1
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Faitova T, Coelho M, Da Cunha-Bang C, Ozturk S, Kartal E, Bork P, Seiffert M, Niemann CU. The diversity of the microbiome impacts chronic lymphocytic leukemia development in mice and humans. Haematologica 2024; 109:3237-3250. [PMID: 38721725 PMCID: PMC11443378 DOI: 10.3324/haematol.2023.284693] [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] [Received: 11/17/2023] [Accepted: 04/30/2024] [Indexed: 10/02/2024] Open
Abstract
The gut microbiota plays a critical role in maintaining a healthy human body and its dysregulation is associated with various diseases. In this study, we investigated the influence of gut microbiome diversity on the development of chronic lymphocytic leukemia (CLL). Analysis of stool samples from 59 CLL patients revealed individual and heterogeneous microbiome compositions, but allowed for grouping of patients according to their microbiome diversity. Interestingly, CLL patients with lower microbiome diversity and an enrichment of bacteria linked to poor health suffered from a more advanced or aggressive form of CLL. In the Eµ-TCL1 mouse model of CLL, we observed a faster course of disease when mice were housed in high hygiene conditions. Shotgun DNA sequencing of fecal samples showed that this was associated with a lower microbiome diversity which was dominated by Mucispirillum and Parabacteroides genera in comparison to mice kept under lower hygiene conditions. In conclusion, we applied taxonomic microbiome analyses to demonstrate a link between gut microbiome diversity and the clinical course of CLL in humans, as well as the development of CLL in mice. Our novel data serve as a basis for further investigations to decipher the pathological and mechanistic role of intestinal microbiota in CLL development.
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Affiliation(s)
| | - Mariana Coelho
- Department of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Biosciences of the University of Heidelberg, Heidelberg
| | | | - Selcen Ozturk
- Department of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg
| | - Ece Kartal
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany; Department of Bioinformatics, Biocenter, University of Wurzburg, Wurzburg, Germany; Yonsei Frontier Lab (YFL), Yonsei University, Seoul, South Korea; Max Delbruck Center for Molecular Medicine, Berlin
| | - Martina Seiffert
- Department of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg. m.seiffert@dkfzheidelberg
| | - Carsten U Niemann
- Department of Hematology, Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen.
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2
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Li Y, Zhang R, Fu C, Jiang Q, Zhang P, Zhang Y, Chen J, Tao K, Chen WH, Zeng X. Intratumoral microbiome promotes liver metastasis and dampens adjuvant imatinib treatment in gastrointestinal stromal tumor. Cancer Lett 2024; 601:217149. [PMID: 39117066 DOI: 10.1016/j.canlet.2024.217149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 06/06/2024] [Accepted: 07/28/2024] [Indexed: 08/10/2024]
Abstract
Understanding the determinants of long-term liver metastasis (LM) outcomes in gastrointestinal stromal tumor (GIST) patients is crucial. We established the feature selection model of intratumoral microbiome at the surgery, achieving robust predictive accuracies of 0.953 and 0.897 AUCs in discovery (n = 74) and validation (n = 34) cohorts, respectively. Notably, despite the significant reduction in LM occurrence with adjuvant imatinib (AI) treatment, intratumoral microbiome exerted independently stronger effects on post-operative LM. Employing both 16S and full-length rRNA sequencing, we pinpoint intracellular Shewanella algae as a foremost LM risk factor in both AI- and non-AI-treated patients. Experimental validation confirmed S. algae's intratumoral presence in GIST, along with migration/invasion-promoting effects on GIST cells. Furthermore, S. algae promoted LM and impeded AI treatment in metastatic mouse models. Our findings advocate for incorporating intratumoral microbiome evaluation at surgery, and propose S. algae as a therapeutic target for LM suppression in GIST.
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Affiliation(s)
- Yanze Li
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China; Department of Computer Science, School of Science, Aalto University, Helsinki, Finland
| | - Ruizhi Zhang
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chengbo Fu
- Department of Computer Science, School of Science, Aalto University, Helsinki, Finland
| | - Qi Jiang
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Peng Zhang
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yong Zhang
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jingchao Chen
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Kaixiong Tao
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Wei-Hua Chen
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China; Institution of Medical Artificial Intelligence, Binzhou Medical University, Yantai, 264003, China.
| | - Xiangyu Zeng
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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3
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Wirbel J, Essex M, Forslund SK, Zeller G. A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies. Genome Biol 2024; 25:247. [PMID: 39322959 PMCID: PMC11423519 DOI: 10.1186/s13059-024-03390-9] [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: 12/30/2023] [Accepted: 09/06/2024] [Indexed: 09/27/2024] Open
Abstract
BACKGROUND In microbiome disease association studies, it is a fundamental task to test which microbes differ in their abundance between groups. Yet, consensus on suitable or optimal statistical methods for differential abundance testing is lacking, and it remains unexplored how these cope with confounding. Previous differential abundance benchmarks relying on simulated datasets did not quantitatively evaluate the similarity to real data, which undermines their recommendations. RESULTS Our simulation framework implants calibrated signals into real taxonomic profiles, including signals mimicking confounders. Using several whole meta-genome and 16S rRNA gene amplicon datasets, we validate that our simulated data resembles real data from disease association studies much more than in previous benchmarks. With extensively parametrized simulations, we benchmark the performance of nineteen differential abundance methods and further evaluate the best ones on confounded simulations. Only classic statistical methods (linear models, the Wilcoxon test, t-test), limma, and fastANCOM properly control false discoveries at relatively high sensitivity. When additionally considering confounders, these issues are exacerbated, but we find that adjusted differential abundance testing can effectively mitigate them. In a large cardiometabolic disease dataset, we showcase that failure to account for covariates such as medication causes spurious association in real-world applications. CONCLUSIONS Tight error control is critical for microbiome association studies. The unsatisfactory performance of many differential abundance methods and the persistent danger of unchecked confounding suggest these contribute to a lack of reproducibility among such studies. We have open-sourced our simulation and benchmarking software to foster a much-needed consolidation of statistical methodology for microbiome research.
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Affiliation(s)
- Jakob Wirbel
- Structural and Computational Biology Unit (SCB), European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Morgan Essex
- Experimental and Clinical Research Center (ECRC), a cooperation of the Max-Delbrück Center and Charité-Universitätsmedizin, Berlin, Germany
- Max-Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Charité-Universitätsmedizin Berlin (a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin), Berlin, Germany
| | - Sofia Kirke Forslund
- Structural and Computational Biology Unit (SCB), European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
- Experimental and Clinical Research Center (ECRC), a cooperation of the Max-Delbrück Center and Charité-Universitätsmedizin, Berlin, Germany.
- Max-Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany.
- Charité-Universitätsmedizin Berlin (a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin), Berlin, Germany.
- German Center for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany.
| | - Georg Zeller
- Structural and Computational Biology Unit (SCB), European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
- Center for Infectious Diseases (LUCID), Leiden University, Leiden University Medical Center (LUMC), Leiden, Netherlands.
- Center for Microbiome Analyses and Therapeutics (CMAT), Leiden University Medical Center, Leiden, Netherlands.
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4
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Boverhoff D, Kool J, Pijnacker R, Ducarmon QR, Zeller G, Shetty S, Sie S, Mulder AC, van der Klis F, Franz E, Mughini-Gras L, van Baarle D, Fuentes S. Profiling the fecal microbiome and its modulators across the lifespan in the Netherlands. Cell Rep 2024; 43:114729. [PMID: 39264809 DOI: 10.1016/j.celrep.2024.114729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/23/2024] [Accepted: 08/22/2024] [Indexed: 09/14/2024] Open
Abstract
Defining what constitutes a healthy microbiome throughout our lives remains an ongoing challenge. Understanding to what extent host and environmental factors can influence it has been the primary motivation for large population studies worldwide. Here, we describe the fecal microbiome of 3,746 individuals (0-87 years of age) in a nationwide study in the Netherlands, in association with extensive questionnaires. We validate previous findings, such as infant-adult trajectories, and explore the collective impact of our variables, which explain over 40% of the variation in microbiome composition. We identify associations with less explored factors, particularly those ethnic related, which show the largest impact on the adult microbiome composition, diversity, metabolic profiles, and CAZy (carbohydrate-active enzyme) repertoires. Understanding the sources of microbiome variability is crucial, given its potential as a modifiable target with therapeutic possibilities. With this work, we aim to serve as a foundational element for the design of health interventions and fundamental research.
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Affiliation(s)
- David Boverhoff
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands; Virology & Immunology Research, Department of Medical Microbiology and Infection Prevention, University Medical Center Groningen, Groningen, the Netherlands
| | - Jolanda Kool
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Roan Pijnacker
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Quinten R Ducarmon
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Georg Zeller
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Sudarshan Shetty
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands; Virology & Immunology Research, Department of Medical Microbiology and Infection Prevention, University Medical Center Groningen, Groningen, the Netherlands
| | - Stephan Sie
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Annemieke Christine Mulder
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Fiona van der Klis
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Eelco Franz
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Lapo Mughini-Gras
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands; Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Debbie van Baarle
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands; Virology & Immunology Research, Department of Medical Microbiology and Infection Prevention, University Medical Center Groningen, Groningen, the Netherlands
| | - Susana Fuentes
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands.
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5
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Liu Z, Sun Y, Li Y, Ma A, Willaims NF, Jahanbahkshi S, Hoyd R, Wang X, Zhang S, Zhu J, Xu D, Spakowicz D, Ma Q, Liu B. An Explainable Graph Neural Framework to Identify Cancer-Associated Intratumoral Microbial Communities. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2403393. [PMID: 39225619 DOI: 10.1002/advs.202403393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/26/2024] [Indexed: 09/04/2024]
Abstract
Microbes are extensively present among various cancer tissues and play critical roles in carcinogenesis and treatment responses. However, the underlying relationships between intratumoral microbes and tumors remain poorly understood. Here, a MIcrobial Cancer-association Analysis using a Heterogeneous graph transformer (MICAH) to identify intratumoral cancer-associated microbial communities is presented. MICAH integrates metabolic and phylogenetic relationships among microbes into a heterogeneous graph representation. It uses a graph transformer to holistically capture relationships between intratumoral microbes and cancer tissues, which improves the explainability of the associations between identified microbial communities and cancers. MICAH is applied to intratumoral bacterial data across 5 cancer types and 5 fungi datasets, and its generalizability and reproducibility are demonstrated. After experimentally testing a representative observation using a mouse model of tumor-microbe-immune interactions, a result consistent with MICAH's identified relationship is observed. Source tracking analysis reveals that the primary known contributor to a cancer-associated microbial community is the organs affected by the type of cancer. Overall, this graph neural network framework refines the number of microbes that can be used for follow-up experimental validation from thousands to tens, thereby helping to accelerate the understanding of the relationship between tumors and intratumoral microbiomes.
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Affiliation(s)
- Zhaoqian Liu
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China
- College of Sciences, Xi'an University of Science and Technology, Xi'an, Shanxi, 710054, China
| | - Yuhan Sun
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China
| | - Yingjie Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Anjun Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The Ohio State University, Columbus, OH, 43210, USA
| | - Nyelia F Willaims
- Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
| | - Shiva Jahanbahkshi
- Department of Food Science and Technology, College of Food, Agricultural, and Environmental Sciences, The Ohio State University, Columbus, OH, 43210, USA
| | - Rebecca Hoyd
- Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
| | - Xiaoying Wang
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The Ohio State University, Columbus, OH, 43210, USA
| | - Shiqi Zhang
- Department of Human Sciences, College of Education and Human Ecology, The Ohio State University, Columbus, OH, 43210, USA
| | - Jiangjiang Zhu
- Department of Human Sciences, College of Education and Human Ecology, The Ohio State University, Columbus, OH, 43210, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65201, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65201, USA
| | - Daniel Spakowicz
- Pelotonia Institute for Immuno-Oncology, The Ohio State University, Columbus, OH, 43210, USA
- Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
| | - Qin Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The Ohio State University, Columbus, OH, 43210, USA
| | - Bingqiang Liu
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China
- Shandong National Center for Applied Mathematics, Jinan, Shandong, 250199, China
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6
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Hosseiniyan Khatibi SM, Dimaano NG, Veliz E, Sundaresan V, Ali J. Exploring and exploiting the rice phytobiome to tackle climate change challenges. PLANT COMMUNICATIONS 2024:101078. [PMID: 39233440 DOI: 10.1016/j.xplc.2024.101078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 08/07/2024] [Accepted: 09/02/2024] [Indexed: 09/06/2024]
Abstract
The future of agriculture is uncertain under the current climate change scenario. Climate change directly and indirectly affects the biotic and abiotic elements that control agroecosystems, jeopardizing the safety of the world's food supply. A new area that focuses on characterizing the phytobiome is emerging. The phytobiome comprises plants and their immediate surroundings, involving numerous interdependent microscopic and macroscopic organisms that affect the health and productivity of plants. Phytobiome studies primarily focus on the microbial communities associated with plants, which are referred to as the plant microbiome. The development of high-throughput sequencing technologies over the past 10 years has dramatically advanced our understanding of the structure, functionality, and dynamics of the phytobiome; however, comprehensive methods for using this knowledge are lacking, particularly for major crops such as rice. Considering the impact of rice production on world food security, gaining fresh perspectives on the interdependent and interrelated components of the rice phytobiome could enhance rice production and crop health, sustain rice ecosystem function, and combat the effects of climate change. Our review re-conceptualizes the complex dynamics of the microscopic and macroscopic components in the rice phytobiome as influenced by human interventions and changing environmental conditions driven by climate change. We also discuss interdisciplinary and systematic approaches to decipher and reprogram the sophisticated interactions in the rice phytobiome using novel strategies and cutting-edge technology. Merging the gigantic datasets and complex information on the rice phytobiome and their application in the context of regenerative agriculture could lead to sustainable rice farming practices that are resilient to the impacts of climate change.
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Affiliation(s)
| | - Niña Gracel Dimaano
- International Rice Research Institute, Los Baños, Laguna, Philippines; College of Agriculture and Food Science, University of the Philippines Los Baños, Los Baños, Laguna, Philippines
| | - Esteban Veliz
- College of Biological Sciences, University of California, Davis, Davis, CA, USA
| | - Venkatesan Sundaresan
- College of Biological Sciences, University of California, Davis, Davis, CA, USA; College of Agricultural and Environmental Sciences, University of California, Davis, Davis, CA, USA
| | - Jauhar Ali
- International Rice Research Institute, Los Baños, Laguna, Philippines.
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7
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Gao W, Lin W, Li Q, Chen W, Yin W, Zhu X, Gao S, Liu L, Li W, Wu D, Zhang G, Zhu R, Jiao N. Identification and validation of microbial biomarkers from cross-cohort datasets using xMarkerFinder. Nat Protoc 2024; 19:2803-2830. [PMID: 38745111 DOI: 10.1038/s41596-024-00999-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 03/05/2024] [Indexed: 05/16/2024]
Abstract
Microbial signatures have emerged as promising biomarkers for disease diagnostics and prognostics, yet their variability across different studies calls for a standardized approach to biomarker research. Therefore, we introduce xMarkerFinder, a four-stage computational framework for microbial biomarker identification with comprehensive validations from cross-cohort datasets, including differential signature identification, model construction, model validation and biomarker interpretation. xMarkerFinder enables the identification and validation of reproducible biomarkers for cross-cohort studies, along with the establishment of classification models and potential microbiome-induced mechanisms. Originally developed for gut microbiome research, xMarkerFinder's adaptable design makes it applicable to various microbial habitats and data types. Distinct from existing biomarker research tools that typically concentrate on a singular aspect, xMarkerFinder uniquely incorporates a sophisticated feature selection process, specifically designed to address the heterogeneity between different cohorts, extensive internal and external validations, and detailed specificity assessments. Execution time varies depending on the sample size, selected algorithm and computational resource. Accessible via GitHub ( https://github.com/tjcadd2020/xMarkerFinder ), xMarkerFinder supports users with diverse expertise levels through different execution options, including step-to-step scripts with detailed tutorials and frequently asked questions, a single-command execution script, a ready-to-use Docker image and a user-friendly web server ( https://www.biosino.org/xmarkerfinder ).
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Affiliation(s)
- Wenxing Gao
- The Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China
| | - Weili Lin
- The Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China
| | - Qiang Li
- National Genomics Data Center & Bio-Med Big Data Center, Chinese Academy of Sciences Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, P. R. China
| | - Wanning Chen
- The Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China
| | - Wenjing Yin
- The Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China
| | - Xinyue Zhu
- The Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China
| | - Sheng Gao
- The Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China
| | - Lei Liu
- The Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China
| | - Wenjie Li
- Shanghai Southgene Technology Co., Ltd., Shanghai, P. R. China
| | - Dingfeng Wu
- National Clinical Research Center for Child Health, the Children's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, P. R. China
| | - Guoqing Zhang
- National Genomics Data Center & Bio-Med Big Data Center, Chinese Academy of Sciences Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, P. R. China.
| | - Ruixin Zhu
- The Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China.
| | - Na Jiao
- National Clinical Research Center for Child Health, the Children's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, P. R. China.
- State Key Laboratory of Genetic Engineering, Fudan Microbiome Center, School of Life Sciences, Fudan University, Shanghai, P. R. China.
