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Vich Vila A, Zhang J, Liu M, Faber KN, Weersma RK. Untargeted faecal metabolomics for the discovery of biomarkers and treatment targets for inflammatory bowel diseases. Gut 2024:gutjnl-2023-329969. [PMID: 39002973 DOI: 10.1136/gutjnl-2023-329969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Accepted: 06/23/2024] [Indexed: 07/15/2024]
Abstract
The gut microbiome has been recognised as a key component in the pathogenesis of inflammatory bowel diseases (IBD), and the wide range of metabolites produced by gut bacteria are an important mechanism by which the human microbiome interacts with host immunity or host metabolism. High-throughput metabolomic profiling and novel computational approaches now allow for comprehensive assessment of thousands of metabolites in diverse biomaterials, including faecal samples. Several groups of metabolites, including short-chain fatty acids, tryptophan metabolites and bile acids, have been associated with IBD. In this Recent Advances article, we describe the contribution of metabolomics research to the field of IBD, with a focus on faecal metabolomics. We discuss the latest findings on the significance of these metabolites for IBD prognosis and therapeutic interventions and offer insights into the future directions of metabolomics research.
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Affiliation(s)
- Arnau Vich Vila
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
- Department of Genetics, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
| | - Jingwan Zhang
- Department of Medicine & Therapeutics, The Chinese University of Hong Kong, Hong Kong (SAR), People's Republic of China
- Microbiota I-Center (MagIC), Hong Kong (SAR), People's Republic of China
| | - Moting Liu
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
| | - Klaas Nico Faber
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
| | - Rinse K Weersma
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
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Mohr AE, Ortega-Santos CP, Whisner CM, Klein-Seetharaman J, Jasbi P. Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare. Biomedicines 2024; 12:1496. [PMID: 39062068 PMCID: PMC11274472 DOI: 10.3390/biomedicines12071496] [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: 04/15/2024] [Revised: 06/25/2024] [Accepted: 06/28/2024] [Indexed: 07/28/2024] Open
Abstract
The field of multi-omics has witnessed unprecedented growth, converging multiple scientific disciplines and technological advances. This surge is evidenced by a more than doubling in multi-omics scientific publications within just two years (2022-2023) since its first referenced mention in 2002, as indexed by the National Library of Medicine. This emerging field has demonstrated its capability to provide comprehensive insights into complex biological systems, representing a transformative force in health diagnostics and therapeutic strategies. However, several challenges are evident when merging varied omics data sets and methodologies, interpreting vast data dimensions, streamlining longitudinal sampling and analysis, and addressing the ethical implications of managing sensitive health information. This review evaluates these challenges while spotlighting pivotal milestones: the development of targeted sampling methods, the use of artificial intelligence in formulating health indices, the integration of sophisticated n-of-1 statistical models such as digital twins, and the incorporation of blockchain technology for heightened data security. For multi-omics to truly revolutionize healthcare, it demands rigorous validation, tangible real-world applications, and smooth integration into existing healthcare infrastructures. It is imperative to address ethical dilemmas, paving the way for the realization of a future steered by omics-informed personalized medicine.
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Affiliation(s)
- Alex E. Mohr
- Systems Precision Engineering and Advanced Research (SPEAR), Theriome Inc., Phoenix, AZ 85004, USA; (A.E.M.); (C.P.O.-S.); (C.M.W.); (J.K.-S.)
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Biodesign Institute Center for Health Through Microbiomes, Arizona State University, Tempe, AZ 85281, USA
| | - Carmen P. Ortega-Santos
- Systems Precision Engineering and Advanced Research (SPEAR), Theriome Inc., Phoenix, AZ 85004, USA; (A.E.M.); (C.P.O.-S.); (C.M.W.); (J.K.-S.)
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA
| | - Corrie M. Whisner
- Systems Precision Engineering and Advanced Research (SPEAR), Theriome Inc., Phoenix, AZ 85004, USA; (A.E.M.); (C.P.O.-S.); (C.M.W.); (J.K.-S.)
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Biodesign Institute Center for Health Through Microbiomes, Arizona State University, Tempe, AZ 85281, USA
| | - Judith Klein-Seetharaman
- Systems Precision Engineering and Advanced Research (SPEAR), Theriome Inc., Phoenix, AZ 85004, USA; (A.E.M.); (C.P.O.-S.); (C.M.W.); (J.K.-S.)
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Paniz Jasbi
- Systems Precision Engineering and Advanced Research (SPEAR), Theriome Inc., Phoenix, AZ 85004, USA; (A.E.M.); (C.P.O.-S.); (C.M.W.); (J.K.-S.)
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Jensen IT, Janss L, Radutoiu S, Waagepetersen R. Compositionally aware estimation of cross-correlations for microbiome data. PLoS One 2024; 19:e0305032. [PMID: 38941272 PMCID: PMC11213360 DOI: 10.1371/journal.pone.0305032] [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: 10/18/2023] [Accepted: 05/22/2024] [Indexed: 06/30/2024] Open
Abstract
In the field of microbiome studies, it is of interest to infer correlations between abundances of different microbes (here referred to as operational taxonomic units, OTUs). Several methods taking the compositional nature of the sequencing data into account exist. However, these methods cannot infer correlations between OTU abundances and other variables. In this paper we introduce the novel methods SparCEV (Sparse Correlations with External Variables) and SparXCC (Sparse Cross-Correlations between Compositional data) for quantifying correlations between OTU abundances and either continuous phenotypic variables or components of other compositional datasets, such as transcriptomic data. SparCEV and SparXCC both assume that the average correlation in the dataset is zero. Iterative versions of SparCEV and SparXCC are proposed to alleviate bias resulting from deviations from this assumption. We compare these new methods to empirical Pearson cross-correlations after applying naive transformations of the data (log and log-TSS). Additionally, we test the centered log ratio transformation (CLR) and the variance stabilising transformation (VST). We find that CLR and VST outperform naive transformations, except when the correlation matrix is dense. SparCEV and SparXCC outperform CLR and VST when the number of OTUs is small and perform similarly to CLR and VST for large numbers of OTUs. Adding the iterative procedure increases accuracy for SparCEV and SparXCC for all cases, except when the average correlation in the dataset is close to zero or the correlation matrix is dense. These results are consistent with our theoretical considerations.
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Affiliation(s)
- Ib Thorsgaard Jensen
- Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
- Department of Mathematical Sciences, Aalborg University, Aalborg, Denmark
| | - Luc Janss
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Simona Radutoiu
- Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
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Choi Y, Lee SJ, Kim HS, Eom JS, Jo SU, Guan LL, Lee SS. Metataxonomic and metabolomic profiling revealed Pinus koraiensis cone essential oil reduced methane emission through affecting ruminal microbial interactions and host-microbial metabolism. Anim Microbiome 2024; 6:37. [PMID: 38943213 PMCID: PMC11212255 DOI: 10.1186/s42523-024-00325-4] [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: 02/08/2024] [Accepted: 06/18/2024] [Indexed: 07/01/2024] Open
Abstract
BACKGROUND Pinus koraiensis cone essential oil (PEO) contains functional compounds such as monoterpene hydrocarbons, and the administration of PEO reduced methane (CH4) emissions during growing phase of goats. However, the mode of action of PEO driven CH4 reduction is not known, especially how the administration of PEO can affect rumen microbiota and host metabolism in goats during the fattening phase. This study aimed to elucidate the potential microbial and host responses PEO supplementation in goats using metataxonomics (prokaryotes and protozoa) and metabolomics (rumen fluid and serum). RESULTS Ten fattening Korean native goats were divided into two dietary groups: control (CON; basal diet without additives) and PEO (basal diet + 1.5 g/d of PEO) with a 2 × 2 crossover design and the treatment lasted for 11 weeks. Administration of PEO reduced CH4 concentrations in the exhaled gas from eructation by 12.0-13.6% (P < 0.05). Although the microbial composition of prokaryotes (bacteria and archaea) and protozoa in the rumen was not altered after PEO administration. MaAsLin2 analysis revealed that the abundance of Selenomonas, Christensenellaceae R-7 group, and Anaerovibrio were enriched in the rumen of PEO supplemented goats (Q < 0.1). Co-occurrence network analysis revealed that Lachnospiraceae AC2044 group and Anaerovibrio were the keystone taxa in the CON and PEO groups, respectively. Methane metabolism (P < 0.05) was enriched in the CON group, whereas metabolism of sulfur (P < 0.001) and propionate (P < 0.1) were enriched in the PEO group based on microbial predicted functions. After PEO administration, the abundance of 11 rumen and 4 serum metabolites increased, whereas that of 25 rumen and 14 serum metabolites decreased (P < 0.1). Random forest analysis identified eight ruminal metabolites that were altered after PEO administration, among which four were associated with propionate production, with predictive accuracy ranging from 0.75 to 0.88. Additionally, we found that serum sarcosine (serum metabolite) was positively correlated with CH4 emission parameters and abundance of Methanobrevibacter in the rumen (|r|≥ 0.5, P < 0.05). CONCLUSIONS This study revealed that PEO administration reduced CH4 emission from of fattening goats with altered microbial interactions and metabolites in the rumen and host. Importantly, PEO administration affected utilizes various mechanisms such as formate, sulfur, methylated amines metabolism, and propionate production, collectively leading to CH4 reduction. The knowledge is important for future management strategies to maintain animal production and health while mitigate CH4 emission.
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Affiliation(s)
- Y Choi
- Division of Applied Life Science (BK21), Gyeongsang National University, Jinju, 52828, Republic of Korea
- Institute of Agriculture and Life Science (IALS), Gyeongsang National University, Jinju, 52828, Republic of Korea
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada
- Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - S J Lee
- Institute of Agriculture and Life Science (IALS), Gyeongsang National University, Jinju, 52828, Republic of Korea
- Institute of Agriculture and Life Science and University-Centered Labs, Gyeongsang National University, Jinju, 52828, Republic of Korea
| | - H S Kim
- Institute of Agriculture and Life Science (IALS), Gyeongsang National University, Jinju, 52828, Republic of Korea
| | - J S Eom
- Institute of Agriculture and Life Science (IALS), Gyeongsang National University, Jinju, 52828, Republic of Korea
| | - S U Jo
- Division of Applied Life Science (BK21), Gyeongsang National University, Jinju, 52828, Republic of Korea
- Institute of Agriculture and Life Science (IALS), Gyeongsang National University, Jinju, 52828, Republic of Korea
| | - L L Guan
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada.
- Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.
| | - S S Lee
- Division of Applied Life Science (BK21), Gyeongsang National University, Jinju, 52828, Republic of Korea.
- Institute of Agriculture and Life Science (IALS), Gyeongsang National University, Jinju, 52828, Republic of Korea.
- Institute of Agriculture and Life Science and University-Centered Labs, Gyeongsang National University, Jinju, 52828, Republic of Korea.
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Song Y, Yao S, Li X, Wang T, Jiang X, Bolan N, Warren CR, Northen TR, Chang SX. Soil metabolomics: Deciphering underground metabolic webs in terrestrial ecosystems. ECO-ENVIRONMENT & HEALTH 2024; 3:227-237. [PMID: 38680731 PMCID: PMC11047296 DOI: 10.1016/j.eehl.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 02/05/2024] [Accepted: 03/04/2024] [Indexed: 05/01/2024]
Abstract
Soil metabolomics is an emerging approach for profiling diverse small molecule metabolites, i.e., metabolomes, in the soil. Soil metabolites, including fatty acids, amino acids, lipids, organic acids, sugars, and volatile organic compounds, often contain essential nutrients such as nitrogen, phosphorus, and sulfur and are directly linked to soil biogeochemical cycles driven by soil microorganisms. This paper presents an overview of methods for analyzing soil metabolites and the state-of-the-art of soil metabolomics in relation to soil nutrient cycling. We describe important applications of metabolomics in studying soil carbon cycling and sequestration, and the response of soil organic pools to changing environmental conditions. This includes using metabolomics to provide new insights into the close relationships between soil microbiome and metabolome, as well as responses of soil metabolome to plant and environmental stresses such as soil contamination. We also highlight the advantage of using soil metabolomics to study the biogeochemical cycles of elements and suggest that future research needs to better understand factors driving soil function and health.
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Affiliation(s)
- Yang Song
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shi Yao
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaona Li
- School of Environment and Ecology, Jiangnan University, Wuxi 225127, China
| | - Tao Wang
- Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610299, China
| | - Xin Jiang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Nanthi Bolan
- School of Agriculture and Environment, The University of Western Australia, Nedland, WA-6009, Australia
- The UWA Institute of Agriculture, The University of Western Australia, Nedland, WA-6009, Australia
- Healthy Environments and Lives (HEAL) National Research Network, Australia
| | - Charles R. Warren
- School of Life and Environmental Sciences, University of Sydney, Heydon-Laurence Building A08, NSW 2006, Australia
| | - Trent R. Northen
- Environmental Genomics and System Biology Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Scott X. Chang
- Department of Renewable Resources, University of Alberta, Edmonton, Alberta T6G 2E3, Canada
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6
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De Filippis F, Valentino V, Sequino G, Borriello G, Riccardi MG, Pierri B, Cerino P, Pizzolante A, Pasolli E, Esposito M, Limone A, Ercolini D. Exposure to environmental pollutants selects for xenobiotic-degrading functions in the human gut microbiome. Nat Commun 2024; 15:4482. [PMID: 38802370 PMCID: PMC11130323 DOI: 10.1038/s41467-024-48739-7] [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: 02/19/2024] [Accepted: 05/08/2024] [Indexed: 05/29/2024] Open
Abstract
Environmental pollutants from different chemical families may reach the gut microbiome, where they can be metabolized and transformed. However, how our gut symbionts respond to the exposure to environmental pollution is still underexplored. In this observational, cohort study, we aim to investigate the influence of environmental pollution on the gut microbiome composition and potential activity by shotgun metagenomics. We select as a case study a population living in a highly polluted area in Campania region (Southern Italy), proposed as an ideal field for exposomic studies and we compare the fecal microbiome of 359 subjects living in areas with high, medium and low environmental pollution. We highlight changes in gut microbiome composition and functionality that were driven by pollution exposure. Subjects from highly polluted areas show higher blood concentrations of dioxin and heavy metals, as well as an increase in microbial genes related to degradation and/or resistance to these molecules. Here we demonstrate the dramatic effect that environmental xenobiotics have on gut microbial communities, shaping their composition and boosting the selection of strains with degrading capacity. The gut microbiome can be considered as a pivotal player in the environment-health interaction that may contribute to detoxifying toxic compounds and should be taken into account when developing risk assessment models. The study was registered at ClinicalTrials.gov with the identifier NCT05976126.
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Affiliation(s)
- Francesca De Filippis
- Department of Agricultural Sciences, University of Naples Federico II, Via Università, 100, Portici, Italy
- Task Force on Microbiome Studies, University of Naples Federico II, Corso Umberto I, 40, Napoli, Italy
- Istituto Zooprofilattico Sperimentale del Mezzogiorno, Via Salute, 2, Portici, Italy
| | - Vincenzo Valentino
- Department of Agricultural Sciences, University of Naples Federico II, Via Università, 100, Portici, Italy
| | - Giuseppina Sequino
- Department of Agricultural Sciences, University of Naples Federico II, Via Università, 100, Portici, Italy
| | - Giorgia Borriello
- Istituto Zooprofilattico Sperimentale del Mezzogiorno, Via Salute, 2, Portici, Italy
| | | | - Biancamaria Pierri
- National Reference Centre for the Analysis and Study of the Correlation between Environment, Animal and Human, Via Salute, 2, Portici, Italy
| | - Pellegrino Cerino
- National Reference Centre for the Analysis and Study of the Correlation between Environment, Animal and Human, Via Salute, 2, Portici, Italy
| | - Antonio Pizzolante
- National Reference Centre for the Analysis and Study of the Correlation between Environment, Animal and Human, Via Salute, 2, Portici, Italy
| | - Edoardo Pasolli
- Department of Agricultural Sciences, University of Naples Federico II, Via Università, 100, Portici, Italy
- Task Force on Microbiome Studies, University of Naples Federico II, Corso Umberto I, 40, Napoli, Italy
| | - Mauro Esposito
- National Reference Centre for the Analysis and Study of the Correlation between Environment, Animal and Human, Via Salute, 2, Portici, Italy
| | - Antonio Limone
- Istituto Zooprofilattico Sperimentale del Mezzogiorno, Via Salute, 2, Portici, Italy
| | - Danilo Ercolini
- Department of Agricultural Sciences, University of Naples Federico II, Via Università, 100, Portici, Italy.
