapy (Figure 3). Neuroblastoma is the second most common extracranial solid malignancy of childhood. High-risk neuroblastoma is defined by age, stage, and biological features of the tumor, including MYCN amplification, which occurs in some 20%–25% of cases (31). Because MYCN-amplified tumor cells require glutamine catabolism for survival and growth (15), glutamine-based PET at the time of standard diagnostic imaging could help predict which tumors would be likely to respond to inhibitors of glutamine metabolism. Infusion of 13C-glutamine coordinated with the diagnostic biopsy could then enable inspection of 13C enrichment in glutamine-derived metabolites from the tumor, confirming the activity of glutamine catabolic pathways. Following on evidence from mouse models of neuroblastoma, treatment could then include agents directed against glutamine catabolism (15). Of note, some tumors were sensitive to the ATF4 agonist fenretinide (FRT), alone or in combination with EGCG. Importantly, FRT has already been the focus of a Phase I clinical trial in children with solid tumors, including neuroblastoma, and was fairly well tolerated (81).

A strategy to integrate glutamine metabolism into the diagnosis, classificaFigure 3A strategy to integrate glutamine metabolism into the diagnosis, classification, treatment, and monitoring of neuroblastoma. Neuroblastoma commonly presents in children as an abdominal mass. A standard evaluation of a child with suspected neuroblastoma includes measurement of urine catecholamines, a bone scan, and full-body imaging with meta-iodobenzylguanidine (MIBG), all of which contribute to diagnosis and disease staging. In animal models, a subset of these tumors requires glutamine metabolism. This finding implies that approaches to image, quantify, or block glutamine metabolism (highlighted in red) in human neuroblastoma could be incorporated into the diagnosis and management of this disease. In particular, glutamine metabolic studies may help predict which tumors would respond to therapies targeting glutamine metabolism. HVA, homovanillic acid; VMA, vanillylmandelic acid.

Conclusions

Glutamine is a versatile nutrient required for the survival and growth of a potentially large subset of tumors. Work over the next several years should produce a more accurate picture of the molecular determinants of glutamine addiction and the identification of death pathways that execute cells when glutamine catabolism is impaired. Advancement of glutamine-based imaging into clinical practice should soon make it possible to differentiate tumors that take up glutamine from those that do not. Finally, the development of safe, high-potency inhibitors of key metabolic nodes should facilitate therapeutic regimens featuring inhibition of glutamine metabolism.

Therapeutic strategies impacting cancer cell glutamine metabolism

The metabolic adaptations that support oncogenic growth can also render cancer cells dependent on certain nutrients. Along with the Warburg effect, increased utilization of glutamine is one of the metabolic hallmarks of the transformed state. Glutamine catabolism is positively regulated by multiple oncogenic signals, including those transmitted by the Rho family of GTPases and by c-Myc. The recent identification of mechanistically distinct inhibitors of glutaminase, which can selectively block cellular transformation, has revived interest in the possibility of targeting glutamine metabolism in cancer therapy. Here, we outline the regulation and roles of glutamine metabolism within cancer cells and discuss possible strategies for, and the consequences of, impacting these processes therapeutically.

Cancer cell metabolism & glutamine addiction

Interest in the metabolic changes characteristic of malignant transformation has undergone a renaissance of sorts in the cancer biology and pharmaceutical communities. However, the recognition that an important connection exists between cellular metabolism and cancer began nearly a century ago with the work of Otto Warburg [13]. Warburg found that rapidly proliferating tumor cells exhibit elevated glucose uptake and glycolytic flux, and furthermore that much of the pyruvate generated by glycolysis is reduced to lactate rather than undergoing mitochondrial oxidation via the tricarboxylic acid (TCA) cycle (Figure 1). This phenomenon persists even under aerobic conditions (‘aerobic glycolysis’), and is known as the Warburg effect [4]. Warburg proposed that aerobic glycolysis was caused by defective mitochondria in cancer cells, but it is now known that mitochondrial dysfunction is relatively rare and that most tumors have an unimpaired capacity for oxidative phosphorylation [5]. In fact, the most important selective advantages provided by the Warburg effect are still debated. Although aerobic glycolysis is an inefficient way to produce ATP (2 ATP/glucose vs ~36 ATP/glucose by complete oxidation), a high glycolytic flux can generate ATP rapidly and furthermore can provide a biosynthetic advantage by supplying precursors and reducing equivalents for the synthesis of macromolecules [4]. The mechanisms underlying the Warburg effect are also not yet fully resolved, although it is increasingly clear that a number of oncogenes and tumor suppressors contribute to the phenomenon. The PI3K/Akt/mTORC1 signaling axis, for example, is a key regulator of aerobic glycolysis and biosynthesis, driving the surface expression of nutrient transporters and the upregulation of glycolytic enzymes [6]. The HIF transcription factor also upregulates expression of glucose transporters and glycolytic enzymes in response to hypoxia and growth factors (or loss of the von Hippel–Landau [VHL] tumor suppressor), and the oncogenic transcription factor c-Myc similarly induces expression of proteins important for glycolysis [6].

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4154374/bin/nihms610340f1.jpg

Cell proliferation requires metabolic reprogramming

A second major change in the metabolic program of many cancer cells, and the primary focus of this review, is the alteration of glutamine metabolism. Glutamine is the major carrier of nitrogen between organs, and the most abundant amino acid in plasma [7]. It is also a key nutrient for numerous intracellular processes including oxidative metabolism and ATP generation, biosynthesis of proteins, lipids and nucleic acids, and also redox homeostasis and the regulation of signal transduction pathways [810]. Although most mammalian cells are capable of synthesizing glutamine, the demand for this amino acid can become so great during rapid proliferation that an additional extracellular supply is required; hence glutamine is considered conditionally essential [11]. Indeed, many cancer cells are ‘glutamine addicted’, and cannot survive in the absence of an exogenous glutamine supply [12,13].

An important step in the elevation of glutamine catabolism is the activation of the mitochondrial enzyme glutaminase, which catalyzes the hydrolysis of glutamine to generate glutamate and ammonium. The subsequent deamination of glutamate releases a second ammonium to yield the TCA cycle intermediate α-ketoglutarate (α-KG), a reaction catalyzed by glutamate dehydrogenase (GLUD1). This series of reactions is particularly important in rapidly proliferating cells, in which a considerable proportion of the TCA cycle metabolite citrate is exported from mitochondria in order to generate cytosolic acetyl-CoA for lipid biosynthesis [14]. Replenishment of TCA cycle intermediates (anaplerosis) is therefore required, and glutamine often serves as the key anaplerotic substrate through its conversion via glutamate to α-KG (Figure 1).

Mammals express two genes for glutaminase enzymes [1517]. The GLS gene encodes a protein initially characterized in kidney and thus called kidney-type glutaminase (KGA), although this enzyme and its shorter splice variant glutaminase C (GAC), collectively referred to as GLS, are now known to be widely distributed [1820]. The KGA and GAC isoforms share identical N-terminal and catalytic domains, encoded by exons 1–14 of the GLS gene, but have distinct C-termini derived from exon 15 in the case of GAC and exons 16–19 in the case of KGA [21]. Upregulation of GLS, in particular the GAC iso-form, is common in cancer cells and the degree of GLS overexpression correlates with both the degree of malignancy and the tumor grade in human breast cancer samples [22,23]. The GLS2 gene encodes a protein originally discovered and characterized in liver, which has thus been referred to as liver-type glutaminase and, more recently, as glutaminase 2 (GLS2) [15].

Both KGA and GAC can be activated by inorganic phosphate (Pi), and this activation correlates closely with a dimer-to-tetramer transition for each enzyme [7, 22]. As the concentration of Pi is raised the apparent catalytic constant, kcatapp, increases and simultaneously the apparent Michaelis constant, Kmapp, decreases; consequently the catalytic efficiency rises dramatically, especially in the case of GAC [22]. x-ray crystal structures of GAC and KGA in different states indicate that the positioning of a key loop within each monomer (Glu312 to Pro329), located between the active site and the dimer–dimer interface, is critical for mediating tetramerization-induced activation [22,24]. Given the ability of Pi to promote tetramerization and activation of GAC and KGA, it has been proposed that the elevated mitochondrial Pi levels found under hypoxic conditions, which are commonly encountered in the tumor microenvironment, could be one trigger for GLS activation [22].

Oncogenic alterations affecting glutamine metabolism

At least two classes of cellular signals regulate glutamine metabolism, influencing both the expression level and the enzymatic activity of GLS. The transcription factor c-Myc can suppress the expression of microRNAs miR-23a and miR-23b and, in doing so, upregulates GLS (specifically GAC) expression [13,25]. Independent of changes in GAC expression, oncogenic diffuse B-cell lymphoma protein (Dbl), a GEF for Rho GTPases and oncogenic variants of downstream Rho GTPases are able to signal to activate GAC in a manner that is dependent on NF-κB [23]. Mitochondria isolated from Dbl- or Rho GTPase-transformed NIH-3T3 fibroblasts demonstrate significantly higher basal glutaminase activity than mitochondria isolated from non-transformed cells [23]. Furthermore, the enzymatic activity of GAC immunoprecipitated from Dbl-transformed cells is elevated relative to GAC from non-transformed cells, indicating the presence of activating post-translational modification(s) [23]. Indeed, when GAC isolated from Dbl-transformed cells is treated with alkaline phosphatase, basal enzymatic activity is dramatically reduced [23]. Collectively, these findings point to phosphorylation events underlying the activation of GAC in transformed cells. Similarly, phosphorylation-dependent regulation of KGA activity downstream of the Raf-Mek-Erk signaling axis occurs in response to EGF stimulation [24].

It is becoming clear that, in addition to c-Myc and Dbl, many other oncogenic signals and environmental conditions can impact cellular glutamine metabolism. Loss of the retinoblastoma tumor suppressor, for example, leads to a marked increase in glutamine uptake and catabolism, and renders mouse embryonic fibroblasts dependent on exogenous glutamine [26]. Cells transformed by KRAS also illustrate increased expression of genes associated with glutamine metabolism and a corresponding increased utilization of glutamine for anabolic synthesis [27]. In fact, KRAS signaling appears to induce glutamine dependence, since the deleterious effects of glutamine withdrawal in KRAS-driven cells can be rescued by expression of a dominant-negative GEF for Ras [28]. Downstream of Ras, the Raf-MEK-ERK signaling pathway has been implicated in the upregulation of glutamine uptake and metabolism [24,29]. A recent study using human pancreatic ductal adenocarcinoma cells identified a novel KRAS-regulated metabolic pathway, through which glutamine supports cell growth [30]. Proliferation of KRAS-mutant pancreatic ductal adenocarcinoma cells depends on GLS-catalyzed production of glutamate, but not on downstream deamination of glutamate to α-KG; instead, transaminase-mediated glutamate metabolism is essential for growth. Glutamine-derived aspartate is subsequently transported into the cytoplasm where it is converted by aspartate transaminase into oxaloacetate, which can be used to generate malate and pyruvate. The series of reactions maintains NADPH levels and thus the cellular redox state [30].