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8
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Li J, O’Toole PW. Disease-associated microbiome signature species in the gut. PNAS NEXUS 2024; 3:pgae352. [PMID: 39228810 PMCID: PMC11370893 DOI: 10.1093/pnasnexus/pgae352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 08/12/2024] [Indexed: 09/05/2024]
Abstract
There is an accumulation of evidence that the human gut microbiota plays a role in maintaining health, and that an altered gut microbiota (sometimes called dysbiosis) associates with risk for many noncommunicable diseases. However, the dynamics of disease-linked bacteria in the gut and other body sites remain unclear. If microbiome alterations prove causative in particular diseases, therapeutic intervention may be possible. Furthermore, microbial signature taxa have been established for the diagnosis of some diseases like colon cancer. We identified 163 disease-enriched and 98 disease-depleted gut microbiome signature taxa at species-level resolution (signature species) from 10 meta-analyses of multiple diseases such as colorectal cancer, ulcerative colitis, Crohn's disease, irritable bowel syndrome, pancreatic cancer, and COVID-19 infection. Eight signature species were enriched and nine were depleted across at least half of the diseases studied. Compared with signature species depleted in diseases, a significantly higher proportion of disease-enriched signature species were identified as extra-intestinal (primarily oral) inhabitants, had been reported in bacteremia cases from the literature, and were aerotolerant anaerobes. These findings highlight the potential involvement of oral microbes, bacteremia isolates, and aerotolerant anaerobes in disease-associated gut microbiome alterations, and they have implications for patient care and disease management.
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Affiliation(s)
- Junhui Li
- APC Microbiome Ireland, University College Cork, Cork T12 K8AF, Ireland
- School of Microbiology, University College Cork, Cork T12 K8AF, Ireland
| | - Paul W O’Toole
- APC Microbiome Ireland, University College Cork, Cork T12 K8AF, Ireland
- School of Microbiology, University College Cork, Cork T12 K8AF, Ireland
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9
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Khan MW, Cruz de Jesus V, Mittermuller BA, Sareen S, Lee V, Schroth RJ, Hu P, Chelikani P. Role of socioeconomic factors and interkingdom crosstalk in the dental plaque microbiome in early childhood caries. Cell Rep 2024; 43:114635. [PMID: 39154338 DOI: 10.1016/j.celrep.2024.114635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 06/04/2024] [Accepted: 07/30/2024] [Indexed: 08/20/2024] Open
Abstract
Early childhood caries (ECC) is influenced by microbial and host factors, including social, behavioral, and oral health. In this cross-sectional study, we analyze interkingdom dynamics in the dental plaque microbiome and its association with host variables. We use 16S rRNA and ITS1 amplicon sequencing on samples collected from preschool children and analyze questionnaire data to examine the social determinants of oral health. The results indicate a significant enrichment of Streptococcus mutans and Candida dubliniensis in ECC samples, in contrast to Neisseria oralis in caries-free children. Our interkingdom correlation analysis reveals that Candida dubliniensis is strongly correlated with both Neisseria bacilliformis and Prevotella veroralis in ECC. Additionally, ECC shows significant associations with host variables, including oral health status, age, place of residence, and mode of childbirth. This study provides empirical evidence associating the oral microbiome with socioeconomic and behavioral factors in relation to ECC, offering insights for developing targeted prevention strategies.
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Affiliation(s)
- Mohd Wasif Khan
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada; Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada
| | - Vivianne Cruz de Jesus
- Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada; Manitoba Chemosensory Biology Research Group, Department of Oral Biology, University of Manitoba, Winnipeg, MB, Canada; Department of Preventive Dental Science, University of Manitoba, Winnipeg, MB, Canada
| | - Betty-Anne Mittermuller
- Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada; Manitoba Chemosensory Biology Research Group, Department of Oral Biology, University of Manitoba, Winnipeg, MB, Canada; Department of Preventive Dental Science, University of Manitoba, Winnipeg, MB, Canada
| | - Shaan Sareen
- Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada; Manitoba Chemosensory Biology Research Group, Department of Oral Biology, University of Manitoba, Winnipeg, MB, Canada
| | - Victor Lee
- Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada; Department of Preventive Dental Science, University of Manitoba, Winnipeg, MB, Canada
| | - Robert J Schroth
- Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada; Manitoba Chemosensory Biology Research Group, Department of Oral Biology, University of Manitoba, Winnipeg, MB, Canada; Department of Preventive Dental Science, University of Manitoba, Winnipeg, MB, Canada; Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, MB, Canada
| | - Pingzhao Hu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada; Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada; Department of Biochemistry, Western University, London, ON, Canada.
| | - Prashen Chelikani
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada; Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada; Manitoba Chemosensory Biology Research Group, Department of Oral Biology, University of Manitoba, Winnipeg, MB, Canada; Department of Physiology and Pathophysiology, University of Manitoba, Winnipeg, MB, Canada.
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10
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Raajaraam L, Raman K. Modeling Microbial Communities: Perspective and Challenges. ACS Synth Biol 2024; 13:2260-2270. [PMID: 39148432 DOI: 10.1021/acssynbio.4c00116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Microbial communities are immensely important due to their widespread presence and profound impact on various facets of life. Understanding these complex systems necessitates mathematical modeling, a powerful tool for simulating and predicting microbial community behavior. This review offers a critical analysis of metabolic modeling and highlights key areas that would greatly benefit from broader discussion and collaboration. Moreover, we explore the challenges and opportunities linked to the intricate nature of these communities, spanning data generation, modeling, and validation. We are confident that ongoing advancements in modeling techniques, such as machine learning, coupled with interdisciplinary collaborations, will unlock the full potential of microbial communities across diverse applications.
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Affiliation(s)
- Lavanya Raajaraam
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems mEdicine, IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| | - Karthik Raman
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems mEdicine, IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
- Department of Data Science and AI, Wadhwani School of Data Science and Artificial Intelligence, IIT Madras, Chennai 600 036, India
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11
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Khan MW, Fung DLX, Schroth RJ, Chelikani P, Hu P. A cross-cohort analysis of dental plaque microbiome in early childhood caries. iScience 2024; 27:110447. [PMID: 39104404 PMCID: PMC11298647 DOI: 10.1016/j.isci.2024.110447] [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: 11/10/2023] [Revised: 05/05/2024] [Accepted: 07/01/2024] [Indexed: 08/07/2024] Open
Abstract
Early childhood caries (ECC) is a multifactorial disease with a microbiome playing a significant role in caries progression. Understanding changes at the microbiome level in ECC is required to develop diagnostic and preventive strategies. In our study, we combined data from small independent cohorts to compare microbiome composition using a unified pipeline and applied a batch correction to avoid the pitfalls of batch effects. Our meta-analysis identified common biomarker species between different studies. We identified the best machine learning method for the classification of ECC versus caries-free samples and compared the performance of this method using a leave-one-dataset-out approach. Our random forest model was found to be generalizable when used in combination with other studies. While our results highlight the potential microbial species involved in ECC and disease classification, we also mentioned the limitations that can serve as a guide for future researchers to design and use appropriate tools for such analyses.
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Affiliation(s)
- Mohd Wasif Khan
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada
- Children’s Hospital Research Institute of Manitoba, Winnipeg, MB, Canada
| | | | - Robert J. Schroth
- Children’s Hospital Research Institute of Manitoba, Winnipeg, MB, Canada
- Department of Preventive Dental Science, University of Manitoba, Winnipeg, MB, Canada
- Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, MB, Canada
| | - Prashen Chelikani
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada
- Children’s Hospital Research Institute of Manitoba, Winnipeg, MB, Canada
- Manitoba Chemosensory Biology Research Group, Department of Oral Biology, University of Manitoba, Winnipeg, MB, Canada
| | - Pingzhao Hu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada
- Children’s Hospital Research Institute of Manitoba, Winnipeg, MB, Canada
- Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada
- Department of Biochemistry, Western University, London, ON, Canada
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12
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Lee S, Portlock T, Le Chatelier E, Garcia-Guevara F, Clasen F, Oñate FP, Pons N, Begum N, Harzandi A, Proffitt C, Rosario D, Vaga S, Park J, von Feilitzen K, Johansson F, Zhang C, Edwards LA, Lombard V, Gauthier F, Steves CJ, Gomez-Cabrero D, Henrissat B, Lee D, Engstrand L, Shawcross DL, Proctor G, Almeida M, Nielsen J, Mardinoglu A, Moyes DL, Ehrlich SD, Uhlen M, Shoaie S. Global compositional and functional states of the human gut microbiome in health and disease. Genome Res 2024; 34:967-978. [PMID: 39038849 PMCID: PMC11293553 DOI: 10.1101/gr.278637.123] [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: 10/19/2023] [Accepted: 06/05/2024] [Indexed: 07/24/2024]
Abstract
The human gut microbiota is of increasing interest, with metagenomics a key tool for analyzing bacterial diversity and functionality in health and disease. Despite increasing efforts to expand microbial gene catalogs and an increasing number of metagenome-assembled genomes, there have been few pan-metagenomic association studies and in-depth functional analyses across different geographies and diseases. Here, we explored 6014 human gut metagenome samples across 19 countries and 23 diseases by performing compositional, functional cluster, and integrative analyses. Using interpreted machine learning classification models and statistical methods, we identified Fusobacterium nucleatum and Anaerostipes hadrus with the highest frequencies, enriched and depleted, respectively, across different disease cohorts. Distinct functional distributions were observed in the gut microbiomes of both westernized and nonwesternized populations. These compositional and functional analyses are presented in the open-access Human Gut Microbiome Atlas, allowing for the exploration of the richness, disease, and regional signatures of the gut microbiota across different cohorts.
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Affiliation(s)
- Sunjae Lee
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, United Kingdom
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), 61005, Gwangju, Republic of Korea
| | - Theo Portlock
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | | | - Fernando Garcia-Guevara
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, United Kingdom
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Frederick Clasen
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, United Kingdom
| | | | - Nicolas Pons
- University Paris-Saclay, INRAE, MetaGenoPolis, 78350 Jouy-en-Josas, France
| | - Neelu Begum
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, United Kingdom
| | - Azadeh Harzandi
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, United Kingdom
| | - Ceri Proffitt
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, United Kingdom
| | - Dorines Rosario
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, United Kingdom
| | - Stefania Vaga
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, United Kingdom
| | - Junseok Park
- Department of Bio and Brain Engineering, KAIST, Yuseong-gu, Daejeon 305-701, Republic of Korea
| | - Kalle von Feilitzen
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Fredric Johansson
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Lindsey A Edwards
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, United Kingdom
- Institute of Liver Studies, Department of Inflammation Biology, School of Immunology and Microbial Sciences, King's College London, London SE5 9NU, United Kingdom
| | - Vincent Lombard
- INRAE, USC1408 Architecture et Fonction des Macromolécules Biologiques (AFMB), Marseille 13288, France
- Architecture et Fonction des Macromolécules Biologiques (AFMB), CNRS, Aix-Marseille University, Marseille 13288, France
| | - Franck Gauthier
- University Paris-Saclay, INRAE, MetaGenoPolis, 78350 Jouy-en-Josas, France
| | - Claire J Steves
- Department of Twin Research & Genetic Epidemiology, King's College London, London WC2R 2LS, United Kingdom
| | - David Gomez-Cabrero
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, United Kingdom
- Translational Bioinformatics Unit, Navarrabiomed, Universidad Pública de Navarra (UPNA), IdiSNA, 31008 Pamplona, Spain
- Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Bernard Henrissat
- Department of Biological Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Department of Biotechnology and Biomedicine, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Doheon Lee
- Department of Bio and Brain Engineering, KAIST, Yuseong-gu, Daejeon 305-701, Republic of Korea
| | - Lars Engstrand
- Centre for Translational Microbiome Research (CTMR), Department of Microbiology, Tumour and Cell Biology, Karolinska Institutet, 171 65 Stockholm, Sweden
| | - Debbie L Shawcross
- Institute of Liver Studies, Department of Inflammation Biology, School of Immunology and Microbial Sciences, King's College London, London SE5 9NU, United Kingdom
| | - Gordon Proctor
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, United Kingdom
| | - Mathieu Almeida
- University Paris-Saclay, INRAE, MetaGenoPolis, 78350 Jouy-en-Josas, France
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
- BioInnovation Institute, DK-2200 Copenhagen N, Denmark
| | - Adil Mardinoglu
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, United Kingdom
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - David L Moyes
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, United Kingdom
| | - Stanislav Dusko Ehrlich
- University Paris-Saclay, INRAE, MetaGenoPolis, 78350 Jouy-en-Josas, France
- Department of Clinical and Movement Neurosciences, University College London, London NW3 2PF, United Kingdom
| | - Mathias Uhlen
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, SE-171 21, Sweden;
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT, United Kingdom;
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, SE-171 21, Sweden
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13
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Palanikumar I, Sinha H, Raman K. Panera: An innovative framework for surmounting uncertainty in microbial community modeling using pan-genera metabolic models. iScience 2024; 27:110358. [PMID: 39092173 PMCID: PMC11292516 DOI: 10.1016/j.isci.2024.110358] [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: 11/30/2023] [Revised: 04/10/2024] [Accepted: 06/20/2024] [Indexed: 08/04/2024] Open
Abstract
Utilization of 16S rRNA data in constraint-based modeling to characterize microbial communities confronts a major hurdle of lack of species-level resolution, impeding the construction of community models. We introduce "Panera," an innovative framework designed to model communities under this uncertainty and yet perform metabolic inferences using pan-genus metabolic models (PGMMs). We demonstrated PGMMs' utility for comprehending the metabolic capabilities of a genus and in characterizing community models using amplicon data. The unique, adaptable nature of PGMMs unlocks their potential in building hybrid communities, combining genome-scale metabolic models (GSMMs) and PGMMs. Notably, these models provide predictions comparable to the standard GSMM-based community models, while achieving a nearly 46% reduction in error compared to the genus model-based communities. In essence, "Panera" presents a potent and effective approach to aid in metabolic modeling by enabling robust predictions of community metabolic potential when dealing with amplicon data, and offers insights into genus-level metabolic landscapes.
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Affiliation(s)
- Indumathi Palanikumar
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems mEdicine (IBSE), IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| | - Himanshu Sinha
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems mEdicine (IBSE), IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| | - Karthik Raman
- Department of Biotechnology, Bhupat Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems mEdicine (IBSE), IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
- Department of Data Science and AI, Wadhwani School of Data Science and AI, IIT Madras, Chennai 600 036, India
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14
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Güven Gülhan Ü, Nikerel E, Çakır T, Erdoğan Sevilgen F, Durmuş S. Species-level identification of enterotype-specific microbial markers for colorectal cancer and adenoma. Mol Omics 2024; 20:397-416. [PMID: 38780313 DOI: 10.1039/d4mo00016a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Enterotypes have been shown to be an important factor for population stratification based on gut microbiota composition, leading to a better understanding of human health and disease states. Classifications based on compositional patterns will have implications for personalized microbiota-based solutions. There have been limited enterotype based studies on colorectal adenoma and cancer. Here, an enterotype-based meta-analysis of fecal shotgun metagenomic studies was performed, including 1579 samples of healthy controls (CTR), colorectal adenoma (ADN) and colorectal cancer (CRC) in total. Gut microbiota of healthy people were clustered into three enterotypes (Ruminococcus-, Bacteroides- and Prevotella-dominated enterotypes). Reference-based enterotype assignments were performed for CRC and ADN samples, using the supervised machine learning algorithm, K-nearest neighbors. Differential abundance analyses and random forest classification were conducted on each enterotype between healthy controls and CRC-ADN groups, revealing novel enterotype-specific microbial markers for non-invasive CRC screening strategies. Furthermore, we identified microbial species unique to each enterotype that play a role in the production of secondary bile acids and short-chain fatty acids, unveiling the correlation between cancer-associated gut microbes and dietary patterns. The enterotype-based approach in this study is promising in elucidating the mechanisms of differential gut microbiome profiles, thereby improving the efficacy of personalized microbiota-based solutions.
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Affiliation(s)
- Ünzile Güven Gülhan
- Department of Bioengineering, Gebze Technical University, Gebze, TR 41400, Turkey.
| | - Emrah Nikerel
- Department of Genetics and Bioengineering, Yeditepe University, Istanbul, TR 34755, Turkey
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Gebze, TR 41400, Turkey.
- PhiTech Bioinformatics, Gebze, TR 41470, Turkey
| | - Fatih Erdoğan Sevilgen
- The Institute for Data Science & Artificial Intelligence, Boğaziçi University, Istanbul, TR 34342, Turkey
- PhiTech Bioinformatics, Gebze, TR 41470, Turkey
| | - Saliha Durmuş
- Department of Bioengineering, Gebze Technical University, Gebze, TR 41400, Turkey.
