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Xu K, Zhang L, Wang T, Yu T, Zhao X, Zhang Y. Transcriptomics reveals dynamic changes in the "gene profiles" of rat supraspinatus tendon at three different time points after diabetes induction. BMC Med Genomics 2024; 17:122. [PMID: 38711057 DOI: 10.1186/s12920-024-01899-3] [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: 02/14/2024] [Accepted: 04/30/2024] [Indexed: 05/08/2024] Open
Abstract
OBJECTIVE There is increasing evidence that type 2 diabetes mellitus (T2DM) is an independent risk factor for the occur of tendinopathy. Therefore, this study is the first to explore the dynamic changes of the "gene profile" of supraspinatus tendon in rats at different time points after T2DM induction through transcriptomics, providing potential molecular markers for exploring the pathogenesis of diabetic tendinopathy. METHODS A total of 40 Sprague-Dawley rats were randomly divided into normal (NG, n = 10) and T2DM groups (T2DM, n = 30) and subdivided into three groups according to the duration of diabetes: T2DM-4w, T2DM-8w, and T2DM-12w groups; the duration was calculated from the time point of T2DM rat model establishment. The three comparison groups were set up in this study, T2DM-4w group vs. NG, T2DM-8w group vs. NG, and T2DM-12w group vs. NG. Differentially expressed genes (DEGs) in 3 comparison groups were screened. The intersection of the three comparison groups' DEGs was defined as key genes that changed consistently in the supraspinatus tendon after diabetes induction. Cluster analysis, gene ontology (GO) functional annotation analysis and Kyoto encyclopedia of genes and genomes (KEGG) functional annotation and enrichment analysis were performed for DEGs. RESULTS T2DM-4w group vs. NG, T2DM-8w group vs. NG, and T2DM-12w group vs. NG detected 519 (251 up-regulated and 268 down-regulated), 459 (342 up-regulated and 117 down-regulated) and 328 (255 up-regulated and 73 down-regulated) DEGs, respectively. 103 key genes of sustained changes in the supraspinatus tendon following induction of diabetes, which are the first identified biomarkers of the supraspinatus tendon as it progresses through the course of diabetes.The GO analysis results showed that the most significant enrichment in biological processes was calcium ion transmembrane import into cytosol (3 DEGs). The most significant enrichment in cellular component was extracellular matrix (9 DEGs). The most significant enrichment in molecular function was glutamate-gated calcium ion channel activity (3 DEGs). The results of KEGG pathway enrichment analysis showed that there were 17 major pathways (p < 0.05) that diabetes affected supratinusculus tendinopathy, including cAMP signaling pathway and Calcium signaling pathway. CONCLUSIONS Transcriptomics reveals dynamic changes in the"gene profiles"of rat supraspinatus tendon at three different time points after diabetes induction. The 103 DEGs identified in this study may provide potential molecular markers for exploring the pathogenesis of diabetic tendinopathy, and the 17 major pathways enriched in KEGG may provide new ideas for exploring the pathogenesis of diabetic tendinopathy.
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Affiliation(s)
- Kuishuai Xu
- Department of Sports Medicine, The Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong, China
| | - Liang Zhang
- Department of Abdominal Ultrasound, Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong, China
| | - Tianrui Wang
- Department of Traumatology, The Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong, China
| | - Tengbo Yu
- Department of Orthopedic Surgery, Qingdao Municipal Hospital, Qingdao, 266000, Shandong, China
| | - Xia Zhao
- Department of Sports Medicine, The Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong, China.
| | - Yingze Zhang
- Department of Sports Medicine, The Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong, China.
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Baron C, Cherkaoui S, Therrien-Laperriere S, Ilboudo Y, Poujol R, Mehanna P, Garrett ME, Telen MJ, Ashley-Koch AE, Bartolucci P, Rioux JD, Lettre G, Rosiers CD, Ruiz M, Hussin JG. Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results. iScience 2023; 26:108473. [PMID: 38077122 PMCID: PMC10709128 DOI: 10.1016/j.isci.2023.108473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 08/24/2023] [Accepted: 11/13/2023] [Indexed: 12/20/2023] Open
Abstract
Metabolite genome-wide association studies (mGWAS) have advanced our understanding of the genetic control of metabolite levels. However, interpreting these associations remains challenging due to a lack of tools to annotate gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we introduce the shortest reactional distance (SRD) metric, drawing from the comprehensive KEGG database, to enhance the biological interpretation of mGWAS results. We applied this approach to three independent mGWAS, including a case study on sickle cell disease patients. Our analysis reveals an enrichment of small SRD values in reported mGWAS pairs, with SRD values significantly correlating with mGWAS p values, even beyond the standard conservative thresholds. We demonstrate the utility of SRD annotation in identifying potential false negatives and inaccuracies within current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs, suitable to integrate statistical evidence to biological networks.