- Task Force on Microbiome Studies, University of Naples Federico II, Corso Umberto I, 40, Napoli, Italy.
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Wu S, Zhou H, Chen D, Lu Y, Li Y, Qiao J. Multi-omic analysis tools for microbial metabolites prediction. Brief Bioinform 2024; 25:bbae264. [PMID: 38859767 PMCID: PMC11165163 DOI: 10.1093/bib/bbae264] [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: 02/03/2024] [Revised: 05/08/2024] [Indexed: 06/12/2024] Open
Abstract
How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.
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Affiliation(s)
- Shengbo Wu
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
| | - Haonan Zhou
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Danlei Chen
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
| | - Yutong Lu
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
| | - Yanni Li
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
| | - Jianjun Qiao
- School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Zhejiang Institute of Tianjin University, Shaoxing, Shaoxing 312300, China
- Key Laboratory of Systems Bioengineering, Ministry of Education (Tianjin University), Tianjin 300072, China
- Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China
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Tang J, Baker JL. The salivary virome during childhood dental caries. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.22.595360. [PMID: 38826395 PMCID: PMC11142174 DOI: 10.1101/2024.05.22.595360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
While many studies have examined the bacterial taxa associated with dental caries, the most common chronic infectious disease globally, little is known about the caries-associated virome. In this study, the salivary viromes of 21 children with severe caries (>2 dentin lesions) and 23 children with healthy dentition were examined. 2,485 viral metagenome-assembled genomes (vMAGs) were identified, binned, and quantified from the metagenomic assemblies. These vMAGs were mostly phage, and represented 1,547 unique species-level vOTUs, 247 of which appear to be novel. The metagenomes were also queried for all 3,835 unique species-level vOTUs of DNA viruses with a human host on NCBI Virus, however all but Human betaherpesvirus 7 were at very low abundance in the saliva. The oral viromes of the children with caries exhibited significantly different beta diversity compared to the oral virome of the children with healthy dentition; several vOTUs predicted to infect Pauljensenia and Neisseria were strongly correlated with health, and two vOTUs predicted to infect Saccharibacteria and Prevotella histicola, respectively, were correlated with caries. Co-occurrence analysis indicated that phage typically co-occurred with both their predicted hosts and with bacteria that were themselves associated with the same disease status. Overall, this study provided the sequences of 53 complete or nearly complete novel oral phages and illustrated the significance of the oral virome in the context of dental caries, which has been largely overlooked. This work represents an important step towards the identification and study of phage therapy candidates which treat or prevent caries pathogenesis.
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Affiliation(s)
- Jonah Tang
- Department of Oral Rehabilitation & Biosciences, OHSU School of Dentistry, Portland, OR, USA
| | - Jonathon L Baker
- Department of Oral Rehabilitation & Biosciences, OHSU School of Dentistry, Portland, OR, USA
- Genomic Medicine Group, J. Craig Venter Institute, La Jolla, CA, USA
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9
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Xiang Y, Yu Y, Wang J, Li W, Rong Y, Ling H, Chen Z, Qian Y, Han X, Sun J, Yang Y, Chen L, Zhao C, Li J, Chen K. Neural network establishes co-occurrence links between transformation products of the contaminant and the soil microbiome. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 924:171287. [PMID: 38423316 DOI: 10.1016/j.scitotenv.2024.171287] [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: 10/09/2023] [Revised: 02/24/2024] [Accepted: 02/24/2024] [Indexed: 03/02/2024]
Abstract
It remains challenging to establish reliable links between transformation products (TPs) of contaminants and corresponding microbes. This challenge arises due to the sophisticated experimental regime required for TP discovery and the compositional nature of 16S rRNA gene amplicon sequencing and mass spectrometry datasets, which can potentially confound statistical inference. In this study, we present a new strategy by combining the use of 2H-labeled Stable Isotope-Assisted Metabolomics (2H-SIAM) with a neural network-based algorithm (i.e., MMvec) to explore links between TPs of pyrene and the soil microbiome. The links established by this novel strategy were further validated using different approaches. Briefly, a metagenomic study provided indirect evidence for the established links, while the identification of pyrene degraders from soils, and a DNA-based stable isotope probing (DNA-SIP) study offered direct evidence. The comparison among different approaches, including Pearson's and Spearman's correlations, further confirmed the superior performance of our strategy. In conclusion, we summarize the unique features of the combined use of 2H-SIAM and MMvec. This study not only addresses the challenges in linking TPs to microbes but also introduces an innovative and effective approach for such investigations. Environmental Implication: Taxonomically diverse bacteria performing successive metabolic steps of the contaminant were firstly depicted in the environmental matrix.
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Affiliation(s)
- Yuhui Xiang
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, PR China
| | - Yansong Yu
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, PR China
| | - Jiahui Wang
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, PR China
| | - Weiwei Li
- Hubei Key Laboratory of Pollution Damage Assessment and Environmental Health Risk Prevention and Control, Hubei Provincial Academy of Eco-Environmental Sciences, Wuhan 430074, PR China
| | - Yu Rong
- Hubei Key Laboratory of Pollution Damage Assessment and Environmental Health Risk Prevention and Control, Hubei Provincial Academy of Eco-Environmental Sciences, Wuhan 430074, PR China
| | - Haibo Ling
- Hubei Key Laboratory of Pollution Damage Assessment and Environmental Health Risk Prevention and Control, Hubei Provincial Academy of Eco-Environmental Sciences, Wuhan 430074, PR China
| | - Zhongbing Chen
- Department of Applied Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha 16500, Czech Republic
| | - Yiguang Qian
- Research Center for Environmental Ecology and Engineering, School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, PR China
| | - Xiaole Han
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, PR China
| | - Jie Sun
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, PR China
| | - Yuyi Yang
- Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, PR China
| | - Liang Chen
- Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, PR China
| | - Chao Zhao
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, PR China
| | - Juying Li
- Shenzhen Key Laboratory of Environmental Chemistry and Ecological Remediation, College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518060, PR China.
| | - Ke Chen
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, PR China.
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10
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Aguilar C, Alwali A, Mair M, Rodriguez-Orduña L, Contreras-Peruyero H, Modi R, Roberts C, Sélem-Mojica N, Licona-Cassani C, Parkinson EI. Actinomycetota bioprospecting from ore-forming environments. Microb Genom 2024; 10:001253. [PMID: 38743050 PMCID: PMC11165632 DOI: 10.1099/mgen.0.001253] [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: 11/21/2023] [Accepted: 04/26/2024] [Indexed: 05/16/2024] Open
Abstract
Natural products from Actinomycetota have served as inspiration for many clinically relevant therapeutics. Despite early triumphs in natural product discovery, the rate of unearthing new compounds has decreased, necessitating inventive approaches. One promising strategy is to explore environments where survival is challenging. These harsh environments are hypothesized to lead to bacteria developing chemical adaptations (e.g. natural products) to enable their survival. This investigation focuses on ore-forming environments, particularly fluoride mines, which typically have extreme pH, salinity and nutrient scarcity. Herein, we have utilized metagenomics, metabolomics and evolutionary genome mining to dissect the biodiversity and metabolism in these harsh environments. This work has unveiled the promising biosynthetic potential of these bacteria and has demonstrated their ability to produce bioactive secondary metabolites. This research constitutes a pioneering endeavour in bioprospection within fluoride mining regions, providing insights into uncharted microbial ecosystems and their previously unexplored natural products.
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Affiliation(s)
- César Aguilar
- Department of Chemistry, Purdue University, West Lafayette, IN, 47907, USA
| | - Amir Alwali
- Department of Chemistry, Purdue University, West Lafayette, IN, 47907, USA
| | - Madeline Mair
- Department of Chemistry, Purdue University, West Lafayette, IN, 47907, USA
| | | | | | - Ramya Modi
- Department of Chemistry, Purdue University, West Lafayette, IN, 47907, USA
| | - Carson Roberts
- Department of Chemistry, Purdue University, West Lafayette, IN, 47907, USA
| | | | | | - Elizabeth Ivy Parkinson
- Department of Chemistry, Purdue University, West Lafayette, IN, 47907, USA
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN, 47907, USA
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11
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Yin YH, Li CH, Huang HP, Zhang C, Zhang SB, Li SM, Chen J. A novel enzyme-based functional correlation algorithm for multi-omics reveals the potential mechanisms of traditional Chinese medicines: Taking Jian-Pi-Yi-Shen formula as an example. J Pharm Biomed Anal 2024; 241:115973. [PMID: 38237547 DOI: 10.1016/j.jpba.2024.115973] [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: 10/17/2023] [Revised: 12/21/2023] [Accepted: 01/08/2024] [Indexed: 02/21/2024]
Abstract
The integrated analysis of host metabolome and intestinal microbiome is an opportunity to explore the complex therapeutic mechanisms of traditional Chinese medicines. Currently, researchers mainly employ various statistical correlation analytical methods to investigate metabolome-microbiome correlations. However, these conventional correlation techniques often focus on statistical correlations and their biological meanings are always ignored, especially the functional relevance between them. Here, we developed a novel enzyme-based functional correlation (EBFC) algorithm to further improve the interpretability and the identified scope of microbe-metabolite correlations based on the conventional Spearman's analysis. The proposed EBFC algorithm is successfully utilized to reveal the therapeutic mechanisms of Jian-Pi-Yi-Shen (JPYS) formula on the treatment of adenine-induced chronic kidney disease (CKD) rats. JPYS, a TCM formula for treating CKD, has beneficial clinical effects. We tentatively revealed the potential mechanism of JPYS for treating CKD rats from the perspective of the serum metabolome, gut microbiome, and their interactions. Specifically, 11 metabolites and 19 bacterial genera in the CKD rats were significantly regulated to approaching normal status after JPYS treatment, suggesting that JPYS could ameliorate the pathological symptoms of CKD rats by reshaping the disturbed metabolome and gut microbiota. Further correlation analysis between the significantly perturbed metabolites, microbiota, and the related enzymes provided more strong evidence for the study of host metabolism-microbiota interactions and the therapeutic mechanism of JPYS on CKD rats. In conclusion, these findings will help us to deeply understand the pathogenesis of CKD and provide new insights into the therapeutic mechanism of JPYS.
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Affiliation(s)
- Ying-Hao Yin
- Shenzhen Key Laboratory of Hospital Chinese Medicine Preparation, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen 518033, China
| | - Chang-Hui Li
- Shenzhen Key Laboratory of Hospital Chinese Medicine Preparation, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen 518033, China
| | - Hai-Piao Huang
- Shenzhen Key Laboratory of Hospital Chinese Medicine Preparation, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen 518033, China
| | - Chi Zhang
- Shenzhen Key Laboratory of Hospital Chinese Medicine Preparation, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen 518033, China
| | - Shang-Bin Zhang
- Shenzhen Key Laboratory of Hospital Chinese Medicine Preparation, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen 518033, China
| | - Shun-Min Li
- Shenzhen Key Laboratory of Hospital Chinese Medicine Preparation, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen 518033, China
| | - Jianping Chen
- Shenzhen Key Laboratory of Hospital Chinese Medicine Preparation, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen 518033, China.
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12
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Santangelo BE, Apgar M, Colorado ASB, Martin CG, Sterrett J, Wall E, Joachimiak MP, Hunter LE, Lozupone CA. Integrating biological knowledge for mechanistic inference in the host-associated microbiome. Front Microbiol 2024; 15:1351678. [PMID: 38638909 PMCID: PMC11024261 DOI: 10.3389/fmicb.2024.1351678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/26/2024] [Indexed: 04/20/2024] Open
Abstract
Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.
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Affiliation(s)
- Brook E. Santangelo
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Madison Apgar
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | | | - Casey G. Martin
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - John Sterrett
- Department of Integrative Physiology, University of Colorado, Boulder, CO, United States
| | - Elena Wall
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Marcin P. Joachimiak
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Biosystems Data Science Department, Berkeley, CA, United States
| | - Lawrence E. Hunter
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Catherine A. Lozupone
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
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13
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P. Gomes PW, Mannochio-Russo H, Mao J, Zhao HN, Ancira J, Tipton CD, Dorrestein PC, Li M. Co-occurrence network analysis reveals the alterations of the skin microbiome and metabolome in adults with mild to moderate atopic dermatitis. mSystems 2024; 9:e0111923. [PMID: 38319107 PMCID: PMC10949451 DOI: 10.1128/msystems.01119-23] [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: 10/19/2023] [Accepted: 01/04/2024] [Indexed: 02/07/2024] Open
Abstract
Skin microbiome can be altered in patients with atopic dermatitis (AD). An understanding of the changes from healthy to atopic skin can help develop new targets for treatment by identifying microbial and molecular biomarkers. This study investigates the skin microbiome and metabolome of healthy adult subjects and lesion (ADL) and non-lesion (ADNL) of AD patients by 16S rRNA gene sequencing and mass spectrometry, respectively. Samples from AD patients showed alterations in the diversity and composition of the skin microbiome, with ADL skin having the greatest divergence. Staphylococcus species, especially S. aureus, were significantly increased in AD patients. Metabolomic profiles were also different between the groups. Dipeptide derivatives are more abundant in ADL, which may be related to skin inflammation. Co-occurrence network analysis of the microbiome and metabolomics data revealed higher co-occurrence of metabolites and bacteria in healthy ADNL compared to ADL. S. aureus co-occurred with dipeptide derivatives in ADL, while phytosphingosine-derived compounds showed co-occurrences with commensal bacteria, for example, Paracoccus sp., Pseudomonas sp., Prevotella bivia, Lactobacillus iners, Anaerococcus sp., Micrococcus sp., Corynebacterium ureicelerivorans, Corynebacterium massiliense, Streptococcus thermophilus, and Roseomonas mucosa, in healthy and ADNL groups. Therefore, these findings provide valuable insights into how AD affects the human skin metabolome and microbiome.IMPORTANCEThis study provides valuable insight into changes in the skin microbiome and associated metabolomic profiles in an adult population with mild to moderate atopic dermatitis. It also identifies new therapeutic targets that may be useful for developing personalized treatments for individuals with atopic dermatitis based on their unique skin microbiome and metabolic profiles.
