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Avci FG. Unraveling bacterial stress responses: implications for next-generation antimicrobial solutions. World J Microbiol Biotechnol 2024; 40:285. [PMID: 39073503 PMCID: PMC11286680 DOI: 10.1007/s11274-024-04090-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 07/18/2024] [Indexed: 07/30/2024]
Abstract
The accelerated spread of antimicrobial-resistant bacteria has caused a serious health problem and rendered antimicrobial treatments ineffective. Innovative approaches are crucial to overcome the health threat posed by resistant pathogens and prevent the emergence of untreatable infections. Triggering stress responses in bacteria can diminish susceptibility to various antimicrobials by inducing resistance mechanisms. Therefore, a thorough understanding of stress response control, especially in relation to antimicrobial resistance, offers valuable perspectives for innovative and efficient therapeutic approaches to combat antimicrobial resistance. The aim of this study was to evaluate the stress responses of 8 different bacteria by analyzing reporter metabolites, around which significant alterations were observed, using a pathway-driven computational approach. For this purpose, the transcriptomic data that the bacterial pathogens were grown under 11 different stress conditions mimicking the human host environments were integrated with the genome-scale metabolic models of 8 pathogenic species (Enterococcus faecalis OG1R, Escherichia coli EPEC O127:H6 E2348/69, Escherichia coli ETEC H10407, Escherichia coli UPEC 536, Klebsiella pneumoniae MGH 78578, Pseudomonas aeruginosa PAO1, Staphylococcus aureus MRSA252, and Staphylococcus aureus MSSA476). The resulting reporter metabolites were enriched in multiple metabolic pathways, with cofactor biosynthesis being the most important. The results of this study will serve as a guide for the development of antimicrobial agents as they provide a first insight into potential drug targets.
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Affiliation(s)
- Fatma Gizem Avci
- Department of Bioengineering, Faculty of Engineering and Natural Sciences, Üsküdar University, Istanbul, Türkiye.
- Genetics of Prokaryotes, Faculty of Biology and Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany.
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Sharma V, Khokhar M, Panigrahi P, Gadwal A, Setia P, Purohit P. Advancements, Challenges, and clinical implications of integration of metabolomics technologies in diabetic nephropathy. Clin Chim Acta 2024; 561:119842. [PMID: 38969086 DOI: 10.1016/j.cca.2024.119842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 06/25/2024] [Accepted: 06/29/2024] [Indexed: 07/07/2024]
Abstract
BACKGROUND Diabetic nephropathy (DN), a severe complication of diabetes, involves a range of renal abnormalities driven by metabolic derangements. Metabolomics, revealing dynamic metabolic shifts in diseases like DN and offering insights into personalized treatment strategies, emerges as a promising tool for improved diagnostics and therapies. METHODS We conducted an extensive literature review to examine how metabolomics contributes to the study of DN and the challenges associated with its implementation in clinical practice. We identified and assessed relevant studies that utilized metabolomics methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to assess their efficacy in diagnosing DN. RESULTS Metabolomics unveils key pathways in DN progression, highlighting glucose metabolism, dyslipidemia, and mitochondrial dysfunction. Biomarkers like glycated albumin and free fatty acids offer insights into DN nuances, guiding potential treatments. Metabolomics detects small-molecule metabolites, revealing disease-specific patterns for personalized care. CONCLUSION Metabolomics offers valuable insights into the molecular mechanisms underlying DN progression and holds promise for personalized medicine approaches. Further research in this field is warranted to elucidate additional metabolic pathways and identify novel biomarkers for early detection and targeted therapeutic interventions in DN.
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Affiliation(s)
- V Sharma
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - M Khokhar
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - P Panigrahi
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - A Gadwal
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - P Setia
- Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India
| | - P Purohit
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan 342005, India.
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Moyer DC, Reimertz J, Segrè D, Fuxman Bass JI. Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.24.600481. [PMID: 38979177 PMCID: PMC11230171 DOI: 10.1101/2024.06.24.600481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Genome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organism's metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually. Results We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organism's genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organism's genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends. Conclusions MACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.
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Jiang F, Ruan Y, Chen XH, Yu HL, Cheng T, Duan XY, Liu YG, Zhang HY, Zhang QY. Metabolites of pathogenic microorganisms database (MPMdb) and its seed metabolite applications. Microbiol Spectr 2024; 12:e0234223. [PMID: 38391229 PMCID: PMC10986615 DOI: 10.1128/spectrum.02342-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 01/23/2024] [Indexed: 02/24/2024] Open
Abstract
Seed metabolites are the combination of essential compounds required by an organism across various potential environmental conditions. The seed metabolites screening framework based on the network topology approach can capture important biological information of species. This study aims to identify comprehensively the relationship between seed metabolites and pathogenic bacteria. A large-scale data set was compiled, describing the seed metabolite sets and metabolite sets of 124,192 pathogenic strains from 34 genera, by constructing genome-scale metabolic models. The enrichment analysis method was used to screen the specific seed metabolites of each species/genus of pathogenic bacteria. The metabolites of pathogenic microorganisms database (MPMdb) (http://qyzhanglab.hzau.edu.cn/MPMdb/) was established for browsing, searching, predicting, or downloading metabolites and seed metabolites of pathogenic microorganisms. Based on the MPMdb, taxonomic and phylogenetic analyses of pathogenic bacteria were performed according to the function of seed metabolites and metabolites. The results showed that the seed metabolites could be used as a feature for microorganism chemotaxonomy, and they could mirror the phylogeny of pathogenic bacteria. In addition, our screened specific seed metabolites of pathogenic bacteria can be used not only for further tapping the nutritional resources and identifying auxotrophies of pathogenic bacteria but also for designing targeted bactericidal compounds by combining with existing antimicrobial agents.IMPORTANCEMetabolites serve as key communication links between pathogenic microorganisms and hosts, with seed metabolites being crucial for microbial growth, reproduction, external communication, and host infection. However, the large-scale screening of metabolites and the identification of seed metabolites have always been the main technical bottleneck due to the low throughput and costly analysis. Genome-scale metabolic models have become a recognized research paradigm to investigate the metabolic characteristics of species. The developed metabolites of pathogenic microorganisms database in this study is committed to systematically predicting and identifying the metabolites and seed metabolites of pathogenic microorganisms, which could provide a powerful resource platform for pathogenic bacteria research.
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Affiliation(s)
- Feng Jiang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Yao Ruan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Xiao-Hui Chen
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Hai-Long Yu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Ting Cheng
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Xin-Ya Duan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Yan-Guang Liu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Qing-Ye Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
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Meng W, Pan H, Sha Y, Zhai X, Xing A, Lingampelly SS, Sripathi SR, Wang Y, Li K. Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases. Metabolites 2024; 14:93. [PMID: 38392985 PMCID: PMC10890086 DOI: 10.3390/metabo14020093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 01/17/2024] [Accepted: 01/25/2024] [Indexed: 02/25/2024] Open
Abstract
The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.
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Affiliation(s)
- Weiyu Meng
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Hongxin Pan
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Yuyang Sha
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Xiaobing Zhai
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Abao Xing
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | | | - Srinivasa R Sripathi
- Henderson Ocular Stem Cell Laboratory, Retina Foundation of the Southwest, Dallas, TX 75231, USA
| | - Yuefei Wang
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Kefeng Li
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
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Srivastava A, Vinod PK. Identification and Characterization of Metabolic Subtypes of Endometrial Cancer Using a Systems-Level Approach. Metabolites 2023; 13:metabo13030409. [PMID: 36984849 PMCID: PMC10054278 DOI: 10.3390/metabo13030409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 03/03/2023] [Accepted: 03/05/2023] [Indexed: 03/12/2023] Open
Abstract
Endometrial cancer (EC) is the most common gynecological cancer worldwide. Understanding metabolic adaptation and its heterogeneity in tumor tissues may provide new insights and help in cancer diagnosis, prognosis, and treatment. In this study, we investigated metabolic alterations of EC to understand the variations in metabolism within tumor samples. Integration of transcriptomics data of EC (RNA-Seq) and the human genome-scale metabolic network was performed to identify the metabolic subtypes of EC and uncover the underlying dysregulated metabolic pathways and reporter metabolites in each subtype. The relationship between metabolic subtypes and clinical variables was explored. Further, we correlated the metabolic changes occurring at the transcriptome level with the genomic alterations. Based on metabolic profile, EC patients were stratified into two subtypes (metabolic subtype-1 and subtype-2) that significantly correlated to patient survival, tumor stages, mutation, and copy number variations. We observed the co-activation of the pentose phosphate pathway, one-carbon metabolism, and genes involved in controlling estrogen levels in metabolic subtype-2, which is linked to poor survival. PNMT and ERBB2 are also upregulated in metabolic subtype-2 samples and present on the same chromosome locus 17q12, which is amplified. PTEN and TP53 mutations show mutually exclusive behavior between subtypes and display a difference in survival. This work identifies metabolic subtypes with distinct characteristics at the transcriptome and genome levels, highlighting the metabolic heterogeneity within EC.
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Xu L, Ma Y, Fang C, Peng Z, Gao F, Moll JM, Qin S, Yu Q, Hou Y, Kristiansen K, Fang W, Brix S, Zhang L. Genomic and microbial factors affect the prognosis of anti-pd-1 immunotherapy in nasopharyngeal carcinoma. Front Oncol 2022; 12:953884. [PMID: 36059644 PMCID: PMC9428750 DOI: 10.3389/fonc.2022.953884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/27/2022] [Indexed: 11/17/2022] Open
Abstract
Antibodies targeting the programmed cell death protein-1 (PD-1) molecule have been reported to hold promising antitumor activities in patients with nasopharyngeal carcinoma (NPC). However, only a small subset of NPC patients benefits from the anti-PD-1 monotherapy and factors that affect the treatment response need further investigation. This study aimed to examine the impact of multiple genetic and environmental factors on outcome of anti-PD-1 immunotherapy by identifying tumor size, tumor mutation burden (TMB) based on whole exon sequencing, human leukocyte antigen class I (HLA-I) homo-/heterozygosity and supertypes, blood Epstein-Barr virus (EBV) DNA load, T cell proportions, and interferon-γ(IFN-γ) levels in a cohort of 57 NPC patients that received Nivolumab or Camrelizumab treatment. Moreover, we profiled the longitudinal changes in gut microbiota composition using shotgun metagenomics sequencing. We observed that high TMB combined with HLA-I heterozygosity was associated with improved clinical outcomes. In agreement with previous studies, we found that patients with higher plasma EBV DNA load showed worse progression-free survival. We found no evidence for an effect of gut bacterial diversity on the treatment response, but identified a higher abundance of seven specific gut bacteria at baseline of non-responders, including Blautia wexlera and Blautia obeum, as well as four other bacteria belonging to the Clostridiales order, and one Erysipelatoclostridium. Combined, this study provides insight into the influence of several genetic and environmental factors on anti-PD-1 immunotherapy responses in NPC patients.
