Leaders in Pharmaceutical Business Intelligence Group, LLC, Doing Business As LPBI Group, Newton, MA

Autophagy-Modulating Proteins and Small Molecules Candidate Targets for Cancer Therapy: Commentary of Bioinformatics Approaches

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

and

Article Architect: Aviva Lev-Ari, PhD, RN

This article represent a Commission of Dr. Lev-Ari from Dr. Bernstein to author a new article on Autophagy for Predictive Therapeutic Targets. 

Background By Aviva Lev-Ari, PhD, RN

Section 1:

Correspondence with Dr. Philip L. Lorenzi, System Biology Department, MD Anderson Cancer Center

From: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Date: Thu, 01 May 2014 18:13:53 -0400

To: <PLLorenzi@MDanderson.org

Subject: attn: Dr. Lorenzi –>>>>> From AVIVA LEV-ARI, PhD — we met @BioIT at your Lecture on 4/30/2014

Dear Dr. Lorenzi,

It was a great pleasure to attend your lecture YESTERDAY @BioIT 2014.

[I covered it for the Scientific Press].

FEW POINTS I wish to share with you:

#1: Your expertise is Autophagy.  

I am pleased to share with you the work on Dr. Larry h Bernstein in this space that was published in our Open Access Online Scientific Journal

http://pharmaceuticalintelligence.com

Autophagy: Selective articles by Larry H. Bernstein, MD, FCAP

http://pharmaceuticalintelligence.com/2014/05/01/autophagy-selective-articles-by-larry-h-bernstein-md-fcap/

#2: Our e-Books in Genomics and in Cancer Genomics

I am pleased to share with you the following two e-Books from our BioMed e-Series:

Volume One in our Series B: Frontiers in Genomics Research 

[Editor: Dr. SJ Williams, et al]

Genomics Orientations for Individualized Medicine

http://pharmaceuticalintelligence.com/biomed-e-books/genomics-orientations-for-personalized-medicine/volume-one-genomics-orientations-for-personalized-medicine/

Volume One in our Series C: e-Books on Cancer & Oncology

[Editor: Dr. SJ Williams, et al]

Cancer Biology and Genomics for Disease Diagnosis

http://pharmaceuticalintelligence.com/biomed-e-books/series-c-e-books-on-cancer-oncology/cancer-biology-and-genomics-for-disease-diagnosis/

#3: Our Cancer Volume Two (work-in-progress)

Therapies in Cancer: Surgery, Radiation, Chemo and Immunotherapies 

[Editor: Dr. I. Golan, et al.]

http://pharmaceuticalintelligence.com/biomed-e-books/series-c-e-books-on-cancer-oncology/volume-2-immunotherapy-in-oncology/

I wish to invite you to contribute one or more articles from your Team in the Systems Biology Department of UT, MD Anderson Cancer Center

Please also e-mail me the electronic version of the paper you delivered @ World Bio-IT on 4/30/2014, thus, it can be included in this e-Book.

#4: Our Genomics Volume Two (work-in-progress)

Genomics Volume TWO in our Series B: Frontiers in Genomics Research

Genomics Methodologies: NGS, BioInformatics & Simulations: the Genome Ontology

http://pharmaceuticalintelligence.com/biomed-e-books/genomics-orientations-for-personalized-medicine/volume-two-genomics-methodologies-ngs-bioinformatics-simulations-and-the-genome-ontology/

I wish to invite you to contribute one or more articles from your Team in the Systems Biology Department of UT, MD Anderson Cancer Center to our TBA Genomics Volume Two.

I am looking forward to your e-mail reply. Please share these links with your Team at MD Anderson Cancer Center.

Best Regards,

Aviva Lev-Ari, PhD, RN

1-617-244-4024

avivalev-ari@alum.berkeley.edu

BioMed e-Books Series – Editor-in-Chief

BioMed e-Series

Section 2:

Correspondence with Dr. Larry H Bernstein, Chief Scientist, Leaders in Pharmaceutical Business Intelligence

From: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>
Date: Wed, 17 Sep 2014 19:59:30 -0400
To: “Dr. Larry Bernstein” <larry.bernstein@gmail.com>

I wish to have in OUR Journal one ARTICLE on autophagy for predictive therapeutic targets, that has FOUR Parts:

Part 1:: Motivation in Medicine to pursue Predictive Therapeutics

Part 2:  Exposition of the Sources Available for the pursuit Predictive Therapeutics as used in Lorenzi [Thomson DB, …]

Part 3:  ALL the material you presented below – Knowledge Architecture of Autophagy Genomics

Part 4:  Reference to Lorenzzi paper and 1/2 a page by you What is the significance of his engagement to Personalized MEDICINE and pathways to achieve optimal Patients therapeutic OUTCOMES

Thank you

After that, you will share the article above on all its Four Parts to some knowledgeable and respected colleagues.

What was Aviva’s role: the sand grain irritating the oyster

1.  I commissioned you to write on a topic few understand better than YOU.
[I will ADD to Lorenzzi’s Post in the Journal, MY OWN e-mail to Him presenting your work on Autophagy in the Journal
[compilation of your articles]]

2.  I brought to your attention Seminal Sources:
2.1     Lorenzi and
2.2     Moskowitz

3.  You are enjoying this endeavor on autophagy very much – indirectly I brought you pleasure

4.  We will be the first to have an article on autophagy for predictive therapeutic targets

5.  You will have FRESH Goods (Four Parts) to share with some knowledgeable and respected colleagues INCLUDING LORENZZI and Moskowitz

THiS IS THE DIFINITION OF SYMBIOSIS, yes, you did put aside your work for this GEM of a Topic, this is going to be one of your most beautiful diamonds, because only you understand the signaling pathways, only you are able to build the material you called “Final Piece” — BUT AVIVA called it Part 3, above

GREAT WORK the rewards of recognition will be huge, Dr. Larry

STAY Healthy – MEDICAL SCIENCES needs their Champion.

Fondly,

Aviva

This Article has Four Parts

Part I: Motivation in Medicine to pursue Predictive Therapeutics

Part II:  Exposition of the Sources Available for the pursuit Predictive Therapeutics as used in Lorenzi [Thomson DB, …]

Part III:  A Deconstruction of the Autophagy Study: Knowledge Architecture of Autophagy Genomics

Part IV:  What is the significance of his engagement to Personalized MEDICINE and pathways to achieve optimal Patients therapeutic OUTCOMES

A Curated Census of Autophagy-Modulating Proteins and Small Molecules Candidate Targets for Cancer Therapy

Reporter: Aviva Lev-Ari, PhD, RN

PERMISSION to re-Publish

From: “Lorenzi,Philip L” <PLLorenzi@mdanderson.org>

Date: Fri, 23 May 2014 15:26:09 +0000

To: ‘Aviva Lev-Ari’ <AvivaLev-Ari@alum.berkeley.edu>

Subject: RE: attn: Dr. Lorenzi –>>>>> From AVIVA LEV-ARI, PhD — we met @BioIT at your Lecture on 4/30/2014

Hi Aviva,

Our autophagy pathway article has just been published online.  It is open source.  Here is the URL:https://www.landesbioscience.com/journals/autophagy/2013AUTO0694R2.pdf

Please feel free to use it as you wish (we purchased the commercial open source option, so you are free to use it for commercial purposes).

Kind regards,

Phil

Resource

http://www.landesbioscience.com Autophagy 1

Autophagy 10:7, 1–11; July 2014; © 2014 Landes Bioscience

A Curated Census of Autophagy-Modulating Proteins and Small Molecules Candidate Targets for Cancer Therapy

Philip L Lorenzi,1,†,* Sofie Claerhout,2,†,‡ Gordon B Mills,2 and John N Weinstein1,2

1Department of Bioinformatics and Computational Biology; The University of Texas MD Anderson Cancer Center; Houston, TX US A;

2Department of Systems Biology; The University of Texas MD Anderson Cancer Center; Houston, TX US A

†These authors contributed equally to this work.

‡Current affiliation: VIB Center for the Biology of Disease and Department of Human Genetics, KU Leuven; Leuven, Belgium

Part I

Curated Content Implications for Personalized Medicine by Larry H Bernstein, MD, FCAP

This article is included in a series of postings on concepts exploring pathways for predictive therapeutics. This is a time when the pharmaceutical industry is repurposing drugs that have been in use for new indications, and also attempting to discover toxicities in the early phases of development, expecting to eliminate large losses in clinical trial failures.

Leaders in Pharmaceutical Intelligence has had extensive coverage of genomics and of transcriptomics as well as anabolic and catabolic metabolic pathways that form the basis for understanding

• l protein,
• l glycogen,
• l lipid and
• l glycolipid synthesis,

essential for maintaining and renewing the body architecture, and the

• l glycolytic,
• l gluconeogenesis,and
• l respiratory pathways for burning fuels.

There is normally a balanced interaction

• l between the eukaryotic cell and the external environment
• l that dictates adaptive responses throughout life,
• l even as adaptive responses become diminished

after peak of aging, related to hormonal responses, diet, and activity.

In the course of these discussions there has been an exploration of druggable therapeutic targets related mostly to genomic expression and transcription, and this has been accompanied by major signaling pathways, which have received considerable attention with respect to therapeutic targets. A listing of some of these articles appears in the Appendix that follows. This includes a more recent attention to miRNAs, mtRNAs, siRNAs, and a feedback loop

• l also involving proteins and/or histone regulation.

This discussion elaborates new ideas involving

• l the cell death, or apoptosis pathway(s), of which
• l there is not just one.

This is quite novel. In the observations on carcinogenesis by Otto Warburg, based on careful manometric measurements of tissue slices of 1 cell thickness, he observed that

• l malignant cells utilize glycolysis with the generation of lactic acid

He surmised that cancer cells have impaired respiration, the mechanism for which was not then known. If there is a Yin and a Yan in the evolution of this story,

• l the Yin is the catabolic generation of energy (and heat) with utilization of ATP, and H⁺ transfers equivalent to electrons in the inner membrane of the mitochondria.
• l the Yan is the creative destruction of organelles and proteins (proteolysis), with recycling of the amino acids (except that some are indispensable and must be replaced from a dietary source, e.g., methionine, which is involved with acetyl CoA and high energy ~P).

The creative destruction is referred to as apoptosis. Articles on apoptosis are also listed in the Appendix.

The role of apoptosis in the neoplastic imbalance between mitochondrial energy generation and glycolysis for generation of new cells leading to metastasis, can be viewed, as it is here, as

• l an imbalance between the mitochondrial involvement in anabolic syntheses,
• l precluding mitochondrial energy utilization, and
• l a concomitant suppression of the apoptosis pathways.

This work is an important venture into new possibilities for therapeutic targets and design of biopharmaceutical agents.

The work hereby presented is substantially difficult to read even for the knowledgeable reader, and especially for those not educated in medicine, genetics, molecular biology, or statistical methodology, so it has been broken down for both clarity and accuracy in communication.
It has already been mentioned that the pharmaceutical industry is under great pressure to change. It is faced with patent expiration of many drugs, and there has been great cost in developing new drugs with consolidations occurring in the industry because of financial losses in failures associated with toxicities or with lack of reaching acceptable results in the Primary Outcomes of Clinical Trials. This is a transitional phase of the creative destruction that is leading to a Personalized Medicine pharmaceutical refocusing that has benefitted from the completion of the Human Genome Project in the first decade of the 21^st century.   Much has happened in the ensuing years, including the recent completion of a preliminary Human Proteome. This is important because as one grasps the curated study presented, even though it depends on genomic nucleotide associations, the product of the discovery is related to the expression of the genome and to metabolic pathways interactions and control. Ultimately, we are moving into a highly technology intensive -OMICS- based biological and medical science that combines genomics, proteomics, transcriptomics, and metabolomics.

It is the map that fills the uncompleted Atlas comprised of metabolic pathways and signaling pathways. In this case, the cancer pharmacotherapy is subjected to the focused examination of cellular ‘apoptosis” or autophagy, which involves the lysosome, the endoplasmic reticulum, and the mitochondria, and perhaps the nucleus and cytoskeleton. Autophagy is in a relationship to energy metabolism and to synthetic processes. These are in disequilibrium when we refer to cancer.