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Affiliation(s)
- Cantin Baron
- Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Montréal, QC, Canada
- Montreal Heart Institute, Montréal, QC, Canada
| | - Sarah Cherkaoui
- Montreal Heart Institute, Montréal, QC, Canada
- Division of Oncology and Children’s Research Center, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Center, Université Paris-Saclay, Villejuif, France
| | | | - Yann Ilboudo
- Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Montréal, QC, Canada
- Montreal Heart Institute, Montréal, QC, Canada
| | | | | | - Melanie E. Garrett
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - Marilyn J. Telen
- Division of Hematology, Department of Medicine, Duke University Medical Center, Durham, NC, USA
| | | | - Pablo Bartolucci
- Université Paris Est Créteil, Hôpitaux Universitaires Henri Mondor, APHP, Sickle cell referral center – UMGGR, Créteil, France
- Université Paris Est Créteil, IMRB, Laboratory of excellence LABEX, Créteil, France
| | - John D. Rioux
- Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Montréal, QC, Canada
- Montreal Heart Institute, Montréal, QC, Canada
- Département de Médecine, Université de Montréal, Montréal, QC, Canada
| | - Guillaume Lettre
- Montreal Heart Institute, Montréal, QC, Canada
- Département de Médecine, Université de Montréal, Montréal, QC, Canada
| | - Christine Des Rosiers
- Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Montréal, QC, Canada
- Montreal Heart Institute, Montréal, QC, Canada
- Département de Nutrition, Université de Montréal, Montréal, QC, Canada
| | - Matthieu Ruiz
- Montreal Heart Institute, Montréal, QC, Canada
- Département de Nutrition, Université de Montréal, Montréal, QC, Canada
| | - Julie G. Hussin
- Montreal Heart Institute, Montréal, QC, Canada
- Département de Médecine, Université de Montréal, Montréal, QC, Canada
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3
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Baron C, Cherkaoui S, Therrien-Laperriere S, Ilboudo Y, Poujol R, Mehanna P, Garrett ME, Telen MJ, Ashley-Koch AE, Bartolucci P, Rioux JD, Lettre G, Des Rosiers C, Ruiz M, Hussin JG. Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.22.533869. [PMID: 36993181 PMCID: PMC10055409 DOI: 10.1101/2023.03.22.533869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Studies combining metabolomics and genetics, known as metabolite genome-wide association studies (mGWAS), have provided valuable insights into our understanding of the genetic control of metabolite levels. However, the biological interpretation of these associations remains challenging due to a lack of existing tools to annotate mGWAS gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we computed the shortest reactional distance (SRD) based on the curated knowledge of the KEGG database to explore its utility in enhancing the biological interpretation of results from three independent mGWAS, including a case study on sickle cell disease patients. Results show that, in reported mGWAS pairs, there is an excess of small SRD values and that SRD values and p-values significantly correlate, even beyond the standard conservative thresholds. The added-value of SRD annotation is shown for identification of potential false negative hits, exemplified by the finding of gene-metabolite associations with SRD ≤1 that did not reach standard genome-wide significance cut-off. The wider use of this statistic as an mGWAS annotation would prevent the exclusion of biologically relevant associations and can also identify errors or gaps in current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs that can be used to integrate statistical evidence to biological networks.
