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Genome-Scale Model-Based Identification of Metabolite Indicators for Early Detection of Kidney Toxicity.
Pannala VR, Vinnakota KC, Estes SK, Trenary I, OˈBrien TP, Printz RL, Papin JA, Reifman J, Oyama T, Shiota M, Young JD, Wallqvist A
(2020) Toxicol Sci 173: 293-312
MeSH Terms: Animals, Biomarkers, Gene Expression Profiling, Gentamicins, Kidney, Liver, Male, Metabolic Networks and Pathways, Metabolome, Rats, Rats, Sprague-Dawley
Show Abstract · Added March 5, 2020
Identifying early indicators of toxicant-induced organ damage is critical to provide effective treatment. To discover such indicators and the underlying mechanisms of toxicity, we used gentamicin as an exemplar kidney toxicant and performed systematic perturbation studies in Sprague Dawley rats. We obtained high-throughput data 7 and 13 h after administration of a single dose of gentamicin (0.5 g/kg) and identified global changes in genes in the liver and kidneys, metabolites in the plasma and urine, and absolute fluxes in central carbon metabolism. We used these measured changes in genes in the liver and kidney as constraints to a rat multitissue genome-scale metabolic network model to investigate the mechanism of gentamicin-induced kidney toxicity and identify metabolites associated with changes in tissue gene expression. Our experimental analysis revealed that gentamicin-induced metabolic perturbations could be detected as early as 7 h postexposure. Our integrated systems-level analyses suggest that changes in kidney gene expression drive most of the significant metabolite alterations in the urine. The analyses thus allowed us to identify several significantly enriched injury-specific pathways in the kidney underlying gentamicin-induced toxicity, as well as metabolites in these pathways that could serve as potential early indicators of kidney damage.
© The Author(s) 2020. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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11 MeSH Terms
Bacterial Pathogens Hijack the Innate Immune Response by Activation of the Reverse Transsulfuration Pathway.
Gobert AP, Latour YL, Asim M, Finley JL, Verriere TG, Barry DP, Milne GL, Luis PB, Schneider C, Rivera ES, Lindsey-Rose K, Schey KL, Delgado AG, Sierra JC, Piazuelo MB, Wilson KT
(2019) mBio 10:
MeSH Terms: Animals, Bacteria, Gene Silencing, Helicobacter pylori, Histones, Humans, Immune Evasion, Immunity, Innate, Immunoglobulins, Macrophages, Male, Metabolic Networks and Pathways, Mice, Mice, Inbred C57BL, Nitric Oxide Synthase Type II, Phosphatidylinositol 3-Kinases, Polyamines, RAW 264.7 Cells, Spermidine, Spermine, Sulfur, Transcription Factors
Show Abstract · Added November 1, 2019
The reverse transsulfuration pathway is the major route for the metabolism of sulfur-containing amino acids. The role of this metabolic pathway in macrophage response and function is unknown. We show that the enzyme cystathionine γ-lyase (CTH) is induced in macrophages infected with pathogenic bacteria through signaling involving phosphatidylinositol 3-kinase (PI3K)/MTOR and the transcription factor SP1. This results in the synthesis of cystathionine, which facilitates the survival of pathogens within myeloid cells. Our data demonstrate that the expression of CTH leads to defective macrophage activation by (i) dysregulation of polyamine metabolism by depletion of -adenosylmethionine, resulting in immunosuppressive putrescine accumulation and inhibition of spermidine and spermine synthesis, and (ii) increased histone H3K9, H3K27, and H3K36 di/trimethylation, which is associated with gene expression silencing. Thus, CTH is a pivotal enzyme of the innate immune response that disrupts host defense. The induction of the reverse transsulfuration pathway by bacterial pathogens can be considered an unrecognized mechanism for immune escape. Macrophages are professional immune cells that ingest and kill microbes. In this study, we show that different pathogenic bacteria induce the expression of cystathionine γ-lyase (CTH) in macrophages. This enzyme is involved in a metabolic pathway called the reverse transsulfuration pathway, which leads to the production of numerous metabolites, including cystathionine. Phagocytized bacteria use cystathionine to better survive in macrophages. In addition, the induction of CTH results in dysregulation of the metabolism of polyamines, which in turn dampens the proinflammatory response of macrophages. In conclusion, pathogenic bacteria can evade the host immune response by inducing CTH in macrophages.