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Affiliation(s)
- Paulo Wender P. Gomes
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA
| | - Helena Mannochio-Russo
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA
| | - Junhong Mao
- Colgate−Palmolive Company, Piscataway, New Jersey, USA
| | - Haoqi Nina Zhao
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA
| | | | | | - Pieter C. Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA
- Department of Pediatrics, University of California, San Diego, California, USA
| | - Min Li
- Colgate−Palmolive Company, Piscataway, New Jersey, USA
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14
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Nothias LF, Schmid R, Garlet A, Cameron H, Leoty-Okombi S, André-Frei V, Fuchs R, Dorrestein PC, Ternes P. Functional metabolomics of the human scalp: a metabolic niche for Staphylococcus epidermidis. mSystems 2024; 9:e0035623. [PMID: 38206014 PMCID: PMC10878091 DOI: 10.1128/msystems.00356-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 11/29/2023] [Indexed: 01/12/2024] Open
Abstract
Although metabolomics data acquisition and analysis technologies have become increasingly sophisticated over the past 5-10 years, deciphering a metabolite's function from a description of its structure and its abundance in a given experimental setting is still a major scientific and intellectual challenge. To point out ways to address this "data to knowledge" challenge, we developed a functional metabolomics strategy that combines state-of-the-art data analysis tools and applied it to a human scalp metabolomics data set: skin swabs from healthy volunteers with normal or oily scalp (Sebumeter score 60-120, n = 33; Sebumeter score > 120, n = 41) were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), yielding four metabolomics data sets for reversed phase chromatography (C18) or hydrophilic interaction chromatography (HILIC) separation in electrospray ionization (ESI) + or - ionization mode. Following our data analysis strategy, we were able to obtain increasingly comprehensive structural and functional annotations, by applying the Global Natural Product Social Networking (M. Wang, J. J. Carver, V. V. Phelan, L. M. Sanchez, et al., Nat Biotechnol 34:828-837, 2016, https://doi.org/10.1038/nbt.3597), SIRIUS (K. Dührkop, M. Fleischauer, M. Ludwig, A. A. Aksenov, et al., Nat Methods 16:299-302, 2019, https://doi.org/10.1038/s41592-019-0344-8), and MicrobeMASST (S. ZuffaS, R. Schmid, A. Bauermeister, P. W, P. Gomes, et al., bioRxiv:rs.3.rs-3189768, 2023, https://doi.org/10.21203/rs.3.rs-3189768/v1) tools. We finally combined the metabolomics data with a corresponding metagenomic sequencing data set using MMvec (J. T. Morton, A. A. Aksenov, L. F. Nothias, J. R. Foulds, et. al., Nat Methods 16:1306-1314, 2019, https://doi.org/10.1038/s41592-019-0616-3), gaining insights into the metabolic niche of one of the most prominent microbes on the human skin, Staphylococcus epidermidis.IMPORTANCESystems biology research on host-associated microbiota focuses on two fundamental questions: which microbes are present and how do they interact with each other, their host, and the broader host environment? Metagenomics provides us with a direct answer to the first part of the question: it unveils the microbial inhabitants, e.g., on our skin, and can provide insight into their functional potential. Yet, it falls short in revealing their active role. Metabolomics shows us the chemical composition of the environment in which microbes thrive and the transformation products they produce. In particular, untargeted metabolomics has the potential to observe a diverse set of metabolites and is thus an ideal complement to metagenomics. However, this potential often remains underexplored due to the low annotation rates in MS-based metabolomics and the necessity for multiple experimental chromatographic and mass spectrometric conditions. Beyond detection, prospecting metabolites' functional role in the host/microbiome metabolome requires identifying the biological processes and entities involved in their production and biotransformations. In the present study of the human scalp, we developed a strategy to achieve comprehensive structural and functional annotation of the metabolites in the human scalp environment, thus diving one step deeper into the interpretation of "omics" data. Leveraging a collection of openly accessible software tools and integrating microbiome data as a source of functional metabolite annotations, we finally identified the specific metabolic niche of Staphylococcus epidermidis, one of the key players of the human skin microbiome.
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Affiliation(s)
- Louis-Félix Nothias
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, California, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA
| | - Robin Schmid
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, California, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czechia
| | | | - Hunter Cameron
- BASF Corporation, Research Triangle Park, North Carolina, USA
| | | | | | | | - Pieter C. Dorrestein
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, California, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA
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15
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Ban S, Cheng W, Wang X, Niu J, Wu Q, Xu Y. Predicting the final metabolic profile based on the succession-related microbiota during spontaneous fermentation of the starter for Chinese liquor making. mSystems 2024; 9:e0058623. [PMID: 38206013 PMCID: PMC10878095 DOI: 10.1128/msystems.00586-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 12/08/2023] [Indexed: 01/12/2024] Open
Abstract
Microbial inoculation is an effective way to improve the quality of fermented foods via affecting the microbiota structure. However, it is unclear how the inoculation regulates the microbiota structure, and it is still difficult to directionally control the microbiota function via the inoculation. In this work, using the spontaneous fermentation of the starter (Daqu) for Chinese liquor fermentation as a case, we inoculated different microbiota groups at different time points in Daqu fermentation, and analyzed the effect of the inoculation on the final metabolic profile of Daqu. The inoculated microbiota and inoculated time points both significantly affected the final metabolites via regulating the microbial succession (P < 0.001), and multiple inoculations can promote deterministic assembly. Twenty-seven genera were identified to be related to microbial succession, and drove the variation of 121 metabolites. We then constructed an elastic network model to predict the profile of these 121 metabolites based on the abundances of 27 succession-related genera in Daqu fermentation. Procrustes analysis showed that the model could accurately predict the metabolic abundances (average Spearman correlation coefficients >0.3). This work revealed the effect of inoculation on the microbiota succession and the metabolic profile. The established predicted model of metabolic profile would be beneficial for directionally improving the food quality.IMPORTANCEThis work revealed the importance of microbial succession to microbiota structure and metabolites. Multi-inoculations would promote deterministic assembly. It would facilitate the regulation of microbiota structure and metabolic profile. In addition, we established a model to predict final metabolites based on microbial genera related to microbial succession. This model was beneficial for optimizing the inoculation of the microbiota. This work would be helpful for controlling the spontaneous food fermentation and directionally improving the food quality.
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Affiliation(s)
- Shibo Ban
- Lab of Brewing Microbiology and Applied Enzymology, Key Laboratory of Industrial Biotechnology of Ministry of Education, State Key Laboratory of Food Science and Technology, School of Biotechnology, Jiangnan University, Wuxi, China
| | - Wei Cheng
- Sichuan Langjiu Group Co., Ltd, Luzhou, China
| | - Xi Wang
- Sichuan Langjiu Group Co., Ltd, Luzhou, China
| | - Jiao Niu
- Sichuan Langjiu Group Co., Ltd, Luzhou, China
| | - Qun Wu
- Lab of Brewing Microbiology and Applied Enzymology, Key Laboratory of Industrial Biotechnology of Ministry of Education, State Key Laboratory of Food Science and Technology, School of Biotechnology, Jiangnan University, Wuxi, China
| | - Yan Xu
- Lab of Brewing Microbiology and Applied Enzymology, Key Laboratory of Industrial Biotechnology of Ministry of Education, State Key Laboratory of Food Science and Technology, School of Biotechnology, Jiangnan University, Wuxi, China
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16
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Zhang JS, Huang S, Chen Z, Chu CH, Takahashi N, Yu OY. Application of omics technologies in cariology research: A critical review with bibliometric analysis. J Dent 2024; 141:104801. [PMID: 38097035 DOI: 10.1016/j.jdent.2023.104801] [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: 09/19/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 12/19/2023] Open
Abstract
OBJECTIVES To review the application of omics technologies in the field of cariology research and provide critical insights into the emerging opportunities and challenges. DATA & SOURCES Publications on the application of omics technologies in cariology research up to December 2022 were sourced from online databases, including PubMed, Web of Science and Scopus. Two independent reviewers assessed the relevance of the publications to the objective of this review. STUDY SELECTION Studies that employed omics technologies to investigate dental caries were selected from the initial pool of identified publications. A total of 922 publications with one or more omics technologies adopted were included for comprehensive bibliographic analysis. (Meta)genomics (676/922, 73 %) is the predominant omics technology applied for cariology research in the included studies. Other applied omics technologies are metabolomics (108/922, 12 %), proteomics (105/922, 11 %), and transcriptomics (76/922, 8 %). CONCLUSION This study identified an emerging trend in the application of multiple omics technologies in cariology research. Omics technologies possess significant potential in developing strategies for the detection, staging evaluation, risk assessment, prevention, and management of dental caries. Despite the numerous challenges that lie ahead, the integration of multi-omics data obtained from individual biological samples, in conjunction with artificial intelligence technology, may offer potential avenues for further exploration in caries research. CLINICAL SIGNIFICANCE This review presented a comprehensive overview of the application of omics technologies in cariology research and discussed the advantages and challenges of using these methods to detect, assess, predict, prevent, and treat dental caries. It contributes to steering research for improved understanding of dental caries and advancing clinical translation of cariology research outcomes.
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Affiliation(s)
| | - Shi Huang
- Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, PR China
| | - Zigui Chen
- Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, PR China; Department of Microbiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, PR China
| | - Chun-Hung Chu
- Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, PR China
| | - Nobuhiro Takahashi
- Division of Oral Ecology and Biochemistry, Tohoku University Graduate School of Dentistry, Sendai, Japan
| | - Ollie Yiru Yu
- Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, PR China.
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17
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Roach J, Mital R, Haffner JJ, Colwell N, Coats R, Palacios HM, Liu Z, Godinho JLP, Ness M, Peramuna T, McCall LI. Microbiome metabolite quantification methods enabling insights into human health and disease. Methods 2024; 222:81-99. [PMID: 38185226 DOI: 10.1016/j.ymeth.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 10/27/2023] [Accepted: 12/13/2023] [Indexed: 01/09/2024] Open
Abstract
Many of the health-associated impacts of the microbiome are mediated by its chemical activity, producing and modifying small molecules (metabolites). Thus, microbiome metabolite quantification has a central role in efforts to elucidate and measure microbiome function. In this review, we cover general considerations when designing experiments to quantify microbiome metabolites, including sample preparation, data acquisition and data processing, since these are critical to downstream data quality. We then discuss data analysis and experimental steps to demonstrate that a given metabolite feature is of microbial origin. We further discuss techniques used to quantify common microbial metabolites, including short-chain fatty acids (SCFA), secondary bile acids (BAs), tryptophan derivatives, N-acyl amides and trimethylamine N-oxide (TMAO). Lastly, we conclude with challenges and future directions for the field.
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Affiliation(s)
- Jarrod Roach
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Rohit Mital
- Department of Biology, University of Oklahoma
| | - Jacob J Haffner
- Department of Anthropology, University of Oklahoma; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma
| | - Nathan Colwell
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Randy Coats
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Horvey M Palacios
- Department of Anthropology, University of Oklahoma; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma
| | - Zongyuan Liu
- Department of Chemistry and Biochemistry, University of Oklahoma
| | | | - Monica Ness
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Thilini Peramuna
- Department of Chemistry and Biochemistry, University of Oklahoma
| | - Laura-Isobel McCall
- Department of Chemistry and Biochemistry, University of Oklahoma; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma; Department of Chemistry and Biochemistry, San Diego State University.
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18
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Zulfiqar M, Singh V, Steinbeck C, Sorokina M. Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities. Crit Rev Microbiol 2024:1-40. [PMID: 38270170 DOI: 10.1080/1040841x.2024.2306465] [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: 03/13/2023] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.
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Affiliation(s)
- Mahnoor Zulfiqar
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Vinay Singh
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Maria Sorokina
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Data Science and Artificial Intelligence, Research and Development, Pharmaceuticals, Bayer, Berlin, Germany
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19
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Chen Y, Li Y, Fan Y, Chen S, Chen L, Chen Y, Chen Y. Gut microbiota-driven metabolic alterations reveal gut-brain communication in Alzheimer's disease model mice. Gut Microbes 2024; 16:2302310. [PMID: 38261437 PMCID: PMC10807476 DOI: 10.1080/19490976.2024.2302310] [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: 06/19/2023] [Accepted: 01/03/2024] [Indexed: 01/25/2024] Open
Abstract
The gut microbiota (GM) and its metabolites affect the host nervous system and are involved in the pathogeneses of various neurological diseases. However, the specific GM alterations under pathogenetic pressure and their contributions to the "microbiota - metabolite - brain axis" in Alzheimer's disease (AD) remain unclear. Here, we investigated the GM and the fecal, serum, cortical metabolomes in APP/PS1 and wild-type (WT) mice, revealing distinct hub bacteria in AD mice within scale-free GM networks shared by both groups. Moreover, we identified diverse peripheral - central metabolic landscapes between AD and WT mice that featured bile acids (e.g. deoxycholic and isodeoxycholic acid) and unsaturated fatty acids (e.g. 11Z-eicosenoic and palmitoleic acid). Machine-learning models revealed the relationships between the differential/hub bacteria and these metabolic signatures from the periphery to the brain. Notably, AD-enriched Dubosiella affected AD occurrence via cortical palmitoleic acid and vice versa. Considering the transgenic background of the AD mice, we propose that Dubosiella enrichment impedes AD progression via the synthesis of palmitoleic acid, which has protective properties against inflammation and metabolic disorders. We identified another association involving fecal deoxycholic acid-mediated interactions between the AD hub bacteria Erysipelatoclostridium and AD occurrence, which was corroborated by the correlation between deoxycholate levels and cognitive scores in humans. Overall, this study elucidated the GM network alterations, contributions of the GM to peripheral - central metabolic landscapes, and mediatory roles of metabolites between the GM and AD occurrence, thus revealing the critical roles of bacteria in AD pathogenesis and gut - brain communications under pathogenetic pressure.
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Affiliation(s)
- Yijing Chen
- Chinese Academy of Sciences Key Laboratory of Brain Connectome and Manipulation, Shenzhen Key Laboratory of Translational Research for Brain Diseases, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen–Hong Kong Institute of Brain Science–Shenzhen Fundamental Research Institutions, Shenzhen, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen, China
| | - Yinhu Li
- Chinese Academy of Sciences Key Laboratory of Brain Connectome and Manipulation, Shenzhen Key Laboratory of Translational Research for Brain Diseases, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen–Hong Kong Institute of Brain Science–Shenzhen Fundamental Research Institutions, Shenzhen, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen, China
| | - Yingying Fan
- Chinese Academy of Sciences Key Laboratory of Brain Connectome and Manipulation, Shenzhen Key Laboratory of Translational Research for Brain Diseases, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen–Hong Kong Institute of Brain Science–Shenzhen Fundamental Research Institutions, Shenzhen, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen, China
| | - Shuai Chen
- Chinese Academy of Sciences Key Laboratory of Brain Connectome and Manipulation, Shenzhen Key Laboratory of Translational Research for Brain Diseases, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen–Hong Kong Institute of Brain Science–Shenzhen Fundamental Research Institutions, Shenzhen, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen, China
| | - Li Chen
- Department of Neurology, Shenzhen Children’s Hospital, Shenzhen, China
| | - Yuewen Chen
- Chinese Academy of Sciences Key Laboratory of Brain Connectome and Manipulation, Shenzhen Key Laboratory of Translational Research for Brain Diseases, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen–Hong Kong Institute of Brain Science–Shenzhen Fundamental Research Institutions, Shenzhen, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen, China
| | - Yu Chen
- Chinese Academy of Sciences Key Laboratory of Brain Connectome and Manipulation, Shenzhen Key Laboratory of Translational Research for Brain Diseases, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen–Hong Kong Institute of Brain Science–Shenzhen Fundamental Research Institutions, Shenzhen, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen, China
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20
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Lum GR, Ha SM, Olson CA, Blencowe M, Paramo J, Reyes B, Matsumoto JH, Yang X, Hsiao EY. Ketogenic diet therapy for pediatric epilepsy is associated with alterations in the human gut microbiome that confer seizure resistance in mice. Cell Rep 2023; 42:113521. [PMID: 38070135 PMCID: PMC10769314 DOI: 10.1016/j.celrep.2023.113521] [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: 03/20/2023] [Revised: 07/13/2023] [Accepted: 11/14/2023] [Indexed: 12/30/2023] Open
Abstract
The gut microbiome modulates seizure susceptibility and the anti-seizure effects of the ketogenic diet (KD) in animal models, but whether these relationships translate to KD therapies for human epilepsy is unclear. We find that the clinical KD alters gut microbial function in children with refractory epilepsy. Colonizing mice with KD-associated microbes promotes seizure resistance relative to matched pre-treatment controls. Select metagenomic and metabolomic features, including those related to anaplerosis, fatty acid β-oxidation, and amino acid metabolism, are seen with human KD therapy and preserved upon microbiome transfer to mice. Mice colonized with KD-associated gut microbes exhibit altered hippocampal transcriptomes, including pathways related to ATP synthesis, glutathione metabolism, and oxidative phosphorylation, and are linked to susceptibility genes identified in human epilepsy. Our findings reveal key microbial functions that are altered by KD therapies for pediatric epilepsy and linked to microbiome-induced alterations in brain gene expression and seizure protection in mice.