There are portions of the study that are left for the reader to refer to the published work because they are of interest only to those working in the discipline. However, they are identified here to point out the challenge encountered by the researchers. They point out the ambiguous relationships of dual modulators of autophagy and suggest that they are not likely to be good drug targets, if the aim is to modulate autophagy. This is the case for two large groups encountered. Also, pathway analysis was not always in agreement with siRNA screening. In other instances, false negatives occur as a result of cases that may exhibit a very small contribution of negative cell death modulators, which justifies identification of modulation in more than one test system.

The publication of the autophagy pathway article has just been released online.  It is open source.  Refer to the URL: https://www.landesbioscience.com/journals/autophagy/2013AUTO0694R2.pdf

It has been granted the commercial open source option, with permission from the author to use its content for educational purposes.

Part II  

Exposition of the Sources Available for the pursuit Predictive Therapeutics as used in Lorenzi 

 Exposition of the Sources Available for the pursuit Predictive Therapeutics as used in Lorenzi [Thomson DB, …]

The average reader will not have access to the Reuters-Thomson data base used to access the siRNAs.  So we can partition the problem into

1. access the data
2. to generate a hypothesis, and
3. to validate the concept.

In forming a hypothesis, as in this case, the data has to be classified

• based on siRNAs and their association with
• negative or positive effect as modulator of some autophagy pathway.

Several types of autophagy are identified, so

• each of these is a separate regulatory target.

I have extracted the same type of data used in the Lorenzi paper from a selection of 90 of 190 publicationi in Science Diect/Scopus using the search words autophagy and cancer and siRNA.  In most of these article the study is aimed at identifying a potential cancer therapeutic target.  The studies are two part directed.

1. Select a potential drug and identify the target of either autophagy or apoptosis, or both.
2. Use inhibitors and siRNA to block the action against the target, thereby validating the assumed mechanism

The Table I create is insufficient to deal with the problems identified in the Lorenzi publication that follows.  It does clearly identify features that stand out:

• The coexistence of both autophagy and apoptosis in many studies, indicating
  • Apoptosis leads to cell death and is selectively related to toxicity to the cancer cell
  • Autophagy is related to the maintenance of cellular structures, e.g., mitochondria, endoplasmic reticulum
• Autophagy and apoptosis are in some equilibrium that may be manipulated by the effect of attacking a drug target
• Only very few studies identify an attack on mitochondria that disrupts the membranous structure and releases cytochrome c, and generates ROS
  • The generation of ROS (H₂O₂) and disruption of glutamate reductase, possible formation of RNS leads to apoptosis

The table is shown below:

Table. Selections from 88 of 190 sources documents in 2013-2014 from autophagy, siRNA, and cancer in ScienceDirect/Scopus
notables: *crosstalk between apoptosis and autophagy

Having the data, it is necessary to form combinatorial classes.

The classification of blood types emerged at the turn of the last century firstly as a result of the work of Paul Ehrlich establishing a groundwork for immunology, and then later Karl Landsteiner’s seminal work in laying the foundation for the blood groups.

The kind of features that we readily identify are gram stain positivity, colonies on agar, cocciform or bacillary shape, outer capsule, motility, the clusters formed, the metabolic features in growth media, and even the expected antibiotic reactivity. Thus, we have an example: in Table 2.

Table 2. Typical classes of bacteria

GROUP 4

Description:

Gram Negative, Aerobic/Microaerophilic rods and cocci
Key differences are: pigments/fluorescent, motility, growth requirements, denitrification, morphology, and oxidase,

read Genera descriptions

Examples:

Acinetobacter, Pseudomonas, Beijerinckia

GROUP 5

Description:

Facultatively Anaerobic Gram negative rods
Key differences are: growth factors, morph., gram rxn., oxidase rxn.,

read Genera descriptions

Examples: Family Enterobacteriaceae and Vibrionaceae

GROUP 17

Description: Gram-Positive Cocci
Key differences are: oxygen requirements, morph., growth requirements (45^o C and supplements),

read Genera descriptions

Examples: Micrococcus, Staphylococcus, Streptococcus, Enterococcus, Lactococcus

Table 2 does not further divide into subclasses, which requires metabolic differentiation in growth media.

The data  used in this study is not numerical with a continuous or other variable, in this case, just an siRNA type (feature) associated with suppression or activation of one or more pathways.  So it is unnecessary to consider some heavily used and elegant methods for this discussion. The problem at hand is as much related to clinical diagnostics as it is critical for research.

The computer architecture that the physician uses to view the results is  not recombined from the rigid lists into a structured format that readily enables the physician to interpret the report. Consequently the results are not presented as the end-user would like. In order to optimize the interface for physician, the system would have a “front-to-back” design, with the call up for any patient ideally consisting of a design that presents the crucial information that the physician would likely act on in an easily accessible manner. The problem of the user having to adjust to what

• the system confronts them with is described by Didner (1) in an internal Bell Labs memo approved for external release. The key point being
• that each item used has to be closely related to a corresponding criterion needed for a decision.

I have to throw some light on the approach to this problem. The closest analogy I can use, somewhat simpler in view of there being fewer associations, is from the pioneering work on microbial organisms by Eugene Rypka, PhD, from the University of New Mexico, Albuquerque, NM.
What is important is the critical value of the information provided. The disciplines required in blood banking and in microbiology have only been recently automated, but their importance is readily understood.  Even though there may be a large number of measured values, the variety is reduced by this compression, even though there is risk of loss of information. Yet the real issue is how a combination of variables falls into a table with meaningful information. He describes how
syndromic classification is uniquely valuable for clinical laboratory information by amplifying information in the course of making a pattern-identifiable syndromic classification.

Syndromic Classification: A Process for Amplifying Information Using S-Clustering
Eugene Rypka, PhD  Nutrition 1996; 12(11/12),

In a previous issue of Nutrition, Drs. Bernstein and Pleban use the method of S-clustering to aid in nutritional classification of patients directly on-line. Classification of this type is called primary or syndromic classification.* It is created by a process called separatory (S-) clustering (E. Rypka, unpublished observations). The authors use S-clustering in Table I.
S-clustering extracts features (analytes, variables) from endogenous data that amplify or maximize structural information to create classes of patients (pathophysiologic events) which are the most disjointed or separable. S-clustering differs from other classificatory methods because it finds in a database a theoretic-or more-number of variables with the required variety that map closest to an ideal, theoretic, or structural information standard. In Table I of their article, Bernstein and Pleban’ indicate there would have to be 3 ^5 =  243 rows to show all possible patterns. In Table II of this article, I have used a 3^3 = 27 row truth table to convey the notion of mapping amplified information to an ideal, theoretic standard using just the first three columns. Variables are scaled for use in S-clustering.

SCALING AND SEPARATION

Scaling is important because it allows using variables or message sources, with different numbers of message choices (number bases) in the same matrix.

VARIABLES

The feature “glasses” is a nominal variable. It is categorically scaled to either 0 or 1. Its number base. r, is two. An agglutination test to determine an antibody titer may be scored 0 + ++ +++ ++++. The scaling is 0 1 2 3 4. The “titer of antibody ( Abx) ” is ordered from least to most in terms of the amount of agglutination. “Titer of Abx” is an ordinal variable,
r = 5. There is an interval for “reference range”. These may both be scaled. Once an analyte’s value is known, we tend to think relationally about it such as, “Is the value low, normal, high, or high high?“, etc. Scaling reduces the amount of variety (a measure of a system’s complexity) for classifying data sets.

Amplifying Information Using S-Clustering and Relationship to Kullback-Liebler Distance: An Application to Myocardial Infarction
Larry H Bernstein, MD  http://pharmaceuticalintelligence.com/2012/09/22/amplifying-information-using-s-clustering/

The usual accepted method for determining the decision value of a predictive variable is the Receiver Operator Characteristic Curve, which requires a mapping of each value of the variable against the percent with disease on the Y-axis.   This requires a review of every case entered into the study.  The ROC curve is done to validate a study to classify data on leukemia markers for research purposes as shown by Jay Magidson in his demonstation of  Correlated Component Regression (2012)(Statistical Innovations, Inc.)  The test for the contribution of each predictor is measured by Akaike Information Criteria and Bayes Information Criteria, which have proved to be critically essential tests over the last 20 years.

Inability to classify information is a major problem in deriving and validating hypotheses from PRIMARY data sets necessary to establish a measure of outcome effectiveness.  When using quantitative data, decision limits have to be determined that best distinguish the populations investigated.   We are concerned with accurate assignment into uniquely verifiable groups in test relationships. Uncertainty in assigning to a supervisory classification can only be relieved by providing sufficient data.

A method for examining the endogenous information in the data is used to determine decision points.  The reference or null set is defined as a class having no information.  When information is present in the data, the entropy (uncertainty in the data set) is reduced by the amount of information provided.  This is measureable and may be referred to as the Kullback-Liebler distance, which was extended by Akaike to include statistical theory.   An approach is devised using EW Rypka’s S-Clustering has been created to find optimal decision values that separate the groups being classified.  Further, it is possible to obtain PRIMARY data on-line and continually creating primary classifications (learning matrices).  From the primary classifications test-minimized sets of features are determined with optimal useful and sufficient information for accurately distinguishing elements (patients).  Primary classifications can be continually created from PRIMARY data.  More recent and complex work in classifying hematology data using a 30,000 patient data set and 16 variables to identify the anemias, moderate SIRS, sepsis, lymphocytic and platelet disorders has been  published and recently presented.  Another classification for malnutrition and stress hypermetabolism is now validated and in press in the journal Nutrition (2012), Elsevier.

Rudolph RA, Bernstein LH, Babb J. Information induction for predicting acute myocardial infarction. Clin Chem 1988; 34(10):2031-2038. ICID: 825568.

Rypka EW. Methods to evaluate and develop the decision process in the selection of tests. Clin Lab Med 1992; 12:355

Rypka EW. Syndromic Classification: A process for amplifying information using S-Clustering. Nutrition 1996; 12(12):827-9.

Christianson R. Foundations of inductive reasoning. 1964.  Entropy Publications. Lincoln, MA.

G David, LH Bernstein, RR Coifman. Generating Evidence Based Interpretation of Hematology Screens via Anomaly Characterization. Open Clinical Chemistry Journal 2011; 4 (1):10-16. 1874-2416/11 2011 Bentham Open.  ICID: 939928

G David; LH Bernstein; RR Coifman. The Automated Malnutrition Assessment. Accepted 29 April 2012.
http://www.nutritionjrnl.com. Nutrition (2012), http://dx.doi.org:/10.1016/j.nut.2012.04.017.

Keywords: Network Algorithm; unsupervised classification; characteristic metric; characteristic profile; data characterization.

We can characterize a unique profile for each patient and map similar patients into a classification. The laboratory parameters were sufficient for the automated risk prediction. We propose a simple, workable algorithm that provides assistance for interpreting any set of data from the screen of a blood analysis with high accuracy, reliability, and inter-operability with an electronic medical record. This has been made possible at least recently as a result of advances in mathematics, low computational costs, and rapid transmission of the necessary data for computation.

The item below is chosen only for identifying the type of problem encountered by the siRNA nomenclature, and only because it involves the inner membrane transport system, with several linked variables and involving Fe/S/and O2.
What we have here: Isu (scaffold protein),

Hsp70 Ss,1, J-protein Jac1 and nucleotide release factor Mge1 – acting as sulfur and iron donors.

In the siRNA targets – we have to look for the effect of siRNA as a modulator of this system.
(Only a piece of the puzzle).

Conserved system for assembly of Fe/S clusters and their insertion into proteins
http://www.biochem.wisc.edu/faculty/craig/lab/iron.aspx

An Fe/S cluster is transiently assembled on a scaffold protein, called Isu, prior to transfer to the recipient protein. Additional proteins act as sulfur and iron donors. In addition, an Hsp70 chaperone system, consisting of the Hsp70 Ssq1, the J-protein Jac1 and the nucleotide release factor Mge1, is required for efficient Fe/S cluster biogenesis. Both in vivo and in vitro evidence supports a role for the chaperones in transfer of the cluster to recipient proteins.

