Protocol for performing metabolic pathway-based subtyping of breast tumors.

Here, we present a protocol for analyzing the global metabolic landscape in breast tumors for the purpose of metabolism-based patient stratification. We describe steps for analyzing 1,454 metabolic genes representing 90 metabolic pathways and subjecting them to an algorithm that calculates the deregulation score of 90 pathways in each tumor sample, thus converting gene-level information into pathway-level information. We then detail procedures for performing clustering analysis to identify metabolic subtypes and using machine learning to develop a signature representing each subtype. For complete details on the use and execution of this protocol, please refer to Iqbal et al.1.

Leveraging metabolic modeling and machine learning to uncover modulators of quiescence depth.

Quiescence, a temporary withdrawal from the cell cycle, plays a key role in tissue homeostasis and regeneration. Quiescence is increasingly viewed as a continuum between shallow and deep quiescence, reflecting different potentials to proliferate. The depth of quiescence is altered in a range of diseases and during aging. Here, we leveraged genome-scale metabolic modeling (GEM) to define the metabolic and epigenetic changes that take place with quiescence deepening. We discovered contrasting changes in lipid catabolism and anabolism and diverging trends in histone methylation and acetylation. We then built a multi-cell type machine learning model that accurately predicts quiescence depth in diverse biological contexts. Using both machine learning and genome-scale flux simulations, we performed high-throughput screening of chemical and genetic modulators of quiescence and identified novel small molecule and genetic modulators with relevance to cancer and aging.

Metabolic stratification of human breast tumors reveal subtypes of clinical and therapeutic relevance.

Extensive metabolic heterogeneity in breast cancers has limited the deployment of metabolic therapies. To enable patient stratification, we studied the metabolic landscape in breast cancers (∼3000 patients combined) and identified three subtypes with increasing degrees of metabolic deregulation. Subtype M1 was found to be dependent on bile-acid biosynthesis, whereas M2 showed reliance on methionine pathway, and M3 engaged fatty-acid, nucleotide, and glucose metabolism. The extent of metabolic alterations correlated strongly with tumor aggressiveness and patient outcome. This pattern was reproducible in independent datasets and using in vivo tumor metabolite data. Using machine-learning, we identified robust and generalizable signatures of metabolic subtypes in tumors and cell lines. Experimental inhibition of metabolic pathways in cell lines representing metabolic subtypes revealed subtype-specific sensitivity, therapeutically relevant drugs, and promising combination therapies. Taken together, metabolic stratification of breast cancers can thus aid in predicting patient outcome and designing precision therapies.

Single-cell RNA-sequencing identifies anti-cancer immune phenotypes in the early lung metastatic niche during breast cancer.

Microenvironmental changes in the early metastatic niche may be exploited to identify therapeutic targets to inhibit secondary tumor formation and improve disease outcomes. We dissected the developing lung metastatic niche in a model of metastatic, triple-negative breast cancer using single-cell RNA-sequencing. Lungs were extracted from mice at 7-, 14-, or 21 days after tumor inoculation corresponding to the pre-metastatic, micro-metastatic, and metastatic niche, respectively. The progression of the metastatic niche was marked by an increase in neutrophil infiltration (5% of cells at day 0 to 81% of cells at day 21) and signaling pathways corresponding to the hallmarks of cancer. Importantly, the pre-metastatic and early metastatic niche were composed of immune cells with an anti-cancer phenotype not traditionally associated with metastatic disease. As expected, the metastatic niche exhibited pro-cancer phenotypes. The transition from anti-cancer to pro-cancer phenotypes was directly associated with neutrophil and monocyte behaviors at these time points. Predicted metabolic, transcription factor, and receptor-ligand signaling suggested that changes in the neutrophils likely induced the transitions in the other immune cells. Conditioned medium generated by cells extracted from the pre-metastatic niche successfully inhibited tumor cell proliferation and migration in vitro and the in vivo depletion of pre-metastatic neutrophils and monocytes worsened survival outcomes, thus validating the anti-cancer phenotype of the developing niche. Genes associated with the early anti-cancer response could act as biomarkers that could serve as targets for the treatment of early metastatic disease. Such therapies have the potential to revolutionize clinical outcomes in metastatic breast cancer.

Acetyl-CoA metabolism drives epigenome change and contributes to carcinogenesis risk in fatty liver disease.