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Affiliation(s)
- Cantin Baron
- Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Québec, Canada
- Montreal Heart Institute, Québec, Canada
| | - Sarah Cherkaoui
- Montreal Heart Institute, Québec, Canada
- Division of Oncology and Children’s Research Center, University Children’s Hospital Zurich, University of Zurich, Switzerland
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Center, Université Paris-Saclay, Villejuif, France
| | | | - Yann Ilboudo
- Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Québec, Canada
- Montreal Heart Institute, Québec, Canada
| | | | | | - Melanie E. Garrett
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - Marilyn J. Telen
- Division of Hematology, Department of Medicine, Duke University Medical Center, Durham, NC, USA
| | | | - Pablo Bartolucci
- Université Paris Est Créteil, Hôpitaux Universitaires Henri Mondor, APHP, Sickle cell referral center – UMGGR, Créteil, France
- Université Paris Est Créteil, IMRB, Laboratory of excellence LABEX, Créteil, France
| | - John D. Rioux
- Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Québec, Canada
- Montreal Heart Institute, Québec, Canada
- Département de Médecine, Université de Montréal, Québec, Canada
| | - Guillaume Lettre
- Montreal Heart Institute, Québec, Canada
- Département de Médecine, Université de Montréal, Québec, Canada
| | - Christine Des Rosiers
- Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Québec, Canada
- Montreal Heart Institute, Québec, Canada
- Département de Nutrition, Université de Montréal, Québec, Canada
| | - Matthieu Ruiz
- Montreal Heart Institute, Québec, Canada
- Département de Nutrition, Université de Montréal, Québec, Canada
| | - Julie G. Hussin
- Montreal Heart Institute, Québec, Canada
- Département de Médecine, Université de Montréal, Québec, Canada
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Munro D, Wang T, Chitre AS, Polesskaya O, Ehsan N, Gao J, Gusev A, Woods LS, Saba L, Chen H, Palmer A, Mohammadi P. The regulatory landscape of multiple brain regions in outbred heterogeneous stock rats. Nucleic Acids Res 2022; 50:10882-10895. [PMID: 36263809 PMCID: PMC9638908 DOI: 10.1093/nar/gkac912] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/17/2022] [Accepted: 10/05/2022] [Indexed: 11/14/2022] Open
Abstract
Heterogeneous Stock (HS) rats are a genetically diverse outbred rat population that is widely used for studying genetics of behavioral and physiological traits. Mapping Quantitative Trait Loci (QTL) associated with transcriptional changes would help to identify mechanisms underlying these traits. We generated genotype and transcriptome data for five brain regions from 88 HS rats. We identified 21 392 cis-QTLs associated with expression and splicing changes across all five brain regions and validated their effects using allele specific expression data. We identified 80 cases where eQTLs were colocalized with genome-wide association study (GWAS) results from nine physiological traits. Comparing our dataset to human data from the Genotype-Tissue Expression (GTEx) project, we found that the HS rat data yields twice as many significant eQTLs as a similarly sized human dataset. We also identified a modest but highly significant correlation between genetic regulatory variation among orthologous genes. Surprisingly, we found less genetic variation in gene regulation in HS rats relative to humans, though we still found eQTLs for the orthologs of many human genes for which eQTLs had not been found. These data are available from the RatGTEx data portal (RatGTEx.org) and will enable new discoveries of the genetic influences of complex traits.
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Affiliation(s)
- Daniel Munro
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA,Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, USA
| | - Tengfei Wang
- Department of Pharmacology, Addiction Science and Toxicology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Apurva S Chitre
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Oksana Polesskaya
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Nava Ehsan
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, USA
| | - Jianjun Gao
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Alexander Gusev
- Division of Population Sciences, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Leah C Solberg Woods
- Section of Molecular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Laura M Saba
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Hao Chen
- Department of Pharmacology, Addiction Science and Toxicology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Abraham A Palmer
- Correspondence may also be addressed to Abraham A. Palmer. Tel: +1 858 534 2093;
| | - Pejman Mohammadi
- To whom correspondence should be addressed. Tel: +1 858 784 8746;
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5
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Clark KC, Kwitek AE. Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome. Compr Physiol 2021; 12:3045-3084. [PMID: 34964118 PMCID: PMC9373910 DOI: 10.1002/cphy.c210010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.
