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Li Y, He W, Liu S, Hu X, He Y, Song X, Yin J, Nie S, Xie M. Innovative omics strategies in fermented fruits and vegetables: Unveiling nutritional profiles, microbial diversity, and future prospects. Compr Rev Food Sci Food Saf 2024; 23:e70030. [PMID: 39379298 DOI: 10.1111/1541-4337.70030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 09/06/2024] [Accepted: 09/08/2024] [Indexed: 10/10/2024]
Abstract
Fermented fruits and vegetables (FFVs) are not only rich in essential nutrients but also contain distinctive flavors, prebiotics, and metabolites. Although omics techniques have gained widespread recognition as an analytical strategy for FFVs, its application still encounters several challenges due to the intricacies of biological systems. This review systematically summarizes the advances, obstacles and prospects of genomics, transcriptomics, proteomics, metabolomics, and multi-omics strategies in FFVs. It is evident that beyond traditional applications, such as the exploration of microbial diversity, protein expression, and metabolic pathways, omics techniques exhibit innovative potential in deciphering stress response mechanisms and uncovering spoilage microorganisms. The adoption of multi-omics strategies is paramount to acquire a multidimensional network fusion, thereby mitigating the limitations of single omics strategies. Although substantial progress has been made, this review underscores the necessity for a comprehensive repository of omics data and the establishment of universal databases to ensure precision in predictions. Furthermore, multidisciplinary integration with other physical or biochemical approaches is imperative, as it enriches our comprehension of this intricate process.
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Affiliation(s)
- Yuhao Li
- State Key Laboratory of Food Science and Resources, China-Canada Joint Laboratory of Food Science and Technology (Nanchang), Key Laboratory of Bioactive Polysaccharides of Jiangxi Province, Nanchang University, Nanchang, China
| | - Weiwei He
- State Key Laboratory of Food Science and Resources, China-Canada Joint Laboratory of Food Science and Technology (Nanchang), Key Laboratory of Bioactive Polysaccharides of Jiangxi Province, Nanchang University, Nanchang, China
| | - Shuai Liu
- State Key Laboratory of Food Science and Resources, China-Canada Joint Laboratory of Food Science and Technology (Nanchang), Key Laboratory of Bioactive Polysaccharides of Jiangxi Province, Nanchang University, Nanchang, China
| | - Xiaoyi Hu
- State Key Laboratory of Food Science and Resources, China-Canada Joint Laboratory of Food Science and Technology (Nanchang), Key Laboratory of Bioactive Polysaccharides of Jiangxi Province, Nanchang University, Nanchang, China
| | - Yuxing He
- State Key Laboratory of Food Science and Resources, China-Canada Joint Laboratory of Food Science and Technology (Nanchang), Key Laboratory of Bioactive Polysaccharides of Jiangxi Province, Nanchang University, Nanchang, China
| | - Xiaoxiao Song
- State Key Laboratory of Food Science and Resources, China-Canada Joint Laboratory of Food Science and Technology (Nanchang), Key Laboratory of Bioactive Polysaccharides of Jiangxi Province, Nanchang University, Nanchang, China
| | - Junyi Yin
- State Key Laboratory of Food Science and Resources, China-Canada Joint Laboratory of Food Science and Technology (Nanchang), Key Laboratory of Bioactive Polysaccharides of Jiangxi Province, Nanchang University, Nanchang, China
| | - Shaoping Nie
- State Key Laboratory of Food Science and Resources, China-Canada Joint Laboratory of Food Science and Technology (Nanchang), Key Laboratory of Bioactive Polysaccharides of Jiangxi Province, Nanchang University, Nanchang, China
| | - Mingyong Xie
- State Key Laboratory of Food Science and Resources, China-Canada Joint Laboratory of Food Science and Technology (Nanchang), Key Laboratory of Bioactive Polysaccharides of Jiangxi Province, Nanchang University, Nanchang, China
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Marín-Sáez J, Hernández-Mesa M, Cano-Sancho G, García-Campaña AM. Analytical challenges and opportunities in the study of endocrine disrupting chemicals within an exposomics framework. Talanta 2024; 279:126616. [PMID: 39067205 DOI: 10.1016/j.talanta.2024.126616] [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: 05/06/2024] [Revised: 07/11/2024] [Accepted: 07/23/2024] [Indexed: 07/30/2024]
Abstract
Exposomics aims to measure human exposures throughout the lifespan and the changes they produce in the human body. Exposome-scale studies have significant potential to understand the interplay of environmental factors with complex multifactorial diseases widespread in our society and whose origin remain unclear. In this framework, the study of the chemical exposome aims to cover all chemical exposures and their effects in human health but, today, this goal still seems unfeasible or at least very challenging, which makes the exposome for now only a concept. Furthermore, the study of the chemical exposome faces several methodological challenges such as moving from specific targeted methodologies towards high-throughput multitargeted and non-targeted approaches, guaranteeing the availability and quality of biological samples to obtain quality analytical data, standardization of applied analytical methodologies, as well as the statistical assignment of increasingly complex datasets, or the identification of (un)known analytes. This review discusses the various steps involved in applying the exposome concept from an analytical perspective. It provides an overview of the wide variety of existing analytical methods and instruments, highlighting their complementarity to develop combined analytical strategies to advance towards the chemical exposome characterization. In addition, this review focuses on endocrine disrupting chemicals (EDCs) to show how studying even a minor part of the chemical exposome represents a great challenge. Analytical strategies applied in an exposomics context have shown great potential to elucidate the role of EDCs in health outcomes. However, translating innovative methods into etiological research and chemical risk assessment will require a multidisciplinary effort. Unlike other review articles focused on exposomics, this review offers a holistic view from the perspective of analytical chemistry and discuss the entire analytical workflow to finally obtain valuable results.
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Affiliation(s)
- Jesús Marín-Sáez
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Campus Fuentenueva s/n, E-18071, Granada, Spain; Research Group "Analytical Chemistry of Contaminants", Department of Chemistry and Physics, Research Centre for Mediterranean Intensive Agrosystems and Agri-Food Biotechnology (CIAIMBITAL), University of Almeria, Agrifood Campus of International Excellence, ceiA3, E-04120, Almeria, Spain.
| | - Maykel Hernández-Mesa
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Campus Fuentenueva s/n, E-18071, Granada, Spain.
| | | | - Ana M García-Campaña
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Campus Fuentenueva s/n, E-18071, Granada, Spain
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Garfinkle EAR, Nallagatla P, Sahoo B, Dang J, Balood M, Cotton A, Franke C, Mitchell S, Wilson T, Gruber TA. CBFA2T3-GLIS2 mediates transcriptional regulation of developmental pathways through a gene regulatory network. Nat Commun 2024; 15:8747. [PMID: 39384814 PMCID: PMC11464917 DOI: 10.1038/s41467-024-53158-9] [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: 03/06/2023] [Accepted: 10/03/2024] [Indexed: 10/11/2024] Open
Abstract
CBFA2T3-GLIS2 is a fusion oncogene found in pediatric acute megakaryoblastic leukemia that is associated with a poor prognosis. We establish a model of CBFA2T3-GLIS2 driven acute megakaryoblastic leukemia that allows the distinction of fusion specific changes from those that reflect the megakaryoblast lineage of this leukemia. Using this model, we map fusion genome wide binding that in turn imparts the characteristic transcriptional signature. A network of transcription factor genes bound and upregulated by the fusion are found to have downstream effects that result in dysregulated signaling of developmental pathways including NOTCH, Hedgehog, TGFβ, and WNT. Transcriptional regulation is mediated by homo-dimerization and binding of the ETO transcription factor through the nervy homology region 2. Loss of nerve homology region 2 abrogated the development of leukemia, leading to downregulation of JAK/STAT, Hedgehog, and NOTCH transcriptional signatures. These data contribute to the understanding of CBFA2T3-GLIS2 mediated leukemogenesis and identify potential therapeutic vulnerabilities for future studies.
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Affiliation(s)
| | - Pratima Nallagatla
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Binay Sahoo
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Jinjun Dang
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Mohammad Balood
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Anitria Cotton
- Division of Experimental Hematology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Camryn Franke
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Sharnise Mitchell
- Department of Chemical Biology and Therapeutics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Taylor Wilson
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Tanja A Gruber
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
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Gough EK, Edens TJ, Carr L, Robertson RC, Mutasa K, Ntozini R, Chasekwa B, Geum HM, Baharmand I, Gill SK, Mutasa B, Mbuya MNN, Majo FD, Tavengwa N, Francis F, Tome J, Evans C, Kosek M, Prendergast AJ, Manges AR. Bifidobacterium longum and microbiome maturation modify a nutrient intervention for stunting in Zimbabwean infants. EBioMedicine 2024; 108:105362. [PMID: 39341154 PMCID: PMC11467582 DOI: 10.1016/j.ebiom.2024.105362] [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: 04/13/2024] [Revised: 09/06/2024] [Accepted: 09/10/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND Small-quantity lipid-based nutrient supplements (SQ-LNS), which has been widely tested to reduce child stunting, has largely modest effects to date, but the mechanisms underlying these modest effects are unclear. Child stunting is a longstanding indicator of chronic undernutrition and it remains a prevalent public health problem. The infant gut microbiome may be a key contributor to stunting; and mother and infant fucosyltransferase (FUT) phenotypes are important determinants of infant microbiome composition. METHODS We investigated whether mother-infant FUT status (n = 792) and infant gut microbiome composition (n = 354 fecal specimens from 172 infants) modified the impact of an infant and young child feeding (IYCF) intervention, that included SQ-LNS, on stunting at age 18 months in secondary analysis of a randomized trial in rural Zimbabwe. FINDINGS We found that the impact of the IYCF intervention on stunting was modified by: (i) mother-infant FUT2+/FUT3- phenotype (difference-in-differences -32.6% [95% CI: -55.3%, -9.9%]); (ii) changes in species composition that reflected microbiome maturation (difference-in-differences -68.1% [95% CI: -99.0%, -28.5%); and (iii) greater relative abundance of B. longum (differences-in-differences 49.1% [95% CI: 26.6%, 73.6%]). The dominant strains of B. longum when the intervention started were most similar to the proficient milk oligosaccharide utilizer subspecies infantis, which decreased with infant age and differed by mother-infant FUT2+/FUT3- phenotypes. INTERPRETATION These findings indicate that a persistently "younger" microbiome at initiation of the intervention reduced its benefits on stunting in areas with a high prevalence of growth restriction. FUNDING Bill and Melinda Gates Foundation, UK DFID/Aid, Wellcome Trust, Swiss Agency for Development and Cooperation, US National Institutes of Health, UNICEF, and Nutricia Research Foundation.
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Affiliation(s)
- Ethan K Gough
- Department of International Health, Johns Hopkins Bloomberg School of Public Health; Baltimore, MD, USA.
| | | | - Lynnea Carr
- Department of Microbiology and Immunology, University of British Columbia; Vancouver, BC, Canada
| | | | - Kuda Mutasa
- Zvitambo Institute for Maternal and Child Health Research, Harare, Zimbabwe
| | - Robert Ntozini
- Zvitambo Institute for Maternal and Child Health Research, Harare, Zimbabwe
| | - Bernard Chasekwa
- Zvitambo Institute for Maternal and Child Health Research, Harare, Zimbabwe
| | - Hyun Min Geum
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Iman Baharmand
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Sandeep K Gill
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Batsirai Mutasa
- Zvitambo Institute for Maternal and Child Health Research, Harare, Zimbabwe
| | - Mduduzi N N Mbuya
- Zvitambo Institute for Maternal and Child Health Research, Harare, Zimbabwe; Global Alliance for Improved Nutrition, Washington, DC, 20036, USA
| | - Florence D Majo
- Zvitambo Institute for Maternal and Child Health Research, Harare, Zimbabwe
| | - Naume Tavengwa
- Zvitambo Institute for Maternal and Child Health Research, Harare, Zimbabwe
| | - Freddy Francis
- Department of Experimental Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Joice Tome
- Zvitambo Institute for Maternal and Child Health Research, Harare, Zimbabwe
| | - Ceri Evans
- Blizard Institute, Queen Mary University of London, London, UK; Zvitambo Institute for Maternal and Child Health Research, Harare, Zimbabwe; Department of Clinical Infection, Microbiology and Immunology, University of Liverpool, Liverpool, UK
| | - Margaret Kosek
- University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Andrew J Prendergast
- Blizard Institute, Queen Mary University of London, London, UK; Zvitambo Institute for Maternal and Child Health Research, Harare, Zimbabwe
| | - Amee R Manges
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada; British Columbia Centre for Disease Control (BCCDC), Vancouver, BC, Canada
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Perdomo-Quinteiro P, Belmonte-Hernández A. Knowledge Graphs for drug repurposing: a review of databases and methods. Brief Bioinform 2024; 25:bbae461. [PMID: 39325460 PMCID: PMC11426166 DOI: 10.1093/bib/bbae461] [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: 05/22/2024] [Revised: 08/07/2024] [Accepted: 09/11/2024] [Indexed: 09/27/2024] Open
Abstract
Drug repurposing has emerged as a effective and efficient strategy to identify new treatments for a variety of diseases. One of the most effective approaches for discovering potential new drug candidates involves the utilization of Knowledge Graphs (KGs). This review comprehensively explores some of the most prominent KGs, detailing their structure, data sources, and how they facilitate the repurposing of drugs. In addition to KGs, this paper delves into various artificial intelligence techniques that enhance the process of drug repurposing. These methods not only accelerate the identification of viable drug candidates but also improve the precision of predictions by leveraging complex datasets and advanced algorithms. Furthermore, the importance of explainability in drug repurposing is emphasized. Explainability methods are crucial as they provide insights into the reasoning behind AI-generated predictions, thereby increasing the trustworthiness and transparency of the repurposing process. We will discuss several techniques that can be employed to validate these predictions, ensuring that they are both reliable and understandable.
