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Computational modeling of methionine cycle-based metabolism and DNA methylation and the implications for anti-cancer drug response prediction.

ABSTRACT: The relationship between metabolism and methylation is considered to be an important aspect of cancer development and drug efficacy. However, it remains poorly defined how to apply this aspect to improve preclinical disease characterization and clinical treatment outcome. Using available molecular information from Kyoto Encyclopedia of Genes and Genomes (KEGG) and literature, we constructed a large-scale knowledge-based metabolic in silico model. For the purpose of model validation, we applied data from the Cancer Cell Line Encyclopedia (CCLE) to investigate computationally the impact of metabolism on chemotherapy efficacy. In our model, different metabolic components such as MAT2A, ATP6V0E1, NNMT involved in methionine cycle correlate with biologically measured chemotherapy outcome (IC50) that are in agreement with findings of independent studies. These proteins are potentially also involved in cellular methylation processes. In addition, several components such as 3,4-dihydoxymandelate, PAPSS2, UPP1 from metabolic pathways involved in the production of purine and pyrimidine correlate with IC50. This study clearly demonstrates that complex computational approaches can reflect findings of biological experiments. This demonstrates their high potential to grasp complex issues within systems medicine such as response prediction, biomarker identification using available data resources.

SUBMITTER: Zhang M

PROVIDER: S-EPMC5989406 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Computational modeling of methionine cycle-based metabolism and DNA methylation and the implications for anti-cancer drug response prediction.

Zhang Mengying M Saad Christian C Le Lien L Halfter Kathrin K Bauer Bernhard B Mansmann Ulrich R UR Li Jian J

Oncotarget 20180221 32

The relationship between metabolism and methylation is considered to be an important aspect of cancer development and drug efficacy. However, it remains poorly defined how to apply this aspect to improve preclinical disease characterization and clinical treatment outcome. Using available molecular information from Kyoto Encyclopedia of Genes and Genomes (KEGG) and literature, we constructed a large-scale knowledge-based metabolic <i>in silico</i> model. For the purpose of model validation, we ap ...[more]

PMID: 29875994

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Project description:Accurately predicting the response of a cancer patient to a therapeutic agent remains an important challenge in precision medicine. With the rise of data science, researchers have applied computational models to study the drug inhibition effects on cancers based on cancer genomics and transcriptomics. Moreover, a common epigenetic modification, DNA methylation, has been related to the occurrence and development of cancer, as well as drug effectiveness. Therefore, it is helpful for improvement of drug response prediction through exploring the relationship between DNA methylation and drug effectiveness. Here, we proposed a computational model to predict drug responses in cancers through integration of cancer genomics, transcriptomics, epigenomics, and compound chemical properties. Meanwhile, we applied a regularized regression model (Least Absolute Shrinkage and Selection Operator, lasso) to detect the methylation sites that were closely related to drug effectiveness. The prediction models were trained on a well-known pharmacogenomics data resource, Genomics of Drug Sensitivity in Cancer (GDSC). The cross-validation indicates that the performance of the prediction model using DNA methylation is comparable to that of using other cancer omics, including oncogene mutation and gene expression data. It indicates the important role of DNA methylation in prediction of drug responses. Encyclopedia of DNA Elements (ENCODE) and Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST2) database analyses suggest that the methylation sites associated with drug effectiveness are mainly located in the transcription factor (TF) binding region. Therefore, we hypothesized that the sensitivity of cancer cells to drugs could be regulated by changing the methylation modification of TF binding region. In conclusion, we confirmed the important role of DNA methylation in prediction of drug responses, and provided some methylation sites that closely related to the drug effectiveness, which may be a great regulatory target for improvement of drug treatment effects on cancer patients.

Project description:Homocysteine-Methionine (HM) cycle produces universal methyl group donor S-adenosylmethione (SAM), methyltransferase inhibitor S-adenosylhomocysteine (SAH) and homocysteine (Hcy). Hyperhomocysteinemia (HHcy) is established as an independent risk factor for cardiovascular disease (CVD) and other degenerative disease. We selected 115 genes in the extended HM cycle (31 metabolic enzymes and 84 methyltransferases), examined their protein subcellular location/partner protein, investigated their mRNA levels and mapped their corresponding histone methylation status in 35 disease conditions via mining a set of public databases and intensive literature research. We have 6 major findings. 1) All HM metabolic enzymes are located only in the cytosol except for cystathionine-?-synthase (CBS), which was identified in both cytosol and nucleus. 2) Eight disease conditions encountered only histone hypomethylation on 8 histone residues (H3R2/K4/R8/K9/K27/K36/K79 and H4R3). Nine disease conditions had only histone hypermethylation on 8 histone residues (H3R2/K4/K9/K27/K36/K79 and H4R3/K20). 3) We classified 9 disease types with differential HM cycle expression pattern. Eleven disease conditions presented most 4 HM cycle pathway suppression. 4) Three disease conditions had all 4 HM cycle pathway suppression and only histone hypomethylation on H3R2/K4/R8/K9/K36 and H4R3. 5) Eleven HM cycle metabolic enzymes interact with 955 proteins. 6) Five paired HM cycle proteins interact with each other. We conclude that HM cycle is a key metabolic sensor system which mediates receptor-independent metabolism-associated danger signal recognition and modulates SAM/SAH-dependent methylation in disease conditions and that hypomethylation on frequently modified histone residues is a key mechanism for metabolic disorders, autoimmune disease and CVD. We propose that HM metabolism takes place in the cytosol, that nuclear methylation equilibration requires a nuclear-cytosol transfer of SAM/SAH/Hcy, and that Hcy clearance is essential for genetic protection.

Dataset Information

Computational modeling of methionine cycle-based metabolism and DNA methylation and the implications for anti-cancer drug response prediction.

Publications

Computational modeling of methionine cycle-based metabolism and DNA methylation and the implications for anti-cancer drug response prediction.

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