1
|
Yang L, Wang Q, He L, Sun X. The critical role of tumor microbiome in cancer immunotherapy. Cancer Biol Ther 2024; 25:2301801. [PMID: 38241173 PMCID: PMC10802201 DOI: 10.1080/15384047.2024.2301801] [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: 10/24/2023] [Accepted: 01/01/2024] [Indexed: 01/21/2024] Open
Abstract
In recent years, the microbiome has shown an integral role in cancer immunotherapy and has become a prominent and widely studied topic. A full understanding of the interactions between the tumor microbiome and various immunotherapies offers opportunities for immunotherapy of cancer. This review scrutinizes the composition of the tumor microbiome, the mechanism of microbial immune regulation, the influence of tumor microorganisms on tumor metastasis, and the interaction between tumor microorganisms and immunotherapy. In addition, this review also summarizes the challenges and opportunities of immunotherapy through tumor microbes, as well as the prospects and directions for future related research. In conclusion, the potential of microbial immunotherapy to enhance treatment outcomes for cancer patients should not be underestimated. Through this review, it is hoped that more research on tumor microbial immunotherapy will be done to better solve the treatment problems of cancer patients.
Collapse
Affiliation(s)
- Liu Yang
- School of Clinical Medicine, The Affiliated Hospital, Southwest Medical University, Luzhou, China
| | - Qi Wang
- Department of Gastroenterology, Affiliated Hospital of Jiangsu University, Jiangsu University, Zhenjiang, China
| | - Lijuan He
- Department of Health Management Center, The Affiliated Hospital, Southwest Medical University, Luzhou, China
| | - Xingyu Sun
- Department of Gynecology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, China
| |
Collapse
|
2
|
Piperni E, Nguyen LH, Manghi P, Kim H, Pasolli E, Andreu-Sánchez S, Arrè A, Bermingham KM, Blanco-Míguez A, Manara S, Valles-Colomer M, Bakker E, Busonero F, Davies R, Fiorillo E, Giordano F, Hadjigeorgiou G, Leeming ER, Lobina M, Masala M, Maschio A, McIver LJ, Pala M, Pitzalis M, Wolf J, Fu J, Zhernakova A, Cacciò SM, Cucca F, Berry SE, Ercolini D, Chan AT, Huttenhower C, Spector TD, Segata N, Asnicar F. Intestinal Blastocystis is linked to healthier diets and more favorable cardiometabolic outcomes in 56,989 individuals from 32 countries. Cell 2024:S0092-8674(24)00692-5. [PMID: 38981480 DOI: 10.1016/j.cell.2024.06.018] [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: 05/12/2023] [Revised: 02/23/2024] [Accepted: 06/11/2024] [Indexed: 07/11/2024]
Abstract
Diet impacts human health, influencing body adiposity and the risk of developing cardiometabolic diseases. The gut microbiome is a key player in the diet-health axis, but while its bacterial fraction is widely studied, the role of micro-eukaryotes, including Blastocystis, is underexplored. We performed a global-scale analysis on 56,989 metagenomes and showed that human Blastocystis exhibits distinct prevalence patterns linked to geography, lifestyle, and dietary habits. Blastocystis presence defined a specific bacterial signature and was positively associated with more favorable cardiometabolic profiles and negatively with obesity (p < 1e-16) and disorders linked to altered gut ecology (p < 1e-8). In a diet intervention study involving 1,124 individuals, improvements in dietary quality were linked to weight loss and increases in Blastocystis prevalence (p = 0.003) and abundance (p < 1e-7). Our findings suggest a potentially beneficial role for Blastocystis, which may help explain personalized host responses to diet and downstream disease etiopathogenesis.