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Affiliation(s)
- Pablo Perdomo-Quinteiro
- Grupo de Aplicación de Telecomunicaciones Visuales, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Avenida Complutense 30, 28040 Madrid, Spain
| | - Alberto Belmonte-Hernández
- Grupo de Aplicación de Telecomunicaciones Visuales, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Avenida Complutense 30, 28040 Madrid, Spain
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Nguyen QH, Nguyen H, Oh EC, Nguyen T. Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review. Brief Bioinform 2024; 25:bbae498. [PMID: 39397425 PMCID: PMC11471905 DOI: 10.1093/bib/bbae498] [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: 05/22/2024] [Revised: 09/03/2024] [Accepted: 10/02/2024] [Indexed: 10/15/2024] Open
Abstract
Metabolite profiling is a powerful approach for the clinical diagnosis of complex diseases, ranging from cardiometabolic diseases, cancer, and cognitive disorders to respiratory pathologies and conditions that involve dysregulated metabolism. Because of the importance of systems-level interpretation, many methods have been developed to identify biologically significant pathways using metabolomics data. In this review, we first describe a complete metabolomics workflow (sample preparation, data acquisition, pre-processing, downstream analysis, etc.). We then comprehensively review 24 approaches capable of performing functional analysis, including those that combine metabolomics data with other types of data to investigate the disease-relevant changes at multiple omics layers. We discuss their availability, implementation, capability for pre-processing and quality control, supported omics types, embedded databases, pathway analysis methodologies, and integration techniques. We also provide a rating and evaluation of each software, focusing on their key technique, software accessibility, documentation, and user-friendliness. Following our guideline, life scientists can easily choose a suitable method depending on method rating, available data, input format, and method category. More importantly, we highlight outstanding challenges and potential solutions that need to be addressed by future research. To further assist users in executing the reviewed methods, we provide wrappers of the software packages at https://github.com/tinnlab/metabolite-pathway-review-docker.
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Affiliation(s)
- Quang-Huy Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, United States
| | - Ha Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, United States
| | - Edwin C Oh
- Department of Internal Medicine, UNLV School of Medicine, University of Nevada, Las Vegas, NV 89154, United States
| | - Tin Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, United States
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Novoa-Del-Toro EM, Witting M. Navigating common pitfalls in metabolite identification and metabolomics bioinformatics. Metabolomics 2024; 20:103. [PMID: 39305388 PMCID: PMC11416380 DOI: 10.1007/s11306-024-02167-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Accepted: 08/31/2024] [Indexed: 09/25/2024]
Abstract
BACKGROUND Metabolomics, the systematic analysis of small molecules in a given biological system, emerged as a powerful tool for different research questions. Newer, better, and faster methods have increased the coverage of metabolites that can be detected and identified in a shorter amount of time, generating highly dense datasets. While technology for metabolomics is still advancing, another rapidly growing field is metabolomics data analysis including metabolite identification. Within the next years, there will be a high demand for bioinformaticians and data scientists capable of analyzing metabolomics data as well as chemists capable of using in-silico tools for metabolite identification. However, metabolomics is often not included in bioinformatics curricula, nor does analytical chemistry address the challenges associated with advanced in-silico tools. AIM OF REVIEW In this educational review, we briefly summarize some key concepts and pitfalls we have encountered in a collaboration between a bioinformatician (originally not trained for metabolomics) and an analytical chemist. We identified that many misunderstandings arise from differences in knowledge about metabolite annotation and identification, and the proper use of bioinformatics approaches for these tasks. We hope that this article helps other bioinformaticians (as well as other scientists) entering the field of metabolomics bioinformatics, especially for metabolite identification, to quickly learn the necessary concepts for a successful collaboration with analytical chemists. KEY SCIENTIFIC CONCEPTS OF REVIEW We summarize important concepts related to LC-MS/MS based non-targeted metabolomics and compare them with other data types bioinformaticians are potentially familiar with. Drawing these parallels will help foster the learning of key aspects of metabolomics.
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Affiliation(s)
- Elva María Novoa-Del-Toro
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP- Purpan, UPS, 180 chemin de Tournefeuille St-Martin-du-Touch, BP 3, Toulouse Cedex, 31931, France
| | - Michael Witting
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, 85764, Neuherberg, Germany.
- Chair of Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, 85354, Freising-Weihenstephan, Germany.
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Ponce-de-Leon M, Wang-Sattler R, Peters A, Rathmann W, Grallert H, Artati A, Prehn C, Adamski J, Meisinger C, Linseisen J. Stool and blood metabolomics in the metabolic syndrome: a cross-sectional study. Metabolomics 2024; 20:105. [PMID: 39306637 PMCID: PMC11416374 DOI: 10.1007/s11306-024-02166-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Accepted: 08/27/2024] [Indexed: 09/25/2024]
Abstract
INTRODUCTION/OBJECTIVES Changes in the stool metabolome have been poorly studied in the metabolic syndrome (MetS). Moreover, few studies have explored the relationship of stool metabolites with circulating metabolites. Here, we investigated the associations between stool and blood metabolites, the MetS and systemic inflammation. METHODS We analyzed data from 1,370 participants of the KORA FF4 study (Germany). Metabolites were measured by Metabolon, Inc. (untargeted) in stool, and using the AbsoluteIDQ® p180 kit (targeted) in blood. Multiple linear regression models, adjusted for dietary pattern, age, sex, physical activity, smoking status and alcohol intake, were used to estimate the associations of metabolites with the MetS, its components and high-sensitivity C-reactive protein (hsCRP) levels. Partial correlation and Multi-Omics Factor Analysis (MOFA) were used to investigate the relationship between stool and blood metabolites. RESULTS The MetS was significantly associated with 170 stool and 82 blood metabolites. The MetS components with the highest number of associations were triglyceride levels (stool) and HDL levels (blood). Additionally, 107 and 27 MetS-associated metabolites (in stool and blood, respectively) showed significant associations with hsCRP levels. We found low partial correlation coefficients between stool and blood metabolites. MOFA did not detect shared variation across the two datasets. CONCLUSIONS The MetS, particularly dyslipidemia, is associated with multiple stool and blood metabolites that are also associated with systemic inflammation. Further studies are necessary to validate our findings and to characterize metabolic alterations in the MetS. Although our analyses point to weak correlations between stool and blood metabolites, additional studies using integrative approaches are warranted.
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Affiliation(s)
- Mariana Ponce-de-Leon
- Institute for Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany.
- Epidemiology, Medical Faculty, Universität Augsburg, Augsburg, Germany.
| | - Rui Wang-Sattler
- Institute of Translational Genomics, Helmholtz Munich, Munich-Neuherberg, Germany
- German Center for Diabetes Research (DZD), Partner Neuherberg, Munich-Neuherberg, Germany
| | - Annette Peters
- Institute for Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
- German Center for Diabetes Research (DZD), Partner Neuherberg, Munich-Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Munich, Munich-Neuherberg, Germany
- Munich Heart Alliance, German Center for Cardiovascular Health (DZHK E.V), Munich, Germany
| | - Wolfgang Rathmann
- German Diabetes Center (DDZ), Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, Munich-Neuherberg, Germany
| | - Harald Grallert
- German Center for Diabetes Research (DZD), Partner Neuherberg, Munich-Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Munich, Munich-Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Munich, Munich-Neuherberg, Germany
| | - Anna Artati
- Metabolomics and Proteomics Core, Helmholtz Munich, Munich-Neuherberg, Germany
| | - Cornelia Prehn
- Metabolomics and Proteomics Core, Helmholtz Munich, Munich-Neuherberg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Munich, Munich-Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Christa Meisinger
- Epidemiology, Medical Faculty, Universität Augsburg, Augsburg, Germany
| | - Jakob Linseisen
- Institute for Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
- Epidemiology, Medical Faculty, Universität Augsburg, Augsburg, Germany
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Lewis F, Shoieb D, Azmoun S, Colicino E, Jin Y, Chi J, Gu H, Placidi D, Padovani A, Pilotto A, Pepe F, Turla M, Crippa P, Wang X, Lucchini RG. Metabolomic and Lipidomic Analysis of Manganese-Associated Parkinsonism: a Case-Control Study in Brescia, Italy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.04.24313002. [PMID: 39281765 PMCID: PMC11398432 DOI: 10.1101/2024.09.04.24313002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/18/2024]
Abstract
Background and Objectives Excessive Manganese (Mn) exposure is neurotoxic and can cause Mn-Induced Parkinsonism (MnIP), marked by cognitive and motor dysfunction. Although metabolomic and lipidomic research in Parkinsonism (PD) patients exists, it remains limited. This study hypothesizes distinct metabolomic and lipidomic profiles based on exposure status, disease diagnosis, and their interaction. Methods We used a case-control design with a 2×2 factorial framework to investigate the metabolomic and lipidomic alterations associated with Mn exposure and their link to PD. The study population of 97 individuals was divided into four groups: non-exposed controls (n=23), exposed controls (n=25), non-exposed with PD (n=26) and exposed with PD (n=23). Cases, defined by at least two cardinal PD features (excluding vascular, iatrogenic, and traumatic origins), were recruited from movement disorder clinics in four hospitals in Brescia, Northern Italy. Controls, free from neurological or psychiatric conditions, were selected from the same hospitals. Exposed subjects resided in metallurgic regions (Val Camonica and Bagnolo Mella) for at least 8 continuous years, while non-exposed subjects lived in low-exposure areas around Lake Garda and Brescia city. We conducted untargeted analyses of metabolites and lipids in whole blood samples using ultra-high-performance liquid chromatography (UHPLC) and mass spectrometry (MS), followed by statistical analyses including Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA), and Two-Way Analysis of Covariance (ANCOVA). Results Metabolomic analysis revealed modulation of alanine, aspartate, and glutamate metabolism (Impact=0.05, p=0.001) associated with disease effect; butanoate metabolism (Impact=0.03, p=0.004) with the exposure effect; and vitamin B6 metabolism (Impact=0.08, p=0.03) with the interaction effect. Differential relative abundances in 3-sulfoxy-L-Tyrosine (β=1.12, FDR p<0.001), glycocholic acid (β=0.48, FDR p=0.03), and palmitelaidic acid (β=0.30, FDR p<0.001) were linked to disease, exposure, and interaction effects, respectively. In the lipidome, ferroptosis (Pathway Lipids=11, FDR p=0.03) associated with the disease effect and sphingolipid signaling (Pathway Lipids=9, FDR p=0.04) associated with the interaction effect were significantly altered. Lipid classes triacylglycerols, ceramides, and phosphatidylethanolamines showed differential relative abundances associated with disease, exposure, and interaction effects, respectively. Discussion These findings suggest that PD and Mn exposure induce unique metabolomic and lipidomic changes, potentially serving as biomarkers for MnIP and warranting further study.
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Affiliation(s)
- Freeman Lewis
- Environmental Health Sciences, Florida International University, 11200 SW 8th St, Miami, 33199, Florida, USA
| | - Daniel Shoieb
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Viale Europa 11, Brescia, 25123, Italy
| | - Somaiyeh Azmoun
- Environmental Health Sciences, Florida International University, 11200 SW 8th St, Miami, 33199, Florida, USA
| | - Elena Colicino
- Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, 17 E 102nd St, New York, 10029, New York, USA
| | - Yan Jin
- Environmental Health Sciences, Florida International University, 11200 SW 8th St, Miami, 33199, Florida, USA
| | - Jinhua Chi
- Environmental Health Sciences, Florida International University, 11200 SW 8th St, Miami, 33199, Florida, USA
| | - Haiwei Gu
- Environmental Health Sciences, Florida International University, 11200 SW 8th St, Miami, 33199, Florida, USA
| | - Donatella Placidi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Viale Europa 11, Brescia, 25123, Italy
| | - Alessandro Padovani
- Department of Clinical and Experimental Sciences, University of Brescia, Viale Europa 11, Brescia, 25123, Italy and Department of continuity of care and frailty, Neurology Unit, ASST Spedali Civili Hospital, Brescia, Italy
| | - Andrea Pilotto
- Department of Clinical and Experimental Sciences, University of Brescia, Viale Europa 11, Brescia, 25123, Italy and Department of continuity of care and frailty, Neurology Unit, ASST Spedali Civili Hospital, Brescia, Italy
| | - Fulvio Pepe
- Clinic of Neurology, Poliambulanza Foundation, Brescia, Italy
| | - Marinella Turla
- Clinic of Neurology, Esine Hospital of Valcamonica, Brescia, Italy
| | | | - Xuexia Wang
- Department of Biostatistics, Florida International University, 11200 SW 8th St, Miami, 33199, Florida, USA
| | - Roberto G Lucchini
- Environmental Health Sciences, Florida International University, 11200 SW 8th St, Miami, 33199, Florida, USA
- Department of Biomedical, Metabolic and Neurosciences, University of Modena and Reggio Emilia, Via Universitá, 4, Modena, 610101, Italy
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10
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Matar IK, Muhammad ZA, Gomha SM, Al-Hussain SA, Al-Ali M, Zaki MEA, El-Khouly AS. Novel 3-substituted coumarins inspire a custom pharmacology prediction pipeline: an anticancer discovery adventure. Future Med Chem 2024; 16:1761-1776. [PMID: 39230519 PMCID: PMC11457655 DOI: 10.1080/17568919.2024.2379232] [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: 04/06/2024] [Accepted: 07/03/2024] [Indexed: 09/05/2024] Open
Abstract
Aim: This research aims to expand the established pharmacological space of tumor-associated carbonic anhydrases (TACAs) by exploring the synthetically accessible chemical space of 3-substituted coumarins, with the help of in silico pharmacology prediction.Materials & methods: 52 novel 3-substituted coumarins were sketched, prioritizing synthetic feasibility. Their pharmacological potentials were estimated using a custom machine-learning approach. 17 compounds were predicted as cytotoxic against HeLa cells by interfering with TACAs. Those compounds were synthesized and biologically tested against HeLa cells. The three most potent compounds were assayed against multiple carbonic anhydrases, and their enzyme binding mechanism(s) were studied using molecular docking.Results: Experimental results exhibited pronounced consensus with in silico pharmacology predictions.Conclusion: Novel 3-substituted coumarins are herein dispatched to the cancer therapeutics space.