Other recent studies have revealed that another pathway for glutamine metabolism can be essential under hypoxic conditions, and also in cancer cells with mitochondrial defects or loss of the VHL tumor suppressor [3135]. In these situations, glutamine-derived α-KG undergoes reductive carboxylation by IDH1 or IDH2 to generate citrate, which can be exported from mitochondria to support lipogenesis (Figure 1). Activation of HIF is both necessary and sufficient for driving the reductive carboxylation phenotype in renal cell carcinoma, and suppression of HIF activity can induce a switch from glutamine-mediated lipogenesis back to glucose-mediated lipogenesis [32,35]. Furthermore, loss of VHL and consequent downstream activation of HIF renders renal cell carcinoma cells sensitive to inhibitors of GLS [35]. Evidently, the metabolic routes through which glutamine supports cancer cell proliferation vary with genetic background and with microenvironmental conditions. Nevertheless, it is increasingly clear that diverse oncogenic signals promote glutamine utilization and furthermore that hypoxia, a common condition within poorly vascularized tumors, increases glutamine dependence.

…….

Consistent with the critical role of TCA cycle anaplerosis in cancer cell proliferation, a range of glutamine-dependent cancer cell lines are sensitive to silencing or inhibition of GLS [23,93]. Although loss of GLS suppresses proliferation, in some cases the induction of a compensatory anaplerotic mechanism mediated by pyruvate carboxylase (PC) allows the use of glucose- rather than glutamine-derived carbon for anaplerosis [93]. Low glutamine conditions render glioblastoma cells completely dependent on PC for proliferation; reciprocally, glucose deprivation causes them to become dependent on GLUD1, presumably as a mediator of glutamine-dependent anaplerosis [94]. These studies provide insight into the possibility of inhibiting glutamine-dependent TCA cycle anaplerosis (e.g., with 968 or BPTES) and indicate that high expression of PC could represent a means of resistance to GLS inhibitors.

In c-Myc-induced human Burkitt lymphoma P493 cells, entry of glucose-derived carbon into the TCA cycle is attenuated under hypoxia, whereas glutamine oxidation via the TCA cycle persists [95]. Upon complete withdrawal of glucose, the TCA cycle continues to function and is driven by glutamine. The proportions of viable and proliferating cell populations are almost identical in glucose-replete and -deplete conditions so long as glutamine is present. Inhibition of GLS by BPTES causes a decrease in ATP and glutathione levels, with a simultaneous increase in reactive oxygen species production. Strikingly, whereas BPTES treatment under aerobic conditions suppresses proliferation, under hypoxic conditions it results in cell death, an effect ascribed to glutamine’s critical roles in alleviating oxidative stress in addition to supporting bioenergetics.

In addition to deamidation, glutamine-derived carbon can also reach the TCA cycle through transamination [96], and recent studies indicate that inhibition of this process could be a promising strategy for cancer treatment [30,97,98]. The transaminase inhibitor amino-oxyacetate selectively suppresses proliferation of the aggressive breast cancer cell line MDA-MB-231 relative to normal human mammary epithelial cells, and similar effects were observed with siRNA knockdown of aspartate transaminase [97]. Treatment with amino-oxyacetate killed glutamine-dependent glioblastoma cells, in a manner that could be rescued by α-KG and was dependent on c-Myc expression [13]. Transaminase inhibitors have also been found to suppress both anchorage-dependent and anchorage-independent growth of lung carcinoma cells [98].

Reductive carboxylation

The central metabolic precursor for fatty acid biosynthesis is acetyl-CoA, which can be generated from pyruvate in the mitochondria by pyruvate dehydrogenase. Since acetyl-CoA cannot cross the inner mitochondrial membrane, it is exported to the cytosol via the citrate shuttle following its condensation with oxaloacetate in the TCA cycle (Figure 3). In the cytosol, citrate is converted back to acetyl-CoA and oxaloacetate in a reaction catalyzed by ATP citrate lyase. In addition to its synthesis from glycolytic pyruvate, citrate can also be generated by reductive carboxylation of α-KG [99]. Across a range of cancer cell lines, 10–25% of lipogenic acetyl-CoA is generated from glutamine via this reductive pathway; indeed, reductive metabolism is the primary route for incorporation of glutamine, glutamate and α-KG carbon into lipids [32]. Some of the reductive carboxylation of α-KG is catalyzed by cytosolic IDH1, as well as by mitochondrial IDH2 and/or IDH3.

In A549 lung carcinoma cells, glutamine dependence and reductive carboxylation flux increases under hypoxic conditions [32,34], such that glutamine-derived α-KG accounts for approximately 80% of the carbon used for de novo lipogenesis. Similarly, in melanoma cells, the major source of carbon for acetyl-CoA, citrate and fatty acids switches from glucose under normoxia to glutamine (via reductive carboxylation) under hypoxia [31]. The hypoxic switch to reductive glutamine metabolism is dependent on HIF, and constitutive activation of HIF is sufficient to induce the preferential reductive metabolism of α-KG even under normoxic conditions [32]. Tumor cells with mitochondrial defects, such as electron-transport chain mutations/inhibition, also use glutamine-dependent reductive carboxylation as the major pathway for citrate generation, and loss of electron-transport chain activity is sufficient to induce a switch from glucose to glutamine as the primary source of lipogenic carbon [33].

Together these studies indicate that mitochondrial defects/inhibition, and/or hypoxia, might sensitize cancer cells to inhibition of GLS. The fact that P493 cells are more sensitive to BPTES under hypoxic conditions could in part be explained by an increased reliance on glutamine-dependent reductive carboxylation for lipogenesis [95]. Intriguingly, cancer cells harboring neoenzymatic mutations in IDH1, which results in production of the oncometabolite 2-hydroxyglutarate, are also sensitized to GLS inhibition [100]. 2-hydroxyglutarate is generated primarily from glutamine-derived α-KG [100,101], and therefore tumors expressing mutant IDH might be especially susceptible to alterations in α-KG levels.

……

As with all therapies, the potential side effects of strategies impacting glutamine metabolism must be seriously considered. The widespread use of l-asparaginase to lower plasma asparagine and glutamine concentrations in ALL patients demonstrates the potential for glutamine metabolism to be safely targeted, and also sheds light on potential toxicological consequences. For example, glutamine is known to be essential for the proliferation of lymphocytes, macrophages and neutrophils, and immunosuppression is a known side effect of l-asparaginase treatment, requiring close monitoring [11,105]. Evidence from early trials using glutamine-mimetic anti-metabolites, such as l-DON, indicates that these unselective molecules can cause excessive gastrointestinal toxicity and neurotoxicity. Within the brain, GLS converts glutamine into the neurotransmitter glutamate in neurons; astrocytes then take up synaptically released glutamate and convert it back to glutamine, which is subsequently transported back to neurons [106,107].

……

It has become clear during the past decade that altered metabolism plays a critical, in some cases even causal, role in the development and maintenance of cancers. It is now accepted that virtually all oncogenes and tumor suppressors impact metabolic pathways [5]. Furthermore, mutations in certain metabolic enzymes (e.g., isocitrate dehydrogenase, succinate dehydrogenase and fumarate hydratase) are associated with both familial and sporadic human cancers [113]. With this realization has come a renewed interest in the possibility of selectively targeting the metabolism of cancer cells as a therapeutic strategy. The use of l-asparaginase to treat ALL by depleting plasma asparagine and glutamine levels and the promising outcome of the first use of dichloroacetate (which acts, at least in part, through its inhibition of the metabolic enzyme pyruvate dehydrogenase kinase) in glioblastoma patients [114,115], support the notion that cancer metabolism can be safely and effectively targeted in the clinic. The metabolic adaptations of cancer cells must balance the requirements for modestly increased ATP synthesis, dramatically upregulated macromolecular biosynthesis and maintenance of redox balance. By serving as a carbon source for energy generation, a carbon and nitrogen source for biosynthesis and a precursor of the cellular antioxidant glutathione, glutamine is able to contribute to each of these requirements.

The countless combinations of genetic alterations that are found in human neo-plasias mean that there is not a single rigid metabolic program that is characteristic of all transformed cells. This perhaps explains why some current anti-metabolite chemotherapies (e.g., those targeting nucleotide synthesis) are effective only for certain malignancies. A deeper understanding of the metabolic alterations within specific genetic contexts will allow for better-targeted therapeutic interventions. Furthermore, it seems highly likely that combination therapies based on drug synergisms will be especially important for exploiting therapeutic windows within which cancer cells, but not normal cells, are impacted [37]. Glucose and glutamine metabolic pathways, for example, might be able to compensate for one another under some circumstances. When glucose metabolism is impaired in glioblastoma cells, glutamine catabolism becomes essential for survival [94]; reciprocally, suppression of GLS expression causes cells to become fully dependent on glucose-driven TCA cycle anaplerosis via PC [93]. The implication is that PC inhibition could synergize with GLS inhibition.

A topic warranting further investigation is the role that GLS2 plays in cellular metabolism. GLS, in particular the GAC isoform, is upregulated downstream of oncogenes and downregulated by tumor suppressors, and is essential for growth of many cancer cells. In contrast, GLS2 is activated by the ‘universal’ tumor suppressor p53, and furthermore is significantly downregulated in liver tumors and can block transformed characteristics of some cancer cells when overexpressed [116118]. Emphasizing the importance of genetic context, it was recently reported that GLS2 is significantly upregulated in neuroblastomas overexpressing N-Myc [119]. There are various possible explanations for the apparently different roles of two enzymes that catalyze the same reaction. Because the regulation of GLS and GLS2 is distinct, they will be called up under different conditions. The two enzymes have different kinetic characteristics, and therefore might influence energy metabolism and antioxidant defense in different manners [20]. There is also evidence that GLS2 may act, directly or indirectly, as a transcription factor [118]. Finally, it is possible that the different interactions of GLS and GLS2 with other proteins are responsible for their apparently different roles.