BACKGROUND: The incidence of non-alcoholic fatty liver disease (NAFLD)-associated hepatocellular carcinoma (HCC) is increasing worldwide, but the steps in precancerous hepatocytes which lead to HCC driver mutations are not well understood. Here we provide evidence that metabolically driven histone hyperacetylation in steatotic hepatocytes can increase DNA damage to initiate carcinogenesis. METHODS: Global epigenetic state was assessed in liver samples from high-fat diet or high-fructose diet rodent models, as well as in cultured immortalized human hepatocytes (IHH cells). The mechanisms linking steatosis, histone acetylation and DNA damage were investigated by computational metabolic modelling as well as through manipulation of IHH cells with metabolic and epigenetic inhibitors. Chromatin immunoprecipitation and next-generation sequencing (ChIP-seq) and transcriptome (RNA-seq) analyses were performed on IHH cells. Mutation locations and patterns were compared between the IHH cell model and genome sequence data from preneoplastic fatty liver samples from patients with alcohol-related liver disease and NAFLD. RESULTS: Genome-wide histone acetylation was increased in steatotic livers of rodents fed high-fructose or high-fat diet. In vitro, steatosis relaxed chromatin and increased DNA damage marker γH2AX, which was reversed by inhibiting acetyl-CoA production. Steatosis-associated acetylation and γH2AX were enriched at gene clusters in telomere-proximal regions which contained HCC tumour suppressors in hepatocytes and human fatty livers. Regions of metabolically driven epigenetic change also had increased levels of DNA mutation in non-cancerous tissue from NAFLD and alcohol-related liver disease patients. Finally, genome-scale network modelling indicated that redox balance could be a key contributor to this mechanism. CONCLUSIONS: Abnormal histone hyperacetylation facilitates DNA damage in steatotic hepatocytes and is a potential initiating event in hepatocellular carcinogenesis.

Lipid exposure activates gene expression changes associated with estrogen receptor negative breast cancer.

Improved understanding of local breast biology that favors the development of estrogen receptor negative (ER-) breast cancer (BC) would foster better prevention strategies. We have previously shown that overexpression of specific lipid metabolism genes is associated with the development of ER- BC. We now report results of exposure of MCF-10A and MCF-12A cells, and mammary organoids to representative medium- and long-chain polyunsaturated fatty acids. This exposure caused a dynamic and profound change in gene expression, accompanied by changes in chromatin packing density, chromatin accessibility, and histone posttranslational modifications (PTMs). We identified 38 metabolic reactions that showed significantly increased activity, including reactions related to one-carbon metabolism. Among these reactions are those that produce S-adenosyl-L-methionine for histone PTMs. Utilizing both an in-vitro model and samples from women at high risk for ER- BC, we show that lipid exposure engenders gene expression, signaling pathway activation, and histone marks associated with the development of ER- BC.

Metabolism, HDACs, and HDAC Inhibitors: A Systems Biology Perspective.

Histone deacetylases (HDACs) are epigenetic enzymes that play a central role in gene regulation and are sensitive to the metabolic state of the cell. The cross talk between metabolism and histone acetylation impacts numerous biological processes including development and immune function. HDAC inhibitors are being explored for treating cancers, viral infections, inflammation, neurodegenerative diseases, and metabolic disorders. However, how HDAC inhibitors impact cellular metabolism and how metabolism influences their potency is unclear. Discussed herein are recent applications and future potential of systems biology methods such as high throughput drug screens, cancer cell line profiling, single cell sequencing, proteomics, metabolomics, and computational modeling to uncover the interplay between metabolism, HDACs, and HDAC inhibitors. The synthesis of new systems technologies can ultimately help identify epigenomic and metabolic biomarkers for patient stratification and the design of effective therapeutics.

The deacylase SIRT5 supports melanoma viability by influencing chromatin dynamics.

Cutaneous melanoma remains the most lethal skin cancer, and ranks third among all malignancies in terms of years of life lost. Despite the advent of immune checkpoint and targeted therapies, only roughly half of patients with advanced melanoma achieve a durable remission. Sirtuin 5 (SIRT5) is a member of the sirtuin family of protein deacylases that regulates metabolism and other biological processes. Germline Sirt5 deficiency is associated with mild phenotypes in mice. Here we showed that SIRT5 was required for proliferation and survival across all cutaneous melanoma genotypes tested, as well as uveal melanoma, a genetically distinct melanoma subtype that arises in the eye and is incurable once metastatic. Likewise, SIRT5 was required for efficient tumor formation by melanoma xenografts and in an autochthonous mouse Braf Pten-driven melanoma model. Via metabolite and transcriptomic analyses, we found that SIRT5 was required to maintain histone acetylation and methylation levels in melanoma cells, thereby promoting proper gene expression. SIRT5-dependent genes notably included MITF, a key lineage-specific survival oncogene in melanoma, and the c-MYC proto-oncogene. SIRT5 may represent a druggable genotype-independent addiction in melanoma.

A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs.