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Affiliation(s)
- Karen C Clark
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Anne E Kwitek
- Department of Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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6
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Xu W, Hu X, Anwaier A, Wang J, Liu W, Tian X, Zhu W, Ma C, Wan F, Shi G, Qu YY, Zhang H, Ye D. Fatty Acid Synthase Correlates With Prognosis-Related Abdominal Adipose Distribution and Metabolic Disorders of Clear Cell Renal Cell Carcinoma. Front Mol Biosci 2021; 7:610229. [PMID: 33569391 PMCID: PMC7868388 DOI: 10.3389/fmolb.2020.610229] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 12/04/2020] [Indexed: 12/22/2022] Open
Abstract
Purpose: Lipid metabolism reprogramming is a major pathway in tumor evolution. This study investigated fatty acid synthase (FASN) mRNA expression in anthropometric adipose tissue and elucidated the prognostic value and potential mechanism of clear cell renal cell carcinoma (ccRCC). Materials and Methods: Transcription profiles were obtained from 533 ccRCC samples in The Cancer Genome Atlas (TCGA) cohorts. Real-time quantitative PCR (RT-qPCR) and immunohistochemistry were performed to detect FASN expression in 380 paired ccRCC and normal tissues from the Fudan University Shanghai Cancer Center (FUSCC). Visceral adipose tissue (VAT) and subcutaneous adipose tissue were at the level of the umbilicus as measured by magnetic resonance imaging (MRI). Non-targeted metabolomics and in vitro experiments were used to reveal the biological functions of FASN. Results: Increased FASN expression was significantly relevant to advanced T, N, and American Joint Committee on Cancer (AJCC) stages (p < 0.01) and significantly correlated to poor progression-free survival (PFS) and overall survival (OS) of 913 ccRCC patients in FUSCC and TCGA cohorts. Pearson's correlation coefficient indicated that FASN amplification was positively correlated to VAT% (r = 0.772, p < 0.001), which significantly correlated to poor PFS (HR = 2.066, p = 0.028) and OS (HR = 2.773, p = 0.023) in the FUSCC cohort. Transient inhibition or overexpression of FASN significantly regulated A498 and 786O cell proliferation and migration by regulating epithelial–mesenchymal transition. Inhibition of FASN led to a higher apoptotic rate and decreased lipid droplet formation compared with normal control in ccRCC cells. Non-targeted metabolomics showed that decreased de novo lipogenesis might be required to sustain an elevation of glycolytic activity in 786O cells by regulating galactinol, dl-lactate, N-acetylaspartylglutamate, and sucrose, thereby participating in carcinogenesis and progression of ccRCC. Conclusion: This study demonstrated that FASN expression is positively related to aggressive cell proliferation, migration, apoptosis, and lipid droplet formation and regulates metabolic disorders of the ccRCC microenvironment. Additionally, elevated FASN mRNA expression is significantly correlated to the abdominal obesity distribution, especially VAT%, which is a significant predictor of a poor prognosis for ccRCC patients.
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Affiliation(s)
- Wenhao Xu
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China
| | - Xiaoxin Hu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Aihetaimujiang Anwaier
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China
| | - Jun Wang
- Department of Urology, Sun Yat-sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in Southern China, Guangzhou, China
| | - Wangrui Liu
- Department of Neurosurgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xi Tian
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China
| | - Wenkai Zhu
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China
| | - Chunguang Ma
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China
| | - Fangning Wan
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China
| | - Guohai Shi
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China
| | - Yuan-Yuan Qu
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China
| | - Hailiang Zhang
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China
| | - Dingwei Ye
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Fudan University Shanghai Medical College, Shanghai, China
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Linke V, Overmyer KA, Miller IJ, Brademan DR, Hutchins PD, Trujillo EA, Reddy TR, Russell JD, Cushing EM, Schueler KL, Stapleton DS, Rabaglia ME, Keller MP, Gatti DM, Keele GR, Pham D, Broman KW, Churchill GA, Attie AD, Coon JJ. A large-scale genome-lipid association map guides lipid identification. Nat Metab 2020; 2:1149-1162. [PMID: 32958938 PMCID: PMC7572687 DOI: 10.1038/s42255-020-00278-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 08/11/2020] [Indexed: 12/13/2022]
Abstract
Despite the crucial roles of lipids in metabolism, we are still at the early stages of comprehensively annotating lipid species and their genetic basis. Mass spectrometry-based discovery lipidomics offers the potential to globally survey lipids and their relative abundances in various biological samples. To discover the genetics of lipid features obtained through high-resolution liquid chromatography-tandem mass spectrometry, we analysed liver and plasma from 384 diversity outbred mice, and quantified 3,283 molecular features. These features were mapped to 5,622 lipid quantitative trait loci and compiled into a public web resource termed LipidGenie. The data are cross-referenced to the human genome and offer a bridge between genetic associations in humans and mice. Harnessing this resource, we used genome-lipid association data as an additional aid to identify a number of lipids, for example gangliosides through their association with B4galnt1, and found evidence for a group of sex-specific phosphatidylcholines through their shared locus. Finally, LipidGenie's ability to query either mass or gene-centric terms suggests acyl-chain-specific functions for proteins of the ABHD family.