Let us put this configuration into a truth table:

Reaction is Fe/S cluster coupled to Isu

Step 1: Fe/S cluster biogenesis
donors: Hsp70 Ssq1  Jac1   Mge1

Hsp70 Ssq1

Jac1                                   Fe/S  cluster

Mge1

Step 2: FE/S cluster + Isu

siRNAs are in the class of microRNA,

• which are regulatory molecules, as I point out.

The field has had abundant work from the DNA revolution, but this is not directed at the genetic code per se. These molecules

•  interact with the chromatin that associates with DNA,

and are directed at

•  the function of initiating or suppressing pathways

1.  Connected to proliferation and with
2. depression of mitochondrial oxidative phosphorylation and ETC.

The mechanisms are intricately tied in with

•  the rate of glycolysis in carcinogenesis as well as
• protein breakdown and membrane repair.

I had not considered the following coordinated in autophagy

• cell proliferation of cancer cells (immortality?),

• loss of cell cohesion and metastasis,

• there has to be regeneration of membranes
  (which is a tearing down and recycling of amino acids).

Pyridine nucleotide coenzymes

The burning of fuel in mitochondria generates 36 ATP, which may be glucose or lipids (the Fyodor Lynen cycle), and in the process there is hydrogen transfer in reduction of NADH to NAD+.  In the synthetic process, NADPH is required.  That is why the pyridine nucleotide transferase and also the hexose monophosphate shunt were brought into view.  When Otto Warburg visited Kornberg and Horecker at the NIH, they provided him with a supply of the purified NADPH.

The pentose phosphate pathway (also called the phosphogluconate pathway and thehexose monophosphate shunt) is a biochemical pathway parallel to glycolysis that generatesNADPH and pentoses (5-carbon sugars). While it does involve oxidation of glucose, its primary role is anabolic rather than catabolic.

The pentose phosphate pathway appears to have a very ancient evolutionary origin. The reactions of this pathway are (mostly) enzyme catalysed in modern cells. They also occur however non-enzymatically under conditions that replicate those of the Archean ocean, and are then catalyzed bymetal ions, ferrous iron Fe(II) in particular. The origins of the pathway could thus date back to the prebiotic world.

The primary results of the pathway are:

• The generation of reducing equivalents, in the form of NADPH, used in reductive biosynthesis reactions within cells (e.g. fatty acid synthesis).
• Production of ribose-5-phosphate (R5P), used in the synthesis of nucleotides and nucleic acids.
• Production of erythrose-4-phosphate (E4P), used in the synthesis of aromatic amino acids.

In mammals, the PPP occurs exclusively in the cytoplasm, and is found to be most active in the liver, mammary gland and adrenal cortex in the human. The PPP is one of the three main ways the body creates molecules with reducing power, accounting for approximately 60% of NADPH production in humans.

Oxidative phase

In this phase, two molecules of NADP⁺ are reduced to NADPH, utilizing the energy from the conversion of glucose-6-phosphate into ribulose 5-phosphate

The overall reaction for this process is:

Glucose 6-phosphate + 2 NADP⁺ + H₂O → ribulose 5-phosphate + 2 NADPH + 2 H⁺ + CO₂

Nonoxidative phase

Net reaction: 3 ribulose-5-phosphate → 1 ribose-5-phosphate + 2 xylulose-5-phosphate → 2 fructose-6-phosphate + glyceraldehyde-3-phosphate

Regulation

Glucose-6-phosphate dehydrogenase is the rate-controlling enzyme of this pathway. It is allosterically stimulated by NADP⁺. The ratio of NADPH:NADP⁺ is normally about 100:1 in liver cytosol. This makes the cytosol a highly-reducing environment. An NADPH-utilizing pathway forms NADP⁺, which stimulates Glucose-6-phosphate dehydrogenase to produce more NADPH. This step is also inhibited by acetyl CoA

Role of NADPH 

One of the uses of NADPH in the cell is to prevent oxidative stress. It reduces glutathione via glutathione reductase, which converts reactive H₂O₂ into H₂O by glutathione peroxidase. If absent, the H₂O₂ would be converted to hydroxyl free radicals by Fenton chemistry, which can attack the cell. Erythrocytes, for example, generate a large amount of NADPH through the pentose phosphate pathway to use in the reduction of glutathione.

This has to be compared with the lens of the eye. Reticulocytes lose their nuclei and have no mitochondria, and the same for the crystalline lens.  86% glycolysis, the remaining PPP.  But the lens is not involved in oxygen transfer across membranes.  Need to discuss with Harry Maisel.

Hydrogen peroxide is also generated for phagocytes in a process often referred to as a respiratory burst.

So this treatment of carcinogenesis and potential targets is really getting to the heart of the targeting of cellular dysregulation, which is actually a changed equilibrium under the circumstances of the cellular environment.  The energetics of the reactions are calculated according to established rules in physical chemistry that go back to Boltzmann.  The Nobel Laureate and giant in Quantum Theory, Erwin Schroedinger, in “What is Life?”  posited that it was not possible to derive a mathematical model that can predict life because of the millions of concurrent reactions.  His view was not that different than – Prigogine, who noted that you can’t know the initial state.

Flavoproteins

Fumarate reductase is the enzyme that converts fumarate to succinate, and is important in anaerobic respiration.

Succinate + acceptor <=> fumarate + reduced acceptor

Fumarate reductase couples the reduction of fumarate to succinate to the oxidation of quinol to quinone, in a reaction opposite to that catalysed by the related complex II of the respiratory chain (succinate dehydrogenase).

Fumarate reductase complex includes three subunits.

1. Subunit A contains the site of fumarate reduction and a covalently bound flavin adenine dinucleotide prosthetic group FADH2.
2. Subunit B contains three iron-sulphur centres.
3. The menaquinol-oxidizing subunit C consists of five membrane-spanning, primarily helical segments and binds two haem b
4. The D subunit may be required to anchor the catalytic components of the fumarate reductase complex to the cytoplasmic membrane.

http://www.ebi.ac.uk/pdbe-srv/view/images/entry/2bs3600.png
Mitochondrial tumour suppressors: a genetic and biochemical update
Eyal Gottlieb & Ian P. M. Tomlinson, Nature Reviews Cancer 5, 857-866 (November 2005) http://dx.doi.org:/10.1038/nrc1737

Succinate dehydrogenase or succinate-coenzyme Q reductase (SQR) or respiratory Complex II is an enzyme complex, bound to the inner mitochondrial membrane of mammalian mitochondria and many bacterial cells. It is the only enzyme that participates in both the citric acid cycle and the electron transport chain.

In step 6 of the citric acid cycle, SQR catalyzes the oxidation of succinate to fumarate with the reduction of ubiquinone to ubiquinol. This occurs in the inner mitochondrial membrane by coupling the two reactions together.

Prerequisites for ubiquinone analogs to prevent mitochondrial permeability transition-induced cell death.

Belliere J¹, Devun F, Cottet-Rousselle C, Batandier C, Leverve X, Fontaine E.  Author information
J Bioenerg Biomembr. 2012 Feb;44(1):207-12. doi: 10.1007/s10863-012-9406-7. Erratum in  J Bioenerg Biomembr. 2012 Jun;44(3):397.

The permeability transition pore (PTP) is a mitochondrial inner membrane channel involved in cell death.

• The inhibition of PTP opening has been proved to be an
• effective strategy to prevent cell death induced by oxidative stress.

Several ubiquinone analogs are known to powerfully inhibit PTP opening with an effect depending on the studied cell line. Here, we have studied the effects of ubiquinone 0 (Ub(0)), ubiquinone 5 (Ub(5)) and ubiquinone 10 (Ub(10)) on PTP regulation, H(2)O(2) production and cell viability in U937 cells. We found that Ub(0) induced both PTP opening and H(2)O(2) production. Ub(5) did not regulate PTP opening yet induced H(2)O(2) production. Ub(10) potently inhibited PTP opening yet induced H(2)O(2) production. Both Ub(0) and Ub(5) induced cell death, whereas Ub(10) was not toxic. Moreover,

• Ub(10) prevented tert-butyl hydroperoxide-induced
• PTP opening and subsequent cell death.

We conclude that PTP-inhibitor ubiquinone analogs are able to prevent PTP opening-induced cell death

• only if they are not toxic per se, which is the case
• when they have no or low pro-oxidant activity.
  PMID: 22246424

A notable reaction related to oxidative stress having a potential role in carcinogenesis as it does in chronic diseases is nitric oxide. Nitric oxide synthases (EC 1.14.13.39) (NOSs) are a family of enzymes catalyzing the production of nitric oxide (NO) from L-arginine. The complex reaction involves the transfer of electrons from NADPH. It has three isoenzymes – eNOS, iNOS, and nNOS. Endothelial NOS (eNOS), also known as nitric oxide synthase 3 (NOS3) or constitutive NOS (cNOS), is an enzyme that in humans is encoded by the NOS3 gene.

Isolation of nitric oxide synthetase, a calmodulin-requiring enzyme.

Bredt DS¹, Snyder SH.  Author information
PNAS 1990 Jan;87(2):682-5.

Nitric oxide mediates

1. vascular relaxing effects of endothelial cells,
2. cytotoxic actions of macrophages and neutrophils, and
3. influences of excitatory amino acids on cerebellar cyclic GMP.

Its enzymatic formation from arginine by a soluble enzyme associated with stoichiometric production of citrulline

• requires NADPH and Ca2+.

We show that nitric oxide synthetase activity requires calmodulin. Utilizing a 2′,5′-ADP affinity column eluted with NADPH, we have purified nitric oxide synthetase 6000-fold to homogeneity from rat cerebellum. The native enzyme appears to be a monomer.
PMID: 1689048

Nitric oxide synthase: Structural studies using anti-peptide antibodies

V Riveros-Moreno^*, C Beddell and S Moncad
Eur J Biochem 1993 Aug; 215(3), pages 801–808.
http://dx.doi.org:/10.1111/j.1432-1033.1993.tb18095.x

We present the characterisation of anti-NOS antibodies in relation to the possible

• conformation of the enzyme and
• an immunological comparison between two isoforms of NO synthase: constitutive (rat brain) and inducible (macrophage).

Peptide regions predicted to be exposed, flexible or substantially in core, have produced antibodies that were able to recognise the native protein. Most of the selected anti-peptide antibodies were

• not able to cross-react with the inducible macrophage enzyme.

The macrophage enzyme was able to compete weakly with the binding of the constitutive enzyme to its own antibody, but 10 times more inducible protein was required. We propose that conformational epitopes are responsible for the cross-reaction.

Nitric oxide synthases: Roles, tolls, and controls

Carl Nathan, Qiao-wen Xie
Department of Medicine Cornell University Medical College New York, NY.  Cell 1994 Sep; 78(6), p 915–918.
http://dx.doi.org/10.1016/0092-8674(94)90266-6

Part III

A Deconstruction of the Autophagy Study

by Larry H Bernstein, MD, FCAP

Autophagy 10:7, 1–11; July 2014; © 2014 Landes Bioscience
http://www.landesbioscience.com

Introduction

The term “autophagy” was coined in the 1960s to describe a

• l “self-eating” process in which cell constituents are delivered to

lysosomes for degradation.¹

Autophagy has been divided into

3 major classes:

• l macroautophagy,
• l microautophagy,² and
• l chaperone-mediated autophagy.³

Because macroautophagy (which includes organelle-specific subpathways such as mitophagy) is by far the most common, we focus on it here, referring to it generically as “autophagy.” Autophagy plays a context-dependent role in cancer, as explained in a recent elegant review.⁴

Elimination of damaged cellular components through autophagy

• l suppresses tissue injury and tumor initiation.