Tuberculosis (TB) is the deadliest infectious disease worldwide. The design of new treatments for TB is hindered by the large number of candidate drugs, drug combinations, dosing choices, and complex pharmaco-kinetics/dynamics (PK/PD). Here we study the interplay of these factors in designing combination therapies by linking a machine-learning model, INDIGO-MTB, which predicts in vitro drug interactions using drug transcriptomics, with a multi-scale model of drug PK/PD and pathogen-immune interactions called GranSim. We calculate an in vivo drug interaction score (iDIS) from dynamics of drug diffusion, spatial distribution, and activity within lesions against various pathogen sub-populations. The iDIS of drug regimens evaluated against non-replicating bacteria significantly correlates with efficacy metrics from clinical trials. Our approach identifies mechanisms that can amplify synergistic or mitigate antagonistic drug interactions in vivo by modulating the relative distribution of drugs. Our mechanistic framework enables efficient evaluation of in vivo drug interactions and optimization of combination therapies.

Purine metabolism regulates DNA repair and therapy resistance in glioblastoma.

Intratumoral genomic heterogeneity in glioblastoma (GBM) is a barrier to overcoming therapy resistance. Treatments that are effective independent of genotype are urgently needed. By correlating intracellular metabolite levels with radiation resistance across dozens of genomically-distinct models of GBM, we find that purine metabolites, especially guanylates, strongly correlate with radiation resistance. Inhibiting GTP synthesis radiosensitizes GBM cells and patient-derived neurospheres by impairing DNA repair. Likewise, administration of exogenous purine nucleosides protects sensitive GBM models from radiation by promoting DNA repair. Neither modulating pyrimidine metabolism nor purine salvage has similar effects. An FDA-approved inhibitor of GTP synthesis potentiates the effects of radiation in flank and orthotopic patient-derived xenograft models of GBM. High expression of the rate-limiting enzyme of de novo GTP synthesis is associated with shorter survival in GBM patients. These findings indicate that inhibiting purine synthesis may be a promising strategy to overcome therapy resistance in this genomically heterogeneous disease.

Nutrient Sensing by Histone Marks: Reading the Metabolic Histone Code Using Tracing, Omics, and Modeling.

Several metabolites serve as substrates for histone modifications and communicate changes in the metabolic environment to the epigenome. Technologies such as metabolomics and proteomics have allowed us to reconstruct the interactions between metabolic pathways and histones. These technologies have shed light on how nutrient availability can have a dramatic effect on various histone modifications. This metabolism-epigenome cross talk plays a fundamental role in development, immune function, and diseases like cancer. Yet, major challenges remain in understanding the interactions between cellular metabolism and the epigenome. How the levels and fluxes of various metabolites impact epigenetic marks is still unclear. Discussed herein are recent applications and the potential of systems biology methods such as flux tracing and metabolic modeling to address these challenges and to uncover new metabolic-epigenetic interactions. These systems approaches can ultimately help elucidate how nutrients shape the epigenome of microbes and mammalian cells.

Common biochemical properties of metabolic genes recurrently dysregulated in tumors.

BACKGROUND: Tumor initiation and progression are associated with numerous metabolic alterations. However, the biochemical drivers and constraints that contribute to metabolic gene dysregulation are unclear. METHODS: Here, we present MetOncoFit, a computational model that integrates 142 metabolic features that can impact tumor fitness, including enzyme catalytic activity, pathway association, network topology, and reaction flux. MetOncoFit uses genome-scale metabolic modeling and machine-learning to quantify the relative importance of various metabolic features in predicting cancer metabolic gene expression, copy number variation, and survival data. RESULTS: Using MetOncoFit, we performed a meta-analysis of 9 cancer types and over 4500 samples from TCGA, Prognoscan, and COSMIC tumor databases. MetOncoFit accurately predicted enzyme differential expression and its impact on patient survival using the 142 attributes of metabolic enzymes. Our analysis revealed that enzymes with high catalytic activity were frequently upregulated in many tumors and associated with poor survival. Topological analysis also identified specific metabolites that were hot spots of dysregulation. CONCLUSIONS: MetOncoFit integrates a broad range of datasets to understand how biochemical and topological features influence metabolic gene dysregulation across various cancer types. MetOncoFit was able to achieve significantly higher accuracy in predicting differential expression, copy number variation, and patient survival than traditional modeling approaches. Overall, MetOncoFit illuminates how enzyme activity and metabolic network architecture influences tumorigenesis.

Inferring Metabolic Flux from Time-Course Metabolomics.

The metabolic activity of a mammalian cell changes dynamically over time and is tied to the changing metabolic demands of cellular processes such as cell differentiation and proliferation. While experimental tools like time-course metabolomics and flux tracing can measure the dynamics of a few pathways, they are unable to infer fluxes at the whole network level. To address this limitation, we have developed the Dynamic Flux Activity (DFA) algorithm, a genome-scale modeling approach that uses time-course metabolomics to predict dynamic flux rewiring during transitions between metabolic states. This chapter provides a protocol for applying DFA to characterize the dynamic metabolic activity of various cancer cell lines.