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Affiliation(s)
- Gregory R Lum
- Department of Integrative Biology & Physiology, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Sung Min Ha
- Department of Integrative Biology & Physiology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Christine A Olson
- Department of Integrative Biology & Physiology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Montgomery Blencowe
- Department of Integrative Biology & Physiology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jorge Paramo
- UCLA Goodman-Luskin Microbiome Center, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Beck Reyes
- Department of Pediatrics, Division of Pediatric Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Joyce H Matsumoto
- Department of Pediatrics, Division of Pediatric Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Xia Yang
- Department of Integrative Biology & Physiology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Elaine Y Hsiao
- Department of Integrative Biology & Physiology, University of California, Los Angeles, Los Angeles, CA 90095, USA; UCLA Goodman-Luskin Microbiome Center, Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine, Los Angeles, CA 90095, USA.
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21
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Ruiz-Perez D, Gimon I, Sazal M, Mathee K, Narasimhan G. Unfolding and De-confounding: Biologically meaningful causal inference from longitudinal multi-omic networks using METALICA. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.12.571384. [PMID: 38168315 PMCID: PMC10760167 DOI: 10.1101/2023.12.12.571384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state-of-the-art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps to identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery and network inference algorithms applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.
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Affiliation(s)
- Daniel Ruiz-Perez
- Bioinformatics Research Group (BioRG), Florida International University, Miami, FL 33199, USA
| | - Isabella Gimon
- Bioinformatics Research Group (BioRG), Florida International University, Miami, FL 33199, USA
| | - Musfiqur Sazal
- Bioinformatics Research Group (BioRG), Florida International University, Miami, FL 33199, USA
| | - Kalai Mathee
- Florida International University, Miami, FL 33199, USA
- Biomolecular Sciences Institute, Florida International University, Miami, FL 33199, USA
| | - Giri Narasimhan
- Bioinformatics Research Group (BioRG), Florida International University, Miami, FL 33199, USA
- Biomolecular Sciences Institute, Florida International University, Miami, FL 33199, USA
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22
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Mei S. A Multi-Label Learning Framework for Predicting Chemical Classes and Biological Activities of Natural Products from Biosynthetic Gene Clusters. J Chem Ecol 2023; 49:681-695. [PMID: 37779180 DOI: 10.1007/s10886-023-01452-z] [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: 01/15/2023] [Revised: 08/28/2023] [Accepted: 09/13/2023] [Indexed: 10/03/2023]
Abstract
Natural products (NP) or secondary metabolites, as a class of small chemical molecules that are naturally synthesized by chromosomally clustered biosynthesis genes (also called biosynthetic gene clusters, BGCs) encoded enzymes or enzyme complexes, mediates the bioecological interactions between host and microbiota and provides a natural reservoir for screening drug-like therapeutic pharmaceuticals. In this work, we propose a multi-label learning framework to functionally annotate natural products or secondary metabolites solely from their catalytical biosynthetic gene clusters without experimentally conducting NP structural resolutions. All chemical classes and bioactivities constitute the label space, and the sequence domains of biosynthetic gene clusters that catalyse the biosynthesis of natural products constitute the feature space. In this multi-label learning framework, a joint representation of features (BGCs domains) and labels (natural products annotations) is efficiently learnt in an integral and low-dimensional space to accurately define the inter-class boundaries and scale to the learning problem of many imbalanced labels. Computational results on experimental data show that the proposed framework achieves satisfactory multi-label learning performance, and the learnt patterns of BGCs domains are transferrable across bacteria, or even across kingdom, for instance, from bacteria to Arabidopsis thaliana. Lastly, take Arabidopsis thaliana and its rhizosphere microbiome for example, we propose a pipeline combining existing BGCs identification tools and this proposed framework to find and functionally annotate novel natural products for downstream bioecological studies in terms of plant-microbiota-soil interactions and plant environmental adaption.
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Affiliation(s)
- Suyu Mei
- Software College, Shenyang Normal University, Shenyang, 110034, China.
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23
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Thukral M, Allen AE, Petras D. Progress and challenges in exploring aquatic microbial communities using non-targeted metabolomics. THE ISME JOURNAL 2023; 17:2147-2159. [PMID: 37857709 PMCID: PMC10689791 DOI: 10.1038/s41396-023-01532-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 09/27/2023] [Accepted: 10/02/2023] [Indexed: 10/21/2023]
Abstract
Advances in bioanalytical technologies are constantly expanding our insights into complex ecosystems. Here, we highlight strategies and applications that make use of non-targeted metabolomics methods in aquatic chemical ecology research and discuss opportunities and remaining challenges of mass spectrometry-based methods to broaden our understanding of environmental systems.
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Affiliation(s)
- Monica Thukral
- University of California San Diego, Scripps Institution of Oceanography, La Jolla, CA, USA
- J. Craig Venter Institute, Microbial and Environmental Genomics Group, La Jolla, CA, USA
| | - Andrew E Allen
- University of California San Diego, Scripps Institution of Oceanography, La Jolla, CA, USA
- J. Craig Venter Institute, Microbial and Environmental Genomics Group, La Jolla, CA, USA
| | - Daniel Petras
- University of Tuebingen, CMFI Cluster of Excellence, Tuebingen, Germany.
- University of California Riverside, Department of Biochemistry, Riverside, CA, USA.
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24
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Li Z, Selim A, Kuehn S. Statistical prediction of microbial metabolic traits from genomes. PLoS Comput Biol 2023; 19:e1011705. [PMID: 38113208 PMCID: PMC10729968 DOI: 10.1371/journal.pcbi.1011705] [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: 08/16/2023] [Accepted: 11/22/2023] [Indexed: 12/21/2023] Open
Abstract
The metabolic activity of microbial communities is central to their role in biogeochemical cycles, human health, and biotechnology. Despite the abundance of sequencing data characterizing these consortia, it remains a serious challenge to predict microbial metabolic traits from sequencing data alone. Here we culture 96 bacterial isolates individually and assay their ability to grow on 10 distinct compounds as a sole carbon source. Using these data as well as two existing datasets, we show that statistical approaches can accurately predict bacterial carbon utilization traits from genomes. First, we show that classifiers trained on gene content can accurately predict bacterial carbon utilization phenotypes by encoding phylogenetic information. These models substantially outperform predictions made by constraint-based metabolic models automatically constructed from genomes. This result solidifies our current knowledge about the strong connection between phylogeny and metabolic traits. However, phylogeny-based predictions fail to predict traits for taxa that are phylogenetically distant from any strains in the training set. To overcome this we train improved models on gene presence/absence to predict carbon utilization traits from gene content. We show that models that predict carbon utilization traits from gene presence/absence can generalize to taxa that are phylogenetically distant from the training set either by exploiting biochemical information for feature selection or by having sufficiently large datasets. In the latter case, we provide evidence that a statistical approach can identify putatively mechanistic genes involved in metabolic traits. Our study demonstrates the potential power for predicting microbial phenotypes from genotypes using statistical approaches.
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Affiliation(s)
- Zeqian Li
- Center for the Physics of Evolving Systems, The University of Chicago, Chicago, Illinois, United States of America
- Department of Ecology and Evolution, The University of Chicago, Chicago, Illinois, United States of America
- Department of Physics, The University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Ahmed Selim
- Graduate Program in Biophysical Sciences, The University of Chicago, Chicago, Illinois, United States of America
| | - Seppe Kuehn
- Center for the Physics of Evolving Systems, The University of Chicago, Chicago, Illinois, United States of America
- Department of Ecology and Evolution, The University of Chicago, Chicago, Illinois, United States of America
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25
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Nie S, Wang A, Chen X, Gong Y, Yuan Y. Microbial-Related Metabolites May Be Involved in Eight Major Biological Processes and Represent Potential Diagnostic Markers in Gastric Cancer. Cancers (Basel) 2023; 15:5271. [PMID: 37958446 PMCID: PMC10649575 DOI: 10.3390/cancers15215271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 10/26/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023] Open
Abstract
Metabolites associated with microbes regulate human immunity, inhibit bacterial colonization, and promote pathogenicity. Integrating microbe and metabolome research in GC provides a direction for understanding the microbe-associated pathophysiological process of metabolic changes and disease occurrence. The present study included 30 GC patients with 30 cancerous tissues and paired non-cancerous tissues (NCs) as controls. LC-MS/MS metabolomics and 16S rRNA sequencing were performed to obtain the metabolic and microbial characteristics. Integrated analysis of the microbes and metabolomes was conducted to explore the coexistence relationship between the microbial and metabolic characteristics of GC and to identify microbial-related metabolite diagnostic markers. The metabolic analysis showed that the overall metabolite distribution differed between the GC tissues and the NC tissues: 25 metabolites were enriched in the NC tissues and 42 metabolites were enriched in the GC tissues. The α and β microbial diversities were higher in the GC tissues than in the NC tissues, with 11 differential phyla and 52 differential genera. In the correlation and coexistence integrated analysis, 66 differential metabolites were correlated and coexisted, with specific differential microbes. The microbes in the GC tissue likely regulated eight metabolic pathways. In the efficacy evaluation of the microbial-related differential metabolites in the diagnosis of GC, 12 differential metabolites (area under the curve [AUC] >0.9) exerted relatively high diagnostic efficiency, and the combined diagnostic efficacy of 5 to 6 microbial-related differential metabolites was higher than the diagnostic efficacy of a single feature. Therefore, microbial diversity and metabolite distribution differed between the GC tissues and the NC tissues. Microbial-related metabolites may be involved in eight major metabolism-based biological processes in GC and represent potential diagnostic markers.
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Affiliation(s)
- Siru Nie
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Hospital of China Medical University, Shenyang 110001, China; (S.N.); (A.W.); (X.C.)
- Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, The First Hospital of China Medical University, Shenyang 110001, China
- Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, The First Hospital of China Medical University, Shenyang 110001, China
| | - Ang Wang
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Hospital of China Medical University, Shenyang 110001, China; (S.N.); (A.W.); (X.C.)
- Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, The First Hospital of China Medical University, Shenyang 110001, China
- Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, The First Hospital of China Medical University, Shenyang 110001, China
| | - Xiaohui Chen
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Hospital of China Medical University, Shenyang 110001, China; (S.N.); (A.W.); (X.C.)
- Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, The First Hospital of China Medical University, Shenyang 110001, China
- Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, The First Hospital of China Medical University, Shenyang 110001, China
| | - Yuehua Gong
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Hospital of China Medical University, Shenyang 110001, China; (S.N.); (A.W.); (X.C.)
- Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, The First Hospital of China Medical University, Shenyang 110001, China
- Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, The First Hospital of China Medical University, Shenyang 110001, China
| | - Yuan Yuan
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Hospital of China Medical University, Shenyang 110001, China; (S.N.); (A.W.); (X.C.)
- Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, The First Hospital of China Medical University, Shenyang 110001, China
- Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, The First Hospital of China Medical University, Shenyang 110001, China
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26
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Dilmore AH, Martino C, Neth BJ, West KA, Zemlin J, Rahman G, Panitchpakdi M, Meehan MJ, Weldon KC, Blach C, Schimmel L, Kaddurah-Daouk R, Dorrestein PC, Knight R, Craft S. Effects of a ketogenic and low-fat diet on the human metabolome, microbiome, and foodome in adults at risk for Alzheimer's disease. Alzheimers Dement 2023; 19:4805-4816. [PMID: 37017243 PMCID: PMC10551050 DOI: 10.1002/alz.13007] [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: 09/06/2022] [Revised: 01/17/2023] [Accepted: 01/23/2023] [Indexed: 04/06/2023]
Abstract
INTRODUCTION The ketogenic diet (KD) is an intriguing therapeutic candidate for Alzheimer's disease (AD) given its protective effects against metabolic dysregulation and seizures. Gut microbiota are essential for KD-mediated neuroprotection against seizures as well as modulation of bile acids, which play a major role in cholesterol metabolism. These relationships motivated our analysis of gut microbiota and metabolites related to cognitive status following a controlled KD intervention compared with a low-fat-diet intervention. METHODS Prediabetic adults, either with mild cognitive impairment (MCI) or cognitively normal (CN), were placed on either a low-fat American Heart Association diet or high-fat modified Mediterranean KD (MMKD) for 6 weeks; then, after a 6-week washout period, they crossed over to the alternate diet. We collected stool samples for shotgun metagenomics and untargeted metabolomics at five time points to investigate individuals' microbiome and metabolome throughout the dietary interventions. RESULTS Participants with MCI on the MMKD had lower levels of GABA-producing microbes Alistipes sp. CAG:514 and GABA, and higher levels of GABA-regulating microbes Akkermansia muciniphila. MCI individuals with curcumin in their diet had lower levels of bile salt hydrolase-containing microbes and an altered bile acid pool, suggesting reduced gut motility. DISCUSSION Our results suggest that the MMKD may benefit adults with MCI through modulation of GABA levels and gut-transit time.
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Affiliation(s)
- Amanda Hazel Dilmore
- Department of Pediatrics, University of California San Diego, La Jolla, CA
- Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA
| | - Cameron Martino
- Department of Pediatrics, University of California San Diego, La Jolla, CA
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA
- Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA
| | | | - Kiana A. West
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA
| | - Jasmine Zemlin
- Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA
| | - Gibraan Rahman
- Department of Pediatrics, University of California San Diego, La Jolla, CA
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA
| | - Morgan Panitchpakdi
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA
| | - Michael J. Meehan
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA
| | - Kelly C. Weldon
- Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA
| | - Colette Blach
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC
- Department of Medicine, Duke University, Durham, NC
- Duke Institute of Brain Sciences, Duke University, Durham, NC
| | - Leyla Schimmel
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC
- Department of Medicine, Duke University, Durham, NC
- Duke Institute of Brain Sciences, Duke University, Durham, NC
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC
- Department of Medicine, Duke University, Durham, NC
- Duke Institute of Brain Sciences, Duke University, Durham, NC
| | - Pieter C Dorrestein
- Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, CA
- Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA
- Department of Bioengineering, University of California San Diego, La Jolla, CA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA
| | - Suzanne Craft
- Department of Internal Medicine, Section on Geriatrics and Gerontology, Wake Forest School of Medicine, Winston-Salem, NC
| | - Alzheimer’s Gut Microbiome Project Consortium
- Department of Pediatrics, University of California San Diego, La Jolla, CA
- Department of Medicine, Duke University, Durham, NC
- Department of Internal Medicine, Section on Geriatrics and Gerontology, Wake Forest School of Medicine, Winston-Salem, NC
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27
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Collart L, Jiang D, Halsey KH. The volatilome reveals microcystin concentration, microbial composition, and oxidative stress in a critical Oregon freshwater lake. mSystems 2023; 8:e0037923. [PMID: 37589463 PMCID: PMC10654074 DOI: 10.1128/msystems.00379-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 07/03/2023] [Indexed: 08/18/2023] Open
Abstract
IMPORTANCE Harmful algal blooms are among the most significant threats to drinking water safety. Blooms dominated by cyanobacteria can produce potentially harmful toxins and, despite intensive research, toxin production remains unpredictable. We measured gaseous molecules in Upper Klamath Lake, Oregon, over 2 years and used them to predict the presence and concentration of the cyanotoxin, microcystin, and microbial community composition. Subsets of gaseous compounds were identified that are associated with microcystin production during oxidative stress, pointing to ecosystem-level interactions leading to microcystin contamination. Our approach shows potential for gaseous molecules to be harnessed in monitoring critical waterways.