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Affiliation(s)
- Islam K Matar
- Department of Chemistry, Saint Mary's University, Halifax, Nova Scotia, B3H 3C3, Canada
- Department of Chemistry & Physics, Mount Saint Vincent University, Halifax, Nova Scotia, B3M 2J6, Canada
| | - Zeinab A Muhammad
- Department of Pharmaceutical Chemistry, National Organization for Drug Control & Research (NODCAR), Giza, 12311, Egypt
| | - Sobhi M Gomha
- Department of Chemistry, Faculty of Science, Islamic University of Madinah, Madinah, 42351, Saudi Arabia
| | - Sami A Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11623, Saudi Arabia
| | - Maha Al-Ali
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11623, Saudi Arabia
| | - Magdi EA Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11623, Saudi Arabia
| | - Ahmed S El-Khouly
- Department of Organic & Medicinal Chemistry, Faculty of Pharmacy, University of Sadat City, 32897, Egypt
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, Jerash University, 26150, Jordan
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11
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Luo Y, Zhang W, Qin G. Metabolomics in diabetic nephropathy: Unveiling novel biomarkers for diagnosis (Review). Mol Med Rep 2024; 30:156. [PMID: 38963028 PMCID: PMC11258608 DOI: 10.3892/mmr.2024.13280] [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: 03/22/2024] [Accepted: 06/21/2024] [Indexed: 07/05/2024] Open
Abstract
Diabetic nephropathy (DN) also known as diabetic kidney disease, is a major microvascular complication of diabetes and a leading cause of end‑stage renal disease (ESRD), which affects the morbidity and mortality of patients with diabetes. Despite advancements in diabetes care, current diagnostic methods, such as the determination of albuminuria and the estimated glomerular filtration rate, are limited in sensitivity and specificity, often only identifying kidney damage after considerable morphological changes. The present review discusses the potential of metabolomics as an approach for the early detection and management of DN. Metabolomics is the study of metabolites, the small molecules produced by cellular processes, and may provide a more sensitive and specific diagnostic tool compared with traditional methods. For the purposes of this review, a systematic search was conducted on PubMed and Google Scholar for recent human studies published between 2011 and 2023 that used metabolomics in the diagnosis of DN. Metabolomics has demonstrated potential in identifying metabolic biomarkers specific to DN. The ability to detect a broad spectrum of metabolites with high sensitivity and specificity may allow for earlier diagnosis and better management of patients with DN, potentially reducing the progression to ESRD. Furthermore, metabolomics pathway analysis assesses the pathophysiological mechanisms underlying DN. On the whole, metabolomics is a potential tool in the diagnosis and management of DN. By providing a more in‑depth understanding of metabolic alterations associated with DN, metabolomics could significantly improve early detection, enable timely interventions and reduce the healthcare burdens associated with this condition.
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Affiliation(s)
- Yuanyuan Luo
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan 450052, P.R. China
| | - Wei Zhang
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan 450052, P.R. China
| | - Guijun Qin
- Department of Endocrinology and Metabolism, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan 450052, P.R. China
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12
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Sola F, Ayala D, Pulido M, Ayala R, López-Cerero L, Hernández I, Ruiz D. ginmappeR: an unified approach for integrating gene and protein identifiers across biological sequence databases. BIOINFORMATICS ADVANCES 2024; 4:vbae129. [PMID: 39262905 PMCID: PMC11387618 DOI: 10.1093/bioadv/vbae129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 08/09/2024] [Accepted: 08/27/2024] [Indexed: 09/13/2024]
Abstract
Summary The proliferation of biological sequence data, due to developments in molecular biology techniques, has led to the creation of numerous open access databases on gene and protein sequencing. However, the lack of direct equivalence between identifiers across these databases difficults data integration. To address this challenge, we introduce ginmappeR, an integrated R package facilitating the translation of gene and protein identifiers between databases. By providing a unified interface, ginmappeR streamlines the integration of diverse data sources into biological workflows, so it enhances efficiency and user experience. Availability and implementation from Bioconductor: https://bioconductor.org/packages/ginmappeR.
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Affiliation(s)
- Fernando Sola
- SCORE Lab, DEAL, University of Seville, ETSII, 41012 Seville, Spain
| | - Daniel Ayala
- SCORE Lab, DEAL, University of Seville, ETSII, 41012 Seville, Spain
| | - Marina Pulido
- Department of Microbiology, University of Seville, 41009 Seville, Spain
- Institute of Biomedicine of Seville, Virgen Macarena University Hospital, CSIC, University of Seville, 41013 Seville, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Infecciosas (CIBERINFEC), 28029 Madrid, Spain
| | - Rafael Ayala
- Molecular Cryo-Electron Microscopy Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa 904-0411, Japan
| | - Lorena López-Cerero
- Department of Microbiology, University of Seville, 41009 Seville, Spain
- Institute of Biomedicine of Seville, Virgen Macarena University Hospital, CSIC, University of Seville, 41013 Seville, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Infecciosas (CIBERINFEC), 28029 Madrid, Spain
| | - Inma Hernández
- SCORE Lab, DEAL, University of Seville, ETSII, 41012 Seville, Spain
| | - David Ruiz
- SCORE Lab, DEAL, University of Seville, ETSII, 41012 Seville, Spain
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13
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Fresnais L, Perin O, Riu A, Grall R, Ott A, Fromenty B, Gallardo JC, Stingl M, Frainay C, Jourdan F, Poupin N. A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques. BMC Bioinformatics 2024; 25:234. [PMID: 38992584 PMCID: PMC11238488 DOI: 10.1186/s12859-024-05845-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 06/14/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes. RESULTS To address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA. CONCLUSION The proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module ( https://pypi.org/project/manamodeller/ ) and jupyter notebooks ( https://github.com/LouisonF/MANA ).
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Affiliation(s)
- Louison Fresnais
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France.
| | - Olivier Perin
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Anne Riu
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Romain Grall
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Alban Ott
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Bernard Fromenty
- Institut NUMECAN (Nutrition Metabolisms and Cancer) UMR_A 1317, UMR_S 1241, INSERM, Univ Rennes, INRAE, 35000, Rennes, France
| | - Jean-Clément Gallardo
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Maximilian Stingl
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Clément Frainay
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Fabien Jourdan
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Nathalie Poupin
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
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14
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Elizarraras JM, Liao Y, Shi Z, Zhu Q, Pico A, Zhang B. WebGestalt 2024: faster gene set analysis and new support for metabolomics and multi-omics. Nucleic Acids Res 2024; 52:W415-W421. [PMID: 38808672 PMCID: PMC11223849 DOI: 10.1093/nar/gkae456] [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: 03/08/2024] [Revised: 05/07/2024] [Accepted: 05/14/2024] [Indexed: 05/30/2024] Open
Abstract
Enrichment analysis, crucial for interpreting genomic, transcriptomic, and proteomic data, is expanding into metabolomics. Furthermore, there is a rising demand for integrated enrichment analysis that combines data from different studies and omics platforms, as seen in meta-analysis and multi-omics research. To address these growing needs, we have updated WebGestalt to include enrichment analysis capabilities for both metabolites and multiple input lists of analytes. We have also significantly increased analysis speed, revamped the user interface, and introduced new pathway visualizations to accommodate these updates. Notably, the adoption of a Rust backend reduced gene set enrichment analysis time by 95% from 270.64 to 12.41 s and network topology-based analysis by 89% from 159.59 to 17.31 s in our evaluation. This performance improvement is also accessible in both the R package and a newly introduced Python package. Additionally, we have updated the data in the WebGestalt database to reflect the current status of each source and have expanded our collection of pathways, networks, and gene signatures. The 2024 WebGestalt update represents a significant leap forward, offering new support for metabolomics, streamlined multi-omics analysis capabilities, and remarkable performance enhancements. Discover these updates and more at https://www.webgestalt.org.
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Affiliation(s)
- John M Elizarraras
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yuxing Liao
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Zhiao Shi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Qian Zhu
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Alexander R Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
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15
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Xavier JB. Machine learning of cellular metabolic rewiring. Biol Methods Protoc 2024; 9:bpae048. [PMID: 39011352 PMCID: PMC11249387 DOI: 10.1093/biomethods/bpae048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 06/14/2024] [Accepted: 07/01/2024] [Indexed: 07/17/2024] Open
Abstract
Metabolic rewiring allows cells to adapt their metabolism in response to evolving environmental conditions. Traditional metabolomics techniques, whether targeted or untargeted, often struggle to interpret these adaptive shifts. Here, we introduce MetaboLiteLearner, a lightweight machine learning framework that harnesses the detailed fragmentation patterns from electron ionization (EI) collected in scan mode during gas chromatography/mass spectrometry to predict changes in the metabolite composition of metabolically adapted cells. When tested on breast cancer cells with different preferences to metastasize to specific organs, MetaboLiteLearner predicted the impact of metabolic rewiring on metabolites withheld from the training dataset using only the EI spectra, without metabolite identification or pre-existing knowledge of metabolic networks. Despite its simplicity, the model learned captured shared and unique metabolomic shifts between brain- and lung-homing metastatic lineages, suggesting cellular adaptations associated with metastasis to specific organs. Integrating machine learning and metabolomics paves the way for new insights into complex cellular adaptations.
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Affiliation(s)
- Joao B Xavier
- Program for Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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16
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Zanetti KA, Guo L, Husain D, Kelly RS, Lasky-Su J, Broadhurst D, Wheelock CE. Workshop report - interdisciplinary metabolomic epidemiology: the pathway to clinical translation. Metabolomics 2024; 20:60. [PMID: 38773013 PMCID: PMC11108898 DOI: 10.1007/s11306-024-02111-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 03/19/2024] [Indexed: 05/23/2024]
Abstract
Metabolomic epidemiology studies are complex and require a broad array of domain expertise. Although many metabolite-phenotype associations have been identified; to date, few findings have been translated to the clinic. Bridging this gap requires understanding of both the underlying biology of these associations and their potential clinical implications, necessitating an interdisciplinary team approach. To address this need in metabolomic epidemiology, a workshop was held at Metabolomics 2023 in Niagara Falls, Ontario, Canada that highlighted the domain expertise needed to effectively conduct these studies -- biochemistry, clinical science, epidemiology, and assay development for biomarker validation -- and emphasized the role of interdisciplinary teams to move findings towards clinical translation.
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Affiliation(s)
- Krista A Zanetti
- Office of Nutrition Research, Division of Program Coordination, Planning, and Strategic Initiatives, Office of the Director, National Institutes of Health, Bethesda, MD, USA.
| | | | - Deeba Husain
- Mass Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Rachel S Kelly
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - David Broadhurst
- School of Science, Edith Cowan University, Joondalup, Perth, Western Australia
| | - Craig E Wheelock
- Unit of Integrative Metabolomics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Respiratory Medicine and Allergy, Karolinska University Hospital, Stockholm, Sweden
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17
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Sun B, Zhang Y, Chen K, Sun L. Metabolomics captures the differential metabolites in the replication pathway of snakehead vesiculovirus regulated by glutamine. DISEASES OF AQUATIC ORGANISMS 2024; 158:101-114. [PMID: 38661141 DOI: 10.3354/dao03786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Snakehead vesiculovirus (SHVV) is a negative-sense single-stranded RNA virus that infects snakehead fish. This virus leads to illness and mortality, causing significant economic losses in the snakehead aquaculture industry. The replication and spread of SHVV in cells, which requires glutamine as a nitrogen source, is accompanied by alterations in intracellular metabolites. However, the metabolic mechanisms underlying the inhibition of viral replication by glutamine deficiency are poorly understood. This study utilized liquid chromatography-mass spectrometry to measure the differential metabolites between the channel catfish Parasilurus asotus ovary cell line infected with SHVV under glutamine-containing and glutamine-deprived conditions. Results showed that the absence of glutamine regulated 4 distinct metabolic pathways and influenced 9 differential metabolites. The differential metabolites PS(16:0/16:0), 5,10-methylene-THF, and PS(18:0/18:1(9Z)) were involved in amino acid metabolism. In the nuclear metabolism functional pathway, differential metabolites of guanosine were observed. In the carbohydrate metabolism pathway, differential metabolites of UDP-d-galacturonate were detected. In the signal transduction pathway, differential metabolites of SM(d18:1/20:0), SM(d18:1/22:1(13Z)), SM(d18:1/24:1(15 Z)), and sphinganine were found. Among them, PS(18:0/18:1(9Z)), PS(16:0/16:0), and UDP-d-galacturonate were involved in the synthesis of phosphatidylserine and glycoprotein. The compound 5,10-methylene-THF provided raw materials for virus replication, and guanosine and sphingosine are related to virus virulence. The differential metabolites may collectively participate in the replication, packaging, and proliferation of SHVV under glutamine deficiency. This study provides new insights and potential metabolic targets for combating SHVV infection in aquaculture through metabolomics approaches.
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Affiliation(s)
- Binbin Sun
- School of Life Sciences, Jiangsu University, Zhenjiang 212013, PR China
| | - Yulei Zhang
- Guangdong South China Sea Key Laboratory of Aquaculture for Aquatic Economic Animals, Guangdong Ocean University, Zhanjiang 524088, PR China
| | - Keping Chen
- School of Life Sciences, Jiangsu University, Zhenjiang 212013, PR China
| | - Lindan Sun
- School of Life Sciences, Jiangsu University, Zhenjiang 212013, PR China
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18
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Wang Y, Cai X, Ma Y, Yang Y, Pan CW, Zhu X, Ke C. Metabolomics on depression: A comparison of clinical and animal research. J Affect Disord 2024; 349:559-568. [PMID: 38211744 DOI: 10.1016/j.jad.2024.01.053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 12/13/2023] [Accepted: 01/04/2024] [Indexed: 01/13/2024]
Abstract
BACKGROUND Depression is a major cause of suicide and mortality worldwide. This study aims to conduct a systematic review to identify metabolic biomarkers and pathways for major depressive disorder (MDD), a prevalent subtype of clinical depression. METHODS We searched for metabolomics studies on depression published between January 2000 and January 2023 in the PubMed and Web of Science databases. The reported metabolic biomarkers were systematically evaluated and compared. Pathway analysis was implemented using MetaboAnalyst 5.0. RESULTS We included 26 clinical studies on MDD and 78 metabolomics studies on depressive-like animal models. A total of 55 and 77 high-frequency metabolites were reported consistently in two-thirds of clinical and murine studies, respectively. In the comparison between murine and clinical studies, we identified 9 consistently changed metabolites (tryptophan, tyrosine, phenylalanine, methionine, fumarate, valine, deoxycholic acid, pyruvate, kynurenic acid) in the blood, 1 consistently altered metabolite (indoxyl sulfate) in the urine and 14 disturbed metabolic pathways in both types of studies. These metabolic dysregulations and pathways are mainly implicated in enhanced inflammation, impaired neuroprotection, reduced energy metabolism, increased oxidative stress damage and disturbed apoptosis, laying solid molecular foundations for MDD. LIMITATIONS Due to unavailability of original data like effect-size results in many metabolomics studies, a meta-analysis cannot be conducted, and confounding factors cannot be fully ruled out. CONCLUSIONS This systematic review delineated metabolic biomarkers and pathways related to depression in the murine and clinical samples, providing opportunities for early diagnosis of MDD and the development of novel diagnostic targets.
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Affiliation(s)
- Yibo Wang
- Suzhou Medical College of Soochow University, Suzhou, China
| | - Xinyi Cai
- Suzhou Medical College of Soochow University, Suzhou, China
| | - Yuchen Ma
- Suzhou Medical College of Soochow University, Suzhou, China
| | - Yang Yang
- Suzhou Medical College of Soochow University, Suzhou, China
| | - Chen-Wei Pan
- School of Public Health, Suzhou Medical College of Soochow University, Suzhou, China
| | - Xiaohong Zhu
- Suzhou Centers for Disease Control and Prevention, Suzhou, China.
| | - Chaofu Ke
- School of Public Health, Suzhou Medical College of Soochow University, Suzhou, China.