Tissue of origin dictates GOT1 dependence and confers synthetic lethality to radiotherapy.

BACKGROUND: Metabolic programs in cancer cells are influenced by genotype and the tissue of origin. We have previously shown that central carbon metabolism is rewired in pancreatic ductal adenocarcinoma (PDA) to support proliferation through a glutamate oxaloacetate transaminase 1 (GOT1)-dependent pathway. METHODS: We utilized a doxycycline-inducible shRNA-mediated strategy to knockdown GOT1 in PDA and colorectal cancer (CRC) cell lines and tumor models of similar genotype. These cells were analyzed for the ability to form colonies and tumors to test if tissue type impacted GOT1 dependence. Additionally, the ability of GOT1 to impact the response to chemo- and radiotherapy was assessed. Mechanistically, the associated specimens were examined using a combination of steady-state and stable isotope tracing metabolomics strategies and computational modeling. Statistics were calculated using GraphPad Prism 7. One-way ANOVA was performed for experiments comparing multiple groups with one changing variable. Student's t test (unpaired, two-tailed) was performed when comparing two groups to each other. Metabolomics data comparing three PDA and three CRC cell lines were analyzed by performing Student's t test (unpaired, two-tailed) between all PDA metabolites and CRC metabolites. RESULTS: While PDA exhibits profound growth inhibition upon GOT1 knockdown, we found CRC to be insensitive. In PDA, but not CRC, GOT1 inhibition disrupted glycolysis, nucleotide metabolism, and redox homeostasis. These insights were leveraged in PDA, where we demonstrate that radiotherapy potently enhanced the effect of GOT1 inhibition on tumor growth. CONCLUSIONS: Taken together, these results illustrate the role of tissue type in dictating metabolic dependencies and provide new insights for targeting metabolism to treat PDA.

Tying Metabolic Branches With Histone Tails Using Systems Biology.

Histone modifications represent an innate cellular mechanism to link nutritional status to gene expression. Metabolites such as acetyl-CoA and S-adenosyl methionine influence gene expression by serving as substrates for modification of histones. Yet, we lack a predictive model for determining histone modification levels based on cellular metabolic state. The numerous metabolic pathways that intersect with histone marks makes it highly challenging to understand their interdependencies. Here, we highlight new systems biology tools to unravel the impact of nutritional cues and metabolic fluxes on histone modifications.

Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling.

Metabolism is an emerging stem cell hallmark tied to cell fate, pluripotency, and self-renewal, yet systems-level understanding of stem cell metabolism has been limited by the lack of genome-scale network models. Here, we develop a systems approach to integrate time-course metabolomics data with a computational model of metabolism to analyze the metabolic state of naive and primed murine pluripotent stem cells. Using this approach, we find that one-carbon metabolism involving phosphoglycerate dehydrogenase, folate synthesis, and nucleotide synthesis is a key pathway that differs between the two states, resulting in differential sensitivity to anti-folates. The model also predicts that the pluripotency factor Lin28 regulates this one-carbon metabolic pathway, which we validate using metabolomics data from Lin28-deficient cells. Moreover, we identify and validate metabolic reactions related to S-adenosyl-methionine production that can differentially impact histone methylation in naive and primed cells. Our network-based approach provides a framework for characterizing metabolic changes influencing pluripotency and cell fate.

Transcriptional regulatory dynamics drive coordinated metabolic and neural response to social challenge in mice.

Agonistic encounters are powerful effectors of future behavior, and the ability to learn from this type of social challenge is an essential adaptive trait. We recently identified a conserved transcriptional program defining the response to social challenge across animal species, highly enriched in transcription factor (TF), energy metabolism, and developmental signaling genes. To understand the trajectory of this program and to uncover the most important regulatory influences controlling this response, we integrated gene expression data with the chromatin landscape in the hypothalamus, frontal cortex, and amygdala of socially challenged mice over time. The expression data revealed a complex spatiotemporal patterning of events starting with neural signaling molecules in the frontal cortex and ending in the modulation of developmental factors in the amygdala and hypothalamus, underpinned by a systems-wide shift in expression of energy metabolism-related genes. The transcriptional signals were correlated with significant shifts in chromatin accessibility and a network of challenge-associated TFs. Among these, the conserved metabolic and developmental regulator ESRRA was highlighted for an especially early and important regulatory role. Cell-type deconvolution analysis attributed the differential metabolic and developmental signals in this social context primarily to oligodendrocytes and neurons, respectively, and we show that ESRRA is expressed in both cell types. Localizing ESRRA binding sites in cortical chromatin, we show that this nuclear receptor binds both differentially expressed energy-related and neurodevelopmental TF genes. These data link metabolic and neurodevelopmental signaling to social challenge, and identify key regulatory drivers of this process with unprecedented tissue and temporal resolution.