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Affiliation(s)
- Lindsay Collart
- Department of Microbiology, Oregon State University, Corvallis, Oregon, USA
| | - Duo Jiang
- Department of Statistics, Oregon State University, Corvallis, Oregon, USA
| | - Kimberly H. Halsey
- Department of Microbiology, Oregon State University, Corvallis, Oregon, USA
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28
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Zhang M, Tang H, Yuan Y, Ou Z, Chen Z, Xu Y, Fu X, Zhao Z, Sun Y. The Role of Indoor Microbiome and Metabolites in Shaping Children's Nasal and Oral Microbiota: A Pilot Multi-Omic Analysis. Metabolites 2023; 13:1040. [PMID: 37887365 PMCID: PMC10608577 DOI: 10.3390/metabo13101040] [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: 08/31/2023] [Revised: 09/21/2023] [Accepted: 09/27/2023] [Indexed: 10/28/2023] Open
Abstract
Maintaining a diverse and well-balanced nasal and oral microbiota is vital for human health. However, the impact of indoor microbiome and metabolites on nasal and oral microbiota remains largely unknown. Fifty-six children in Shanghai were surveyed to complete a questionnaire about their personal and environmental characteristics. The indoor microbiome and metabolites from vacuumed indoor dust were profiled via shotgun metagenomics and untargeted liquid chromatography-mass spectrometry (LC-MS). The nasal and oral microbiota in children was characterized using full-length 16S rRNA sequencing from PacBio. Associations between personal/environmental characteristics and the nasal/oral microbiota were calculated using PERMANOVA and regression analyses. We identified 6247, 431, and 342 microbial species in the indoor dust, nasal, and oral cavities, respectively. The overall nasal and oral microbial composition showed significant associations with environmental tobacco smoke (ETS) exposure during pregnancy and early childhood (p = 0.005 and 0.03, respectively), and the abundance of total indoor flavonoids and two mycotoxins (deoxynivalenol and nivalenol) (p = 0.01, 0.02, and 0.03, respectively). Notably, the abundance of several flavonoids, such as baicalein, eupatilin, isoliquiritigenin, tangeritin, and hesperidin, showed positive correlations with alpha diversity and the abundance of protective microbial taxa in nasal and oral cavities (p < 0.02), suggesting their potential beneficial roles in promoting nasal/oral health. Conversely, high carbohydrate/fat food intake and ETS exposure diminished protective microorganisms while augmenting risky microorganisms in the nasal/oral cavities. Further, potential microbial transfer was observed from the indoor environment to the childhood oral cavity (Moraxella catarrhalis, Streptococcus mitis, and Streptococcus salivarius), which could potentially increase virulence factors related to adherence and immune modulation and vancomycin resistance genes in children. This is the first study to reveal the association between the indoor microbiome/metabolites and nasal/oral microbiota using multi-omic approaches. These findings reveal potential protective and risk factors related to the indoor microbial environment.
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Affiliation(s)
- Mei Zhang
- Guangdong Provincial Key Laboratory of Protein Function and Regulation in Agricultural Organisms, College of Life Sciences, South China Agricultural University, Guangzhou 510642, China; (M.Z.); (Y.Y.); (Z.O.)
| | - Hao Tang
- School of Public Health, Fudan University, Shanghai 200032, China; (H.T.); (Y.X.)
| | - Yiwen Yuan
- Guangdong Provincial Key Laboratory of Protein Function and Regulation in Agricultural Organisms, College of Life Sciences, South China Agricultural University, Guangzhou 510642, China; (M.Z.); (Y.Y.); (Z.O.)
| | - Zheyuan Ou
- Guangdong Provincial Key Laboratory of Protein Function and Regulation in Agricultural Organisms, College of Life Sciences, South China Agricultural University, Guangzhou 510642, China; (M.Z.); (Y.Y.); (Z.O.)
| | - Zhuoru Chen
- Children’s Hospital of Fudan University, Shanghai 201102, China;
| | - Yanyi Xu
- School of Public Health, Fudan University, Shanghai 200032, China; (H.T.); (Y.X.)
| | - Xi Fu
- Guangdong Provincial Engineering Research Center of Public Health Detection and Assessment, School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510006, China;
| | - Zhuohui Zhao
- School of Public Health, Fudan University, Shanghai 200032, China; (H.T.); (Y.X.)
- Key Laboratory of Public Health Safety of the Ministry of Education, NHC Key Laboratory of Health Technology Assessment (Fudan University), Shanghai Typhoon Institute/CMA, Shanghai Key Laboratory of Meteorology and Health, Shanghai 200030, China
| | - Yu Sun
- Guangdong Provincial Key Laboratory of Protein Function and Regulation in Agricultural Organisms, College of Life Sciences, South China Agricultural University, Guangzhou 510642, China; (M.Z.); (Y.Y.); (Z.O.)
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29
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Jariyasopit N, Khoomrung S. Mass spectrometry-based analysis of gut microbial metabolites of aromatic amino acids. Comput Struct Biotechnol J 2023; 21:4777-4789. [PMID: 37841334 PMCID: PMC10570628 DOI: 10.1016/j.csbj.2023.09.032] [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: 05/04/2023] [Revised: 09/24/2023] [Accepted: 09/24/2023] [Indexed: 10/17/2023] Open
Abstract
Small molecules derived from gut microbiota have been increasingly investigated to better understand the functional roles of the human gut microbiome. Microbial metabolites of aromatic amino acids (AAA) have been linked to many diseases, such as metabolic disorders, chronic kidney diseases, inflammatory bowel disease, diabetes, and cancer. Important microbial AAA metabolites are often discovered via global metabolite profiling of biological specimens collected from humans or animal models. Subsequent metabolite identity confirmation and absolute quantification using targeted analysis enable comparisons across different studies, which can lead to the establishment of threshold concentrations of potential metabolite biomarkers. Owing to their excellent selectivity and sensitivity, hyphenated mass spectrometry (MS) techniques are often employed to identify and quantify AAA metabolites in various biological matrices. Here, we summarize the developments over the past five years in MS-based methodology for analyzing gut microbiota-derived AAA. Sample preparation, method validation, analytical performance, and statistical methods for correlation analysis are discussed, along with future perspectives.
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Affiliation(s)
- Narumol Jariyasopit
- Siriraj Center of Research Excellence in Metabolomics and Systems Biology (SiCORE-MSB), Faculty of Medicine Siriraj Hospital Mahidol University, Bangkok 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital Mahidol University, Bangkok 10700, Thailand
| | - Sakda Khoomrung
- Siriraj Center of Research Excellence in Metabolomics and Systems Biology (SiCORE-MSB), Faculty of Medicine Siriraj Hospital Mahidol University, Bangkok 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital Mahidol University, Bangkok 10700, Thailand
- Department of Biochemistry, Faculty of Medicine Siriraj Hospital Mahidol University, Bangkok 10700, Thailand
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30
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Arıkan M, Muth T. Integrated multi-omics analyses of microbial communities: a review of the current state and future directions. Mol Omics 2023; 19:607-623. [PMID: 37417894 DOI: 10.1039/d3mo00089c] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
Integrated multi-omics analyses of microbiomes have become increasingly common in recent years as the emerging omics technologies provide an unprecedented opportunity to better understand the structural and functional properties of microbial communities. Consequently, there is a growing need for and interest in the concepts, approaches, considerations, and available tools for investigating diverse environmental and host-associated microbial communities in an integrative manner. In this review, we first provide a general overview of each omics analysis type, including a brief history, typical workflow, primary applications, strengths, and limitations. Then, we inform on both experimental design and bioinformatics analysis considerations in integrated multi-omics analyses, elaborate on the current approaches and commonly used tools, and highlight the current challenges. Finally, we discuss the expected key advances, emerging trends, potential implications on various fields from human health to biotechnology, and future directions.
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Affiliation(s)
- Muzaffer Arıkan
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey.
- Department of Medical Biology, Faculty of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Thilo Muth
- Section eScience (S.3), Federal Institute for Materials Research and Testing (BAM), Berlin, Germany.
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Baker JL. Illuminating the oral microbiome and its host interactions: recent advancements in omics and bioinformatics technologies in the context of oral microbiome research. FEMS Microbiol Rev 2023; 47:fuad051. [PMID: 37667515 PMCID: PMC10503653 DOI: 10.1093/femsre/fuad051] [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: 01/31/2023] [Revised: 08/02/2023] [Accepted: 09/01/2023] [Indexed: 09/06/2023] Open
Abstract
The oral microbiota has an enormous impact on human health, with oral dysbiosis now linked to many oral and systemic diseases. Recent advancements in sequencing, mass spectrometry, bioinformatics, computational biology, and machine learning are revolutionizing oral microbiome research, enabling analysis at an unprecedented scale and level of resolution using omics approaches. This review contains a comprehensive perspective of the current state-of-the-art tools available to perform genomics, metagenomics, phylogenomics, pangenomics, transcriptomics, proteomics, metabolomics, lipidomics, and multi-omics analysis on (all) microbiomes, and then provides examples of how the techniques have been applied to research of the oral microbiome, specifically. Key findings of these studies and remaining challenges for the field are highlighted. Although the methods discussed here are placed in the context of their contributions to oral microbiome research specifically, they are pertinent to the study of any microbiome, and the intended audience of this includes researchers would simply like to get an introduction to microbial omics and/or an update on the latest omics methods. Continued research of the oral microbiota using omics approaches is crucial and will lead to dramatic improvements in human health, longevity, and quality of life.
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Affiliation(s)
- Jonathon L Baker
- Department of Oral Rehabilitation & Biosciences, School of Dentistry, Oregon Health & Science University, 3181 Sam Jackson Park Road, Portland, OR 97202, United States
- Genomic Medicine Group, J. Craig Venter Institute, La Jolla, CA 92037, United States
- Department of Pediatrics, UC San Diego School of Medicine, La Jolla, CA 92093, United States
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32
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Mannochio-Russo H, Swift SOI, Nakayama KK, Wall CB, Gentry EC, Panitchpakdi M, Caraballo-Rodriguez AM, Aron AT, Petras D, Dorrestein K, Dorrestein TK, Williams TM, Nalley EM, Altman-Kurosaki NT, Martinelli M, Kuwabara JY, Darcy JL, Bolzani VS, Wegley Kelly L, Mora C, Yew JY, Amend AS, McFall-Ngai M, Hynson NA, Dorrestein PC, Nelson CE. Microbiomes and metabolomes of dominant coral reef primary producers illustrate a potential role for immunolipids in marine symbioses. Commun Biol 2023; 6:896. [PMID: 37653089 PMCID: PMC10471604 DOI: 10.1038/s42003-023-05230-1] [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: 12/01/2022] [Accepted: 08/08/2023] [Indexed: 09/02/2023] Open
Abstract
The dominant benthic primary producers in coral reef ecosystems are complex holobionts with diverse microbiomes and metabolomes. In this study, we characterize the tissue metabolomes and microbiomes of corals, macroalgae, and crustose coralline algae via an intensive, replicated synoptic survey of a single coral reef system (Waimea Bay, O'ahu, Hawaii) and use these results to define associations between microbial taxa and metabolites specific to different hosts. Our results quantify and constrain the degree of host specificity of tissue metabolomes and microbiomes at both phylum and genus level. Both microbiome and metabolomes were distinct between calcifiers (corals and CCA) and erect macroalgae. Moreover, our multi-omics investigations highlight common lipid-based immune response pathways across host organisms. In addition, we observed strong covariation among several specific microbial taxa and metabolite classes, suggesting new metabolic roles of symbiosis to further explore.
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Affiliation(s)
- Helena Mannochio-Russo
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA.
- Department of Biochemistry and Organic Chemistry, Institute of Chemistry, São Paulo State University, Araraquara, SP, 14800-060, Brazil.
| | - Sean O I Swift
- Daniel K. Inouye Center for Microbial Oceanography: Research and Education, Department of Oceanography and Sea Grant College Program, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA.
| | - Kirsten K Nakayama
- Pacific Biosciences Research Center, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Christopher B Wall
- Pacific Biosciences Research Center, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
- Ecology Behavior and Evolution Section, Department of Biological Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Emily C Gentry
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Morgan Panitchpakdi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Andrés M Caraballo-Rodriguez
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Allegra T Aron
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Chemistry and Biochemistry, University of Denver, Denver, CO, 80210, USA
| | - Daniel Petras
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
- Cluster of Excellence "Controlling Microbes to Fight Infections" (CMFI), University of Tuebingen, Tuebingen, Germany
| | - Kathleen Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | | | - Taylor M Williams
- Marine Option Program, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Eileen M Nalley
- Hawai'i Sea Grant College Program, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Noam T Altman-Kurosaki
- School of Biological Sciences, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, GA, 30332, USA
| | | | - Jeff Y Kuwabara
- Marine Option Program, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - John L Darcy
- Pacific Biosciences Research Center, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Vanderlan S Bolzani
- Department of Biochemistry and Organic Chemistry, Institute of Chemistry, São Paulo State University, Araraquara, SP, 14800-060, Brazil
| | - Linda Wegley Kelly
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, CA, USA
| | - Camilo Mora
- Geography, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Joanne Y Yew
- Pacific Biosciences Research Center, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Anthony S Amend
- Pacific Biosciences Research Center, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Margaret McFall-Ngai
- Pacific Biosciences Research Center, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Nicole A Hynson
- Pacific Biosciences Research Center, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
| | - Pieter C Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Craig E Nelson
- Daniel K. Inouye Center for Microbial Oceanography: Research and Education, Department of Oceanography and Sea Grant College Program, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA
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Fu T, Huan T, Rahman G, Zhi H, Xu Z, Oh TG, Guo J, Coulter S, Tripathi A, Martino C, McCarville JL, Zhu Q, Cayabyab F, Low B, He M, Xing S, Vargas F, Yu RT, Atkins A, Liddle C, Ayres J, Raffatellu M, Dorrestein PC, Downes M, Knight R, Evans RM. Paired microbiome and metabolome analyses associate bile acid changes with colorectal cancer progression. Cell Rep 2023; 42:112997. [PMID: 37611587 PMCID: PMC10903535 DOI: 10.1016/j.celrep.2023.112997] [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: 11/07/2022] [Revised: 05/08/2023] [Accepted: 08/01/2023] [Indexed: 08/25/2023] Open
Abstract
Colorectal cancer (CRC) is driven by genomic alterations in concert with dietary influences, with the gut microbiome implicated as an effector in disease development and progression. While meta-analyses have provided mechanistic insight into patients with CRC, study heterogeneity has limited causal associations. Using multi-omics studies on genetically controlled cohorts of mice, we identify diet as the major driver of microbial and metabolomic differences, with reductions in α diversity and widespread changes in cecal metabolites seen in high-fat diet (HFD)-fed mice. In addition, non-classic amino acid conjugation of the bile acid cholic acid (AA-CA) increased with HFD. We show that AA-CAs impact intestinal stem cell growth and demonstrate that Ileibacterium valens and Ruminococcus gnavus are able to synthesize these AA-CAs. This multi-omics dataset implicates diet-induced shifts in the microbiome and the metabolome in disease progression and has potential utility in future diagnostic and therapeutic developments.