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19
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Maushagen J, Addin NS, Schuppert C, Ward-Caviness CK, Nattenmüller J, Adamski J, Peters A, Bamberg F, Schlett CL, Wang-Sattler R, Rospleszcz S. Serum metabolite signatures of cardiac function and morphology in individuals from a population-based cohort. Biomark Res 2024; 12:31. [PMID: 38444025 PMCID: PMC10916302 DOI: 10.1186/s40364-024-00578-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/24/2024] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND Changes in serum metabolites in individuals with altered cardiac function and morphology may exhibit information about cardiovascular disease (CVD) pathway dysregulations and potential CVD risk factors. We aimed to explore associations of cardiac function and morphology, evaluated using magnetic resonance imaging (MRI) with a large panel of serum metabolites. METHODS Cross-sectional data from CVD-free individuals from the population-based KORA cohort were analyzed. Associations between 3T-MRI-derived left ventricular (LV) function and morphology parameters (e.g., volumes, filling rates, wall thickness) and markers of carotid plaque with metabolite profile clusters and single metabolites as outcomes were assessed by adjusted multinomial logistic regression and linear regression models. RESULTS In 360 individuals (mean age 56.3 years; 41.9% female), 146 serum metabolites clustered into three distinct profiles that reflected high-, intermediate- and low-CVD risk. Higher stroke volume (relative risk ratio (RRR): 0.53, 95%-CI [0.37; 0.76], p-value < 0.001) and early diastolic filling rate (RRR: 0.51, 95%-CI [0.37; 0.71], p-value < 0.001) were most strongly protectively associated against the high-risk profile compared to the low-risk profile after adjusting for traditional CVD risk factors. Moreover, imaging markers were associated with 10 metabolites in linear regression. Notably, negative associations of stroke volume and early diastolic filling rate with acylcarnitine C5, and positive association of function parameters with lysophosphatidylcholines, diacylphosphatidylcholines, and acylalkylphosphatidylcholines were observed. Furthermore, there was a negative association of LV wall thickness with alanine, creatinine, and symmetric dimethylarginine. We found no significant associations with carotid plaque. CONCLUSIONS Serum metabolite signatures are associated with cardiac function and morphology even in individuals without a clinical indication of CVD.
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Affiliation(s)
- Juliane Maushagen
- Institute of Epidemiology, Helmholtz Munich, Neuherberg, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Medical Faculty, Ludwig- Maximilians-Universität (LMU), München, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Nuha Shugaa Addin
- Institute of Epidemiology, Helmholtz Munich, Neuherberg, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Medical Faculty, Ludwig- Maximilians-Universität (LMU), München, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Christopher Schuppert
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Cavin K Ward-Caviness
- Center for Public Health and Environmental Assessment, U.S. EPA, Chapel Hill, NC, USA
| | - Johanna Nattenmüller
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, 117597, Singapore, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Munich, Neuherberg, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Medical Faculty, Ludwig- Maximilians-Universität (LMU), München, Germany
- German Center for Diabetes Research, DZD, Neuherberg, Germany
- German Center for Cardiovascular Disease Research (DZHK), Munich Heart Alliance, Munich, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Christopher L Schlett
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Rui Wang-Sattler
- German Center for Diabetes Research, DZD, Neuherberg, Germany
- Institute of Translational Genomics, Helmholtz Munich, Neuherberg, Germany
| | - Susanne Rospleszcz
- Institute of Epidemiology, Helmholtz Munich, Neuherberg, Germany.
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Medical Faculty, Ludwig- Maximilians-Universität (LMU), München, Germany.
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany.
- German Center for Cardiovascular Disease Research (DZHK), Munich Heart Alliance, Munich, Germany.
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20
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Gough EK, Edens TJ, Carr L, Robertson RC, Mutasa K, Ntozini R, Chasekwa B, Geum HM, Baharmand I, Gill SK, Mutasa B, Mbuya MNN, Majo FD, Tavengwa N, Francis F, Tome J, Evans C, Kosek M, Prendergast AJ, Manges AR. Bifidobacterium longum modifies a nutritional intervention for stunting in Zimbabwean infants. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.18.24301438. [PMID: 38293149 PMCID: PMC10827232 DOI: 10.1101/2024.01.18.24301438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Child stunting is an indicator of chronic undernutrition and reduced human capital. However, it remains a poorly understood public health problem. Small-quantity lipid-based nutrient supplements (SQ-LNS) have been widely tested to reduce stunting, but have modest effects. The infant intestinal microbiome may contribute to stunting, and is partly shaped by mother and infant histo-blood group antigens (HBGA). We investigated whether mother-infant fucosyltransferase status, which governs HBGA, and the infant gut microbiome modified the impact of SQ-LNS on stunting at age 18 months among Zimbabwean infants in the SHINE Trial ( NCT01824940 ). We found that mother-infant fucosyltransferase discordance and Bifidobacterium longum reduced SQ-LNS efficacy. Infant age-related microbiome shifts in B. longum subspecies dominance from infantis , a proficient human milk oligosaccharide utilizer, to suis or longum , proficient plant-polysaccharide utilizers, were partly influenced by discordance in mother-infant FUT2+/FUT3- phenotype, suggesting that a "younger" microbiome at initiation of SQ-LNS reduces its benefits on stunting.
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21
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Wieder C, Cooke J, Frainay C, Poupin N, Bowler R, Jourdan F, Kechris KJ, Lai RPJ, Ebbels T. PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration. PLoS Comput Biol 2024; 20:e1011814. [PMID: 38527092 PMCID: PMC10994553 DOI: 10.1371/journal.pcbi.1011814] [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: 01/10/2024] [Revised: 04/04/2024] [Accepted: 03/11/2024] [Indexed: 03/27/2024] Open
Abstract
As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. PathIntegrate is available as an open-source Python package.
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Affiliation(s)
- Cecilia Wieder
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Juliette Cooke
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Clement Frainay
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Nathalie Poupin
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Russell Bowler
- National Jewish Health, Denver, Colorado, United States of America
| | - Fabien Jourdan
- MetaboHUB-Metatoul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Katerina J. Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Rachel PJ Lai
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Timothy Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
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22
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Fischer N, Costa CP, Hur M, Kirkwood JS, Woodard SH. Impacts of neonicotinoid insecticides on bumble bee energy metabolism are revealed under nectar starvation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169388. [PMID: 38104805 DOI: 10.1016/j.scitotenv.2023.169388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 12/19/2023]
Abstract
Bumble bees are an important group of insects that provide essential pollination services as a consequence of their foraging behaviors. These pollination services are driven, in part, by energetic exchanges between flowering plants and individual bees. Thus, it is important to examine bumble bee energy metabolism and explore how it might be influenced by external stressors contributing to declines in global pollinator populations. Two stressors that are commonly encountered by bees are insecticides, such as the neonicotinoids, and nutritional stress, resulting from deficits in pollen and nectar availability. Our study uses a metabolomic approach to examine the effects of neonicotinoid insecticide exposure on bumble bee metabolism, both alone and in combination with nutritional stress. We hypothesized that exposure to imidacloprid disrupts bumble bee energy metabolism, leading to changes in key metabolites involved in central carbon metabolism. We tested this by exposing Bombus impatiens workers to imidacloprid according to one of three exposure paradigms designed to explore how chronic versus more acute (early or late) imidacloprid exposure influences energy metabolite levels, then also subjecting them to artificial nectar starvation. The strongest effects of imidacloprid were observed when bees also experienced nectar starvation, suggesting a combinatorial effect of neonicotinoids and nutritional stress on bumble bee energy metabolism. Overall, this study provides important insights into the mechanisms underlying the impact of neonicotinoid insecticides on pollinators, and underscores the need for further investigation into the complex interactions between environmental stressors and energy metabolism.
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Affiliation(s)
- Natalie Fischer
- Department of Entomology, University of California, Riverside, Riverside, CA, USA.
| | - Claudinéia P Costa
- Department of Entomology, University of California, Riverside, Riverside, CA, USA
| | - Manhoi Hur
- IIGB Metabolomics Core Facility, University of California, Riverside, Riverside, CA, USA
| | - Jay S Kirkwood
- IIGB Metabolomics Core Facility, University of California, Riverside, Riverside, CA, USA
| | - S Hollis Woodard
- Department of Entomology, University of California, Riverside, Riverside, CA, USA.
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23
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Gao Y, Zhang G, Jiang S, Liu Y. Wekemo Bioincloud: A user-friendly platform for meta-omics data analyses. IMETA 2024; 3:e175. [PMID: 38868508 PMCID: PMC10989175 DOI: 10.1002/imt2.175] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/23/2024] [Accepted: 01/23/2024] [Indexed: 06/14/2024]
Abstract
The increasing application of meta-omics approaches to investigate the structure, function, and intercellular interactions of microbial communities has led to a surge in available data. However, this abundance of human and environmental microbiome data has exposed new scalability challenges for existing bioinformatics tools. In response, we introduce Wekemo Bioincloud-a specialized platform for -omics studies. This platform offers a comprehensive analysis solution, specifically designed to alleviate the challenges of tool selection for users in the face of expanding data sets. As of now, Wekemo Bioincloud has been regularly equipped with 22 workflows and 65 visualization tools, establishing itself as a user-friendly and widely embraced platform for studying diverse data sets. Additionally, the platform enables the online modification of vector outputs, and the registration-independent personalized dashboard system ensures privacy and traceability. Wekemo Bioincloud is freely available at https://www.bioincloud.tech/.
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Affiliation(s)
- Yunyun Gao
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhenChina
| | - Guoxing Zhang
- Shenzhen Wekemo Technology Group Co., Ltd.ShenzhenChina
| | - Shunyao Jiang
- Shenzhen Wekemo Technology Group Co., Ltd.ShenzhenChina
| | - Yong‐Xin Liu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhenChina
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24
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Kuligowski J, Pérez-Rubio Á, Moreno-Torres M, Soluyanova P, Pérez-Rojas J, Rienda I, Pérez-Guaita D, Pareja E, Trullenque-Juan R, Castell JV, Verheijen M, Caiment F, Jover R, Quintás G. Cluster-Partial Least Squares (c-PLS) regression analysis: Application to miRNA and metabolomic data. Anal Chim Acta 2024; 1286:342052. [PMID: 38049234 DOI: 10.1016/j.aca.2023.342052] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 11/20/2023] [Indexed: 12/06/2023]
Abstract
BACKGROUND Biomedicine and biological research frequently involve analyzing large datasets generated by high-throughput technologies like genomics, transcriptomics, miRNomics, and metabolomics. Pathway analysis is a common computational approach used to understand the impact of experimental conditions, phenotypes, or interventions on biological pathways and networks. This involves statistical analysis of omic data to identify differentially expressed variables and mapping them onto predefined pathways. Analyzing such datasets often requires multivariate techniques to extract meaningful insights such as Partial Least Squares (PLS). Variable selection strategies like interval-PLS (iPLS) help improve understanding and predictive performance by identifying informative variables or intervals. However, iPLS is suboptimal to treat omic data such as metabolic or miRNA profiles, where features cannot be distributed along a continuous dimension describing their relationships as in e.g., vibrational or nuclear magnetic resonance spectroscopy. RESULTS This study introduces a novel variable selection approach called cluster PLS (c-PLS) that aims to assess the joint impact of variable groups selected based on biological characteristics (such as miRNA-regulated metabolic pathway or lipid classes) on the predictive performance of a multivariate model. The usefulness of c-PLS is shown using miRNomic and metabolomic datasets obtained from the analysis of 24 liver tissue biopsies collected in the frame of a clinical study of steatosis. SIGNIFICANCE AND NOVELTY Results obtained show that c-PLS enables analyzing the effect of biologically relevant variable clusters, facilitating the identification of biological processes associated with the independent variable, and the prioritization of the biological factors influencing model performance, thereby improving the understanding of the biological factors driving model predictions. While the strategy is tested for the evaluation of PLS models, it could be extended to other linear and non-linear multivariate models.
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Affiliation(s)
- Julia Kuligowski
- Neonatal Research Group, Health Research Institute La Fe, Valencia, Spain
| | - Álvaro Pérez-Rubio
- Servicio de Cirugía General y Aparato Digestivo, Hospital Universitario Dr. Peset, Valencia, Spain
| | - Marta Moreno-Torres
- Departamento de Bioquímica y Biología Molecular, Universidad de Valencia, Valencia, Spain; Experimental Hepatology and Liver Transplant Unit, Health Research Institute La Fe, Valencia, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Madrid, Spain
| | - Polina Soluyanova
- Departamento de Bioquímica y Biología Molecular, Universidad de Valencia, Valencia, Spain; Experimental Hepatology and Liver Transplant Unit, Health Research Institute La Fe, Valencia, Spain
| | - Judith Pérez-Rojas
- Pathology Department, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Iván Rienda
- Pathology Department, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - David Pérez-Guaita
- Departamento de Química Analítica, Universidad de Valencia, Burjassot, Spain
| | - Eugenia Pareja
- Servicio de Cirugía General y Aparato Digestivo, Hospital Universitario Dr. Peset, Valencia, Spain; Experimental Hepatology and Liver Transplant Unit, Health Research Institute La Fe, Valencia, Spain
| | - Ramón Trullenque-Juan
- Servicio de Cirugía General y Aparato Digestivo, Hospital Universitario Dr. Peset, Valencia, Spain
| | - José V Castell
- Departamento de Bioquímica y Biología Molecular, Universidad de Valencia, Valencia, Spain; Experimental Hepatology and Liver Transplant Unit, Health Research Institute La Fe, Valencia, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Madrid, Spain
| | - Marcha Verheijen
- Department of Toxicogenomics, GROW- school for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands
| | - Florian Caiment
- Department of Toxicogenomics, GROW- school for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands
| | - Ramiro Jover
- Departamento de Bioquímica y Biología Molecular, Universidad de Valencia, Valencia, Spain; Experimental Hepatology and Liver Transplant Unit, Health Research Institute La Fe, Valencia, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Madrid, Spain
| | - Guillermo Quintás
- Metabolomics and bioanalysis, Leitat Technological Center, Terrassa, Spain.
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25
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Mahajan P, Fiehn O, Barupal D. IDSL.GOA: gene ontology analysis for interpreting metabolomic datasets. Sci Rep 2024; 14:1299. [PMID: 38221536 PMCID: PMC10788336 DOI: 10.1038/s41598-024-51992-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 01/11/2024] [Indexed: 01/16/2024] Open
Abstract
Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2393 metabolic GO terms and associated 3144 genes, 1,492 EC annotations, and 2621 metabolites. IDSL.GOA analysis of a case study of older versus young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR < 0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/ .