Collapse
Affiliation(s)
- Elisa Piperni
- Department CIBIO, University of Trento, Trento, Italy; IEO, Istituto Europeo di Oncologia IRCSS, Milan, Italy
| | - Long H Nguyen
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA; Harvard Chan Microbiome in Public Health Center, Boston, MA, USA
| | - Paolo Manghi
- Department CIBIO, University of Trento, Trento, Italy
| | - Hanseul Kim
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Edoardo Pasolli
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy
| | - Sergio Andreu-Sánchez
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Pediatrics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Alberto Arrè
- Department CIBIO, University of Trento, Trento, Italy; Zoe Ltd, London, UK
| | - Kate M Bermingham
- Zoe Ltd, London, UK; Department of Nutritional Sciences, King's College London, London, UK
| | | | - Serena Manara
- Department CIBIO, University of Trento, Trento, Italy
| | | | | | - Fabio Busonero
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | | | - Edoardo Fiorillo
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | | | | | - Emily R Leeming
- Department of Twins Research and Genetic Epidemiology, King's College London, London, UK
| | - Monia Lobina
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | - Marco Masala
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | - Andrea Maschio
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | | | - Mauro Pala
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | - Maristella Pitzalis
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
| | | | - Jingyuan Fu
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Pediatrics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Alexandra Zhernakova
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Simone M Cacciò
- Department of Infectious Diseases, Istituto Superiore Di Sanità, Rome, Italy
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy; Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy
| | - Sarah E Berry
- Department of Nutritional Sciences, King's College London, London, UK
| | - Danilo Ercolini
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA; Harvard Chan Microbiome in Public Health Center, Boston, MA, USA
| | - Curtis Huttenhower
- Harvard T.H. Chan School of Public Health, Boston, MA, USA; The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Tim D Spector
- Department of Twins Research and Genetic Epidemiology, King's College London, London, UK
| | - Nicola Segata
- Department CIBIO, University of Trento, Trento, Italy; IEO, Istituto Europeo di Oncologia IRCSS, Milan, Italy; Department of Twins Research and Genetic Epidemiology, King's College London, London, UK.
| | | |
Collapse
|
3
|
Mei Z, Wang F, Bhosle A, Dong D, Mehta R, Ghazi A, Zhang Y, Liu Y, Rinott E, Ma S, Rimm EB, Daviglus M, Willett WC, Knight R, Hu FB, Qi Q, Chan AT, Burk RD, Stampfer MJ, Shai I, Kaplan RC, Huttenhower C, Wang DD. Strain-specific gut microbial signatures in type 2 diabetes identified in a cross-cohort analysis of 8,117 metagenomes. Nat Med 2024:10.1038/s41591-024-03067-7. [PMID: 38918632 DOI: 10.1038/s41591-024-03067-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 05/14/2024] [Indexed: 06/27/2024]
Abstract
The association of gut microbial features with type 2 diabetes (T2D) has been inconsistent due in part to the complexity of this disease and variation in study design. Even in cases in which individual microbial species have been associated with T2D, mechanisms have been unable to be attributed to these associations based on specific microbial strains. We conducted a comprehensive study of the T2D microbiome, analyzing 8,117 shotgun metagenomes from 10 cohorts of individuals with T2D, prediabetes, and normoglycemic status in the United States, Europe, Israel and China. Dysbiosis in 19 phylogenetically diverse species was associated with T2D (false discovery rate < 0.10), for example, enriched Clostridium bolteae and depleted Butyrivibrio crossotus. These microorganisms also contributed to community-level functional changes potentially underlying T2D pathogenesis, for example, perturbations in glucose metabolism. Our study identifies within-species phylogenetic diversity for strains of 27 species that explain inter-individual differences in T2D risk, such as Eubacterium rectale. In some cases, these were explained by strain-specific gene carriage, including loci involved in various mechanisms of horizontal gene transfer and novel biological processes underlying metabolic risk, for example, quorum sensing. In summary, our study provides robust cross-cohort microbial signatures in a strain-resolved manner and offers new mechanistic insights into T2D.
Collapse
Affiliation(s)
- Zhendong Mei
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Fenglei Wang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Amrisha Bhosle
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Danyue Dong
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Raaj Mehta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- 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
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Andrew Ghazi
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yancong Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yuxi Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Ehud Rinott
- Department of Medicine, Hebrew University and Hadassah Medical Center, Jerusalem, Israel
| | - Siyuan Ma
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric B Rimm
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Martha Daviglus
- Institute for Minority Health Research, University of Illinois Chicago, Chicago, IL, USA
| | - Walter C Willett
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Rob Knight
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Frank B Hu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Qibin Qi
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Andrew T Chan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- 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
| | - Robert D Burk
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Obstetrics, Gynecology and Women's Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Meir J Stampfer
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Iris Shai
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Faculty of Health Sciences, The Health and Nutrition Innovative International Research Center, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Curtis Huttenhower
- 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.
- Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Dong D Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| |
Collapse
|
4
|
Byndloss M, Devkota S, Duca F, Niess JH, Nieuwdorp M, Orho-Melander M, Sanz Y, Tremaroli V, Zhao L. The gut microbiota and diabetes: research, translation, and clinical applications - 2023 Diabetes, Diabetes Care, and Diabetologia Expert Forum. Diabetologia 2024:10.1007/s00125-024-06198-1. [PMID: 38910152 DOI: 10.1007/s00125-024-06198-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 05/23/2024] [Indexed: 06/25/2024]
Abstract
This article summarises the state of the science on the role of the gut microbiota (GM) in diabetes from a recent international expert forum organised by Diabetes, Diabetes Care, and Diabetologia, which was held at the European Association for the Study of Diabetes 2023 Annual Meeting in Hamburg, Germany. Forum participants included clinicians and basic scientists who are leading investigators in the field of the intestinal microbiome and metabolism. Their conclusions were as follows: (1) the GM may be involved in the pathophysiology of type 2 diabetes, as microbially produced metabolites associate both positively and negatively with the disease, and mechanistic links of GM functions (e.g. genes for butyrate production) with glucose metabolism have recently emerged through the use of Mendelian randomisation in humans; (2) the highly individualised nature of the GM poses a major research obstacle, and large cohorts and a deep-sequencing metagenomic approach are required for robust assessments of associations and causation; (3) because single time point sampling misses intraindividual GM dynamics, future studies with repeated measures within individuals are needed; and (4) much future research will be required to determine the applicability of this expanding knowledge to diabetes diagnosis and treatment, and novel technologies and improved computational tools will be important to achieve this goal.
Collapse
Affiliation(s)
- Mariana Byndloss
- Vanderbilt University Medical Center, Nashville, TN, USA
- Howard Hughes Medical Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Suzanne Devkota
- Cedars-Sinai Medical Center, Human Microbiome Research Institute, Los Angeles, CA, USA
| | | | - Jan Hendrik Niess
- Department of Biomedicine, University of Basel, Basel, Switzerland
- Department of Gastroenterology and Hepatology, University Digestive Healthcare Center, Clarunis, Basel, Switzerland
| | - Max Nieuwdorp
- Department of Internal and Vascular Medicine, Amsterdam University Medical Centers, Amsterdam, the Netherlands
- Amsterdam Diabeter Center, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Marju Orho-Melander
- Department of Clinical Sciences in Malmö, Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - Yolanda Sanz
- Institute of Agrochemistry and Food Technology, Spanish National Research Council (IATA-CSIC), Valencia, Spain.
| | - Valentina Tremaroli
- Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Liping Zhao
- Department of Biochemistry and Microbiology, School of Environmental and Biological Sciences, Rutgers University, New Brunswick, NJ, USA
| |
Collapse
|
5
|
Lange E, Kranert L, Krüger J, Benndorf D, Heyer R. Microbiome modeling: a beginner's guide. Front Microbiol 2024; 15:1368377. [PMID: 38962127 PMCID: PMC11220171 DOI: 10.3389/fmicb.2024.1368377] [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: 01/10/2024] [Accepted: 05/27/2024] [Indexed: 07/05/2024] Open
Abstract
Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.
Collapse
Affiliation(s)
- Emanuel Lange
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Lena Kranert
- Institute for Automation Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Jacob Krüger
- Engineering of Software-Intensive Systems, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Dirk Benndorf
- Applied Biosciences and Bioprocess Engineering, Anhalt University of Applied Sciences, Köthen, Germany
| | - Robert Heyer
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Multidimensional Omics Data Analysis, Faculty of Technology, Bielefeld University, Bielefeld, Germany
| |
Collapse
|
6
|
Wang B, Luan Y. Evaluation of normalization methods for predicting quantitative phenotypes in metagenomic data analysis. Front Genet 2024; 15:1369628. [PMID: 38903761 PMCID: PMC11188486 DOI: 10.3389/fgene.2024.1369628] [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: 01/12/2024] [Accepted: 05/13/2024] [Indexed: 06/22/2024] Open
Abstract
Genotype-to-phenotype mapping is an essential problem in the current genomic era. While qualitative case-control predictions have received significant attention, less emphasis has been placed on predicting quantitative phenotypes. This emerging field holds great promise in revealing intricate connections between microbial communities and host health. However, the presence of heterogeneity in microbiome datasets poses a substantial challenge to the accuracy of predictions and undermines the reproducibility of models. To tackle this challenge, we investigated 22 normalization methods that aimed at removing heterogeneity across multiple datasets, conducted a comprehensive review of them, and evaluated their effectiveness in predicting quantitative phenotypes in three simulation scenarios and 31 real datasets. The results indicate that none of these methods demonstrate significant superiority in predicting quantitative phenotypes or attain a noteworthy reduction in Root Mean Squared Error (RMSE) of the predictions. Given the frequent occurrence of batch effects and the satisfactory performance of batch correction methods in predicting datasets affected by these effects, we strongly recommend utilizing batch correction methods as the initial step in predicting quantitative phenotypes. In summary, the performance of normalization methods in predicting metagenomic data remains a dynamic and ongoing research area. Our study contributes to this field by undertaking a comprehensive evaluation of diverse methods and offering valuable insights into their effectiveness in predicting quantitative phenotypes.