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Affiliation(s)
- Liqin Xu
- BGI-Shenzhen, Shenzhen, China
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
- Latvia MGI Tech SIA, Marupe, Latvia
| | - Yuxiang Ma
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Chao Fang
- BGI-Shenzhen, Shenzhen, China
- China National GeneBank, BGI-Shenzhen, Shenzhen, China
- Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Zhuobing Peng
- BGI-Shenzhen, Shenzhen, China
- China National GeneBank, BGI-Shenzhen, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Fangfang Gao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Janne Marie Moll
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
| | | | - Qichao Yu
- BGI-Shenzhen, Shenzhen, China
- China National GeneBank, BGI-Shenzhen, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Yong Hou
- BGI-Shenzhen, Shenzhen, China
- Latvia MGI Tech SIA, Marupe, Latvia
- China National GeneBank, BGI-Shenzhen, Shenzhen, China
| | - Karsten Kristiansen
- BGI-Shenzhen, Shenzhen, China
- Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, Copenhagen, Denmark
- Institute of Metagenomics, Qingdao-Europe Advanced Institute for Life Sciences, Qingdao, China
- *Correspondence: Karsten Kristiansen, ; Wenfeng Fang, ; Susanne Brix, ; Li Zhang,
| | - Wenfeng Fang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- *Correspondence: Karsten Kristiansen, ; Wenfeng Fang, ; Susanne Brix, ; Li Zhang,
| | - Susanne Brix
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs. Lyngby, Denmark
- Institute of Metagenomics, Qingdao-Europe Advanced Institute for Life Sciences, Qingdao, China
- *Correspondence: Karsten Kristiansen, ; Wenfeng Fang, ; Susanne Brix, ; Li Zhang,
| | - Li Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- *Correspondence: Karsten Kristiansen, ; Wenfeng Fang, ; Susanne Brix, ; Li Zhang,
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Fang C, Fang W, Xu L, Gao F, Hou Y, Zou H, Ma Y, Moll JM, Yang Y, Wang D, Huang Y, Ren H, Zhao H, Qin S, Zhong H, Li J, Liu S, Yang H, Wang J, Brix S, Kristiansen K, Zhang L. Distinct Functional Metagenomic Markers Predict the Responsiveness to Anti-PD-1 Therapy in Chinese Non-Small Cell Lung Cancer Patients. Front Oncol 2022; 12:837525. [PMID: 35530307 PMCID: PMC9069064 DOI: 10.3389/fonc.2022.837525] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/21/2022] [Indexed: 12/24/2022] Open
Abstract
Background Programmed death 1 (PD-1) and the ligand of PD-1 (PD-L1) are central targets for immune-checkpoint therapy (ICT) blocking immune evasion-related pathways elicited by tumor cells. A number of PD-1 inhibitors have been developed, but the efficacy of these inhibitors varies considerably and is typically below 50%. The efficacy of ICT has been shown to be dependent on the gut microbiota, and experiments using mouse models have even demonstrated that modulation of the gut microbiota may improve efficacy of ICT. Methods We followed a Han Chinese cohort of 85 advanced non-small cell lung cancer (NSCLC) patients, who received anti-PD-1 antibodies. Tumor biopsies were collected before treatment initiation for whole exon sequencing and variant detection. Fecal samples collected biweekly during the period of anti-PD-1 antibody administration were used for metagenomic sequencing. We established gut microbiome abundance profiles for identification of significant associations between specific microbial taxa, potential functionality, and treatment responses. A prediction model based on random forest was trained using selected markers discriminating between the different response groups. Results NSCLC patients treated with antibiotics exhibited the shortest survival time. Low level of tumor-mutation burden and high expression level of HLA-E significantly reduced progression-free survival. We identified metagenomic species and functional pathways that differed in abundance in relation to responses to ICT. Data on differential enrichment of taxa and predicted microbial functions in NSCLC patients responding or non-responding to ICT allowed the establishment of random forest algorithm-adopted models robustly predicting the probability of whether or not a given patient would benefit from ICT. Conclusions Overall, our results identified links between gut microbial composition and immunotherapy efficacy in Chinese NSCLC patients indicating the potential for such analyses to predict outcome prior to ICT.
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Affiliation(s)
- Chao Fang
- Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, Copenhagen, Denmark
- BGI-Shenzhen, Shenzhen, China
| | - Wenfeng Fang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Liqin Xu
- BGI-Shenzhen, Shenzhen, China
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | - Fangfang Gao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yong Hou
- Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, Copenhagen, Denmark
- BGI-Shenzhen, Shenzhen, China
| | - Hua Zou
- BGI-Shenzhen, Shenzhen, China
| | - Yuxiang Ma
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Janne Marie Moll
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | - Yunpeng Yang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | | | - Yan Huang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Huahui Ren
- Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, Copenhagen, Denmark
- BGI-Shenzhen, Shenzhen, China
| | - Hongyun Zhao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | | | - Huanzi Zhong
- Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, Copenhagen, Denmark
- BGI-Shenzhen, Shenzhen, China
| | - Junhua Li
- BGI-Shenzhen, Shenzhen, China
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | | | - Huanming Yang
- BGI-Shenzhen, Shenzhen, China
- James D. Watson Institute of Genome Sciences, Hangzhou, China
| | - Jian Wang
- BGI-Shenzhen, Shenzhen, China
- James D. Watson Institute of Genome Sciences, Hangzhou, China
| | - Susanne Brix
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
- *Correspondence: Susanne Brix, ; Karsten Kristiansen, ; Li Zhang,
| | - Karsten Kristiansen
- Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, Copenhagen, Denmark
- BGI-Shenzhen, Shenzhen, China
- Institute of Metagenomics, Qingdao-Europe Advance Institute for Life Sciences, BGI-Qingdao, Qingdao, China
- *Correspondence: Susanne Brix, ; Karsten Kristiansen, ; Li Zhang,
| | - Li Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
- *Correspondence: Susanne Brix, ; Karsten Kristiansen, ; Li Zhang,
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Louis JM, Agarwal A, Mondal S, Talukdar I. A global analysis on the differential regulation of RNA binding proteins (RBPs) by TNF–α as potential modulators of metabolic syndromes. BBA ADVANCES 2022; 2:100037. [PMID: 37082594 PMCID: PMC10074950 DOI: 10.1016/j.bbadva.2021.100037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 12/12/2021] [Accepted: 12/15/2021] [Indexed: 11/25/2022] Open
Abstract
Metabolic syndrome (MetS) is associated with a group of conditions, which enhances the risk of diabetes, heart diseases and stroke in the affected individuals. Earlier reports from our lab have shown that Tumor necrosis factor-α (TNF-α) significantly modulates the expression of 56 genes at the alternative splicing level which are involved in various signaling and metabolic pathways (MetS genes) connected to MetS. These MetS genes were predicted to interact with various RNA-binding proteins (RBPs) when exposed to TNF-α, resulting changes in their alternative splicing patterns. Here we are presenting data of an RNA-Seq analysis, which identified 1218 unique, and significantly regulated genes by TNF-α, 15% of which are RBPs . Among the 1218 genes, 204 genes have been identified as MetS genes by the ingenuity pathway analysis, and 10% of the MetS genes are found as RBPs. Our results also show that TNF-α changes the phosphorylation status of certain RBPs such as SR proteins, crucial players in alternative splicing, possibly via changing the activation status of certain upstream signaling molecules which also act as upstream kinases for these proteins. Taken together, these findings suggest that TNF-α influences the regulation of the RBPs at the various levels for their expression, which may lead to the alteration of the splicing pattern of the MetS genes. MetS genes acting as RBPs and are modulated by TNF-α, predict the existence of highly interconnected mechanisms which require further analysis to understand their dual roles on the onset of these diseases.
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Ozden F, Siper MC, Acarsoy N, Elmas T, Marty B, Qi X, Cicek AE. DORMAN: Database of Reconstructed MetAbolic Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1474-1480. [PMID: 31581093 DOI: 10.1109/tcbb.2019.2944905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Genome-scale reconstructed metabolic networks have provided an organism specific understanding of cellular processes and their relations to phenotype. As they are deemed essential to study metabolism, the number of organisms with reconstructed metabolic networks continues to increase. This everlasting research interest lead to the development of online systems/repositories that store existing reconstructions and enable new model generation, integration, and constraint-based analyses. While features that support model reconstruction are widely available, current systems lack the means to help users who are interested in analyzing the topology of the reconstructed networks. Here, we present the Database of Reconstructed Metabolic Networks - DORMAN. DORMAN is a centralized online database that stores SBML-based reconstructed metabolic networks published in the literature, and provides web-based computational tools for visualizing and analyzing the model topology. Novel features of DORMAN are (i) interactive visualization interface that allows rendering of the complete network as well as editing and exporting the model, (ii) hierarchical navigation that provides efficient access to connected entities in the model, (iii) built-in query interface that allow posing topological queries, and finally, and (iv) model comparison tool that enables comparing models with different nomenclatures, using approximate string matching. DORMAN is online and freely accessible at http://ciceklab.cs.bilkent.edu.tr/dorman.
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Uppal K. Models of Metabolomic Networks. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11615-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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Morris AR, Stanton DL, Roman D, Liu AC. Systems Level Understanding of Circadian Integration with Cell Physiology. J Mol Biol 2020; 432:3547-3564. [PMID: 32061938 DOI: 10.1016/j.jmb.2020.02.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 01/31/2020] [Accepted: 02/03/2020] [Indexed: 02/07/2023]
Abstract
The mammalian circadian clock regulates a wide variety of physiological and behavioral processes. In turn, its disruption is associated with sleep deficiency, metabolic syndrome, neurological and psychiatric disorders, and cancer. At the turn of the century, the circadian clock was determined to be regulated by a transcriptional negative feedback mechanism composed of a dozen core clock genes. More recently, large-scale genomic studies have expanded the clock into a complex network composed of thousands of gene outputs and inputs. A major task of circadian research is to utilize systems biological approaches to uncover the governing principles underlying cellular oscillatory behavior and advance understanding of biological functions at the genomic level with spatiotemporal resolution. This review focuses on the genes and pathways that provide inputs to the circadian clock. Several emerging examples include AMP-activated protein kinase AMPK, nutrient/energy sensor mTOR, NAD+-dependent deacetylase SIRT1, hypoxia-inducible factor HIF1α, oxidative stress-inducible factor NRF2, and the proinflammatory factor NF-κB. Among others that continue to be revealed, these input pathways reflect the extensive interplay between the clock and cell physiology through the regulation of core clock genes and proteins. While the scope of this crosstalk is well-recognized, precise molecular links are scarce, and the underlying regulatory mechanisms are not well understood. Future research must leverage genetic and genomic tools and technologies, network analysis, and computational modeling to characterize additional modifiers and input pathways. This systems-based framework promises to advance understanding of the circadian timekeeping system and may enable the enhancement of circadian functions through related input pathways.
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Affiliation(s)
- Andrew R Morris
- Department of Physiology and Functional Genomics, University of Florida College of Medicine, Gainesville, FL, United States of America
| | - Daniel L Stanton
- Department of Animal Sciences, University of Florida Institute of Food and Agricultural Sciences, Gainesville, FL, United States of America
| | - Destino Roman
- Department of Physiology and Functional Genomics, University of Florida College of Medicine, Gainesville, FL, United States of America
| | - Andrew C Liu
- Department of Physiology and Functional Genomics, University of Florida College of Medicine, Gainesville, FL, United States of America.
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13
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Poupin N, Corlu A, Cabaton NJ, Dubois-Pot-Schneider H, Canlet C, Person E, Bruel S, Frainay C, Vinson F, Maurier F, Morel F, Robin MA, Fromenty B, Zalko D, Jourdan F. Large-Scale Modeling Approach Reveals Functional Metabolic Shifts during Hepatic Differentiation. J Proteome Res 2018; 18:204-216. [PMID: 30394098 DOI: 10.1021/acs.jproteome.8b00524] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Being able to explore the metabolism of broad metabolizing cells is of critical importance in many research fields. This article presents an original modeling solution combining metabolic network and omics data to identify modulated metabolic pathways and changes in metabolic functions occurring during differentiation of a human hepatic cell line (HepaRG). Our results confirm the activation of hepato-specific functionalities and newly evidence modulation of other metabolic pathways, which could not be evidenced from transcriptomic data alone. Our method takes advantage of the network structure to detect changes in metabolic pathways that do not have gene annotations and exploits flux analyses techniques to identify activated metabolic functions. Compared to the usual cell-specific metabolic network reconstruction approaches, it limits false predictions by considering several possible network configurations to represent one phenotype rather than one arbitrarily selected network. Our approach significantly enhances the comprehensive and functional assessment of cell metabolism, opening further perspectives to investigate metabolic shifts occurring within various biological contexts.