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Affiliation(s)
- Priyanka Mahajan
- Integrated Data Science Laboratory for Metabolomics and Exposomics, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, 10954, USA
| | - Oliver Fiehn
- NIH-West Coast Metabolomics Center, University of California, Davis, CA, 95616, USA
| | - Dinesh Barupal
- Integrated Data Science Laboratory for Metabolomics and Exposomics, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, 10954, USA.
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26
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Chang X, Yan S, Zhang Y, Zhang Y, Li L, Gao Z, Lin X, Chi X. GINv2.0: a comprehensive topological network integrating molecular interactions from multiple knowledge bases. NPJ Syst Biol Appl 2024; 10:4. [PMID: 38218959 PMCID: PMC10787761 DOI: 10.1038/s41540-024-00330-y] [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: 08/19/2023] [Accepted: 01/02/2024] [Indexed: 01/15/2024] Open
Abstract
Knowledge bases have been instrumental in advancing biological research, facilitating pathway analysis and data visualization, which are now widely employed in the scientific community. Despite the establishment of several prominent knowledge bases focusing on signaling, metabolic networks, or both, integrating these networks into a unified topological network has proven to be challenging. The intricacy of molecular interactions and the diverse formats employed to store and display them contribute to the complexity of this task. In a prior study, we addressed this challenge by introducing a "meta-pathway" structure that integrated the advantages of the Simple Interaction Format (SIF) while accommodating reaction information. Nevertheless, the earlier Global Integrative Network (GIN) was limited to reliance on KEGG alone. Here, we present GIN version 2.0, which incorporates human molecular interaction data from ten distinct knowledge bases, including KEGG, Reactome, and HumanCyc, among others. We standardized the data structure, gene IDs, and chemical IDs, and conducted a comprehensive analysis of the consistency among the ten knowledge bases before combining all unified interactions into GINv2.0. Utilizing GINv2.0, we investigated the glycolysis process and its regulatory proteins, revealing coordinated regulations on glycolysis and autophagy, particularly under glucose starvation. The expanded scope and enhanced capabilities of GINv2.0 provide a valuable resource for comprehensive systems-level analyses in the field of biological research. GINv2.0 can be accessed at: https://github.com/BIGchix/GINv2.0 .
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Affiliation(s)
- Xiao Chang
- Department of Dermatology and Venereal Disease, Xuan Wu Hospital, Beijing, 100053, China
| | - Shen Yan
- Agricultural Information Institute, Chinese Academy of Agricultural Science, Beijing, 100081, China
| | - Yizheng Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yingchun Zhang
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Luyang Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhanyu Gao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xuefei Lin
- Department of Dermatology and Venereal Disease, Xuan Wu Hospital, Beijing, 100053, China
| | - Xu Chi
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
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27
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Wieder C, Cooke J, Frainay C, Poupin N, Bowler R, Jourdan F, Kechris KJ, Lai RP, Ebbels T. PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.09.574780. [PMID: 38260498 PMCID: PMC10802464 DOI: 10.1101/2024.01.09.574780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. The PathIntegrate Python package is available at https://github.com/cwieder/PathIntegrate.
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Affiliation(s)
- Cecilia Wieder
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Juliette Cooke
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Clement Frainay
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Nathalie Poupin
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Russell Bowler
- National Jewish Health, 1400 Jackson Street, Denver, CO, 80206, USA
| | - Fabien Jourdan
- MetaboHUB-Metatoul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Katerina J Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Rachel Pj Lai
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Timothy Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
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28
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Zhang J, Sun Y, Ren L, Chen L, Nie L, Shavandi A, Yunusov KE, Aharodnikau UE, Solomevich SO, Jiang G. Red Blood Cell Membrane-Camouflaged Polydopamine and Bioactive Glass Composite Nanoformulation for Combined Chemo/Chemodynamic/Photothermal Therapy. ACS Biomater Sci Eng 2024; 10:442-454. [PMID: 38047725 DOI: 10.1021/acsbiomaterials.3c01239] [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] [Indexed: 12/05/2023]
Abstract
Combinations of different therapeutic strategies, including chemotherapy (CT), chemodynamic therapy (CDT), and photothermal therapy (PTT), are needed to effectively address evolving drug resistance and the adverse effects of traditional cancer treatment. Herein, a camouflage composite nanoformulation (TCBG@PR), an antitumor agent (tubercidin, Tub) loaded into Cu-doped bioactive glasses (CBGs) and subsequently camouflaged by polydopamine (PDA), and red blood cell membranes (RBCm), was successfully constructed for targeted and synergetic antitumor therapies by combining CT of Tub, CDT of doped copper ions, and PTT of PDA. In addition, the TCBG@PRs composite nanoformulation was camouflaged with a red blood cell membrane (RBCm) to improve biocompatibility, longer blood retention times, and excellent cellular uptake properties. It integrated with long circulation and multimodal synergistic treatment (CT, CDT, and PTT) with the benefit of RBCms to avoid immune clearance for efficient targeted delivery to tumor locations, producing an "all-in-one" nanoplatform. In vivo results showed that the TCBG@PRs composite nanoformulation prolonged blood circulation and improved tumor accumulation. The combination of CT, CDT, and PTT therapies enhanced the antitumor therapeutic activity, and light-triggered drug release reduced systematic toxicity and increased synergistic antitumor effects.
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Affiliation(s)
- Junhao Zhang
- School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
- International Scientific and Technological Cooperation Base of Intelligent Biomaterials and Functional Fibers, Hangzhou 310018, China
| | - Yanfang Sun
- College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
| | - Luping Ren
- School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
- International Scientific and Technological Cooperation Base of Intelligent Biomaterials and Functional Fibers, Hangzhou 310018, China
| | - Lianxu Chen
- School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
- International Scientific and Technological Cooperation Base of Intelligent Biomaterials and Functional Fibers, Hangzhou 310018, China
| | - Lei Nie
- College of Life Sciences, Xinyang Normal University, Xinyang 464000, China
| | - Amin Shavandi
- École polytechnique de Bruxelles, Université libre de Bruxelles (ULB), 3BIO10 BioMatter, Avenue F.D. Roosevelt, 50 - CP 165/61, Brussels 1050, Belgium
| | - Khaydar E Yunusov
- Institute of Polymer Chemistry and Physics, Uzbekistan Academy of Sciences, Tashkent 100128, Uzbekistan
| | - Uladzislau E Aharodnikau
- Research Institute for Physical Chemical Problems of the Belarusian State University, Minsk 220030, Belarus
| | - Sergey O Solomevich
- Research Institute for Physical Chemical Problems of the Belarusian State University, Minsk 220030, Belarus
| | - Guohua Jiang
- School of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
- International Scientific and Technological Cooperation Base of Intelligent Biomaterials and Functional Fibers, Hangzhou 310018, China
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29
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Wishart DS, Kruger R, Sivakumaran A, Harford K, Sanford S, Doshi R, Khetarpal N, Fatokun O, Doucet D, Zubkowski A, Jackson H, Sykes G, Ramirez-Gaona M, Marcu A, Li C, Yee K, Garros C, Rayat D, Coleongco J, Nandyala T, Gautam V, Oler E. PathBank 2.0-the pathway database for model organism metabolomics. Nucleic Acids Res 2024; 52:D654-D662. [PMID: 37962386 PMCID: PMC10767802 DOI: 10.1093/nar/gkad1041] [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: 09/15/2023] [Revised: 10/19/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023] Open
Abstract
PathBank (https://pathbank.org) and its predecessor database, the Small Molecule Pathway Database (SMPDB), have been providing comprehensive metabolite pathway information for the metabolomics community since 2010. Over the past 14 years, these pathway databases have grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in computing technology. This year's update, PathBank 2.0, brings a number of important improvements and upgrades that should make the database more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of primary or canonical pathways (from 1720 to 6951); (ii) a massive increase in the total number of pathways (from 110 234 to 605 359); (iii) significant improvements to the quality of pathway diagrams and pathway descriptions; (iv) a strong emphasis on drug metabolism and drug mechanism pathways; (v) making most pathway images more slide-compatible and manuscript-compatible; (vi) adding tools to support better pathway filtering and selecting through a more complete pathway taxonomy; (vii) adding pathway analysis tools for visualizing and calculating pathway enrichment. Many other minor improvements and updates to the content, the interface and general performance of the PathBank website have also been made. Overall, we believe these upgrades and updates should greatly enhance PathBank's ease of use and its potential applications for interpreting metabolomics data.
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Affiliation(s)
- David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 2B7, Canada
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB T6G 2H7, Canada
| | - Ray Kruger
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Aadhavya Sivakumaran
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Karxena Harford
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Selena Sanford
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Rahil Doshi
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Nitya Khetarpal
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Omolola Fatokun
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Daphnee Doucet
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Ashley Zubkowski
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Hayley Jackson
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Gina Sykes
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Miguel Ramirez-Gaona
- Department of Plant Breeding, Wageningen University and Research, 6708 PBWageningen, Gelderland, Netherlands
| | - Ana Marcu
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Carin Li
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Kristen Yee
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Christiana Garros
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Dorsa Yahya Rayat
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Jeanne Coleongco
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Tharuni Nandyala
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Eponine Oler
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
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Mahajan P, Fiehn O, Barupal D. IDSL.GOA: Gene Ontology Analysis for Interpreting Metabolomic datasets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.25.534225. [PMID: 37034715 PMCID: PMC10081191 DOI: 10.1101/2023.03.25.534225] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/17/2023]
Abstract
Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2,393 metabolic GO terms and associated 3,144 genes, 1,492 EC annotations, and 2,621 metabolites. IDSL.GOA analysis of a case study of older vs young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR <0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/.
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Affiliation(s)
- Priyanka Mahajan
- Integrated Data Science Laboratory for Metabolomics and Exposomics, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, USA 10954
| | - Oliver Fiehn
- NIH-West Coast Metabolomics Center, University of California, Davis, California, 95616, USA
| | - Dinesh Barupal
- Integrated Data Science Laboratory for Metabolomics and Exposomics, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, USA 10954
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Puvvula J, Manz KE, Braun JM, Pennell KD, DeFranco EA, Ho SM, Leung YK, Huang S, Vuong AM, Kim SS, Percy ZP, Bhashyam P, Lee R, Jones DP, Tran V, Kim DV, Chen A. Maternal and newborn metabolomic changes associated with urinary polycyclic aromatic hydrocarbon metabolite concentrations at delivery: an untargeted approach. Metabolomics 2023; 20:6. [PMID: 38095785 DOI: 10.1007/s11306-023-02074-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: 08/07/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023]
Abstract
INTRODUCTION Prenatal exposure to polycyclic aromatic hydrocarbons (PAHs) has been associated with adverse human health outcomes. To explore the plausible associations between maternal PAH exposure and maternal/newborn metabolomic outcomes, we conducted a cross-sectional study among 75 pregnant people from Cincinnati, Ohio. METHOD We quantified 8 monohydroxylated PAH metabolites in maternal urine samples collected at delivery. We then used an untargeted high-resolution mass spectrometry approach to examine alterations in the maternal (n = 72) and newborn (n = 63) serum metabolome associated with PAH metabolites. Associations between individual maternal urinary PAH metabolites and maternal/newborn metabolome were assessed using linear regression adjusted for maternal and newborn factors while accounting for multiple testing with the Benjamini-Hochberg method. We then conducted functional analysis to identify potential biological pathways. RESULTS Our results from the metabolome-wide associations (MWAS) indicated that an average of 1% newborn metabolome features and 2% maternal metabolome features were associated with maternal urinary PAH metabolites. Individual PAH metabolite concentrations in maternal urine were associated with maternal/newborn metabolome related to metabolism of vitamins, amino acids, fatty acids, lipids, carbohydrates, nucleotides, energy, xenobiotics, glycan, and organic compounds. CONCLUSION In this cross-sectional study, we identified associations between urinary PAH concentrations during late pregnancy and metabolic features associated with several metabolic pathways among pregnant women and newborns. Further studies are needed to explore the mediating role of the metabolome in the relationship between PAHs and adverse pregnancy outcomes.
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Affiliation(s)
- Jagadeesh Puvvula
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Kathrine E Manz
- School of Engineering, Brown University, Providence, RI, USA
| | - Joseph M Braun
- Department of Epidemiology, Brown University, Providence, RI, USA
| | - Kurt D Pennell
- School of Engineering, Brown University, Providence, RI, USA
| | - Emily A DeFranco
- Department of Obstetrics and Gynecology, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Shuk-Mei Ho
- Department of Pharmacology and Toxicology, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Yuet-Kin Leung
- Department of Pharmacology and Toxicology, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Shouxiong Huang
- Department of Environmental & Public Health Sciences, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Ann M Vuong
- Department of Epidemiology and Biostatistics, School of Public Health, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Stephani S Kim
- Health Research, Battelle Memorial Institute, Columbus, OH, USA
| | - Zana P Percy
- Department of Environmental & Public Health Sciences, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Priyanka Bhashyam
- College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Raymund Lee
- College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Dean P Jones
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University, Atlanta, GA, USA
| | - Vilinh Tran
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University, Atlanta, GA, USA
| | - Dasom V Kim
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Aimin Chen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Hermawan A, Wulandari F, Yudi Utomo R, Asmah Susidarti R, Kirihata M, Meiyanto E. Transcriptomics analyses reveal the effects of Pentagamaboronon-0-ol on PI3K/Akt and cell cycle of HER2+ breast cancer cells. Saudi Pharm J 2023; 31:101847. [PMID: 38028209 PMCID: PMC10652209 DOI: 10.1016/j.jsps.2023.101847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/23/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Monoclonal antibodies and targeted therapies against HER2+ breast cancer has improved overall and disease-free survival in patients; however, encountering drug resistance causes recurrence, necessitating the development of newer HER2-targeted medications. A curcumin analog PGB-0-ol showed most cytotoxicity against HCC1954 HER2+ breast cancer cells than against other subtypes of breast cancer cells. Objective Here, we employed next-generation sequencing technology to elucidate the molecular mechanism underlying the effect of PGB-0-ol on HCC1954 HER2+ breast cancer cells. Methods The molecular mechanism underlying the action of PGB-0-ol on HCC1954 HER2+ breast cancer cells was determined using next-generation sequencing technologies. Additional bioinformatics studies were performed, including gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, disease-gene, and drug-gene associations, network topology analysis (NTA), and gene set enrichment analysis (GSEA). Results We detected 2,263 differentially expressed genes (DEGs) (1,459 upregulated and 804 downregulated) in the PGB-0-ol- and DMSO-treated HCC1954 cells. KEGG enrichment data revealed the control of phosphatidylinositol signaling system, and ErbB signaling following PGB-0-ol treatment. Gene ontology (GO) enrichment analysis demonstrated that these DEGs governed cell cycle, participated in the mitotic spindle and nuclear membrane, and controlled kinase activity at the molecular level. According to the NTA data for GO enrichment, GSEA data for KEGG, drug-gene and disease-gene, PGB-0-ol regulated PI3K/Akt signaling and cell cycle in breast cancer. Overall, our investigation revealed the transcriptomic profile of PGB-0-ol-treated HCC1954 breast cancer cells following PGB-0-ol therapy. Bioinformatics analyses showed that PI3K/Akt signaling and cell cycle was modulated. However, further studies are required to validate the findings of this study.
