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Prince N, Liang D, Tan Y, Alshawabkeh A, Angel EE, Busgang SA, Chu SH, Cordero JF, Curtin P, Dunlop AL, Gilbert-Diamond D, Giulivi C, Hoen AG, Karagas MR, Kirchner D, Litonjua AA, Manjourides J, McRitchie S, Meeker JD, Pathmasiri W, Perng W, Schmidt RJ, Watkins DJ, Weiss ST, Zens MS, Zhu Y, Lasky-Su JA, Kelly RS. Metabolomic data presents challenges for epidemiological meta-analysis: a case study of childhood body mass index from the ECHO consortium. Metabolomics 2024; 20:16. [PMID: 38267770 PMCID: PMC11099615 DOI: 10.1007/s11306-023-02082-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 12/12/2023] [Indexed: 01/26/2024]
Abstract
INTRODUCTION Meta-analyses across diverse independent studies provide improved confidence in results. However, within the context of metabolomic epidemiology, meta-analysis investigations are complicated by differences in study design, data acquisition, and other factors that may impact reproducibility. OBJECTIVE The objective of this study was to identify maternal blood metabolites during pregnancy (> 24 gestational weeks) related to offspring body mass index (BMI) at age two years through a meta-analysis framework. METHODS We used adjusted linear regression summary statistics from three cohorts (total N = 1012 mother-child pairs) participating in the NIH Environmental influences on Child Health Outcomes (ECHO) Program. We applied a random-effects meta-analysis framework to regression results and adjusted by false discovery rate (FDR) using the Benjamini-Hochberg procedure. RESULTS Only 20 metabolites were detected in all three cohorts, with an additional 127 metabolites detected in two of three cohorts. Of these 147, 6 maternal metabolites were nominally associated (P < 0.05) with offspring BMI z-scores at age 2 years in a meta-analytic framework including at least two studies: arabinose (Coefmeta = 0.40 [95% CI 0.10,0.70], Pmeta = 9.7 × 10-3), guanidinoacetate (Coefmeta = - 0.28 [- 0.54, - 0.02], Pmeta = 0.033), 3-ureidopropionate (Coefmeta = 0.22 [0.017,0.41], Pmeta = 0.033), 1-methylhistidine (Coefmeta = - 0.18 [- 0.33, - 0.04], Pmeta = 0.011), serine (Coefmeta = - 0.18 [- 0.36, - 0.01], Pmeta = 0.034), and lysine (Coefmeta = - 0.16 [- 0.32, - 0.01], Pmeta = 0.044). No associations were robust to multiple testing correction. CONCLUSIONS Despite including three cohorts with large sample sizes (N > 100), we failed to identify significant metabolite associations after FDR correction. Our investigation demonstrates difficulties in applying epidemiological meta-analysis to clinical metabolomics, emphasizes challenges to reproducibility, and highlights the need for standardized best practices in metabolomic epidemiology.
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Affiliation(s)
- Nicole Prince
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Donghai Liang
- Gangarosa Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Youran Tan
- Gangarosa Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Akram Alshawabkeh
- Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA
| | - Elizabeth Esther Angel
- Department of Public Health Sciences, School of Medicine, University of California Davis, Davis, CA, 95616, USA
| | - Stefanie A Busgang
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Su H Chu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - José F Cordero
- Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, USA
| | - Paul Curtin
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Anne L Dunlop
- Department of Gynecology and Obstetrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Diane Gilbert-Diamond
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
- Department of Medicine, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
- Department of Pediatrics, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
| | - Cecilia Giulivi
- Department of Molecular Biosciences, School of Veterinary Medicine, University of California Davis, Davis, CA, 95616, USA
| | - Anne G Hoen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
| | - Margaret R Karagas
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
| | - David Kirchner
- Department of Nutrition, Gillings School of Global Public Health, Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Augusto A Litonjua
- Division of Pediatric Pulmonary Medicine, Golisano Children's Hospital at Strong, University of Rochester Medical Center, Rochester, NY, USA
| | | | - Susan McRitchie
- Department of Nutrition, Gillings School of Global Public Health, Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - John D Meeker
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Wimal Pathmasiri
- Department of Nutrition, Gillings School of Global Public Health, Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Wei Perng
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Rebecca J Schmidt
- Department of Public Health Sciences, School of Medicine, University of California Davis, Davis, CA, 95616, USA
- MIND Institute, School of Medicine, University of California Davis, Davis, CA, 95616, USA
| | - Deborah J Watkins
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael S Zens
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
| | - Yeyi Zhu
- Kaiser Permanente Northern California Division of Research, Oakland, CA, USA
| | - Jessica A Lasky-Su
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Rachel S Kelly
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA.