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22 MeSH Terms
Extrinsic and Intrinsic Immunometabolism Converge: Perspectives on Future Research and Therapeutic Development for Obesity.
Caslin HL, Hasty AH
(2019) Curr Obes Rep 8: 210-219
MeSH Terms: Adaptive Immunity, Adipose Tissue, Animals, Energy Metabolism, Epigenesis, Genetic, Humans, Immunity, Immunologic Memory, Iron, Macrophages, Metabolic Diseases, Metabolic Networks and Pathways, MicroRNAs, Obesity
Show Abstract · Added March 3, 2020
PURPOSE OF REVIEW - Research over the past decade has shown that immunologic and metabolic pathways are intricately linked. This burgeoning field of immunometabolism includes intrinsic and extrinsic pathways and is known to be associated with obesity-accelerated metabolic disease. Intrinsic immunometabolism includes the study of fuel utilization and bioenergetic pathways that influence immune cell function. Extrinsic immunometabolism includes the study of immune cells and products that influence systemic metabolism.
RECENT FINDINGS - Th2 immunity, macrophage iron handling, adaptive immune memory, and epigenetic regulation of immunity, which all require intrinsic metabolic changes, play a role in systemic metabolism and metabolic function, linking the two arms of immunometabolism. Together, this suggests that targeting intrinsic immunometabolism can directly affect immune function and ultimately systemic metabolism. We highlight important questions for future basic research that will help improve translational research and provide therapeutic targets to help establish new treatments for obesity and associated metabolic disorders.
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Metabolic plasticity meets gene regulation.
Paudel BB, Quaranta V
(2019) Proc Natl Acad Sci U S A 116: 3370-3372
MeSH Terms: Biochemical Phenomena, Gene Expression Regulation, Humans, Metabolic Networks and Pathways, Neoplasms, Neuronal Plasticity
Added March 23, 2019
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6 MeSH Terms
Metabolic network-based predictions of toxicant-induced metabolite changes in the laboratory rat.
Pannala VR, Wall ML, Estes SK, Trenary I, O'Brien TP, Printz RL, Vinnakota KC, Reifman J, Shiota M, Young JD, Wallqvist A
(2018) Sci Rep 8: 11678
MeSH Terms: Acetaminophen, Animals, Animals, Laboratory, Gene Expression Regulation, Glycogenolysis, Liver, Male, Metabolic Flux Analysis, Metabolic Networks and Pathways, Metabolome, Pyruvates, Rats, Sprague-Dawley
Show Abstract · Added March 28, 2019
In order to provide timely treatment for organ damage initiated by therapeutic drugs or exposure to environmental toxicants, we first need to identify markers that provide an early diagnosis of potential adverse effects before permanent damage occurs. Specifically, the liver, as a primary organ prone to toxicants-induced injuries, lacks diagnostic markers that are specific and sensitive to the early onset of injury. Here, to identify plasma metabolites as markers of early toxicant-induced injury, we used a constraint-based modeling approach with a genome-scale network reconstruction of rat liver metabolism to incorporate perturbations of gene expression induced by acetaminophen, a known hepatotoxicant. A comparison of the model results against the global metabolic profiling data revealed that our approach satisfactorily predicted altered plasma metabolite levels as early as 5 h after exposure to 2 g/kg of acetaminophen, and that 10 h after treatment the predictions significantly improved when we integrated measured central carbon fluxes. Our approach is solely driven by gene expression and physiological boundary conditions, and does not rely on any toxicant-specific model component. As such, it provides a mechanistic model that serves as a first step in identifying a list of putative plasma metabolites that could change due to toxicant-induced perturbations.
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12 MeSH Terms
Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics.