- PhiTech Bioinformatics, Gebze, TR 41470, Turkey
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15
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Derosa L, Iebba V, Silva CAC, Piccinno G, Wu G, Lordello L, Routy B, Zhao N, Thelemaque C, Birebent R, Marmorino F, Fidelle M, Messaoudene M, Thomas AM, Zalcman G, Friard S, Mazieres J, Audigier-Valette C, Sibilot DM, Goldwasser F, Scherpereel A, Pegliasco H, Ghiringhelli F, Bouchard N, Sow C, Darik I, Zoppi S, Ly P, Reni A, Daillère R, Deutsch E, Lee KA, Bolte LA, Björk JR, Weersma RK, Barlesi F, Padilha L, Finzel A, Isaksen ML, Escudier B, Albiges L, Planchard D, André F, Cremolini C, Martinez S, Besse B, Zhao L, Segata N, Wojcik J, Kroemer G, Zitvogel L. Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome. Cell 2024; 187:3373-3389.e16. [PMID: 38906102 DOI: 10.1016/j.cell.2024.05.029] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/16/2024] [Accepted: 05/14/2024] [Indexed: 06/23/2024]
Abstract
The gut microbiota influences the clinical responses of cancer patients to immunecheckpoint inhibitors (ICIs). However, there is no consensus definition of detrimental dysbiosis. Based on metagenomics (MG) sequencing of 245 non-small cell lung cancer (NSCLC) patient feces, we constructed species-level co-abundance networks that were clustered into species-interacting groups (SIGs) correlating with overall survival. Thirty-seven and forty-five MG species (MGSs) were associated with resistance (SIG1) and response (SIG2) to ICIs, respectively. When combined with the quantification of Akkermansia species, this procedure allowed a person-based calculation of a topological score (TOPOSCORE) that was validated in an additional 254 NSCLC patients and in 216 genitourinary cancer patients. Finally, this TOPOSCORE was translated into a 21-bacterial probe set-based qPCR scoring that was validated in a prospective cohort of NSCLC patients as well as in colorectal and melanoma patients. This approach could represent a dynamic diagnosis tool for intestinal dysbiosis to guide personalized microbiota-centered interventions.
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Affiliation(s)
- Lisa Derosa
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France; Department of Medical Oncology, Gustave Roussy, Villejuif, France.
| | - Valerio Iebba
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
| | - Carolina Alves Costa Silva
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
| | | | - Guojun Wu
- Center for Nutrition, Microbiome and Health, New Jersey Institute for Food, Nutrition and Health, Rutgers University, New Brunswick, NJ, USA; Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA; Rutgers-Jiaotong Joint Laboratory for Microbiome and Human Health, New Brunswick, NJ, USA
| | - Leonardo Lordello
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
| | - Bertrand Routy
- Centre Hospitalier de l'Université de Montréal (CHUM), Hematology-Oncology Division, Department of Medicine, Montréal, QC, Canada; Centre de Recherche du CHUM (CRCHUM), Montréal, QC, Canada
| | - Naisi Zhao
- Department of Public Health and Community Medicine, School of Medicine, Tufts University, Boston, MA 02111, USA
| | - Cassandra Thelemaque
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
| | - Roxanne Birebent
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
| | - Federica Marmorino
- Unit of Medical Oncology 2, University Hospital of Pisa, Pisa, Italy; Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Marine Fidelle
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
| | | | | | - Gerard Zalcman
- Université Paris Cité, Thoracic Oncology Department-CIC1425/CLIP2 Paris-Nord, Bichat-Claude Bernard Hospital, AP-HP, Paris, France
| | - Sylvie Friard
- Pneumology Department, Foch Hospital, Suresnes, France
| | - Julien Mazieres
- Centre Hospitalier Universitaire de Toulouse, Toulouse, France
| | | | - Denis Moro- Sibilot
- Department of Thoracic Oncology, Centre Hospitalier Universitaire, Grenoble, France
| | - François Goldwasser
- INSERM U1016-CNRS UMR8104, Paris Cité University, Paris, France; Department of Medical Oncology, Cochin Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France; Immunomodulatory Therapies Multidisciplinary Study Group (CERTIM), Paris, France
| | - Arnaud Scherpereel
- Department of Pulmonary and Thoracic Oncology, University of Lille, University Hospital (CHU), Lille, France
| | | | - François Ghiringhelli
- Cancer Biology Transfer Platform, Centre Georges-François Leclerc, Dijon, France; Centre de Recherche INSERM LNC-UMR1231, Dijon, France; Department of Medical Oncology, Centre Georges-François Leclerc, Dijon, France
| | | | - Cissé Sow
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
| | - Ines Darik
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
| | - Silvia Zoppi
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France; Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Pierre Ly
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France
| | - Anna Reni
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France; Section of Oncology, Department of Medicine, University of Verona School of Medicine and Verona University Hospital Trust, Verona, Italy
| | | | - Eric Deutsch
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Department of Radiation Oncology, Gustave Roussy, Villejuif, France; INSERM U1030, Radiothérapie Moléculaire et Innovation Thérapeutique, Villejuif, France
| | - Karla A Lee
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Laura A Bolte
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, the Netherlands
| | - Johannes R Björk
- 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
| | - Fabrice Barlesi
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Department of Medical Oncology, Gustave Roussy, Villejuif, France
| | - Lucas Padilha
- Bio-Me AS, Oslo Science Park, Gaustadalléen 21, Oslo, Norway
| | - Ana Finzel
- Bio-Me AS, Oslo Science Park, Gaustadalléen 21, Oslo, Norway
| | | | - Bernard Escudier
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Department of Medical Oncology, Gustave Roussy, Villejuif, France
| | - Laurence Albiges
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Department of Medical Oncology, Gustave Roussy, Villejuif, France
| | - David Planchard
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Department of Medical Oncology, Gustave Roussy, Villejuif, France
| | - Fabrice André
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Department of Medical Oncology, Gustave Roussy, Villejuif, France
| | - Chiara Cremolini
- Unit of Medical Oncology 2, University Hospital of Pisa, Pisa, Italy; Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Stéphanie Martinez
- Service des Maladies Respiratoires, Centre Hospitalier d'Aix-en-Provence, Aix-en-Provence, France
| | - Benjamin Besse
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Department of Medical Oncology, Gustave Roussy, Villejuif, France
| | - Liping Zhao
- Center for Nutrition, Microbiome and Health, New Jersey Institute for Food, Nutrition and Health, Rutgers University, New Brunswick, NJ, USA; Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA; Rutgers-Jiaotong Joint Laboratory for Microbiome and Human Health, New Brunswick, NJ, USA; State Key Laboratory of Microbial Metabolism, Ministry of Education Laboratory of Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Nicola Segata
- Department CIBIO, University of Trento, Trento, Italy; IEO, European Institute of Oncology IRCCS, Milan, Italy
| | | | - Guido Kroemer
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Centre de Recherche des Cordeliers, Equipe labellisée-Ligue contre le cancer, Université de Paris Cité, Sorbonne Université, Institut Universitaire de France, Inserm U1138, Paris, France; Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France; Institut du Cancer Paris CARPEM, Department of Biology, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Laurence Zitvogel
- Gustave Roussy Cancer Campus, ClinicObiome, Villejuif, France; Université Paris-Saclay, Ile-de-France, France; Institut National de la Santé et de la Recherche Médicale (INSERM) U1015, Equipe Labellisée-Ligue Nationale contre le Cancer, Villejuif, France; Center of Clinical Investigations in Biotherapies of Cancer (BIOTHERIS) 1428, Villejuif, France.
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16
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Zhu J, Sun C, Li M, Hu G, Zhao XM, Chen WH. Compared to histamine-2 receptor antagonist, proton pump inhibitor induces stronger oral-to-gut microbial transmission and gut microbiome alterations: a randomised controlled trial. Gut 2024; 73:1087-1097. [PMID: 38050061 PMCID: PMC11187400 DOI: 10.1136/gutjnl-2023-330168] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 11/06/2023] [Indexed: 12/06/2023]
Abstract
OBJECTIVE We aim to compare the effects of proton pump inhibitors (PPIs) and histamine-2 receptor antagonists (H2RAs) on the gut microbiota through longitudinal analysis. DESIGN Healthy volunteers were randomly assigned to receive either PPI (n=23) or H2RA (n=26) daily for seven consecutive days. We collected oral (saliva) and faecal samples before and after the intervention for metagenomic next-generation sequencing. We analysed intervention-induced alterations in the oral and gut microbiome including microbial abundance and growth rates, oral-to-gut transmissions, and compared differences between the PPI and H2RA groups. RESULTS Both interventions disrupted the gut microbiota, with PPIs demonstrating more pronounced effects. PPI usage led to a significantly higher extent of oral-to-gut transmission and promoted the growth of specific oral microbes in the gut. This led to a significant increase in both the number and total abundance of oral species present in the gut, including the identification of known disease-associated species like Fusobacterium nucleatum and Streptococcus anginosus. Overall, gut microbiome-based machine learning classifiers could accurately distinguish PPI from non-PPI users, achieving an area under the receiver operating characteristic curve (AUROC) of 0.924, in contrast to an AUROC of 0.509 for H2RA versus non-H2RA users. CONCLUSION Our study provides evidence that PPIs have a greater impact on the gut microbiome and oral-to-gut transmission than H2RAs, shedding light on the mechanism underlying the higher risk of certain diseases associated with prolonged PPI use. TRIAL REGISTRATION NUMBER ChiCTR2300072310.
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Affiliation(s)
- Jiaying Zhu
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Chuqing Sun
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Min Li
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Guoru Hu
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Xing-Ming Zhao
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- International Human Phenome Institutes (Shanghai), Shanghai, China
| | - Wei-Hua Chen
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Henan, China
- Medical Artificial Intelligence Research Institute, Binzhou Medical University, Yantai, China
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17
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Pusadkar V, Mazumder A, Azad A, Patil D, Azad RK. Deciphering Microbial Shifts in the Gut and Lung Microbiomes of COVID-19 Patients. Microorganisms 2024; 12:1058. [PMID: 38930440 PMCID: PMC11205787 DOI: 10.3390/microorganisms12061058] [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: 05/09/2024] [Revised: 05/17/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
COVID-19, caused by SARS-CoV-2, results in respiratory and cardiopulmonary infections. There is an urgent need to understand not just the pathogenic mechanisms of this disease but also its impact on the physiology of different organs and microbiomes. Multiple studies have reported the effects of COVID-19 on the gastrointestinal microbiota, such as promoting dysbiosis (imbalances in the microbiome) following the disease's progression. Deconstructing the dynamic changes in microbiome composition that are specifically correlated with COVID-19 patients remains a challenge. Motivated by this problem, we implemented a biomarker discovery pipeline to identify candidate microbes specific to COVID-19. This involved a meta-analysis of large-scale COVID-19 metagenomic data to decipher the impact of COVID-19 on the human gut and respiratory microbiomes. Metagenomic studies of the gut and respiratory microbiomes of COVID-19 patients and of microbiomes from other respiratory diseases with symptoms similar to or overlapping with COVID-19 revealed 1169 and 131 differentially abundant microbes in the human gut and respiratory microbiomes, respectively, that uniquely associate with COVID-19. Furthermore, by utilizing machine learning models (LASSO and XGBoost), we demonstrated the power of microbial features in separating COVID-19 samples from metagenomic samples representing other respiratory diseases and controls (healthy individuals), achieving an overall accuracy of over 80%. Overall, our study provides insights into the microbiome shifts occurring in COVID-19 patients, shining a new light on the compositional changes.
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Affiliation(s)
- Vaidehi Pusadkar
- Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX 76203, USA;
| | - Anirudh Mazumder
- Texas Academy of Mathematics and Science, University of North Texas, Denton, TX 76203, USA
| | - Abhijay Azad
- Texas Academy of Mathematics and Science, University of North Texas, Denton, TX 76203, USA
| | - Deepti Patil
- Texas Academy of Mathematics and Science, University of North Texas, Denton, TX 76203, USA
| | - Rajeev K. Azad
- Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX 76203, USA;
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18
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Schluter J, Hussey G, Valeriano J, Zhang C, Sullivan A, Fenyö D. The MTIST platform: a microbiome time series inference standardized test. RESEARCH SQUARE 2024:rs.3.rs-4343683. [PMID: 38766187 PMCID: PMC11100882 DOI: 10.21203/rs.3.rs-4343683/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
The human gut microbiome is a promising therapeutic target, but interventions are hampered by our limited understanding of microbial ecosystems. Here, we present a platform to develop, evaluate, and score approaches to learn ecological interactions from microbiome time series data. The microbiome time series inference standardized test (MTIST) comprises: a simulation framework for the in silico generation of microbiome study data akin to what is obtained with quantitative next-generation sequencing approaches, a compilation of a large curated data set generated by the simulation framework representing 648 simulated microbiome studies containing 18,360 time series, with a total of 2,182,800 species abundance measurements, and a scoring method to rank ecological inference algorithms. We use the MTIST platform to rank five implementations of microbiome inference approaches, revealing that while all algorithms performed well on ecosystems with few species (3 and 10), all algorithms failed to infer most interaction in a large ecosystem with 100 member species. However, we do find that the strongest interactions within a large ecosystem are inferred with higher success by all algorithms. Finally, we use the MTIST platform to compare different microbiome study designs, characterizing tradeoffs between samples per subject and number of subjects. Interestingly, we find that when only few samples can be collected per subject, ecological inference is most successful when these samples are collected with highest feasible temporal frequency. Taken together, we provide a computational tool to aid the development of better microbiome ecosystem inference approaches, which will be crucial towards the development of reliable and predictable therapeutic approaches that target the microbiome ecosystem.
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Affiliation(s)
| | | | - João Valeriano
- Centre Interdisciplinaire de Nanoscience de Marseille, Aix-Marseille Université
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19
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Qin Y, Tong X, Mei WJ, Cheng Y, Zou Y, Han K, Yu J, Jie Z, Zhang T, Zhu S, Jin X, Wang J, Yang H, Xu X, Zhong H, Xiao L, Ding PR. Consistent signatures in the human gut microbiome of old- and young-onset colorectal cancer. Nat Commun 2024; 15:3396. [PMID: 38649355 PMCID: PMC11035630 DOI: 10.1038/s41467-024-47523-x] [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: 06/01/2023] [Accepted: 04/03/2024] [Indexed: 04/25/2024] Open
Abstract
The incidence of young-onset colorectal cancer (yCRC) has been increasing in recent decades, but little is known about the gut microbiome of these patients. Most studies have focused on old-onset CRC (oCRC), and it remains unclear whether CRC signatures derived from old patients are valid in young patients. To address this, we assembled the largest yCRC gut metagenomes to date from two independent cohorts and found that the CRC microbiome had limited association with age across adulthood. Differential analysis revealed that well-known CRC-associated taxa, such as Clostridium symbiosum, Peptostreptococcus stomatis, Parvimonas micra and Hungatella hathewayi were significantly enriched (false discovery rate <0.05) in both old- and young-onset patients. Similar strain-level patterns of Fusobacterium nucleatum, Bacteroides fragilis and Escherichia coli were observed for oCRC and yCRC. Almost all oCRC-associated metagenomic pathways had directionally concordant changes in young patients. Importantly, CRC-associated virulence factors (fadA, bft) were enriched in both oCRC and yCRC compared to their respective controls. Moreover, the microbiome-based classification model had similar predication accuracy for CRC status in old- and young-onset patients, underscoring the consistency of microbial signatures across different age groups.
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Affiliation(s)
- Youwen Qin
- BGI Research, Shenzhen, 518083, China.
- BGI Genomics, Shenzhen, 518083, China.
| | - Xin Tong
- BGI Research, Shenzhen, 518083, China
| | - Wei-Jian Mei
- Department of Colorectal Surgery, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Yanshuang Cheng
- Department of Colorectal Surgery, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Yuanqiang Zou
- BGI Research, Shenzhen, 518083, China
- Shenzhen Engineering Laboratory of Detection and Intervention of Human Intestinal Microbiome, Shenzhen, China
| | - Kai Han
- Department of Colorectal Surgery, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Jiehai Yu
- Department of Colorectal Surgery, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Zhuye Jie
- BGI Research, Shenzhen, 518083, China
| | - Tao Zhang
- BGI Research, Shenzhen, 518083, China
- Shenzhen Key Laboratory of Human commensal microorganisms and Health Research, Shenzhen, China
- BGI Research, Wuhan, 430074, China
| | - Shida Zhu
- BGI Genomics, Shenzhen, 518083, China
| | - Xin Jin
- BGI Research, Shenzhen, 518083, China
| | - Jian Wang
- BGI Research, Shenzhen, 518083, China
| | | | - Xun Xu
- BGI Research, Shenzhen, 518083, China
| | - Huanzi Zhong
- BGI Research, Shenzhen, 518083, China
- BGI Genomics, Shenzhen, 518083, China
| | - Liang Xiao
- BGI Research, Shenzhen, 518083, China
- Shenzhen Engineering Laboratory of Detection and Intervention of Human Intestinal Microbiome, Shenzhen, China
| | - Pei-Rong Ding
- Department of Colorectal Surgery, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China.