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Khan AA, Al-Mahrouqi N, Al-Yahyaee A, Al-Sayegh H, Al-Harthy M, Al-Zadjali S. Deciphering Urogenital Cancers through Proteomic Biomarkers: A Systematic Review and Meta-Analysis. Cancers (Basel) 2023; 16:22. [PMID: 38201450 PMCID: PMC10778028 DOI: 10.3390/cancers16010022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/20/2023] [Accepted: 12/04/2023] [Indexed: 01/12/2024] Open
Abstract
Urogenital cancers, which include prostate, bladder, and kidney malignancies, exert a substantial impact on global cancer-related morbidity and mortality. Proteomic biomarkers, emerging as valuable tools, aim to enhance early detection, prognostic accuracy, and the development of personalized therapeutic strategies. This study undertook a comprehensive systematic review and meta-analysis of the existing literature investigating the role and potential of proteomic biomarkers in plasma, tissue, and urine samples in urogenital cancers. Our extensive search across several databases identified 1879 differentially expressed proteins from 37 studies, signifying their potential as unique biomarkers for these cancers. A meta-analysis of the significantly differentially expressed proteins was executed, accentuating the findings through visually intuitive volcano plots. A functional enrichment analysis unveiled their significant involvement in diverse biological processes, including signal transduction, immune response, cell communication, and cell growth. A pathway analysis highlighted the participation of key pathways such as the nectin adhesion pathway, TRAIL signaling pathway, and integrin signaling pathways. These findings not only pave the way for future investigations into early detection and targeted therapeutic approaches but also underscore the fundamental role of proteomics in advancing our understanding of the molecular mechanisms underpinning urogenital cancer pathogenesis. Ultimately, these findings hold remarkable potential to significantly enhance patient care and improve clinical outcomes.
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Affiliation(s)
- Aafaque Ahmad Khan
- Research Laboratories, Sultan Qaboos Comprehensive Cancer Care and Research Center, Muscat 123, Oman; (N.A.-M.); (A.A.-Y.); (H.A.-S.); (S.A.-Z.)
| | - Nahad Al-Mahrouqi
- Research Laboratories, Sultan Qaboos Comprehensive Cancer Care and Research Center, Muscat 123, Oman; (N.A.-M.); (A.A.-Y.); (H.A.-S.); (S.A.-Z.)
| | - Aida Al-Yahyaee
- Research Laboratories, Sultan Qaboos Comprehensive Cancer Care and Research Center, Muscat 123, Oman; (N.A.-M.); (A.A.-Y.); (H.A.-S.); (S.A.-Z.)
| | - Hasan Al-Sayegh
- Research Laboratories, Sultan Qaboos Comprehensive Cancer Care and Research Center, Muscat 123, Oman; (N.A.-M.); (A.A.-Y.); (H.A.-S.); (S.A.-Z.)
| | - Munjid Al-Harthy
- Medical Oncology Department, Urogenital Cancers Program, Sultan Qaboos Comprehensive Cancer Care and Research Center, Muscat 123, Oman;
| | - Shoaib Al-Zadjali
- Research Laboratories, Sultan Qaboos Comprehensive Cancer Care and Research Center, Muscat 123, Oman; (N.A.-M.); (A.A.-Y.); (H.A.-S.); (S.A.-Z.)