 

Mitochondria as biosynthetic factories for cancer proliferation

Christopher S Ahn and Christian M Metallo

Cancer & Metabolism (2015) 3:1      http://dx.doi.org:/10.1186/s40170-015-0128-2

Unchecked growth and proliferation is a hallmark of cancer, and numerous oncogenic mutations reprogram cellular metabolism to fuel these processes. As a central metabolic organelle, mitochondria execute critical biochemical functions for the synthesis of fundamental cellular components, including fatty acids, amino acids, and nucleotides. Despite the extensive interest in the glycolytic phenotype of many cancer cells, tumors contain fully functional mitochondria that support proliferation and survival. Furthermore, tumor cells commonly increase flux through one or more mitochondrial pathways, and pharmacological inhibition of mitochondrial metabolism is emerging as a potential therapeutic strategy in some cancers. Here, we review the biosynthetic roles of mitochondrial metabolism in tumors and highlight specific cancers where these processes are activated.

………………

Recent characterizations of metabolic enzymes as tumor suppressors and oncogene-driven metabolic reprogramming have reinvigorated interest in cancer metabolism. Although therapies targeting metabolic processes have long been a staple in cancer treatment (e.g. inhibition of folate metabolism via methotrexate), the focused therapeutic potential surrounding these findings have generated a renewed appreciation for Otto Warburg’s work almost a century ago. Warburg observed that tumor cells ferment much of the glucose taken up during growth to lactate, thus using glycolysis as a major means of adenosine triphosphate (ATP) regeneration [1]. However, the observation of decreased respiration in cancer cells and idea that “the respiration of all cancer cells is damaged” belies the critical role of mitochondria in biosynthesis and cell survival [1]. On the contrary, functional mitochondria are present in all proliferative cells within our body (including all tumors), as they are responsible for converting the diverse nutrients available to cells into the fundamental building blocks required for cell growth. These organelles execute numerous functions in cancer cells to promote tumor growth and survival in response to stress. Here, we outline the critical biosynthetic functions served by mitochondria within tumors (Figure 1). Although many of these functions are similarly important in normal, proliferating cells, we have attempted to highlight potential points where mitochondrial metabolism may be therapeutically targeted to slow cancer growth. This review is organized by specific metabolic pathways or processes (i.e., glucose metabolism and lipogenesis, amino acid metabolism, and nucleotide biosynthesis). Tumors or cancer cell types where enzymes in each pathway have been specifically observed to by dysregulated are described within the text and summarized in Table 1.

https://static-content.springer.com/image/art%3A10.1186%2Fs40170-015-0128-2/MediaObjects/40170_2015_128_Fig1_HTML.gif

Figure 1

Biosynthetic nodes within mitochondria. Metabolic pathways within mitochondria that contribute to biosynthesis in cancer and other proliferating cells. TCA metabolism and FOCM enable cells to convert carbohydrates and amino acids to lipids, non-essential amino acids, nucleotides (including purines used for cofactor synthesis), glutathione, heme, and other cellular components. Critical biosynthetic routes are indicated by yellow arrows. Enzymatic reactions that are dependent on redox-sensitive cofactors are depicted in red.  https://static-content.springer.com/image/art%3A10.1186%2Fs40170-015-0128-2/MediaObjects/40170_2015_128_Fig1_HTML.gif

Table 1

Overview of mitochondrial biosynthetic enzymes important in cancer

TCA cycle, anaplerosis, and AcCoA metabolism

Cancers in which three or more mitochondrial enzymes have been studied and found to be differentially regulated (or mutated, as indicated) in cancers vs. control groups are included. Dysregulation of each enzyme was demonstrated in clinical tumors samples, animal models, or cell lines at the levels of genes, mRNA, protein, metabolites, and/or flux.

https://static-content.springer.com/image/art%3A10.1186%2Fs40170-015-0128-2/MediaObjects/40170_2015_128_Fig2_HTML.gif

Figure 2

Coordination of carbon and nitrogen metabolism across amino acids. Glutamate and aKG are key substrates in numerous transamination reactions and can also serve as precursors for glutamine, proline, and the TCA cycle. Mitochondrial enzymes catalyzing these reactions are highlighted in blue, and TCA cycle intermediates are highlighted in orange (pyruvate enters the TCA cycle as acetyl-CoA or oxaloacetate).
https://static-content.springer.com/image/art%3A10.1186%2Fs40170-015-0128-2/MediaObjects/40170_2015_128_Fig2_HTML.gif

https://static-content.springer.com/image/art%3A10.1186%2Fs40170-015-0128-2/MediaObjects/40170_2015_128_Fig3_HTML.gif

Figure 3

Biosynthetic sources for purine and pyrimidine synthesis. Sources and fates of nitrogen, carbon, and oxygen atoms are colored as indicated. Italicized metabolites can be sourced from the mitochondria or cytosol. The double bond formed by the action of DHODH/ubiquinone is also indicated.      https://static-content.springer.com/image/art%3A10.1186%2Fs40170-015-0128-2/MediaObjects/40170_2015_128_Fig3_HTML.gif

Mitochondria operate as both engine and factory in eukaryotes, coordinating cellular energy production and the availability of fundamental building blocks that are required for cell proliferation. Cancer cells must therefore balance their relative bioenergetic and biosynthetic needs to grow, proliferate, and survive within the physical constraints of energy and mass conservation. In contrast to quiescent cells, which predominantly use oxidative mitochondrial metabolism to produce ATP and uptake glucose at much lower rates than proliferating cells, tumor cells exhibit increased glycolytic rates to provide an elevated flux of substrate for biosynthetic pathways, including those executed within mitochondria. Given these higher rates of nutrient utilization, metabolic flux through mitochondrial pathways and the associated ROS production can often be higher in cancer cells. Not surprisingly, activation of cellular antioxidant response pathways is commonly observed in cancer or subpopulations of cells within tumors [46,78]. Cellular compartmentalization affords a degree of protection from such damaging side products of metabolism, and methods which are able to deconvolute the relative contributions of each cellular compartment (e.g. mitochondria, cytosol, peroxisome, etc.) to cancer metabolism will be crucial to more completely understand the metabolism of cancer cells in the future [74,79]. Ultimately, while mitochondrial dysregulation is widely considered to be a hallmark of cancer, numerous mitochondrial functions remain critical for tumor growth and are emerging as clinical targets.

Following this point, it comes as no surprise that mitochondrial metabolism is highly active in virtually all tumors (i.e., cancer cells, stroma, or both), and investigators have begun targeting these pathways to explore potential efficacy. Indeed, some evidence suggests that biguanides such as metformin or phenformin may limit tumor incidence and burden in humans and animals [80,81]. These effects are presumably due, at least in part, to complex I inhibition of the ETC, which significantly perturbs mitochondrial function [82,83]. However, more insights are needed into the mechanisms of these compounds in patients to determine the therapeutic potential of targeting this and other components of mitochondria. In developing new therapies that target cancer metabolism, researchers will face challenges similar to those that are relevant for many established chemotherapies since deleterious effects on normal proliferating cells that also depend on mitochondrial metabolism (and aerobic glycolysis) are likely to arise.

As we acquire a more detailed picture of how specific genetic modifications in a patient’s tumor correlate with its metabolic profile, opportunities for designing targeted or combinatorial therapies will become increasingly apparent. Cancer therapies that address tumor-specific mitochondrial dysregulation and dysfunction may be particularly effective. For example, some cancer cells harbor mutations in TCA enzymes (e.g., FH, SDH, IDH2) or regulatory proteins that control mitophagy (i.e., LKB1) [84]. Such tumors may be compromised with respect to some aspects of mitochondrial biosynthesis and dependent on alternate pathways for growth and/or survival such that synthetically lethal targets emerge. Ultimately, such strategies will require clinicians and researchers to coordinate metabolic, biochemical, and genetic information in the design of therapeutic strategies.

 

David Terrano, M.D., Ph.D. commented on your update
“Not well versed in Nat peptides so I could not say. I also hesitate with any PNAS paper because those in their academy tend to have a fast track to publication. It has been that way since at least early 2000’s wh n I began research. I don’t doubt their goal and approach (this same group leads the way in methylation-based diagnosis of CNS neoplasms, which is apparently highly accurate). But when I see “dying cells” I know what that means biochemically and look for those hallmarks. Organ specific oligonucleosomes would be a nice cell death surrogate. “

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Writer and curator: Larry H. Bernstein, MD, FCAP and
Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013-01-23/larryhbern/Regulation-of-somatic-stem-cell-function/

There is an explosion of work-in-progress in applications to regenerative medicine using inducible pluripotent stem cells in both endothelial and cardiomyocyte postischemic repair, and also in post bone marrow radiation restoration, with benefits and hazards.  The following article is quite novel in that it deals with stem cell regulation by DNA methylation.  Therefore, it deals with the essentiality of methylation of DNA in epigenetic regulation.

This is the fourth discussion of a several part series leading from the genome, to protein synthesis (1), posttranslational modification of proteins (2), examples of protein effects on metabolism and signaling pathways (3), and leading to disruption of signaling pathways in disease (4), and effects leading to mutagenesis.

1.  A Primer on DNAand DNA Replication

2.  Overview of translational medicine

3.  Genes, proteomes, and their interaction

4. Regulation of somatic stem cell Function

5.  Proteomics – The Pathway to Understanding and Decision-making in Medicine

6.  Genomics, Proteomics and standards

7.  Long Non-coding RNAs Can Encode Proteins After All

8.  Proteins and cellular adaptation to stress

9.  Loss of normal growth regulation

 

Posttranslational modification is a step in protein biosynthesis. Proteins are created by ribosomes translating mRNA into polypeptide chains. These polypeptide chains undergo
PTM before becoming the mature protein product.

Regulation of somatic stem cell Function by DNA Methylation and Genomic Imprinting

Mo Li1, Na Young Kim1, Shigeo Masuda1 and Juan Carlos izpisua Belmonte1,2 1Salk institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA 92037, USA. 2Center of Regenerative Medicine in Barcelona, Dr Aiguader, 88, 08003 Barcelona, Spain. Corresponding author email: mli@salk.edu

Cell & Tissue Transplantation & Therapy 2013:5 19–23
http://dx.doi.org/10.4137/CTTT.S12142
This article is available from http://www.la-press.com

Abstract:

Epigenetic regulation is essential for self-renewal and differentiation of somatic stem cells, including

  • hematopoietic stem cells (HSCs) and
  • neural stem cells (NSCs).