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20
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Hu X, Yu C, He Y, Zhu S, Wang S, Xu Z, You S, Jiao Y, Liu SL, Bao H. Integrative metagenomic analysis reveals distinct gut microbial signatures related to obesity. BMC Microbiol 2024; 24:119. [PMID: 38580930 PMCID: PMC10996249 DOI: 10.1186/s12866-024-03278-5] [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: 10/13/2023] [Accepted: 03/26/2024] [Indexed: 04/07/2024] Open
Abstract
Obesity is a metabolic disorder closely associated with profound alterations in gut microbial composition. However, the dynamics of species composition and functional changes in the gut microbiome in obesity remain to be comprehensively investigated. In this study, we conducted a meta-analysis of metagenomic sequencing data from both obese and non-obese individuals across multiple cohorts, totaling 1351 fecal metagenomes. Our results demonstrate a significant decrease in both the richness and diversity of the gut bacteriome and virome in obese patients. We identified 38 bacterial species including Eubacterium sp. CAG:274, Ruminococcus gnavus, Eubacterium eligens and Akkermansia muciniphila, and 1 archaeal species, Methanobrevibacter smithii, that were significantly altered in obesity. Additionally, we observed altered abundance of five viral families: Mesyanzhinovviridae, Chaseviridae, Salasmaviridae, Drexlerviridae, and Casjensviridae. Functional analysis of the gut microbiome indicated distinct signatures associated to obesity and identified Ruminococcus gnavus as the primary driver for function enrichment in obesity, and Methanobrevibacter smithii, Akkermansia muciniphila, Ruminococcus bicirculans, and Eubacterium siraeum as functional drivers in the healthy control group. Additionally, our results suggest that antibiotic resistance genes and bacterial virulence factors may influence the development of obesity. Finally, we demonstrated that gut vOTUs achieved a diagnostic accuracy with an optimal area under the curve of 0.766 for distinguishing obesity from healthy controls. Our findings offer comprehensive and generalizable insights into the gut bacteriome and virome features associated with obesity, with the potential to guide the development of microbiome-based diagnostics.
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Affiliation(s)
- Xinliang Hu
- Genomics Research Center, Key Laboratory of Gut Microbiota and Pharmacogenomics of Heilongjiang Province, State-Province Key Laboratory of Biomedicine-Pharmaceutics of China, College of Pharmacy, Harbin Medical University, Harbin, China
- Harbin Medical University-University of Calgary Cumming School of Medicine Centre for Infection and Genomics, Harbin Medical University, Harbin, China
| | - Chong Yu
- Genomics Research Center, Key Laboratory of Gut Microbiota and Pharmacogenomics of Heilongjiang Province, State-Province Key Laboratory of Biomedicine-Pharmaceutics of China, College of Pharmacy, Harbin Medical University, Harbin, China
- Harbin Medical University-University of Calgary Cumming School of Medicine Centre for Infection and Genomics, Harbin Medical University, Harbin, China
| | - Yuting He
- Genomics Research Center, Key Laboratory of Gut Microbiota and Pharmacogenomics of Heilongjiang Province, State-Province Key Laboratory of Biomedicine-Pharmaceutics of China, College of Pharmacy, Harbin Medical University, Harbin, China
- Harbin Medical University-University of Calgary Cumming School of Medicine Centre for Infection and Genomics, Harbin Medical University, Harbin, China
| | - Songling Zhu
- Genomics Research Center, Key Laboratory of Gut Microbiota and Pharmacogenomics of Heilongjiang Province, State-Province Key Laboratory of Biomedicine-Pharmaceutics of China, College of Pharmacy, Harbin Medical University, Harbin, China
- Harbin Medical University-University of Calgary Cumming School of Medicine Centre for Infection and Genomics, Harbin Medical University, Harbin, China
| | - Shuang Wang
- Department of Biopharmaceutical Sciences (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China), College of Pharmacy, Harbin Medical University, Harbin, China
| | - Ziqiong Xu
- Genomics Research Center, Key Laboratory of Gut Microbiota and Pharmacogenomics of Heilongjiang Province, State-Province Key Laboratory of Biomedicine-Pharmaceutics of China, College of Pharmacy, Harbin Medical University, Harbin, China
- Harbin Medical University-University of Calgary Cumming School of Medicine Centre for Infection and Genomics, Harbin Medical University, Harbin, China
| | - Shaohui You
- Genomics Research Center, Key Laboratory of Gut Microbiota and Pharmacogenomics of Heilongjiang Province, State-Province Key Laboratory of Biomedicine-Pharmaceutics of China, College of Pharmacy, Harbin Medical University, Harbin, China
- Harbin Medical University-University of Calgary Cumming School of Medicine Centre for Infection and Genomics, Harbin Medical University, Harbin, China
| | - Yanlei Jiao
- Genomics Research Center, Key Laboratory of Gut Microbiota and Pharmacogenomics of Heilongjiang Province, State-Province Key Laboratory of Biomedicine-Pharmaceutics of China, College of Pharmacy, Harbin Medical University, Harbin, China
- Harbin Medical University-University of Calgary Cumming School of Medicine Centre for Infection and Genomics, Harbin Medical University, Harbin, China
| | - Shu-Lin Liu
- Genomics Research Center, Key Laboratory of Gut Microbiota and Pharmacogenomics of Heilongjiang Province, State-Province Key Laboratory of Biomedicine-Pharmaceutics of China, College of Pharmacy, Harbin Medical University, Harbin, China.
- Harbin Medical University-University of Calgary Cumming School of Medicine Centre for Infection and Genomics, Harbin Medical University, Harbin, China.
| | - Hongxia Bao
- Genomics Research Center, Key Laboratory of Gut Microbiota and Pharmacogenomics of Heilongjiang Province, State-Province Key Laboratory of Biomedicine-Pharmaceutics of China, College of Pharmacy, Harbin Medical University, Harbin, China.
- Harbin Medical University-University of Calgary Cumming School of Medicine Centre for Infection and Genomics, Harbin Medical University, Harbin, China.
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21
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Asnicar F, Thomas AM, Passerini A, Waldron L, Segata N. Machine learning for microbiologists. Nat Rev Microbiol 2024; 22:191-205. [PMID: 37968359 DOI: 10.1038/s41579-023-00984-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/03/2023] [Indexed: 11/17/2023]
Abstract
Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding and the machine learning tools frequently used in basic and clinical research range from classification and regression to clustering and dimensionality reduction. In this Review, we examine the main machine learning concepts, tasks and applications that are relevant for experimental and clinical microbiologists. We provide the minimal toolbox for a microbiologist to be able to understand, interpret and use machine learning in their experimental and translational activities.
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Affiliation(s)
- Francesco Asnicar
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy
| | - Andrew Maltez Thomas
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy
| | - Andrea Passerini
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Levi Waldron
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy.
- Department of Epidemiology and Biostatistics, City University of New York, New York, NY, USA.
| | - Nicola Segata
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy.
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy.
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22
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Muller E, Shiryan I, Borenstein E. Multi-omic integration of microbiome data for identifying disease-associated modules. Nat Commun 2024; 15:2621. [PMID: 38521774 PMCID: PMC10960825 DOI: 10.1038/s41467-024-46888-3] [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: 01/22/2024] [Accepted: 03/08/2024] [Indexed: 03/25/2024] Open
Abstract
Multi-omic studies of the human gut microbiome are crucial for understanding its role in disease across multiple functional layers. Nevertheless, integrating and analyzing such complex datasets poses significant challenges. Most notably, current analysis methods often yield extensive lists of disease-associated features (e.g., species, pathways, or metabolites), without capturing the multi-layered structure of the data. Here, we address this challenge by introducing "MintTea", an intermediate integration-based approach combining canonical correlation analysis extensions, consensus analysis, and an evaluation protocol. MintTea identifies "disease-associated multi-omic modules", comprising features from multiple omics that shift in concord and that collectively associate with the disease. Applied to diverse cohorts, MintTea captures modules with high predictive power, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome study, MintTea identifies a module with serum glutamate- and TCA cycle-related metabolites, along with bacterial species linked to insulin resistance. In another dataset, MintTea identifies a module associated with late-stage colorectal cancer, including Peptostreptococcus and Gemella species and fecal amino acids, in line with these species' metabolic activity and their coordinated gradual increase with cancer development. This work demonstrates the potential of advanced integration methods in generating systems-level, multifaceted hypotheses underlying microbiome-disease interactions.
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Affiliation(s)
- Efrat Muller
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Itamar Shiryan
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Elhanan Borenstein
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.
- Santa Fe Institute, Santa Fe, NM, USA.
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23
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Maghini DG, Oduaran OH, Wirbel J, Olubayo LAI, Smyth N, Mathema T, Belger CW, Agongo G, Boua PR, Choma SSR, Gómez-Olivé FX, Kisiangani I, Mashaba GR, Micklesfield L, Mohamed SF, Nonterah EA, Norris S, Sorgho H, Tollman S, Wafawanaka F, Tluway F, Ramsay M, Bhatt AS, Hazelhurst S. Expanding the human gut microbiome atlas of Africa. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.13.584859. [PMID: 38559015 PMCID: PMC10980044 DOI: 10.1101/2024.03.13.584859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Population studies are crucial in understanding the complex interplay between the gut microbiome and geographical, lifestyle, genetic, and environmental factors. However, populations from low- and middle-income countries, which represent ~84% of the world population, have been excluded from large-scale gut microbiome research. Here, we present the AWI-Gen 2 Microbiome Project, a cross-sectional gut microbiome study sampling 1,803 women from Burkina Faso, Ghana, Kenya, and South Africa. By intensively engaging with communities that range from rural and horticultural to urban informal settlements and post-industrial, we capture population diversity that represents a far greater breadth of the world's population. Using shotgun metagenomic sequencing, we find that study site explains substantially more microbial variation than disease status. We identify taxa with strong geographic and lifestyle associations, including loss of Treponema and Cryptobacteroides species and gain of Bifidobacterium species in urban populations. We uncover a wealth of prokaryotic and viral novelty, including 1,005 new bacterial metagenome-assembled genomes, and identify phylogeography signatures in Treponema succinifaciens. Finally, we find a microbiome signature of HIV infection that is defined by several taxa not previously associated with HIV, including Dysosmobacter welbionis and Enterocloster sp. This study represents the largest population-representative survey of gut metagenomes of African individuals to date, and paired with extensive clinical biomarkers, demographic data, and lifestyle information, provides extensive opportunity for microbiome-related discovery and research.
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Affiliation(s)
- Dylan G Maghini
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
- Department of Medicine (Hematology), Stanford University, Stanford, CA, USA
| | - Ovokeraye H Oduaran
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Jakob Wirbel
- Department of Medicine (Hematology), Stanford University, Stanford, CA, USA
| | - Luicer A Ingasia Olubayo
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Natalie Smyth
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Theophilous Mathema
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Carl W Belger
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Godfred Agongo
- Department of Biochemistry and Forensic Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana
| | - Palwendé R Boua
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
- Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santé, Burkina Faso
| | - Solomon SR Choma
- DIMAMO Population Health Research Centre, University of Limpopo, South Africa
| | - F Xavier Gómez-Olivé
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), Faculty of Health Sciences, School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| | | | - Given R Mashaba
- DIMAMO Population Health Research Centre, University of Limpopo, South Africa
| | - Lisa Micklesfield
- SAMRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | | | | | - Shane Norris
- SAMRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- School of Human Development and Health, University of Southampton, Southampton, United Kingdom
| | - Hermann Sorgho
- Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santé, Burkina Faso
| | - Stephen Tollman
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), Faculty of Health Sciences, School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| | - Floidy Wafawanaka
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), Faculty of Health Sciences, School of Public Health, University of the Witwatersrand, Johannesburg, South Africa
| | - Furahini Tluway
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Ami S Bhatt
- Department of Medicine (Hematology, Blood and Marrow Transplantation), Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
- School of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
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Misiak B, Pawlak E, Rembacz K, Kotas M, Żebrowska-Różańska P, Kujawa D, Łaczmański Ł, Piotrowski P, Bielawski T, Samochowiec J, Samochowiec A, Karpiński P. Associations of gut microbiota alterations with clinical, metabolic, and immune-inflammatory characteristics of chronic schizophrenia. J Psychiatr Res 2024; 171:152-160. [PMID: 38281465 DOI: 10.1016/j.jpsychires.2024.01.036] [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: 09/02/2023] [Revised: 12/31/2023] [Accepted: 01/22/2024] [Indexed: 01/30/2024]
Abstract
The present study had the following aims: 1) to compare gut microbiota composition in patients with schizophrenia and controls and 2) to investigate the association of differentially abundant bacterial taxa with markers of inflammation, intestinal permeability, lipid metabolism, and glucose homeostasis as well as clinical manifestation. A total of 115 patients with schizophrenia during remission of positive and disorganization symptoms, and 119 controls were enrolled. Altogether, 32 peripheral blood markers were assessed. A higher abundance of Eisenbergiella, Family XIII AD3011 group, Eggerthella, Hungatella, Lactobacillus, Olsenella, Coprobacillus, Methanobrevibacter, Ligilactobacillus, Eubacterium fissicatena group, and Clostridium innocuum group in patients with schizophrenia was found. The abundance of Paraprevotella and Bacteroides was decreased in patients with schizophrenia. Differentially abundant genera were associated with altered levels of immune-inflammatory markers, zonulin, lipid profile components, and insulin resistance. Moreover, several correlations of differentially abundant genera with cognitive impairment, higher severity of negative symptoms, and worse social functioning were observed. The association of Methanobrevibacter abundance with the level of negative symptoms, cognition, and social functioning appeared to be mediated by the levels of interleukin-6 and RANTES. In turn, the association of Hungatella with the performance of attention was mediated by the levels of zonulin. The findings indicate that compositional alterations of gut microbiota observed in patients with schizophrenia correspond with clinical manifestation, intestinal permeability, subclinical inflammation, lipid profile alterations, and impaired glucose homeostasis. Subclinical inflammation and impaired gut permeability might mediate the association of gut microbiota alterations with psychopathological symptoms and cognitive impairment.
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Affiliation(s)
- Błażej Misiak
- Department of Psychiatry, Wroclaw Medical University, Wroclaw, Poland.
| | - Edyta Pawlak
- Laboratory of Immunopathology, Department of Experimental Therapy, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Krzysztof Rembacz
- Laboratory of Immunopathology, Department of Experimental Therapy, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Marek Kotas
- Laboratory of Immunopathology, Department of Experimental Therapy, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Paulina Żebrowska-Różańska
- Laboratory of Genomics & Bioinformatics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Dorota Kujawa
- Laboratory of Genomics & Bioinformatics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Łukasz Łaczmański
- Laboratory of Genomics & Bioinformatics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Patryk Piotrowski
- Department of Psychiatry, Wroclaw Medical University, Wroclaw, Poland
| | - Tomasz Bielawski
- Department of Psychiatry, Wroclaw Medical University, Wroclaw, Poland
| | - Jerzy Samochowiec
- Department of Psychiatry, Pomeranian Medical University, Szczecin, Poland
| | - Agnieszka Samochowiec
- Department of Clinical Psychology, Institute of Psychology, University of Szczecin, Poland
| | - Paweł Karpiński
- Laboratory of Genomics & Bioinformatics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland; Department of Genetics, Wroclaw Medical University, Wroclaw, Poland
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Wyss J, Raselli T, Wyss A, Telzerow A, Rogler G, Krupka N, Yilmaz B, Schmidt TSB, Misselwitz B. Development of non-alcoholic steatohepatitis is associated with gut microbiota but not with oxysterol enzymes CH25H, EBI2, or CYP7B1 in mice. BMC Microbiol 2024; 24:69. [PMID: 38418983 PMCID: PMC10900623 DOI: 10.1186/s12866-024-03195-7] [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: 09/06/2023] [Accepted: 01/11/2024] [Indexed: 03/02/2024] Open
Abstract
Liver steatosis is the most frequent liver disorder and its advanced stage, non-alcoholic steatohepatitis (NASH), will soon become the main reason for liver fibrosis and cirrhosis. The "multiple hits hypothesis" suggests that progression from simple steatosis to NASH is triggered by multiple factors including the gut microbiota composition. The Epstein Barr virus induced gene 2 (EBI2) is a receptor for the oxysterol 7a, 25-dihydroxycholesterol synthesized by the enzymes CH25H and CYP7B1. EBI2 and its ligand control activation of immune cells in secondary lymphoid organs and the gut. Here we show a concurrent study of the microbial dysregulation and perturbation of the EBI2 axis in a mice model of NASH.We used mice with wildtype, or littermates with CH25H-/-, EBI2-/-, or CYP7B1-/- genotypes fed with a high-fat diet (HFD) containing high amounts of fat, cholesterol, and fructose for 20 weeks to induce liver steatosis and NASH. Fecal and small intestinal microbiota samples were collected, and microbiota signatures were compared according to genotype and NASH disease state.We found pronounced differences in microbiota composition of mice with HFD developing NASH compared to mice did not developing NASH. In mice with NASH, we identified significantly increased 33 taxa mainly belonging to the Clostridiales order and/ or the family, and significantly decreased 17 taxa. Using an Elastic Net algorithm, we suggest a microbiota signature that predicts NASH in animals with a HFD from the microbiota composition with moderate accuracy (area under the receiver operator characteristics curve = 0.64). In contrast, no microbiota differences regarding the studied genotypes (wildtype vs knock-out CH25H-/-, EBI2-/-, or CYP7B1-/-) were observed.In conclusion, our data confirm previous studies identifying the intestinal microbiota composition as a relevant marker for NASH pathogenesis. Further, no link of the EBI2 - oxysterol axis to the intestinal microbiota was detectable in the current study.