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Paul KC, Zhang K, Walker DI, Sinsheimer J, Yu Y, Kusters C, Del Rosario I, Folle AD, Keener AM, Bronstein J, Jones DP, Ritz B. Untargeted serum metabolomics reveals novel metabolite associations and disruptions in amino acid and lipid metabolism in Parkinson's disease. Mol Neurodegener 2023; 18:100. [PMID: 38115046 PMCID: PMC10731845 DOI: 10.1186/s13024-023-00694-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 12/06/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Untargeted high-resolution metabolomic profiling provides simultaneous measurement of thousands of metabolites. Metabolic networks based on these data can help uncover disease-related perturbations across interconnected pathways. OBJECTIVE Identify metabolic disturbances associated with Parkinson's disease (PD) in two population-based studies using untargeted metabolomics. METHODS We performed a metabolome-wide association study (MWAS) of PD using serum-based untargeted metabolomics data derived from liquid chromatography with high-resolution mass spectrometry (LC-HRMS) using two distinct population-based case-control populations. We also combined our results with a previous publication of 34 metabolites linked to PD in a large-scale, untargeted MWAS to assess external validation. RESULTS LC-HRMS detected 4,762 metabolites for analysis (HILIC: 2716 metabolites; C18: 2046 metabolites). We identified 296 features associated with PD at FDR<0.05, 134 having a log2 fold change (FC) beyond ±0.5 (228 beyond ±0.25). Of these, 104 were independently associated with PD in both discovery and replication studies at p<0.05 (170 at p<0.10), while 27 were associated with levodopa-equivalent dose among the PD patients. Intriguingly, among the externally validated features were the microbial-related metabolites, p-cresol glucuronide (FC=2.52, 95% CI=1.67, 3.81, FDR=7.8e-04) and p-cresol sulfate. P-cresol glucuronide was also associated with motor symptoms among patients. Additional externally validated metabolites associated with PD include phenylacetyl-L-glutamine, trigonelline, kynurenine, biliverdin, and pantothenic acid. Novel associations include the anti-inflammatory metabolite itaconate (FC=0.79, 95% CI=0.73, 0.86; FDR=2.17E-06) and cysteine-S-sulfate (FC=1.56, 95% CI=1.39, 1.75; FDR=3.43E-11). Seventeen pathways were enriched, including several related to amino acid and lipid metabolism. CONCLUSIONS Our results revealed PD-associated metabolites, confirming several previous observations, including for p-cresol glucuronide, and newly implicating interesting metabolites, such as itaconate. Our data also suggests metabolic disturbances in amino acid and lipid metabolism and inflammatory processes in PD.
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Affiliation(s)
- Kimberly C Paul
- Department of Neurology, UCLA David Geffen School of Medicine, Los Angeles, CA, USA.