However, in an established tumor,

• l autophagy promotes cancer progression

1. by the substrates generated in metabolic activities

autophagy is suppressed
ATP generated is inefficient by glycolysis (2 ATP)
mitochondria are in reversal of ATP generation with TPNH from hexose monophosphate shunt used for syntheses

2. Maintaining functional mitochondria, andmitochondrial oxidative phosphorylation with formation of
   ”active acetate” (acetyl Co A) and use of the ECP is downgraded
3. fostering survival during and after therapy.Survival is dependent on both stage and cellular anaplasia
   Cellular anaplasia would be correlated with both mitotic index and
   suppression of apoptosis

Autophagy, a programmed process in which cell contents are

• l delivered to lysosomes for degradation, appears to have
• l both tumor-suppressive and tumor-promoting functions;

both stimulation and inhibition of autophagy have been reported to induce cancer cell death, and particular genes and proteins have been associated both positively and negatively with autophagy. To provide a basis for incisive analysis of those complexities and ambiguities and to guide development of new autophagy-targeted treatments for cancer, we have compiled a comprehensive, curated inventory of autophagy modulators by integrating information from published

1. siRNA screens,
2. multiple pathway analysis algorithms, and
3. extensive, manually curated text-mining of the literature.

The resulting inventory includes 739 proteins and 385 chemicals (including drugs, small molecules, and metabolites). Because autophagy is still at an early stage of investigation, we provide extensive analysis of our sources of information and their complex relationships with each other. We conclude with a discussion of novel strategies that could potentially be used to target autophagy for cancer therapy. ²

Although autophagy has been subjected to intensive investigation,
the complex networks that regulate the process in human diseases

• l have only begun to be elucidated.

One recent report described the combined use of

• l protein expression,
• l immunoprecipitation,
• l and mass spectrometry

to identify an “Autophagy- Interaction Network” composed of 409 candidate interacting proteins with 751 discrete interactions.⁵ Another report described the use of mass spectrometry to identify 728 proteins apparently associated with autophagosomes.⁶ There is also an autophagy database.⁷ However,

• l for identification of candidate cancer drug targets, it is important to assess
• l the functional contribution of each protein to the modulation of autophagy,
• l not just its apparent association with the process.

In this analysis, we focused first on the identification of causal relationships through reanalysis of 4 macroautophagy-specific human cell line siRNA screens.^8-11

Results

The Venn diagram in Figure 1A shows

1. the sizes of the siRNA libraries and
2. the numbers of hits in each screen.

The correspondence of results was only moderate, in part because autophagy was defined

• l by different end-points,
• l in different cell types, and
• l using different methods.

The analysis of the siRNA screens was complemented with

1. extensive pathway analyses of the relevant literature,
2. and extensive curation.

The results were even less

• l AM ENABLE TO FORMING MEANINGFUL CLUSTERS (Fig. 1B),

1. suggesting the unsettled, complex nature of autophagy and
2. its many functional relationships with important cellular processes.

To provide the most comprehensive list of candidate autophagy modulators possible, we focused on

• l the union of the various sets in Figure 1B.

The individual data sets (or any Boolean combination thereof) can be interrogated

• l to assess the nature of the evidence for a candidate molecule.

To provide crude indices of the likelihood that a candidate is a true positive, we list

1. the number of siRNA screens in which it was identified and
2. its rank in terms of literature references that support it as a modulator of autophagy.

Our principal aim is to highlight candidate targets for autophagy-related therapy of cancer. The reader should keep in mind the repeated reference to identified proteins and complexes as central to the findings.

Integrated analysis of candidate autophagy modulating genes

The union of siRNA and text-mining data yielded 739 apparent autophagy-modulating entities (proteins and complexes). A truncated subset of the top-ranked hits is provided in Table 1. We parsed the full set of results into 7 tables (Table S1A–S1G), with each entity appearing in only 1 table according to its direction of autophagy modulation (positive or negative).

A Venn diagram of the integrated siRNA-pathway analysis results (Fig. 2A) serves as a guide. We also present a detailed pathway schematic of the autophagy process that puts many entities from the census in their functional contexts (Fig. 3). Since one goal of this analysis was to highlight potential targets and strategies for treatment of cancer, we focus our discussion in each section below

• l on the therapeutic potential of top-ranked autophagy modulators.

………………………………………………………………………………………

Positive modulators of autophagy

1. Text-mining and
2. siRNA screening

led, respectively, to the identification of 288 and 96 positive modulators of autophagy (including dual positive-negative modulators) .

There were 7 entities in the intersection of the 2 sets
(Fig. 2A; Table S1).

Of those 7 consensus genes (listed in Table S1G), 2—

1. KIF5B and
2. RELA—

have been reported to modulate cell death, mainly negatively,^14,15

• l suggesting that inhibiting them may induce cell death.

When 4 or more sets are being shown as circles, it is not in general mathematically possible to represent them and their intersections graphically to scale with accuracy. VennMaster provides an optimization algorithm that achieves a compromise representation.

However, the 8 sets overlap in such a complex way that the fit could be improved by manually changing the circles for sets 6 and 7 to ellipses of approximately the right dimensions.

No viable inhibitors of KIF5B were found by text mining the scientific literature, but a number of RELA inhibitors have been reported. In particular, text mining helped us identify a report in which

• ^l ~50% inhibition of RELA activity was observed
• ^l after treatment with the antioxidant acetylcysteine (Table S1H).¹⁶

Another report identified

• ^l sesquiterpenes as inhibitors of RELA.¹⁷

Hence, acetylcysteine or sesquiterpenes could be used to test the hypothesis that

• ^l inhibiting autophagy through inhibition of RELA can induce cancer cell death.
• ^l the effect of RELA on autophagy may vary depending on the nature of the DNA damage-inducing stimulus.¹⁸

Negative modulators of autophagy

1. Text-mining and
2. siRNA screening

led to the identification of 160 and 248 negative modulators (including dual positive-negative modulators) of autophagy, respectively, with

• l 12 entities in the intersection of the 2 sets ( 2A; Table S1).

To further prioritize candidate drug targets within that overlapping set of 12 autophagy modulators (listed in Table S1F),

we surmised that the best candidates are those whose

• l inhibition (by a drug) is expected to result in cell death.

Inhibiting a NEG modulator results in cell death

Eight of the 12 proteins—

• l AURKA, CLCF1, CXCL12, EP300, FGFR1, IGF1, LIF, and SOD1
• l have been reported to modulate cell death negatively,

suggesting that inhibition of those proteins might induce cell death (?)

To assess those 8 proteins as possible drug targets, we used Pathway Studio to analyze their pathway relationships,

1. identifying IGF1 as a key node
2. with connections to 7 of the other entities (Fig. S1).

The possibility that inhibition of a highly connected pathway node could have a greater effect than inhibition of a less connected one prompts the hypothesis that

• l inhibition of IGF1 could have a greater effect than
• l inhibition of 1 of the other targets.

Furthermore, text-mining also identified 3 agents

1. acrylonitrile,
2. tempol, and
3. UO126

that inhibit IGF1 and also 1 of the other 7 proteins.

UO126

• l used as a MAP2K1/MEK1 inhibitor
• l also inhibits IGF1¹⁹ and EP300²⁰

stands out as the top candidate to test the hypothesis that stimulating autophagy

• l through inhibition of IGF1 and EP300 could
• l induce cell death in established cancers.

stimulation of autophagy with UO126 might also represent a viable

• l cancer prevention strategy,

as autophagy in normal cells would be expected to

• l suppress the initiation of cancer

1. through elimination of damaged proteins and abnormal mitochondria and
2. by preventing accumulation of DNA damage.⁴

Several interesting negative modulators of autophagy were identified by siRNA screening alone (Table S1D).

• l CSNK1A1 was the highest ranking hit from siRNA screen 3

2 additional casein kinase (CSNK) family members,

• l CSNK1G2 and CSNK2A2,

were found to be negative modulators of autophagy by siRNA screening (Table S1D),

• l suggesting casein kinases as possible targets.

Currently available CSNK inhibitors include:

• ^l heparin, which has been reported to inhibit both CSNK1A1²¹ and CSNK2A2,²²  
• ^l ionomycin, which has been reported to inhibit CSNK1A1,²³
• ^l suramin,²² 2-aminopurine,24 and 6-dimethyladenine,²⁴

which have been reported to inhibit CSNK2A2.

Hence, those agents alone or in combination with other drugs could be used to test the hypothesis that stimulating autophagy through inhibition of casein kinases results in cancer cell death.

Dual modulators of autophagy

An unexpected result of our analysis was the number of

genes with data supporting both positive and negative modulation

of autophagy (Table S1C). Those genes necessitated additional

investigation to probe the apparent discrepancies.

We found several genes in Table S1C—

• l AGER, GNAI1, TLR3, and TNF—
• l Were positive modulators by text mining
• l yet negative modulators by siRNA screens.

those genes are actually positive modulators of autophagy
(i.e., the text-mined results are more persuasive).

• 1) Pathway Studio identified multiple references supporting
  positive modulatory roles for AGER and TNF.
• 2) TNFis a positive modulator of the cell death pathway known as necroptosis
• l negatively modulated by CASP8.^25-29
• l CASP8 is expressed in only 16% of neuroblastoma cell lines³⁰
• l the H4 neuroblastoma cell line used in siRNA screen 2 might have been deficient in CASP8 (making a positive result) or
• l Another negative modulator of necroptosis present rendered the H4 line
• l susceptible to uncontrolled necroptosis.²⁸

Silencing TNF in such a context would

1. alleviate uncontrolled necroptosis and might
2. induce autophagy to facilitate recovery.

That explanation may extend to the discrepancies observed for AGER, GNAI1, and TLR3 as well, since those discrepancies were

• l also associated with siRNA screen 2 and the H4 cell line.

Overall, each of the aforementioned genes may positively modulate autophagy under most circumstances, and the cell line used in siRNA screen 2 may simply represent

• l an unusual genetic context in which those genes act as negative modulators of autophagy.

These observations suggest the importance of conducting siRNA screens against multiple cell types

1. to corroborate results across multiple genetic contexts
2. to reduce contradictory observations when merged with text mining results.

The inherent sampling of data from a wide range of sources and cell types is an advantage of text mining.

For another set of dual autophagy modulators in Table S1C—
at least one of the relationships was supported by only one text reference. In each case, analogous to the discussion of the H4 cell line above, the single reference may have been derived from context-dependent analytical conditions not reflected in other studies ³¹ BAK1 is reported to be a negative modulator of autophagy only in the experimental context of dual BAK1 and BAX knockdown; ³² and should therefore generally be considered a positive modulator of autophagy. E2F1, however, is identified as a negative autophagy modulator by just one reference,³³ but

• l the mechanism appears to be physiologically relevant.

GNAI3 is a GTPase that

• l negatively modulates autophagy in the active, GTP-bound form
• l and positively modulates autophagy in the inactive GDP-bound form³⁴—

GNAI3 should be considered a negative modulator of autophagy, even though there is only a single reference to it as a negative modulator. Overall, for the aforementioned set of 21 genes, the assignment of positive or negative modulation of autophagy is generally in favor of one direction.

For the next set of genes, the conclusions are even less clear. Nine entities identified as dual modulators of autophagy did not appear to exhibit a dominant direction of autophagy modulation (Table S1C). The first gene, BAX, is reported to be a negative modulator of autophagy only in the context of dual BAK1 and BAX knockdown;^26,32 BAX should, therefore, be considered a positive modulator of autophagy.

MAPK14 exhibits evidence favoring a negative modulatory role in autophagy; a recent publication describes a mechanism through which MAPK14 negatively modulates autophagy via phosphorylation complexes.

Three small molecules depicted are

1. glutamine (Gln),
2. phosphatidylinositol 3-phosphate (PtdIns3P),
3. phosphatidylethanolamine (PE).

Three established cargo selectors include SQSTM1/p62, NBR1, and WDFY3/ALFY. The yellow dotted line in the elongation/closure stage indicates recycling of PE and LC3 by ATG4 following degradation of ATG5.²⁸

MYC, is probably a true dual modulator whose direction of autophagy modulation depends on genetic context.
PRKCD is probably a negative modulator of autophagy.
The dual modulation reported for TP53 appears to be

• l attributable to cellular localization;
• l nuclear TP53 activates stress-induced autophagy genes transcriptionally,³⁰ whereas
• l cytoplasmic TP53 inhibits basal autophagy by an unknown mechanism.³⁶

Overall, the ambiguous relationships of dual modulators of autophagy suggest that they are not likely to be good drug targets if the aim is to modulate autophagy.