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Affiliation(s)
- Nathalie Poupin
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Anne Corlu
- Université Rennes, INSERM, INRA, Institut NUMECAN (Nutrition Metabolisms and Cancer), UMR_A 1341, UMR_S 1241 , F-35000 Rennes , France
| | - Nicolas J Cabaton
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Hélène Dubois-Pot-Schneider
- Université Rennes, INSERM, INRA, Institut NUMECAN (Nutrition Metabolisms and Cancer), UMR_A 1341, UMR_S 1241 , F-35000 Rennes , France
| | - Cécile Canlet
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Elodie Person
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Sandrine Bruel
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Clément Frainay
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Florence Vinson
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Florence Maurier
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Fabrice Morel
- Université Rennes, INSERM, INRA, Institut NUMECAN (Nutrition Metabolisms and Cancer), UMR_A 1341, UMR_S 1241 , F-35000 Rennes , France
| | - Marie-Anne Robin
- Université Rennes, INSERM, INRA, Institut NUMECAN (Nutrition Metabolisms and Cancer), UMR_A 1341, UMR_S 1241 , F-35000 Rennes , France
| | - Bernard Fromenty
- Université Rennes, INSERM, INRA, Institut NUMECAN (Nutrition Metabolisms and Cancer), UMR_A 1341, UMR_S 1241 , F-35000 Rennes , France
| | - Daniel Zalko
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Fabien Jourdan
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
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14
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Machado D, Andrejev S, Tramontano M, Patil KR. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res 2018; 46:7542-7553. [PMID: 30192979 PMCID: PMC6125623 DOI: 10.1093/nar/gky537] [Citation(s) in RCA: 310] [Impact Index Per Article: 51.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 05/17/2018] [Accepted: 05/29/2018] [Indexed: 12/26/2022] Open
Abstract
Genome-scale metabolic models are instrumental in uncovering operating principles of cellular metabolism, for model-guided re-engineering, and unraveling cross-feeding in microbial communities. Yet, the application of genome-scale models, especially to microbial communities, is lagging behind the availability of sequenced genomes. This is largely due to the time-consuming steps of manual curation required to obtain good quality models. Here, we present an automated tool, CarveMe, for reconstruction of species and community level metabolic models. We introduce the concept of a universal model, which is manually curated and simulation ready. Starting with this universal model and annotated genome sequences, CarveMe uses a top-down approach to build single-species and community models in a fast and scalable manner. We show that CarveMe models perform closely to manually curated models in reproducing experimental phenotypes (substrate utilization and gene essentiality). Additionally, we build a collection of 74 models for human gut bacteria and test their ability to reproduce growth on a set of experimentally defined media. Finally, we create a database of 5587 bacterial models and demonstrate its potential for fast generation of microbial community models. Overall, CarveMe provides an open-source and user-friendly tool towards broadening the use of metabolic modeling in studying microbial species and communities.
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Affiliation(s)
- Daniel Machado
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Sergej Andrejev
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Melanie Tramontano
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany
| | - Kiran Raosaheb Patil
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany
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15
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Zelezniak A, Vowinckel J, Capuano F, Messner CB, Demichev V, Polowsky N, Mülleder M, Kamrad S, Klaus B, Keller MA, Ralser M. Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts. Cell Syst 2018; 7:269-283.e6. [PMID: 30195436 PMCID: PMC6167078 DOI: 10.1016/j.cels.2018.08.001] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 05/29/2018] [Accepted: 07/31/2018] [Indexed: 02/08/2023]
Abstract
A challenge in solving the genotype-to-phenotype relationship is to predict a cell’s metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype. The proteome of kinase knockouts is dominated by enzyme abundance changes The enzyme expression profiles of kinase knockouts are non-redundant Metabolism is regulated by many expression changes acting in concert Machine learning accurately predicts the metabolome from enzyme abundance
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Affiliation(s)
- Aleksej Zelezniak
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK; Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Jakob Vowinckel
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK; Biognosys AG, Schlieren, Switzerland
| | - Floriana Capuano
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
| | - Christoph B Messner
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK
| | - Vadim Demichev
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK; Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
| | - Nicole Polowsky
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
| | - Michael Mülleder
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK; Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
| | - Stephan Kamrad
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK; Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Bernd Klaus
- Centre for Statistical Data Analysis, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Markus A Keller
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK; Medical University of Innsbruck, Innsbruck, Austria
| | - Markus Ralser
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK; Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK; Department of Biochemistry, Charité Universitaetsmedizin Berlin, Berlin, Germany.
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16
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Ding X, Lin S, Weng H, Liang J. Separation and determination of the enantiomers of lactic acid and 2-hydroxyglutaric acid by chiral derivatization combined with gas chromatography and mass spectrometry. J Sep Sci 2018; 41:2576-2584. [DOI: 10.1002/jssc.201701555] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2018] [Revised: 03/22/2018] [Accepted: 03/22/2018] [Indexed: 12/22/2022]
Affiliation(s)
- Xuemei Ding
- Department of Pharmaceutical Analysis, School of Pharmacy; Fudan University; Shanghai P. R. China
| | - Shuhai Lin
- Department of Biochemistry and Molecular Cell Biology, School of Medicine; Shanghai Jiao Tong University; Shanghai P. R. China
| | - Hongbo Weng
- Department of Pharmacology, School of Pharmacy; Fudan University; Shanghai P. R. China
| | - Jianying Liang
- Department of Pharmaceutical Analysis, School of Pharmacy; Fudan University; Shanghai P. R. China
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17
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Metabolomic analysis reveals altered metabolic pathways in a rat model of gastric carcinogenesis. Oncotarget 2018; 7:60053-60073. [PMID: 27527852 PMCID: PMC5312368 DOI: 10.18632/oncotarget.11049] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2016] [Accepted: 07/07/2016] [Indexed: 12/27/2022] Open
Abstract
Gastric cancer (GC) is one of the most malignant tumors with a poor prognosis. Alterations in metabolic pathways are inextricably linked to GC progression. However, the underlying molecular mechanisms remain elusive. We performed NMR-based metabolomic analysis of sera derived from a rat model of gastric carcinogenesis, revealed significantly altered metabolic pathways correlated with the progression of gastric carcinogenesis. Rats were histologically classified into four pathological groups (gastritis, GS; low-grade gastric dysplasia, LGD; high-grade gastric dysplasia, HGD; GC) and the normal control group (CON). The metabolic profiles of the five groups were clearly distinguished from each other. Furthermore, significant inter-metabolite correlations were extracted and used to reconstruct perturbed metabolic networks associated with the four pathological stages compared with the normal stage. Then, significantly altered metabolic pathways were identified by pathway analysis. Our results showed that oxidative stress-related metabolic pathways, choline phosphorylation and fatty acid degradation were continually disturbed during gastric carcinogenesis. Moreover, amino acid metabolism was perturbed dramatically in gastric dysplasia and GC. The GC stage showed more changed metabolite levels and more altered metabolic pathways. Two activated pathways (glycolysis; glycine, serine and threonine metabolism) substantially contributed to the metabolic alterations in GC. These results lay the basis for addressing the molecular mechanisms underlying gastric carcinogenesis and extend our understanding of GC progression.
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18
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Özcan E, Çakır T. Genome-Scale Brain Metabolic Networks as Scaffolds for the Systems Biology of Neurodegenerative Diseases: Mapping Metabolic Alterations. ADVANCES IN NEUROBIOLOGY 2018; 21:195-217. [PMID: 30334223 DOI: 10.1007/978-3-319-94593-4_7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Systems-based investigation of diseases requires integrated analysis of cellular networks and high-throughput data of gene products. The use of genome-scale metabolic networks for such integration has led to the elucidation of cellular mechanisms for several cell types from microorganisms to plants. It has become easier and cheaper to generate high-throughput data over years in the form of transcriptome, proteome and metabolome. This has tremendously improved the quality and quantity of information extracted from such data enabling the documentation of active pathways and reactions in cell metabolism. A number of omics-based datasets for several neurodegenerative diseases are now available in public repositories. This increases the potential of using genome-scale brain metabolic networks as a scaffold for this type of data to map metabolic alterations for the purpose of elucidating disease mechanisms and for the diagnosis and treatment of such disorders. This chapter first reviews omics data collected for neurodegenerative diseases to map their effect on metabolism. Later, the potential for genome-scale metabolic modeling of such data is reviewed and discussed in light of recently reconstructed brain metabolic networks at genome-scale.
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Affiliation(s)
- Emrah Özcan
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey.
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19
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Rani J, Mittal I, Pramanik A, Singh N, Dube N, Sharma S, Puniya BL, Raghunandanan MV, Mobeen A, Ramachandran S. T2DiACoD: A Gene Atlas of Type 2 Diabetes Mellitus Associated Complex Disorders. Sci Rep 2017; 7:6892. [PMID: 28761062 PMCID: PMC5537262 DOI: 10.1038/s41598-017-07238-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Accepted: 06/28/2017] [Indexed: 12/11/2022] Open
Abstract
We performed integrative analysis of genes associated with type 2 Diabetes Mellitus (T2DM) associated complications by automated text mining with manual curation and also gene expression analysis from Gene Expression Omnibus. They were analysed for pathogenic or protective role, trends, interaction with risk factors, Gene Ontology enrichment and tissue wise differential expression. The database T2DiACoD houses 650 genes, and 34 microRNAs associated with T2DM complications. Seven genes AGER, TNFRSF11B, CRK, PON1, ADIPOQ, CRP and NOS3 are associated with all 5 complications. Several genes are studied in multiple years in all complications with high proportion in cardiovascular (75.8%) and atherosclerosis (51.3%). T2DM Patients' skeletal muscle tissues showed high fold change in differentially expressed genes. Among the differentially expressed genes, VEGFA is associated with several complications of T2DM. A few genes ACE2, ADCYAP1, HDAC4, NCF1, NFE2L2, OSM, SMAD1, TGFB1, BDNF, SYVN1, TXNIP, CD36, CYP2J2, NLRP3 with details of protective role are catalogued. Obesity is clearly a dominant risk factor interacting with the genes of T2DM complications followed by inflammation, diet and stress to variable extents. This information emerging from the integrative approach used in this work could benefit further therapeutic approaches. The T2DiACoD is available at www.http://t2diacod.igib.res.in/ .
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Affiliation(s)
- Jyoti Rani
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India
| | - Inna Mittal
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India
| | - Atreyi Pramanik
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India
| | - Namita Singh
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India
| | - Namita Dube
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India
| | - Smriti Sharma
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India
| | - Bhanwar Lal Puniya
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India
| | - Muthukurussi Varieth Raghunandanan
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India
| | - Ahmed Mobeen
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India
- Academy of Scientific and Innovative Research, CSIR-IGIB South Campus, New Delhi, 110025, India
| | - Srinivasan Ramachandran
- G N Ramachandran Knowledge of Centre, Council of Scientific and Industrial Research - Institute of Genomics and Integrative Biology (CSIR-IGIB), Room No. 130, Mathura Road, New Delhi, 110025, India.
- Academy of Scientific and Innovative Research, CSIR-IGIB South Campus, New Delhi, 110025, India.