Collapse
Affiliation(s)
- Beibei Wang
- Frontier Science Center for Nonlinear Expectations, Ministry of Education, Qingdao, China
- Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China
- School of Mathematics, Shandong University, Jinan, China
| | - Yihui Luan
- Frontier Science Center for Nonlinear Expectations, Ministry of Education, Qingdao, China
- Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China
- School of Mathematics, Shandong University, Jinan, China
| |
Collapse
|
7
|
Tamayo M, Olivares M, Ruas-Madiedo P, Margolles A, Espín JC, Medina I, Moreno-Arribas MV, Canals S, Mirasso CR, Ortín S, Beltrán-Sanchez H, Palloni A, Tomás-Barberán FA, Sanz Y. How Diet and Lifestyle Can Fine-Tune Gut Microbiomes for Healthy Aging. Annu Rev Food Sci Technol 2024; 15:283-305. [PMID: 38941492 DOI: 10.1146/annurev-food-072023-034458] [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: 06/30/2024]
Abstract
Many physical, social, and psychological changes occur during aging that raise the risk of developing chronic diseases, frailty, and dependency. These changes adversely affect the gut microbiota, a phenomenon known as microbe-aging. Those microbiota alterations are, in turn, associated with the development of age-related diseases. The gut microbiota is highly responsive to lifestyle and dietary changes, displaying a flexibility that also provides anactionable tool by which healthy aging can be promoted. This review covers, firstly, the main lifestyle and socioeconomic factors that modify the gut microbiota composition and function during healthy or unhealthy aging and, secondly, the advances being made in defining and promoting healthy aging, including microbiome-informed artificial intelligence tools, personalized dietary patterns, and food probiotic systems.
Collapse
Affiliation(s)
- M Tamayo
- Institute of Agrochemistry and Food Technology, Spanish National Research Council (IATA-CSIC), Valencia, Spain;
- Faculty of Medicine, Autonomous University of Madrid (UAM), Spain
| | - M Olivares
- Institute of Agrochemistry and Food Technology, Spanish National Research Council (IATA-CSIC), Valencia, Spain;
| | | | - A Margolles
- Health Research Institute (ISPA), Asturias, Spain
| | - J C Espín
- Laboratory of Food & Health, Group of Quality, Safety, and Bioactivity of Plant Foods, Centro de Edafología y Biología Aplicada del Segura (CEBAS-CSIC), Murcia, Spain
| | - I Medina
- Instituto de Investigaciones Marinas, Spanish National Research Council (IIM-CSIC), Vigo, Spain
| | | | - S Canals
- Instituto de Neurociencias, Universidad Miguel Hernández-CSIC, Sant Joan d'Alacant, Spain
| | - C R Mirasso
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, Palma de Mallorca, Spain
| | - S Ortín
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, Palma de Mallorca, Spain
| | - H Beltrán-Sanchez
- Department of Community Health Sciences, Fielding School of Public Health and California Center for Population Research, University of California, Los Angeles, California, USA
| | - A Palloni
- Department of Sociology, University of Wisconsin, Madison, Wisconsin, USA
| | - F A Tomás-Barberán
- Laboratory of Food & Health, Group of Quality, Safety, and Bioactivity of Plant Foods, Centro de Edafología y Biología Aplicada del Segura (CEBAS-CSIC), Murcia, Spain
| | - Y Sanz
- Institute of Agrochemistry and Food Technology, Spanish National Research Council (IATA-CSIC), Valencia, Spain;
| |
Collapse
|
8
|
Teixeira M, Silva F, Ferreira RM, Pereira T, Figueiredo C, Oliveira HP. A review of machine learning methods for cancer characterization from microbiome data. NPJ Precis Oncol 2024; 8:123. [PMID: 38816569 PMCID: PMC11139966 DOI: 10.1038/s41698-024-00617-7] [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: 01/15/2024] [Accepted: 05/17/2024] [Indexed: 06/01/2024] Open
Abstract
Recent studies have shown that the microbiome can impact cancer development, progression, and response to therapies suggesting microbiome-based approaches for cancer characterization. As cancer-related signatures are complex and implicate many taxa, their discovery often requires Machine Learning approaches. This review discusses Machine Learning methods for cancer characterization from microbiome data. It focuses on the implications of choices undertaken during sample collection, feature selection and pre-processing. It also discusses ML model selection, guiding how to choose an ML model, and model validation. Finally, it enumerates current limitations and how these may be surpassed. Proposed methods, often based on Random Forests, show promising results, however insufficient for widespread clinical usage. Studies often report conflicting results mainly due to ML models with poor generalizability. We expect that evaluating models with expanded, hold-out datasets, removing technical artifacts, exploring representations of the microbiome other than taxonomical profiles, leveraging advances in deep learning, and developing ML models better adapted to the characteristics of microbiome data will improve the performance and generalizability of models and enable their usage in the clinic.