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Affiliation(s)
- Ting Fu
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Tao Huan
- Department of Chemistry, UBC Faculty of Science, Vancouver Campus, Vancouver, BC V6T 1Z4, Canada
| | - Gibraan Rahman
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Hui Zhi
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Zhenjiang Xu
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Tae Gyu Oh
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Jian Guo
- Department of Chemistry, UBC Faculty of Science, Vancouver Campus, Vancouver, BC V6T 1Z4, Canada
| | - Sally Coulter
- Storr Liver Centre, Westmead Institute for Medical Research and Sydney Medical School, University of Sydney, Westmead, NSW 2145, Australia
| | - Anupriya Tripathi
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Cameron Martino
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Justin L McCarville
- Molecular and Systems Physiology Laboratory, Gene Expression Laboratory, NOMIS Center for Immunobiology and Microbial Pathogenesis, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Qiyun Zhu
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Fritz Cayabyab
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Brian Low
- Department of Chemistry, UBC Faculty of Science, Vancouver Campus, Vancouver, BC V6T 1Z4, Canada
| | - Mingxiao He
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Shipei Xing
- Department of Chemistry, UBC Faculty of Science, Vancouver Campus, Vancouver, BC V6T 1Z4, Canada
| | - Fernando Vargas
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ruth T Yu
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Annette Atkins
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Christopher Liddle
- Storr Liver Centre, Westmead Institute for Medical Research and Sydney Medical School, University of Sydney, Westmead, NSW 2145, Australia
| | - Janelle Ayres
- Molecular and Systems Physiology Laboratory, Gene Expression Laboratory, NOMIS Center for Immunobiology and Microbial Pathogenesis, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Manuela Raffatellu
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA; Chiba University-UC San Diego Center for Mucosal Immunity, Allergy, and Vaccines (CU-UCSD cMAV), La Jolla, CA 92093, USA
| | - Pieter C Dorrestein
- UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA; Department of Engineering, University of California, San Diego, La Jolla, CA 92093, USA; Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Michael Downes
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Rob Knight
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; UCSD Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA; Department of Engineering, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Ronald M Evans
- Gene Expression Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
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Zhu J, Xie H, Yang Z, Chen J, Yin J, Tian P, Wang H, Zhao J, Zhang H, Lu W, Chen W. Statistical modeling of gut microbiota for personalized health status monitoring. MICROBIOME 2023; 11:184. [PMID: 37596617 PMCID: PMC10436630 DOI: 10.1186/s40168-023-01614-x] [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: 01/19/2023] [Accepted: 07/06/2023] [Indexed: 08/20/2023]
Abstract
BACKGROUND The gut microbiome is closely associated with health status, and any microbiota dysbiosis could considerably impact the host's health. In addition, many active consortium projects have generated many reference datasets available for large-scale retrospective research. However, a comprehensive monitoring framework that analyzes health status and quantitatively present bacteria-to-health contribution has not been thoroughly investigated. METHODS We systematically developed a statistical monitoring diagram for personalized health status prediction and analysis. Our framework comprises three elements: (1) a statistical monitoring model was established, the health index was constructed, and the health boundary was defined; (2) healthy patterns were identified among healthy people and analyzed using contrast learning; (3) the contribution of each bacterium to the health index of the diseased population was analyzed. Furthermore, we investigated disease proximity using the contribution spectrum and discovered multiple multi-disease-related targets. RESULTS We demonstrated and evaluated the effectiveness of the proposed monitoring framework for tracking personalized health status through comprehensive real-data analysis using the multi-study cohort and another validation cohort. A statistical monitoring model was developed based on 92 microbial taxa. In both the discovery and validation sets, our approach achieved balanced accuracies of 0.7132 and 0.7026, and AUC of 0.80 and 0.76, respectively. Four health patterns were identified in healthy populations, highlighting variations in species composition and metabolic function across these patterns. Furthermore, a reasonable correlation was found between the proposed health index and host physiological indicators, diversity, and functional redundancy. The health index significantly correlated with Shannon diversity ([Formula: see text]) and species richness ([Formula: see text]) in the healthy samples. However, in samples from individuals with diseases, the health index significantly correlated with age ([Formula: see text]), species richness ([Formula: see text]), and functional redundancy ([Formula: see text]). Personalized diagnosis is achieved by analyzing the contribution of each bacterium to the health index. We identified high-contribution species shared across multiple diseases by analyzing the contribution spectrum of these diseases. CONCLUSIONS Our research revealed that the proposed monitoring framework could promote a deep understanding of healthy microbiomes and unhealthy variations and served as a bridge toward individualized therapy target discovery and precise modulation. Video Abstract.
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Affiliation(s)
- Jinlin Zhu
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu, 214122, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Heqiang Xie
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu, 214122, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Zixin Yang
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu, 214122, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Jing Chen
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu, 214122, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Jialin Yin
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu, 214122, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Peijun Tian
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu, 214122, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Hongchao Wang
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu, 214122, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Jianxin Zhao
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu, 214122, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, 214122, China
- (Yangzhou) Institute of Food Biotechnology, Jiangnan University, Yangzhou, Jiangsu, 225004, China
| | - Hao Zhang
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu, 214122, China
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, 214122, China
- (Yangzhou) Institute of Food Biotechnology, Jiangnan University, Yangzhou, Jiangsu, 225004, China
- National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, Jiangsu, 214122, China
- Wuxi Translational Medicine Research Center, Jiangsu Translational Medicine Research Institute Wuxi Branch, Wuxi, Jiangsu, China
| | - Wenwei Lu
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu, 214122, China.
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, 214122, China.
- (Yangzhou) Institute of Food Biotechnology, Jiangnan University, Yangzhou, Jiangsu, 225004, China.
- International Joint Research Laboratory for Pharmabiotics & Antibiotic Resistance, Jiangnan University, Wuxi, Jiangsu, 214122, China.
| | - Wei Chen
- State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu, 214122, China.
- School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu, 214122, China.
- National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, Jiangsu, 214122, China.
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Vich Vila A, Hu S, Andreu-Sánchez S, Collij V, Jansen BH, Augustijn HE, Bolte LA, Ruigrok RAAA, Abu-Ali G, Giallourakis C, Schneider J, Parkinson J, Al-Garawi A, Zhernakova A, Gacesa R, Fu J, Weersma RK. Faecal metabolome and its determinants in inflammatory bowel disease. Gut 2023; 72:1472-1485. [PMID: 36958817 PMCID: PMC10359577 DOI: 10.1136/gutjnl-2022-328048] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 03/05/2023] [Indexed: 03/25/2023]
Abstract
OBJECTIVE Inflammatory bowel disease (IBD) is a multifactorial immune-mediated inflammatory disease of the intestine, comprising Crohn's disease and ulcerative colitis. By characterising metabolites in faeces, combined with faecal metagenomics, host genetics and clinical characteristics, we aimed to unravel metabolic alterations in IBD. DESIGN We measured 1684 different faecal metabolites and 8 short-chain and branched-chain fatty acids in stool samples of 424 patients with IBD and 255 non-IBD controls. Regression analyses were used to compare concentrations of metabolites between cases and controls and determine the relationship between metabolites and each participant's lifestyle, clinical characteristics and gut microbiota composition. Moreover, genome-wide association analysis was conducted on faecal metabolite levels. RESULTS We identified over 300 molecules that were differentially abundant in the faeces of patients with IBD. The ratio between a sphingolipid and L-urobilin could discriminate between IBD and non-IBD samples (AUC=0.85). We found changes in the bile acid pool in patients with dysbiotic microbial communities and a strong association between faecal metabolome and gut microbiota. For example, the abundance of Ruminococcus gnavus was positively associated with tryptamine levels. In addition, we found 158 associations between metabolites and dietary patterns, and polymorphisms near NAT2 strongly associated with coffee metabolism. CONCLUSION In this large-scale analysis, we identified alterations in the metabolome of patients with IBD that are independent of commonly overlooked confounders such as diet and surgical history. Considering the influence of the microbiome on faecal metabolites, our results pave the way for future interventions targeting intestinal inflammation.
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Affiliation(s)
- Arnau Vich Vila
- Department of Genetics, University Medical Centre, Groningen, The Netherlands
- Department of Pediatrics, University Medical Centre, Groningen, The Netherlands
| | - Shixian Hu
- Department of Genetics, University Medical Centre, Groningen, The Netherlands
- Department of Pediatrics, University Medical Centre, Groningen, The Netherlands
| | - Sergio Andreu-Sánchez
- Department of Pediatrics, University Medical Centre, Groningen, The Netherlands
- Department of Gastroenterology and Hepatology, University Medical Centre, Groningen, The Netherlands
| | - Valerie Collij
- Department of Genetics, University Medical Centre, Groningen, The Netherlands
- Department of Pediatrics, University Medical Centre, Groningen, The Netherlands
| | - Bernadien H Jansen
- Department of Genetics, University Medical Centre, Groningen, The Netherlands
| | - Hannah E Augustijn
- Department of Pediatrics, University Medical Centre, Groningen, The Netherlands
| | - Laura A Bolte
- Department of Genetics, University Medical Centre, Groningen, The Netherlands
| | - Renate A A A Ruigrok
- Department of Genetics, University Medical Centre, Groningen, The Netherlands
- Department of Pediatrics, University Medical Centre, Groningen, The Netherlands
| | - Galeb Abu-Ali
- Gastroenterology Drug Discovery Unit, Takeda Pharmaceutical, Cambridge, Massachusetts, USA
| | - Cosmas Giallourakis
- Gastroenterology Drug Discovery Unit, Takeda Pharmaceutical, Cambridge, Massachusetts, USA
| | - Jessica Schneider
- Gastroenterology Drug Discovery Unit, Takeda Pharmaceutical, Cambridge, Massachusetts, USA
| | - John Parkinson
- Gastroenterology Drug Discovery Unit, Takeda Pharmaceutical, Cambridge, Massachusetts, USA
| | - Amal Al-Garawi
- Gastroenterology Drug Discovery Unit, Takeda Pharmaceutical, Cambridge, Massachusetts, USA
| | | | - Ranko Gacesa
- Department of Genetics, University Medical Centre, Groningen, The Netherlands
- Department of Pediatrics, University Medical Centre, Groningen, The Netherlands
| | - Jingyuan Fu
- Department of Pediatrics, University Medical Centre, Groningen, The Netherlands
- Department of Gastroenterology and Hepatology, University Medical Centre, Groningen, The Netherlands
| | - Rinse K Weersma
- Department of Genetics, University Medical Centre, Groningen, The Netherlands
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Morton JT, Jin DM, Mills RH, Shao Y, Rahman G, McDonald D, Zhu Q, Balaban M, Jiang Y, Cantrell K, Gonzalez A, Carmel J, Frankiensztajn LM, Martin-Brevet S, Berding K, Needham BD, Zurita MF, David M, Averina OV, Kovtun AS, Noto A, Mussap M, Wang M, Frank DN, Li E, Zhou W, Fanos V, Danilenko VN, Wall DP, Cárdenas P, Baldeón ME, Jacquemont S, Koren O, Elliott E, Xavier RJ, Mazmanian SK, Knight R, Gilbert JA, Donovan SM, Lawley TD, Carpenter B, Bonneau R, Taroncher-Oldenburg G. Multi-level analysis of the gut-brain axis shows autism spectrum disorder-associated molecular and microbial profiles. Nat Neurosci 2023:10.1038/s41593-023-01361-0. [PMID: 37365313 DOI: 10.1038/s41593-023-01361-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 05/13/2023] [Indexed: 06/28/2023]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut-brain axis (GBA) has been implicated in ASD although with limited reproducibility across studies. In this study, we developed a Bayesian differential ranking algorithm to identify ASD-associated molecular and taxa profiles across 10 cross-sectional microbiome datasets and 15 other datasets, including dietary patterns, metabolomics, cytokine profiles and human brain gene expression profiles. We found a functional architecture along the GBA that correlates with heterogeneity of ASD phenotypes, and it is characterized by ASD-associated amino acid, carbohydrate and lipid profiles predominantly encoded by microbial species in the genera Prevotella, Bifidobacterium, Desulfovibrio and Bacteroides and correlates with brain gene expression changes, restrictive dietary patterns and pro-inflammatory cytokine profiles. The functional architecture revealed in age-matched and sex-matched cohorts is not present in sibling-matched cohorts. We also show a strong association between temporal changes in microbiome composition and ASD phenotypes. In summary, we propose a framework to leverage multi-omic datasets from well-defined cohorts and investigate how the GBA influences ASD.
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Affiliation(s)
- James T Morton
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Biostatistics & Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Dong-Min Jin
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, USA
| | | | - Yan Shao
- Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK
| | - Gibraan Rahman
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Daniel McDonald
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Qiyun Zhu
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
- Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA
| | - Metin Balaban
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Yueyu Jiang
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Kalen Cantrell
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Antonio Gonzalez
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Julie Carmel
- Azrieli Faculty of Medicine, Bar Ilan University, Safed, Israel
| | | | - Sandra Martin-Brevet
- Laboratory for Research in Neuroimaging, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Kirsten Berding
- Division of Nutritional Sciences, University of Illinois, Urbana, IL, USA
| | - Brittany D Needham
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - María Fernanda Zurita
- Microbiology Institute and Health Science College, Universidad San Francisco de Quito, Quito, Ecuador
| | - Maude David
- Departments of Microbiology & Pharmaceutical Sciences, Oregon State University, Corvallis, OR, USA
| | - Olga V Averina
- Vavilov Institute of General Genetics Russian Academy of Sciences, Moscow, Russia
| | - Alexey S Kovtun
- Vavilov Institute of General Genetics Russian Academy of Sciences, Moscow, Russia
- Skolkovo Institute of Science and Technology, Skolkovo, Russia
| | - Antonio Noto
- Department of Biomedical Sciences, School of Medicine, University of Cagliari, Cagliari, Italy
| | - Michele Mussap
- Laboratory Medicine, Department of Surgical Sciences, School of Medicine, University of Cagliari, Cagliari, Italy
| | - Mingbang Wang
- Shanghai Key Laboratory of Birth Defects, Division of Neonatology, Children's Hospital of Fudan University, National Center for Children's Health, Shanghai, China
- Microbiome Therapy Center, South China Hospital, Health Science Center, Shenzhen University, Shenzhen, China
| | - Daniel N Frank
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Ellen Li
- Department of Medicine, Division of Gastroenterology and Hepatology, Stony Brook University, Stony Brook, NY, USA
| | - Wenhao Zhou
- Shanghai Key Laboratory of Birth Defects, Division of Neonatology, Children's Hospital of Fudan University, National Center for Children's Health, Shanghai, China
| | - Vassilios Fanos
- Neonatal Intensive Care Unit and Neonatal Pathology, Department of Surgical Sciences, School of Medicine, University of Cagliari, Cagliari, Italy
| | - Valery N Danilenko
- Vavilov Institute of General Genetics Russian Academy of Sciences, Moscow, Russia
| | - Dennis P Wall
- Pediatrics (Systems Medicine), Biomedical Data Science, and Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Paúl Cárdenas
- Institute of Microbiology, COCIBA, Universidad San Francisco de Quito, Quito, Ecuador
| | - Manuel E Baldeón
- Facultad de Ciencias Médicas, de la Salud y la Vida, Universidad Internacional del Ecuador, Quito, Ecuador
| | - Sébastien Jacquemont
- Sainte Justine Hospital Research Center, Montréal, QC, Canada
- Department of Pediatrics, Université de Montréal, Montréal, QC, Canada
| | - Omry Koren
- Azrieli Faculty of Medicine, Bar Ilan University, Safed, Israel
| | - Evan Elliott
- Azrieli Faculty of Medicine, Bar Ilan University, Safed, Israel
- The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar Ilan University, Ramat Gan, Israel
| | - Ramnik J Xavier
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
- Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital, Boston, MA, USA
| | - Sarkis K Mazmanian
- Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Rob Knight
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, California, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, California, USA
| | - Jack A Gilbert
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, California, USA
- Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA
| | - Sharon M Donovan
- Division of Nutritional Sciences, University of Illinois, Urbana, IL, USA
| | - Trevor D Lawley
- Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK
| | - Bob Carpenter
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, USA
- Prescient Design, a Genentech Accelerator, New York, NY, USA
| | - Gaspar Taroncher-Oldenburg
- Gaspar Taroncher Consulting, Philadelphia, PA, USA.