The role of DNA methylation, a key epigenetic pathway,

  • in regulating somatic stem cell function
    • under physiological conditions and during aging

has been intensively investigated.

Accumulating evidence highlights the dynamic nature of

  • the DNAmethylome
    • during lineage commitment of somatic stem cells and
  • the pivotal role of DNAmethyltransferases in
    • stem cell self-renewal and differentiation.

Recent studies on genomic imprinting have shed light on

  • the imprinted gene network (IGN) in somatic stem cells,
  1. where a subset of imprinted genes remain expressed and
  2. are important for maintaining self-renewal of these cells.

Together with emerging technologies, elucidation of the epigenetic mechanisms regulating somatic stem cells with normal or pathological functions may contribute to the development of regenerative medicine.

Keywords: somatic stem cells, epigenetics, DNA methylation, genomic imprinting, hematopoietic stem cells, neural stem cells

Introduction

In adult animals, somatic stem cells (also known as adult stem cells) are responsible for maintaining tissue homeostasis and participate in tissue regeneration under injury conditions. Self-renewal and differentiation are two important aspects of somatic stem cell function. Epigenetic mechanisms underlying these processes have been intensively investigated. With the increasing ability

  • to identify and manipulate somatic stem cell populations from diverse tissues,
  • it is possible to dissect the epigenetic pathways that are
  1. either unique for a specific tissue or
  2. universally important in regulating stemness and differentiation.

Epigenetic control of somatic stem cell function exists at various levels, including

  • DNA methylation,
  • histone modification, and
  • higher-order chromatin structure dynamics.

Here, we focus on recent progress in our understanding of how

  • DNA methylation regulates somatic stem cell function.

DNA Methylation and stem cell Function

The role of DNA methylation in somatic stem cell compartments has gained increasing attention. Recent  evidence has shown that

  • DNA methylation is dynamically regulated during somatic stem cell differentiation and aging.1

A study of methylomes of human hematopoietic stem cells (HSCs) and two mature hematopoietic lineages,

  • including B cells and neutrophils, showed that
    • hypomethylated regions of lineage-specific genes often become methylated in opposing lineages, and that
    • progenitors display an intermediate methylation pattern

that is poised for lineage-specific resolution.2

Another study compared genome-wide promoter DNA methylation in human cord blood hematopoietic progenitor cells (HPCs) with

  • that in mobilized peripheral blood HPCs from aged individuals.

It was found that aged HPCs lose DNA methylation in a subset of genes that are hypomethylated in differentiated myeloid cells and

  • gain de novo DNA methylation at polycomb repressive complex 2 (PRC2) target sites.3

It was hypothesized that such epigenetic changes contribute to age-related loss of HSC function, such as a bias toward myeloid lineages. Recently, Beerman et al. studied the global DNA methylation landscape of HSCs in the context of

  • age-associated decline of HSC function.4

Over- all, the DNA methylation landscape remains stable during HSC ontogeny. However, HSCs isolated from old mice display higher global DNA methylation. Interestingly, they observed

  • localized DNA methylation changes in genomic regions associated with hematopoietic lineage differentiation.

These methylation changes preferentially map to genes

  • that are expressed in downstream progenitor and effector cells.

For example, genes that are important for the lymphoid and erythroid lineages

  • become methylated in “old” HSCs,

which is consistent with

  • the decline of lymphopoiesis and erythropoiesis during aging.

Additionally, inducing HSC proliferation by 5-fluorouracil treatment or

  • by limiting the number of transplantedHSCs
    • recapitulates the functional decline and DNA methylation changes during physiological aging.

A closer examination of the overlapping genes with significant DNA methylation changes during aging or enforced proliferation showed

  • an enrichment of DNA hypermethylation at PRC2 target loci,

echoing the observation by Bocker et al. in human HSCs.

Interestingly, a recent report showed that epigenetic alterations such as DNA hypermethylation that are accrued during aging,

  • can be fully reset by somatic reprogramming,

raising an interesting possibility that these aging-related epigenetic defects may be reserved by small molecules.5

Methylation of cytosines at CpG dinucleotides is catalyzed by three key enzymes.

DNA (cytosine-5)- methyltransferase 1 (DNMT1) is responsible for maintaining DNA methylation patterns during DNA replication

  • by methylating the newly synthesized hemi-methylated DNA.

The other two DNA methyltransferases, DNMT3a and DNMT3b,

  • are not DNA replication-dependent and can methylate fully unmethylated DNA de novo.

They are responsible for establishing new DNA methylation patterns during development.

DNMT3a, a gene required for neurogenesis,

  • is expressed in postnatal neural stem cells (NSCs).

In NSCs, DNMT3a methylates non-proximal promoter regions, such as gene bodies and intergenic regions. Surprisingly, rather than silencing gene expression,

DNMT3a-mediated DNA methylation in gene bodies antagonizes Polycomb-dependent repression and

  • facilitates the expression of neurogenic genes.6

The role of DNMT3a in HSCs has also been investigated. Both Dnmt3a and Dnmt3b are expressed in HSCs. An earlier study did not identify any defects in HSC function when Dnmt3a or Dnmt3b was removed.  However,

  • HSCs lackingboth of these de novomethyltransferases
    • fail to self-renew, yet retain the capacity to differentiate.7

A more recent study re-examined

  • the consequences of Dnmt3a loss in HSCs and
  • uncovered a progressive defect in differentiation that is only manifested during serial transplantation.8

At the molecular level, while Dnmt3a loss results in the expected hypomethylation at some loci,

  • it counterintuitively causes hypermethylation in even more regions.8

This seemingly paradoxical result echoes the  unconventional role of Dnmt3a in transcriptional  activation in NSCs (as discussed above). Both cases suggest a more complex regulatory function of DNMT3a that is

  • beyond simply methylating DNA.

In contrast, the loss of Dnmt1 produces more dramatic and immediate phenotypes in HSCs, manifested

  • in premature HSC exhaustion and
  • block of lymphoid differentiation,

highlighting the distinct requirements for different DNA methyltransferases in HSCs.9,10

Genomic Imprinting and stemness

DNA methylation also underlies genomic imprinting, which is an

  • evolutionarily conserved epigenetic mechanism of ensuring appropriate gene dosage during development.

One allele of the imprinted genes is

  • epigenetically marked by DNA methylation to be silenced according to the parental origin.

The pattern of imprinting

  • is established in germ cells and maintained in somatic cells.

Imprinted genes are thought to play critical roles in organismal growth and are relatively downregulated after birth.11 Recently, a series of reports demonstrated that

  • a subset of imprinted genes belonging to the purported imprinted gene network (IGN)12
  • remain expressed in somatic stem cells and
  • are important for maintaining self-renewal of these cells.

Through gene expression profiling, one group identified that several members of the IGN are expressed in

  1. murine muscle,
  2. epidermal, and
  3. long-term hematopoietic stem cells
  4. as well as in human epidermal and hematopoietic stem cells.13

In particular, the paternally expressed gene 3 (Peg3) gene was shown by another group

  • to mark cycling and quiescent stem cells in a wide variety of mouse tissues.14

The role of imprinted genes in regulating somatic stem cell function has been examined in two types of tissues.

In bronchioalveolar stem cells (BASCs), a lung epithelial stem cell population,

  • expression of IGN members is required for their self-renewal.

Bmi1, a polycomb repressive  complex 1 (PRC1) subunit,

  • is essential for controlling the expression of imprinted genes in BASCs without affecting their imprinting status.15

In Bmi1 mutant BASCs,  many members of the IGN become derepressed,

  • including p57, H19, Dlk1, Peg3, Ndn, Mest, Gtl2, Grb10, Plagl1, and Igf2.

Knockdown of p57, which is the most differentially expressed imprinted gene between normal and mutant BASCs,

  • partially rescues the self-renewal defect of lung stem cells.

Interestingly, insufficient levels of p57 also inhibit self-renewal of lung stem cells. Because p57 expression

  • remains monoallelic in Bmi1 knockdown cells,
  • Bmi1 is thought to maintain an appropriate level of expression from the expressed allele of p57.15

Another IGN member- delta-like homologue 1 (Dlk1) has been shown to be important for postnatal neurogenesis. Interestingly, in this context,

  • Dlk1 loses its imprinting in postnatal neural stem cells and niche astrocytes.16

These studies suggest that modulating IGN may represent another

  • epigenetic mechanism for balancing self-renewal and differentiation in somatic stem cells.

Thus, somatic stem cells either co-opt or remodel these developmental pathways involving the IGN

  • to fulfill the needs of tissue homeostasis during the adult stage.

In summary, several factors participate in regulating the epigenome of somatic stem cells.

Perturbations in the epigenome of somatic stem cells,

  • either during organismal aging or under pathological conditions,

will tip the balance between self-renewal and differentiation of somatic stem cells (Fig. 1). A detailed understanding of the mechanisms underlying these changes will likely result in novel therapeutic approaches targeting somatic stem cells.

Figure 1. The epigenome of somatic stem cells is regulated by diverse factors.

Future perspectives The epigenetic mechanisms governing self-renewal and differentiation of somatic stem cells are likely to be complex because of the diverse needs of different tissues. It would be interesting to determine whether a common mechanism, such as the IGN, exists across different somatic stem cells. Additionally, study- ing epigenetic pathways that are specific to one type of somatic stem cell requires the isolation of these cells and their differentiated progeny, which is more practical in model organisms than in humans. Along these lines, developing robust in vitro culture methods for human somatic stem cells and protocols for differentiating these cells into specific lineages are critical for uncovering epigenetic pathways that are unique to human somatic stem cells. In recent years, the field has seen a great improvement in methods of directed differentiation of human embryonic stem cells and induced pluripotent stem cells (iPSCs). For example, it is relatively straightforward to produce high-purity cell populations that resemble neural stem cells or mesenchymal stem cells from iPSCs.17

These methodologies not only are useful for studying the normal function of somatic stem cells, but also provide an exciting opportunity for understanding the role of somatic stem cells in disease pathology and a platform to screen for drugs. A recent study under- scored the usefulness of this approach. Liu et al. studied neural stem cells derived from Parkinson’s disease human iPSCs and uncovered previously unknown defects in nuclear morphology and epigenetic regulation in these derived NSCs.18 The cellular defects only menifest in “aged” neural stem cells, which is consistent with the fact that Parkinson’s disease pri- marily manifests in old age. More  importantly, this study identified neural stem cell as a potential target of therapeutic intervention for Parkinson’s disease.