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Affiliation(s)
- Adam Hermawan
- Laboratory of Macromolecular Engineering, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada Sekip Utara II, 55281, Yogyakarta, Indonesia
- Cancer Chemoprevention Research Center, Faculty of Pharmacy, Universitas Gadjah Mada Sekip Utara II, 55281, Yogyakarta, Indonesia
- Laboratory of Advanced Pharmaceutical Sciences. APSLC Building, Faculty of Pharmacy, Universitas Gadjah Mada Sekip Utara II, 55281, Yogyakarta, Indonesia
| | - Febri Wulandari
- Cancer Chemoprevention Research Center, Faculty of Pharmacy, Universitas Gadjah Mada Sekip Utara II, 55281, Yogyakarta, Indonesia
| | - Rohmad Yudi Utomo
- Cancer Chemoprevention Research Center, Faculty of Pharmacy, Universitas Gadjah Mada Sekip Utara II, 55281, Yogyakarta, Indonesia
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada Sekip Utara II, 55281, Yogyakarta, Indonesia
| | - Ratna Asmah Susidarti
- Cancer Chemoprevention Research Center, Faculty of Pharmacy, Universitas Gadjah Mada Sekip Utara II, 55281, Yogyakarta, Indonesia
- Laboratory of Medicinal Chemistry, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada Sekip Utara II, 55281, Yogyakarta, Indonesia
| | - Mitsunori Kirihata
- Research Center for BNCT, Osaka Metropolitan University, 1-2, Gakuen-cho, Naka-ku, Sakai, Osaka 599-8570, Japan
| | - Edy Meiyanto
- Laboratory of Macromolecular Engineering, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada Sekip Utara II, 55281, Yogyakarta, Indonesia
- Cancer Chemoprevention Research Center, Faculty of Pharmacy, Universitas Gadjah Mada Sekip Utara II, 55281, Yogyakarta, Indonesia
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Yao Y, Schneider A, Wolf K, Zhang S, Wang-Sattler R, Peters A, Breitner S. Longitudinal associations between metabolites and immediate, short- and medium-term exposure to ambient air pollution: Results from the KORA cohort study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 900:165780. [PMID: 37495154 DOI: 10.1016/j.scitotenv.2023.165780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/21/2023] [Accepted: 07/23/2023] [Indexed: 07/28/2023]
Abstract
BACKGROUND Short-term exposure to air pollution has been reported to be associated with cardiopulmonary diseases, but the underlying mechanisms remain unclear. This study aimed to investigate changes in serum metabolites associated with immediate, short- and medium-term exposures to ambient air pollution. METHODS We used data from the German population-based Cooperative Health Research in the Region of Augsburg (KORA) S4 survey (1999-2001) and two follow-up examinations (F4: 2006-08 and FF4: 2013-14). Mass-spectrometry-based targeted metabolomics was used to quantify metabolites among serum samples. Only participants with repeated metabolites measurements were included in this analysis. We collected daily averages of fine particles (PM2.5), coarse particles (PMcoarse), nitrogen dioxide (NO2), and ozone (O3) at urban background monitors located in Augsburg, Germany. Covariate-adjusted generalized additive mixed-effects models were used to examine the associations between immediate (2-day average of same day and previous day as individual's blood withdrawal), short- (2-week moving average), and medium-term exposures (8-week moving average) to air pollution and metabolites. We further performed pathway analysis for the metabolites significantly associated with air pollutants in each exposure window. RESULTS Of 9,620 observations from 4,261 study participants, we included 5,772 (60.0%) observations from 2,583 (60.6%) participants in this analysis. Out of 108 metabolites that passed quality control, multiple significant associations between metabolites and air pollutants with several exposure windows were identified at a Bonferroni corrected p-value threshold (p < 3.9 × 10-5). We found the highest number of associations for NO2, particularly at the medium-term exposure windows. Among the identified metabolic pathways based on the metabolites significantly associated with air pollutants, the glycerophospholipid metabolism was the most robust pathway in different air pollutants exposures. CONCLUSIONS Our study suggested that short- and medium-term exposure to air pollution might induce alterations of serum metabolites, particularly in metabolites involved in metabolic pathways related to inflammatory response and oxidative stress.
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Affiliation(s)
- Yueli Yao
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Faculty of Medicine, Pettenkofer School of Public Health, LMU Munich, Munich, Germany.
| | - Alexandra Schneider
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Kathrin Wolf
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Siqi Zhang
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Rui Wang-Sattler
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Faculty of Medicine, Pettenkofer School of Public Health, LMU Munich, Munich, Germany; German Center for Diabetes Research (DZD), Munich-Neuherberg, Germany; German Centre for Cardiovascular Research (DZHK), Partner Site Munich, Munich, Germany
| | - Susanne Breitner
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Faculty of Medicine, Pettenkofer School of Public Health, LMU Munich, Munich, Germany
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Hosseini E, Amirjannati N, Henkel R, Bazrafkan M, Moghadasfar H, Gilany K. Targeted Amino Acids Profiling of Human Seminal Plasma from Teratozoospermia Patients Using LC-MS/MS. Reprod Sci 2023; 30:3285-3295. [PMID: 37264261 DOI: 10.1007/s43032-023-01272-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 05/13/2023] [Indexed: 06/03/2023]
Abstract
Identifying the metabolome of human seminal plasma (HSP) is a new research area to screen putative biomarkers of infertility. This case-control study was performed on HSP specimens of 15 infertile patients with teratozoospermia (defined as normal sperm morphology < 4%) and 12 confirmed fertile normozoospermic men as the control group to investigate the seminal metabolic signature and whether there are differences in the metabolome between two groups. HSPs were subjected to LC-MS-MS analysis. MetaboAnalyst5.0 software was utilized for statistical analysis. Different univariate and multivariate analyses were used, including T-tests, fold change analysis, random forest (RF), and metabolite set enrichment analysis (MSEA). Teratozoospermic samples contained seventeen significantly different amino acids. Upregulated metabolites include glutamine, asparagine, and glycylproline, whereas downregulated metabolites include cysteine, γ-aminobutyric acid, histidine, hydroxylysine, hydroxyproline, glycine, proline, methionine, ornithine, tryptophan, aspartic acid, argininosuccinic acid, α-aminoadipic acid, and β-aminoisobutyric acid. RF algorithm defined a set of 15 metabolites that constitute the significant features of teratozoospermia. In particular, increased glutamine, asparagine, and decreased cysteine, tryptophan, glycine, and valine were strong predictors of teratozoospemia. The most affected metabolic pathways in teratozoospermic men are the aminoacyl-tRNA, arginine, valine-leucine, and isoleucine biosynthesis. Altered metabolites detected in teratozoospermia were responsible for various roles in sperm functions that classified into four subgroups as follows: related metabolites to antioxidant function, energy production, sperm function, and spermatogenesis. The altered amino acid metabolome identified in this study may be related to the etiology of teratozoospermia, and may provide novel insight into potential biomarkers of male infertility for therapeutic targets.
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Affiliation(s)
- Elham Hosseini
- Zanjan Metabolic Diseases Research Center, Zanjan University of Medical Sciences, Zanjan, Iran
- Department of Obstetrics and Gynecology, Mousavi Hospital, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Naser Amirjannati
- Department of Andrology and Embryology, Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
| | - Ralf Henkel
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Medical Bioscience, University of the Western Cape, Bellville, South Africa
- LogixX Pharma, Theale, Berkshire, UK
| | - Mahshid Bazrafkan
- Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
| | - Hanieh Moghadasfar
- Reproductive Immunology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
| | - Kambiz Gilany
- Reproductive Immunology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran.
- Integrative Oncology Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran.
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Mengelkoch S, Miryam Schüssler-Fiorenza Rose S, Lautman Z, Alley JC, Roos LG, Ehlert B, Moriarity DP, Lancaster S, Snyder MP, Slavich GM. Multi-omics approaches in psychoneuroimmunology and health research: Conceptual considerations and methodological recommendations. Brain Behav Immun 2023; 114:475-487. [PMID: 37543247 PMCID: PMC11195542 DOI: 10.1016/j.bbi.2023.07.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 07/04/2023] [Accepted: 07/30/2023] [Indexed: 08/07/2023] Open
Abstract
The field of psychoneuroimmunology (PNI) has grown substantially in both relevance and prominence over the past 40 years. Notwithstanding its impressive trajectory, a majority of PNI studies are still based on a relatively small number of analytes. To advance this work, we suggest that PNI, and health research in general, can benefit greatly from adopting a multi-omics approach, which involves integrating data across multiple biological levels (e.g., the genome, proteome, transcriptome, metabolome, lipidome, and microbiome/metagenome) to more comprehensively profile biological functions and relate these profiles to clinical and behavioral outcomes. To assist investigators in this endeavor, we provide an overview of multi-omics research, highlight recent landmark multi-omics studies investigating human health and disease risk, and discuss how multi-omics can be applied to better elucidate links between psychological, nervous system, and immune system activity. In doing so, we describe how to design high-quality multi-omics studies, decide which biological samples (e.g., blood, stool, urine, saliva, solid tissue) are most relevant, incorporate behavioral and wearable sensing data into multi-omics research, and understand key data quality, integration, analysis, and interpretation issues. PNI researchers are addressing some of the most interesting and important questions at the intersection of psychology, neuroscience, and immunology. Applying a multi-omics approach to this work will greatly expand the horizon of what is possible in PNI and has the potential to revolutionize our understanding of mind-body medicine.
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Affiliation(s)
- Summer Mengelkoch
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA.
| | | | - Ziv Lautman
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Jenna C Alley
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Lydia G Roos
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Benjamin Ehlert
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Daniel P Moriarity
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | | | | | - George M Slavich
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA.
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Vorperian SK, DeFelice BC, Buonomo JA, Chinchinian HJ, Gray IJ, Yan J, Mach KE, La V, Lee TJ, Liao JC, Lafayette R, Loeb GB, Bertozzi CR, Quake SR. Multiomics characterization of cell type repertoires for urine liquid biopsies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.20.563226. [PMID: 37961398 PMCID: PMC10634682 DOI: 10.1101/2023.10.20.563226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Urine is assayed alongside blood in medicine, yet current clinical diagnostic tests utilize only a small fraction of its total biomolecular repertoire, potentially foregoing high-resolution insights into human health and disease. In this work, we characterized the joint landscapes of transcriptomic and metabolomic signals in human urine. We also compared the urine transcriptome to plasma cell-free RNA, identifying a distinct cell type repertoire and enrichment for metabolic signal. Untargeted metabolomic measurements identified a complementary set of pathways to the transcriptomic analysis. Our findings suggest that urine is a promising biofluid yielding prognostic and detailed insights for hard-to-biopsy tissues with low representation in the blood, offering promise for a new generation of liquid biopsies.
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Xavier JB. Machine learning of cellular metabolic rewiring. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.11.552957. [PMID: 37645838 PMCID: PMC10462012 DOI: 10.1101/2023.08.11.552957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Metabolic rewiring allows cells to adapt their metabolism in response to evolving environmental conditions. Traditional metabolomics techniques, whether targeted or untargeted, often struggle to interpret these adaptive shifts. Here, we introduce MetaboLiteLearner, a machine learning framework that harnesses the detailed fragmentation patterns from electron ionization (EI) collected in scan mode during gas chromatography/mass spectrometry (GC/MS) to predict abundance changes in metabolically adapted cells. When tested on breast cancer cells with different preferences to metastasize to specific organs, MetaboLiteLearner predicted the impact of metabolic rewiring on metabolites withheld from the training dataset using only the EI spectra, without metabolite identification or pre-existing knowledge of metabolic networks. The model learned captures shared and unique metabolomic shifts between brain- and lung-homing metastatic lineages, suggesting potential organ-tailored cellular adaptations. Integrating machine learning and metabolomics paves the way for new insights into complex cellular adaptations.
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Affiliation(s)
- Joao B Xavier
- Program for Computational and Systems Biology, Sloan Kettering Institute for Cancer Research
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38
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Hallan SI, Øvrehus MA, Darshi M, Montemayor D, Langlo KA, Bruheim P, Sharma K. Metabolic Differences in Diabetic Kidney Disease Patients with Normoalbuminuria versus Moderately Increased Albuminuria. KIDNEY360 2023; 4:1407-1418. [PMID: 37612821 PMCID: PMC10615383 DOI: 10.34067/kid.0000000000000248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/17/2023] [Indexed: 08/25/2023]
Abstract
Key Points The pathophysiological mechanisms of diabetic kidney disease (DKD) with normal (nonalbuminuric DKD) versus moderately increased albuminuria (A-DKD) are not well-understood. Fatty acid biosynthesis and oxydation, gluconeogenesis, TCA cycle, and glucose-alanine cycle were more disturbed in patients with A-DKD compared with those with nonalbuminuric DKD with identical eGFR. DKD patients with and without microalbuminuria could represent different clinical phenotypes. Background The pathophysiological mechanisms of diabetic kidney disease (DKD) with normal versus moderately increased albuminuria (nonalbuminuric DKD [NA-DKD] and A-DKD) are currently not well-understood and could have implications for diagnosis and treatment. Methods Fourteen patients with NA-DKD with urine albumin–creatinine ratio <3 mg/mmol, 26 patients with A-DKD with albumin–creatinine ratio 3–29 mg/mmol, and 60 age- and sex-matched healthy controls were randomly chosen from a population-based cohort study (Nord-Trøndelag Health Study-3, Norway). Seventy-four organic acids, 21 amino acids, 21 biogenic acids, 40 acylcarnitines, 14 sphingomyelins, and 88 phosphatidylcholines were quantified in urine. One hundred forty-six patients with diabetes from the US-based Chronic Renal Insufficiency Cohort study were used to verify main findings. Results Patients with NA-DKD and A-DKD had similar age, kidney function, diabetes treatment, and other traditional risk factors. Still, partial least-squares discriminant analysis showed strong metabolite-based separation (R2, 0.82; Q2, 0.52), with patients with NA-DKD having a metabolic profile positioned between the profiles of healthy controls and patients with A-DKD. Seventy-five metabolites contributed significantly to separation between NA-DKD and A-DKD (variable importance in projection scores ≥1.0) with propionylcarnitine (C3), phosphatidylcholine C38:4, medium-chained (C8) fatty acid octenedioic acid, and lactic acid as the top metabolites (variable importance in projection scores, 2.7–2.2). Compared with patients with NA-DKD, those with A-DKD had higher levels of short-chained acylcarnitines, higher long-chained fatty acid levels with more double bounds, higher branched-chain amino acid levels, and lower TCA cycle intermediates. The main findings were similar by random forest analysis and in the Chronic Renal Insufficiency Cohort study. Formal enrichment analysis indicated that fatty acid biosynthesis and oxydation, gluconeogenesis, TCA cycle, and glucose-alanine cycle were more disturbed in patients with A-DKD compared with those with NA-DKD with identical eGFR. We also found indications of a Warburg-like effect in patients with A-DKD (i.e. , metabolism of glucose to lactate despite adequate oxygen). Conclusion DKD patients with normoalbuminuria differ substantially in their metabolic disturbances compared with patients with moderately increase albuminuria and could represent different clinical phenotypes.