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Affiliation(s)
- Vanessa Linke
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Katherine A Overmyer
- Morgridge Institute for Research, Madison, WI, USA
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Ian J Miller
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Dain R Brademan
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Paul D Hutchins
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Edna A Trujillo
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Thiru R Reddy
- Morgridge Institute for Research, Madison, WI, USA
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Emily M Cushing
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Kathryn L Schueler
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Donald S Stapleton
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Mary E Rabaglia
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Mark P Keller
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | | | | | - Duy Pham
- The Jackson Laboratory, Bar Harbor, ME, USA
| | - Karl W Broman
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Alan D Attie
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA.
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8
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Li H, Shi X, Jiang H, Kang J, Yu M, Li Q, Yu K, Chen Z, Pan H, Chen W. CMap analysis identifies Atractyloside as a potential drug candidate for type 2 diabetes based on integration of metabolomics and transcriptomics. J Cell Mol Med 2020; 24:7417-7426. [PMID: 32469143 PMCID: PMC7339182 DOI: 10.1111/jcmm.15357] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 02/06/2020] [Accepted: 02/21/2020] [Indexed: 12/13/2022] Open
Abstract
Background This research aimed at exploring the mechanisms of alterations of metabolites and pathways in T2D from the perspective of metabolomics and transcriptomics, as well as uncovering novel drug candidate for T2D treatment. Methods Metabolites in human plasma from 42 T2D patients and 45 non‐diabetic volunteers were detected by liquid chromatography‐mass spectrometer (LC‐MS). Microarray dataset of the transcriptome was obtained from Gene Expression Omnibus (GEO) database. Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to conduct pathway enrichment analysis. Connectivity Map (CMap) was employed to select potential drugs for T2D therapy. In vivo assay was performed to verify above findings. The protein expression levels of ME1, ME2 and MDH1 were detected by Western blot to determine the status of NAD/NADH cofactor system. Results In our study, differentially expressed metabolites were selected out between healthy samples and T2D samples with selection criteria P value < .05, |Fold Change| > 2, including N‐acetylglutamate and Malate. Genes set enrichment analysis (GSEA) revealed that 34 pathways were significantly enriched in T2D. Based on CMap analysis and animal experiments, Atractyloside was identified as a potential novel drug for T2D treatment via targeting ME1, ME2 and MDH1 and regulating the NAD/NADH cofactor system. Conclusion The present research revealed differentially expressed metabolites and genes, as well as significantly altered pathways in T2D via an integration of metabolomics, transcriptomics and CMap analysis. It was also demonstrated that comprehensive analysis based on metabolomics and transcriptomics was an effective approach for identification and verification of metabolic biomarkers and alternated pathways.
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Affiliation(s)
- Hailong Li
- Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Health Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaodong Shi
- Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Health Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hua Jiang
- Institute for Emergency and Disaster Medicine, Sichuan Provincial People's Hospital, Sichuan Academy of Medical Sciences, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Junren Kang
- Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Health Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Miao Yu
- Department of Health Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qifei Li
- Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Health Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kang Yu
- Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Health Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhengju Chen
- Pooling Medical Research Institutes, Hangzhou, China
| | - Hui Pan
- Department of Health Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wei Chen
- Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Health Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Beijing Key Laboratory of the Innovative Development of Functional Staple and the Nutritional Intervention for Chronic Disease, Beijing, China
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9
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Zhou S, Morgante F, Geisz MS, Ma J, Anholt RRH, Mackay TFC. Systems genetics of the Drosophila metabolome. Genome Res 2020; 30:392-405. [PMID: 31694867 PMCID: PMC7111526 DOI: 10.1101/gr.243030.118] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 03/11/2019] [Indexed: 02/06/2023]
Abstract
How effects of DNA sequence variants are transmitted through intermediate endophenotypes to modulate organismal traits remains a central question in quantitative genetics. This problem can be addressed through a systems approach in a population in which genetic polymorphisms, gene expression traits, metabolites, and complex phenotypes can be evaluated on the same genotypes. Here, we focused on the metabolome, which represents the most proximal link between genetic variation and organismal phenotype, and quantified metabolite levels in 40 lines of the Drosophila melanogaster Genetic Reference Panel. We identified sex-specific modules of genetically correlated metabolites and constructed networks that integrate DNA sequence variation and variation in gene expression with variation in metabolites and organismal traits, including starvation stress resistance and male aggression. Finally, we asked to what extent SNPs and metabolites can predict trait phenotypes and generated trait- and sex-specific prediction models that provide novel insights about the metabolomic underpinnings of complex phenotypes.