Targeted modification of the human genome is  another technological advancement that is on the horizon to greatly facilitate the dissection of epige- netic pathways in somatic stem cells. Although gene targeting in somatic stem cells has been historically challenging, there have been encouraging successful reports following development of new genome-e diting technologies, such as Helper-dependent adenovi- ral vectors, TALENs, and CAS9/CRISPR. With the development of these new technologies, it seems that the stage has been set for a new wave of discoveries in epigenetic mechanisms of somatic stem cells.

References

1. Li M, Liu GH, Izpisua Belmonte JC. Navigating the epigenetic landscape of pluripotent stem cells. Nat Rev Mol Cell Biol. 2012;13(8):524–535.

2. Hodges E, Molaro A, Dos Santos CO, et al. Directional DNA methylation changes and complex intermediate states accompany lineage specificity in the adult hematopoietic compartment. Mol Cell. 2011;44(1):17–28.

3. Bocker MT, Hellwig I, Breiling A, Eckstein V, Ho AD, Lyko F. Genome- wide promoter DNA methylation dynamics of human hematopoietic progen- itor cells during differentiation and aging. Blood. 2011;117(19):e182–e189.

4. Beerman I, Bock C, Garrison BS, et al. Proliferation-dependent alterations of the DNA methylation landscape underlie hematopoietic stem cell aging. Cell Stem Cell. 2013;12(4):413–425.

5. Wahlestedt M, Norddahl GL, Sten G, et al. An epigenetic component of hematopoietic stem cell aging amenable to reprogramming into a young state. Blood. 2013;121(21):4257–4264.

6. Wu H, Coskun V, Tao J, et al. Dnmt3a-dependent nonpromoter DNA methylation facilitates transcription of neurogenic genes. Science. 2010; 329(5990):444–448.

7. Tadokoro Y, Ema H, Okano M, Li E, Nakauchi H. De novo DNA meth- yltransferase is essential for self-renewal, but not for differentiation, in hematopoietic stem cells. J Exp Med. 2007;204(4):715–722.

8. Challen GA, Sun D, Jeong M, et al. Dnmt3a is essential for hematopoietic stem cell differentiation. Nat Genet. 2011;44(1):23–31.

9. Broske AM, Vockentanz L, Kharazi S, et al. DNA methylation protects hematopoietic stem cell multipotency from myeloerythroid restriction. Nat Genet. 2009;41(11):1207–1215.

10. Trowbridge JJ, Snow JW, Kim J, Orkin SH. DNA methyltransferase 1 is essential for and uniquely regulates hematopoietic stem and progenitor cells. Cell Stem Cell. 2009;5(4):442–449.

11. Wood AJ, Oakey RJ. Genomic imprinting in mammals: emerging themes and established theories. PLoS Genet. 2006;2(11):e147.

12. Lui JC, Finkielstain GP, Barnes KM, Baron J. An imprinted gene network that controls mammalian somatic growth is down-regulated during postna- tal growth deceleration in multiple organs. Am J Physiol Regul Integr Comp Physiol. 2008;295(1):R189–R196.

13. Berg JS, Lin KK, Sonnet C, et al. Imprinted genes that regulate early mam- malian growth are coexpressed in somatic stem cells. PLoS One. 2011; 6(10):e26410.

14. Besson V, Smeriglio P, Wegener A, et al. PW1 gene/paternally expressed gene 3 (PW1/Peg3) identifies multiple adult stem and progenitor cell popu- lations. Proc Natl Acad Sci U S A. 2011;108(28):11470–11475.

15. Zacharek SJ, Fillmore CM, Lau AN, et al. Lung stem cell self-renewal relies on BMI1-dependent control of expression at imprinted loci. Cell Stem Cell. 2011;9(3):272–281.

16. Ferron SR, Charalambous M, Radford E, et al. Postnatal loss of Dlk1 imprinting in stem cells and niche astrocytes regulates neurogenesis. Nature. 2011;475(7356):381–385.

17. Li W, Sun W, Zhang Y, et al. Rapid induction and long-term self-renewal of primitive neural precursors from human embryonic stem cells by small molecule inhibitors. Proc Natl Acad Sci U S A. 2011;108(20):8299–8304.

18. Liu GH, Qu J, Suzuki K, et al. Progressive degeneration of human neural stem cells caused by pathogenic LRRK2. Nature. 2012;491(7425):603–607.

 

Additional References in Leaders in Pharmaceutical Intelligence

Proteomics and Biomarker Discovery

https://pharmaceuticalintelligence.com/2012/08/21/proteomics-and-biomarker-discovery/

Developments in the Genomics and Proteomics of Type 2 Diabetes Mellitus and Treatment Targets

https://pharmaceuticalintelligence.com/2013/12/08/developments-in-the-genomics-and-proteomics-of-type-2-diabetes-mellitus-and-treatment-targets/

Immune activation, immunity, antibacterial activity

https://pharmaceuticalintelligence.com/2014/07/06/immune-activation-immunity-antibacterial-activity/

Ubiquitin-Proteosome pathway, Autophagy, the Mitochondrion, Proteolysis and Cell Apoptosis: Part III

https://pharmaceuticalintelligence.com/2013/02/14/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis-reconsidered/

Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis

https://pharmaceuticalintelligence.com/2012/10/30/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis/

Research on inflammasomes opens therapeutic ways for treatment of rheumatoid arthritis

https://pharmaceuticalintelligence.com/2014/07/12/research-on-inflammasomes-opens-therapeutic-ways-for-treatment-of-rheumatoid-arthritis/

Update on mitochondrial function, respiration, and associated disorders

https://pharmaceuticalintelligence.com/2014/07/08/update-on-mitochondrial-function-respiration-and-associated-disorders/

MIT Scientists on Proteomics: All the Proteins in the Mitochondrial Matrix identified

https://pharmaceuticalintelligence.com/2013/02/03/mit-scientists-on-proteomics-all-the-proteins-in-the-mitochondrial-matrix-identified/

Mitochondrial Damage and Repair under Oxidative Stress

https://pharmaceuticalintelligence.com/2012/10/28/mitochondrial-damage-and-repair-under-oxidative-stress/

Bzzz! Are fruitflies like us?

https://pharmaceuticalintelligence.com/2014/07/07/bzzz-are-fruitflies-like-us/

Discovery of Imigliptin, a Novel Selective DPP-4 Inhibitor for the Treatment of Type 2 Diabetes

https://pharmaceuticalintelligence.com/2014/06/25/discovery-of-imigliptin-a-novel-selective-dpp-4-inhibitor-for-the-treatment-of-type-2-diabetes/

Molecular biology mystery unravelled

https://pharmaceuticalintelligence.com/2014/06/22/molecular-biology-mystery-unravelled/

Gene Switch Takes Blood Cells to Leukemia and Back Again

https://pharmaceuticalintelligence.com/2014/06/20/gene-switch-takes-blood-cells-to-leukemia-and-back-again/

Wound-healing role for microRNAs in colon offer new insight to inflammatory bowel diseases

https://pharmaceuticalintelligence.com/2014/06/19/wound-healing-role-for-micrornas-in-colon-offer-new-insight-to-inflammatory-bowel-diseases/

Targeting a key driver of cancer

https://pharmaceuticalintelligence.com/2014/06/20/targeting-a-key-driver-of-cancer/

Tang Prize for 2014: Immunity and Cancer

https://pharmaceuticalintelligence.com/2014/06/20/tang-prize-for-2014-immunity-and-cancer/

Confined Indolamine 2, 3 dioxygenase (IDO) Controls the Hemeostasis of Immune Responses for Good and Bad                             Demet Sag, PhD

https://pharmaceuticalintelligence.com/2013/07/31/confined-indolamine-2-3-dehydrogenase-controls-the-hemostasis-of-immune-responses-for-good-and-bad/

3:45 – 4:15, 2014, Scott Lowe “Tumor suppressor and tumor maintenance genes”

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Targeted genome editing by lentiviral protein transduction of zinc-finger and TAL-effector nucleases          Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2014/06/04/targeted-genome-editing-by-lentiviral-protein-transduction-of-zinc-finger-and-tal-effector-nucleases/

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Larry H. Bernstein, MD, FCAP, reviewer and curator

https://pharmaceuticalintelligence.com/2013-12-09/larryhbern/VEGF-activation-and-signaling,-lysine-methylation,-and-activation-of-receptor-tyrosine-kinase

Lysine Methylation Promotes VEGFR-2 Activation and Angiogenesis

 Edward J. Hartsough1*, Rosana D. Meyer1*, Vipul Chitalia2, Yan Jiang3, Victor E. Marquez4, Irina V. Zhdanova5, Janice Weinberg6, Catherine E. Costello3, and Nader Rahimi1{dagger}
 1 Departments of Pathology and Ophthalmology, School of Medicine, Boston University Medical Campus, Boston, MA 02118, USA.
2 Harvard-MIT Division of Health Science and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
3 Department of Biochemistry and Center for Biomedical Mass Spectrometry, School of Medicine, Boston University Medical Campus, Boston, MA 02118, USA.
4 Chemical Biology Laboratory, National Cancer Institute at Frederick, Frederick, MD 21702, USA.
5 Department of Anatomy and Neurobiology, Boston University Medical Campus, Boston, MA 02118, USA.
6 School of Public Health, Boston University Medical Campus, Boston, MA 02118, USA.
Activation of vascular endothelial growth factor receptor-2 (VEGFR-2), an endothelial cell receptor tyrosine kinase,
  • promotes tumor angiogenesis and ocular neovascularization.
We report the methylation of VEGFR-2 at multiple Lys and Arg residues, including Lys1041,
  • a residue that is proximal to the activation loop of the kinase domain.
Methylation of VEGFR-2 was
  • independent of ligand binding and
  • was not regulated by ligand stimulation.
Methylation of Lys1041 enhanced tyrosine phosphorylation and kinase activity in response to ligands. Additionally, interfering with the methylation of VEGFR-2 by pharmacological inhibition or by site-directed mutagenesis revealed that
  • methylation of Lys1041 was required for VEGFR-2–mediated angiogenesis
    • in zebrafish and
    • tumor growth in mice.
We propose that methylation of Lys1041 promotes the activation of VEGFR-2 and that
  • similar posttranslational modification could also regulate the activity of other receptor tyrosine kinases.
{dagger} Corresponding author. E-mail: nrahimi@bu.edu
Citation: E. J. Hartsough, R. D. Meyer, V. Chitalia, Y. Jiang, V. E. Marquez, I. V. Zhdanova, J. Weinberg, C. E. Costello, N. Rahimi, Lysine Methylation Promotes VEGFR-2 Activation and Angiogenesis. Sci. Signal. 6, ra104 (2013).