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Affiliation(s)
- Jacqueline Wyss
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Tina Raselli
- Department of Gastroenterology and Hepatology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Annika Wyss
- Department of Gastroenterology and Hepatology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Anja Telzerow
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Gerhard Rogler
- Department of Gastroenterology and Hepatology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Niklas Krupka
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Bahtiyar Yilmaz
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Maurice Müller Laboratories, Department for Biomedical Research, University of Bern, 3008, Bern, Switzerland
| | - Thomas S B Schmidt
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
| | - Benjamin Misselwitz
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Department of Gastroenterology and Hepatology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
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26
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Iqbal S, Begum F, Ullah I, Jalal N, Shaw P. Peeling off the layers from microbial dark matter (MDM): recent advances, future challenges, and opportunities. Crit Rev Microbiol 2024:1-21. [PMID: 38385313 DOI: 10.1080/1040841x.2024.2319669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 02/10/2024] [Indexed: 02/23/2024]
Abstract
Microbes represent the most common organisms on Earth; however, less than 2% of microbial species in the environment can undergo cultivation for study under laboratory conditions, and the rest of the enigmatic, microbial world remains mysterious, constituting a kind of "microbial dark matter" (MDM). In the last two decades, remarkable progress has been made in culture-dependent and culture-independent techniques. More recently, studies of MDM have relied on culture-independent techniques to recover genetic material through either unicellular genomics or shotgun metagenomics to construct single-amplified genomes (SAGs) and metagenome-assembled genomes (MAGs), respectively, which provide information about evolution and metabolism. Despite the remarkable progress made in the past decades, the functional diversity of MDM still remains uncharacterized. This review comprehensively summarizes the recently developed culture-dependent and culture-independent techniques for characterizing MDM, discussing major challenges, opportunities, and potential applications. These activities contribute to expanding our knowledge of the microbial world and have implications for various fields including Biotechnology, Bioprospecting, Functional genomics, Medicine, Evolutionary and Planetary biology. Overall, this review aims to peel off the layers from MDM, shed light on recent advancements, identify future challenges, and illuminate the exciting opportunities that lie ahead in unraveling the secrets of this intriguing microbial realm.
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Affiliation(s)
- Sajid Iqbal
- Oujiang Lab (Zhejiang Laboratory for Regenerative Medicine, Vision, and Brain Health), Wenzhou, China
- School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, China
| | - Farida Begum
- Department of Biochemistry, Abdul Wali Khan University Mardan, Mardan, Pakistan
| | - Ihsan Ullah
- College of Chemical Engineering, Fuzhou University, Fuzhou, China
| | - Nasir Jalal
- Oujiang Lab (Zhejiang Laboratory for Regenerative Medicine, Vision, and Brain Health), Wenzhou, China
| | - Peter Shaw
- Oujiang Lab (Zhejiang Laboratory for Regenerative Medicine, Vision, and Brain Health), Wenzhou, China
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Kim MJ, Jung DR, Lee JM, Kim I, Son H, Kim ES, Shin JH. Microbial dysbiosis index for assessing colitis status in mouse models: A systematic review and meta-analysis. iScience 2024; 27:108657. [PMID: 38205250 PMCID: PMC10777064 DOI: 10.1016/j.isci.2023.108657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 09/07/2023] [Accepted: 12/04/2023] [Indexed: 01/12/2024] Open
Abstract
Although countless gut microbiome studies on colitis using mouse models have been carried out, experiments with small sample sizes have encountered reproducibility limitations because of batch effects and statistical errors. In this study, dextran-sodium-sulfate-induced microbial dysbiosis index (DiMDI) was introduced as a reliable dysbiosis index that can be used to assess the state of microbial dysbiosis in DSS-induced mouse models. Meta-analysis of 189 datasets from 11 independent studies was performed to construct the DiMDI. Microbial dysbiosis biomarkers, Muribaculaceae, Alistipes, Turicibacter, and Bacteroides, were selected through four different feature selection methods and used to construct the DiMDI. This index demonstrated a high accuracy of 82.3% and showed strong robustness (88.9%) in the independent cohort. Therefore, DiMDI may be used as a standard for assessing microbial imbalance in DSS-induced mouse models and may contribute to the development of reliable colitis microbiome studies in mouse experiments.
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Affiliation(s)
- Min-Ji Kim
- Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Da-Ryung Jung
- Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Ji-Min Lee
- Cell & Matrix Research Institute, Kyungpook National University, Daegu 41940, Republic of Korea
| | - Ikwhan Kim
- NGS Core Facility, Kyungpook National University, Daegu 41566, Republic of Korea
| | - HyunWoo Son
- Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Eun Soo Kim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea
| | - Jae-Ho Shin
- Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea
- NGS Core Facility, Kyungpook National University, Daegu 41566, Republic of Korea
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28
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Mulders MCF, Audhoe AS, Van Koetsveld PM, Feelders RA, Hofland LJ, de Herder WW, Kraaij R, Hofland J. Midgut neuroendocrine tumor patients have a depleted gut microbiome with a discriminative signature. Eur J Cancer 2024; 197:113472. [PMID: 38100919 DOI: 10.1016/j.ejca.2023.113472] [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: 07/03/2023] [Revised: 11/28/2023] [Accepted: 11/30/2023] [Indexed: 12/17/2023]
Abstract
RATIONALE When compared to other types of cancer, the prevalence of midgut neuroendocrine tumors (NET) has disproportionally increased over the past decades. To date, there has been very little progress in discovering (epi)genetic drivers and treatment options for these tumors. Recent microbiome research has revealed that enteroendocrine cells communicate with the intestinal microbiome and has provided novel treatment targets for various other cancer types. Hence, our aim was to analyze the role of the gut microbiome in midgut NET patients. METHODS Fecal samples, prospectively collected from patients and control subjects, were analyzed with next generation 16S sequencing. Patients with neuroendocrine carcinomas and recent antibiotics use were excluded. Relevant variables were extracted from questionnaires and electronic health records. Microbial composition was compared between patients and controls as well as between groups within the patient cohort. RESULTS 87 midgut NET patients and 95 controls were included. Midgut NET patients had a less rich and diverse gut microbiome than controls (p < 0.001). Moreover, we identified 31 differentially abundant species and a gut microbial signature consisting of 17 species that was predictive of midgut NET presence with an area under the receiver operating characteristic curve of 0.863. Gut microbial composition was not directly associated with the presence of the carcinoid syndrome, tumor grade or multifocality. Nonetheless, we did observe a potential link between microbial diversity and the presence of carcinoid syndrome symptoms within the subset of patients with elevated 5-hydroxyindolacetic acid levels. CONCLUSION Midgut NET patients have an altered gut microbiome which suggests a role in NET development and could provide novel targets for microbiome-based diagnostics and therapeutics.
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Affiliation(s)
- M C F Mulders
- ENETS Center of Excellence, Section of Endocrinology, Department of Internal Medicine, Erasmus Medical Center and Erasmus Medical Center Cancer Institute, Rotterdam, the Netherlands.
| | - A S Audhoe
- ENETS Center of Excellence, Section of Endocrinology, Department of Internal Medicine, Erasmus Medical Center and Erasmus Medical Center Cancer Institute, Rotterdam, the Netherlands
| | - P M Van Koetsveld
- ENETS Center of Excellence, Section of Endocrinology, Department of Internal Medicine, Erasmus Medical Center and Erasmus Medical Center Cancer Institute, Rotterdam, the Netherlands
| | - R A Feelders
- ENETS Center of Excellence, Section of Endocrinology, Department of Internal Medicine, Erasmus Medical Center and Erasmus Medical Center Cancer Institute, Rotterdam, the Netherlands
| | - L J Hofland
- ENETS Center of Excellence, Section of Endocrinology, Department of Internal Medicine, Erasmus Medical Center and Erasmus Medical Center Cancer Institute, Rotterdam, the Netherlands
| | - W W de Herder
- ENETS Center of Excellence, Section of Endocrinology, Department of Internal Medicine, Erasmus Medical Center and Erasmus Medical Center Cancer Institute, Rotterdam, the Netherlands
| | - R Kraaij
- Laboratory of Population Genomics, Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - J Hofland
- ENETS Center of Excellence, Section of Endocrinology, Department of Internal Medicine, Erasmus Medical Center and Erasmus Medical Center Cancer Institute, Rotterdam, the Netherlands
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29
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Essex M, Rios Rodriguez V, Rademacher J, Proft F, Löber U, Markó L, Pleyer U, Strowig T, Marchand J, Kirwan JA, Siegmund B, Forslund SK, Poddubnyy D. Shared and Distinct Gut Microbiota in Spondyloarthritis, Acute Anterior Uveitis, and Crohn's Disease. Arthritis Rheumatol 2024; 76:48-58. [PMID: 37471465 DOI: 10.1002/art.42658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 06/26/2023] [Accepted: 07/13/2023] [Indexed: 07/22/2023]
Abstract
OBJECTIVE Spondyloarthritis (SpA) is a group of immune-mediated diseases highly concomitant with nonmusculoskeletal inflammatory disorders, such as acute anterior uveitis (AAU) and Crohn's disease (CD). The gut microbiome represents a promising avenue to elucidate shared and distinct underlying pathophysiology. METHODS We performed 16S ribosomal RNA sequencing on stool samples of 277 patients (72 CD, 103 AAU, and 102 SpA) included in the German Spondyloarthritis Inception Cohort and 62 back pain controls without any inflammatory disorder. Discriminatory statistical methods were used to disentangle microbial disease signals from one another and a wide range of potential confounders. Patients were naive to or had not received treatment with biological disease-modifying antirheumatic drugs (DMARDs) for >3 months before enrollment, providing a better approximation of a true baseline disease signal. RESULTS We identified a shared, immune-mediated disease signal represented by low abundances of Lachnospiraceae taxa relative to controls, most notably Fusicatenibacter, which was most abundant in controls receiving nonsteroidal antiinflammatory drug monotherapy and implied to partially mediate higher serum C-reactive protein. Patients with SpA showed an enrichment of Collinsella, whereas human leukocyte antigen (HLA)-B27+ individuals displayed enriched Faecalibacterium. CD patients had higher abundances of a Ruminococcus taxon, and previous conventional/synthetic DMARD therapy was associated with increased Akkermansia. CONCLUSION Our work supports the existence of a common gut dysbiosis in SpA and related inflammatory pathologies. We reveal shared and disease-specific microbial associations and suggest potential mediators of disease activity. Validation studies are needed to clarify the role of Fusicatenibacter in gut-joint inflammation, and metagenomic resolution is needed to understand the relationship between Faecalibacterium commensals and HLA-B27.
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Affiliation(s)
- Morgan Essex
- Experimental and Clinical Research Center (ECRC; a cooperation of the Max Delbrück Center and Charité-Universitätsmedizin), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), and Charité-Universitätsmedizin Berlin (a corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin), Berlin, Germany
| | - Valeria Rios Rodriguez
- Medical Department of Gastroenterology, Infectiology and Rheumatology, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Judith Rademacher
- Medical Department of Gastroenterology, Infectiology and Rheumatology, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, and Berlin Institute of Health (BIH) at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Fabian Proft
- Medical Department of Gastroenterology, Infectiology and Rheumatology, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Ulrike Löber
- ECRC, MDC, Charité-Universitätsmedizin Berlin and German Center for Cardiovascular Research (DZHK), Berlin, Germany
| | - Lajos Markó
- ECRC, MDC, Charité-Universitätsmedizin Berlin and German Center for Cardiovascular Research (DZHK), Berlin, Germany
| | - Uwe Pleyer
- Department of Ophthalmology, Campus Virchow, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Till Strowig
- Helmholtz Center for Infection Research, Braunschweig, Germany, and Cluster of Excellence RESIST (EXC 2155), Hannover Medical School and Center for Individualized Infection Medicine (CiiM; a joint venture between the Helmholtz Center for Infection Research and the Hannover Medical School), Hannover, Germany
| | - Jérémy Marchand
- MDC and BIH Metabolomics Platform at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Jennifer A Kirwan
- MDC and BIH Metabolomics Platform at Charité-Universitätsmedizin Berlin, Berlin, Germany, and University of Nottingham School of Veterinary Medicine and Science, Loughborough, UK
| | - Britta Siegmund
- Medical Department of Gastroenterology, Infectiology and Rheumatology, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Sofia K Forslund
- ECRC, MDC, Charité-Universitätsmedizin Berlin, and DZHK, Berlin, and Structural and Computational Biology Unit, EMBL, Heidelberg, Germany
| | - Denis Poddubnyy
- Department of Gastroentergology, Infectiology and Rheumatology, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin and German Rheumatism Research Center (DRFZ), Berlin, Germany
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30
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Monshizadeh M, Ye Y. Incorporating metabolic activity, taxonomy and community structure to improve microbiome-based predictive models for host phenotype prediction. Gut Microbes 2024; 16:2302076. [PMID: 38214657 PMCID: PMC10793686 DOI: 10.1080/19490976.2024.2302076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 01/02/2024] [Indexed: 01/13/2024] Open
Abstract
We developed MicroKPNN, a prior-knowledge guided interpretable neural network for microbiome-based human host phenotype prediction. The prior knowledge used in MicroKPNN includes the metabolic activities of different bacterial species, phylogenetic relationships, and bacterial community structure, all in a shallow neural network. Application of MicroKPNN to seven gut microbiome datasets (involving five different human diseases including inflammatory bowel disease, type 2 diabetes, liver cirrhosis, colorectal cancer, and obesity) shows that incorporation of the prior knowledge helped improve the microbiome-based host phenotype prediction. MicroKPNN outperformed fully connected neural network-based approaches in all seven cases, with the most improvement of accuracy in the prediction of type 2 diabetes. MicroKPNN outperformed a recently developed deep-learning based approach DeepMicro, which selects the best combination of autoencoder and machine learning approach to make predictions, in all of the seven cases. Importantly, we showed that MicroKPNN provides a way for interpretation of the predictive models. Using importance scores estimated for the hidden nodes, MicroKPNN could provide explanations for prior research findings by highlighting the roles of specific microbiome components in phenotype predictions. In addition, it may suggest potential future research directions for studying the impacts of microbiome on host health and diseases. MicroKPNN is publicly available at https://github.com/mgtools/MicroKPNN.
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Affiliation(s)
- Mahsa Monshizadeh
- Computer Science Department, Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA
| | - Yuzhen Ye
- Computer Science Department, Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA
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31
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Chanda D, De D. Meta-analysis reveals obesity associated gut microbial alteration patterns and reproducible contributors of functional shift. Gut Microbes 2024; 16:2304900. [PMID: 38265338 PMCID: PMC10810176 DOI: 10.1080/19490976.2024.2304900] [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: 07/24/2023] [Accepted: 01/09/2024] [Indexed: 01/25/2024] Open
Abstract
The majority of cohort-specific studies associating gut microbiota with obesity are often contradictory; thus, the replicability of the signature remains questionable. Moreover, the species that drive obesity-associated functional shifts and their replicability remain unexplored. Thus, we aimed to address these questions by analyzing gut microbial metagenome sequencing data to develop an in-depth understanding of obese host-gut microbiota interactions using 3329 samples (Obese, n = 1494; Control, n = 1835) from 17 different countries, including both 16S rRNA gene and metagenomic sequence data. Fecal metagenomic data from diverse geographical locations were curated, profiled, and pooled using a machine learning-based approach to identify robust global signatures of obesity. Furthermore, gut microbial species and pathways were systematically integrated through the genomic content of the species to identify contributors to obesity-associated functional shifts. The community structure of the obese gut microbiome was evaluated, and a reproducible depletion of diversity was observed in the obese compared to the lean gut. From this, we infer that the loss of diversity in the obese gut is responsible for perturbations in the healthy microbial functional repertoire. We identified 25 highly predictive species and 37 pathway associations as signatures of obesity, which were validated with remarkably high accuracy (AUC, Species: 0.85, and pathway: 0.80) with an independent validation dataset. We observed a reduction in short-chain fatty acid (SCFA) producers (several Alistipes species, Odoribacter splanchnicus, etc.) and depletion of promoters of gut barrier integrity (Akkermansia muciniphila and Bifidobacterium longum) in obese guts. Our analysis underlines SCFAs and purine/pyrimidine biosynthesis, carbohydrate metabolism pathways in control individuals, and amino acid, enzyme cofactor, and peptidoglycan biosynthesis pathway enrichment in obese individuals. We also mapped the contributors to important obesity-associated functional shifts and observed that these are both dataset-specific and shared across the datasets. In summary, a comprehensive analysis of diverse datasets unveils species specifically contributing to functional shifts and consistent gut microbial patterns associated to obesity.
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Affiliation(s)
- Deep Chanda
- Laboratory of Cellular Differentiation & Metabolic Disorder, Department of Biotechnology, National Institute of Technology, Durgapur, India
| | - Debojyoti De
- Laboratory of Cellular Differentiation & Metabolic Disorder, Department of Biotechnology, National Institute of Technology, Durgapur, India
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32
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Vänni P, Tejesvi MV, Paalanne N, Aagaard K, Ackermann G, Camargo CA, Eggesbø M, Hasegawa K, Hoen AG, Karagas MR, Kolho KL, Laursen MF, Ludvigsson J, Madan J, Ownby D, Stanton C, Stokholm J, Tapiainen T. Machine-learning analysis of cross-study samples according to the gut microbiome in 12 infant cohorts. mSystems 2023; 8:e0036423. [PMID: 37874156 PMCID: PMC10734493 DOI: 10.1128/msystems.00364-23] [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: 04/14/2023] [Accepted: 09/13/2023] [Indexed: 10/25/2023] Open
Abstract
IMPORTANCE There are challenges in merging microbiome data from diverse research groups due to the intricate and multifaceted nature of such data. To address this, we utilized a combination of machine-learning (ML) models to analyze 16S sequencing data from a substantial set of gut microbiome samples, sourced from 12 distinct infant cohorts that were gathered prospectively. Our initial focus was on the mode of delivery due to its prior association with changes in infant gut microbiomes. Through ML analysis, we demonstrated the effective merging and comparison of various gut microbiome data sets, facilitating the identification of robust microbiome biomarkers applicable across varied study populations.