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Affiliation(s)
- Shanshan Zhou
- Program in Genetics, W.M. Keck Center for Behavioral Biology and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Fabio Morgante
- Program in Genetics, W.M. Keck Center for Behavioral Biology and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Matthew S Geisz
- Program in Genetics, W.M. Keck Center for Behavioral Biology and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Junwu Ma
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, JiangXi Agricultural University, JiangXi, China
| | - Robert R H Anholt
- Program in Genetics, W.M. Keck Center for Behavioral Biology and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Trudy F C Mackay
- Program in Genetics, W.M. Keck Center for Behavioral Biology and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA
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10
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Azad RK, Shulaev V. Metabolomics technology and bioinformatics for precision medicine. Brief Bioinform 2019; 20:1957-1971. [PMID: 29304189 PMCID: PMC6954408 DOI: 10.1093/bib/bbx170] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 11/29/2017] [Indexed: 12/14/2022] Open
Abstract
Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.
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Affiliation(s)
| | - Vladimir Shulaev
- Corresponding author: Vladimir Shulaev, Department of Biological Sciences, BioDiscovery Institute, University of North Texas, Denton, TX 76210, USA. Tel.: 940-369-5368; Fax: 940-565-3821; E-mail:
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11
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Gomez-Cabrero D, Tarazona S, Ferreirós-Vidal I, Ramirez RN, Company C, Schmidt A, Reijmers T, Paul VVS, Marabita F, Rodríguez-Ubreva J, Garcia-Gomez A, Carroll T, Cooper L, Liang Z, Dharmalingam G, van der Kloet F, Harms AC, Balzano-Nogueira L, Lagani V, Tsamardinos I, Lappe M, Maier D, Westerhuis JA, Hankemeier T, Imhof A, Ballestar E, Mortazavi A, Merkenschlager M, Tegner J, Conesa A. STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse. Sci Data 2019; 6:256. [PMID: 31672995 PMCID: PMC6823427 DOI: 10.1038/s41597-019-0202-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 09/02/2019] [Indexed: 12/30/2022] Open
Abstract
Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.
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Affiliation(s)
- David Gomez-Cabrero
- Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain
- Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Science for Life Laboratory, Solna, Sweden
| | - Sonia Tarazona
- Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Valencia, Spain
| | - Isabel Ferreirós-Vidal
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Ricardo N Ramirez
- Department of Developmental and Cell Biology and Center for Complex Biological Systems, University of California, Irvine, CA, USA
| | - Carlos Company
- Chromatin and Disease Group, Cancer Epigenetics and Biology Programme (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Spain
| | - Andreas Schmidt
- Protein Analysis Unit, Biomedical Center, Ludwig Maximilian University of Munich, Munich, Germany
| | - Theo Reijmers
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands
| | | | - Francesco Marabita
- Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Javier Rodríguez-Ubreva
- Chromatin and Disease Group, Cancer Epigenetics and Biology Programme (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Spain
| | - Antonio Garcia-Gomez
- Chromatin and Disease Group, Cancer Epigenetics and Biology Programme (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Spain
| | - Thomas Carroll
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Lee Cooper
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Ziwei Liang
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Gopuraja Dharmalingam
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Frans van der Kloet
- Centre for Human Metabolomics, Faculty of Natural Sciences, North-West University (Potchefstroom Campus), Potchefstroom, South Africa
| | - Amy C Harms
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands
| | - Leandro Balzano-Nogueira
- Microbiology and Cell Science Department, Institute for Food and Agricultural Research, Genetics Institute, University of Florida, Gainesville, Florida, USA
| | - Vincenzo Lagani
- Computer Science Department, University of Crete, Heraklion, Greece
- Institute of Chemical Biology, Ilia State University, Tbilisi, Georgia, United States
| | - Ioannis Tsamardinos
- Institute of Chemical Biology, Ilia State University, Tbilisi, Georgia, United States
- Gnosis Data Analysis PC, Heraklion, Greece
| | - Michael Lappe
- QIAGEN Aarhus A/S, Silkeborgvej 2, 8000, Aarhus, Denmark
| | | | - Johan A Westerhuis
- Centre for Human Metabolomics, Faculty of Natural Sciences, North-West University (Potchefstroom Campus), Potchefstroom, South Africa
- Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Thomas Hankemeier
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands
| | - Axel Imhof
- Protein Analysis Unit, Biomedical Center, Ludwig Maximilian University of Munich, Munich, Germany
| | - Esteban Ballestar
- Chromatin and Disease Group, Cancer Epigenetics and Biology Programme (PEBC), Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Spain
| | - Ali Mortazavi
- Department of Developmental and Cell Biology and Center for Complex Biological Systems, University of California, Irvine, CA, USA
| | - Matthias Merkenschlager
- MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK.
| | - Jesper Tegner
- Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.