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20
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Havas KM, Milchevskaya V, Radic K, Alladin A, Kafkia E, Garcia M, Stolte J, Klaus B, Rotmensz N, Gibson TJ, Burwinkel B, Schneeweiss A, Pruneri G, Patil KR, Sotillo R, Jechlinger M. Metabolic shifts in residual breast cancer drive tumor recurrence. J Clin Invest 2017; 127:2091-2105. [PMID: 28504653 DOI: 10.1172/jci89914] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 03/16/2017] [Indexed: 12/22/2022] Open
Abstract
Tumor recurrence is the leading cause of breast cancer-related death. Recurrences are largely driven by cancer cells that survive therapeutic intervention. This poorly understood population is referred to as minimal residual disease. Here, using mouse models that faithfully recapitulate human disease together with organoid cultures, we have demonstrated that residual cells acquire a transcriptionally distinct state from normal epithelium and primary tumors. Gene expression changes and functional characterization revealed altered lipid metabolism and elevated ROS as hallmarks of the cells that survive tumor regression. These residual cells exhibited increased oxidative DNA damage, potentiating the acquisition of somatic mutations during hormonal-induced expansion of the mammary cell population. Inhibition of either cellular fatty acid synthesis or fatty acid transport into mitochondria reduced cellular ROS levels and DNA damage, linking these features to lipid metabolism. Direct perturbation of these hallmarks in vivo, either by scavenging ROS or by halting the cyclic mammary cell population expansion, attenuated tumor recurrence. Finally, these observations were mirrored in transcriptomic and histological signatures of residual cancer cells from neoadjuvant-treated breast cancer patients. These results highlight the potential of lipid metabolism and ROS as therapeutic targets for reducing tumor recurrence in breast cancer patients.
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Affiliation(s)
- Kristina M Havas
- EMBL Monterotondo, Adriano Buzzati-Traverso Campus, Monterotondo, Italy.,Istituto Firc di Oncologia Molecolare (IFOM) the Italian Foundation for Cancer Research (FIRC) Institute of Molecular Oncology, Milan, Italy
| | | | | | | | | | | | - Jens Stolte
- EMBL Monterotondo, Adriano Buzzati-Traverso Campus, Monterotondo, Italy
| | | | - Nicole Rotmensz
- Division of Epidemiology and Biostatistics European Institute of Oncology, Milan, Italy
| | | | - Barbara Burwinkel
- Molecular Biology of Breast Cancer, University Women's Clinic, Heidelberg, Germany
| | - Andreas Schneeweiss
- Gynecologic Oncology, National Center for Tumor Diseases, University of Heidelberg, Heidelberg, Germany
| | - Giancarlo Pruneri
- Department of Pathology, Biobank for Translational Medicine Unit, European Institute of Oncology, Milan and University of Milan, School of Medicine, Milan, Italy
| | | | - Rocio Sotillo
- EMBL Monterotondo, Adriano Buzzati-Traverso Campus, Monterotondo, Italy.,Division of Molecular Thoracic Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
| | - Martin Jechlinger
- EMBL Monterotondo, Adriano Buzzati-Traverso Campus, Monterotondo, Italy.,EMBL Heidelberg, Heidelberg, Germany
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21
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Biomedical applications of cell- and tissue-specific metabolic network models. J Biomed Inform 2017; 68:35-49. [DOI: 10.1016/j.jbi.2017.02.014] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2016] [Revised: 02/21/2017] [Accepted: 02/23/2017] [Indexed: 12/17/2022]
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22
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In silico analysis of human metabolism: Reconstruction, contextualization and application of genome-scale models. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.coisb.2017.01.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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23
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Møller AB, Kampmann U, Hedegaard J, Thorsen K, Nordentoft I, Vendelbo MH, Møller N, Jessen N. Altered gene expression and repressed markers of autophagy in skeletal muscle of insulin resistant patients with type 2 diabetes. Sci Rep 2017; 7:43775. [PMID: 28252104 PMCID: PMC5333153 DOI: 10.1038/srep43775] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 01/30/2017] [Indexed: 12/15/2022] Open
Abstract
This case-control study was designed to investigate the gene expression profile in skeletal muscle from severely insulin resistant patients with long-standing type 2 diabetes (T2D), and to determine associated signaling pathways. Gene expression profiles were examined by whole transcriptome, strand-specific RNA-sequencing and associated signaling was determined by western blot. We identified 117 differentially expressed gene transcripts. Ingenuity Pathway Analysis related these differences to abnormal muscle morphology and mitochondrial dysfunction. Despite a ~5-fold difference in plasma insulin, we did not observe any difference in phosphorylation of AKT or AS160, although other insulin-sensitive cascades, as mTOR/4EBP1, had retained their sensitivity. Autophagy-related gene (ATG14, RB1CC1/FIP200, GABARAPL1, SQSTM1/p62, and WIPI1) and protein (LC3BII, SQSTM1/p62 and ATG5) expression were decreased in skeletal muscle from the patients, and this was associated with a trend to increased phosphorylation of the insulin-sensitive regulatory transcription factor FOXO3a. These data show that gene expression is highly altered and related to mitochondrial dysfunction and abnormal morphology in skeletal muscle from severely insulin resistant patients with T2D, and that this is associated with decreased expression of autophagy-related genes and proteins. We speculate that prolonged treatment with high doses of insulin may suppress autophagy thereby generating a vicious cycle maintaining insulin resistance.
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Affiliation(s)
- Andreas Buch Møller
- Research Laboratory for Biochemical Pathology, Department of Clinical Medicine, Aarhus University, Denmark
| | - Ulla Kampmann
- Department of Internal Medicine and Endocrinology, Aarhus University Hospital, Denmark
| | - Jakob Hedegaard
- Department of Molecular Medicine, Aarhus University Hospital, Denmark
| | - Kasper Thorsen
- Department of Molecular Medicine, Aarhus University Hospital, Denmark
| | - Iver Nordentoft
- Department of Molecular Medicine, Aarhus University Hospital, Denmark
| | - Mikkel Holm Vendelbo
- Department of Nuclear Medicine and PET Center, Aarhus University Hospital, Denmark
| | - Niels Møller
- Department of Internal Medicine and Endocrinology, Aarhus University Hospital, Denmark.,Medical Research Laboratory, Department of Clinical Medicine, Aarhus University Hospital, Denmark
| | - Niels Jessen
- Research Laboratory for Biochemical Pathology, Department of Clinical Medicine, Aarhus University, Denmark.,Department of Molecular Medicine, Aarhus University Hospital, Denmark.,Department of Clinical Pharmacology, Aarhus University Hospital, Denmark
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24
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Lv W, Wang Q, Chen H, Jiang Y, Zheng J, Shi M, Xu Y, Han J, Li C, Zhang R. Prioritization of rheumatoid arthritis risk subpathways based on global immune subpathway interaction network and random walk strategy. MOLECULAR BIOSYSTEMS 2016; 11:2986-97. [PMID: 26289534 DOI: 10.1039/c5mb00247h] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The initiation and development of rheumatoid arthritis (RA) is closely related to mutual dysfunction of multiple pathways. Furthermore, some similar molecular mechanisms are shared between RA and other immune diseases. Therefore it is vital to reveal the molecular mechanism of RA through searching for subpathways of immune diseases and investigating the crosstalk effect among subpathways. Here we exploited an integrated approach combining both construction of a subpathway-subpathway interaction network and a random walk strategy to prioritize RA risk subpathways. Our research can be divided into three parts: (1) acquisition of risk genes and identification of risk subpathways of 85 immune diseases by using subpathway-lenient distance similarity (subpathway-LDS) method; (2) construction of a global immune subpathway interaction (GISI) network with subpathways identified by subpathway-LDS; (3) optimization of RA risk subpathways by random walk strategy based on GISI network. The results showed that our method could effectively identify RA risk subpathways, such as MAPK signaling pathway, prostate cancer pathway and chemokine signaling pathway. The integrated strategy considering crosstalk between immune subpathways significantly improved the effect of risk subpathway identification. With the development of GWAS, our method will provide insight into exploring molecular mechanisms of immune diseases and might be a promising approach for studying other diseases.
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Affiliation(s)
- Wenhua Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.
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25
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Abstract
The concept of allostery in which macromolecules switch between two different conformations is a central theme in biological processes ranging from gene regulation to cell signaling to enzymology. Allosteric enzymes pervade metabolic processes, yet a simple and unified treatment of the effects of allostery in enzymes has been lacking. In this work, we take a step toward this goal by modeling allosteric enzymes and their interaction with two key molecular players-allosteric regulators and competitive inhibitors. We then apply this model to characterize existing data on enzyme activity, comment on how enzyme parameters (such as substrate binding affinity) can be experimentally tuned, and make novel predictions on how to control phenomena such as substrate inhibition.
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Affiliation(s)
- Tal Einav
- Department of Physics, California Institute of Technology , Pasadena, California 91125, United States
| | - Linas Mazutis
- Institute of Biotechnology, Vilnius University , Vilnius, 02241 Lithuania
| | - Rob Phillips
- Department of Applied Physics and Division of Biology, California Institute of Technology , Pasadena, California 91125, United States
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26
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Shaked I, Oberhardt M, Atias N, Sharan R, Ruppin E. Metabolic Network Prediction of Drug Side Effects. Cell Syst 2016; 2:209-13. [DOI: 10.1016/j.cels.2016.03.001] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Revised: 12/11/2015] [Accepted: 03/01/2016] [Indexed: 11/17/2022]
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27
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Zhao Y, Wang Y, Zou L, Huang J. Reconstruction and applications of consensus yeast metabolic network based on RNA sequencing. FEBS Open Bio 2016; 6:264-75. [PMID: 27239440 PMCID: PMC4821349 DOI: 10.1002/2211-5463.12033] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Revised: 01/08/2016] [Accepted: 01/13/2016] [Indexed: 11/06/2022] Open
Abstract
One practical application of genome-scale metabolic reconstructions is to interrogate multispecies relationships. Here, we report a consensus metabolic model in four yeast species (Saccharomyces cerevisiae, S. paradoxus, S. mikatae, and S. bayanus) by integrating metabolic network simulations with RNA sequencing (RNA-seq) datasets. We generated high-resolution transcriptome maps of four yeast species through de novo assembly and genome-guided approaches. The transcriptomes were annotated and applied to build the consensus metabolic network, which was verified using independent RNA-seq experiments. The expression profiles reveal that the genes involved in amino acid and lipid metabolism are highly coexpressed. The diverse phenotypic characteristics, such as cellular growth and gene deletions, can be simulated using the metabolic model. We also explored the applications of the consensus model in metabolic engineering using yeast-specific reactions and biofuel production as examples. Similar strategies will benefit communities studying genome-scale metabolic networks of other organisms.