Small-molecule modulators of autophagy

Just as we used Pathway Studio to compile a list of autophagy-modulating genes, proteins, and complexes, we next used Pathway Studio to identify 385 small molecule modulators of autophagy: 95 negative modulators (Table S1H), 268 positive modulators (Table S1I), and 22 compounds that have been reported to modulate autophagy both negatively and positively (Table S1J). A Venn diagram summarizes the results (Fig. 2B), and a truncated subset of top-ranked positive and negative hits is provided in Table 2. Together with the genes, proteins, and complexes discussed previously, the identification of small molecule modulators of autophagy completes our census.

Although it was reassuring to see that text mining identified a number of established autophagy inhibitors, including bafilomycin A1, chloroquine, and hydroxychloroquine, a number of the small molecules in Table S1H are worthy of fresh attention.

First, the “amino acids” entity was the most highly referenced negative modulator of autophagy after 3-methyladenine. Interestingly, arginine, asparagine, leucine, and phenylalanine were the only individual amino acids to make the list.

Glutamine, as discussed later, is reported to be a dual modulator of autophagy.

Another noteworthy negative modulator of autophagy is oxygen; indeed hypoxia in the tumor microenvironment may

1. drive autophagy and
2. promote tumorigenesis.^37-40

Table S1I lists 268 small molecules reported to modulate autophagy positively. In general, most chemotherapeutic agents induce autophagy;

• l tamoxifen,
• l imatinib, and
• l bortezomib,

for example, were highly cited positive modulators.

• l the MTOR inhibitor rapamycin was

the top-ranked positive modulator of autophagy and its clinical analogs everolimus and temsirolimus were also identified.

The second highest-ranking positive modulator of autophagy was

• l “reactive oxygen species” (ROS).

Damaged mitochondria are primary sources of ROS and, accordingly, are thought to induce a form of autophagy known as mitophagy.⁴ Peroxide and nitric oxide, which interact with ROS to form reactive nitrogen species, are also highly referenced as positive modulators of autophagy. Further supporting the importance of ROS as a positive modulator, a number of antioxidants have been reported to inhibit autophagy, including

1. acetylcysteine,
2. ascorbic acid,
3. butylhydroxyanisole,
4. glutathione,
5. lipoic acid,
6. tiron (a cell-permeable superoxide scavenger), and
7. vitamin E

(Table S1H).

Two final entities worth noting are the sphingolipids and ceramides, both of which are highly referenced as positive modulators of autophagy (Table S1I).

Table S1J lists 22 small molecules that both inhibit and stimulate autophagy according to text-mining results. If we exclude chemicals for which one of the relationships is supported by only 1 reference, 9 compounds were still identified as dual modulators of autophagy. How can a molecule both negatively and positively modulate autophagy? The following examples provide potential mechanisms.

First, AICAR was originally described as an AMPK-dependent inhibitor of autophagy,⁴¹ but more recent work identifies AMPK-independent inhibition of autophagy by AICAR, possibly through inhibition of the class III PtdIns3K (whose catalytic subunit is PIK3C3/VPS34) to BECN1.^42,43 Hence, cellular and genetic contexts appear to determine the direction of autophagy modulation by AICAR.

Second, it is not surprising to find ATP as a positive modulator, since autophagy is an active process. The primary mechanism by which ATP modulates autophagy involves activation of AMPK in response to a decrease of the ATP/AMP ratio.⁴⁴ Therefore,

• l “an increase in AMP” decreases the “ATP/AMP ratio.”

That said, ATP should theoretically feed back negatively on autophagy, since a primary function of autophagy is to generate energy to survive stress, and that function must be turned off when sufficient energy and nutrients have been generated.

Third, glucose withdrawal has been extensively described to induce autophagy,^45,46 but high glucose/hyperglycemia can also induce autophagy through MTOR^47-51 and potentially through generation of ROS.^52-54

A fourth entity, glutamine, is in the spotlight because of the many pathways in which it functions. Recently, the pathways that anabolize and catabolize glutamine (mediated by glutamine synthetase (GLUL) and glutaminase (GLS), respectively) have been found to modulate autophagy upstream of RRAG GTPases,^55,56 implicating glutamine as a critical node in the modulation of autophagy (Fig. 3). Specifically,

• l glutamine can positively modulate autophagy through
• l glutaminolysis via the production of ammonia,⁵⁷
• l a positive modulator of autophagy (Table S1I).

Like other amino acids, however, glutamine negatively modulates autophagy through

1. RRAG GTPases,^35,55  
2. MTOR signaling,^56,58 and
3. EIF2A-ATF4 signaling.^59-61

Finally, metformin is another interesting small molecule reported to modulate autophagy both positively and negatively. Both inhibition and stimulation of autophagy by metformin appear to be

• l AMPK-dependent,^62,63

but the exact mechanisms are still under investigation.

Overall, these and a number of additional chemicals listed in Table S1J appear to be dual modulators of autophagy, but

• l in most cases additional studies would be required to define

1. the molecular determinants and
2. the contexts of such diverse behavior.

Novel autophagy-based therapeutic strategies

We next wanted to leverage the integrated results

• l to propose novel autophagy-targeted strategies for treating cancer.

Inhibition of autophagy, reportedly augments the efficacy of a number of therapeutic agents, which provdes a clear rationale for combining inhibitors of autophagy with other agents in clinical trials. A number of such trials are already underway,⁶⁵ and the previous sections discussed a few new strategies worth testing. But based on new information from studies of the cell death pathway known as necroptosis, one trial is worth particular attention:

1. evaluation of hydroxychloroquine (HCQ) (Table 2; Table S1H)
2. in combination with the rapamycin (Table 2; Table S1I) analog temsirolimus.

As single agents, allosteric TORC1 (Table 1; Table S1A) inhibitors like

• l temsirolimus have shown limited activity in clinical trials,⁶⁶

Perhaps because autophagy is induced as a prosurvival mechanism.

Another possible explanation for the limited clinical activity

of rapalogs, however, is the finding that

• l the primary executors of necroptosis, RIPK1 and RIPK3,
  are localized to mitochondria.^67,68

That observation prompts the hypothesis that

Hence, a combination of a TORC1 inhibitor
(i.e., autophagy stimulator)

• l like temsirolimus with
• l an autophagy inhibitor like HCQ
  • n could sensitize cancer cells to necroptotic cell death.

On that basis,⁷¹ we propose that

• l combination therapies consisting of at least one mitophagy inhibitor and
• l one non-mitochondrial autophagy stimulator

might be useful in the treatment of cancer. The results of our analysis suggest additional autophagy inhibitors and stimulators worth evaluating in that regard (i.e., the positive and negative modulators listed in Table 2).

Another cancer treatment strategy that was prioritized by our integrated analysis of autophagy-modulating proteins and small molecules is based on targeting RELA (Table 1; Table S1G) for inhibition of autophagy.

Text mining identified a report in which

• l RELA activity was inhibited ~50% by treatment with

1. the antioxidant acetylcysteine (Table S1H),¹⁶ and
2. Sesquiterpenes were also found to inhibit RELA,¹⁷

Then a nontargeted strategy using

3. acetylcysteine or a sesquiterpene (e.g., helenalin)
4. in combination with rapamycin

may be worth evaluation as a proof-of-concept. A possible drawback of

acetylcysteine therapy, however, could be toxicity associated with

• l its ability to break disulfide bonds and
• l disrupt redox homeostasis.

The combination of rapamycin with acetylcysteine, however,

might circumvent that possibility due to

• l the antioxidant activity of acetylcysteine.

A second therapeutic approach based on autophagy inhibition and the results of our analysis entails the use of oxygen. Hypoxia in tumor stroma has been observed to promote tumorigenesis and autophagy,⁷² prompting the hypothesis that

• l pharmacological delivery of oxygen (e.g., using red blood cell as carriers⁷³) could be useful in treatment of some cancers.

Our analysis also enabled us to prioritize 2 top candidate drug targets for stimulating autophagy—

1. IGF1 (Table 1; Table S1F) and
2. key amino acids (Table 2; Table S1H).

As discussed previously, the MEK inhibitor UO126

3. is a candidate inhibitor of IGF1 that could be tested
4. in combination with an autophagy inhibitor such as HCQ as a proof-of-concept

prior to the development of molecules specifically targeted to IGF1.

As for inhibition of amino acids, since our analysis identified

• l asparagine and glutamine

as important negative modulators of autophagy, and since

• l ammonia is known to stimulate autophagy potently,⁴⁸
• l evaluation of L-asparaginase as an autophagy stimulator is particularly interesting.

L-asparaginase enzymatically releases ammonia from asparagine and glutamine

• l in the process of catabolizing the 2 amino acids into
• l aspartic acid and glutamic acid

Because L-asparaginase has been confirmed

• l to induce autophagy,⁷⁴
• l testing it in combination with HCQ would be a
• l worthwhile proof-of-concept experiment.

L-asparaginase has been used clinically since the 1970s to treat acute lymphoblastic leukemias.^75,76

In special cases, inhibiting autophagy with single agents may

be therapeutically effective. Aggressive tumors (e.g., those with

constitutive RAS activation) have adapted to survive

• l with high rates of autophagy and have been proposed to be “addicted” to autophagy.⁷⁷

Therefore, inhibiting autophagy may decrease

• l the tumorigenicity of RAS-expressing cancer cell line models.^77,78

In support of that hypothesis, inhibition of autophagy

• l by atg7 knockout in BRAFV600E-driven or KRASG12D-driven
  lung cancers
• l altered tumor fate by diverting aggressive cancers
  to more benign disease.^79,80

That observation suggests the possibility that

1. increased RAS activation and
2. inhibition of autophagy

could be “synthetically lethal” in cancer patients or at least

• l could significantly decrease tumor burden.

Conclusion

we have integrated analyses of published

• l siRNA screen data and pathway-based text-mining
• l to construct an extensive inventory of

1. genes,
2. proteins,
3. complexes, and
4. small molecules

that appear to modulate autophagy (Table S1).

The inventory and analysis offer novel features:

1. i) analysis and annotation of the direction (positive or negative)
   of autophagy modulation;
2. ii) a semiquantitative (in the case of text-mined results) or quantitative (in the case of siRNA screen results) index for
   – estimating the strength of evidence behind each entity
   reported as a modulator of autophagy;

iii) a model of the autophagy pathway (Fig. 3)

– that incorporates new information from our analysis;

1. iv) an indication of the possible utility, at least in concept,

– of combining an inhibitor of autophagy (e.g., an inhibitor of RELA)     – with a stimulator of autophagy (e.g., rapamycin or L-asparaginase),   – particularly if the inhibitor affects mitophagy and the stimulator affects
– a non-mitochondrial form of autophagy;

1. v) Venn diagrams and associated quantitative analyses that indicate the sometimes surprising relationships (or lack thereof) among the different types of evidence in this complex, often confusing field.
   A future challenge will be to determine

• l the specificity of all modulators (genes and compounds) that regulate autophagy,
• l preferably in isogenic autophagy-wild-type and autophagy–deficient cells.

Materials and Methods

siRNA Screens

We compiled data from 4 published, macroautophagy-specific siRNA screens in human cell lines: siRNA screen 1 (753 siRNA pools targeting 705 genes)8 yielded 7 validated hits; siRNA screen 2 (21,121 siRNA pools targeting 16,492 genes)9 yielded 148 validated hits; siRNA screen 3 (726 individual kinase-targeted siRNAs)10 yielded 21 validated hits; and siRNA screen 4 (21,121 siRNA pools targeting 16,492 genes)11 yielded 169 validated hits. The data and explanatory details are provided in Table S2A. The Venn diagrams in Figure 1A (drawn to scale) show the sizes of the siRNA libraries and the numbers of hits in each screen.

To achieve consistency throughout the analysis, it was necessary to pre-process the published data. For each screen, we i) translated redundant or outdated gene names or symbols into HUGO gene symbols, ii) excluded data in the rare cases in which we could not resolve naming ambiguities, and iii) assigned rank values to the genes based on number of references to provide a rough quantitative basis for comparing strength of evidence.