- Science for Life Laboratory, Solna, Sweden.
- Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
| | - Ana Conesa
- Microbiology and Cell Science Department, Institute for Food and Agricultural Research, Genetics Institute, University of Florida, Gainesville, Florida, USA.
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12
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Otto GW, Kaisaki PJ, Brial F, Le Lay A, Cazier JB, Mott R, Gauguier D. Conserved properties of genetic architecture of renal and fat transcriptomes in rat models of insulin resistance. Dis Model Mech 2019; 12:dmm.038539. [PMID: 31213483 PMCID: PMC6679378 DOI: 10.1242/dmm.038539] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Accepted: 05/20/2019] [Indexed: 12/19/2022] Open
Abstract
To define renal molecular mechanisms that are affected by permanent hyperglycaemia and might promote phenotypes relevant to diabetic nephropathy, we carried out linkage analysis of genome-wide gene transcription in the kidneys of F2 offspring from the Goto-Kakizaki (GK) rat model of type 2 diabetes and normoglycaemic Brown Norway (BN) rats. We mapped 2526 statistically significant expression quantitative trait loci (eQTLs) in the cross. More than 40% of eQTLs mapped in the close vicinity of the linked transcripts, underlying possible cis-regulatory mechanisms of gene expression. We identified eQTL hotspots on chromosomes 5 and 9 regulating the expression of 80-165 genes, sex or cross direction effects, and enriched metabolic and immunological processes by segregating GK alleles. Comparative analysis with adipose tissue eQTLs in the same cross showed that 496 eQTLs, in addition to the top enriched biological pathways, are conserved in the two tissues. Extensive similarities in eQTLs mapped in the GK rat and in the spontaneously hypertensive rat (SHR) suggest a common aetiology of disease phenotypes common to the two strains, including insulin resistance, which is a prominent pathophysiological feature in both GK rats and SHRs. Our data shed light on shared and tissue-specific molecular mechanisms that might underlie aetiological aspects of insulin resistance in the context of spontaneously occurring hyperglycaemia and hypertension. Summary: Kidney and fat expression QTL mapping in rat models of spontaneously occurring insulin resistance associated with either diabetes or hypertension reveals conserved gene expression regulation, suggesting shared aetiology of disease phenotypes.
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Affiliation(s)
- Georg W Otto
- Genetics and Genomic Medicine, University College London Institute of Child Health, 30 Guilford Street, London WC1N 1EH, United Kingdom
| | - Pamela J Kaisaki
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7BN, United Kingdom
| | - Francois Brial
- University Paris Descartes, INSERM UMR 1124, 45 rue des Saint-Pères, 75006 Paris, France
| | - Aurélie Le Lay
- University Paris Descartes, INSERM UMR 1124, 45 rue des Saint-Pères, 75006 Paris, France
| | - Jean-Baptiste Cazier
- Centre for Computational Biology, Medical School, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Richard Mott
- University College London Genetics Institute, Gower Street, London WC1E 6BT, United Kingdom
| | - Dominique Gauguier
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7BN, United Kingdom .,University Paris Descartes, INSERM UMR 1124, 45 rue des Saint-Pères, 75006 Paris, France.,McGill University and Genome Quebec Innovation Centre, 740 Doctor Penfield Avenue, Montreal, QC H3A 0G1, Canada
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13
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Leuthold P, Schwab M, Hofmann U, Winter S, Rausch S, Pollak MN, Hennenlotter J, Bedke J, Schaeffeler E, Haag M. Simultaneous Extraction of RNA and Metabolites from Single Kidney Tissue Specimens for Combined Transcriptomic and Metabolomic Profiling. J Proteome Res 2018; 17:3039-3049. [PMID: 30091608 DOI: 10.1021/acs.jproteome.8b00199] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Tissue analysis represents a powerful tool for the investigation of disease pathophysiology. However, the heterogeneous nature of tissue samples, in particular of neoplastic, may affect the outcome of such analysis and hence obscure interpretation of results. Thus, comprehensive isolation and extraction of transcripts and metabolites from an identical tissue specimen would minimize variations and enable the economic use of biopsy material which is usually available in limited amounts. Here we demonstrate a fast and simple protocol for combined transcriptomics and metabolomics analysis in homogenates prepared from one single tissue sample. Metabolites were recovered by protein precipitation from lysates originally prepared for RNA isolation and were analyzed by LC-QTOF-MS after HILIC and RPLC separation, respectively. Strikingly, although ion suppression was observed, over 80% of the 2885 detected metabolic features could be extracted and analyzed with high reproducibility (CV ≤ 20%). Moreover fold changes of different tumor and nontumor kidney tissues were correlated to an established metabolomics protocol and revealed a strong correlation ( rp ≥ 0.75). In order to demonstrate the feasibility of the combined analysis of RNA and metabolites, the protocol was applied to kidney tissue of metformin treated mice to investigate drug induced alterations.
