Genome-scale network model of metabolism and histone acetylation reveals metabolic dependencies of histone deacetylase inhibitors

Shen, Fangzhou; Boccuto, Luigi; Pauly, Rini; Srikanth, Sujata; Chandrasekaran, Sriram

doi:10.1186/s13059-019-1661-z

Cited by 37 publications

(51 citation statements)

References 75 publications

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“…Finally, metabolism is a dynamic process occurring over a wide timescale and is controlled by several regulatory levels. Incorporating additional parameters into metabolic modeling, such as post-translational modifications, proteomics, metabolomics, epigenetic markers, and metabolite feedback would further improve our ability to understand these diverse rewiring strategies in cancer cells [18,48,49].…”

Section: Discussionmentioning

confidence: 99%

Common biochemical properties of metabolic genes recurrently dysregulated in tumors

et al. 2020

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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.

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Section: Discussionmentioning

confidence: 99%

Common biochemical properties of metabolic genes recurrently dysregulated in tumors

et al. 2020

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“…Consequently, acetylation has been found to increase in both nutrient excess and starvation conditions. [ 32,33 ]…”

Section: Metabolic–epigenetic Cross Talk Is Complex and Context‐specificmentioning

confidence: 99%

“…Notably, a new computational model based on CBM for directly simulating the dynamics of histone acetylation was recently developed by Shen et al. [ 32 ] This enabled them to predict the impact of metabolic alterations on histone acetylation (Figure 3). To simulate acetylation using the metabolic network model, the authors added biochemical reactions corresponding to histone acetylation and synthesis of acetyl‐CoA in the nucleus.…”

Section: Constraint‐based Modeling Can Predict and Interpret Metabolimentioning

confidence: 99%

“…In contrast, the mechanistic models (CBMs) employed in studies highlighted above were built using biochemical data on enzymes and substrates curated from literature. [ 32 ] Using advanced machine‐learning algorithms like Deep learning may soon make it possible to directly predict histone modifications and ramifications of metabolic alterations on the epigenome without knowledge of underlying mechanism. While mechanistic modeling is limited to known reactions or interactions in literature, machine‐learning algorithms are ideally suited for uncovering novel interactions.…”

Section: Next‐generation Technologies For Discovering New Metabolic–ementioning

confidence: 99%

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

Campit

Meliki²,

Youngson

et al. 2020

BioEssays

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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.

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“…For example, DNA and histone methyltransferases use the metabolite S -adenosyl methionine (SAM) as a methyl donor and are highly responsive to substrate levels. 3 Thus, alterations to metabolism of the cell can affect substrate levels in the nucleus resulting in altered histone modifications. 2,4 However, the interconnectedness of the metabolic network makes it highly challenging to predict the impact of nutrient changes on histone modifications.…”

Section: Introductionmentioning

confidence: 99%

Tying Metabolic Branches With Histone Tails Using Systems Biology

Chandrasekaran

2019

Genet Epigenet

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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.

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Genome-scale network model of metabolism and histone acetylation reveals metabolic dependencies of histone deacetylase inhibitors

Cited by 37 publications

References 75 publications

Common biochemical properties of metabolic genes recurrently dysregulated in tumors

Common biochemical properties of metabolic genes recurrently dysregulated in tumors

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

Tying Metabolic Branches With Histone Tails Using Systems Biology

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