| | - Keren Zhang
- Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA, USA
| | - Douglas I Walker
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Janet Sinsheimer
- Department of Human Genetics, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
- Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, CA, USA
| | - Yu Yu
- Center for Health Policy Research, UCLA Fielding School of Public Health, Los Angeles, CA, USA
| | - Cynthia Kusters
- Department of Human Genetics, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - Irish Del Rosario
- Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA, USA
| | - Aline Duarte Folle
- Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA, USA
| | - Adrienne M Keener
- Department of Neurology, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
- Parkinson's Disease Research, Education, and Clinical Center, Greater Los Angeles Veterans Affairs Medical Center, Los Angeles, CA, USA
| | - Jeff Bronstein
- Department of Neurology, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - Dean P Jones
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA
| | - Beate Ritz
- Department of Neurology, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
- Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA, USA
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Roointan A, Ghaeidamini M, Shafieizadegan S, Hudkins KL, Gholaminejad A. Metabolome panels as potential noninvasive biomarkers for primary glomerulonephritis sub-types: meta-analysis of profiling metabolomics studies. Sci Rep 2023; 13:20325. [PMID: 37990116 PMCID: PMC10663527 DOI: 10.1038/s41598-023-47800-7] [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: 03/13/2023] [Accepted: 11/18/2023] [Indexed: 11/23/2023] Open
Abstract
Primary glomerulonephritis diseases (PGDs) are known as the top causes of chronic kidney disease worldwide. Renal biopsy, an invasive method, is the main approach to diagnose PGDs. Studying the metabolome profiles of kidney diseases is an inclusive approach to identify the disease's underlying pathways and discover novel non-invasive biomarkers. So far, different experiments have explored the metabolome profiles in different PGDs, but the inconsistencies might hinder their clinical translations. The main goal of this meta-analysis study was to achieve consensus panels of dysregulated metabolites in PGD sub-types. The PGDs-related metabolome profiles from urine samples in humans were selected in a comprehensive search. Amanida package in R software was utilized for performing the meta-analysis. Through sub-type analyses, the consensus list of metabolites in each category was obtained. To identify the most affected pathways, functional enrichment analysis was performed. Also, a gene-metabolite network was constructed to identify the key metabolites and their connected proteins. After a vigorous search, among the 11 selected studies (15 metabolite profiles), 270 dysregulated metabolites were recognized in urine of 1154 PGDs and control samples. Through sub-type analyses by Amanida package, the consensus list of metabolites in each category was obtained. Top dysregulated metabolites (vote score of ≥ 4 or ≤ - 4) in PGDs urines were selected as main panel of meta-metabolites including glucose, leucine, choline, betaine, dimethylamine, fumaric acid, citric acid, 3-hydroxyisovaleric acid, pyruvic acid, isobutyric acid, and hippuric acid. The enrichment analyses results revealed the involvement of different biological pathways such as the TCA cycle and amino acid metabolisms in the pathogenesis of PGDs. The constructed metabolite-gene interaction network revealed the high centralities of several metabolites, including pyruvic acid, leucine, and choline. The identified metabolite panels could shed a light on the underlying pathological pathways and be considered as non-invasive biomarkers for the diagnosis of PGD sub-types.
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Affiliation(s)
- Amir Roointan
- Regenerative Medicine Research Center, Faculty of Medicine, Isfahan University of Medical Sciences, Hezar Jarib St., Isfahan, 81746-73461, Iran
| | - Maryam Ghaeidamini
- Regenerative Medicine Research Center, Faculty of Medicine, Isfahan University of Medical Sciences, Hezar Jarib St., Isfahan, 81746-73461, Iran
| | - Saba Shafieizadegan
- Regenerative Medicine Research Center, Faculty of Medicine, Isfahan University of Medical Sciences, Hezar Jarib St., Isfahan, 81746-73461, Iran
| | - Kelly L Hudkins
- Department of Laboratory Medicine and Pathology, University of Washington, School of Medicine, Seattle, USA
| | - Alieh Gholaminejad
- Regenerative Medicine Research Center, Faculty of Medicine, Isfahan University of Medical Sciences, Hezar Jarib St., Isfahan, 81746-73461, Iran.
