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Inference of cancer mechanisms through computational systems analysis.

ABSTRACT: Large amounts of metabolomics data have been accumulated to study metabolic alterations in cancer that allow cancer cells to synthesize molecular materials necessary for cell growth and proliferation. Although metabolic reprogramming in cancer was discovered almost a century ago, the underlying biochemical mechanisms are still unclear. We show that metabolomics data can be used to infer likely biochemical mechanisms associated with cancer. The proposed inference method is data-driven and quite generic; its efficacy is demonstrated by the analysis of changes in purine metabolism of human renal cell carcinoma. The method and results are essentially unbiased and tolerate noise in the data well. The proposed method correctly identified and accurately quantified primary enzymatic alterations in cancer, and these account for over 80% of the metabolic alterations in the investigated carcinoma. Interestingly, the two primary action sites are not the most sensitive reaction steps in purine metabolism, which implies that sensitivity analysis is not a valid approach for identifying cancer targets. The proposed method exhibits statistically high precision and robustness even for analyses of moderately incomplete metabolomics data. By permitting analyses of individual metabolic profiles, the method may become a tool of personalized precision medicine.

SUBMITTER: Qi Z

PROVIDER: S-EPMC5330810 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Inference of cancer mechanisms through computational systems analysis.

Qi Zhen Z Voit Eberhard O EO

Molecular bioSystems 20170201 3

Large amounts of metabolomics data have been accumulated to study metabolic alterations in cancer that allow cancer cells to synthesize molecular materials necessary for cell growth and proliferation. Although metabolic reprogramming in cancer was discovered almost a century ago, the underlying biochemical mechanisms are still unclear. We show that metabolomics data can be used to infer likely biochemical mechanisms associated with cancer. The proposed inference method is data-driven and quite g ...[more]

PMID: 28112324

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Project description:BackgroundColorectal cancer is one of the most prevalent causes of cancer death. It has been studied extensively for a long time, and numerous genetic and epigenetic events have been associated with the disease. However, its molecular mechanisms are still unclear. High-throughput metabolomics data, combined with customized computational systems modeling, can assist our understanding of some of these mechanisms by revealing connections between alterations in enzymatic activities and their consequences for a person's metabolic profile. Of particular importance in this context is purine metabolism, as it provides the nucleotides needed for cell proliferation.Methods and findingsWe employ a computational systems approach to infer molecular mechanisms associated with purine metabolism in colorectal carcinoma. The approach uses a dynamic model of purine metabolism as the simulation system and metabolomics data as input. The execution of large-scale Monte Carlo simulations and optimization with the model permits a step-wise reduction in possibly affected enzyme mechanisms, from which likely targets emerge.ConclusionsAccording to our results, some enzymes in the purine pathway system are very unlikely the targets of colorectal carcinoma. In fact, only three enzymatic steps emerge with statistical confidence as most likely being affected, namely: amidophosphoribosyltransferase (ATASE), 5'-nucleotidase (5NUC), and the xanthine oxidase/dehydrogenase (XD) reactions. The first of these enzymes catalyzes the first committed step of de novo purine biosynthesis, while the other two enzymes are associated with critical purine salvage pathways. The identification of these enzymes is statistically significant and robust. In addition, the results suggest potential secondary targets. The computational method cannot discern whether the inferred mechanisms constitute symptoms of colorectal carcinoma, or whether they might be causative and critical components of the uncontrolled cellular growth in cancer. The inferred molecular mechanisms present testable hypotheses that suggest targeted experiments for future studies of colorectal carcinoma and might eventually lead to improved diagnosis and treatment.

Project description:BackgroundThe growing discipline of structural systems pharmacology is applied prospectively in this study to predict pharmacological outcomes of antibacterial compounds in Escherichia coli K12. This work builds upon previously established methods for structural prediction of ligand binding pockets on protein molecules and utilizes and expands upon the previously developed genome scale model of metabolism integrated with protein structures (GEM-PRO) for E. coli, structurally accounting for protein complexes. Carefully selected case studies are demonstrated to display the potential for this structural systems pharmacology framework in discovery and development of antibacterial compounds.ResultsThe prediction framework for antibacterial activity of compounds was validated for a control set of well-studied compounds, recapitulating experimentally-determined protein binding interactions and deleterious growth phenotypes resulting from these interactions. The antibacterial activity of fosfomycin, sulfathiazole, and trimethoprim were accurately predicted, and as a negative control glucose was found to have no predicted antibacterial activity. Previously uncharacterized mechanisms of action were predicted for compounds with known antibacterial properties, including (1-hydroxyheptane-1,1-diyl)bis(phosphonic acid) and cholesteryl oleate. Five candidate inhibitors were predicted for a desirable target protein without any known inhibitors, tryptophan synthase ? subunit (TrpB). In addition to the predictions presented, this effort also included significant expansion of the previously developed GEM-PRO to account for physiological assemblies of protein complex structures with activities included in the E. coli K12 metabolic network.ConclusionsThe structural systems pharmacology framework presented in this study was shown to be effective in the prediction of molecular mechanisms of antibacterial compounds. The study provides a promising proof of principle for such an approach to antibacterial development and raises specific molecular and systemic hypotheses about antibacterials that are amenable to experimental testing. This framework, and perhaps also the specific predictions of antibacterials, is extensible to developing antibacterial treatments for pathogenic E. coli and other bacterial pathogens.

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Inference of cancer mechanisms through computational systems analysis.

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Inference of cancer mechanisms through computational systems analysis.

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