- Simons Foundation Autism Research Initiative, Simons Foundation, New York, NY, USA.
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Pu J, Yang J, Lu S, Jin D, Luo X, Xiong Y, Bai X, Zhu W, Huang Y, Wu S, Niu L, Liu L, Xu J. Species-Level Taxonomic Characterization of Uncultured Core Gut Microbiota of Plateau Pika. Microbiol Spectr 2023; 11:e0349522. [PMID: 37067438 PMCID: PMC10269723 DOI: 10.1128/spectrum.03495-22] [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: 08/31/2022] [Accepted: 02/13/2023] [Indexed: 04/18/2023] Open
Abstract
Rarely has the vast diversity of bacteria on Earth been profiled, particularly on inaccessible plateaus. These uncultured microbes, which are also known as "microbial dark matter," may play crucial roles in maintaining the ecosystem and are linked to human health, regarding pathogenicity and prebioticity. The plateau pika (Ochotona curzoniae) is a small burrowing steppe lagomorph that is endemic to the Qinghai-Tibetan Plateau and is a keystone species in the maintenance of ecological balance. We used a combination of full-length 16S rRNA amplicon sequencing, shotgun metagenomics, and metabolomics to elucidate the species-level community structure and the metabolic potential of the gut microbiota of the plateau pika. Using a full-length 16S rRNA metataxonomic approach, we clustered 618 (166 ± 35 per sample) operational phylogenetic units (OPUs) from 105 plateau pika samples and assigned them to 215 known species, 226 potentially new species, and 177 higher hierarchical taxa. Notably, 39 abundant OPUs (over 60% total relative abundance) are found in over 90% of the samples, thereby representing a "core microbiota." They are all classified as novel microbial lineages, from the class to the species level. Using metagenomic reads, we independently assembled and binned 109 high-quality, species-level genome bins (SGBs). Then, a precise taxonomic assignment was performed to clarify the phylogenetic consistency of the SGBs and the 16S rRNA amplicons. Thus, the majority of the core microbes possess their genomes. SGBs belonging to the genus Treponema, the families Muribaculaceae, Lachnospiraceae, and Oscillospiraceae, and the order Eubacteriales are abundant in the metagenomic samples. In addition, multiple CAZymes are detected in these SGBs, indicating their efficient utilization of plant biomass. As the most widely connected metabolite with the core microbiota, tryptophan may relate to host environmental adaptation. Our investigation allows for a greater comprehension of the composition and functional capacity of the gut microbiota of the plateau pika. IMPORTANCE The great majority of microbial species remain uncultured, severely limiting their taxonomic characterization and biological understanding. The plateau pika (Ochotona curzoniae) is a small burrowing steppe lagomorph that is endemic to the Qinghai-Tibetan Plateau and is considered to be the keystone species in the maintenance of ecological stability. We comprehensively investigated the gut microbiota of the plateau pika via a multiomics endeavor. Combining full-length 16S rRNA metataxonomics, shotgun metagenomics, and metabolomics, we elucidated the species-level taxonomic assignment of the core uncultured intestinal microbiota of the plateau pika and revealed their correlation to host nutritional metabolism and adaptation. Our findings provide insights into the microbial diversity and biological significance of alpine animals.
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Affiliation(s)
- Ji Pu
- State Key Laboratory of Infectious Disease Prevention and Control and National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jing Yang
- State Key Laboratory of Infectious Disease Prevention and Control and National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- Research Units of Discovery of Unknown Bacteria and Function, Chinese Academy of Medical Sciences, Beijing, China
| | - Shan Lu
- State Key Laboratory of Infectious Disease Prevention and Control and National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- Research Units of Discovery of Unknown Bacteria and Function, Chinese Academy of Medical Sciences, Beijing, China
| | - Dong Jin
- State Key Laboratory of Infectious Disease Prevention and Control and National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- Research Units of Discovery of Unknown Bacteria and Function, Chinese Academy of Medical Sciences, Beijing, China
| | - Xuelian Luo
- State Key Laboratory of Infectious Disease Prevention and Control and National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yanwen Xiong
- State Key Laboratory of Infectious Disease Prevention and Control and National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiangning Bai
- State Key Laboratory of Infectious Disease Prevention and Control and National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Wentao Zhu
- State Key Laboratory of Infectious Disease Prevention and Control and National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yuyuan Huang
- State Key Laboratory of Infectious Disease Prevention and Control and National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Shusheng Wu
- Yushu Prefecture Center for Disease Control and Prevention, Yushu, China
| | - Lina Niu
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, Hainan Medical University, Haikou, China
| | - Liyun Liu
- State Key Laboratory of Infectious Disease Prevention and Control and National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jianguo Xu
- State Key Laboratory of Infectious Disease Prevention and Control and National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
- Research Units of Discovery of Unknown Bacteria and Function, Chinese Academy of Medical Sciences, Beijing, China
- Institute of Public Health, Nankai University, Tianjing, China
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38
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Correia GD, Marchesi JR, MacIntyre DA. Moving beyond DNA: towards functional analysis of the vaginal microbiome by non-sequencing-based methods. Curr Opin Microbiol 2023; 73:102292. [PMID: 36931094 DOI: 10.1016/j.mib.2023.102292] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 03/17/2023]
Abstract
Over the last two decades, sequencing-based methods have revolutionised our understanding of niche-specific microbial complexity. In the lower female reproductive tract, these approaches have enabled identification of bacterial compositional structures associated with health and disease. Application of metagenomics and metatranscriptomics strategies have provided insight into the putative function of these communities but it is increasingly clear that direct measures of microbial and host cell function are required to understand the contribution of microbe-host interactions to pathophysiology. Here we explore and discuss current methods and approaches, many of which rely upon mass-spectrometry, being used to capture functional insight into the vaginal mucosal interface. In addition to improving mechanistic understanding, these methods offer innovative solutions for the development of diagnostic and therapeutic strategies designed to improve women's health.
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Affiliation(s)
- Gonçalo Ds Correia
- Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK; March of Dimes Prematurity Research Centre at Imperial College London, London, UK
| | - Julian R Marchesi
- March of Dimes Prematurity Research Centre at Imperial College London, London, UK; Centre for Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Imperial College London, Imperial College London, London W2 1NY, UK
| | - David A MacIntyre
- Institute of Reproductive and Developmental Biology, Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK; March of Dimes Prematurity Research Centre at Imperial College London, London, UK.
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39
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Feng Y, Zhang M, Liu Y, Yang X, Wei F, Jin X, Liu D, Guo Y, Hu Y. Quantitative microbiome profiling reveals the developmental trajectory of the chicken gut microbiota and its connection to host metabolism. IMETA 2023; 2:e105. [PMID: 38868437 PMCID: PMC10989779 DOI: 10.1002/imt2.105] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/06/2023] [Accepted: 03/15/2023] [Indexed: 06/14/2024]
Abstract
Revealing the assembly and succession of the chicken gut microbiota is critical for a better understanding of its role in chicken physiology and metabolism. However, few studies have examined dynamic changes of absolute chicken gut microbes using the quantitative microbiome profiling (QMP) method. Here, we revealed the developmental trajectory of the broiler chicken gut bacteriome and mycobiome by combining high-throughput sequencing with a microbial load quantification assay. We showed that chicken gut microbiota abundance and diversity reached a plateau at 7 days posthatch (DPH), forming segment-specific community types after 1 DPH. The bacteriome was more impacted by deterministic processes, and the mycobiome was more affected by stochastic processes. We also observed stage-specific microbes in different gut segments, and three microbial occurrence patterns including "colonization," "disappearance," and "core" were defined. The microbial co-occurrence networks were very different among gut segments, with more positive associations than negative associations. Furthermore, we provided links between the absolute changes in chicken gut microbiota and their serum metabolite variations. Time-course untargeted metabolomics revealed six metabolite clusters with different changing patterns of abundance. The foregut microbiota had more connections with chicken serum metabolites, and the gut microbes were closely related to chicken lipid and amino acid metabolism. The present study provided a full landscape of chicken gut microbiota development in a quantitative manner, and the associations between gut microbes and chicken serum metabolites further highlight the impact of gut microbiota in chicken growth and development.
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Affiliation(s)
- Yuqing Feng
- State Key Laboratory of Animal Nutrition, College of Animal Science and TechnologyChina Agricultural UniversityBeijingChina
| | - Meihong Zhang
- State Key Laboratory of Animal Nutrition, College of Animal Science and TechnologyChina Agricultural UniversityBeijingChina
| | - Yan Liu
- State Key Laboratory of Animal Nutrition, College of Animal Science and TechnologyChina Agricultural UniversityBeijingChina
| | - Xinyue Yang
- State Key Laboratory of Animal Nutrition, College of Animal Science and TechnologyChina Agricultural UniversityBeijingChina
| | - Fuxiao Wei
- State Key Laboratory of Animal Nutrition, College of Animal Science and TechnologyChina Agricultural UniversityBeijingChina
| | - Xiaolu Jin
- State Key Laboratory of Animal Nutrition, College of Animal Science and TechnologyChina Agricultural UniversityBeijingChina
| | - Dan Liu
- State Key Laboratory of Animal Nutrition, College of Animal Science and TechnologyChina Agricultural UniversityBeijingChina
| | - Yuming Guo
- State Key Laboratory of Animal Nutrition, College of Animal Science and TechnologyChina Agricultural UniversityBeijingChina
| | - Yongfei Hu
- State Key Laboratory of Animal Nutrition, College of Animal Science and TechnologyChina Agricultural UniversityBeijingChina
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40
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Lee CY, Dillard LR, Papin JA, Arnold KB. New perspectives into the vaginal microbiome with systems biology. Trends Microbiol 2023; 31:356-368. [PMID: 36272885 DOI: 10.1016/j.tim.2022.09.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 09/19/2022] [Accepted: 09/21/2022] [Indexed: 10/28/2022]
Abstract
The vaginal microbiome (VMB) is critical to female reproductive health; however, the mechanisms associated with optimal and non-optimal states remain poorly understood due to the complex community structure and dynamic nature. Quantitative systems biology techniques applied to the VMB have improved understanding of community composition and function using primarily statistical methods. In contrast, fewer mechanistic models that use a priori knowledge of VMB features to develop predictive models have been implemented despite their use for microbiomes at other sites, including the gastrointestinal tract. Here, we explore systems biology approaches that have been applied in the VMB, highlighting successful techniques and discussing new directions that hold promise for improving understanding of health and disease.
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Affiliation(s)
- Christina Y Lee
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Lillian R Dillard
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA; Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Kelly B Arnold
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
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Peng S, Luo M, Long D, Liu Z, Tan Q, Huang P, Shen J, Pu S. Full-length 16S rRNA gene sequencing and machine learning reveal the bacterial composition of inhalable particles from two different breeding stages in a piggery. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 253:114712. [PMID: 36863163 DOI: 10.1016/j.ecoenv.2023.114712] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 02/15/2023] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
Bacterial loading aggravates the harm of particulate matter (PM) to public health and ecological systems, especially in operations of concentrated animal production. This study aimed to explore the characteristics and influencing factors of bacterial components of inhalable particles at a piggery. The morphology and elemental composition of coarse particles (PM10, aerodynamic diameter ≤ 10 µm) and fine particles (PM2.5, aerodynamic diameter ≤ 2.5 µm) were analyzed. Full-length 16 S rRNA sequencing technology was used to identify bacterial components according to breeding stage, particle size, and diurnal rhythm. Machine learning (ML) algorithms were used to further explore the relationship between bacteria and the environment. The results showed that the morphology of particles in the piggery differed, and the morphologies of the suspected bacterial components were elliptical deposited particles. Full-length 16 S rRNA indicated that most of the airborne bacteria in the fattening and gestation houses were bacilli. The analysis of beta diversity and difference between samples showed that the relative abundance of some bacteria in PM2.5 was significantly higher than that in PM10 at the same pig house (P < 0.01). There were significant differences in the bacterial composition of inhalable particles between the fattening and gestation houses (P < 0.01). The aggregated boosted tree (ABT) model showed that PM2.5 had a great influence on airborne bacteria among air pollutants. Fast expectation-maximization microbial source tracking (FEAST) showed that feces was a major potential source of airborne bacteria in pig houses (contribution 52.64-80.58 %). These results will provide a scientific basis for exploring the potential risks of airborne bacteria in a piggery to human and animal health.
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Affiliation(s)
- Siyi Peng
- Chongqing Academy of Animal Sciences, No. 51, Changlong Avenue, Rong chang District, Chongqing 402460, China; College of Animal Science and Technology, Southwest University, Chongqing 402460, China
| | - Min Luo
- Chongqing Academy of Animal Sciences, No. 51, Changlong Avenue, Rong chang District, Chongqing 402460, China
| | - Dingbiao Long
- Chongqing Academy of Animal Sciences, No. 51, Changlong Avenue, Rong chang District, Chongqing 402460, China; Scientific Observation and Experiment Station of Livestock Equipment Engineering in Southwest, Ministry of Agriculture and Rural Affairs, Chongqing 402460, China; Innovation and Entrepreneurship Team for Livestock Environment Control and Equipment R&D, Chongqing 402460, China; National Center of Technology Innovation for pigs, Chongqing 402460, China
| | - Zuohua Liu
- Chongqing Academy of Animal Sciences, No. 51, Changlong Avenue, Rong chang District, Chongqing 402460, China; National Center of Technology Innovation for pigs, Chongqing 402460, China; College of Animal Science and Technology, Southwest University, Chongqing 402460, China
| | - Qiong Tan
- Chongqing Academy of Animal Sciences, No. 51, Changlong Avenue, Rong chang District, Chongqing 402460, China; National Center of Technology Innovation for pigs, Chongqing 402460, China
| | - Ping Huang
- Chongqing Academy of Animal Sciences, No. 51, Changlong Avenue, Rong chang District, Chongqing 402460, China; National Center of Technology Innovation for pigs, Chongqing 402460, China
| | - Jie Shen
- Chongqing Academy of Animal Sciences, No. 51, Changlong Avenue, Rong chang District, Chongqing 402460, China; National Center of Technology Innovation for pigs, Chongqing 402460, China
| | - Shihua Pu
- Chongqing Academy of Animal Sciences, No. 51, Changlong Avenue, Rong chang District, Chongqing 402460, China; Scientific Observation and Experiment Station of Livestock Equipment Engineering in Southwest, Ministry of Agriculture and Rural Affairs, Chongqing 402460, China; Innovation and Entrepreneurship Team for Livestock Environment Control and Equipment R&D, Chongqing 402460, China; National Center of Technology Innovation for pigs, Chongqing 402460, China.