Ding L, Bailey MH, Porta-Pardo E, Thorsson V, Colaprico A, Bertrand D, Gibbs DL, Weerasinghe A, Huang KL, Tokheim C, Cortés-Ciriano I, Jayasinghe R, Chen F, Yu L, Sun S, Olsen C, Kim J, Taylor AM, Cherniack AD, Akbani R, Suphavilai C, Nagarajan N, Stuart JM, Mills GB, Wyczalkowski MA, Vincent BG, Hutter CM, Zenklusen JC, Hoadley KA, Wendl MC, Shmulevich L, Lazar AJ, Wheeler DA, Getz G, Cancer Genome Atlas Research Network
(2018) Cell 173: 305-320.e10
MeSH Terms: Carcinogenesis, DNA Repair, Databases, Genetic, Genes, Neoplasm, Genomics, Humans, Metabolic Networks and Pathways, Microsatellite Instability, Mutation, Neoplasms, Transcriptome, Tumor Microenvironment
Show Abstract · Added October 30, 2019
The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing.
Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
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The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma.
Ricketts CJ, De Cubas AA, Fan H, Smith CC, Lang M, Reznik E, Bowlby R, Gibb EA, Akbani R, Beroukhim R, Bottaro DP, Choueiri TK, Gibbs RA, Godwin AK, Haake S, Hakimi AA, Henske EP, Hsieh JJ, Ho TH, Kanchi RS, Krishnan B, Kwiatkowski DJ, Lui W, Merino MJ, Mills GB, Myers J, Nickerson ML, Reuter VE, Schmidt LS, Shelley CS, Shen H, Shuch B, Signoretti S, Srinivasan R, Tamboli P, Thomas G, Vincent BG, Vocke CD, Wheeler DA, Yang L, Kim WY, Robertson AG, Cancer Genome Atlas Research Network, Spellman PT, Rathmell WK, Linehan WM
(2018) Cell Rep 23: 313-326.e5
MeSH Terms: Biomarkers, Tumor, Carcinoma, Renal Cell, Cyclin-Dependent Kinase Inhibitor p16, DNA-Binding Proteins, Genome, Human, Humans, Kidney Neoplasms, Metabolic Networks and Pathways, Nuclear Proteins, PTEN Phosphohydrolase, Phenotype, Survival Analysis, Transcription Factors, Tumor Suppressor Proteins, Ubiquitin Thiolesterase
Show Abstract · Added October 30, 2019
Renal cell carcinoma (RCC) is not a single disease, but several histologically defined cancers with different genetic drivers, clinical courses, and therapeutic responses. The current study evaluated 843 RCC from the three major histologic subtypes, including 488 clear cell RCC, 274 papillary RCC, and 81 chromophobe RCC. Comprehensive genomic and phenotypic analysis of the RCC subtypes reveals distinctive features of each subtype that provide the foundation for the development of subtype-specific therapeutic and management strategies for patients affected with these cancers. Somatic alteration of BAP1, PBRM1, and PTEN and altered metabolic pathways correlated with subtype-specific decreased survival, while CDKN2A alteration, increased DNA hypermethylation, and increases in the immune-related Th2 gene expression signature correlated with decreased survival within all major histologic subtypes. CIMP-RCC demonstrated an increased immune signature, and a uniform and distinct metabolic expression pattern identified a subset of metabolically divergent (MD) ChRCC that associated with extremely poor survival.
Published by Elsevier Inc.
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15 MeSH Terms
Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers.
Peng X, Chen Z, Farshidfar F, Xu X, Lorenzi PL, Wang Y, Cheng F, Tan L, Mojumdar K, Du D, Ge Z, Li J, Thomas GV, Birsoy K, Liu L, Zhang H, Zhao Z, Marchand C, Weinstein JN, Cancer Genome Atlas Research Network, Bathe OF, Liang H
(2018) Cell Rep 23: 255-269.e4
MeSH Terms: Cell Line, Tumor, Core Binding Factor Alpha 2 Subunit, Drug Resistance, Neoplasm, HEK293 Cells, Humans, Metabolic Networks and Pathways, Neoplasms, Snail Family Transcription Factors, Transcriptome
Show Abstract · Added October 30, 2019
Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1-master regulators of carbohydrate metabolic subtypes-modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility.
Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.
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Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types.
Ge Z, Leighton JS, Wang Y, Peng X, Chen Z, Chen H, Sun Y, Yao F, Li J, Zhang H, Liu J, Shriver CD, Hu H, Cancer Genome Atlas Research Network, Piwnica-Worms H, Ma L, Liang H
(2018) Cell Rep 23: 213-226.e3
MeSH Terms: Cell Line, Tumor, Gene Expression Regulation, Neoplastic, Genome, Human, Genomics, Humans, Metabolic Networks and Pathways, Neoplasms, Oncogene Proteins, Ubiquitination
Show Abstract · Added October 30, 2019
Protein ubiquitination is a dynamic and reversible process of adding single ubiquitin molecules or various ubiquitin chains to target proteins. Here, using multidimensional omic data of 9,125 tumor samples across 33 cancer types from The Cancer Genome Atlas, we perform comprehensive molecular characterization of 929 ubiquitin-related genes and 95 deubiquitinase genes. Among them, we systematically identify top somatic driver candidates, including mutated FBXW7 with cancer-type-specific patterns and amplified MDM2 showing a mutually exclusive pattern with BRAF mutations. Ubiquitin pathway genes tend to be upregulated in cancer mediated by diverse mechanisms. By integrating pan-cancer multiomic data, we identify a group of tumor samples that exhibit worse prognosis. These samples are consistently associated with the upregulation of cell-cycle and DNA repair pathways, characterized by mutated TP53, MYC/TERT amplification, and APC/PTEN deletion. Our analysis highlights the importance of the ubiquitin pathway in cancer development and lays a foundation for developing relevant therapeutic strategies.
Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
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Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas.
Campbell JD, Yau C, Bowlby R, Liu Y, Brennan K, Fan H, Taylor AM, Wang C, Walter V, Akbani R, Byers LA, Creighton CJ, Coarfa C, Shih J, Cherniack AD, Gevaert O, Prunello M, Shen H, Anur P, Chen J, Cheng H, Hayes DN, Bullman S, Pedamallu CS, Ojesina AI, Sadeghi S, Mungall KL, Robertson AG, Benz C, Schultz A, Kanchi RS, Gay CM, Hegde A, Diao L, Wang J, Ma W, Sumazin P, Chiu HS, Chen TW, Gunaratne P, Donehower L, Rader JS, Zuna R, Al-Ahmadie H, Lazar AJ, Flores ER, Tsai KY, Zhou JH, Rustgi AK, Drill E, Shen R, Wong CK, Cancer Genome Atlas Research Network, Stuart JM, Laird PW, Hoadley KA, Weinstein JN, Peto M, Pickering CR, Chen Z, Van Waes C
(2018) Cell Rep 23: 194-212.e6
MeSH Terms: Carcinoma, Squamous Cell, Cell Line, Tumor, DNA Methylation, Epithelial-Mesenchymal Transition, Gene Expression Regulation, Neoplastic, Genomics, Humans, Metabolic Networks and Pathways, Polymorphism, Genetic
Show Abstract · Added October 30, 2019
This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smoking and/or human papillomavirus (HPV). SCCs harbor 3q, 5p, and other recurrent chromosomal copy-number alterations (CNAs), DNA mutations, and/or aberrant methylation of genes and microRNAs, which are correlated with the expression of multi-gene programs linked to squamous cell stemness, epithelial-to-mesenchymal differentiation, growth, genomic integrity, oxidative damage, death, and inflammation. Low-CNA SCCs tended to be HPV(+) and display hypermethylation with repression of TET1 demethylase and FANCF, previously linked to predisposition to SCC, or harbor mutations affecting CASP8, RAS-MAPK pathways, chromatin modifiers, and immunoregulatory molecules. We uncovered hypomethylation of the alternative promoter that drives expression of the ΔNp63 oncogene and embedded miR944. Co-expression of immune checkpoint, T-regulatory, and Myeloid suppressor cells signatures may explain reduced efficacy of immune therapy. These findings support possibilities for molecular classification and therapeutic approaches.
Published by Elsevier Inc.
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