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Affiliation(s)
- Yuqi Zhao
- State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Yunnan China
| | - Yanjie Wang
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences and Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Yunnan China
| | - Lei Zou
- Department of General Surgery First People's Hospital of Yunnan Province Kunming China
| | - Jingfei Huang
- State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Yunnan China; Collaborative Innovation Center for Natural Products and Biological Drugs of Yunnan Kunming Yunnan China
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28
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Lakshmanan M, Lim SH, Mohanty B, Kim JK, Ha SH, Lee DY. Unraveling the Light-Specific Metabolic and Regulatory Signatures of Rice through Combined in Silico Modeling and Multiomics Analysis. PLANT PHYSIOLOGY 2015; 169:3002-20. [PMID: 26453433 PMCID: PMC4677915 DOI: 10.1104/pp.15.01379] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2015] [Accepted: 10/07/2015] [Indexed: 05/21/2023]
Abstract
Light quality is an important signaling component upon which plants orchestrate various morphological processes, including seed germination and seedling photomorphogenesis. However, it is still unclear how plants, especially food crops, sense various light qualities and modulate their cellular growth and other developmental processes. Therefore, in this work, we initially profiled the transcripts of a model crop, rice (Oryza sativa), under four different light treatments (blue, green, red, and white) as well as in the dark. Concurrently, we reconstructed a fully compartmentalized genome-scale metabolic model of rice cells, iOS2164, containing 2,164 unique genes, 2,283 reactions, and 1,999 metabolites. We then combined the model with transcriptome profiles to elucidate the light-specific transcriptional signatures of rice metabolism. Clearly, light signals mediated rice gene expressions, differentially regulating numerous metabolic pathways: photosynthesis and secondary metabolism were up-regulated in blue light, whereas reserve carbohydrates degradation was pronounced in the dark. The topological analysis of gene expression data with the rice genome-scale metabolic model further uncovered that phytohormones, such as abscisate, ethylene, gibberellin, and jasmonate, are the key biomarkers of light-mediated regulation, and subsequent analysis of the associated genes' promoter regions identified several light-specific transcription factors. Finally, the transcriptional control of rice metabolism by red and blue light signals was assessed by integrating the transcriptome and metabolome data with constraint-based modeling. The biological insights gained from this integrative systems biology approach offer several potential applications, such as improving the agronomic traits of food crops and designing light-specific synthetic gene circuits in microbial and mammalian systems.
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Affiliation(s)
- Meiyappan Lakshmanan
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576 (M.L., B.M., D.-Y.L.);Bioprocessing Technology Institute, Agency for Science, Technology and Research, Singapore 138668 (M.L., D.-Y.L.);Metabolic Engineering Division, National Academy of Agricultural Science, Rural Development Administration, Jeonju 560-500, Republic of Korea (S.-H.L.);Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon 406-772, Republic of Korea (J.K.K.); andDepartment of Genetic Engineering and Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin 446-701, Republic of Korea (S.-H.H.)
| | - Sun-Hyung Lim
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576 (M.L., B.M., D.-Y.L.);Bioprocessing Technology Institute, Agency for Science, Technology and Research, Singapore 138668 (M.L., D.-Y.L.);Metabolic Engineering Division, National Academy of Agricultural Science, Rural Development Administration, Jeonju 560-500, Republic of Korea (S.-H.L.);Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon 406-772, Republic of Korea (J.K.K.); andDepartment of Genetic Engineering and Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin 446-701, Republic of Korea (S.-H.H.)
| | - Bijayalaxmi Mohanty
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576 (M.L., B.M., D.-Y.L.);Bioprocessing Technology Institute, Agency for Science, Technology and Research, Singapore 138668 (M.L., D.-Y.L.);Metabolic Engineering Division, National Academy of Agricultural Science, Rural Development Administration, Jeonju 560-500, Republic of Korea (S.-H.L.);Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon 406-772, Republic of Korea (J.K.K.); andDepartment of Genetic Engineering and Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin 446-701, Republic of Korea (S.-H.H.)
| | - Jae Kwang Kim
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576 (M.L., B.M., D.-Y.L.);Bioprocessing Technology Institute, Agency for Science, Technology and Research, Singapore 138668 (M.L., D.-Y.L.);Metabolic Engineering Division, National Academy of Agricultural Science, Rural Development Administration, Jeonju 560-500, Republic of Korea (S.-H.L.);Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon 406-772, Republic of Korea (J.K.K.); andDepartment of Genetic Engineering and Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin 446-701, Republic of Korea (S.-H.H.)
| | - Sun-Hwa Ha
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576 (M.L., B.M., D.-Y.L.);Bioprocessing Technology Institute, Agency for Science, Technology and Research, Singapore 138668 (M.L., D.-Y.L.);Metabolic Engineering Division, National Academy of Agricultural Science, Rural Development Administration, Jeonju 560-500, Republic of Korea (S.-H.L.);Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon 406-772, Republic of Korea (J.K.K.); andDepartment of Genetic Engineering and Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin 446-701, Republic of Korea (S.-H.H.)
| | - Dong-Yup Lee
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576 (M.L., B.M., D.-Y.L.);Bioprocessing Technology Institute, Agency for Science, Technology and Research, Singapore 138668 (M.L., D.-Y.L.);Metabolic Engineering Division, National Academy of Agricultural Science, Rural Development Administration, Jeonju 560-500, Republic of Korea (S.-H.L.);Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon 406-772, Republic of Korea (J.K.K.); andDepartment of Genetic Engineering and Graduate School of Biotechnology, College of Life Sciences, Kyung Hee University, Yongin 446-701, Republic of Korea (S.-H.H.)
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29
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Çakır T. Reporter pathway analysis from transcriptome data: Metabolite-centric versus Reaction-centric approach. Sci Rep 2015; 5:14563. [PMID: 26411587 PMCID: PMC4585941 DOI: 10.1038/srep14563] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2015] [Accepted: 08/28/2015] [Indexed: 12/16/2022] Open
Abstract
A systems-based investigation of the effect of perturbations on metabolic machinery is crucial to elucidate the mechanism behind perturbations. One way to investigate the perturbation-induced changes within the cell metabolism is to focus on pathway-level effects. In this study, three different perturbation types (genetic, environmental and disease-based) are analyzed to compute a list of reporter pathways, metabolic pathways which are significantly affected from a perturbation. The most common omics data type, transcriptome, is used as an input to the bioinformatic analysis. The pathways are scored by two alternative approaches: by averaging the changes in the expression levels of the genes controlling the associated reactions (reaction-centric), and by averaging the changes in the associated metabolites which were scored based on the associated genes (metabolite-centric). The analysis reveals the superiority of the novel metabolite-centric approach over the commonly used reaction-centric approach since it is based on metabolites which better represent the cross-talk among different pathways, enabling a more global and realistic cataloguing of network-wide perturbation effects.
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Affiliation(s)
- Tunahan Çakır
- Gebze Technical University, Department of Bioengineering, 41400, Gebze, Kocaeli, Turkey
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30
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Bailey LB, Stover PJ, McNulty H, Fenech MF, Gregory JF, Mills JL, Pfeiffer CM, Fazili Z, Zhang M, Ueland PM, Molloy AM, Caudill MA, Shane B, Berry RJ, Bailey RL, Hausman DB, Raghavan R, Raiten DJ. Biomarkers of Nutrition for Development-Folate Review. J Nutr 2015; 145:1636S-1680S. [PMID: 26451605 PMCID: PMC4478945 DOI: 10.3945/jn.114.206599] [Citation(s) in RCA: 296] [Impact Index Per Article: 32.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Revised: 12/11/2014] [Accepted: 04/14/2015] [Indexed: 12/13/2022] Open
Abstract
The Biomarkers of Nutrition for Development (BOND) project is designed to provide evidence-based advice to anyone with an interest in the role of nutrition in health. Specifically, the BOND program provides state-of-the-art information and service with regard to selection, use, and interpretation of biomarkers of nutrient exposure, status, function, and effect. To accomplish this objective, expert panels are recruited to evaluate the literature and to draft comprehensive reports on the current state of the art with regard to specific nutrient biology and available biomarkers for assessing nutrients in body tissues at the individual and population level. Phase I of the BOND project includes the evaluation of biomarkers for 6 nutrients: iodine, iron, zinc, folate, vitamin A, and vitamin B-12. This review represents the second in the series of reviews and covers all relevant aspects of folate biology and biomarkers. The article is organized to provide the reader with a full appreciation of folate's history as a public health issue, its biology, and an overview of available biomarkers (serum folate, RBC folate, and plasma homocysteine concentrations) and their interpretation across a range of clinical and population-based uses. The article also includes a list of priority research needs for advancing the area of folate biomarkers related to nutritional health status and development.
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Affiliation(s)
- Lynn B Bailey
- Department of Foods and Nutrition, University of Georgia, Athens, GA;
| | - Patrick J Stover
- Division of Nutritional Sciences, Cornell University, Ithaca, NY
| | - Helene McNulty
- Northern Ireland Centre for Food and Health, Biomedical Sciences Research Institute, University of Ulster, Londonderry, United Kingdom
| | - Michael F Fenech
- Genome Health Nutrigenomics Laboratory, Food, Nutrition, and Bioproducts Flagship, Commonwealth Scientific and Industrial Research Organization, Adelaide, Australia
| | - Jesse F Gregory
- Food Science and Human Nutrition Department, University of Florida, Gainesville, FL
| | - James L Mills
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD
| | | | - Zia Fazili
- National Center for Environmental Health, CDC, Atlanta, GA
| | - Mindy Zhang
- National Center for Environmental Health, CDC, Atlanta, GA
| | - Per M Ueland
- Department of Clinical Science, Univeristy of Bergen, Bergen, Norway
| | - Anne M Molloy
- Institute of Molecular Medicine, Trinity College, Dublin, Ireland
| | - Marie A Caudill
- Division of Nutritional Sciences, Cornell University, Ithaca, NY
| | - Barry Shane
- Department of Nutritional Sciences and Toxicology, University of California-Berkeley, Berkeley, CA
| | - Robert J Berry
- National Center on Birth Defects and Developmental Disabilities, CDC, Atlanta, GA; and
| | | | - Dorothy B Hausman
- Department of Foods and Nutrition, University of Georgia, Athens, GA
| | - Ramkripa Raghavan
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD
| | - Daniel J Raiten
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD;
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31
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Heinken A, Thiele I. Systems biology of host-microbe metabolomics. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2015; 7:195-219. [PMID: 25929487 PMCID: PMC5029777 DOI: 10.1002/wsbm.1301] [Citation(s) in RCA: 69] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Revised: 03/25/2015] [Accepted: 04/01/2015] [Indexed: 12/15/2022]
Abstract
The human gut microbiota performs essential functions for host and well‐being, but has also been linked to a variety of disease states, e.g., obesity and type 2 diabetes. The mammalian body fluid and tissue metabolomes are greatly influenced by the microbiota, with many health‐relevant metabolites being considered ‘mammalian–microbial co‐metabolites’. To systematically investigate this complex host–microbial co‐metabolism, a systems biology approach integrating high‐throughput data and computational network models is required. Here, we review established top‐down and bottom‐up systems biology approaches that have successfully elucidated relationships between gut microbiota‐derived metabolites and host health and disease. We focus particularly on the constraint‐based modeling and analysis approach, which enables the prediction of mechanisms behind metabolic host–microbe interactions on the molecular level. We illustrate that constraint‐based models are a useful tool for the contextualization of metabolomic measurements and can further our insight into host–microbe interactions, yielding, e.g., in potential novel drugs and biomarkers. WIREs Syst Biol Med 2015, 7:195–219. doi: 10.1002/wsbm.1301 For further resources related to this article, please visit the WIREs website. Conflict of interest: The authors have declared no conflicts of interest for this article.