Collapse
Affiliation(s)
- Marco Teixeira
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal.
- Faculty of Engineering, University of Porto, Porto, Portugal.
| | - Francisco Silva
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
- Faculty of Science, University of Porto, Porto, Portugal
| | - Rui M Ferreira
- Ipatimup - Institute of Molecular Pathology and Immunology of the University of Porto, Porto, Portugal
- Instituto de Investigação e Inovação em Saúde, University of Porto, Porto, Portugal
| | - Tania Pereira
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
- Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal
| | - Ceu Figueiredo
- Ipatimup - Institute of Molecular Pathology and Immunology of the University of Porto, Porto, Portugal
- Instituto de Investigação e Inovação em Saúde, University of Porto, Porto, Portugal
- Faculty of Medicine, University of Porto, Porto, Portugal
| | - Hélder P Oliveira
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
- Faculty of Science, University of Porto, Porto, Portugal
| |
Collapse
|
9
|
De Filippis F, Valentino V, Sequino G, Borriello G, Riccardi MG, Pierri B, Cerino P, Pizzolante A, Pasolli E, Esposito M, Limone A, Ercolini D. Exposure to environmental pollutants selects for xenobiotic-degrading functions in the human gut microbiome. Nat Commun 2024; 15:4482. [PMID: 38802370 PMCID: PMC11130323 DOI: 10.1038/s41467-024-48739-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 05/08/2024] [Indexed: 05/29/2024] Open
Abstract
Environmental pollutants from different chemical families may reach the gut microbiome, where they can be metabolized and transformed. However, how our gut symbionts respond to the exposure to environmental pollution is still underexplored. In this observational, cohort study, we aim to investigate the influence of environmental pollution on the gut microbiome composition and potential activity by shotgun metagenomics. We select as a case study a population living in a highly polluted area in Campania region (Southern Italy), proposed as an ideal field for exposomic studies and we compare the fecal microbiome of 359 subjects living in areas with high, medium and low environmental pollution. We highlight changes in gut microbiome composition and functionality that were driven by pollution exposure. Subjects from highly polluted areas show higher blood concentrations of dioxin and heavy metals, as well as an increase in microbial genes related to degradation and/or resistance to these molecules. Here we demonstrate the dramatic effect that environmental xenobiotics have on gut microbial communities, shaping their composition and boosting the selection of strains with degrading capacity. The gut microbiome can be considered as a pivotal player in the environment-health interaction that may contribute to detoxifying toxic compounds and should be taken into account when developing risk assessment models. The study was registered at ClinicalTrials.gov with the identifier NCT05976126.
Collapse
Affiliation(s)
- Francesca De Filippis
- Department of Agricultural Sciences, University of Naples Federico II, Via Università, 100, Portici, Italy
- Task Force on Microbiome Studies, University of Naples Federico II, Corso Umberto I, 40, Napoli, Italy
- Istituto Zooprofilattico Sperimentale del Mezzogiorno, Via Salute, 2, Portici, Italy
| | - Vincenzo Valentino
- Department of Agricultural Sciences, University of Naples Federico II, Via Università, 100, Portici, Italy
| | - Giuseppina Sequino
- Department of Agricultural Sciences, University of Naples Federico II, Via Università, 100, Portici, Italy
| | - Giorgia Borriello
- Istituto Zooprofilattico Sperimentale del Mezzogiorno, Via Salute, 2, Portici, Italy
| | | | - Biancamaria Pierri
- National Reference Centre for the Analysis and Study of the Correlation between Environment, Animal and Human, Via Salute, 2, Portici, Italy
| | - Pellegrino Cerino
- National Reference Centre for the Analysis and Study of the Correlation between Environment, Animal and Human, Via Salute, 2, Portici, Italy
| | - Antonio Pizzolante
- National Reference Centre for the Analysis and Study of the Correlation between Environment, Animal and Human, Via Salute, 2, Portici, Italy
| | - Edoardo Pasolli
- Department of Agricultural Sciences, University of Naples Federico II, Via Università, 100, Portici, Italy
- Task Force on Microbiome Studies, University of Naples Federico II, Corso Umberto I, 40, Napoli, Italy
| | - Mauro Esposito
- National Reference Centre for the Analysis and Study of the Correlation between Environment, Animal and Human, Via Salute, 2, Portici, Italy
| | - Antonio Limone
- Istituto Zooprofilattico Sperimentale del Mezzogiorno, Via Salute, 2, Portici, Italy
| | - Danilo Ercolini
- Department of Agricultural Sciences, University of Naples Federico II, Via Università, 100, Portici, Italy.