Pathways and Text Mining

To complement the siRNA screen data, we used pathway analysis based on text-mining of the literature to identify autophagy modulators. The operational definition of “modulator” used here is rather broad: “an entity (gene, protein, protein complex or small molecule) that has been reported empirically to activate or inhibit autophagy.” Accordingly, knowledge of the mechanism was not required to list a particular entity as an autophagy modulator. That definition of “modulator” applies well to “hits” in siRNA screening, for which there is assumed to be an element of causality. The definition also applies to the major pathway analysis software packages, of which we compared 3: Ingenuity Pathway Analysis (IPA; Ingenuity Systems); MetaCore (Thomson Reuters GeneGo); and Pathway Studio (Elsevier/ Ariadne Genomics). With IPA, we used the advanced search tool to search for the term “autophagy” as a function. That IPA search yielded 218 genes and 123 small molecules (Table S3).

A similar approach using MetaCore yielded 38 genes. Both IPA and MetaCore use manually curated databases, so those hits can be considered in a sense to be validated. Pathway Studio, by contrast, uses automated text mining, which is more susceptible to false positives. Therefore, after using Pathway Studio to mine 10,087 PubMed abstracts and 228 full-text articles from a PubMed search for “autophagy,” we validated the Pathway Studio hits by manually curating (i.e., reading and analyzing) the text from which autophagy modulators were identified. The end result obtained with Pathway Studio was 421 genes and 385 small molecules, which are listed in Table S1. A more thorough description of the Pathway Studio method is provided in Supplementary Methods.

Uncurated pathway analysis based on text mining has its limitations. One is that the molecular definition of autophagy has changed over time and varies even among recent publications. Also, text-mining algorithms may incorrectly assign the direction (negative, unknown, or positive) of autophagy modulation, depending on whether the assay defined autophagy appropriately.

For example, accumulation of MAP1LC3A (also known as LC3A) in autophagosomes has been invoked as an index of autophagy, but MAP1LC3A accumulation can reflect processes other than functional autophagic flux. It can also result from inhibition of autophagosome-lysosome fusion or autolysosome degradation, reflecting an abortive or defective autophagic process.81 To ensure the greatest possible accuracy of the directions assigned and the number of references that support each relationship, we manually curated all of the text-mined relationships by reading ~6,000 extracted text entries describing ~1,000 protein-autophagy and chemical-autophagy relationships. When a text-mining discrepancy was found, we assigned http://www.landesbioscience.com Autophagy 9 the direction of modulation, when possible, based on consensus in the autophagy field, which usually reflected the direction of modulation that occurs under nutrient-replete conditions. For example, MTOR is reported to modulate autophagy positively and separately reported to modulate it negatively (see Table S2 for references and exact sentences). We list MTOR as a negative modulator because it negatively modulates autophagy in the presence of sufficient nutrients. Table S2 contains a complete list of such ambiguities and their resolutions. As we did with siRNA data, the number of literature references that support each textmined relationship was converted to a rank value to serve as a crude quantitative index for comparison across entities and their associations. We provide to-scale Venn diagrams and, in some instances, statistical analyses to aid the reader in assessing the robustness of evidence. See the legend of Figure 1 and Table S4 for explanations of the Venn diagram methodology.

Data quality assessment

Although most of the human genome was covered by the combination of siRNA libraries (Fig. 1A), we discovered a number of limitations: i) only 2 of the screens (numbers 2 and 4) were “genome-wide” (16,492 genes);9,11 ii) as illustrated in Figure 1B, the siRNA screen hits exhibited little intersection with text mining hits; iii) despite the high degree of overlap among siRNA libraries, the hits in the different screens exhibited almost no intersection (Fig. 1A). The only overlap (out of total of 342 hits) consisted of 3 genes in common between screens 2 and 4. Considering that the overlap expected by chance for the 2 independent screens is 1.52 genes, the enrichment over chance is modest (a factor of 1.98); iv) 3 of the 4 primary siRNA screens (i.e., initial screens as opposed to secondary, validation screens) measured autophagy using upstream markers such as MAP1LC3A-II accumulation or localization instead of downstream markers of autophagic flux (i.e., productive autophagy).

The most recent, siRNA screen 4,11 was an exception; it employed a primary screen designed to distinguish hits that induce MAP1LC3A-II accumulation as a result of abortive autophagy from those that induce productive autophagy; v) measures of screen robustness (i.e., Z′-factors) were not reported for any of the screens, and the data necessary to calculate Z′-factors were not provided. Based on a cursory assessment, however, it is not clear that any of the 4 has a sufficiently large dynamic range and sufficiently low variance to yield robust Z′-factors.82 Together, those issues represent 2 types of limitations—ones that are general for siRNA screening, and others that are specific to interrogation of the autophagy pathway.

Pathway analysis also has limitations: i) although the intersections of hits among the 3 software packages IPA, MetaCore, and Pathway Studio were greater than the intersections among siRNA screens (Fig. 1B), a significant number of hits were unique to each software package; ii) as indicated above, the pathway analysis hits showed little intersection with siRNA screening hits. Because Pathway Studio yielded larger intersection with siRNA screening than did IPA or MetaCore (Fig. 1B), we chose Pathway Studio (with our manual curation) as the prime pathway analysis tool; iii) 19% (67/358) of the relationships identified by Pathway Studio were false positives with respect to our manually curated list (Table S2A). The manual curation also rescued 132 “unknown” relationships that would otherwise have been discarded, thereby increasing the number of apparently validated hits from 285 to 417.

Overall, neither siRNA screening nor pathway analysis appeared fully adequate to interrogate the universe of autophagy modulators. Therefore, we chose to focus on the union of validated siRNA and text-mined data sets (i.e., sets 1, 2, 3, 4, and 8 in Fig. 1B). That strategy yielded a more comprehensive census than any individual approach alone, but we provide sufficient data annotation in the Supplementary Tables so that the reader can choose instead to focus on candidates identified by any single siRNA or text-mining data set or any desired combination of intersections and/or unions of the sets. Since we cannot be fully certain whether the limitations reported here are specific to the autophagy pathway or whether they are technical limitations of the various approaches, future analyses of additional pathways will continue to shed light on the informatics issues. What we do know, however, is that the inconsistencies in designation of genes as positive or negative in their regulatory influence reflect uncertainties and context-dependent relationships in the field.

…………………………………………………………

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Acknowledgments

We thank Shelley Herbrich, Kevin Coombes, and Ganiraju

Manyam (UT MD Anderson Cancer Center) for assistance in

dealing with gene names and synonyms and all of the researchers

whose publications formed the basis for this census.

JNW’s and PLL’s work was supported in part by US. National

Cancer Institute (NCI) grant numbers CA143883 (The

Cancer Genome Atlas MD Anderson Genome Data Analysis

Center) and CA083639 (University of Texas MD Anderson

SPORE in Ovarian Cancer), the Cancer Prevention Research

Institute of Texas (CPRIT) grant number RP130397, and also

by the Chapman Foundation and the Michael and Susan Dell

Foundation (honoring Lorraine Dell). GBM’s work was supported

in part by Komen Foundation grants KG 081694 and

FAS0703849, and by the Ovarian Cancer Research Fund. SC’s

work was supported by an Odyssey Program Fellowship, by the

Theodore N. Law Endowment for Scientific Achievement at the

University of Texas MD Anderson Cancer Center, and by NCI

Breast SPORE Career Developmental Project award CA116199.

Supplemental Materials

Supplemental materials may be found here:

http://www.landesbioscience.com/journals/autophagy/article/28773

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Part IV

The Significance of Lorenzi’s Article to

Personalized MEDICINE and Pathways to achieve

Optimal Patients Therapeutic OUTCOMES

Section IV. Implications of PredictiveTherapeutics for Personalized Medicine

This study of “curated proteins for therapeutic discovery is among the first two landmark works of our time. Perhaps the other also deserves notice, and mention needs to be made of the problems that have to be resolved to take this further, both for the pharmaceutical industry and for the patient.

The work uses

1. siRNA screens,
2. multiple pathway analysis algorithms, and
3. extensive, manually curated text-mining of the literature.

Even though it uses siRNA screens, the major focus of the study is proteomics, pathway analysis, and metabolic regulation of cancer cells by autophagy mechanism. It is a truly elegant piece of work, or may I say, an orchestral piece. A large body of work has been highly focused on the funding to support research was directed – in a biological Manhattan Project. Despite that, chronic diseases, and more specifically, cancer, have been resistant to full comprehension. This will take time, but the successes are coming at a rapid pace.

The work is unique in a special way. Look at the title –

A curated census of autophagy-modulating proteins and small molecules: Candidate targets for cancer therapy

The mechanism of energy utilization is from our food sources – carbohydrates and fats, and from breakdown of proteins

the gluconeogenic amino acids from which go into glycolysis, or are recycled into protein. The major high energy pathway is mitochondrial respiration, generating 36 ATPs through acetyl

coenzyme A, and the electron transport chain mediating H⁺ transfers (as electron equivalents). On the other hand, the body is constantly repairing its machinery.

The rate of reconstruction is affected by age, diet, physical activities, and hormonal influences, peaking in the late 30s, and declining past the late 50s. This has more to do with metabolism

than genomic makeup. In the case of skeletal and brain growth, there is a rapid development in the first 5 postnatal

years. There is much to be elucidated about the brain, but much is known about skeletal muscle and bone, and it has contributed to advances in orthopedics and sports medicine. In the growth of long bone (not fibrous bone) growth in length occurs at the margin of the epiphysis and the shaft, called the metaphysis, where articular cartilage is replaced by bone in a succession of osteoclastic removal and osteoblastic matrix formation, and finally a stable bone containing osteocytes. The

osteoclasts remove matrix at 100 m per day, and refill by osteoclasts is at a rate of 1 m per day. In the elderly we see a decline that is expressed in osteoporosis and dementia. In the case of hyperparathyroid disease, there is osteitis fibrosa cystica (cystic bone lesions).

The extreme loss of control in bone repair is expressed as Paget’s disease, in which circumstance there is a wild removal                                                                   and replacement leading to “pumice” bone. The removal of tissue requires lysosomal activity, which is dependent on “hydrolases”, that resorb or break down ( a hydrolase /is an enzyme that catalyzes the hydrolysis of a chemical bond).

The serine hydrolase superfamily is one of the largest known enzyme families comprising approximately ~200 enzymes or 1% of the genes in the human proteome. A characteristic defining feature of this superfamily is the presence of an active site nucleophilic serine that is used for the hydrolysis of substrates. Catalysis proceeds by the formation of an acyl-enzyme intermediate through this serine, followed by water/hydroxide-induced saponification of the intermediate and regeneration of the enzyme. Unlike other non-catalytic serines, the nucleophilic serine of these hydrolases is typically activated by a proton relay involving an acidic residue (e.g. aspartate or glutamate) and a basic residue (usually histidine) although variations on this

mechanism exist.

This study is a most thorough coverage of the types of autophagy, and their role in the complex group of diseases that we define as cancers. Cancer is defined here as a metabolic balance between cell proliferation and cell remodeling, the first being NADPH dependent, and requiring mitochondrial support at the expense of catabolic demands, that are shifted to the glycolytic pathway. The second spares the mitochondrion in a controlled manner. This allows for a systematic destruction and repair, and also for respiration.

What is the significance of this. It is hugely important for where pharmacotherapy can go, with a minimization of unexpected toxicities. In order for this to advance more effectively, it will require further advances in the aggregation of data essential for analytical requirements, and also advances in the analytical methods used for classifying the data, which is attainable.

I pose examples along the lines just mentioned:

• l Proc Natl Acad Sci U S A. 2014 Aug 13. pii: 201404171.

Arginine starvation-associated atypical cellular death
involves mitochondrial dysfunction, nuclear DNA leakage,
and chromatin

autophagy.