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Affiliation(s)
- Almut Heinken
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg
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32
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Cicek AE, Qi X, Cakmak A, Johnson SR, Han X, Alshalwi S, Ozsoyoglu ZM, Ozsoyoglu G. An online system for metabolic network analysis. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau091. [PMID: 25267793 PMCID: PMC4178370 DOI: 10.1093/database/bau091] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Metabolic networks have become one of the centers of attention in life sciences research with the advancements in the metabolomics field. A vast array of studies analyzes metabolites and their interrelations to seek explanations for various biological questions, and numerous genome-scale metabolic networks have been assembled to serve for this purpose. The increasing focus on this topic comes with the need for software systems that store, query, browse, analyze and visualize metabolic networks. PathCase Metabolomics Analysis Workbench (PathCaseMAW) is built, released and runs on a manually created generic mammalian metabolic network. The PathCaseMAW system provides a database-enabled framework and Web-based computational tools for browsing, querying, analyzing and visualizing stored metabolic networks. PathCaseMAW editor, with its user-friendly interface, can be used to create a new metabolic network and/or update an existing metabolic network. The network can also be created from an existing genome-scale reconstructed network using the PathCaseMAW SBML parser. The metabolic network can be accessed through a Web interface or an iPad application. For metabolomics analysis, steady-state metabolic network dynamics analysis (SMDA) algorithm is implemented and integrated with the system. SMDA tool is accessible through both the Web-based interface and the iPad application for metabolomics analysis based on a metabolic profile. PathCaseMAW is a comprehensive system with various data input and data access subsystems. It is easy to work with by design, and is a promising tool for metabolomics research and for educational purposes. Database URL: http://nashua.case.edu/PathwaysMAW/Web
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Affiliation(s)
- Abdullah Ercument Cicek
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Xinjian Qi
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Ali Cakmak
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Stephen R Johnson
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Xu Han
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Sami Alshalwi
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Zehra Meral Ozsoyoglu
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
| | - Gultekin Ozsoyoglu
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15222, USA, Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Department of Computer Science, Istanbul Sehir University, Istanbul 34662, Turkey
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Jia X, Li W, Miao Z, Feng C, Liu Z, He Y, Lv J, Du Y, Hou M, He W, Li D, Chen L. Identification of modules related to programmed cell death in CHD based on EHEN. BIOMED RESEARCH INTERNATIONAL 2014; 2014:475379. [PMID: 25133163 PMCID: PMC4123579 DOI: 10.1155/2014/475379] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2014] [Accepted: 05/28/2014] [Indexed: 01/26/2023]
Abstract
The formation and death of macrophages and foam cells are one of the major factors that cause coronary heart disease (CHD). In our study, based on the Edinburgh Human Metabolic Network (EHMN) metabolic network, we built an enzyme network which was constructed by enzymes (nodes) and reactions (edges) called the Edinburgh Human Enzyme Network (EHEN). By integrating the subcellular location information for the reactions and refining the protein-reaction relationships based on the location information, we proposed a computational approach to select modules related to programmed cell death. The identified module was in the EHEN-mitochondria (EHEN-M) and was confirmed to be related to programmed cell death, CHD pathogenesis, and lipid metabolism in the literature. We expected this method could analyze CHD better and more comprehensively from the point of programmed cell death in subnetworks.
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Affiliation(s)
- Xu Jia
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Zhengqiang Miao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Chenchen Feng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Zhe Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Yuehan He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Junjie Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Youwen Du
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Min Hou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Weiming He
- Institute of Opto-Electronics, Harbin Institute of Technology, Harbin, Heilongjiang 150000, China
| | - Danbin Li
- Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
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Abstract
MOTIVATION Discovering the transcriptional regulatory architecture of the metabolism has been an important topic to understand the implications of transcriptional fluctuations on metabolism. The reporter algorithm (RA) was proposed to determine the hot spots in metabolic networks, around which transcriptional regulation is focused owing to a disease or a genetic perturbation. Using a z-score-based scoring scheme, RA calculates the average statistical change in the expression levels of genes that are neighbors to a target metabolite in the metabolic network. The RA approach has been used in numerous studies to analyze cellular responses to the downstream genetic changes. In this article, we propose a mutual information-based multivariate reporter algorithm (MIRA) with the goal of eliminating the following problems in detecting reporter metabolites: (i) conventional statistical methods suffer from small sample sizes, (ii) as z-score ranges from minus to plus infinity, calculating average scores can lead to canceling out opposite effects and (iii) analyzing genes one by one, then aggregating results can lead to information loss. MIRA is a multivariate and combinatorial algorithm that calculates the aggregate transcriptional response around a metabolite using mutual information. We show that MIRA's results are biologically sound, empirically significant and more reliable than RA. RESULTS We apply MIRA to gene expression analysis of six knockout strains of Escherichia coli and show that MIRA captures the underlying metabolic dynamics of the switch from aerobic to anaerobic respiration. We also apply MIRA to an Autism Spectrum Disorder gene expression dataset. Results indicate that MIRA reports metabolites that highly overlap with recently found metabolic biomarkers in the autism literature. Overall, MIRA is a promising algorithm for detecting metabolic drug targets and understanding the relation between gene expression and metabolic activity. AVAILABILITY AND IMPLEMENTATION The code is implemented in C# language using .NET framework. Project is available upon request.
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Affiliation(s)
- A Ercument Cicek
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA 15213 and Department of Electrical Engineering and Computer Science, School of Engineering, Case Western Reserve University, Cleveland, OH, USA 44106
| | - Kathryn Roeder
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA 15213 and Department of Electrical Engineering and Computer Science, School of Engineering, Case Western Reserve University, Cleveland, OH, USA 44106
| | - Gultekin Ozsoyoglu
- Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA 15213 and Department of Electrical Engineering and Computer Science, School of Engineering, Case Western Reserve University, Cleveland, OH, USA 44106
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35
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Sertbaş M, Ülgen K, Çakır T. Systematic analysis of transcription-level effects of neurodegenerative diseases on human brain metabolism by a newly reconstructed brain-specific metabolic network. FEBS Open Bio 2014; 4:542-53. [PMID: 25061554 PMCID: PMC4104795 DOI: 10.1016/j.fob.2014.05.006] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2014] [Revised: 05/23/2014] [Accepted: 05/27/2014] [Indexed: 01/02/2023] Open
Abstract
Network-oriented analysis is essential to identify those parts of a cell affected by a given perturbation. The effect of neurodegenerative perturbations in the form of diseases of brain metabolism was investigated by using a newly reconstructed brain-specific metabolic network. The developed stoichiometric model correctly represents healthy brain metabolism, and includes 630 metabolic reactions in and between astrocytes and neurons, which are controlled by 570 genes. The integration of transcriptome data of six neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, multiple sclerosis, schizophrenia) with the model was performed to identify reporter features specific and common for these diseases, which revealed metabolites and pathways around which the most significant changes occur. The identified metabolites are potential biomarkers for the pathology of the related diseases. Our model indicated perturbations in oxidative stress, energy metabolism including TCA cycle and lipid metabolism as well as several amino acid related pathways, in agreement with the role of these pathways in the studied diseases. The computational prediction of transcription factors that commonly regulate the reporter metabolites was achieved through binding-site analysis. Literature support for the identified transcription factors such as USF1, SP1 and those from FOX families are known from the literature to have regulatory roles in the identified reporter metabolic pathways as well as in the neurodegenerative diseases. In essence, the reconstructed brain model enables the elucidation of effects of a perturbation on brain metabolism and the illumination of possible machineries in which a specific metabolite or pathway acts as a regulatory spot for cellular reorganization.
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Key Words
- AD, Alzheimer’s disease
- ALS, amyotrophic lateral sclerosis
- Brain metabolic network
- Computational systems biology
- FBA, flux balance analysis
- GABA, gamma-aminobutyric acid
- HD, Huntington’s disease
- KIV, ketoisovalerate
- KLF, Krüppel-like factor
- KMV, alpha-keto-beta-methylvalerate
- MS, multiple sclerosis
- Neurodegenerative diseases
- Neurometabolism
- PCA, principal component analysis
- PD, Parkinson’s disease
- RMA, reporter metabolite analysis
- RPA, reporter pathway analysis
- Reporter metabolite
- SCHZ, schizophrenia
- TCA, tricarboxylic acid
- Transcriptome
- USF, upstream stimulatory factor
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Affiliation(s)
- Mustafa Sertbaş
- Department of Bioengineering, Gebze Institute of Technology, Gebze, Kocaeli, Turkey
- Department of Chemical Engineering, Boğaziçi University, 34342 Bebek, Istanbul, Turkey
| | - Kutlu Ülgen
- Department of Chemical Engineering, Boğaziçi University, 34342 Bebek, Istanbul, Turkey
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Institute of Technology, Gebze, Kocaeli, Turkey
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Zelezniak A, Sheridan S, Patil KR. Contribution of network connectivity in determining the relationship between gene expression and metabolite concentration changes. PLoS Comput Biol 2014; 10:e1003572. [PMID: 24762675 PMCID: PMC3998873 DOI: 10.1371/journal.pcbi.1003572] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Accepted: 03/03/2014] [Indexed: 11/19/2022] Open
Abstract
One of the primary mechanisms through which a cell exerts control over its metabolic state is by modulating expression levels of its enzyme-coding genes. However, the changes at the level of enzyme expression allow only indirect control over metabolite levels, for two main reasons. First, at the level of individual reactions, metabolite levels are non-linearly dependent on enzyme abundances as per the reaction kinetics mechanisms. Secondly, specific metabolite pools are tightly interlinked with the rest of the metabolic network through their production and consumption reactions. While the role of reaction kinetics in metabolite concentration control is well studied at the level of individual reactions, the contribution of network connectivity has remained relatively unclear. Here we report a modeling framework that integrates both reaction kinetics and network connectivity constraints for describing the interplay between metabolite concentrations and mRNA levels. We used this framework to investigate correlations between the gene expression and the metabolite concentration changes in Saccharomyces cerevisiae during its metabolic cycle, as well as in response to three fundamentally different biological perturbations, namely gene knockout, nutrient shock and nutrient change. While the kinetic constraints applied at the level of individual reactions were found to be poor descriptors of the mRNA-metabolite relationship, their use in the context of the network enabled us to correlate changes in the expression of enzyme-coding genes to the alterations in metabolite levels. Our results highlight the key contribution of metabolic network connectivity in mediating cellular control over metabolite levels, and have implications towards bridging the gap between genotype and metabolic phenotype.
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Affiliation(s)
- Aleksej Zelezniak
- European Molecular Biology Laboratory, Heidelberg, Germany
- Technical University of Denmark, Kgs. Lyngby, Denmark
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Drozdov I, Didangelos A, Yin X, Zampetaki A, Abonnenc M, Murdoch C, Zhang M, Ouzounis CA, Mayr M, Tsoka S, Shah AM. Gene Network and Proteomic Analyses of Cardiac Responses to Pathological and Physiological Stress. ACTA ACUST UNITED AC 2013; 6:588-97. [DOI: 10.1161/circgenetics.113.000063] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background—
The molecular mechanisms underlying similarities and differences between physiological and pathological left ventricular hypertrophy (LVH) are of intense interest. Most previous work involved targeted analysis of individual signaling pathways or screening of transcriptomic profiles. We developed a network biology approach using genomic and proteomic data to study the molecular patterns that distinguish pathological and physiological LVH.
Methods and Results—
A network-based analysis using graph theory methods was undertaken on 127 genome-wide expression arrays of in vivo murine LVH. This revealed phenotype-specific pathological and physiological gene coexpression networks. Despite >1650 common genes in the 2 networks, network structure is significantly different. This is largely because of rewiring of genes that are differentially coexpressed in the 2 networks; this novel concept of differential wiring was further validated experimentally. Functional analysis of the rewired network revealed several distinct cellular pathways and gene sets. Deeper exploration was undertaken by targeted proteomic analysis of mitochondrial, myofilament, and extracellular subproteomes in pathological LVH. A notable finding was that mRNA–protein correlation was greater at the cellular pathway level than for individual loci.
