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Zheng S, Qin W, Chen T, Ouyang R, Wang X, Li Q, Zhao Y, Liu X, Wang D, Zhou L, Xu G. Strategy for Comprehensive Detection and Annotation of Gut Microbiota-Related Metabolites Based on Liquid Chromatography-High-Resolution Mass Spectrometry. Anal Chem 2024; 96:2206-2216. [PMID: 38253323 DOI: 10.1021/acs.analchem.3c05219] [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: 01/24/2024]
Abstract
Gut microbiota, widely populating the mammalian gastrointestinal tract, plays an important role in regulating diverse pathophysiological processes by producing bioactive molecules. Extensive detection of these molecules contributes to probing microbiota function but is limited by insufficient identification of existing analytical methods. In this study, a systematic strategy was proposed to detect and annotate gut microbiota-related metabolites on a large scale. A pentafluorophenyl (PFP) column-based liquid chromatography-high-resolution mass spectrometry (LC-HRMS) method was first developed for high-coverage analysis of gut microbiota-related metabolites, which was verified to be stable and robust with a wide linearity range, high sensitivity, satisfactory recovery, and repeatability. Then, an informative database integrating 968 knowledge-based microbiota-related metabolites and 282 sample-sourced ones defined by germ-free (GF)/antibiotic-treated (ABX) models was constructed and subsequently used for targeted extraction and annotation in biological samples. Using pooled feces, plasma, and urine of mice for demonstration application, 672 microbiota-related metabolites were annotated, including 21% neglected by routine nontargeted peak detection. This strategy serves as a useful tool for the comprehensive capture of the intestinal flora metabolome, contributing to our deeper understanding of microbe-host interactions.
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Affiliation(s)
- Sijia Zheng
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wangshu Qin
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
| | - Tiantian Chen
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Runze Ouyang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaolin Wang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
| | - Qi Li
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
| | - Ying Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
| | - Xinyu Liu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
| | - Difei Wang
- Department of Gerontology and Geriatrics, Shengjing Hospital of China Medical University, Shenyang 110022, China
| | - Lina Zhou
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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Effects of microbial-derived biotics (meta/pharma/post-biotics) on the modulation of gut microbiome and metabolome; general aspects and emerging trends. Food Chem 2023; 411:135478. [PMID: 36696721 DOI: 10.1016/j.foodchem.2023.135478] [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] [Received: 08/15/2022] [Revised: 11/20/2022] [Accepted: 01/10/2023] [Indexed: 01/13/2023]
Abstract
Potential effects of metabiotics (probiotics effector molecules or signaling factors), pharmabiotics (pro-functional metabolites produced by gut microbiota (GMB)) and postbiotics (multifunctional metabolites and structural compounds of food-grade microorganisms) on GMB have been rarely reviewed. These multifunctional components have several promising capabilities for prevention, alleviation and treatment of some diseases or disorders. Correlations between these essential biotics and GMB are also very interesting and important in human health and nutrition. Furthermore, these natural bioactives are involved in modulation of the immune function, control of metabolic dysbiosis and regulation of the signaling pathways. This review discusses the potential of meta/pharma/post-biotics as new classes of pharmaceutical agents and their effective mechanisms associated with GMB-host cell to cell communications with therapeutic benefits which are important in balance and the integrity of the host microbiome. In addition, cutting-edge findings about bioinformatics /metabolomics analyses related to GMB and these essential biotics are reviewed.