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Affiliation(s)
- Petri Vänni
- Research Unit of Clinical Medicine, University of Oulu, Oulu, Finland
| | - Mysore V. Tejesvi
- Research Unit of Clinical Medicine, University of Oulu, Oulu, Finland
- Ecology and Genetics, Faculty of Science, University of Oulu, Oulu, Finland
| | - Niko Paalanne
- Research Unit of Clinical Medicine, University of Oulu, Oulu, Finland
- Department of Pediatrics and Adolescent Medicine, Oulu University Hospital, University of Oulu, Oulu, Finland
| | - Kjersti Aagaard
- Department of Obstetrics & Gynecology, Division of Maternal-Fetal Medicine, Baylor College of Medicine and Texas Children’s Hospital, Houston, Texas, USA
| | - Gail Ackermann
- Department of Pediatrics, University of California, San Diego, California, USA
| | - Carlos A. Camargo
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Merete Eggesbø
- Department of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Kohei Hasegawa
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Anne G. Hoen
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
| | - Margaret R. Karagas
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
| | - Kaija-Leena Kolho
- Children’s Hospital, University of Helsinki and HUS, Helsinki, Finland
| | - Martin F. Laursen
- National Food Institute, Technical University of Denmark, Lyngby, Denmark
| | - Johnny Ludvigsson
- Crown Princess Victoria Children’s Hospital and Division of Pediatrics, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Juliette Madan
- Department of Psychiatry, Dartmouth Hitchcock Medical Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA
- Department of Pediatrics, Dartmouth Hitchcock Medical Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA
| | - Dennis Ownby
- Medical College of Georgia, Augusta, Georgia, USA
| | - Catherine Stanton
- Teagasc Food Research Centre & APC Microbiome Ireland, Moorepark, Fermoy, Co. Cork, Ireland
| | - Jakob Stokholm
- Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Department of Food Science, University of Copenhagen, Copenhagen, Denmark
| | - Terhi Tapiainen
- Research Unit of Clinical Medicine, University of Oulu, Oulu, Finland
- Department of Obstetrics & Gynecology, Division of Maternal-Fetal Medicine, Baylor College of Medicine and Texas Children’s Hospital, Houston, Texas, USA
- Biocenter Oulu, University of Oulu, Oulu, Finland
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Kowalski K, Żebrowska-Różańska P, Karpiński P, Kujawa D, Łaczmański Ł, Samochowiec J, Chęć M, Piotrowski P, Misiak B. Profiling gut microbiota signatures associated with the deficit subtype of schizophrenia: Findings from a case-control study. Prog Neuropsychopharmacol Biol Psychiatry 2023; 127:110834. [PMID: 37473955 DOI: 10.1016/j.pnpbp.2023.110834] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/12/2023] [Accepted: 07/17/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Previous studies have reported a variety of gut microbiota alterations in patients with schizophrenia. However, none of these studies has investigated gut microbiota in patients with the deficit subtype of schizophrenia (D-SCZ) that can be characterized by primary and enduring negative symptoms. Therefore, in this study we aimed to profile gut microbiota of individuals with D-SCZ, compared to those with non-deficit schizophrenia (ND-SCZ) and healthy controls (HCs). METHODS A total of 115 outpatients (44 individuals with D-SCZ and 71 individuals with ND-SCZ) during remission of positive and disorganization symptoms as well as 120 HCs were enrolled. Gut microbiota was analyzed using the 16 rRNA amplicon sequencing. Additionally, the levels of C-reactive protein (CRP), glucose and lipid metabolism markers were determined in the peripheral blood samples. RESULTS Altogether 14 genera showed differential abundance in patients with D-SCZ compared to ND-SCZ and HCs, including Candidatus Soleaferrea, Eubacterium, Fusobacterium, Lachnospiraceae UCG-002, Lachnospiraceae UCG-004, Lachnospiraceae UCG-010, Libanicoccus, Limosilactobacillus, Mogibacterium, Peptococcus, Prevotella, Prevotellaceae NK3B31 group, Rikenellaceae RC9 gut group, and Slackia after adjustment for potential confounding factors. Observed alterations were significantly associated with cognitive performance in both groups of patients. Moreover, several significant correlations of differentially abundant genera with the levels of CRP, lipid profile parameters, glucose and insulin were found across all subgroups of participants. CONCLUSION Findings from the present study indicate that individuals with D-SCZ show a distinct profile of gut microbiota alterations that is associated with cognitive performance, metabolic parameters and subclinical inflammation.
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Affiliation(s)
- Krzysztof Kowalski
- Department of Psychiatry, Division of Consultation Psychiatry and Neuroscience, Wroclaw Medical University, Wroclaw, Poland
| | - Paulina Żebrowska-Różańska
- Laboratory of Genomics & Bioinformatics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Paweł Karpiński
- Laboratory of Genomics & Bioinformatics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland; Department of Genetics, Wroclaw Medical University, Wroclaw, Poland
| | - Dorota Kujawa
- Laboratory of Genomics & Bioinformatics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Łukasz Łaczmański
- Laboratory of Genomics & Bioinformatics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Jerzy Samochowiec
- Department of Psychiatry, Pomeranian Medical University, Szczecin, Poland
| | - Magdalena Chęć
- Department of Clinical Psychology, Institute of Psychology, University of Szczecin, Szczecin, Poland
| | - Patryk Piotrowski
- Department of Psychiatry, Division of Consultation Psychiatry and Neuroscience, Wroclaw Medical University, Wroclaw, Poland
| | - Błażej Misiak
- Department of Psychiatry, Wroclaw Medical University, Wroclaw, Poland.
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Henderickx JG, Crobach MJ, Terveer EM, Smits WK, Kuijper EJ, Zwittink RD. Fungal and bacterial gut microbiota differ between Clostridioides difficile colonization and infection. MICROBIOME RESEARCH REPORTS 2023; 3:8. [PMID: 38455084 PMCID: PMC10917615 DOI: 10.20517/mrr.2023.52] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/21/2023] [Accepted: 11/30/2023] [Indexed: 03/09/2024]
Abstract
Aim: The bacterial microbiota is well-recognized for its role in Clostridioides difficile colonization and infection, while fungi and yeasts remain understudied. The aim of this study was to analyze the predictive value of the mycobiota and its interactions with the bacterial microbiota in light of C. difficile colonization and infection. Methods: The mycobiota was profiled by ITS2 sequencing of fecal DNA from C. difficile infection (CDI) patients (n = 29), asymptomatically C. difficile colonization (CDC) patients (n = 38), and hospitalized controls with C. difficile negative stool culture (controls; n = 38). Previously published 16S rRNA gene sequencing data of the same cohort were used additionally for machine learning and fungal-bacterial network analysis. Results: CDI patients were characterized by a significantly higher abundance of Candida spp. (MD 0.270 ± 0.089, P = 0.002) and Candida albicans (MD 0.165 ± 0.082, P = 0.023) compared to controls. Additionally, they were deprived of Aspergillus spp. (MD -0.067 ± 0.026, P = 0.000) and Penicillium spp. (MD -0.118 ± 0.043, P = 0.000) compared to CDC patients. Network analysis revealed a positive association between several fungi and bacteria in CDI and CDC, although the analysis did not reveal a direct association between Clostridioides spp. and fungi. Furthermore, the microbiota machine learning model outperformed the models based on the mycobiota and the joint microbiota-mycobiota model. The microbiota classifier successfully distinguished CDI from CDC [Area Under the Receiver Operating Characteristic (AUROC) = 0.884] and CDI from controls (AUROC = 0.905). Blautia and Bifidobacterium were marker genera associated with CDC patients and controls. Conclusion: The gut mycobiota differs between CDI, CDC, and controls and may affect Clostridioides spp. through indirect interactions. The mycobiota data alone could not successfully discriminate CDC from controls or CDI patients and did not have additional predictive value to the bacterial microbiota data. The identification of bacterial marker genera associated with CDC and controls warrants further investigation.
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Affiliation(s)
- Jannie G.E. Henderickx
- Center for Microbiome Analyses and Therapeutics, Department of Medical Microbiology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
- Department of Medical Microbiology and Leiden University Center of Infectious Diseases (LU-CID), Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
| | - Monique J.T. Crobach
- Department of Medical Microbiology and Leiden University Center of Infectious Diseases (LU-CID), Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
| | - Elisabeth M. Terveer
- Department of Medical Microbiology and Leiden University Center of Infectious Diseases (LU-CID), Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
- Netherlands Donor Feces Bank, Department of Medical Microbiology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
| | - Wiep Klaas Smits
- Center for Microbiome Analyses and Therapeutics, Department of Medical Microbiology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
- Department of Medical Microbiology and Leiden University Center of Infectious Diseases (LU-CID), Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
| | - Ed J. Kuijper
- Center for Microbiome Analyses and Therapeutics, Department of Medical Microbiology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
- Department of Medical Microbiology and Leiden University Center of Infectious Diseases (LU-CID), Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
- Netherlands Donor Feces Bank, Department of Medical Microbiology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
| | - Romy D. Zwittink
- Center for Microbiome Analyses and Therapeutics, Department of Medical Microbiology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
- Department of Medical Microbiology and Leiden University Center of Infectious Diseases (LU-CID), Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
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Liao H, Shang J, Sun Y. GDmicro: classifying host disease status with GCN and deep adaptation network based on the human gut microbiome data. Bioinformatics 2023; 39:btad747. [PMID: 38085234 PMCID: PMC10749762 DOI: 10.1093/bioinformatics/btad747] [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: 06/12/2023] [Revised: 11/16/2023] [Accepted: 12/11/2023] [Indexed: 12/27/2023] Open
Abstract
MOTIVATION With advances in metagenomic sequencing technologies, there are accumulating studies revealing the associations between the human gut microbiome and some human diseases. These associations shed light on using gut microbiome data to distinguish case and control samples of a specific disease, which is also called host disease status classification. Importantly, using learning-based models to distinguish the disease and control samples is expected to identify important biomarkers more accurately than abundance-based statistical analysis. However, available tools have not fully addressed two challenges associated with this task: limited labeled microbiome data and decreased accuracy in cross-studies. The confounding factors, such as the diet, technical biases in sample collection/sequencing across different studies/cohorts often jeopardize the generalization of the learning model. RESULTS To address these challenges, we develop a new tool GDmicro, which combines semi-supervised learning and domain adaptation to achieve a more generalized model using limited labeled samples. We evaluated GDmicro on human gut microbiome data from 11 cohorts covering 5 different diseases. The results show that GDmicro has better performance and robustness than state-of-the-art tools. In particular, it improves the AUC from 0.783 to 0.949 in identifying inflammatory bowel disease. Furthermore, GDmicro can identify potential biomarkers with greater accuracy than abundance-based statistical analysis methods. It also reveals the contribution of these biomarkers to the host's disease status. AVAILABILITY AND IMPLEMENTATION https://github.com/liaoherui/GDmicro.
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Affiliation(s)
- Herui Liao
- Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong (SAR), 518057, China
| | - Jiayu Shang
- Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong (SAR), 518057, China
| | - Yanni Sun
- Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong (SAR), 518057, China
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36
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Yersin S, Garneau JR, Schneeberger PHH, Osman KA, Cercamondi CI, Muhummed AM, Tschopp R, Zinsstag J, Vonaesch P. Gut microbiomes of agropastoral children from the Adadle region of Ethiopia reflect their unique dietary habits. Sci Rep 2023; 13:21342. [PMID: 38049420 PMCID: PMC10696028 DOI: 10.1038/s41598-023-47748-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 11/17/2023] [Indexed: 12/06/2023] Open
Abstract
The composition and function of the intestinal microbiota are major determinants of human health and are strongly influenced by diet, antibiotic treatment, lifestyle and geography. Nevertheless, we currently have only little data on microbiomes of non-westernized communities. We assess the stool microbiota composition in 59 children aged 2-5 years from the Adadle district of Ethiopia, Somali Regional State. Here, milk and starch-rich food are predominant components of the local diet, where the inhabitants live a remote, traditional agropastoral lifestyle. Microbiota composition, function and the resistome were characterized by both 16S rRNA gene amplicon and shotgun metagenomic sequencing and compared to 1471 publicly available datasets from children living in traditional, transitional, and industrial communities with different subsistence strategies. Samples from the Adadle district are low in Bacteroidaceae, and Prevotellaceae, the main bacterial representatives in the feces of children living in industrialized and non-industrialized communities, respectively. In contrast, they had a higher relative abundance in Streptococcaceae, Bifidobacteriaceae and Erysipelatoclostridiaceae. Further, genes involved in degradation pathways of lactose, D-galactose and simple carbohydrates were enriched. Overall, our study revealed a unique composition of the fecal microbiota of these agropastoral children, highlighting the need to further characterize the fecal bacterial composition of human populations living different lifestyles.
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Affiliation(s)
- Simon Yersin
- Department of Fundamental Microbiology, University of Lausanne, 1015, Lausanne, Switzerland
| | - Julian R Garneau
- Department of Fundamental Microbiology, University of Lausanne, 1015, Lausanne, Switzerland
| | - Pierre H H Schneeberger
- Helminth Drug Development Unit, Swiss Tropical and Public Health Institute, Kreuzstrasse 2, 4123, Allschwil, Switzerland
- University of Basel, Petersplatz 1, 4001, Basel, Switzerland
| | | | - Colin Ivano Cercamondi
- Department of Health Sciences and Technology, ETHZ, Rämistrasse 101, 8092, Zurich, Switzerland
| | - Abdifatah Muktar Muhummed
- University of Basel, Petersplatz 1, 4001, Basel, Switzerland
- Jigjiga University, Jigjiga, Ethiopia
- Human and Animal Health Unit, Swiss Tropical and Public Health Institute, Kreuzstrasse 2, 4123, Allschwil, Switzerland
| | - Rea Tschopp
- University of Basel, Petersplatz 1, 4001, Basel, Switzerland
- Human and Animal Health Unit, Swiss Tropical and Public Health Institute, Kreuzstrasse 2, 4123, Allschwil, Switzerland
- Armauer Hansen Research Institute, Jimma Road, 1005, Addis Ababa, Ethiopia
| | - Jakob Zinsstag
- University of Basel, Petersplatz 1, 4001, Basel, Switzerland
- Human and Animal Health Unit, Swiss Tropical and Public Health Institute, Kreuzstrasse 2, 4123, Allschwil, Switzerland
| | - Pascale Vonaesch
- Department of Fundamental Microbiology, University of Lausanne, 1015, Lausanne, Switzerland.
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Angelova IY, Kovtun AS, Averina OV, Koshenko TA, Danilenko VN. Unveiling the Connection between Microbiota and Depressive Disorder through Machine Learning. Int J Mol Sci 2023; 24:16459. [PMID: 38003647 PMCID: PMC10671666 DOI: 10.3390/ijms242216459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023] Open
Abstract
In the last few years, investigation of the gut-brain axis and the connection between the gut microbiota and the human nervous system and mental health has become one of the most popular topics. Correlations between the taxonomic and functional changes in gut microbiota and major depressive disorder have been shown in several studies. Machine learning provides a promising approach to analyze large-scale metagenomic data and identify biomarkers associated with depression. In this work, machine learning algorithms, such as random forest, elastic net, and You Only Look Once (YOLO), were utilized to detect significant features in microbiome samples and classify individuals based on their disorder status. The analysis was conducted on metagenomic data obtained during the study of gut microbiota of healthy people and patients with major depressive disorder. The YOLO method showed the greatest effectiveness in the analysis of the metagenomic samples and confirmed the experimental results on the critical importance of a reduction in the amount of Faecalibacterium prausnitzii for the manifestation of depression. These findings could contribute to a better understanding of the role of the gut microbiota in major depressive disorder and potentially lead the way for novel diagnostic and therapeutic strategies.
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Affiliation(s)
- Irina Y. Angelova
- Vavilov Institute of General Genetics, Russian Academy of Sciences (RAS), 119333 Moscow, Russia; (A.S.K.); (O.V.A.); (V.N.D.)