Conclusions—
This first combined gene network and proteomic analysis of LVH reveals novel insights into the integrated pathomechanisms that distinguish pathological versus physiological phenotypes. In particular, we identify differential gene wiring as a major distinguishing feature of these phenotypes. This approach provides a platform for the investigation of potentially novel pathways in LVH and offers a freely accessible protocol (
http://sites.google.com/site/cardionetworks
) for similar analyses in other cardiovascular diseases.
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Affiliation(s)
- Ignat Drozdov
- From the Cardiovascular Division, King’s College London BHF Centre of Research Excellence, School of Medicine, James Black Centre, London, United Kingdom (I.D., A.D., X.Y., A.Z., M.A., C.M., M.Z., M.M., A.M.S.); and Department of Informatics, School of Natural and Mathematical Sciences, King’s College London, London, United Kingdom (I.D., C.A.O., S.T.)
| | - Athanasios Didangelos
- From the Cardiovascular Division, King’s College London BHF Centre of Research Excellence, School of Medicine, James Black Centre, London, United Kingdom (I.D., A.D., X.Y., A.Z., M.A., C.M., M.Z., M.M., A.M.S.); and Department of Informatics, School of Natural and Mathematical Sciences, King’s College London, London, United Kingdom (I.D., C.A.O., S.T.)
| | - Xiaoke Yin
- From the Cardiovascular Division, King’s College London BHF Centre of Research Excellence, School of Medicine, James Black Centre, London, United Kingdom (I.D., A.D., X.Y., A.Z., M.A., C.M., M.Z., M.M., A.M.S.); and Department of Informatics, School of Natural and Mathematical Sciences, King’s College London, London, United Kingdom (I.D., C.A.O., S.T.)
| | - Anna Zampetaki
- From the Cardiovascular Division, King’s College London BHF Centre of Research Excellence, School of Medicine, James Black Centre, London, United Kingdom (I.D., A.D., X.Y., A.Z., M.A., C.M., M.Z., M.M., A.M.S.); and Department of Informatics, School of Natural and Mathematical Sciences, King’s College London, London, United Kingdom (I.D., C.A.O., S.T.)
| | - Mélanie Abonnenc
- From the Cardiovascular Division, King’s College London BHF Centre of Research Excellence, School of Medicine, James Black Centre, London, United Kingdom (I.D., A.D., X.Y., A.Z., M.A., C.M., M.Z., M.M., A.M.S.); and Department of Informatics, School of Natural and Mathematical Sciences, King’s College London, London, United Kingdom (I.D., C.A.O., S.T.)
| | - Colin Murdoch
- From the Cardiovascular Division, King’s College London BHF Centre of Research Excellence, School of Medicine, James Black Centre, London, United Kingdom (I.D., A.D., X.Y., A.Z., M.A., C.M., M.Z., M.M., A.M.S.); and Department of Informatics, School of Natural and Mathematical Sciences, King’s College London, London, United Kingdom (I.D., C.A.O., S.T.)
| | - Min Zhang
- From the Cardiovascular Division, King’s College London BHF Centre of Research Excellence, School of Medicine, James Black Centre, London, United Kingdom (I.D., A.D., X.Y., A.Z., M.A., C.M., M.Z., M.M., A.M.S.); and Department of Informatics, School of Natural and Mathematical Sciences, King’s College London, London, United Kingdom (I.D., C.A.O., S.T.)
| | - Christos A. Ouzounis
- From the Cardiovascular Division, King’s College London BHF Centre of Research Excellence, School of Medicine, James Black Centre, London, United Kingdom (I.D., A.D., X.Y., A.Z., M.A., C.M., M.Z., M.M., A.M.S.); and Department of Informatics, School of Natural and Mathematical Sciences, King’s College London, London, United Kingdom (I.D., C.A.O., S.T.)
| | - Manuel Mayr
- From the Cardiovascular Division, King’s College London BHF Centre of Research Excellence, School of Medicine, James Black Centre, London, United Kingdom (I.D., A.D., X.Y., A.Z., M.A., C.M., M.Z., M.M., A.M.S.); and Department of Informatics, School of Natural and Mathematical Sciences, King’s College London, London, United Kingdom (I.D., C.A.O., S.T.)
| | - Sophia Tsoka
- From the Cardiovascular Division, King’s College London BHF Centre of Research Excellence, School of Medicine, James Black Centre, London, United Kingdom (I.D., A.D., X.Y., A.Z., M.A., C.M., M.Z., M.M., A.M.S.); and Department of Informatics, School of Natural and Mathematical Sciences, King’s College London, London, United Kingdom (I.D., C.A.O., S.T.)
| | - Ajay M. Shah
- From the Cardiovascular Division, King’s College London BHF Centre of Research Excellence, School of Medicine, James Black Centre, London, United Kingdom (I.D., A.D., X.Y., A.Z., M.A., C.M., M.Z., M.M., A.M.S.); and Department of Informatics, School of Natural and Mathematical Sciences, King’s College London, London, United Kingdom (I.D., C.A.O., S.T.)
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Somvanshi PR, Venkatesh KV. A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics. SYSTEMS AND SYNTHETIC BIOLOGY 2013; 8:99-116. [PMID: 24592295 DOI: 10.1007/s11693-013-9125-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2013] [Accepted: 09/10/2013] [Indexed: 12/28/2022]
Abstract
Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The review highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of cancer and diabetes. We put forth a concept of cell-to-human framework comprising of five modules (data mining, networking, modeling, experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The review overtly emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.
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Affiliation(s)
- Pramod Rajaram Somvanshi
- Biosystems Engineering, Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076 Maharashtra India
| | - K V Venkatesh
- Biosystems Engineering, Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076 Maharashtra India
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39
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Liu Y, Cao DL, Guo LB, Guo SN, Xu JK, Zhuang HF. Amniotic stem cell transplantation therapy for type 1 diabetes: A case report. J Int Med Res 2013; 41:1370-7. [PMID: 23828649 DOI: 10.1177/0300060513487640] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
This case report presents an evaluation of the clinical effects of an allogeneic amniotic cell transplant for the treatment of type 1 diabetes mellitus. A 26-year-old man with type 1 diabetes was treated with stem cells isolated from his neonatal son’s amniotic membrane, collected at birth (2 × 107 cells). The cells, which expressed high levels of cluster of differentiation (CD) 133 and CD34 as assessed by flow cytometry, were infused into the pancreatic dorsal artery through the left femoral artery. The main study outcome was the change in exogenous insulin requirements, which began to decrease 3 days after transplantation. At 3 months post-transplantation, the patient was insulin independent and remained so for 6.2 months. During a 36-month follow-up, the patient’s blood glucose remained under control and insulin treatment was readjusted to a dosage of 8 IU/day. These preliminary data suggest that amniotic membrane stem cell transplantation can improve islet-cell function in response to glucose in vivo, although an alternative explanation (such as a honeymoon period due to reduced glucose toxicity) also has to be considered.
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Affiliation(s)
- Ying Liu
- Stem Cell Clinical Application Centre, Siping Central Hospital, Siping, Jilin Province, China
- Jilin Zhongke Bioengineering Company Ltd, Changchun, Jilin Province, China
| | - Dong-Lin Cao
- Department of Laboratory Medicine, Provincial People’s Hospital, Guangzhou, Guangdong Province, China
| | - Li-Bin Guo
- Stem Cell Clinical Application Centre, Siping Central Hospital, Siping, Jilin Province, China
| | - Sheng-Nan Guo
- Stem Cell Clinical Application Centre, Siping Central Hospital, Siping, Jilin Province, China
| | - Jin-Kai Xu
- Stem Cell Clinical Application Centre, Siping Central Hospital, Siping, Jilin Province, China
| | - Hong-Feng Zhuang
- Stem Cell Clinical Application Centre, Siping Central Hospital, Siping, Jilin Province, China
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40
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Rende D, Baysal N, Kirdar B. Complex disease interventions from a network model for type 2 diabetes. PLoS One 2013; 8:e65854. [PMID: 23776558 PMCID: PMC3679160 DOI: 10.1371/journal.pone.0065854] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2012] [Accepted: 05/02/2013] [Indexed: 12/20/2022] Open
Abstract
There is accumulating evidence that the proteins encoded by the genes associated with a common disorder interact with each other, participate in similar pathways and share GO terms. It has been anticipated that the functional modules in a disease related functional linkage network are informative to reveal significant metabolic processes and disease's associations with other complex disorders. In the current study, Type 2 diabetes associated functional linkage network (T2DFN) containing 2770 proteins and 15041 linkages was constructed. The functional modules in this network were scored and evaluated in terms of shared pathways, co-localization, co-expression and associations with similar diseases. The assembly of top scoring overlapping members in the functional modules revealed that, along with the well known biological pathways, circadian rhythm, diverse actions of nuclear receptors in steroid and retinoic acid metabolisms have significant occurrence in the pathophysiology of the disease. The disease's association with other metabolic and neuromuscular disorders was established through shared proteins. Nuclear receptor NRIP1 has a pivotal role in lipid and carbohydrate metabolism, indicating the need to investigate subsequent effects of NRIP1 on Type 2 diabetes. Our study also revealed that CREB binding protein (CREBBP) and cardiotrophin-1 (CTF1) have suggestive roles in linking Type 2 diabetes and neuromuscular diseases.
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Affiliation(s)
- Deniz Rende
- Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, Troy, New York, United States of America.
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41
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Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013; 138:333-408. [PMID: 23384594 PMCID: PMC3647006 DOI: 10.1016/j.pharmthera.2013.01.016] [Citation(s) in RCA: 511] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 01/22/2013] [Indexed: 02/02/2023]
Abstract
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
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42
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Jerby L, Ruppin E. Predicting drug targets and biomarkers of cancer via genome-scale metabolic modeling. Clin Cancer Res 2013; 18:5572-84. [PMID: 23071359 DOI: 10.1158/1078-0432.ccr-12-1856] [Citation(s) in RCA: 81] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The metabolism of cancer cells is reprogrammed in various ways to support their growth and survival. Studying these phenomena to develop noninvasive diagnostic tools and selective treatments is a promising avenue. Metabolic modeling has recently emerged as a new way to study human metabolism in a systematic, genome-scale manner by using pertinent high-throughput omics data. This method has been shown in various studies to provide fairly accurate estimates of the metabolic phenotype and its modifications following genetic and environmental perturbations. Here, we provide an overview of genome-scale metabolic modeling and its current use to model human metabolism in health and disease. We then describe the initial steps made using it to study cancer metabolism and how it may be harnessed to enhance ongoing experimental efforts to identify drug targets and biomarkers for cancer in a rationale-based manner.
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Affiliation(s)
- Livnat Jerby
- The Blavatnik School of Computer Science, Tel Aviv University, Israel.
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43
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Väremo L, Nookaew I, Nielsen J. Novel insights into obesity and diabetes through genome-scale metabolic modeling. Front Physiol 2013; 4:92. [PMID: 23630502 PMCID: PMC3635026 DOI: 10.3389/fphys.2013.00092] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2013] [Accepted: 04/11/2013] [Indexed: 12/19/2022] Open
Abstract
The growing prevalence of metabolic diseases, such as obesity and diabetes, are putting a high strain on global healthcare systems as well as increasing the demand for efficient treatment strategies. More than 360 million people worldwide are suffering from type 2 diabetes (T2D) and, with the current trends, the projection is that 10% of the global adult population will be affected by 2030. In light of the systemic properties of metabolic diseases as well as the interconnected nature of metabolism, it is necessary to begin taking a holistic approach to study these diseases. Human genome-scale metabolic models (GEMs) are topological and mathematical representations of cell metabolism and have proven to be valuable tools in the area of systems biology. Successful applications of GEMs include the process of gaining further biological and mechanistic understanding of diseases, finding potential biomarkers, and identifying new drug targets. This review will focus on the modeling of human metabolism in the field of obesity and diabetes, showing its vast range of applications of clinical importance as well as point out future challenges.