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Anh NH, Long NP, Min YJ, Ki Y, Kim SJ, Jung CW, Park S, Kwon SW, Lee SJ. Molecular and Metabolic Phenotyping of Hepatocellular Carcinoma for Biomarker Discovery: A Meta-Analysis. Metabolites 2023; 13:1112. [PMID: 37999208 PMCID: PMC10672761 DOI: 10.3390/metabo13111112] [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: 09/25/2023] [Revised: 10/20/2023] [Accepted: 10/24/2023] [Indexed: 11/25/2023] Open
Abstract
Identifying and translating hepatocellular carcinoma (HCC) biomarkers from bench to bedside using mass spectrometry-based metabolomics and lipidomics is hampered by inconsistent findings. Here, we investigated HCC at systemic and metabolism-centric multiomics levels by conducting a meta-analysis of quantitative evidence from 68 cohorts. Blood transcript biomarkers linked to the HCC metabolic phenotype were externally validated and prioritized. In the studies under investigation, about 600 metabolites were reported as putative HCC-associated biomarkers; 39, 20, and 10 metabolites and 52, 12, and 12 lipids were reported in three or more studies in HCC vs. Control, HCC vs. liver cirrhosis (LC), and LC vs. Control groups, respectively. Amino acids, fatty acids (increased 18:1), bile acids, and lysophosphatidylcholine were the most frequently reported biomarkers in HCC. BAX and RAC1 showed a good correlation and were associated with poor prognosis. Our study proposes robust HCC biomarkers across diverse cohorts using a data-driven knowledge-based approach that is versatile and affordable for studying other diseases.
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Affiliation(s)
- Nguyen Hoang Anh
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea; (N.H.A.); (Y.J.M.); (S.J.K.); (C.W.J.); (S.W.K.)
| | - Nguyen Phuoc Long
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan 47392, Republic of Korea
| | - Young Jin Min
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea; (N.H.A.); (Y.J.M.); (S.J.K.); (C.W.J.); (S.W.K.)
| | - Yujin Ki
- School of Mathematics, Statistics and Data Science, Sungshin Women’s University, Seoul 08826, Republic of Korea; (Y.K.); (S.P.)
| | - Sun Jo Kim
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea; (N.H.A.); (Y.J.M.); (S.J.K.); (C.W.J.); (S.W.K.)
| | - Cheol Woon Jung
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea; (N.H.A.); (Y.J.M.); (S.J.K.); (C.W.J.); (S.W.K.)
| | - Seongoh Park
- School of Mathematics, Statistics and Data Science, Sungshin Women’s University, Seoul 08826, Republic of Korea; (Y.K.); (S.P.)
| | - Sung Won Kwon
- College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea; (N.H.A.); (Y.J.M.); (S.J.K.); (C.W.J.); (S.W.K.)
| | - Seul Ji Lee
- College of Pharmacy, Kangwon National University, Chuncheon 24341, Republic of Korea
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Bremer PL, Wohlgemuth G, Fiehn O. The BinDiscover database: a biology-focused meta-analysis tool for 156,000 GC-TOF MS metabolome samples. J Cheminform 2023; 15:66. [PMID: 37475020 PMCID: PMC10359220 DOI: 10.1186/s13321-023-00734-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 07/08/2023] [Indexed: 07/22/2023] Open
Abstract
Metabolomics by gas chromatography/mass spectrometry (GC/MS) provides a standardized and reliable platform for understanding small molecule biology. Since 2005, the West Coast Metabolomics Center at the University of California at Davis has collated GC/MS metabolomics data from over 156,000 samples and 2000 studies into the standardized BinBase database. We believe that the observations from these samples will provide meaningful insight to biologists and that our data treatment and webtool will provide insight to others who seek to standardize disparate metabolomics studies. We here developed an easy-to-use query interface, BinDiscover, to enable intuitive, rapid hypothesis generation for biologists based on these metabolomic samples. BinDiscover creates observation summaries and graphics across a broad range of species, organs, diseases, and compounds. Throughout the components of BinDiscover, we emphasize the use of ontologies to aggregate large groups of samples based on the proximity of their metadata within these ontologies. This adjacency allows for the simultaneous exploration of entire categories such as "rodents", "digestive tract", or "amino acids". The ontologies are particularly relevant for BinDiscover's ontologically grouped differential analysis, which, like other components of BinDiscover, creates clear graphs and summary statistics across compounds and biological metadata. We exemplify BinDiscover's extensive applicability in three showcases across biological domains.