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42
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Integration of Transcriptomics and Non-Targeted Metabolomics Reveals the Underlying Mechanism of Skeletal Muscle Development in Duck during Embryonic Stage. Int J Mol Sci 2023; 24:ijms24065214. [PMID: 36982289 PMCID: PMC10049352 DOI: 10.3390/ijms24065214] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/02/2023] [Accepted: 03/03/2023] [Indexed: 03/11/2023] Open
Abstract
Skeletal muscle is an important economic trait in duck breeding; however, little is known about the molecular mechanisms of its embryonic development. Here, the transcriptomes and metabolomes of breast muscle of Pekin duck from 15 (E15_BM), 21 (E21_BM), and 27 (E27_BM) days of incubation were compared and analyzed. The metabolome results showed that the differentially accumulated metabolites (DAMs), including the up-regulated metabolites, l-glutamic acid, n-acetyl-1-aspartylglutamic acid, l-2-aminoadipic acid, 3-hydroxybutyric acid, bilirubin, and the significantly down-regulated metabolites, palmitic acid, 4-guanidinobutanoate, myristic acid, 3-dehydroxycarnitine, and s-adenosylmethioninamine, were mainly enriched in metabolic pathways, biosynthesis of secondary metabolites, biosynthesis of cofactors, protein digestion and absorption, and histidine metabolism, suggesting that these pathways may play important roles in the muscle development of duck during the embryonic stage. Moreover, a total of 2142 (1552 up-regulated and 590 down-regulated), 4873 (3810 up-regulated and 1063 down-regulated), and 2401 (1606 up-regulated and 795 down-regulated) DEGs were identified from E15_BM vs. E21_BM, E15_BM vs. E27_BM and E21_BM vs. E27_BM in the transcriptome, respectively. The significantly enriched GO terms from biological processes were positive regulation of cell proliferation, regulation of cell cycle, actin filament organization, and regulation of actin cytoskeleton organization, which were associated with muscle or cell growth and development. Seven significant pathways, highly enriched by FYN, PTK2, PXN, CRK, CRKL, PAK, RHOA, ROCK, INSR, PDPK1, and ARHGEF, were focal adhesion, regulation of actin cytoskeleton, wnt signaling pathway, insulin signaling pathway, extracellular matrix (ECM)-receptor interaction, cell cycle, and adherens junction, which participated in regulating the development of skeletal muscle in Pekin duck during the embryonic stage. KEGG pathway analysis of the integrated transcriptome and metabolome indicated that the pathways, including arginine and proline metabolism, protein digestion and absorption, and histidine metabolism, were involved in regulating skeletal muscle development in embryonic Pekin duck. These findings suggested that the candidate genes and metabolites involved in crucial biological pathways may regulate muscle development in the Pekin duck at the embryonic stage, and increased our understanding of the molecular mechanisms underlying the avian muscle development.
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43
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Wang T, Wang XW, Lee-Sarwar KA, Litonjua AA, Weiss ST, Sun Y, Maslov S, Liu YY. Predicting metabolomic profiles from microbial composition through neural ordinary differential equations. NAT MACH INTELL 2023; 5:284-293. [PMID: 38223254 PMCID: PMC10786629 DOI: 10.1038/s42256-023-00627-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 02/03/2023] [Indexed: 03/14/2023]
Abstract
Characterizing the metabolic profile of a microbial community is crucial for understanding its biological function and its impact on the host or environment. Metabolomics experiments directly measuring these profiles are difficult and expensive, while sequencing methods quantifying the species composition of microbial communities are well-developed and relatively cost-effective. Computational methods that are capable of predicting metabolomic profiles from microbial compositions can save considerable efforts needed for metabolomic profiling experimentally. Yet, despite existing efforts, we still lack a computational method with high prediction power, general applicability, and great interpretability. Here we develop a method - mNODE (Metabolomic profile predictor using Neural Ordinary Differential Equations), based on a state-of-the-art family of deep neural network models. We show compelling evidence that mNODE outperforms existing methods in predicting the metabolomic profiles of human microbiomes and several environmental microbiomes. Moreover, in the case of human gut microbiomes, mNODE can naturally incorporate dietary information to further enhance the prediction of metabolomic profiles. Besides, susceptibility analysis of mNODE enables us to reveal microbe-metabolite interactions, which can be validated using both synthetic and real data. The presented results demonstrate that mNODE is a powerful tool to investigate the microbiome-diet-metabolome relationship, facilitating future research on precision nutrition.
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Affiliation(s)
- Tong Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Xu-Wen Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Kathleen A. Lee-Sarwar
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Division of Allergy and Clinical Immunology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Augusto A. Litonjua
- Pediatric Pulmonology, Golisano Children’s Hospital, University of Rochester, Rochester, NY 14642, USA
| | - Scott T. Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Yizhou Sun
- Department of Computer Science, University of California, Los Angeles, USA
| | - Sergei Maslov
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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The Molecular Effect of Wearing Silver-Threaded Clothing on the Human Skin. mSystems 2023; 8:e0092222. [PMID: 36722970 PMCID: PMC9948701 DOI: 10.1128/msystems.00922-22] [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] [Indexed: 02/02/2023] Open
Abstract
With growing awareness that what we put in and on our bodies affects our health and wellbeing, little is still known about the impact of textiles on the human skin. Athletic wear often uses silver threading to improve hygiene, but little is known about its effect on the body's largest organ. In this study, we investigated the impact of such clothing on the skin's chemistry and microbiome. Samples were collected from different body sites of a dozen volunteers over the course of 12 weeks. The changes induced by the antibacterial clothing were specific for individuals, but more so defined by gender and body site. Unexpectedly, the microbial biomass on skin increased in the majority of the volunteers when wearing silver-threaded T-shirts. Although the most abundant taxa remained unaffected, silver caused an increase in diversity and richness of low-abundant bacteria and a decrease in chemical diversity. Both effects were mainly observed for women. The hallmark of the induced changes was an increase in the abundance of various monounsaturated fatty acids (MUFAs), especially in the upper back. Several microbe-metabolite associations were uncovered, including Cutibacterium, detected in the upper back area, which was correlated with the distribution of MUFAs, and Anaerococcus spp. found in the underarms, which were associated with a series of different bile acids. Overall, these findings point to a notable impact of the silver-threaded material on the skin microbiome and chemistry. We observed that relatively subtle changes in the microbiome result in pronounced shifts in molecular composition. IMPORTANCE The impact of silver-threaded material on human skin chemistry and microbiome is largely unknown. Although the most abundant taxa remained unaffected, silver caused an increase in diversity and richness of low-abundant bacteria and a decrease in chemical diversity. The major change was an increase in the abundance of various monounsaturated fatty acids that were also correlated with Cutibacterium. Additionally, Anaerococcus spp., found in the underarms, were associated with different bile acids in the armpit samples. Overall, the impact of the silver-threaded clothing was gender and body site specific.
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Brewer RC, Lanz TV, Hale CR, Sepich-Poore GD, Martino C, Swafford AD, Carroll TS, Kongpachith S, Blum LK, Elliott SE, Blachere NE, Parveen S, Fak J, Yao V, Troyanskaya O, Frank MO, Bloom MS, Jahanbani S, Gomez AM, Iyer R, Ramadoss NS, Sharpe O, Chandrasekaran S, Kelmenson LB, Wang Q, Wong H, Torres HL, Wiesen M, Graves DT, Deane KD, Holers VM, Knight R, Darnell RB, Robinson WH, Orange DE. Oral mucosal breaks trigger anti-citrullinated bacterial and human protein antibody responses in rheumatoid arthritis. Sci Transl Med 2023; 15:eabq8476. [PMID: 36812347 PMCID: PMC10496947 DOI: 10.1126/scitranslmed.abq8476] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 02/02/2023] [Indexed: 02/24/2023]
Abstract
Periodontal disease is more common in individuals with rheumatoid arthritis (RA) who have detectable anti-citrullinated protein antibodies (ACPAs), implicating oral mucosal inflammation in RA pathogenesis. Here, we performed paired analysis of human and bacterial transcriptomics in longitudinal blood samples from RA patients. We found that patients with RA and periodontal disease experienced repeated oral bacteremias associated with transcriptional signatures of ISG15+HLADRhi and CD48highS100A2pos monocytes, recently identified in inflamed RA synovia and blood of those with RA flares. The oral bacteria observed transiently in blood were broadly citrullinated in the mouth, and their in situ citrullinated epitopes were targeted by extensively somatically hypermutated ACPAs encoded by RA blood plasmablasts. Together, these results suggest that (i) periodontal disease results in repeated breaches of the oral mucosa that release citrullinated oral bacteria into circulation, which (ii) activate inflammatory monocyte subsets that are observed in inflamed RA synovia and blood of RA patients with flares and (iii) activate ACPA B cells, thereby promoting affinity maturation and epitope spreading to citrullinated human antigens.
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Affiliation(s)
- R. Camille Brewer
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Tobias V. Lanz
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
- Department of Neurology, Medical Faculty Mannheim, University of Heidelberg, Mannheim, 68167, Germany
| | - Caryn R. Hale
- Rockefeller University, New York City, NY 10065, USA
| | | | - Cameron Martino
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Austin D. Swafford
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
| | - Thomas S. Carroll
- Bioinformatics Resource Center, Rockefeller University, 1230 York Ave., New York, NY 10065, USA
| | - Sarah Kongpachith
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Lisa K. Blum
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Serra E. Elliott
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Nathalie E. Blachere
- Rockefeller University, New York City, NY 10065, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | | | - John Fak
- Rockefeller University, New York City, NY 10065, USA
| | - Vicky Yao
- Department of Computer Science, Rice University, Houston, TX 77005, USA
- Department of Computer Science, Princeton University, Princeton, NJ, 08544, USA
| | - Olga Troyanskaya
- Department of Computer Science, Princeton University, Princeton, NJ, 08544, USA
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA
- Flatiron Institute, Simons Foundation, New York, NY, 10010, USA
| | - Mayu O. Frank
- Rockefeller University, New York City, NY 10065, USA
| | - Michelle S. Bloom
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Shaghayegh Jahanbani
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Alejandro M. Gomez
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Radhika Iyer
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Nitya S. Ramadoss
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Orr Sharpe
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | | | - Lindsay B. Kelmenson
- Division of Rheumatology, University of Colorado - Denver, Aurora, CO, 80045, USA
| | - Qian Wang
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Heidi Wong
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | | | - Mark Wiesen
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Dana T. Graves
- Department of Periodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kevin D. Deane
- Division of Rheumatology, University of Colorado - Denver, Aurora, CO, 80045, USA
| | - V. Michael Holers
- Division of Rheumatology, University of Colorado - Denver, Aurora, CO, 80045, USA
| | - Rob Knight
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Robert B. Darnell
- Rockefeller University, New York City, NY 10065, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - William H. Robinson
- Division of Immunology and Rheumatology, Stanford University, Stanford, CA, 94305, USA
- VA Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Dana E. Orange
- Rockefeller University, New York City, NY 10065, USA
- Hospital for Special Surgery, New York City, NY 10075, USA
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Yang L, Fan W, Xu Y. Chameleon-like microbes promote microecological differentiation of Daqu. Food Microbiol 2023; 109:104144. [DOI: 10.1016/j.fm.2022.104144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/31/2022] [Accepted: 09/12/2022] [Indexed: 11/28/2022]
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Salim F, Mizutani S, Zolfo M, Yamada T. Recent advances of machine learning applications in human gut microbiota study: from observational analysis toward causal inference and clinical intervention. Curr Opin Biotechnol 2023; 79:102884. [PMID: 36623442 DOI: 10.1016/j.copbio.2022.102884] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 02/24/2022] [Accepted: 12/09/2022] [Indexed: 01/08/2023]
Abstract
Statistical methods, especially machine learning, learning(ML), are pivotal for the analyses of large data generated by multiomics human gut microbiota study. These analyses lead to the discovery of microbe-disease associations. Furthermore, recent efforts for more data transparency and accessible analytical tools improved data availability and study reproducibility. Our recent accumulated knowledge on microbe-disease associations brings light to the next questions: what is the role of microbes in disease progression and how can we apply our knowledge of microbiome in clinical settings? Here, we introduce recent studies that implemented ML to answer the questions of causal inference and clinical translation.
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Affiliation(s)
- Felix Salim
- School of Life Science and Technology, Tokyo Institute of Technology
| | - Sayaka Mizutani
- School of Life Science and Technology, Tokyo Institute of Technology; Japan Society for the Promotion of Science
| | - Moreno Zolfo
- School of Life Science and Technology, Tokyo Institute of Technology
| | - Takuji Yamada
- School of Life Science and Technology, Tokyo Institute of Technology; Metagen, Inc.; Metagen Therapeutics, Inc.; digzyme, Inc..
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48
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Haertl T, Owsienko D, Schwinn L, Hirsch C, Eskofier BM, Lang R, Wirtz S, Loos HM. Exploring the interrelationship between the skin microbiome and skin volatiles: A pilot study. Front Ecol Evol 2023. [DOI: 10.3389/fevo.2023.1107463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
Unravelling the interplay between a human’s microbiome and physiology is a relevant task for understanding the principles underlying human health and disease. With regard to human chemical communication, it is of interest to elucidate the role of the microbiome in shaping or generating volatiles emitted from the human body. In this study, we characterized the microbiome and volatile organic compounds (VOCs) sampled from the neck and axilla of ten participants (five male, five female) on two sampling days, by applying different methodological approaches. Volatiles emitted from the respective skin site were collected for 20 min using textile sampling material and analyzed on two analytical columns with varying polarity of the stationary phase. Microbiome samples were analyzed by a culture approach coupled with MALDI-TOF-MS analysis and a 16S ribosomal RNA gene (16S RNA) sequencing approach. Statistical and advanced data analysis methods revealed that classification of body sites was possible by using VOC and microbiome data sets. Higher classification accuracy was achieved by combination of both data pools. Cutibacterium, Staphylococcus, Micrococcus, Streptococcus, Lawsonella, Anaerococcus, and Corynebacterium species were found to contribute to classification of the body sites by the microbiome. Alkanes, esters, ethers, ketones, aldehydes and cyclic structures were used by the classifier when VOC data were considered. The interdisciplinary methodological platform developed here will enable further investigations of skin microbiome and skin VOCs alterations in physiological and pathological conditions.
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49
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Moreno E, Ron R, Serrano-Villar S. The microbiota as a modulator of mucosal inflammation and HIV/HPV pathogenesis: From association to causation. Front Immunol 2023; 14:1072655. [PMID: 36756132 PMCID: PMC9900135 DOI: 10.3389/fimmu.2023.1072655] [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: 10/17/2022] [Accepted: 01/06/2023] [Indexed: 01/24/2023] Open
Abstract
Although the microbiota has largely been associated with the pathogenesis of viral infections, most studies using omics techniques are correlational and hypothesis-generating. The mechanisms affecting the immune responses to viral infections are still being fully understood. Here we focus on the two most important sexually transmitted persistent viruses, HPV and HIV. Sophisticated omics techniques are boosting our ability to understand microbiota-pathogen-host interactions from a functional perspective by surveying the host and bacterial protein and metabolite production using systems biology approaches. However, while these strategies have allowed describing interaction networks to identify potential novel microbiota-associated biomarkers or therapeutic targets to prevent or treat infectious diseases, the analyses are typically based on highly dimensional datasets -thousands of features in small cohorts of patients-. As a result, we are far from getting to their clinical use. Here we provide a broad overview of how the microbiota influences the immune responses to HIV and HPV disease. Furthermore, we highlight experimental approaches to understand better the microbiota-host-virus interactions that might increase our potential to identify biomarkers and therapeutic agents with clinical applications.
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Affiliation(s)
- Elena Moreno
- Department of Infectious Diseases, Hospital Universitario Ramón y Cajal, Facultad de Medicina, Universidad de Alcalá, IRYCIS, Madrid, Spain.,CIBERINFEC, Instituto de Salud Carlos III, Madrid, Spain
| | - Raquel Ron
- Department of Infectious Diseases, Hospital Universitario Ramón y Cajal, Facultad de Medicina, Universidad de Alcalá, IRYCIS, Madrid, Spain.,CIBERINFEC, Instituto de Salud Carlos III, Madrid, Spain
| | - Sergio Serrano-Villar
- Department of Infectious Diseases, Hospital Universitario Ramón y Cajal, Facultad de Medicina, Universidad de Alcalá, IRYCIS, Madrid, Spain.,CIBERINFEC, Instituto de Salud Carlos III, Madrid, Spain
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50
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Patra P, B R D, Kundu P, Das M, Ghosh A. Recent advances in machine learning applications in metabolic engineering. Biotechnol Adv 2023; 62:108069. [PMID: 36442697 DOI: 10.1016/j.biotechadv.2022.108069] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 10/18/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022]
Abstract
Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.
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Affiliation(s)
- Pradipta Patra
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Disha B R
- B.M.S College of Engineering, Basavanagudi, Bengaluru, Karnataka 560019, India
| | - Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Manali Das
- School of Bioscience, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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