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Gimenes de Castro B, Mari Fredi B, Dos Santos Bezerra R, Alcantara QA, Milani Neme CE, Mascarelli DE, Carvalho Tahyra AS, Dos-Santos D, Nappi CR, Santos de Oliveira F, Pereira Freire F, Ballestero G, Menuci Lima JB, de Andrade Bolsoni J, Lourenço Gebenlian J, Lopes Bibo N, Soares Silva N, de Carvalho Santos N, Simionatto Zucherato V, Chagas Peronni K, Guariz Pinheiro D, Dias-Neto E, Gambero Gaspar G, Roberto Bollela V, da Silva Silveira V, Maria Fontes A, Maria Martinez-Rossi N, Nanev Slavov S, Paulo Bianchi Ximenez J, Barbosa F, Araújo Silva W. Metabarcoding approach to identify bacterial community profiling related to nosocomial infection and bacterial trafficking-routes in hospital environments. JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH. PART A 2023; 86:803-815. [PMID: 37565650 DOI: 10.1080/15287394.2023.2243978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Nosocomial infections (NIs) appear in patients under medical care in the hospital. The surveillance of the bacterial communities employing high-resolution 16S rRNA profiling, known as metabarcoding, represents a reliable method to establish factors that may influence the composition of the bacterial population during NIs. The present study aimed to utilize high-resolution 16S rRNA profiling to identify high bacterial diversity by analyzing 11 inside and 10 outside environments from the General Hospital of Ribeirão Preto Medical School, Brazil. Our results identified a high bacterial diversity, and among these, the most abundant bacterial genera linked to NIs were Cutibacterium, Streptococcus, Staphylococcus, and Corynebacterium. A Acinetobacter was detected in cafeterias, bus stops, and adult and pediatric intensive care units (ICUs). Data suggest an association between transport and alimentation areas proximal to the hospital ICU environment. Interestingly, the correlation and clusterization analysis showed the potential of the external areas to directly influence the ICU pediatric department microbial community, including the outpatient's clinic, visitor halls, patient reception, and the closest cafeterias. Our results demonstrate that high-resolution 16S rRNA profiling is a robust and reliable tool for bacterial genomic surveillance. In addition, the metabarcoding approach might help elaborate decontamination policies, and consequently reduce NIs.
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Affiliation(s)
| | - Bruno Mari Fredi
- Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Rafael Dos Santos Bezerra
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
- Regional Blood Center, General Hospital of Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Queren Apuque Alcantara
- Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
- Center for Medical Genomics, General Hospital of Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | | | | | | | - Douglas Dos-Santos
- Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Camilla Rizzo Nappi
- Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | | | | | - Giulia Ballestero
- Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | | | | | | | - Naira Lopes Bibo
- Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | | | | | | | - Kamila Chagas Peronni
- Department of the Research and Innovation, Institute for Cancer Research, Guarapuava, Parana, Brazil
| | - Daniel Guariz Pinheiro
- Department of Technology, School of Agricultural and Veterinarian Sciences, São Paulo State University, Jaboticabal, São Paulo, Brazil
| | - Emmanuel Dias-Neto
- Laboratory of Medical Genomics, International Research Center, A.C. Camargo Cancer Center, Jaboticabal, São Paulo, Brazil
| | - Gilberto Gambero Gaspar
- Department of Internal Medicine, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Valdes Roberto Bollela
- Department of Internal Medicine, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Vanessa da Silva Silveira
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Aparecida Maria Fontes
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Nilce Maria Martinez-Rossi
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Svetoslav Nanev Slavov
- Regional Blood Center, General Hospital of Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - João Paulo Bianchi Ximenez
- Department of Clinical Analyses, Toxicology and Food Science, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Fernando Barbosa
- Department of Clinical Analyses, Toxicology and Food Science, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Wilson Araújo Silva
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
- Regional Blood Center, General Hospital of Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, SP, Brazil
- Department of the Research and Innovation, Institute for Cancer Research, Guarapuava, Parana, Brazil
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Jang B, Chung MG, Lee DS. Association between gut microbial change and acute gastrointestinal toxicity in patients with prostate cancer receiving definitive radiation therapy. Cancer Med 2023; 12:20727-20735. [PMID: 37921267 PMCID: PMC10709749 DOI: 10.1002/cam4.6636] [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: 07/28/2023] [Revised: 09/25/2023] [Accepted: 10/04/2023] [Indexed: 11/04/2023] Open
Abstract
BACKGROUND This prospective study investigated the association between gut microbial changes and acute gastrointestinal toxicities in prostate cancer patients receiving definitive radiation therapy (RT). METHODS Seventy-nine fecal samples were analyzed. Stool samples were collected at the following timepoints: pre-RT (prRT), 2 weeks after the start of RT (RT-2w), 5 weeks after the start of RT (RT-5w), 1 month after completion of RT (poRT-1 m), and 3 months after completion of RT (poRT-3 m). We computed the microbial community polarization index (MCPI) as an indicator of RT-induced dysbiosis. RESULTS Patients experiencing toxicity had lower alpha diversity, especially at RT-2w (p = 0.037) and RT-5w (p = 0.003). Compared to patients without toxicity, the MCPI in those experiencing toxicities was significantly elevated (p = 0.019). In terms of predicted metabolic pathways, we found linearly decreasing pathways, including carbon fixation pathways in prokaryotes (p = 0.035) and the bacterial secretion system (p = 0.005), in patients who experienced toxicities. CONCLUSIONS We showed RT-induced dysbiosis among patients who experienced toxicities. Reduced diversity and elevated RT-related MCPI could be helpfully used for developing individualized RT approaches.
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Affiliation(s)
- Bum‐Sup Jang
- Department of Radiation OncologyCollege of MedicineSeoul National UniversitySeoulKorea
| | - Moon Gyu Chung
- Microbiome centerKorea Research Institute of Bio‐medical ScienceDaejeonKorea
| | - Dong Soo Lee
- Department of Radiation Oncology, College of MedicineThe Catholic University of KoreaSeoulKorea
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Wen T, Niu G, Chen T, Shen Q, Yuan J, Liu YX. The best practice for microbiome analysis using R. Protein Cell 2023; 14:713-725. [PMID: 37128855 PMCID: PMC10599642 DOI: 10.1093/procel/pwad024] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 04/02/2023] [Indexed: 05/03/2023] Open
Abstract
With the gradual maturity of sequencing technology, many microbiome studies have published, driving the emergence and advance of related analysis tools. R language is the widely used platform for microbiome data analysis for powerful functions. However, tens of thousands of R packages and numerous similar analysis tools have brought major challenges for many researchers to explore microbiome data. How to choose suitable, efficient, convenient, and easy-to-learn tools from the numerous R packages has become a problem for many microbiome researchers. We have organized 324 common R packages for microbiome analysis and classified them according to application categories (diversity, difference, biomarker, correlation and network, functional prediction, and others), which could help researchers quickly find relevant R packages for microbiome analysis. Furthermore, we systematically sorted the integrated R packages (phyloseq, microbiome, MicrobiomeAnalystR, Animalcules, microeco, and amplicon) for microbiome analysis, and summarized the advantages and limitations, which will help researchers choose the appropriate tools. Finally, we thoroughly reviewed the R packages for microbiome analysis, summarized most of the common analysis content in the microbiome, and formed the most suitable pipeline for microbiome analysis. This paper is accompanied by hundreds of examples with 10,000 lines codes in GitHub, which can help beginners to learn, also help analysts compare and test different tools. This paper systematically sorts the application of R in microbiome, providing an important theoretical basis and practical reference for the development of better microbiome tools in the future. All the code is available at GitHub github.com/taowenmicro/EasyMicrobiomeR.
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Affiliation(s)
- Tao Wen
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
- The Key Laboratory of Plant Immunity Jiangsu Provincial Key Lab for Organic Solid Waste Utilization Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing 210095, China
| | - Guoqing Niu
- The Key Laboratory of Plant Immunity Jiangsu Provincial Key Lab for Organic Solid Waste Utilization Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing 210095, China
| | - Tong Chen
- National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Qirong Shen
- The Key Laboratory of Plant Immunity Jiangsu Provincial Key Lab for Organic Solid Waste Utilization Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing 210095, China
| | - Jun Yuan
- The Key Laboratory of Plant Immunity Jiangsu Provincial Key Lab for Organic Solid Waste Utilization Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, Nanjing 210095, China
| | - Yong-Xin Liu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
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Shi Z, Hu G, Li MW, Zhang L, Li X, Li L, Wang X, Fu X, Sun Z, Zhang X, Tian L, Li Z, Chen WH, Zhang M. Gut microbiota as non-invasive diagnostic and prognostic biomarkers for natural killer/T-cell lymphoma. Gut 2023; 72:1999-2002. [PMID: 36347595 PMCID: PMC10511952 DOI: 10.1136/gutjnl-2022-328256] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 09/30/2022] [Indexed: 11/09/2022]
Affiliation(s)
- Zhuangzhuang Shi
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Guoru Hu
- Department of Bioinformatics and Systems Biology, Huazhong University of Science and Technology College of Life Sciences and Technology, Wuhan, Hubei, China
| | - Min W Li
- Department of Bioinformatics and Systems Biology, Huazhong University of Science and Technology College of Life Sciences and Technology, Wuhan, Hubei, China
| | - Lei Zhang
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Lymphoma Diagnosis and Treatment Centre of Henan Province, Zhengzhou, Henan, China
| | - Xin Li
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Lymphoma Diagnosis and Treatment Centre of Henan Province, Zhengzhou, Henan, China
| | - Ling Li
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Lymphoma Diagnosis and Treatment Centre of Henan Province, Zhengzhou, Henan, China
| | - Xinhua Wang
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Lymphoma Diagnosis and Treatment Centre of Henan Province, Zhengzhou, Henan, China
| | - Xiaorui Fu
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Lymphoma Diagnosis and Treatment Centre of Henan Province, Zhengzhou, Henan, China
| | - Zhenchang Sun
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Lymphoma Diagnosis and Treatment Centre of Henan Province, Zhengzhou, Henan, China
| | - Xudong Zhang
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Lymphoma Diagnosis and Treatment Centre of Henan Province, Zhengzhou, Henan, China
| | - Li Tian
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Lymphoma Diagnosis and Treatment Centre of Henan Province, Zhengzhou, Henan, China
| | - Zhaoming Li
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Lymphoma Diagnosis and Treatment Centre of Henan Province, Zhengzhou, Henan, China
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Academy of Medical Sciences of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Wei-Hua Chen
- Department of Bioinformatics and Systems Biology, Huazhong University of Science and Technology College of Life Sciences and Technology, Wuhan, Hubei, China
- Institution of Medical Artificial Intelligence, Binzhou Medical University, Yantai, Shandong, China
- College of Life Science, Henan Normal University, Xinxiang, Henan, China
| | - Mingzhi Zhang
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Lymphoma Diagnosis and Treatment Centre of Henan Province, Zhengzhou, Henan, China
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
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42
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Cui Z, Wu Y, Zhang QH, Wang SG, He Y, Huang DS. MV-CVIB: a microbiome-based multi-view convolutional variational information bottleneck for predicting metastatic colorectal cancer. Front Microbiol 2023; 14:1238199. [PMID: 37675425 PMCID: PMC10477591 DOI: 10.3389/fmicb.2023.1238199] [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: 06/11/2023] [Accepted: 08/02/2023] [Indexed: 09/08/2023] Open
Abstract
Introduction Imbalances in gut microbes have been implied in many human diseases, including colorectal cancer (CRC), inflammatory bowel disease, type 2 diabetes, obesity, autism, and Alzheimer's disease. Compared with other human diseases, CRC is a gastrointestinal malignancy with high mortality and a high probability of metastasis. However, current studies mainly focus on the prediction of colorectal cancer while neglecting the more serious malignancy of metastatic colorectal cancer (mCRC). In addition, high dimensionality and small samples lead to the complexity of gut microbial data, which increases the difficulty of traditional machine learning models. Methods To address these challenges, we collected and processed 16S rRNA data and calculated abundance data from patients with non-metastatic colorectal cancer (non-mCRC) and mCRC. Different from the traditional health-disease classification strategy, we adopted a novel disease-disease classification strategy and proposed a microbiome-based multi-view convolutional variational information bottleneck (MV-CVIB). Results The experimental results show that MV-CVIB can effectively predict mCRC. This model can achieve AUC values above 0.9 compared to other state-of-the-art models. Not only that, MV-CVIB also achieved satisfactory predictive performance on multiple published CRC gut microbiome datasets. Discussion Finally, multiple gut microbiota analyses were used to elucidate communities and differences between mCRC and non-mCRC, and the metastatic properties of CRC were assessed by patient age and microbiota expression.
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Affiliation(s)
- Zhen Cui
- Institute of Machine Learning and Systems Biology, College of Electronics and Information Engineering, Tongji University, Shanghai, China
| | - Yan Wu
- College of Electronics and Information Engineering, Tongji University, Shanghai, China
| | - Qin-Hu Zhang
- EIT Institute for Advanced Study, Ningbo, Zhejiang, China
| | - Si-Guo Wang
- Institute of Machine Learning and Systems Biology, College of Electronics and Information Engineering, Tongji University, Shanghai, China
| | - Ying He
- Institute of Machine Learning and Systems Biology, College of Electronics and Information Engineering, Tongji University, Shanghai, China
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Karpiński P, Żebrowska-Różańska P, Kujawa D, Łaczmański Ł, Samochowiec J, Jabłoński M, Plichta P, Piotrowski P, Bielawski T, Misiak B. Gut microbiota alterations in schizophrenia might be related to stress exposure: Findings from the machine learning analysis. Psychoneuroendocrinology 2023; 155:106335. [PMID: 37467542 DOI: 10.1016/j.psyneuen.2023.106335] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 06/04/2023] [Accepted: 07/11/2023] [Indexed: 07/21/2023]
Abstract
Specific mechanisms underlying gut microbiota alterations in schizophrenia remain unknown. We aimed to compare gut microbiota between patients with schizophrenia and controls, taking into consideration exposure stress across lifespan, dietary habits, metabolic parameters and clinical manifestation. A total of 142 participants, including 89 patients with schizophrenia and 52 controls, were recruited. Gut microbiota were analyzed using the 16 S rRNA sequencing. Additionally, biochemical parameters related to glucose homeostasis, lipid profile and inflammation were assessed. Increased abundance of Lactobacillus and Limosilactobacillus as well as decreased abundance of Faecalibacterium and Paraprevotella were found in patients with schizophrenia. The machine learning analysis demonstrated that between-group differences in gut microbiota were associated with psychosocial stress (a history of childhood trauma, greater cumulative exposure to stress across lifespan and higher level of perceived stress), poor nutrition (lower consumption of vegetables and fish products), lipid profile alterations (lower levels of high-density lipoproteins) and cognitive impairment (worse performance of attention). Our findings indicate that gut microbiota alterations in patients with schizophrenia, including increased abundance of lactic acid bacteria (Lactobacillus and Limosilactobacillus) and decreased abundance of bacteria producing short-chain fatty acids (Faecalibacterium and Paraprevotella) might be associated with exposure to stress, poor dietary habits, lipid profile alterations and cognitive impairment.
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Affiliation(s)
- Paweł Karpiński
- Department of Genetics, Wroclaw Medical University, Wroclaw, Poland; Laboratory of Genomics & Bioinformatics, Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Paulina Żebrowska-Różańska
- Laboratory of Genomics & Bioinformatics, Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Dorota Kujawa
- Laboratory of Genomics & Bioinformatics, Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Łukasz Łaczmański
- Laboratory of Genomics & Bioinformatics, Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wroclaw, Poland
| | - Jerzy Samochowiec
- Department and Clinic of Psychiatry, Pomeranian Medical University, Szczecin, Poland
| | - Marcin Jabłoński
- Department and Clinic of Psychiatry, Pomeranian Medical University, Szczecin, Poland
| | - Piotr Plichta
- Department and Clinic of Psychiatry, Pomeranian Medical University, Szczecin, Poland
| | - Patryk Piotrowski
- Department of Psychiatry, Division of Consultation Psychiatry and Neuroscience, Wroclaw Medical University, Wroclaw, Poland
| | - Tomasz Bielawski
- Department and Clinic of Psychiatry, Wroclaw Medical University, Wroclaw, Poland
| | - Błażej Misiak
- Department of Psychiatry, Division of Consultation Psychiatry and Neuroscience, Wroclaw Medical University, Wroclaw, Poland.
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44
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Nguyen LH, Okin D, Drew DA, Battista VM, Jesudasen SJ, Kuntz TM, Bhosle A, Thompson KN, Reinicke T, Lo CH, Woo JE, Caraballo A, Berra L, Vieira J, Huang CY, Das Adhikari U, Kim M, Sui HY, Magicheva-Gupta M, McIver L, Goldberg MB, Kwon DS, Huttenhower C, Chan AT, Lai PS. Metagenomic assessment of gut microbial communities and risk of severe COVID-19. Genome Med 2023; 15:49. [PMID: 37438797 DOI: 10.1186/s13073-023-01202-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 06/13/2023] [Indexed: 07/14/2023] Open
Abstract
BACKGROUND The gut microbiome is a critical modulator of host immunity and is linked to the immune response to respiratory viral infections. However, few studies have gone beyond describing broad compositional alterations in severe COVID-19, defined as acute respiratory or other organ failure. METHODS We profiled 127 hospitalized patients with COVID-19 (n = 79 with severe COVID-19 and 48 with moderate) who collectively provided 241 stool samples from April 2020 to May 2021 to identify links between COVID-19 severity and gut microbial taxa, their biochemical pathways, and stool metabolites. RESULTS Forty-eight species were associated with severe disease after accounting for antibiotic use, age, sex, and various comorbidities. These included significant in-hospital depletions of Fusicatenibacter saccharivorans and Roseburia hominis, each previously linked to post-acute COVID syndrome or "long COVID," suggesting these microbes may serve as early biomarkers for the eventual development of long COVID. A random forest classifier achieved excellent performance when tasked with classifying whether stool was obtained from patients with severe vs. moderate COVID-19, a finding that was externally validated in an independent cohort. Dedicated network analyses demonstrated fragile microbial ecology in severe disease, characterized by fracturing of clusters and reduced negative selection. We also observed shifts in predicted stool metabolite pools, implicating perturbed bile acid metabolism in severe disease. CONCLUSIONS Here, we show that the gut microbiome differentiates individuals with a more severe disease course after infection with COVID-19 and offer several tractable and biologically plausible mechanisms through which gut microbial communities may influence COVID-19 disease course. Further studies are needed to expand upon these observations to better leverage the gut microbiome as a potential biomarker for disease severity and as a target for therapeutic intervention.