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Affiliation(s)
| | - Gert Wohlgemuth
- West Coast Metabolomics Center for Compound Identification, UC Davis Genome Center, University of California, Davis, CA 95616 USA
| | - Oliver Fiehn
- West Coast Metabolomics Center for Compound Identification, UC Davis Genome Center, University of California, Davis, CA 95616 USA
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Habra H, Kachman M, Padmanabhan V, Burant C, Karnovsky A, Meijer J. Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples. J Proteome Res 2022; 21:2936-2946. [PMID: 36367990 DOI: 10.1021/acs.jproteome.2c00371] [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] [Indexed: 11/13/2022]
Abstract
Untargeted liquid chromatography-mass spectrometry metabolomics studies are typically performed under roughly identical experimental settings. Measurements acquired with different LC-MS protocols or following extended time intervals harbor significant variation in retention times and spectral abundances due to altered chromatographic, spectrometric, and other factors, raising many data analysis challenges. We developed a computational workflow for merging and harmonizing metabolomics data acquired under disparate LC-MS conditions. Plasma metabolite profiles were collected from two sets of maternal subjects three years apart using distinct instruments and LC-MS procedures. Metabolomics features were aligned using metabCombiner to generate lists of compounds detected across all experimental batches. We applied data set-specific normalization methods to remove interbatch and interexperimental variation in spectral intensities, enabling statistical analysis on the assembled data matrix. Bioinformatics analyses revealed large-scale metabolic changes in maternal plasma between the first and third trimesters of pregnancy and between maternal plasma and umbilical cord blood. We observed increases in steroid hormones and free fatty acids from the first trimester to term of gestation, along with decreases in amino acids coupled to increased levels in cord blood. This work demonstrates the viability of integrating nonidentically acquired LC-MS metabolomics data and its utility in unconventional metabolomics study designs.
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Affiliation(s)
- Hani Habra
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - Maureen Kachman
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, Michigan 48105, United States
| | - Vasantha Padmanabhan
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan 48109, United States
- Department of Obstetrics & Gynecology, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - Charles Burant
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, Michigan 48105, United States
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - Alla Karnovsky
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
| | - Jennifer Meijer
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan 48109, United States
- Department of Medicine, Geisel School of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire 03756, United States
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Matsuta R, Yamamoto H, Tomita M, Saito R. iDMET: network-based approach for integrating differential analysis of cancer metabolomics. BMC Bioinformatics 2022; 23:508. [PMID: 36443658 PMCID: PMC9706903 DOI: 10.1186/s12859-022-05068-0] [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: 07/08/2022] [Accepted: 11/18/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Comprehensive metabolomic analyses have been conducted in various institutes and a large amount of metabolomic data are now publicly available. To help fully exploit such data and facilitate their interpretation, metabolomic data obtained from different facilities and different samples should be integrated and compared. However, large-scale integration of such data for biological discovery is challenging given that they are obtained from various types of sample at different facilities and by different measurement techniques, and the target metabolites and sensitivities to detect them also differ from study to study. RESULTS We developed iDMET, a network-based approach to integrate metabolomic data from different studies based on the differential metabolomic profiles between two groups, instead of the metabolite profiles themselves. As an application, we collected cancer metabolomic data from 27 previously published studies and integrated them using iDMET. A pair of metabolomic changes observed in the same disease from two studies were successfully connected in the network, and a new association between two drugs that may have similar effects on the metabolic reactions was discovered. CONCLUSIONS We believe that iDMET is an efficient tool for integrating heterogeneous metabolomic data and discovering novel relationships between biological phenomena.