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Affiliation(s)
- Stein I Hallan
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Nephrology, St. Olav Hospital, Trondheim, Norway
| | | | - Manjula Darshi
- Center for Renal Precision Medicine, University of Texas Health San Antonio, San Antonio, Texas
| | - Daniel Montemayor
- Center for Renal Precision Medicine, University of Texas Health San Antonio, San Antonio, Texas
| | - Knut A Langlo
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Nephrology, St. Olav Hospital, Trondheim, Norway
| | - Per Bruheim
- Department of Biotechnology and Food Science, Faculty of Natural Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Kumar Sharma
- Center for Renal Precision Medicine, University of Texas Health San Antonio, San Antonio, Texas
- Department of Nephrology, University of Texas Health San Antonio, San Antonio, Texas
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Lin G, Dong L, Cheng KK, Xu X, Wang Y, Deng L, Raftery D, Dong J. Differential Correlations Informed Metabolite Set Enrichment Analysis to Decipher Metabolic Heterogeneity of Disease. Anal Chem 2023; 95:12505-12513. [PMID: 37557184 DOI: 10.1021/acs.analchem.3c02246] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2023]
Abstract
Metabolic pathways are regarded as functional and basic components of the biological system. In metabolomics, metabolite set enrichment analysis (MSEA) is often used to identify the altered metabolic pathways (metabolite sets) associated with phenotypes of interest (POI), e.g., disease. However, in most studies, MSEA suffers from the limitation of low metabolite coverage. Random walk (RW)-based algorithms can be used to propagate the perturbation of detected metabolites to the undetected metabolites through a metabolite network model prior to MSEA. Nevertheless, most of the existing RW-based algorithms run on a general metabolite network constructed based on public databases, such as KEGG, without taking into consideration the potential influence of POI on the metabolite network, which may reduce the phenotypic specificities of the MSEA results. To solve this problem, a novel pathway analysis strategy, namely, differential correlation-informed MSEA (dci-MSEA), is proposed in this paper. Statistically, differential correlations between metabolites are used to evaluate the influence of POI on the metabolite network, so that a phenotype-specific metabolite network is constructed for RW-based propagation. The experimental results show that dci-MSEA outperforms the conventional RW-based MSEA in identifying the altered metabolic pathways associated with colorectal cancer. In addition, by incorporating the individual-specific metabolite network, the dci-MSEA strategy is easily extended to disease heterogeneity analysis. Here, dci-MSEA was used to decipher the heterogeneity of colorectal cancer. The present results highlight the clustering of colorectal cancer samples with their cluster-specific selection of differential pathways and demonstrate the feasibility of dci-MSEA in heterogeneity analysis. Taken together, the proposed dci-MSEA may provide insights into disease mechanisms and determination of disease heterogeneity.
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Affiliation(s)
- Genjin Lin
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
| | - Liheng Dong
- School of Computing and Data Science, Xiamen University Malaysia, 43600 Sepang, Malaysia
| | - Kian-Kai Cheng
- Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia
| | - Xiangnan Xu
- School of Business and Economics, Humboldt-Universität zu Berlin, Berlin 10099, Germany
| | - Yongpei Wang
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
| | - Lingli Deng
- Department of Information Engineering, East China University of Technology, Nanchang 330013, China
| | - Daniel Raftery
- Northwest Metabolomics Research Center, University of Washington, Seattle, Washington 98109, United States
| | - Jiyang Dong
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
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Bundalian L, Su YY, Chen S, Velluva A, Kirstein AS, Garten A, Biskup S, Battke F, Lal D, Heyne HO, Platzer K, Lin CC, Lemke JR, Le Duc D. Epilepsies of presumed genetic etiology show enrichment of rare variants that occur in the general population. Am J Hum Genet 2023; 110:1110-1122. [PMID: 37369202 PMCID: PMC10357498 DOI: 10.1016/j.ajhg.2023.06.004] [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: 03/13/2023] [Revised: 06/05/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023] Open
Abstract
Previous studies suggested that severe epilepsies, e.g., developmental and epileptic encephalopathies (DEEs), are mainly caused by ultra-rare de novo genetic variants. For milder disease, rare genetic variants could contribute to the phenotype. To determine the importance of rare variants for different epilepsy types, we analyzed a whole-exome sequencing cohort of 9,170 epilepsy-affected individuals and 8,436 control individuals. Here, we separately analyzed three different groups of epilepsies: severe DEEs, genetic generalized epilepsy (GGE), and non-acquired focal epilepsy (NAFE). We required qualifying rare variants (QRVs) to occur in control individuals with an allele count ≥ 1 and a minor allele frequency ≤ 1:1,000, to be predicted as deleterious (CADD ≥ 20), and to have an odds ratio in individuals with epilepsy ≥ 2. We identified genes enriched with QRVs primarily in NAFE (n = 72), followed by GGE (n = 32) and DEE (n = 21). This suggests that rare variants may play a more important role for causality of NAFE than for DEE. Moreover, we found that genes harboring QRVs, e.g., HSGP2, FLNA, or TNC, encode proteins that are involved in structuring the brain extracellular matrix. The present study confirms an involvement of rare variants for NAFE that occur also in the general population, while in DEE and GGE, the contribution of such variants appears more limited.
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Affiliation(s)
- Linnaeus Bundalian
- Institute of Human Genetics, University of Leipzig Medical Center, 04103 Leipzig, Germany.
| | - Yin-Yuan Su
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Siwei Chen
- Analytic and Translational Genetics Unit, Department of Medicine, Boston, MA, USA; Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Akhil Velluva
- Division of General Biochemistry, Rudolf Schönheimer Institute of Biochemistry, Medical Faculty, Leipzig University, 04103 Leipzig, Germany; Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany
| | - Anna Sophia Kirstein
- Pediatric Research Center, University Hospital for Children and Adolescents, Leipzig University, 04103 Leipzig, Germany
| | - Antje Garten
- Pediatric Research Center, University Hospital for Children and Adolescents, Leipzig University, 04103 Leipzig, Germany
| | - Saskia Biskup
- CeGaT GmbH, 72076 Tuebingen, Germany; Hertie-Institute for Clinical Brain Research, 72070 Tubingen, Germany
| | | | - Dennis Lal
- Analytic and Translational Genetics Unit, Department of Medicine, Boston, MA, USA; Massachusetts General Hospital, Boston, MA 02114, USA; Cologne Center for Genomics, University of Cologne, 50937 Cologne, Germany
| | - Henrike O Heyne
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Hasso-Plattner-Institut for Digital Engineering, University of Potsdam, Potsdam, Germany; Hasso Plattner Institute at Mount Sinai, Mount Sinai School of Medicine, New York, NY, USA; Institute for Molecular Medicine Finland: FIMM, University of Helsinki, Helsinki, Finland
| | - Konrad Platzer
- Institute of Human Genetics, University of Leipzig Medical Center, 04103 Leipzig, Germany
| | - Chen-Ching Lin
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Johannes R Lemke
- Institute of Human Genetics, University of Leipzig Medical Center, 04103 Leipzig, Germany; Center for Rare Diseases, University of Leipzig Medical Center, 04103 Leipzig, Germany
| | - Diana Le Duc
- Institute of Human Genetics, University of Leipzig Medical Center, 04103 Leipzig, Germany; Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany.
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Ebbels TMD, van der Hooft JJJ, Chatelaine H, Broeckling C, Zamboni N, Hassoun S, Mathé EA. Recent advances in mass spectrometry-based computational metabolomics. Curr Opin Chem Biol 2023; 74:102288. [PMID: 36966702 PMCID: PMC11075003 DOI: 10.1016/j.cbpa.2023.102288] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 02/16/2023] [Accepted: 02/21/2023] [Indexed: 04/03/2023]
Abstract
The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of metabolomics across a wide array of scientific and medical disciplines. The field continues to expand as modern instrumentation produces datasets with increasing complexity, resolution, and sensitivity. These datasets must be processed, annotated, modeled, and interpreted to enable biological insight. Techniques for visualization, integration (within or between omics), and interpretation of metabolomics data have evolved along with innovation in the databases and knowledge resources required to aid understanding. In this review, we highlight recent advances in the field and reflect on opportunities and innovations in response to the most pressing challenges. This review was compiled from discussions from the 2022 Dagstuhl seminar entitled "Computational Metabolomics: From Spectra to Knowledge".
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Affiliation(s)
- Timothy M D Ebbels
- Section of Bioinformatics, Department of Metabolism, Digestion & Reproduction, Imperial College London, Burlington Danes Building, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.
| | - Justin J J van der Hooft
- Bioinformatics Group, Wageningen University & Research, Wageningen 6708 PB, the Netherlands; Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
| | - Haley Chatelaine
- Informatics Core, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Corey Broeckling
- Bioanalysis and Omics Center, Analytical Resources Core, Colorado State University, Fort Collins, CO, USA
| | - Nicola Zamboni
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Soha Hassoun
- Department of Computer Science, Tufts University, Medford, MA, USA; Department of Chemical and Biological Engineering, Tufts University, Medford, MA, USA
| | - Ewy A Mathé
- Informatics Core, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, USA.
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Rahnenführer J, De Bin R, Benner A, Ambrogi F, Lusa L, Boulesteix AL, Migliavacca E, Binder H, Michiels S, Sauerbrei W, McShane L. Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges. BMC Med 2023; 21:182. [PMID: 37189125 DOI: 10.1186/s12916-023-02858-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 04/03/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND In high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many measurements across the genome, proteome, or metabolome, as well as electronic health records data that have large numbers of variables recorded for each patient. The statistical analysis of such data requires knowledge and experience, sometimes of complex methods adapted to the respective research questions. METHODS Advances in statistical methodology and machine learning methods offer new opportunities for innovative analyses of HDD, but at the same time require a deeper understanding of some fundamental statistical concepts. Topic group TG9 "High-dimensional data" of the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative provides guidance for the analysis of observational studies, addressing particular statistical challenges and opportunities for the analysis of studies involving HDD. In this overview, we discuss key aspects of HDD analysis to provide a gentle introduction for non-statisticians and for classically trained statisticians with little experience specific to HDD. RESULTS The paper is organized with respect to subtopics that are most relevant for the analysis of HDD, in particular initial data analysis, exploratory data analysis, multiple testing, and prediction. For each subtopic, main analytical goals in HDD settings are outlined. For each of these goals, basic explanations for some commonly used analysis methods are provided. Situations are identified where traditional statistical methods cannot, or should not, be used in the HDD setting, or where adequate analytic tools are still lacking. Many key references are provided. CONCLUSIONS This review aims to provide a solid statistical foundation for researchers, including statisticians and non-statisticians, who are new to research with HDD or simply want to better evaluate and understand the results of HDD analyses.
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Affiliation(s)
| | | | - Axel Benner
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Federico Ambrogi
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
- Scientific Directorate, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Lara Lusa
- Department of Mathematics, Faculty of Mathematics, Natural Sciences and Information Technology, University of Primorksa, Koper, Slovenia
- Institute of Biostatistics and Medical Informatics, University of Ljubljana, Ljubljana, Slovenia
| | - Anne-Laure Boulesteix
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany
| | | | - Harald Binder
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Stefan Michiels
- Service de Biostatistique et d'Épidémiologie, Gustave Roussy, Université Paris-Saclay, Villejuif, France
- Oncostat U1018, Inserm, Université Paris-Saclay, Labeled Ligue Contre le Cancer, Villejuif, France
| | - Willi Sauerbrei
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Lisa McShane
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA.
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Zhao K, Rhee SY. Interpreting omics data with pathway enrichment analysis. Trends Genet 2023; 39:308-319. [PMID: 36750393 DOI: 10.1016/j.tig.2023.01.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 11/24/2022] [Accepted: 01/13/2023] [Indexed: 02/09/2023]
Abstract
Pathway enrichment analysis is indispensable for interpreting omics datasets and generating hypotheses. However, the foundations of enrichment analysis remain elusive to many biologists. Here, we discuss best practices in interpreting different types of omics data using pathway enrichment analysis and highlight the importance of considering intrinsic features of various types of omics data. We further explain major components that influence the outcomes of a pathway enrichment analysis, including defining background sets and choosing reference annotation databases. To improve reproducibility, we describe how to standardize reporting methodological details in publications. This article aims to serve as a primer for biologists to leverage the wealth of omics resources and motivate bioinformatics tool developers to enhance the power of pathway enrichment analysis.
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Affiliation(s)
- Kangmei Zhao
- Department of Plant Biology, Carnegie Institution for Science, Stanford, CA 94025, USA.
| | - Seung Yon Rhee
- Department of Plant Biology, Carnegie Institution for Science, Stanford, CA 94025, USA.
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Bundalian L, Su YY, Chen S, Velluva A, Kirstein AS, Garten A, Biskup S, Battke F, Lal D, Heyne HO, Platzer K, Lin CC, Lemke JR, Le Duc D. The role of rare genetic variants enrichment in epilepsies of presumed genetic etiology. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.17.23284702. [PMID: 36974069 PMCID: PMC10041669 DOI: 10.1101/2023.01.17.23284702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Previous studies suggested that severe epilepsies e.g., developmental and epileptic encephalopathies (DEE) are mainly caused by ultra-rare de novo genetic variants. For milder phenotypes, rare genetic variants could contribute to the phenotype. To determine the importance of rare variants for different epilepsy types, we analyzed a whole-exome sequencing cohort of 9,170 epilepsy-affected individuals and 8,436 controls. Here, we separately analyzed three different groups of epilepsies : severe DEEs, genetic generalized epilepsy (GGE), and non-acquired focal epilepsy (NAFE). We required qualifying rare variants (QRVs) to occur in controls at a minor allele frequency ≤ 1:1,000, to be predicted as deleterious (CADD≥20), and to have an odds ratio in epilepsy cases ≥2. We identified genes enriched with QRVs in DEE (n=21), NAFE (n=72), and GGE (n=32) - the number of enriched genes are found greatest in NAFE and least in DEE. This suggests that rare variants may play a more important role for causality of NAFE than in DEE. Moreover, we found that QRV-carrying genes e.g., HSGP2, FLNA or TNC are involved in structuring the brain extracellular matrix. The present study confirms an involvement of rare variants for NAFE, while in DEE and GGE, the contribution of such variants appears more limited.