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Affiliation(s)
- Leif Väremo
- Systems and Synthetic Biology, Department of Chemical and Biological Engineering, Chalmers University of Technology Gothenburg, Sweden
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44
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Leucine and protein metabolism in obese Zucker rats. PLoS One 2013; 8:e59443. [PMID: 23527196 PMCID: PMC3603883 DOI: 10.1371/journal.pone.0059443] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2012] [Accepted: 02/14/2013] [Indexed: 12/15/2022] Open
Abstract
Branched-chain amino acids (BCAAs) are circulating nutrient signals for protein accretion, however, they increase in obesity and elevations appear to be prognostic of diabetes. To understand the mechanisms whereby obesity affects BCAAs and protein metabolism, we employed metabolomics and measured rates of [1-14C]-leucine metabolism, tissue-specific protein synthesis and branched-chain keto-acid (BCKA) dehydrogenase complex (BCKDC) activities. Male obese Zucker rats (11-weeks old) had increased body weight (BW, 53%), liver (107%) and fat (∼300%), but lower plantaris and gastrocnemius masses (−21–24%). Plasma BCAAs and BCKAs were elevated 45–69% and ∼100%, respectively, in obese rats. Processes facilitating these rises appeared to include increased dietary intake (23%), leucine (Leu) turnover and proteolysis [35% per g fat free mass (FFM), urinary markers of proteolysis: 3-methylhistidine (183%) and 4-hydroxyproline (766%)] and decreased BCKDC per g kidney, heart, gastrocnemius and liver (−47–66%). A process disposing of circulating BCAAs, protein synthesis, was increased 23–29% by obesity in whole-body (FFM corrected), gastrocnemius and liver. Despite the observed decreases in BCKDC activities per gm tissue, rates of whole-body Leu oxidation in obese rats were 22% and 59% higher normalized to BW and FFM, respectively. Consistently, urinary concentrations of eight BCAA catabolism-derived acylcarnitines were also elevated. The unexpected increase in BCAA oxidation may be due to a substrate effect in liver. Supporting this idea, BCKAs were elevated more in liver (193–418%) than plasma or muscle, and per g losses of hepatic BCKDC activities were completely offset by increased liver mass, in contrast to other tissues. In summary, our results indicate that plasma BCKAs may represent a more sensitive metabolic signature for obesity than BCAAs. Processes supporting elevated BCAA]BCKAs in the obese Zucker rat include increased dietary intake, Leu and protein turnover along with impaired BCKDC activity. Elevated BCAAs/BCKAs may contribute to observed elevations in protein synthesis and BCAA oxidation.
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45
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Sales V, Patti ME. The Ups and Downs of Insulin Resistance and Type 2 Diabetes: Lessons from Genomic Analyses in Humans. CURRENT CARDIOVASCULAR RISK REPORTS 2012; 7:46-59. [PMID: 23459395 DOI: 10.1007/s12170-012-0283-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
We are in the midst of a worldwide epidemic of type 2 diabetes (T2D) and obesity. Understanding the mechanisms underlying these diseases is critical if we are to halt their progression and ultimately prevent their development. The advent and widespread implementation of microarray technology has allowed analysis of small samples of human skeletal muscle, adipose, liver, pancreas and blood. While patterns differ in each tissue, several dominant themes have emerged from these studies, including altered expression of genes indicating increased inflammation and altered lipid and mitochondrial oxidative metabolism and insulin signaling in patients with T2D, and in some cases, in those at risk for disease. Unraveling which changes in gene expression are primary, and which are secondary to an insulin resistant or diabetes metabolic milieu remains a scientific challenge but we are one step closer.
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Affiliation(s)
- Vicencia Sales
- Research Division, Joslin Diabetes Center, and Department of Medicine, Harvard Medical School ; Department of Biophysics, Federal University of São Paulo, UNIFESP/EPM, São Paulo, SP, Brazil
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46
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Brochado AR, Andrejev S, Maranas CD, Patil KR. Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks. PLoS Comput Biol 2012; 8:e1002758. [PMID: 23133362 PMCID: PMC3486866 DOI: 10.1371/journal.pcbi.1002758] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2012] [Accepted: 09/11/2012] [Indexed: 11/19/2022] Open
Abstract
Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary for predicting the active metabolic state under specific environmental conditions. The objective function to be used in such optimization algorithms is directly linked with the biological hypothesis underlying the model and therefore it is one of the most relevant parameters for successful modeling. Although linear combination of selected fluxes is widely used for formulating metabolic objective functions, we show that the resulting optimization problem is sensitive towards stoichiometry representation of the metabolic network. This undesirable sensitivity leads to different simulation results when using numerically different but biochemically equivalent stoichiometry representations and thereby makes biological interpretation intrinsically subjective and ambiguous. We hereby propose a new method, Minimization of Metabolites Balance (MiMBl), which decouples the artifacts of stoichiometry representation from the formulation of the desired objective functions, by casting objective functions using metabolite turnovers rather than fluxes. By simulating perturbed metabolic networks, we demonstrate that the use of stoichiometry representation independent algorithms is fundamental for unambiguously linking modeling results with biological interpretation. For example, MiMBl allowed us to expand the scope of metabolic modeling in elucidating the mechanistic basis of several genetic interactions in Saccharomyces cerevisiae. One of the challenging tasks in systems biology is to quantitatively predict the metabolic behavior of the cell under given genetic and environmental constraints. To this end, genome-scale metabolic reconstructions and simulation tools are indispensable. The choice of the objective function to be used for simulating genome-scale metabolic models is dependent on the biological context and one of the most relevant parameters for successful modeling. Formulation of the intended objective function often requires the use of multiple fluxes, e.g. the sum of fluxes through ATP-producing reactions. We demonstrate that the existing tools confound biological interpretation of the simulations due to undesired dependence on the representation of stoichiometry and propose a new tool – Minimization of Metabolites Balance (MiMBl). MiMBl allows casting of the desired biological objective functions into linear optimization models and gives consistent simulation results when using numerically different but biochemically equivalent stoichiometry representations. We demonstrate relevance of MiMBl for addressing biological questions through improved predictions of genetic interactions within the yeast metabolic network. Genetic interactions imply functional relationship between the genes and therefore allow assessing different hypotheses for the underlying biological principles. MiMBl explains several of the genetic interactions as outcome of flux re-routing for minimal metabolite turnover adjustments.
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Affiliation(s)
- Ana Rita Brochado
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Center for Microbial Biotechnology, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark
| | - Sergej Andrejev
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Costas D. Maranas
- Department of Chemical Engineering, Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Kiran R. Patil
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- * E-mail:
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47
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Zomorrodi AR, Suthers PF, Ranganathan S, Maranas CD. Mathematical optimization applications in metabolic networks. Metab Eng 2012; 14:672-86. [PMID: 23026121 DOI: 10.1016/j.ymben.2012.09.005] [Citation(s) in RCA: 103] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2012] [Revised: 08/31/2012] [Accepted: 09/14/2012] [Indexed: 11/30/2022]
Abstract
Genome-scale metabolic models are increasingly becoming available for a variety of microorganisms. This has spurred the development of a wide array of computational tools, and in particular, mathematical optimization approaches, to assist in fundamental metabolic network analyses and redesign efforts. This review highlights a number of optimization-based frameworks developed towards addressing challenges in the analysis and engineering of metabolic networks. In particular, three major types of studies are covered here including exploring model predictions, correction and improvement of models of metabolism, and redesign of metabolic networks for the targeted overproduction of a desired compound. Overall, the methods reviewed in this paper highlight the diversity of queries, breadth of questions and complexity of redesigns that are amenable to mathematical optimization strategies.
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Affiliation(s)
- Ali R Zomorrodi
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
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48
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Hoppe A. What mRNA Abundances Can Tell us about Metabolism. Metabolites 2012; 2:614-31. [PMID: 24957650 PMCID: PMC3901220 DOI: 10.3390/metabo2030614] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2012] [Revised: 08/24/2012] [Accepted: 09/04/2012] [Indexed: 01/23/2023] Open
Abstract
Inferring decreased or increased metabolic functions from transcript profiles is at first sight a bold and speculative attempt because of the functional layers in between: proteins, enzymatic activities, and reaction fluxes. However, the growing interest in this field can easily be explained by two facts: the high quality of genome-scale metabolic network reconstructions and the highly developed technology to obtain genome-covering RNA profiles. Here, an overview of important algorithmic approaches is given by means of criteria by which published procedures can be classified. The frontiers of the methods are sketched and critical voices are being heard. Finally, an outlook for the prospects of the field is given.
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Affiliation(s)
- Andreas Hoppe
- Institute for Biochemistry, Charité University Medicine Berlin, Charitéplatz 1, Berlin 10117, Germany.
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49
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Li X, Chen L, Zhang L, Li W, Jia X, Li W, Qu X, Tai J, Feng C, Zhang F, He W. RCM: a novel association approach to search for coronary artery disease genetic related metabolites based on SNPs and metabolic network. Genomics 2012; 100:282-8. [PMID: 22850356 DOI: 10.1016/j.ygeno.2012.07.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2012] [Revised: 06/18/2012] [Accepted: 07/20/2012] [Indexed: 11/17/2022]
Abstract
Integration of genetic and metabolic network holds promise for providing insight into human disease. Coronary artery disease (CAD) is strongly heritable, but the heritability of metabolic compounds has not been evaluated in human metabolic context. Here we performed a genetic-based computational approach within eight sub-cellular networks from Edinburgh Human Metabolic Network to identify significant genetic risk compounds (SGRCs) of CAD. Our results provide the evidence that the high heritabilities of SGRCs played an important role in CAD pathogenesis. Besides, SGRCs were discovered to be strongly associated with lipid metabolism. We also established a possible disease-causing reference table to decipher genetic associations of SGRCs with CAD. Comparing with traditional method, RCM experienced better performance in CAD genetic risk compounds' identification. These findings provided novel insights into CAD pathogenesis from a genetic perspective.
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Affiliation(s)
- Xu Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Hei Longjiang Province, China
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Decreased mitochondrial DNA mutagenesis in human colorectal cancer. PLoS Genet 2012; 8:e1002689. [PMID: 22685414 PMCID: PMC3369930 DOI: 10.1371/journal.pgen.1002689] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2011] [Accepted: 03/20/2012] [Indexed: 01/05/2023] Open
Abstract
Genome instability is regarded as a hallmark of cancer. Human tumors frequently carry clonally expanded mutations in their mitochondrial DNA (mtDNA), some of which may drive cancer progression and metastasis. The high prevalence of clonal mutations in tumor mtDNA has commonly led to the assumption that the mitochondrial genome in cancer is genetically unstable, yet this hypothesis has not been experimentally tested. In this study, we directly measured the frequency of non-clonal (random) de novo single base substitutions in the mtDNA of human colorectal cancers. Remarkably, tumor tissue exhibited a decreased prevalence of these mutations relative to adjacent non-tumor tissue. The difference in mutation burden was attributable to a reduction in C:G to T:A transitions, which are associated with oxidative damage. We demonstrate that the lower random mutation frequency in tumor tissue was also coupled with a shift in glucose metabolism from oxidative phosphorylation to anaerobic glycolysis, as compared to non-neoplastic colon. Together these findings raise the intriguing possibility that fidelity of mitochondrial genome is, in fact, increased in cancer as a result of a decrease in reactive oxygen species-mediated mtDNA damage.
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