Changou CA¹, Chen YR², Xing L³, Yen Y⁴, Chuang FY⁵, Cheng RH³, Bold RJ⁶,

Ann DK⁷, Kung HJ⁸.   Author information

Autophagy is the principal catabolic prosurvival pathway during nutritional starvation. However, excessive autophagy could be cytotoxic, contributing o cell death, but its mechanism remains elusive. Arginine starvation has emerged as a potential therapy for several types of cancers, owing to their tumor-selective deficiency of the arginine metabolism. We demonstrated here that arginine depletion by arginine deiminase induces a cytotoxic autophagy in argininosuccinate synthetase (ASS1)-deficient prostate cancer cells. Advanced microscopic analyses of arginine-deprived dying cells revealed a novel phenotype with giant autophagosome formation, nucleus membrane rupture, and histone-associated DNA leakage encaptured by autophagosomes, which we shall refer to as chromatin autophagy, or chromatophagy. In addition, nuclear inner membrane (lamin A/C) underwent localized rearrangement and outer membrane (NUP98) partially fused with autophagosome membrane. Further analysis showed that prolonged arginine depletion impaired mitochondrial oxidative phosphorylation function and depolarized mitochondrial membrane potential. Thus, reactive oxygen species (ROS) production significantly increased in both cytosolic and mitochondrial fractions, presumably leading to DNA damage accumulation. Addition of ROS scavenger N-acetyl cysteine or knockdown of ATG5 or BECLIN1 attenuated the chromatophagy phenotype. Our data uncover an atypical autophagy-related death pathway and suggest that mitochondrial damage is central to linking arginine starvation and chromatophagy in two distinct cellular compartments. Cancer Discov. 2014 Aug;4(8):905-13.
http://dx.doi.org:/10.1158/2159-8290.CD-14-0362. Epub 2014 May 29.

Autophagy Is Critical for Pancreatic Tumor Growth and Progression in Tumors with p53 Alterations.

Yang A¹, Rajeshkumar NV², Wang X¹, Yabuuchi S³, Alexander BM¹, Chu GC⁴,

Von Hoff DD⁵, Maitra A⁶, Kimmelman AC⁷.   Author information

Pancreatic ductal adenocarcinoma is refractory to available therapies. We have previously shown that these tumors have elevated autophagy and that inhibition of autophagy leads to decreased tumor growth. Using an autochthonous model of pancreatic cancer driven by oncogenic Kras and the stochastic LOH of Trp53, we demonstrate that although genetic ablation of autophagy in the pancreas leads to increased tumor initiation, these

premalignant lesions are impaired in their ability to progress to invasive cancer, leading to prolonged survival. In addition, mouse pancreatic cancer cell lines with differing p53 status are all sensitive to pharmacologic and genetic inhibition of autophagy. Finally, a mouse preclinical trial using cohorts of genetically characterized patient-derived xenografts treated with hydroxychloroquine showed responses across the collection of tumors. Together, our data support the critical role of autophagy in pancreatic cancer and show that inhibition of autophagy may have clinical utility in the treatment of these cancers, independent of p53 status.

SIGNIFICANCE:

Recently, a mouse model with embryonic homozygous Trp53 deletion showed paradoxical effects of autophagy inhibition. We used a mouse model with Trp53 LOH (similar to human tumors), tumor cell lines, and patient-derived xenografts to show that p53 status does not affect response to autophagy inhibition. Cancer Discov; 4(8); 905-13. ©2014 AACR.
See related commentary by Amaravadi an Debnath, p. 873
This article is highlighted in the In This Issue feature, p. 855.
PMID: 24875860   PMCID: PMC4125497   [Available on 2015/2/1]

J Biol Chem. 2014 Aug 22;289(34):23318-28.
http://dx.doi.org:/10.1074/jbc.M114.575183. Epub 2014 Jul 1.

Cancer-associated Isocitrate Dehydrogenase 1 (IDH1) R132H Mutation and d-2-Hydroxyglutarate Stimulate Glutamine Metabolism under Hypoxia.

Reitman ZJ¹, Duncan CG², Poteet E³, Winters A³, Yan LJ³, Gooden DM⁴, Spasojevic I⁵, Boros LG⁶, Yang SH⁷, Yan H⁸.

Author information

Mutations in the cytosolic NADP(+)-dependent isocitrate dehydrogenase (IDH1) occur in several types of cancer, and altered cellular metabolism associated with IDH1 mutations presents unique therapeutic opportunities.
By altering IDH1, these mutations target a critical step in reductive glutamine metabolism, the metabolic pathway that converts glutamine ultimately to acetyl-CoA for biosynthetic processes. While IDH1-mutated cells are sensitive to therapies that target glutamine metabolism, the effect of IDH1 mutations on reductive glutamine metabolism remains poorly understood. To explore this issue, we investigated the effect of a knock-in, single-codon IDH1- mutation on the metabolism of the HCT116 colorectal adenocarcinoma cell line. Here we report the R132H-isobolome by using targeted (13)C isotopomer tracer fate analysis to trace the metabolic fate of glucose and glutamine in this system. We show that introduction of the R132H mutation into IDH1 up-regulates the contribution of glutamine to lipogenesis in hypoxia, but not in normoxia. Treatment of cells with a d-2-hydroxyglutarate (d-2HG) ester

recapitulated these changes, indicating that the alterations observed in the knocked-in cells were mediated by d-2HG produced by the IDH1 mutant. These studies provide a dynamic mechanistic basis for metabolic alterations observed in IDH1-mutated tumors and uncover potential therapeutic targets in IDH1-mutated cancers.   PMID: 24986863

• l Ovarian Cancer Oncogene Found in “Junk DNA”
  http://www.technologynetworks.com/rnai/news.aspx?ID=170127
  Published: Wed, September 10, 2014

Transglutaminase 2 ablation leads to mitophagy impairment associated with a metabolic shift towards aerobic glycolysis.

Rossin F¹, D’Eletto M¹, Falasca L², Sepe S³, Cocco S⁴, Fimia GM⁵, Campanella M⁶, Mastroberardino PG³, Farrace MG¹, Piacentini M⁷. Author information

Macroautophagy selectively degrades dysfunctional mitochondria by a process known as mitophagy. Here we demonstrate the involvement of transglutaminase 2 (TG2) in the turnover and degradation of damaged mitochondria. We demonstrate that in healthy mitochondria, TG2 interacts with the dynamic proteins Drp1 and Fis1. As a consequence of accumulation of damaged mitochondria, cells lacking TG2 increased their aerobic glycolysis and became sensitive to the glycolytic inhibitor 2-deoxy-D-glucose (2-DG). In contrast, TG2-proficient cells are more resistant to 2-DG-induced apoptosis as the caspase 3 is inactivated through the enzyme’s crosslinking activity. The data presented in this study show that TG2 plays a key role in cellular dynamics and consequently influences the energetic metabolism.
Cell Death and Differentiation advance online publication, 25 July 2014; http://dx.doi.org:/10.1038/cdd.2014.106.   PMID: 25060553

• l 2014 Jul 28. http://dx.doi.org/101038/onc.2014.220.

TRIM24 links glucose metabolism with transformation of human mammary epithelial cells.

Pathiraja TN¹, Thakkar KN¹, Jiang S¹, Stratton S¹, Liu Z¹, et al.
Author information

Tripartite motif 24 protein (TRIM24) is a plant omeodomain/bromodomain histone reader, recently associated with poor overall survival of breast-cancer patients. At a molecular level, TRIM24 is a negative regulator of p53 levels and a co-activator of estrogen receptor. However, the role of TRIM24 in breast tumorigenesis remains largely unknown. We used an isogenic human mammary epithelial cell (HMEC) culture model, derived from reduction mammoplasty tissue, and found that ectopic expression of TRIM24 in immortalized HMECs (TRIM24 iHMECs) greatly increased cellular proliferation and induced malignant transformation. Subcutaneous injection of TRIM24 iHMECs in nude mice led to growth of intermediate to high-grade tumors in 60-70% of mice. Molecular analysis of TRIM24 iHMECs revealed a glycolytic and tricarboxylic acid cycle gene signature, alongside increased glucose uptake and activated aerobic glycolysis. Collectively, these results identify a role for TRIM24 in breast tumorigenesis through reprogramming of glucose metabolism in HMECs, further supporting TRIM24 as a viable therapeutic target in breast cancer.
Oncogene advance online publication, 28 July 2014; http://dx.doi.org:/10.1038/onc.2014.220.   PMID: 25065590

• l New Knowledge of Genes Driving Bladder Cancer Points to Targeted Treatments

Mon, 09/15/2014 –   http://www.biosciencetechnology.com/news/2014/09/

The story of cancer care seems so simple: find the mutated gene that causes cancer and turn it off or fix it. But rarely does a single gene cause cancer. More often, many genes are altered together to drive the disease. So the challenge becomes sorting out which altered genes are the most to blame in which cancers. A collaborative study between researchers at the University of Colorado Cancer Center and the National Cancer Institute (NCI) published in the journalClinical Cancer Research takes an important step toward answering this question in bladder cancer.

Specifically, the study examined a mutation-rich layer of the genome called the exome of 54 bladder tumors from primarily Caucasian patients. The study is the first to show

1. alterations in the gene BAP1 in 15 percent of tumors;
2. the gene is a likely tumor suppressor and so
3. bladder cancers with alterations in this gene may be without an important check on the growth and survival of bladder cancer tissue.

Somatic BAP1 alterations contribute to a high frequency of tumors (10/14, 71 percent) with defects in genes encoding

• BRCA1 and BRCA2 pathway proteins,

pathways that have been previously implicated in breast and other cancer types.

More surprising, a second, highly independent genetic pathway was found in 69 percent of 54 tumors, in which

• alterations of the TERT promoter created what is effectively
  1. a second subset of bladder cancer.

The TERT promoter mutations did not significantly correlate with

• somatic alterations in other cancer genes,
  1. indicating that this alteration confers
     a presumed oncogenic benefit
• independent of other cancer gene alterations.

The gene KDM6A was frequently altered in 24 percent of tumors, and the study
shows that experimental

• depletion of KDM6A in human bladder cancer cells
  1. enhanced in vitro proliferation,
  2. in vivo tumor growth, and
  3. cell migration,
• confirming its role as a cancer driver and tumor suppressor in bladder tissue.

The study revealed other surprising relationships between the types of
genetic alterations in bladder tumors.

• BAP1 somatic mutations may correlate with
• papillary features in some bladder tumors – and

1. were significantly more frequent in Caucasian patients than Chinese patients,
2. indicating ethnicity, lifestyle, or exposure may influence somatic BAP1 mutations.

BAP1 and KDM6A mutations significantly co-occurred in tumors, indicating

• they likely supply mutually reinforcing survival advantages to cancer cells.

Finally, just four genes encoding chromatin remodeling enzymes,

1. BAP1,
2. KDM6A,
3. ARID1A, and
4. STAG2,

• were altered in 46 percent of 54 tumors and
• demonstrate a major contribution from somatic alterations
  • o targeting chromatin remodeling functions in bladder cancer.

“Taken together, we have identified new subtypes of bladder cancer that are related by

• somatic and germline genetic alterations

that are observed in patient tumors. These subtypes may be vulnerable to

• subtype-specific therapeutic targeting.

For example, many tumors in this study possessed cells with mutations targeting the

• BRCA DNA repair pathway indicating
• they are likely to be deficient in their ability to repair DNA,”

said Dan Theodorescu, professor of Urology and Pharmacology, director of the University of Colorado Cancer Center and the paper’s senior author.

“Thus the tumor cells should be especially sensitive to

• chemotherapeutic drugs that create DNA damage.

This is an excellent example of a case in which basic science
can now suggest targeted treatments that have the real possibility
to benefit patients,” said Michael Nickerson, staff scientist and lead
author from the National Cancer Institute.
The forging of a cancer-metabolism link and twists in the chain
Posted by Biome on 19 Apr 2013 –

Few phenomena in biology can today be better recognized than the
special metabolic requirements of tumor cells. Not so however ten years
ago, when Grahame Hardie and Dario Alessi discovered that the elusive
upstream kinase that the Hardie lab had been pursuing in their research
on the metabolic regulator AMPK was the tumor suppressor, LKB1, that
the neighbouring Alessi lab was working on at the time.

The resulting paper [1], published in 2003 in what was then Journal of Biology (now BMC Biology), was one [1] of three [2, 3] connecting these two kinases and that helped to swell of a surge of interest in the metabolism of tumor cells that was just beginning at about that time

and is still growing.

To mark the tenth anniversary of the publication both of the paper and
of the journal, BMC Biology invited Hardie and Alessito write about how
their discovery came about and where it has led.