Phosphoproteomic Analysis Implicates the mTORC2-FoxO1 Axis in VEGF Signaling and Feedback Activation of Receptor Tyrosine Kinases

Guanglei Zhuang, Kebing Yu, Zhaoshi Jiang, Alicia Chung, Jenny Yao, Connie Ha, Karen Toy, Robert Soriano, Benjamin Haley, Elizabeth Blackwood, Deepak Sampath, Carlos Bais, Jennie R. Lill, and Napoleone Ferrara (16 April 2013){dagger}
Sci. Signal. 16 April 2013;  6 (271), ra25.    http://dx.doi.org/10.1126/scisignal.2003572
Genentech Inc., 1 DNA Way, South San Francisco, CA 94080, USA.
* These authors contributed equally to this work.{dagger}
{dagger} Present address: Department of Pathology and Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA.
The vascular endothelial growth factor (VEGF) signaling pathway plays a pivotal role in normal development and
  • also represents a major therapeutic target for tumors and intraocular neovascular disorders.
The VEGF receptor tyrosine kinases promote angiogenesis by phosphorylating downstream proteins in endothelial cells. We applied a large-scale proteomic approach to define
  1. the VEGF-regulated phosphoproteome and
  2. its temporal dynamics in human umbilical vein endothelial cells and then
  3. used siRNA (small interfering RNA) screens to investigate the function of a subset of these phosphorylated proteins in VEGF responses.
The PI3K (phosphatidylinositol 3-kinase)–mTORC2 (mammalian target of rapamycin complex 2) axis emerged as central
  1. in activating VEGF-regulated phosphorylation and
  2. increasing endothelial cell viability
    • by suppressing the activity of the transcription factor FoxO1 (forkhead box protein O1),
    • an effect that limited cellular apoptosis and feedback activation of receptor tyrosine kinases.
This FoxO1-mediated feedback loop not only reduced the effectiveness of mTOR inhibitors at decreasing protein phosphorylation and cell survival
  • but also rendered cells more susceptible to PI3K inhibition.
Collectively, our study provides a global and dynamic view of VEGF-regulated phosphorylation events and
  • implicates the mTORC2-FoxO1 axis in VEGF receptor signaling and
  • reprogramming of receptor tyrosine kinases in human endothelial cells.
{ddagger} Corresponding author. E-mail: nferrara@ucsd.edu
Citation: G. Zhuang, K. Yu, Z. Jiang, A. Chung, J. Yao, C. Ha, K. Toy, R. Soriano, B. Haley, E. Blackwood, D. Sampath, C. Bais, J. R. Lill, N. Ferrara, Phosphoproteomic Analysis Implicates the mTORC2-FoxO1 Axis in VEGF Signaling and Feedback Activation of Receptor Tyrosine Kinases. Sci. Signal. 6, ra25 (2013).

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Transthyretin and Lean Body Mass in Stable and Stressed State

Curator: Larry H Bernstein, MD, FCAP

Chapter 20
Plasma Transthyretin Reflects the Fluctuations
of Lean Body Mass in Health and Disease
Yves Ingenbleek
Abstract

Transthyretin (TTR) is a 55-kDa protein secreted mainly by the choroid plexus and the liver. Whereas its intracerebral production appears as a stable secretory process allowing even distribution of intrathecal thyroid hormones, its hepatic synthesis is influenced by nutritional and inflammatory circumstances working concomitantly. Both morbid conditions are governed by distinct pathogenic mechanisms leading to the reduction in size of lean body mass (LBM). The liver production of TTR integrates the dietary and stressful components of any disease spectrum, explaining why it is the sole plasma protein whose evolutionary patterns closely follow the shape outlined by LBM fluctuations. Serial measurement of TTR therefore provides unequalled information on the alterations affecting overall protein nutritional status. Recent advances in TTR physiopathology emphasize the detecting power and preventive role played by the protein in hyperhomocysteinemic states, acquired metabolic disorders currently ascribed to dietary restriction in water-soluble vitamins. Sulfur (S)-deficiency is proposed as an additional causal factor in the sizeable proportion of hyperhomocysteinemic patients characterized by adequate vitamin intake but experiencing varying degrees of nitrogen (N)-depletion. Owing to the fact that N and S coexist in plant and animal tissues within tightly related concentrations, decreasing LBM as an effect of dietary shortage and/or excessive hypercatabolic losses induces proportionate S-losses. Regardless of water-soluble vitamin status, elevation of homocysteine plasma levels is negatively correlated with LBM reduction and declining TTR plasma levels. These findings occur as the result of impaired cystathionine-b-synthase activity, an enzyme initiating the transsulfuration pathway and whose suppression promotes the upstream accumulation and remethylation of homocysteine molecules. Under conditions of N- and S-deficiencies,the maintenance of methionine homeostasis indicates high metabolic priority.
Y. Ingenbleek
Laboratory of Nutrition, University Louis Pasteur Strasbourg
e-mail: yves.ingenbleek@wanadoo.fr
S.J. Richardson and V. Cody (eds.), Recent Advances in Transthyretin Evolution, 329
Structure and Biological Functions,
DOI: 10.1007/978‐3‐642‐00646‐3_20, # Springer‐Verlag Berlin Heidelberg 2009

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Personalized Medicine and Colon Cancer

Author: Tilda Barliya, PhD

According to Dr. Neil Risch a leading expert in statistical genetics and the director of the UCSF Institute for Human Genetics,  “Personalized medicine, in which a suite of molecules measured in a patient’s lab tests can inform decisions about preventing or treating diseases, is becoming a reality” (7).

Colorectal cancer (CRC) is the third most common cancer and the fourth-leading cause of cancer death worldwide despite advances in screening, diagnosis, and treatment. Staging is the only prognostic classification used in clinical practice to select patients for adjuvant chemotherapy. However, pathological staging fails to predict recurrence accurately in many patients undergoing curative surgery for localized CRC (1,2). Most of the patients who are not eligible for surgery need adjuvant chemotherapy in order to avoid relapse or to increase survival. Unfortunately, only a small portion of them shows an objective response to chemotherapy, becoming problematic to correctly predict patients’ clinical outcome (3).

CRC patients are normally being tested for several known biomarkers which falls into 4 main categories (5):

  1. Chromosomal Instability (CIN)
  2. Microsatellite Instability (MSI)
  3. CpG Island methylator phynotype (CIMP)
  4. Global DNA hypomethylation

In the past few years many studies have exploited microarray technology to investigate gene expression profiles (GEPs) in CRC, but no established signature has been found that is useful for clinical practice, especially for predicting prognosis.  Only a subset of CRC patients with MSI tumors have been shown to have better prognosis and probably respond differently to adjuvant chemotherapy compared to microsatellite stable (MSS) cancer patients (6).

Pritchard & Grady have summarized the selected biomarkers that have been evaluated in colon cancer patients (10).

Table 1

Selected Biomarkers That Have Been Evaluated in Colorectal Cancer

Biomarker Molecular Lesion Frequency
in CRC
Prediction Prognosis Diagnosis
KRAS Codon 12/13 activating
mutations; rarely codon
61, 117,146
40% Yes Possible
BRAF V600E activating
mutation
10% Probable Probable Lynch
Syndrome
PIK3CA Helical and kinase
domain mutations
20% Possible Possible
PTEN Loss of protein by IHC 30% Possible
Microsatellite Instability (MSI) Defined as >30%
unstable loci in the NCI
consensus panel or
>40% unstable loci in a
panel of mononucleotide
microsatellite repeats9
15% Probable Yes Lynch
Syndrome
Chromosome Instability (CIN) Aneuploidy 70% Probable Yes
18qLOH Deletion of the long arm
of chromosome 18
50% Probable Probable
CpG Island Methylator
Phenotype (CIMP)
Methylation of at least
three loci from a selected
panel of five markers
15% +/− +/−
Vimentin (VIM) Methylation 75% Early
Detection
TGFBR2 Inactivating Mutations 30%
TP53 Mutations Inactivating Mutations 50%
APC Mutations Inactivating Mutations 70% FAP
CTNNB1 (β-Catenin) Activating Mutations 2%
Mismatch Repair Genes Loss of protein by IHC;
methylation; inactivating
mutations
1–15% Lynch
Syndrome

CRC- colorectal cancer; IHC- immunohistochemistry; FAP- Familial Adenomatous Polyposis

Examples for the great need of personalized medicine tailored according to the patients’ genetics is clearly seen with two specific drugs for CRC:  Cetuximab and panitumumab are two antibodies that were developed to treat colon cancer. However, at first it seemed as if they were a failure because they did not work in many patients. Then, it was discovered that if a cancer cell has a specific genetic mutation, known as K-ras, these drugs do not work.  This is an excellent example of using individual tumor genetics to predict whether or not treatment will work (8).

According to Marisa L et al, however, the molecular classification of CC currently used, which is based on a few common DNA markers as mentioned above (MSI, CpG island methylator phenotype [CIMP], chromosomal instability [CIN], and BRAF and KRAS mutations), needs to be refined.

Genetic Expression Profiles (GEP)

CRC is composed of distinct molecular entities that may develop through multiple pathways on the basis of different molecular features, as a consequence, there may be several prognostic signatures for CRC, each corresponding to a different entity. GEP studies have recently identified at least three distinct molecular subtypes of CC (4). Dr. Marisa Laetitia and her colleagues from the Boige’s lab however, have conducted a very thorough study and identifies 6 distinct clusters for CC patients. Herein, we’ll describe the majority of this study and their results.

Study  Design:

Marisa L et al (1) performed a consensus unsupervised analysis (using an Affymertix chip) of the GEP on tumor tissue sample from 750 patients with stage I to IV CC. Patients were staged according to the American Joint Committee on Cancer tumor node metastasis (TNM) staging system. Of the 750 tumor samples of the CIT cohort, 566 fulfilled RNA quality requirements for GEP analysis. The 566 samples were split into a discovery set (n = 443) and a validation set (n = 123).

