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

Zhang, Mingjie; Saad, Christian; Le, Long Khanh‐Dao; Halfter, Kathrin; Bauer, Bernhard; Mansmann, Ulrich; Li, Jian

doi:10.18632/oncotarget.24547

Cited by 4 publications

(4 citation statements)

References 67 publications

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“…For the calculation of IM.Index (immune-related metabolic index) of each patient, a large-scale model, MCPM, was applied ( 17 ). Laboratory values from each patient were applied as input to initialize the flux of corresponding metabolic reactions ( Supplementary Figure 1 ):…”

Section: Methodsmentioning

confidence: 99%

Survival Factors and Metabolic Pathogenesis in Elderly Patients (≥65) With COVID-19: A Multi-Center Study

et al. 2021

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Background: Elderly patients infected with COVID-19 are reported to be facing a substantially increased risk of mortality. Clinical characteristics, treatment options, and potential survival factors remain under investigation. This study aimed to fill this gap and provide clinically relevant factors associated with survival of elderly patients with COVID-19.Methods: In this multi-center study, elderly patients (age ≥65 years old) with laboratory-confirmed COVID-19 from 4 Wuhan hospitals were included. The clinical end point was hospital discharge or deceased with last date of follow-up on Jul. 08, 2020. Clinical, demographic, and laboratory data were collected. Univariate and multivariate analysis were performed to analyze survival and risk factors. A metabolic flux analysis using a large-scale molecular model was applied to investigate the pathogenesis of SARS-CoV-2 with regard to metabolism pathways.Results: A total of 223 elderly patients infected with COVID-19 were included, 91 (40.8%) were discharged and 132 (59.2%) deceased. Acute respiratory distress syndrome (ARDS) developed in 140 (62.8%) patients, 23 (25.3%) of these patients survived. Multivariate analysis showed that potential risk factors for mortality were elevated D-Dimer (odds ratio: 1.13 [95% CI 1.04 - 1.22], p = 0.005), high immune-related metabolic index (6.42 [95% CI 2.66–15.48], p < 0.001), and increased neutrophil-to-lymphocyte ratio (1.08 [95% 1.03–1.13], p < 0.001). Elderly patients receiving interferon atmotherapy showed an increased probability of survival (0.29 [95% CI 0.17–0.51], p < 0.001). Based on these factors, an algorithm (AlgSurv) was developed to predict survival for elderly patients. The metabolic flux analysis showed that 12 metabolic pathways including phenylalanine (odds ratio: 28.27 [95% CI 10.56–75.72], p < 0.001), fatty acid (15.61 [95% CI 6.66–36.6], p < 0.001), and pyruvate (12.86 [95% CI 5.85–28.28], p < 0.001) showed a consistently lower flux in the survivors vs. the deceased subgroup. This may reflect a key pathogenic mechanism of COVID-19 infection.Conclusion: Several factors such as interferon atmotherapy and recreased activity of specific metabolic pathways were found to be associated with survival of elderly patients. Based on these findings, a survival algorithm (AlgSurv) was developed to assist the clinical stratification for elderly patients. Dysregulation of the metabolic pathways revealed in this study may aid in the drug and vaccine development against COVID-19.

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Section: Methodsmentioning

confidence: 99%

Survival Factors and Metabolic Pathogenesis in Elderly Patients (≥65) With COVID-19: A Multi-Center Study

et al. 2021

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“…The published methionine-cycle based metabolic model (MCPM) was manullay constructed based on on information from literatures and the publicly available database, KEGG [21]. The MCPM consists of 3755 components including gene, protein, compound and others [22]. The MCPM has 4750 reactions that are divided into 30 metabolic pathways.…”

Section: Methodsmentioning

confidence: 99%

Computational analysis of receptor tyrosine kinase inhibitors and cancer metabolism: implications for treatment and discovery of potential therapeutic signatures

Halfter

Zhang

et al. 2019

BMC Cancer

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Background Receptor tyrosine kinase (RTK) inhibitors are frequently used to treat cancers and the results have been mixed, some of these small molecule drugs are highly successful while others show a more modest response. A high number of studies have been conducted to investigate the signaling mechanisms and corresponding therapeutic influence of RTK inhibitors in order to explore the therapeutic potential of RTK inhibitors. However, most of these studies neglected the potential metabolic impact of RTK inhibitors, which could be highly associated with drug efficacy and adverse effects during treatment. Methods In order to fill these knowledge gaps and improve the therapeutic utilization of RTK inhibitors a large-scale computational simulation/analysis over multiple types of cancers with the treatment responses of RTK inhibitors was performed. The pharmacological data of all eight RTK inhibitor and gene expression profiles of 479 cell lines from The Cancer Cell Line Encyclopedia were used. Results The potential metabolic impact of RTK inhibitors on different types of cancers were analyzed resulting in cancer-specific (breast, liver, pancreas, central nervous system) metabolic signatures. Many of these are in line with results from different independent studies, thereby providing indirect verification of the obtained results. Conclusions Our study demonstrates the potential of using a computational approach on signature-based-analysis over multiple cancer types. The results reveal the strength of multiple-cancer analysis over conventional signature-based analysis on a single cancer type. Electronic supplementary material The online version of this article (10.1186/s12885-019-5804-0) contains supplementary material, which is available to authorized users.

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“…This result is in agreement with several independent studies and demonstrates the potential of computational simulation for the purpose of biomarker discovery within systems medicine. 13 Evaluation of gene expression data of 479 cancer cell lines from CCLE using AutoAnalyze took approximately 900 seconds. The simulation duration of CCLE data integrated into the model MCPM with 4750 reactions and 3755 components in AutoAnalyze was approximately 200 seconds.…”

Section: Case Studymentioning

confidence: 99%

“…In a case study, a prediction of treatment response of cell lines and overall survival of patients from different types of cancers yielded satisfactory results. 13…”

Section: Introductionmentioning

confidence: 99%

AutoAnalyze in Systems Biology

Saad

Bauer

Mansmann

et al. 2019

Bioinform Biol Insights

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AutoAnalyze is a highly customizable framework for the visualization and analysis of large-scale model graphs. Originally developed for use in the automotive domain, it also supports efficient computation within molecular networks represented by reaction equations. A static analysis approach is used for efficient treatment-condition-specific simulation. The chosen method relies on the computation of a global network data-flow resulting from the evaluation of individual genetic data. The approach facilitates complex analyses of biological components from a molecular network under specific therapeutic perturbations, as demonstrated in a case study. In addition to simulating the complex networks in a stable and reproducible way, kinetic constants can also be fine-tuned using a genetic algorithm and built-in statistical tools.

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

Cited by 4 publications

References 67 publications

Survival Factors and Metabolic Pathogenesis in Elderly Patients (≥65) With COVID-19: A Multi-Center Study

Survival Factors and Metabolic Pathogenesis in Elderly Patients (≥65) With COVID-19: A Multi-Center Study

Computational analysis of receptor tyrosine kinase inhibitors and cancer metabolism: implications for treatment and discovery of potential therapeutic signatures

AutoAnalyze in Systems Biology

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