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Affiliation(s)
- Patrick Leuthold
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology , Stuttgart , Germany and University of Tübingen, Tübingen, Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology , Stuttgart , Germany and University of Tübingen, Tübingen, Germany.,Department of Clinical Pharmacology , University Hospital Tübingen , Tübingen , Germany.,Department of Pharmacy and Biochemistry , University of Tübingen , Tübingen , Germany
| | - Ute Hofmann
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology , Stuttgart , Germany and University of Tübingen, Tübingen, Germany
| | - Stefan Winter
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology , Stuttgart , Germany and University of Tübingen, Tübingen, Germany
| | - Steffen Rausch
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology , Stuttgart , Germany and University of Tübingen, Tübingen, Germany.,Department of Urology , University Hospital Tübingen , Tübingen , Germany
| | | | - Jörg Hennenlotter
- Department of Urology , University Hospital Tübingen , Tübingen , Germany
| | - Jens Bedke
- Department of Urology , University Hospital Tübingen , Tübingen , Germany
| | - Elke Schaeffeler
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology , Stuttgart , Germany and University of Tübingen, Tübingen, Germany
| | - Mathias Haag
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology , Stuttgart , Germany and University of Tübingen, Tübingen, Germany
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14
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Rodriguez-Martinez A, Ayala R, Posma JM, Dumas ME. Exploring the Genetic Landscape of Metabolic Phenotypes with MetaboSignal. ACTA ACUST UNITED AC 2018; 61:14.14.1-14.14.13. [DOI: 10.1002/cpbi.41] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Andrea Rodriguez-Martinez
- Section of Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, South Kensington Campus, Imperial College London; London United Kingdom
| | - Rafael Ayala
- Section of Structural Biology, Department of Medicine, Faculty of Medicine, South Kensington Campus, Imperial College London; London United Kingdom
| | - Joram M. Posma
- Section of Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, South Kensington Campus, Imperial College London; London United Kingdom
| | - Marc-Emmanuel Dumas
- Section of Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, South Kensington Campus, Imperial College London; London United Kingdom
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15
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Rodriguez-Martinez A, Ayala R, Posma JM, Neves AL, Gauguier D, Nicholson JK, Dumas ME. MetaboSignal: a network-based approach for topological analysis of metabotype regulation via metabolic and signaling pathways. Bioinformatics 2018; 33:773-775. [PMID: 28011775 PMCID: PMC5408820 DOI: 10.1093/bioinformatics/btw697] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 11/03/2016] [Indexed: 12/31/2022] Open
Abstract
Summary MetaboSignal is an R package that allows merging metabolic and signaling pathways reported in the Kyoto Encyclopaedia of Genes and Genomes (KEGG). It is a network-based approach designed to navigate through topological relationships between genes (signaling- or metabolic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape of metabolic phenotypes. Availability and Implementation MetaboSignal is available from Bioconductor: https://bioconductor.org/packages/MetaboSignal/ Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Andrea Rodriguez-Martinez
- Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK
| | - Rafael Ayala
- Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK
| | - Joram M Posma
- Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK
| | - Ana L Neves
- Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK
| | - Dominique Gauguier
- Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK.,Sorbonne Universities, University Pierre & Marie Curie, University Paris Descartes, Sorbonne Paris Cité, INSERMUMR_S 1138, Cordeliers Research Centre, 75006 Paris, France
| | - Jeremy K Nicholson
- Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK
| | - Marc-Emmanuel Dumas
- Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, SW7 2AZ London, UK
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16
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Bihoreau MT, Dumas ME, Lathrop M, Gauguier D. Genomic regulation of type 2 diabetes endophenotypes: Contribution from genetic studies in the Goto-Kakizaki rat. Biochimie 2017; 143:56-65. [DOI: 10.1016/j.biochi.2017.08.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Accepted: 08/20/2017] [Indexed: 11/30/2022]
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