- Task Force on Microbiome Studies, University of Naples Federico II, Corso Umberto I, 40, Napoli, Italy.
| |
Collapse
|
10
|
Gorman ED, Lladser ME. Interpretable metric learning in comparative metagenomics: The adaptive Haar-like distance. PLoS Comput Biol 2024; 20:e1011543. [PMID: 38768195 PMCID: PMC11142682 DOI: 10.1371/journal.pcbi.1011543] [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: 09/26/2023] [Revised: 05/31/2024] [Accepted: 04/25/2024] [Indexed: 05/22/2024] Open
Abstract
Random forests have emerged as a promising tool in comparative metagenomics because they can predict environmental characteristics based on microbial composition in datasets where β-diversity metrics fall short of revealing meaningful relationships between samples. Nevertheless, despite this efficacy, they lack biological insight in tandem with their predictions, potentially hindering scientific advancement. To overcome this limitation, we leverage a geometric characterization of random forests to introduce a data-driven phylogenetic β-diversity metric, the adaptive Haar-like distance. This new metric assigns a weight to each internal node (i.e., split or bifurcation) of a reference phylogeny, indicating the relative importance of that node in discerning environmental samples based on their microbial composition. Alongside this, a weighted nearest-neighbors classifier, constructed using the adaptive metric, can be used as a proxy for the random forest while maintaining accuracy on par with that of the original forest and another state-of-the-art classifier, CoDaCoRe. As shown in datasets from diverse microbial environments, however, the new metric and classifier significantly enhance the biological interpretability and visualization of high-dimensional metagenomic samples.
Collapse
Affiliation(s)
- Evan D. Gorman
- Department of Applied Mathematics, University of Colorado, Boulder, Colorado, United States of America
| | - Manuel E. Lladser
- Department of Applied Mathematics, University of Colorado, Boulder, Colorado, United States of America
| |
Collapse
|
11
|
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.
Collapse
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.
| |
Collapse
|
12
|
Lima HS, Oliveira GFVD, Ferreira RDS, Castro AGD, Silva LCF, Ferreira LDS, Oliveira DADS, Silva LFD, Kasuya MCM, de Paula SO, Silva CCD. Machine learning-based soil quality assessment for enhancing environmental monitoring in iron ore mining-impacted ecosystems. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 356:120559. [PMID: 38471324 DOI: 10.1016/j.jenvman.2024.120559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 02/22/2024] [Accepted: 03/05/2024] [Indexed: 03/14/2024]
Abstract
In November 2015, a catastrophic rupture of the Fundão dam in Mariana (Brazil), resulted in extensive socio-economic and environmental repercussions that persist to this day. In response, several reforestation programs were initiated to remediate the impacted regions. However, accurately assessing soil health in these areas is a complex endeavor. This study employs machine learning techniques to predict soil quality indicators that effectively differentiate between the stages of recovery in these areas. For this, a comprehensive set of soil parameters, encompassing 3 biological, 16 chemical, and 3 physical parameters, were evaluated for samples exposed to mining tailings and those unaffected, totaling 81 and 6 samples, respectively, which were evaluated over 2 years. The most robust model was the decision tree with a restriction of fewer levels to simplify the tree structure. In this model, Cation Exchange Capacity (CEC), Microbial Biomass Carbon (MBC), Base Saturation (BS), and Effective Cation Exchange Capacity (eCEC) emerged as the most pivotal factors influencing model fitting. This model achieved an accuracy score of 92% during training and 93% during testing for determining stages of recovery. The model developed in this study has the potential to revolutionize the monitoring efforts conducted by regulatory agencies in these regions. By reducing the number of parameters that necessitate evaluation, this enhanced efficiency promises to expedite recovery monitoring, simultaneously enhancing cost-effectiveness while upholding the analytical rigor of assessments.