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Affiliation(s)
- Long H Nguyen
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Daniel Okin
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - David A Drew
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Vincent M Battista
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sirus J Jesudasen
- Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Thomas M Kuntz
- Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Amrisha Bhosle
- Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Kelsey N Thompson
- Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Trenton Reinicke
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Chun-Han Lo
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jacqueline E Woo
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Alexander Caraballo
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Lorenzo Berra
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jacob Vieira
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ching-Ying Huang
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Minsik Kim
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Hui-Yu Sui
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Marina Magicheva-Gupta
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Lauren McIver
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Marcia B Goldberg
- Division of Infectious Disease, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Microbiology, Harvard Medical School, Boston, MA, USA
| | - Douglas S Kwon
- Ragon Institute of MGH, Harvard, and MIT, Cambridge, MA, USA
- Division of Infectious Disease, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Curtis Huttenhower
- Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Peggy S Lai
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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45
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Holst AQ, Myers P, Rodríguez-García P, Hermes GDA, Melsaether C, Baker A, Jensen SR, Parschat K. Infant Formula Supplemented with Five Human Milk Oligosaccharides Shifts the Fecal Microbiome of Formula-Fed Infants Closer to That of Breastfed Infants. Nutrients 2023; 15:3087. [PMID: 37513505 PMCID: PMC10383262 DOI: 10.3390/nu15143087] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 06/30/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
Breastmilk is the optimal source of infant nutrition, with short-term and long-term health benefits. Some of these benefits are mediated by human milk oligosaccharides (HMOs), a unique group of carbohydrates representing the third most abundant solid component of human milk. We performed the first clinical study on infant formula supplemented with five different HMOs (5HMO-mix), comprising 2'-fucosyllactose, 3-fucosyllactose, lacto-N-tetraose, 3'-sialyllactose and 6'-sialyllactose at a natural total concentration of 5.75 g/L, and here report the analysis of the infant fecal microbiome. We found an increase in the relative abundance of bifidobacteria in the 5HMO-mix cohort compared with the formula-fed control, specifically affecting bifidobacteria that can produce aromatic lactic acids. 5HMO-mix influenced the microbial composition as early as Week 1, and the observed changes persisted to at least Week 16, including a relative decrease in species with opportunistic pathogenic strains down to the level observed in breastfed infants during the first 4 weeks. We further analyzed the functional potential of the microbiome and observed features shared between 5HMO-mix-supplemented and breastfed infants, such as a relative enrichment in mucus and tyrosine degradation, with the latter possibly being linked to the aromatic lactic acids. The 5HMO-mix supplement, therefore, shifts the infant fecal microbiome closer to that of breastfed infants.
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Affiliation(s)
| | | | | | | | | | - Adam Baker
- Chr. Hansen A/S, 2970 Hoersholm, Denmark
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46
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Manghi P, Blanco-Míguez A, Manara S, NabiNejad A, Cumbo F, Beghini F, Armanini F, Golzato D, Huang KD, Thomas AM, Piccinno G, Punčochář M, Zolfo M, Lesker TR, Bredon M, Planchais J, Glodt J, Valles-Colomer M, Koren O, Pasolli E, Asnicar F, Strowig T, Sokol H, Segata N. MetaPhlAn 4 profiling of unknown species-level genome bins improves the characterization of diet-associated microbiome changes in mice. Cell Rep 2023; 42:112464. [PMID: 37141097 PMCID: PMC10242440 DOI: 10.1016/j.celrep.2023.112464] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 03/10/2023] [Accepted: 04/17/2023] [Indexed: 05/05/2023] Open
Abstract
Mouse models are key tools for investigating host-microbiome interactions. However, shotgun metagenomics can only profile a limited fraction of the mouse gut microbiome. Here, we employ a metagenomic profiling method, MetaPhlAn 4, which exploits a large catalog of metagenome-assembled genomes (including 22,718 metagenome-assembled genomes from mice) to improve the profiling of the mouse gut microbiome. We combine 622 samples from eight public datasets and an additional cohort of 97 mouse microbiomes, and we assess the potential of MetaPhlAn 4 to better identify diet-related changes in the host microbiome using a meta-analysis approach. We find multiple, strong, and reproducible diet-related microbial biomarkers, largely increasing those identifiable by other available methods relying only on reference information. The strongest drivers of the diet-induced changes are uncharacterized and previously undetected taxa, confirming the importance of adopting metagenomic methods integrating metagenomic assemblies for comprehensive profiling.
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Affiliation(s)
- Paolo Manghi
- Department CIBIO, University of Trento, Trento, Italy
| | | | - Serena Manara
- Department CIBIO, University of Trento, Trento, Italy
| | - Amir NabiNejad
- Department CIBIO, University of Trento, Trento, Italy; IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Fabio Cumbo
- Department CIBIO, University of Trento, Trento, Italy
| | | | | | | | - Kun D Huang
- Department CIBIO, University of Trento, Trento, Italy
| | | | | | | | - Moreno Zolfo
- Department CIBIO, University of Trento, Trento, Italy
| | - Till R Lesker
- Department of Microbial Immune Regulation, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Marius Bredon
- Gastroenterology Department, Sorbonne Université, INSERM, Centre de Recherche Saint Antoine, CRSA, AP-HP, Saint Antoine Hospital, 75012 Paris, France; Paris Centre for Microbiome Medicine (PaCeMM) FHU, Paris, France
| | - Julien Planchais
- Paris Centre for Microbiome Medicine (PaCeMM) FHU, Paris, France; INRAE, UMR1319 Micalis & AgroParisTech, Jouy en Josas, France
| | - Jeremy Glodt
- Paris Centre for Microbiome Medicine (PaCeMM) FHU, Paris, France; INRAE, UMR1319 Micalis & AgroParisTech, Jouy en Josas, France
| | | | - Omry Koren
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | - Edoardo Pasolli
- Department of Agricultural Sciences, University of Naples, Naples, Italy
| | | | - Till Strowig
- Department of Microbial Immune Regulation, Helmholtz Centre for Infection Research, Braunschweig, Germany; Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany
| | - Harry Sokol
- Gastroenterology Department, Sorbonne Université, INSERM, Centre de Recherche Saint Antoine, CRSA, AP-HP, Saint Antoine Hospital, 75012 Paris, France; Paris Centre for Microbiome Medicine (PaCeMM) FHU, Paris, France; INRAE, UMR1319 Micalis & AgroParisTech, Jouy en Josas, France
| | - Nicola Segata
- Department CIBIO, University of Trento, Trento, Italy; IEO, European Institute of Oncology IRCCS, Milan, Italy.
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47
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Goubet AG. Could the tumor-associated microbiota be the new multi-faceted player in the tumor microenvironment? Front Oncol 2023; 13:1185163. [PMID: 37287916 PMCID: PMC10242102 DOI: 10.3389/fonc.2023.1185163] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 05/02/2023] [Indexed: 06/09/2023] Open
Abstract
Microorganisms have been identified in tumor specimens for over a century. It is only in recent years that tumor-associated microbiota has become a rapidly expanding field. Assessment techniques encompass methods at the frontiers of molecular biology, microbiology, and histology, requiring a transdisciplinary process to carefully decipher this new component of the tumor microenvironment. Due to the low biomass, the study of tumor-associated microbiota poses technical, analytical, biological, and clinical challenges and must be approached as a whole. To date, several studies have begun to shed light on the composition, functions, and clinical relevance of the tumor-associated microbiota. This new piece of the tumor microenvironment puzzle could potentially change the way we think about and treat patients with cancer.
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Affiliation(s)
- Anne-Gaëlle Goubet
- Department of Pathology and Immunology, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- AGORA Cancer Research Center, Lausanne, Switzerland
- Swiss Cancer Center Léman, Lausanne, Switzerland
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48
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Markandey M, Bajaj A, Verma M, Virmani S, Singh MK, Gaur P, Das P, Srikanth C, Makharia G, Kedia S, Ahuja V. Fecal microbiota transplantation refurbishes the crypt-associated microbiota in ulcerative colitis. iScience 2023; 26:106738. [PMID: 37216124 PMCID: PMC10192942 DOI: 10.1016/j.isci.2023.106738] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/24/2023] [Accepted: 04/20/2023] [Indexed: 05/24/2023] Open
Abstract
A crypt autochthonous microbial population called crypt-associated microbiota (CAM) is localized intimately with gut regenerative and immune machinery. The present report utilizes laser capture microdissection coupled with 16S amplicon sequencing to characterize the CAM in patients with ulcerative colitis (UC) before and after fecal microbiota transplantation with anti-inflammatory diet (FMT-AID). Compositional differences in CAM and its interactions with mucosa-associated microbiota (MAM) were compared between the non-IBD controls and in patients with UC pre- and post-FMT (n = 26). Distinct from the MAM, CAM is dominated by aerobic members of Actinobacteria and Proteobacteria and exhibits resilience of diversity. CAM underwent UC-associated dysbiosis and demonstrated restoration post-FMT-AID. These FMT-restored CAM taxa correlated negatively with disease activity in patients with UC. The positive effects of FMT-AID extended further in refurbishing CAM-MAM interactions, which were obliterated in UC. These results encourage investigation into host-microbiome interactions established by CAM, to understand their role in disease pathophysiology.
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Affiliation(s)
- Manasvini Markandey
- Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India
| | - Aditya Bajaj
- Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India
| | - Mahak Verma
- Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India
| | - Shubi Virmani
- Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India
| | - Mukesh Kumar Singh
- Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India
| | - Preksha Gaur
- Regional Centre for Biotechnology, 3rd Milestone Gurugram-Faridabad Expressway, Faridabad 121001, India
| | - Prasenjit Das
- Department of Pathology, All India Institute of Medical Sciences, New Delhi, India
| | - C.V. Srikanth
- Regional Centre for Biotechnology, 3rd Milestone Gurugram-Faridabad Expressway, Faridabad 121001, India
| | - Govind Makharia
- Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India
| | - Saurabh Kedia
- Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India
| | - Vineet Ahuja
- Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India
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49
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Seelbinder B, Lohinai Z, Vazquez-Uribe R, Brunke S, Chen X, Mirhakkak M, Lopez-Escalera S, Dome B, Megyesfalvi Z, Berta J, Galffy G, Dulka E, Wellejus A, Weiss GJ, Bauer M, Hube B, Sommer MOA, Panagiotou G. Candida expansion in the gut of lung cancer patients associates with an ecological signature that supports growth under dysbiotic conditions. Nat Commun 2023; 14:2673. [PMID: 37160893 PMCID: PMC10169812 DOI: 10.1038/s41467-023-38058-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 04/11/2023] [Indexed: 05/11/2023] Open
Abstract
Candida species overgrowth in the human gut is considered a prerequisite for invasive candidiasis, but our understanding of gut bacteria promoting or restricting this overgrowth is still limited. By integrating cross-sectional mycobiome and shotgun metagenomics data from the stool of 75 male and female cancer patients at risk but without systemic candidiasis, bacterial communities in high Candida samples display higher metabolic flexibility yet lower contributional diversity than those in low Candida samples. We develop machine learning models that use only bacterial taxa or functional relative abundances to predict the levels of Candida genus and species in an external validation cohort with an AUC of 78.6-81.1%. We propose a mechanism for intestinal Candida overgrowth based on an increase in lactate-producing bacteria, which coincides with a decrease in bacteria that regulate short chain fatty acid and oxygen levels. Under these conditions, the ability of Candida to harness lactate as a nutrient source may enable Candida to outcompete other fungi in the gut.
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Affiliation(s)
- Bastian Seelbinder
- Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology- Hans Knöll Institute, Jena, Germany
| | - Zoltan Lohinai
- National Koranyi Institute of Pulmonology, Budapest, Hungary
- Translational Medicine Institute, Semmelweis University, Budapest, Hungary
| | - Ruben Vazquez-Uribe
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Sascha Brunke
- Microbial Pathogenicity Mechanisms, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Germany
| | - Xiuqiang Chen
- Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology- Hans Knöll Institute, Jena, Germany
| | - Mohammad Mirhakkak
- Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology- Hans Knöll Institute, Jena, Germany
| | - Silvia Lopez-Escalera
- Chr. Hansen A/S, Human Health Innovation, Hoersholm, Denmark
- Faculty of Biological Sciences, Friedrich Schiller University, Jena, Germany
| | - Balazs Dome
- National Koranyi Institute of Pulmonology, Budapest, Hungary
- Department of Thoracic Surgery, Medical University of Vienna, Vienna, Austria
- Department of Thoracic Surgery, National Institute of Oncology-Semmelweis University, Budapest, Hungary
| | - Zsolt Megyesfalvi
- National Koranyi Institute of Pulmonology, Budapest, Hungary
- Department of Thoracic Surgery, Medical University of Vienna, Vienna, Austria
- Department of Thoracic Surgery, National Institute of Oncology-Semmelweis University, Budapest, Hungary
| | - Judit Berta
- National Koranyi Institute of Pulmonology, Budapest, Hungary
| | | | - Edit Dulka
- County Hospital of Torokbalint, Torokbalint, Hungary
| | - Anja Wellejus
- Chr. Hansen A/S, Human Health Innovation, Hoersholm, Denmark
| | - Glen J Weiss
- Department of Medicine, UMass Chan Medical School, Worcester, MA, USA
| | - Michael Bauer
- Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, Jena, Germany
- Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany
| | - Bernhard Hube
- Microbial Pathogenicity Mechanisms, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Germany
- Faculty of Biological Sciences, Friedrich Schiller University, Jena, Germany
| | - Morten O A Sommer
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Gianni Panagiotou
- Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology- Hans Knöll Institute, Jena, Germany.
- Faculty of Biological Sciences, Friedrich Schiller University, Jena, Germany.
- Department of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China.
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50
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Sambruni G, Macandog AD, Wirbel J, Cagnina D, Catozzi C, Dallavilla T, Borgo F, Fazio N, Fumagalli-Romario U, Petz WL, Manzo T, Ravenda SP, Zeller G, Nezi L, Schaefer MH. Location and condition based reconstruction of colon cancer microbiome from human RNA sequencing data. Genome Med 2023; 15:32. [PMID: 37131219 PMCID: PMC10155404 DOI: 10.1186/s13073-023-01180-9] [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: 08/18/2022] [Accepted: 04/13/2023] [Indexed: 05/04/2023] Open
Abstract
BACKGROUND The association between microbes and cancer has been reported repeatedly; however, it is not clear if molecular tumour properties are connected to specific microbial colonisation patterns. This is due mainly to the current technical and analytical strategy limitations to characterise tumour-associated bacteria. METHODS Here, we propose an approach to detect bacterial signals in human RNA sequencing data and associate them with the clinical and molecular properties of the tumours. The method was tested on public datasets from The Cancer Genome Atlas, and its accuracy was assessed on a new cohort of colorectal cancer patients. RESULTS Our analysis shows that intratumoural microbiome composition is correlated with survival, anatomic location, microsatellite instability, consensus molecular subtype and immune cell infiltration in colon tumours. In particular, we find Faecalibacterium prausnitzii, Coprococcus comes, Bacteroides spp., Fusobacterium spp. and Clostridium spp. to be strongly associated with tumour properties. CONCLUSIONS We implemented an approach to concurrently analyse clinical and molecular properties of the tumour as well as the composition of the associated microbiome. Our results may improve patient stratification and pave the path for mechanistic studies on microbiota-tumour crosstalk.
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Affiliation(s)
- Gaia Sambruni
- Department of Experimental Oncology, European Institute of Oncology-IRCCS, Milano, Italy
| | - Angeli D Macandog
- Department of Experimental Oncology, European Institute of Oncology-IRCCS, Milano, Italy
| | - Jakob Wirbel
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Danilo Cagnina
- Department of Experimental Oncology, European Institute of Oncology-IRCCS, Milano, Italy
| | - Carlotta Catozzi
- Department of Experimental Oncology, European Institute of Oncology-IRCCS, Milano, Italy
| | - Tiziano Dallavilla
- Department of Experimental Oncology, European Institute of Oncology-IRCCS, Milano, Italy
| | - Francesca Borgo
- Department of Experimental Oncology, European Institute of Oncology-IRCCS, Milano, Italy
- Center for Omics Sciences, IRCCS San Raffaele Institute, Milano, Italy
| | - Nicola Fazio
- Division of Gastrointestinal Medical Oncology and Neuroendocrine Tumors, European Institute of Oncology-IRCCS, Milano, Italy
| | | | - Wanda L Petz
- Digestive Surgery, European Institute of Oncology-IRCCS, Milano, Italy
| | - Teresa Manzo
- Department of Experimental Oncology, European Institute of Oncology-IRCCS, Milano, Italy
| | - Simona P Ravenda
- Division of Gastrointestinal Medical Oncology and Neuroendocrine Tumors, European Institute of Oncology-IRCCS, Milano, Italy
| | - Georg Zeller
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Luigi Nezi
- Department of Experimental Oncology, European Institute of Oncology-IRCCS, Milano, Italy.
| | - Martin H Schaefer
- Department of Experimental Oncology, European Institute of Oncology-IRCCS, Milano, Italy.
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