Several known mutations were used as internal controls, including:

  • The seven most frequent mutations in codons 12 and 13 of KRAS .
  • The BRAF c.1799T>A (p.V600E)
  • TP53mutations (exons 4–9)
  • MSI was analyzed using a panel of five different microsatellite loci from the Bethesda reference panel
  • CIMP status was determined using a panel of five markers (CACNA1G, IGF2, NEUROG1, RUNX3, and SOCS1)

Results:

The results revealed six clusters of samples based on the most variant probe sets. The consensus matrix showed that C2, C3, C4, and C6 appeared as well-individualized clusters, whereas there was more classification overlap between C1 and C5. In other words:

  • Tumors classified as C1, C5, and C6 were more frequently CIN+, CIMP−TP53 mutant, and distal (p<0.001), without any other molecular or clinicopathological features able to discriminate these three clusters clearly.
  • Tumors classified as C2, C4, and C3 were more frequently CIMP+ (59%, 34%, and 18%, respectively, versus <5% in other clusters) and proximal.
  • C2 was enriched for dMMR (68%) and BRAF- mutant tumors (40%).
  • C3 was enriched for KRAS- mutant tumors (87%).

Note: No association between clusters and TNM stage (histopathology) was found, except enrichment for metastatic (31%) tumors in C4.

Figure: These signaling pathways associated with the molecular subtype (by cluster)

Figure 2 Signaling pathways associated with each molecular subtype.

Marisa L et al. Signaling pathways associated with each molecular subtype

These clusters fall into several signaling pathways:

  • up-regulated immune system and cell growth pathways were found in C2, the subtype enriched for dMMR tumors
  • C4 and C6 both showed down-regulation of cell growth and death pathways and up-regulation of the epithelial–mesenchymal transition/motility pathways. displaying “stem cell phenotype–like” GEPs (91%)
  • Most signaling pathways were down-regulated in C1 and C3.
  • In C1, cell communication and immune pathways were down-regulated.
  • In C5, cell communication, Wnt, and metabolism pathways were up-regulated.

These results are further summarized in table 2:

Figure 3 Summary of the main characteristics of the six subtypes.

Marisa L et al. Gene Expression Classification of Colon Cancer into Molecular Subtypes

The authors have identified six robust molecular subtypes of CC individualized by distinct clinicobiological characteristics (as summarized in table 2).

This classification successfully identified the dMMR tumor subtype, and also individualized five other distinct subtypes among pMMR tumors, including three CIN+ CIMP− subtypes representing slightly more than half of the tumors. As expected, mutation of BRAF was associated with the dMMR subtype, but was also frequent in the C4 CIMP+ poor prognosis subtype. TP53– andKRAS-mutant tumors were found in all the subtypes; nevertheless, the C3 subtype, highly enriched in KRAS-mutant CC, was individualized and validated, suggesting a specific role of this mutation in this particular subgroup of CC.

Current Treatments for colon cancer- Table 3 (11) .

Constant S et al. Colon Cancer: Current Treatments and Preclinical Models for the Discovery and Development of New Therapies

Exploratory analysis of each subtype GEP with previously published supervised signatures and relevant deregulated signaling pathways improved the biological relevance of the classification.

The biological relevance of our subtypes was highlighted by significant differences in prognosis. In our unsupervised hierarchical clustering, patients whose tumors were classified as C4 or C6 had poorer RFS than the other patients.

Prognostic analyses based solely on common DNA alterations can distinguish between risk groups, but are still inadequate, as most CCs are pMMR CIMP− BRAFwt.

The markers BRAF-mutant, CIMP+, and dMMR may be useful for classifying a small proportion of cases, but are uninformative for a large number of patients.

Unfortunately, 5 of the 9 anti-CRC drugs approved by the FDA today are basic cytotoxic chemotherapeutics that attack cancer cells at a very fundamental level (i.e. the cell division machinery) without specific targets, resulting in poor effectiveness and strong side-effects (Table 3) (11).

An example for side effects induction mechanisms have also been reported in CRC for the BRAF(V600E) inhibitor Vemurafenib that triggers paradoxical EGFR activation (12).

Summary:

The authors of this study “report a new classification of CC into six robust molecular subtypes that arise through distinct biological pathways and represent novel prognostic subgroups. Our study clearly demonstrates that these gene signatures reflect the molecular heterogeneity of CC. This classification therefore provides a basis for the rational design of robust prognostic signatures for stage II–III CC and for identifying specific, potentially targetable markers for the different subtypes”.

These results further underline the urgent need to expand the standard therapy options by turning to more focused therapeutic strategies: a targeted therapy-for specific subtype profile.. Accordingly, the expansion and the development of new path of therapy, like drugs specifically targeting the self-renewal of intestinal cancer stem cells – a tumor cell population from which CRC is supposed to relapse, remains relevant.

Therefore, the complexity of these results supports the arrival of a personalized medicine, where a careful profiling of tumors will be useful to stratify patient population in order to test drugs sensitivity and combination with the ultimate goal to make treatments safer and more effective.

References:

1. Marisa L,  de Reyniès A, Alex Duval A,  Selves J, Pierre Gaub M, Vescovo L, Etienne-Grimaldi MC, Schiappa R, Guenot D, Ayadi M, Kirzin S, Chazal M, Fléjou JF…Boige V. Gene Expression Classification of Colon Cancer into Molecular Subtypes: Characterization, Validation, and Prognostic Value. PLoS Med May 2013 10(5): e1001453. doi:10.1371. http://www.plosmedicine.org/article/info%3Adoi/10.1371/journal.pmed.1001453

2. Villamil BP, Lopez AR, Prieto SH, Campos GL, Calles A, Lopez- Asenjo JA, Sanz Ortega J, Perez CF, Sastre J, Alfonso R, Caldes T, Sanchez FM and Rubio ED. Colon cancer molecular subtypes identified by expression profiling and associated to stroma, mucinous type and different clinical behavior. BMC Cancer 2012, 12:260.  http://www.biomedcentral.com/1471-2407/12/260/

3. Diaz-Rubio E, Tabernero J, Gomez-Espana A, Massuti B, Sastre J, Chaves M, Abad A, Carrato A, Queralt B, Reina JJ, et al.: Phase III study of capecitabine plus oxaliplatin compared with continuous-infusion fluorouracil plus oxaliplatin as first-line therapy in metastatic colorectal cancer: final report of the Spanish Cooperative Group for the Treatment of Digestive Tumors Trial. J Clin Oncol 2007, 25(27):4224-4230. http://jco.ascopubs.org/content/25/27/4224.long

4. Salazar R, Roepman P, Capella G, Moreno V, Simon I, et al. (2011) Gene expression signature to improve prognosis prediction of stage II and III colorectal cancer. J Clin Oncol 29: 17–24. http://www.ncbi.nlm.nih.gov/pubmed?cmd=Search&doptcmdl=Citation&defaultField=Title%20Word&term=Salazar%5Bauthor%5D%20AND%20Gene%20expression%20signature%20to%20improve%20prognosis%20prediction%20of%20stage%20II%20and%20III%20colorectal%20cancer

5.  By: Global Genome Knowledge. Colorectal Cancer- Personalized Medicine, Now a Clinical Reality.  http://www.srlworld.com/innersense/Voice-135-Colorectal-Cancer-Sept-2012-IS.pdf

6. Popat S, Hubner R and Houlston RS. Systematic review of microsatellite instability and colorectal cancer prognosis. J Clin Oncol. 2005 Jan 20;23(3):609-618. http://www.ncbi.nlm.nih.gov/pubmed/15659508

7. By: Jeffrey Norris. Value of Genomics and Personalized Medicine Is Wrongly Downplayed.http://www.ucsf.edu/news/2012/04/11864/value-genomics-and-personalized-medicine-wrongly-downplayed

8. By: James C Salwitz. The Future is now: Personalized Medicine. http://www.cancer.org/cancer/news/expertvoices/post/2012/04/18/the-future-is-now-personalized-medicine.aspx

9. Jeffrey A. Meyerhardt., and Robert J. Mayer. Systemic Therapy for Colorectal Cancer. N Engl J Med 2005;352:476-487. http://www.med.upenn.edu/gastro/documents/NEJMchemotherapycolorectalcancer.pdf

10. Pritchard CC and Grady WM. Colorectal Cancer Molecular Biology Moves Into Clinical Practice. Gut. Jan 2011 60(1): 116-129.  Gut. 2011 January; 60(1): 116–129http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3006043/

11. Constant S, Huang S, Wiszniewski L andMas C. Colon Cancer: Current Treatments and Preclinical Models for the Discovery and Development of New Therapies.  Pharmacology, Toxicology and Pharmaceutical Science » “Drug Discovery”, book edited by Hany A. El-Shemy, ISBN 978-953-51-0906-8.  http://www.intechopen.com/books/drug-discovery/colon-cancer-current-treatments-and-preclinical-models-for-the-discovery-and-development-of-new-ther

12. Prahallad, C. Sun, S. Huang, F. Di Nicolantonio, R. Salazar, D. Zecchin, R. L. Beijersbergen, A. Bardelli, R. Bernards, 2012 Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature Jan 2012 483 (7387): 100-103. http://www.nature.com/nature/journal/v483/n7387/full/nature10868.html

Other related articles on this Open Access Online Scientific Journal include the following:

*. By Tilda Barliya PhD. Colon Cancer. https://pharmaceuticalintelligence.com/2013/04/30/colon-cancer/

**. By: Tilda Barliya PhD. CD47: Target Therapy for Cancer. https://pharmaceuticalintelligence.com/2013/05/07/cd47-target-therapy-for-cancer/

I. By: Aviva Lev-Ari, PhD, RNCancer Genomic Precision Therapy: Digitized Tumor’s Genome (WGSA) Compared with Genome-native Germ Line: Flash-frozen specimen and Formalin-fixed paraffin-embedded Specimen Needed. https://pharmaceuticalintelligence.com/2013/04/21/cancer-genomic-precision-therapy-digitized-tumors-genome-wgsa-compared-with-genome-native-germ-line-flash-frozen-specimen-and-formalin-fixed-paraffin-embedded-specimen-needed/

II. By: Aviva Lev-Ari, PhD, RN. Critical Gene in Calcium Reabsorption: Variants in the KCNJ and SLC12A1 genes – Calcium Intake and Cancer Protection. https://pharmaceuticalintelligence.com/2013/04/12/critical-gene-in-calcium-reabsorption-variants-in-the-kcnj-and-slc12a1-genes-calcium-intake-and-cancer-protection/