Collapse
Affiliation(s)
- Helena Santiago Lima
- Laboratory of Applied Environmental Microbiology, Department of Microbiology, Federal University of Viçosa, Viçosa, MG, Brazil.
| | | | | | - Alex Gazolla de Castro
- Laboratory of Applied Environmental Microbiology, Department of Microbiology, Federal University of Viçosa, Viçosa, MG, Brazil.
| | - Lívia Carneiro Fidélis Silva
- Laboratory of Applied Environmental Microbiology, Department of Microbiology, Federal University of Viçosa, Viçosa, MG, Brazil.
| | - Letícia de Souza Ferreira
- Laboratory of Applied Environmental Microbiology, Department of Microbiology, Federal University of Viçosa, Viçosa, MG, Brazil.
| | | | | | | | | | - Cynthia Canêdo da Silva
- Laboratory of Applied Environmental Microbiology, Department of Microbiology, Federal University of Viçosa, Viçosa, MG, Brazil.
| |
Collapse
|
13
|
Yi X, He Y, Gao S, Li M. A review of the application of deep learning in obesity: From early prediction aid to advanced management assistance. Diabetes Metab Syndr 2024; 18:103000. [PMID: 38604060 DOI: 10.1016/j.dsx.2024.103000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 01/23/2024] [Accepted: 03/29/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND AND AIMS Obesity is a chronic disease which can cause severe metabolic disorders. Machine learning (ML) techniques, especially deep learning (DL), have proven to be useful in obesity research. However, there is a dearth of systematic reviews of DL applications in obesity. This article aims to summarize the current trend of DL usage in obesity research. METHODS An extensive literature review was carried out across multiple databases, including PubMed, Embase, Web of Science, Scopus, and Medline, to collate relevant studies published from January 2018 to September 2023. The focus was on research detailing the application of DL in the context of obesity. We have distilled critical insights pertaining to the utilized learning models, encompassing aspects of their development, principal results, and foundational methodologies. RESULTS Our analysis culminated in the synthesis of new knowledge regarding the application of DL in the context of obesity. Finally, 40 research articles were included. The final collection of these research can be divided into three categories: obesity prediction (n = 16); obesity management (n = 13); and body fat estimation (n = 11). CONCLUSIONS This is the first review to examine DL applications in obesity. It reveals DL's superiority in obesity prediction over traditional ML methods, showing promise for multi-omics research. DL also innovates in obesity management through diet, fitness, and environmental analyses. Additionally, DL improves body fat estimation, offering affordable and precise monitoring tools. The study is registered with PROSPERO (ID: CRD42023475159).
Collapse
Affiliation(s)
- Xinghao Yi
- Department of Endocrinology, NHC Key Laboratory of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Yangzhige He
- Department of Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing 100730, China
| | - Shan Gao
- Department of Endocrinology, Xuan Wu Hospital, Capital Medical University, Beijing 10053, China
| | - Ming Li
- Department of Endocrinology, NHC Key Laboratory of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China.
| |
Collapse
|
14
|
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: 6] [Impact Index Per Article: 6.0] [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.
Collapse
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.
| |
Collapse
|
15
|
Roy G, Prifti E, Belda E, Zucker JD. Deep learning methods in metagenomics: a review. Microb Genom 2024; 10. [PMID: 38630611 DOI: 10.1099/mgen.0.001231] [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: 04/19/2024] Open
Abstract
The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.
Collapse
Affiliation(s)
- Gaspar Roy
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
| | - Edi Prifti
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
- Sorbonne University, INSERM, Nutriomics, 91 bvd de l'hopital, 75013 Paris, France
| | - Eugeni Belda
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
- Sorbonne University, INSERM, Nutriomics, 91 bvd de l'hopital, 75013 Paris, France
| | - Jean-Daniel Zucker
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
- Sorbonne University, INSERM, Nutriomics, 91 bvd de l'hopital, 75013 Paris, France
| |
Collapse
|
16
|
Sharma D, Lou W, Xu W. phylaGAN: data augmentation through conditional GANs and autoencoders for improving disease prediction accuracy using microbiome data. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae161. [PMID: 38569898 DOI: 10.1093/bioinformatics/btae161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 02/18/2024] [Accepted: 04/01/2024] [Indexed: 04/05/2024]
Abstract
MOTIVATION Research is improving our understanding of how the microbiome interacts with the human body and its impact on human health. Existing machine learning methods have shown great potential in discriminating healthy from diseased microbiome states. However, Machine Learning based prediction using microbiome data has challenges such as, small sample size, imbalance between cases and controls and high cost of collecting large number of samples. To address these challenges, we propose a deep learning framework phylaGAN to augment the existing datasets with generated microbiome data using a combination of conditional generative adversarial network (C-GAN) and autoencoder. Conditional generative adversarial networks train two models against each other to compute larger simulated datasets that are representative of the original dataset. Autoencoder maps the original and the generated samples onto a common subspace to make the prediction more accurate. RESULTS Extensive evaluation and predictive analysis was conducted on two datasets, T2D study and Cirrhosis study showing an improvement in mean AUC using data augmentation by 11% and 5% respectively. External validation on a cohort classifying between obese and lean subjects, with a smaller sample size provided an improvement in mean AUC close to 32% when augmented through phylaGAN as compared to using the original cohort. Our findings not only indicate that the generative adversarial networks can create samples that mimic the original data across various diversity metrics, but also highlight the potential of enhancing disease prediction through machine learning models trained on synthetic data. AVAILABILITY AND IMPLEMENTATION https://github.com/divya031090/phylaGAN.