The distinctive metabolic feature of tumor cells that enables them to
meet the demands of unrestrained growth is the switch from oxidative
generation of ATP to aerobic glycolysis – a phenomenon now well known
as the Warburg effect. Operating this switch is one of the central functions
of the AMP-activated protein kinase (AMPK) that has long been the focus
of research in the Hardie lab. AMPK is an energy sensor that is allosterically
tuned by competitive binding of ATP, ADP and AMP to sites on its g regulatory subunit (its portrait here, with AMP bound at two sites, was kindly provided by Bing Xiao and Stephen Gamblin).

When phosphorylated by LKB1, AMPK responds to depletion of ATP by turning off anabolic reactions required for growth, and turning on catabolic reactions and oxidative phosphorylation – the reverse of the Warburg effect. In this light, it is not surprising that LKB1 is inactivated in some proportion of many different types of tumors.

This early on ignited the hope that metformin, an AMPK-activating drug that is already tried, tested, and very widely used to treat type 2 diabetes, might have anti-tumor potential – an idea that is supported by some evidence from cancer incidence in type 2 diabetics.
Hawley SA, Boudeau J, Reid JL, Mustard KJ, Udd L, Makela TP, Alessi DR, Hardie DG: Complexes between the LKB1 tumor suppressor, STRADa/b and MO25a/b are upstream kinases in the AMP-activated protein kinase cascade. J Biol 2003; 2(4):28.

Woods A, Johnstone SR, Dickerson K, Leiper FC, Fryer LG, Neumann D, Schlattner U, Wallimann T, Carlson M, Carling D: LKB1 is the upstream kinase in the AMP-activated protein kinase cascade. Curr Biol 2003; 13(22):2004-2008.

Shaw RJ, Kosmatka M, Bardeesy N, Hurley RL, Witters LA, DePinho RA, Cantley LC: The tumor suppressor LKB1 kinase directly activates AMP-activated kinase and regulates apoptosis in response to energy stress.
Proc Natl Acad Sci USA 2004; 101(10):3329-3335.

I digress here in reference to an extended reference to matters.
To be sure, Dr. Philip Lorenzi has done an admirable job in evaluating
the potential targets for pharmaceutical development by looking at
siRNA screening, and and a search of the published work on negative
and positive modulators of autophagy, and attention may be directed
at separate pathways in mitophagy, chromatophagy, and ribophagy –
affecting respiration, the chromatin, and the endoplasmic reticulum.
In this respect, autophagy was not known when Warburg, Szent-Gyorgy,
and Sir Hans Krebs, carried out their studies.

Lysosomes were discovered and named by Belgian biologist Christian de
Duve, who eventually received the Nobel Prize in Physiology or Medicine in 1974. Enzymes of the lysosomes are synthesised in the rough endoplasmic reticulum. The enzymes are released from the Golgi apparatus in small vesicles (identified in 1897 by the Italian physician Camillo Golgi) which ultimately fuse with acidic vesicles called endosomes, thus becoming full lysosomes. In the process the enzymes are specifically tagged with mannose 6-phosphate to differentiate them from other enzymes.

Lysosomes are interlinked with three intracellular processes, namely – phagocytosis, endocytosis and autophagy. Synthesis of lysosomal
enzymes are controlled by nuclear genes. Mutations in the genes for
these enzymes are responsible for more than 30 different human genetic diseases, which are collectively known as lysosomal storage diseases. These diseases are due to deficiency in a single lysosomal enzyme that prevent break down of target molecules, and consequently undegraded
materials accumulate within the lysosomes often giving rise to severe clinical symptoms.

LSDs occur with incidences of less than 1:100,000; however, as a group the incidence is about 1:5,000 – 1:10,000. Most of these disorders are autosomal recessively inherited such as Niemann-Pick disease, type C, however a few are X-linked recessively inherited, such as Fabry disease and Hunter syndrome (MPS II). They originate from an abnormal accumulation of substances inside the lysosome. Tay-Sachs disease was the first of these disorders to be described, in 1881, followed by
Gaucher disease in 1882. In the late 1950s and early 1960s, de Duve
and colleagues, using cell fractionation techniques, cytological studies
and biochemical analyses, identified and characterized the lysosome as
a cellular organelle responsible for intracellular digestion and recycling
of macromolecules.
This was the scientific breakthrough that would lead to the understanding of the physiological basis of the Lysosomal Storage Diseases. (Elizabeth Neufeld is most notable for her work in advances in treatment for genetically inherited diseases such as Hurler’s, Hunter’s, and Sanfilippo syndrome. Neufeld discovered that a mixture of Hunter and Hurler cells
underwent some normalization! There appeared to be secretion of ‘corrective factors’, found to be a-L-iduronidase, absent in the Hurler fibroblasts. It turned out that mannose-6-phosphate was the recognition signal for lysosomal enzymes. Neufeld and Kakis made recombinant human and canine DNA, which successfully excreted the enzyme with the M6P signal and was highly corrective! Neufeld spent 35 years of her life developing a treatment for the rare lysosomal enzyme disease. Thanks
to her, enzyme replacement therapy for alpha-L-Iduronidase deficiency
is now an acceptable treatment}.

Recognition: the Wolf Prize, the Albert Lasker Award, Javits Award
for her work on Tay-Sachs disease, National Medal of Science, and membership inNational Academy of Sciences, Chief of the NIH Section
of Human Biochemical Genetics, Chief of the Genetics and Biochemistry Branch of the National Institute of Arthritis, Diabetes, and Digestive and Kidney Diseases (NIADDK), Deputy director of NIADDK’s Division of Intramural Research, Chair of the Biological Chemistry Department at UCLA, chaired the Scientific Advisory Board of the National MPS Society, Hildebrand Award, Gairdner Foundation Award, the International Society
for Clinical Enzymology J. Henry Wilkinson Memorial Award, the Franklin Institute’s Elliot Cresson Medal.

Classic Articles

Pyridine Nucleotide Transhydrogenase. III. Animal
Tissue Transhydrogenases (Kaplan, N. O., Colowick,
S. P., and Neufeld, E. F. (1953) J. Biol. Chem.

205, 1–15)

• “The Hurler Corrective Factor” Barton and Neufeld,
  246: 7773-7779.
• “The Sanfilippo A Corrective Factor” Kresse and
  Neufeld, 247: 2164-2170.
• “The Hunter Corrective Factor” Cantz, et al.,
  247: 5456-5462.

We now see that there are several different major contributors
to autophagy in carcinogenesis, which increases the dimensionality
of our understanding, and may also contribute to the increased
evidence of different classifications of expected cancer cell types.
Furthermore, we now have exposed a possible relationship between
autophagy and mitochondrial impairment that eventuates in the
dependence on glycolysis and impaired respiration in eukaryotic
cancer cells. There is a tie in with nitric oxide and oxidative stress,
and with oxygen supply, and so the indispensable amino acids –
glutamine and arginine, and folic acid and vitamin C can’t be
discounted, but a role for methionine deficiency is not known
(essential for acetyl co A).

Noncoding RNAs

Novel Insights into miRNA in Lung and Heart Inflammatory
Diseases
A Kishore, J Borucka, Jana Petrkova, and M Petrek
Laboratory of Immunogenomics and Immunoproteomics,
Departments of Pathological Physiology and of Medicine, Faculty of
Medicine and Dentistry, Palacky University, Hnevotinska 3, 77515
Olomouc, Czech Republic
MicroRNAs (miRNAs) are noncoding regulatory sequences that govern
posttranscriptional inhibition of genes through binding mainly at regulatory
regions. he regulatory mechanism of miRNAs are inluenced by complex
crosstalk among single nucleotide polymorphisms (SNPs) within miRNA
seed region and epigenetic modiications. Functionally, miRNAs are involved
in basic regulatory mechanisms of cells including inlammation. Thus, miRNA dysregulation, resulting in aberrant expression of a gene, is suggested to play an important role in disease susceptibility. Identiications and usage of potential miRNAs as well as disruption of disease susceptible miRNAs using antagonists, antagomirs, are imperative and may provide a novel therapeutic approach towards combating disease progression.

Long non-coding RNAs as a source of new peptides
J Ruiz-Orera, X Messeguer, JA Subirana, MM Alba
Evolutionary Genomics Group, Research Programme on Biomedical Informatics, Hospital del Mar Research Institute, Universitat Pompeu Fabra; Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya; Real Academia de Ciències i Arts de Barcelona; Catalan Institution for Research and Advanced Studies, Barcelona, Spain

eLife 2014. http://dx.doi.org:/10.7554/eLife.03523.001

Deep transcriptome sequencing has revealed the existence of many transcripts that lack long or conserved open reading frames (ORFs)
and which have been termed long non-coding RNAs (lncRNAs). The
vast majority of lncRNAs are lineage-specific and do not yet have a
known function. In this study, we test the hypothesis that they may
act as a repository for the synthesis of new peptides. We find that
a large fraction of the lncRNAs expressed in cells from six different
species is associated with ribosomes. The patterns of ribosome
protection are consistent with the translation of short peptides.
lncRNAs show similar coding potential and sequence constraints than evolutionary young protein coding sequences, indicating that they
play an important role in de novo protein evolution.

In a previous comment, JEDS Roselino commented: cells, tissues,
organs and systems perform a regulatory work upon our “internal milieu” (common extracellular liquid that bath all our cells) through fast regulatory actions required in order to preserve the values of the components of our “internal milieu” inside a range that we consider is the best for our entire organism.

Examples of this work are the glycemic levels, pH, Na, osmolality. Some
of our cells only affect the “internal milieu”, while others have metabolic
regulation and above that, homeostatic regulatory activity. Both, metabolic
and homeostatic mechanisms are performed by rapid mechanisms that
do not require changes in gene expression. The two major mechanisms
for this kind of fast regulation are changes in protein conformation caused
by binding of effectors directly or indirectly as for instance, through Ca-
binding proteins. Alternatively, by change in conformation caused by
covalent modification of the proteins as for instance, is the case of
phosphorylation.

Therefore, in case you want to display a whole system vision upon regulation of protein activity it is possible to compare it with some
general aspects, not the quantic mechanisms, of Schrödinger´s cat.

• l What is life? The physicist who sparked a revolution in biology

Erwin Schrödinger introduced some of the most important concepts in biology, including the idea of a ‘code’ of life Schrödinger’s book contains something far more important than his attempt to fuse physics and biology. In that lecture 70 years ago, he introduced some of the most important concepts in the history of biology, which continue to frame how we see life. He did not suggest there was a correspondence between each part of the “code-script” and precise biochemical reactions.
Matthew Cobb Thurs 7 Feb 2013 the guardian.com

These examples are in support of the comprehensive study presented above, even if they are targeted to specific types of cancer. The examples seem to also show an interplay between mitophagia, energy metabolism in the balance between oxidative phosphorylation and glycolysis, and genetic regulation, including pathways discussed above. The implications for therapeutic targeting are clearly stated.

The potential for Personalized Medicine is also expressed, but it is a more difficult goal to achieve. I’m not quite sure that the current system of insurance-based coverage is adequate to support the development of a Personalized Medicine infrastructure in the near term, given the development costs, the large number of enrolled patients needed for coverage, and the extremely weak informatics structure that currently exists.

Appendix

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Acknowledgments

We thank Shelley Herbrich, Kevin Coombes, and Ganiraju Manyam (UT MD Anderson Cancer Center) for assistance in dealing with gene names and synonyms and all of the researchers whose publications formed the basis for this census.

JNW’s and PLL’s work was supported in part by US. National Cancer Institute (NCI) grant numbers CA143883 (The Cancer Genome Atlas MD Anderson Genome Data Analysis Center) and CA083639 (University of Texas MD Anderson SPORE in Ovarian Cancer), the Cancer Prevention Research Institute of Texas (CPRIT) grant number RP130397, and also by the Chapman Foundation and the Michael and Susan Dell Foundation (honoring Lorraine Dell). GBM’s work was supported in part by Komen Foundation grants KG 081694 and FAS0703849, and by the Ovarian Cancer Research Fund. SC’s work was supported by an Odyssey Program Fellowship, by the Theodore N. Law Endowment for Scientific Achievement at the University of Texas MD Anderson Cancer Center, and by NCI Breast SPORE Career Developmental Project award CA116199.

Supplemental Materials

Supplemental materials may be found here:

http://www.landesbioscience.com/journals/autophagy/article/28773

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