III.  By: Stephen J. Williams, Ph.DIssues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing. https://pharmaceuticalintelligence.com/2013/04/10/issues-in-personalized-medicine-in-cancer-intratumor-heterogeneity-and-branched-evolution-revealed-by-multiregion-sequencing/

IV. By: Ritu Saxena, Ph.DIn Focus: Targeting of Cancer Stem Cells. https://pharmaceuticalintelligence.com/2013/03/27/in-focus-targeting-of-cancer-stem-cells/

V.  By: Ziv Raviv PhD. Cancer Screening at Sourasky Medical Center Cancer Prevention Center in Tel-Aviv. https://pharmaceuticalintelligence.com/2013/03/25/tel-aviv-sourasky-medical-center-cancer-prevention-center-excellent-example-for-adopting-prevention-of-cancer-as-a-mean-of-fighting-it/

VI. By: Ritu Saxena, PhD. In Focus: Identity of Cancer Stem Cells. https://pharmaceuticalintelligence.com/2013/03/22/in-focus-identity-of-cancer-stem-cells/

VII. By: Dror Nir, PhD. State of the art in oncologic imaging of Colorectal cancers. https://pharmaceuticalintelligence.com/2013/02/02/state-of-the-art-in-oncologic-imaging-of-colorectal-cancers/

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Late Onset of Alzheimer’s Disease and One-carbon Metabolism

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

Abbreviations:

AD (Alzheimer’s disease)

amyloid-beta ()

late onset AD (LOAD)

GSK-3β (glycogen synthase kinase 3-beta)

PP2A (protein phosphatase 2A)

homocysteine (HCY)

S-adenosylmethionine (SAM)

methionine synthase (MS)

betaine-homocysteine methyltransferase (BHMT)

cystathionine beta synthase (CBS)

cysteine (Cys)

glutathione (GSH)

S-adenosylhomocysteine (SAH)

adenosine (Ado)

presenilin 1 (PSEN1)

beta-site APP cleaving enzyme 1 (BACE)

The two main molecular signs of AD are:

  • Extracellular deposits of Amyloid-beta (Aβ) peptides (amyloidogenic pathway) and
  • Intracellular deposits of phosphorylated protein TAU (fibrillogenic pathway)

For many years, both these two pathways (amyloidogenic and fibrillogenic) contended the role of “responsible” for AD onset in the researchers’ debates, even originating respectively the two groups of “BAptists” and “TAUists” scientists. In the recent years, however, these absolutist hypotheses were confuted by the emerging data evidencing that late onset AD (LOAD) has the characteristics to be considered a multifactorial disease and by scientific reports demonstrating possible interconnection between (but not limited to) the two above-mentioned “pathogenic” pathways.

For example, it was demonstrated that

  • GSK-3β (glycogen synthase kinase 3-beta), a phosphorylase involved in tau phosphorylation, is also responsible for APP (Amyloid Precursor Protein) phosphorylation and that
  • Aβ peptides are able to induce GSK-3β.

Among the several possible cocauses and interconnected pathways involved in LOAD onset and progression, a very rapidly emerging topic is related to the role of epigenetics. Moreover, it was hypothesized that methylation impairment could be a common promoter and/or a connection between amyloid and tau pathogenic pathways involving not only DNA methylation but also protein methylation mechanisms. This observation rises from studies on PP2A (protein phosphatase 2A) protein methylation showing that downregulation of neuronal PP2A methylation occurs in affected brain regions from AD patients, causing the accumulation of both phosphorylated tau and APP isoforms and increased secretion of Aβ peptides.

Altered methylation metabolism could represent the connection between B vitamins and LOAD. B vitamins are essential cofactors of homocysteine (HCY) metabolism, also called 1-carbon metabolism. One-carbon metabolism is a complex biochemical pathway regulated by the presence of folate, vitamin B12 and B6 (among other metabolites), and leading to the production of methyl donor molecule S-adenosylmethionine (SAM). High HCY and low B vitamin levels are associated to LOAD, even if a cause-effect relationship is still far to be ascertained; moreover, a clear correlation between HCY and Aβ levels has been found.

In addition, SAM, the principal metabolite in the HCY cycle and the main methyl donor in eukaryotes, appears to be altered in some neurological disorders, including AD. HCY, a thiol containing amino acid produced during the methionine metabolism via the adenosylated compound SAM, once formed is either converted to cysteine by transsulfuration or remethylated to form methionine. In the remethylation pathway HCY is remethylated by the vitamin B12-dependent enzyme methionine synthase (MS) using 5-methyltetrahydrofolate as cosubstrate. Alternatively, mainly in liver, betaine can donate a methyl group in a vitamin B12-independent reaction, catalyzed by betaine-homocysteine methyltransferase (BHMT). In the transsulfuration pathway, HCY can condense with serine to form cystathionine in a reaction catalyzed by the cystathionine beta synthase (CBS), a vitamin B6-dependent enzyme, and the cystathionine is hydrolyzed to cysteine (Cys). Cysteine is used for protein synthesis, metabolized to sulfate, or used for glutathione (GSH) synthesis. The tripeptide GSH is the most abundant intracellular nonprotein thiol, and it is a versatile reductant, serving multiple biological functions, acting, among others, as a quencher of free radicals and a cosubstrate in the enzymatic reduction of peroxides. HCY accumulation causes the accumulation of S-adenosylhomocysteine (SAH) because of the reversibility of the reaction converting SAH to HCY and adenosine (Ado); the equilibrium dynamic favors SAH synthesis. The reaction proceeds in the hydrolytic direction only if HCY and adenosine are efficiently removed. SAH is a strong DNA methyltransferases inhibitor, which reinforces DNA hypomethylation (Chiang et al., 1996). Thus, an alteration of the metabolism through either remethylation or transsulfuration pathways can lead to hyperhomocysteinemia, decrease of SAM/SAH ratio (methylation potential; MP), and alteration of GSH levels, suggesting that hypomethylation is a mechanism through which HCY is involved in vascular disease and AD, together with the oxidative damage. To add insult to injury, oxidative stress also promotes the formation of oxidized derivatives of HCY, like homocysteic acid and homocysteine sulfinic acid. These compounds, through the interaction with glutamate receptors, generate intracellular free radicals.

The first observations about B vitamins or HCY deficiency in neurological disorders were hypothesized in the 80 seconds. Despite this recent acknowledgement, alterations of HCY levels and related compounds were only recently widely recognized as risk factors for LOAD and other forms of dementia. Few mechanisms are suggested as possible protagonists in the toxic pathway of HCY in LOAD onset:

  • oxidative stress and neurotoxicity,
  • vascular damage,
  • alteration of cholesterol and lipids,
  • alteration of protein function by methylation and
  • deregulation of gene expression by DNA methylation.

These results were obtained by using both transgenic and dietary models of hyperhomocysteinemia or altered 1-carbon metabolism. On the one hand, this variety of experimental models allowed to investigate multiple aspects of the biochemical alterations and their consequences; on the other, the lacking of common methods or goals generated a large body of literature in part overlapping for some aspects but fragmentary or incomplete for others. This aspect represents, together with the scarce interplay between clinical/epidemiological and biomolecular research, one of the reasons for the poor relevance given by the scientific community to the role of 1-carbon metabolism in certain diseases like dementia.

A causal connection between 1-carbon alterations:

  • hyperhomocysteinemia,
  • low B vitamins,
  • low SAM, or
  • high SAH

and biological alterations responsible for LOAD onset and progression is still missing. So, it was previously demonstrated that 1-carbon metabolism was related to AD-like hallmarks (increased Aβ production) via PSEN1 (presenilin 1) and BACE (beta-site APP cleaving enzyme 1) upregulation in cellular and animal models. More recently, it was added to the rising literature body dealing with 1-carbon metabolism and GSK-3β and PP2A modulation; it was also demonstrated that PSEN1 promoter is regulated by site-specific DNA methylation in cell cultures and mice and that this modulation of methylation is dependent on the regulation of the DNA methylation machinery. Although all the proposed pathways of HCY toxicity are possibly involved and nonmutually exclusive, as suggested by the multifactorial origin of LOAD, the recent advances in the connection between epigenetics and LOAD (as discussed above) stress a primary role for methylation dishomeostasis dependent on 1-carbon metabolism alterations.

Source References:

http://www.sciencedirect.com/science/article/pii/S0197458011000741

http://www.sciencedirect.com/science/article/pii/0306987784901543

http://www.sciencedirect.com/science/article/pii/S1044743107002953

http://onlinelibrary.wiley.com/doi/10.1196/annals.1297.059/abstract;jsessionid=FE6A683C10230B201295DDF1388DAC68.d02t01

http://www.nejm.org/doi/full/10.1056/NEJMoa011613

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Cardiovascular Disease (CVD) and the Role of Agent Alternatives in endothelial Nitric Oxide Synthase (eNOS) Activation and Nitric Oxide Production

Aviva Lev-Ari, PhD, RN 07/19/2012

https://pharmaceuticalintelligence.com/2012/07/19/cardiovascular-disease-cvd-and-the-role-of-agent-alternatives-in-endothelial-nitric-oxide-synthase-enos-activation-and-nitric-oxide-production/

Mitochondria: More than just the “powerhouse of the cell”

Ritu Saxena, Ph.D, RN 07/09/2012

https://pharmaceuticalintelligence.com/2012/07/09/mitochondria-more-than-just-the-powerhouse-of-the-cell/

Ovarian Cancer and fluorescence-guided surgery: A report

Tilda Barliya PhD, RN 01/19/2013

https://pharmaceuticalintelligence.com/2013/01/19/ovarian-cancer-and-fluorescence-guided-surgery-a-report/

NO Nutritional remedies for hypertension and atherosclerosis. It’s 12 am: do you know where your electrons are?

Meg Baker, Ph.D., Registered Patent Agent, RN 10/07/2012

https://pharmaceuticalintelligence.com/2012/10/07/no-nutritional-remedies-for-hypertension-and-atherosclerosis-its-12-am-do-you-know-where-your-electrons-are/

High Doses of Certain Dietary Supplements Increase Cancer Risk

Prabodh Kandala, PhD, RN 05/17/2012

https://pharmaceuticalintelligence.com/2012/05/17/high-doses-of-certain-dietary-supplements-increase-cancer-risk/

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