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Affiliation(s)
- Linnaeus Bundalian
- Institute of Human Genetics, University of Leipzig Medical Center, 4103 Leipzig, Germany
| | - Yin-Yuan Su
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Siwei Chen
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Akhil Velluva
- Division of General Biochemistry, Rudolf Schönheimer Institute of Biochemistry, Medical Faculty, Leipzig University, 04103, Leipzig, Germany
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103, Leipzig, Germany
| | - Anna Sophia Kirstein
- Pediatric Research Center, University Hospital for Children and Adolescents, Leipzig University, 04103, Leipzig, Germany
| | - Antje Garten
- Pediatric Research Center, University Hospital for Children and Adolescents, Leipzig University, 04103, Leipzig, Germany
| | - Saskia Biskup
- CeGaT GmbH, 72076, Tuebingen, Germany
- Hertie-Institute for Clinical Brain Research, 72070, Tubingen, Germany
| | | | - Dennis Lal
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Cologne Center for Genomics, University of Cologne, 50937 Cologne, Germany
| | - Henrike O Heyne
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Hasso-Plattner-Institut for Digital Engineering, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute at Mount Sinai, Mount Sinai School of Medicine, NY, US
- Institute for Molecular Medicine Finland: FIMM, University of Helsinki, Helsinki, Finland
| | - Konrad Platzer
- Institute of Human Genetics, University of Leipzig Medical Center, 4103 Leipzig, Germany
| | - Chen-Ching Lin
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Johannes R Lemke
- Institute of Human Genetics, University of Leipzig Medical Center, 4103 Leipzig, Germany
- Center for Rare Diseases, University of Leipzig Medical Center, 4103 Leipzig, Germany
| | - Diana Le Duc
- Institute of Human Genetics, University of Leipzig Medical Center, 4103 Leipzig, Germany
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, 04103, Leipzig, Germany
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Figueiredo CC, Balzano-Nogueira L, Bisinotto DZ, Ruiz AR, Duarte GA, Conesa A, Galvão KN, Bisinotto RS. Differences in uterine and serum metabolome associated with metritis in dairy cows. J Dairy Sci 2023; 106:3525-3536. [PMID: 36894419 DOI: 10.3168/jds.2022-22552] [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: 07/19/2022] [Accepted: 11/07/2022] [Indexed: 03/09/2023]
Abstract
Objectives were to evaluate differences in the uterine and serum metabolomes associated with metritis in dairy cows. Vaginal discharge was evaluated using a Metricheck device (Simcro) at 5, 7, and 11 d in milk (DIM; herd 1) or 4, 6, 8, 10, and 12 DIM (herd 2). Cows with reddish or brownish, watery, and fetid discharge were diagnosed with metritis (n = 24). Cows with metritis were paired with herdmates without metritis (i.e., clear mucous vaginal discharge or clear lochia with ≤50% of pus) based on DIM and parity (n = 24). Day of metritis diagnosis was considered study d 0. All cows diagnosed with metritis received antimicrobial therapy. The metabolome of uterine lavage collected on d 0 and 5, and serum samples collected on d 0 were evaluated using untargeted gas chromatography time-of-flight mass spectrometry. Normalized data were subjected to multivariate canonical analysis of population using the MultBiplotR and MixOmics packages in R Studio. Univariate analyses including t-test, principal component analyses, partial least squares discriminant analyses, and pathway analyses were conducted using Metaboanalyst. The uterine metabolome differed between cows with and without metritis on d 0. Differences in the uterine metabolome associated with metritis on d 0 were related to the metabolism of butanoate, amino acids (i.e., glycine, serine, threonine, alanine, aspartate, and glutamate), glycolysis and gluconeogenesis, and the tricarboxylic acid cycle. No differences in the serum metabolome were observed between cows diagnosed with metritis and counterparts without metritis on d 0. Similarly, no differences in uterine metabolome were observed between cows with metritis and counterparts not diagnosed with metritis on d 5. These results indicate that the establishment of metritis in dairy cows is associated with local disturbances in amino acid, lipid, and carbohydrate metabolism in the uterus. The lack of differences in the uterine metabolome on d 5 indicates that processes implicated with the disease are reestablished by d 5 after diagnosis and treatment.
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Affiliation(s)
- C C Figueiredo
- Department of Large Animal Clinical Sciences, D. H. Barron Reproductive and Perinatal Biology Research Program, University of Florida, Gainesville 32610
| | - L Balzano-Nogueira
- Department of Pathology, Immunology, and Laboratory Medicine, Diabetes Institute, University of Florida, Gainesville 32610
| | - D Z Bisinotto
- Department of Large Animal Clinical Sciences, D. H. Barron Reproductive and Perinatal Biology Research Program, University of Florida, Gainesville 32610
| | - A Revilla Ruiz
- Department of Animal Sciences, University of Florida, Gainesville 32611
| | - G A Duarte
- Department of Animal Sciences, University of Florida, Gainesville 32611
| | - A Conesa
- Institute for Integrative Systems Biology, Spanish National Research Council, Paterna 46980, Spain; Department of Microbiology and Cell Sciences, University of Florida, Gainesville 32603
| | - K N Galvão
- Department of Large Animal Clinical Sciences, D. H. Barron Reproductive and Perinatal Biology Research Program, University of Florida, Gainesville 32610.
| | - R S Bisinotto
- Department of Large Animal Clinical Sciences, D. H. Barron Reproductive and Perinatal Biology Research Program, University of Florida, Gainesville 32610.
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Dasgupta S, Ghosh N, Bhattacharyya P, Roy Chowdhury S, Chaudhury K. Metabolomics of asthma, COPD, and asthma-COPD overlap: an overview. Crit Rev Clin Lab Sci 2023; 60:153-170. [PMID: 36420874 DOI: 10.1080/10408363.2022.2140329] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The two common progressive lung diseases, asthma and chronic obstructive pulmonary disease (COPD), are the leading causes of morbidity and mortality worldwide. Asthma-COPD overlap, referred to as ACO, is another complex pulmonary disease that manifests itself with features of both asthma and COPD. The disease has no clear diagnostic or therapeutic guidelines, thereby making both diagnosis and treatment challenging. Though a number of studies on ACO have been documented, gaps in knowledge regarding the pathophysiologic mechanism of this disorder exist. Addressing this issue is an urgent need for improved diagnostic and therapeutic management of the disease. Metabolomics, an increasingly popular technique, reveals the pathogenesis of complex diseases and holds promise in biomarker discovery. This comprehensive narrative review, comprising 99 original research articles in the last five years (2017-2022), summarizes the scientific advances in terms of metabolic alterations in patients with asthma, COPD, and ACO. The analytical tools, nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS), commonly used to study the expression of the metabolome, are discussed. Challenges frequently encountered during metabolite identification and quality assessment are highlighted. Bridging the gap between phenotype and metabotype is envisioned in the future.
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Affiliation(s)
- Sanjukta Dasgupta
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Nilanjana Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, India
| | | | | | - Koel Chaudhury
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, India
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Lu Y, Pang Z, Xia J. Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data. Brief Bioinform 2023; 24:bbac553. [PMID: 36572652 PMCID: PMC9851290 DOI: 10.1093/bib/bbac553] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/31/2022] [Accepted: 11/15/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices. RESULTS We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.
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Affiliation(s)
- Yao Lu
- Department of Microbiology and Immunology, McGill University, Quebec, Canada
| | - Zhiqiang Pang
- Institute of Parasitology, McGill University, Quebec, Canada
| | - Jianguo Xia
- Department of Microbiology and Immunology, McGill University, Quebec, Canada
- Institute of Parasitology, McGill University, Quebec, Canada
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Chicco D, Shiradkar R. Ten quick tips for computational analysis of medical images. PLoS Comput Biol 2023; 19:e1010778. [PMID: 36602952 PMCID: PMC9815662 DOI: 10.1371/journal.pcbi.1010778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Medical imaging is a great asset for modern medicine, since it allows physicians to spatially interrogate a disease site, resulting in precise intervention for diagnosis and treatment, and to observe particular aspect of patients' conditions that otherwise would not be noticeable. Computational analysis of medical images, moreover, can allow the discovery of disease patterns and correlations among cohorts of patients with the same disease, thus suggesting common causes or providing useful information for better therapies and cures. Machine learning and deep learning applied to medical images, in particular, have produced new, unprecedented results that can pave the way to advanced frontiers of medical discoveries. While computational analysis of medical images has become easier, however, the possibility to make mistakes or generate inflated or misleading results has become easier, too, hindering reproducibility and deployment. In this article, we provide ten quick tips to perform computational analysis of medical images avoiding common mistakes and pitfalls that we noticed in multiple studies in the past. We believe our ten guidelines, if taken into practice, can help the computational-medical imaging community to perform better scientific research that eventually can have a positive impact on the lives of patients worldwide.
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Affiliation(s)
- Davide Chicco
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Rakesh Shiradkar
- Department of Biomedical Engineering, Emory University, Atlanta, Georgia, United States of America
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Yao Y, Schneider A, Wolf K, Zhang S, Wang-Sattler R, Peters A, Breitner S. Longitudinal associations between metabolites and long-term exposure to ambient air pollution: Results from the KORA cohort study. ENVIRONMENT INTERNATIONAL 2022; 170:107632. [PMID: 36402035 DOI: 10.1016/j.envint.2022.107632] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/11/2022] [Accepted: 11/12/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Long-term exposure to air pollution has been associated with cardiopulmonary diseases, while the underlying mechanisms remain unclear. OBJECTIVES To investigate changes in serum metabolites associated with long-term exposure to air pollution and explore the susceptibility characteristics. METHODS We used data from the German population-based Cooperative Health Research in the Region of Augsburg (KORA) S4 survey (1999-2001) and two follow-up examinations (F4: 2006-08 and FF4: 2013-14). Mass-spectrometry-based targeted metabolomics was used to quantify metabolites among serum samples. Only participants with repeated metabolites measurements were included in the current analysis. Land-use regression (LUR) models were used to estimate annual average concentrations of ultrafine particles, particulate matter (PM) with an aerodynamic diameter less than 10 μm (PM10), coarse particles (PMcoarse), fine particles, PM2.5 absorbance (a proxy of elemental carbon related to traffic exhaust, PM2.5abs), nitrogen oxides (NO2, NOx), and ozone at individuals' residences. We applied confounder-adjusted mixed-effects regression models to examine the associations between long-term exposure to air pollution and metabolites. RESULTS Among 9,620 observations from 4,261 KORA participants, we included 5,772 (60.0%) observations from 2,583 (60.6%) participants in this analysis. Out of 108 metabolites that passed stringent quality control across three study points in time, we identified nine significant negative associations between phosphatidylcholines (PCs) and ambient pollutants at a Benjamini-Hochberg false discovery rate (FDR) corrected p-value < 0.05. The strongest association was seen for an increase of 0.27 μg/m3 (interquartile range) in PM2.5abs and decreased phosphatidylcholine acyl-alkyl C36:3 (PC ae C36:3) concentrations [percent change in the geometric mean: -2.5% (95% confidence interval: -3.6%, -1.5%)]. CONCLUSIONS Our study suggested that long-term exposure to air pollution is associated with metabolic alterations, particularly in PCs with unsaturated long-chain fatty acids. These findings might provide new insights into potential mechanisms for air pollution-related adverse outcomes.
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Affiliation(s)
- Yueli Yao
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Ludwig-Maximilians-Universität München, Munich, Germany; Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
| | - Alexandra Schneider
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Kathrin Wolf
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Siqi Zhang
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Rui Wang-Sattler
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research, DZD, Munich-Neuherberg, Germany
| | - Annette Peters
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Ludwig-Maximilians-Universität München, Munich, Germany; Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research, DZD, Munich-Neuherberg, Germany; German Centre for Cardiovascular Research, DZHK, Partner Site Munich, Munich, Germany
| | - Susanne Breitner
- Institute for Medical Information Processing, Biometry and Epidemiology - IBE, Ludwig-Maximilians-Universität München, Munich, Germany; Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
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Wieder C, Lai RPJ, Ebbels TMD. Single sample pathway analysis in metabolomics: performance evaluation and application. BMC Bioinformatics 2022; 23:481. [PMID: 36376837 PMCID: PMC9664704 DOI: 10.1186/s12859-022-05005-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 10/25/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Single sample pathway analysis (ssPA) transforms molecular level omics data to the pathway level, enabling the discovery of patient-specific pathway signatures. Compared to conventional pathway analysis, ssPA overcomes the limitations by enabling multi-group comparisons, alongside facilitating numerous downstream analyses such as pathway-based machine learning. While in transcriptomics ssPA is a widely used technique, there is little literature evaluating its suitability for metabolomics. Here we provide a benchmark of established ssPA methods (ssGSEA, GSVA, SVD (PLAGE), and z-score) alongside the evaluation of two novel methods we propose: ssClustPA and kPCA, using semi-synthetic metabolomics data. We then demonstrate how ssPA can facilitate pathway-based interpretation of metabolomics data by performing a case-study on inflammatory bowel disease mass spectrometry data, using clustering to determine subtype-specific pathway signatures. RESULTS While GSEA-based and z-score methods outperformed the others in terms of recall, clustering/dimensionality reduction-based methods provided higher precision at moderate-to-high effect sizes. A case study applying ssPA to inflammatory bowel disease data demonstrates how these methods yield a much richer depth of interpretation than conventional approaches, for example by clustering pathway scores to visualise a pathway-based patient subtype-specific correlation network. We also developed the sspa python package (freely available at https://pypi.org/project/sspa/ ), providing implementations of all the methods benchmarked in this study. CONCLUSION This work underscores the value ssPA methods can add to metabolomic studies and provides a useful reference for those wishing to apply ssPA methods to metabolomics data.
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Affiliation(s)
- Cecilia Wieder
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Rachel P J Lai
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, UK
| | - Timothy M D Ebbels
- Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, UK.
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