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Wang Q, Davis PB, Qi X, Chen SG, Gurney ME, Perry G, Doraiswamy PM, Xu R. Gut-microbiota-microglia-brain interactions in Alzheimer's disease: knowledge-based, multi-dimensional characterization. Alzheimers Res Ther 2021; 13:177. [PMID: 34670619 PMCID: PMC8529734 DOI: 10.1186/s13195-021-00917-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 10/10/2021] [Indexed: 12/26/2022]
Abstract
BACKGROUND Interactions between the gut microbiota, microglia, and aging may modulate Alzheimer's disease (AD) pathogenesis but the precise nature of such interactions is not known. METHODS We developed an integrated multi-dimensional, knowledge-driven, systems approach to identify interactions among microbial metabolites, microglia, and AD. Publicly available datasets were repurposed to create a multi-dimensional knowledge-driven pipeline consisting of an integrated network of microbial metabolite-gene-pathway-phenotype (MGPPN) consisting of 34,509 nodes (216 microbial metabolites, 22,982 genes, 1329 pathways, 9982 mouse phenotypes) and 1,032,942 edges. RESULTS We evaluated the network-based ranking algorithm by showing that abnormal microglia function and physiology are significantly associated with AD pathology at both genetic and phenotypic levels: AD risk genes were ranked at the top 6.4% among 22,982 genes, P < 0.001. AD phenotypes were ranked at the top 11.5% among 9982 phenotypes, P < 0.001. A total of 8094 microglia-microbial metabolite-gene-pathway-phenotype-AD interactions were identified for top-ranked AD-associated microbial metabolites. Short-chain fatty acids (SCFAs) were ranked at the top among prioritized AD-associated microbial metabolites. Through data-driven analyses, we provided evidence that SCFAs are involved in microglia-mediated gut-microbiota-brain interactions in AD at both genetic, functional, and phenotypic levels. CONCLUSION Our analysis produces a novel framework to offer insights into the mechanistic links between gut microbial metabolites, microglia, and AD, with the overall goal to facilitate disease mechanism understanding, therapeutic target identification, and designing confirmatory experimental studies.
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Affiliation(s)
- QuanQiu Wang
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, 2103 Cornell Rd, Cleveland, OH, 44106, USA
| | - Pamela B Davis
- Center for Community Health Integration, Division of General Medical Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Xin Qi
- Department of Physiology and Biophysics, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Shu G Chen
- Department of Pathology, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | | | - George Perry
- College of Sciences, The University of Texas at San Antonio, San Antonio, TX, USA
| | - P Murali Doraiswamy
- Duke University School of Medicine and the Duke Institute for Brain Sciences, Durham, NC, 27710, USA
| | - Rong Xu
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, 2103 Cornell Rd, Cleveland, OH, 44106, USA.
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Zhou M, Wang Q, Zheng C, John Rush A, Volkow ND, Xu R. Drug repurposing for opioid use disorders: integration of computational prediction, clinical corroboration, and mechanism of action analyses. Mol Psychiatry 2021; 26:5286-5296. [PMID: 33432189 PMCID: PMC7797705 DOI: 10.1038/s41380-020-01011-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 12/11/2020] [Accepted: 12/17/2020] [Indexed: 12/13/2022]
Abstract
Morbidity and mortality from opioid use disorders (OUD) and other substance use disorders (SUD) is a major public health crisis, yet there are few medications to treat them. There is an urgency to accelerate SUD medication development. We present an integrated drug repurposing strategy that combines computational prediction, clinical corroboration using electronic health records (EHRs) of over 72.9 million patients and mechanisms of action analysis. Among top-ranked repurposed candidate drugs, tramadol, olanzapine, mirtazapine, bupropion, and atomoxetine were associated with increased odds of OUD remission (adjusted odds ratio: 1.51 [1.38-1.66], 1.90 [1.66-2.18], 1.38 [1.31-1.46], 1.37 [1.29-1.46], 1.48 [1.25-1.76], p value < 0.001, respectively). Genetic and functional analyses showed these five candidate drugs directly target multiple OUD-associated genes including BDNF, CYP2D6, OPRD1, OPRK1, OPRM1, HTR1B, POMC, SLC6A4 and OUD-associated pathways, including opioid signaling, G-protein activation, serotonin receptors, and GPCR signaling. In summary, we developed an integrated drug repurposing approach and identified five repurposed candidate drugs that might be of value for treating OUD patients, including those suffering from comorbid conditions.
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Affiliation(s)
- Mengshi Zhou
- Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University, Cleveland, OH, USA
- Department of Mathematics & Statistics, Saint Cloud State University, Saint Cloud, MN, USA
| | - QuanQiu Wang
- Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University, Cleveland, OH, USA
| | - Chunlei Zheng
- Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University, Cleveland, OH, USA
| | - A John Rush
- Duke University School of Medicine, Durham, NC, USA
- Duke-National University of Singapore, Singapore, Singapore
- Texas-Tech Health Sciences Center, Permian Basin, Odessa, TX, USA
| | - Nora D Volkow
- National Institute on Drug Abuse, National Institutes of Health, Bethesda, MD, USA
| | - Rong Xu
- Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University, Cleveland, OH, USA.
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