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Affiliation(s)
- Rira Matsuta
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0052, Japan
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Kanagawa, 252-8520, Japan
- Human Metabolome Technologies, Inc., 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata, 997-0052, Japan
| | - Hiroyuki Yamamoto
- Human Metabolome Technologies, Inc., 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata, 997-0052, Japan.
| | - Masaru Tomita
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0052, Japan
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Kanagawa, 252-8520, Japan
| | - Rintaro Saito
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0052, Japan
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, Kanagawa, 252-8520, Japan
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Brezmes J, Llambrich M, Cumeras R, Gumà J. Urine NMR Metabolomics for Precision Oncology in Colorectal Cancer. Int J Mol Sci 2022; 23:11171. [PMID: 36232473 PMCID: PMC9569997 DOI: 10.3390/ijms231911171] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 11/16/2022] Open
Abstract
Metabolomics is a fundamental approach to discovering novel biomarkers and their potential use for precision medicine. When applied for population screening, NMR-based metabolomics can become a powerful clinical tool in precision oncology. Urine tests can be more widely accepted due to their intrinsic non-invasiveness. Our review provides the first exhaustive evaluation of NMR metabolomics for the determination of colorectal cancer (CRC) in urine. A specific search in PubMed, Web of Science, and Scopus was performed, and 10 studies met the required criteria. There were no restrictions on the query for study type, leading to not only colorectal cancer samples versus control comparisons, but also prospective studies of surgical effects. With this review, all compounds in the included studies were merged into a database. In doing so, we identified up to 100 compounds in urine samples, and 11 were found in at least three articles. Results were analyzed in three groups: case (CRC and adenomas)/control, pre-/post-surgery, and combining both groups. When combining the case-control and the pre-/post-surgery groups, up to twelve compounds were found to be relevant. Seven down-regulated metabolites in CRC were identified, creatinine, 4-hydroxybenzoic acid, acetone, carnitine, d-glucose, hippuric acid, l-lysine, l-threonine, and pyruvic acid, and three up-regulated compounds in CRC were identified, acetic acid, phenylacetylglutamine, and urea. The pathways and enrichment analysis returned only two pathways significantly expressed: the pyruvate metabolism and the glycolysis/gluconeogenesis pathway. In both cases, only the pyruvic acid (down-regulated in urine of CRC patients, with cancer cell proliferation effect in the tissue) and acetic acid (up-regulated in urine of CRC patients, with chemoprotective effect) were present.
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Affiliation(s)
- Jesús Brezmes
- Metabolomics Interdisciplinary Group, Institut d’Investigació Sanitària Pere Virgili (IISPV), Universitat Rovira i Virgili (URV), 43204 Reus, Spain
- Department of Electrical Electronic Engineering and Automation, Universitat Rovira i Virgili (URV), Institut d’Investigació Sanitària Pere Virgili (IISPV), 43007 Tarragona, Spain
| | - Maria Llambrich
- Metabolomics Interdisciplinary Group, Institut d’Investigació Sanitària Pere Virgili (IISPV), Universitat Rovira i Virgili (URV), 43204 Reus, Spain
- Department of Electrical Electronic Engineering and Automation, Universitat Rovira i Virgili (URV), Institut d’Investigació Sanitària Pere Virgili (IISPV), 43007 Tarragona, Spain
| | - Raquel Cumeras
- Metabolomics Interdisciplinary Group, Institut d’Investigació Sanitària Pere Virgili (IISPV), Universitat Rovira i Virgili (URV), 43204 Reus, Spain
- Department of Electrical Electronic Engineering and Automation, Universitat Rovira i Virgili (URV), Institut d’Investigació Sanitària Pere Virgili (IISPV), 43007 Tarragona, Spain
- Oncology Department, Hospital Universitari Sant Joan de Reus, Institut d’Investigació Sanitària Pere Virgili (IISPV), Universitat Rovira i Virgili (URV), 43204 Reus, Spain
| | - Josep Gumà
- Oncology Department, Hospital Universitari Sant Joan de Reus, Institut d’Investigació Sanitària Pere Virgili (IISPV), Universitat Rovira i Virgili (URV), 43204 Reus, Spain
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