Collapse
Affiliation(s)
- Divya Sharma
- Biostatistics Department, Princess Margaret Cancer Center, University Health Network, Toronto, ON, M5G2C4, Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, M5T3M7, Canada
| | - Wendy Lou
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, M5T3M7, Canada
| | - Wei Xu
- Biostatistics Department, Princess Margaret Cancer Center, University Health Network, Toronto, ON, M5G2C4, Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, M5T3M7, Canada
| |
Collapse
|
17
|
Wang B, Sun F, Luan Y. Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity. Sci Rep 2024; 14:7024. [PMID: 38528097 DOI: 10.1038/s41598-024-57670-2] [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: 09/28/2023] [Accepted: 03/20/2024] [Indexed: 03/27/2024] Open
Abstract
The human microbiome, comprising microorganisms residing within and on the human body, plays a crucial role in various physiological processes and has been linked to numerous diseases. To analyze microbiome data, it is essential to account for inherent heterogeneity and variability across samples. Normalization methods have been proposed to mitigate these variations and enhance comparability. However, the performance of these methods in predicting binary phenotypes remains understudied. This study systematically evaluates different normalization methods in microbiome data analysis and their impact on disease prediction. Our findings highlight the strengths and limitations of scaling, compositional data analysis, transformation, and batch correction methods. Scaling methods like TMM show consistent performance, while compositional data analysis methods exhibit mixed results. Transformation methods, such as Blom and NPN, demonstrate promise in capturing complex associations. Batch correction methods, including BMC and Limma, consistently outperform other approaches. However, the influence of normalization methods is constrained by population effects, disease effects, and batch effects. These results provide insights for selecting appropriate normalization approaches in microbiome research, improving predictive models, and advancing personalized medicine. Future research should explore larger and more diverse datasets and develop tailored normalization strategies for microbiome data analysis.
Collapse
Affiliation(s)
- Beibei Wang
- Frontier Science Center for Nonlinear Expectations, Ministry of Education, Qingdao, 266237, China
- Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China
- School of Mathematics, Shandong University, Jinan, 250100, China
| | - Fengzhu Sun
- Quantitative and Computational Biology Department, University of Southern California, Los Angeles, 90089, USA
| | - Yihui Luan
- Frontier Science Center for Nonlinear Expectations, Ministry of Education, Qingdao, 266237, China.
- Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China.
- School of Mathematics, Shandong University, Jinan, 250100, China.
| |
Collapse
|
18
|
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.
Collapse
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.
| |
Collapse
|
19
|
Huang KD, Amend L, Gálvez EJC, Lesker TR, de Oliveira R, Bielecka A, Blanco-Míguez A, Valles-Colomer M, Ruf I, Pasolli E, Buer J, Segata N, Esser S, Strowig T, Kehrmann J. Establishment of a non-Westernized gut microbiota in men who have sex with men is associated with sexual practices. Cell Rep Med 2024; 5:101426. [PMID: 38366600 PMCID: PMC10982974 DOI: 10.1016/j.xcrm.2024.101426] [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: 04/12/2023] [Revised: 09/07/2023] [Accepted: 01/22/2024] [Indexed: 02/18/2024]
Abstract
The human gut microbiota is influenced by various factors, including health status and environmental conditions, yet considerable inter-individual differences remain unexplained. Previous studies identified that the gut microbiota of men who have sex with men (MSM) is distinct from that of non-MSM. Here, we reveal through species-level microbiota analysis using shotgun metagenomics that the gut microbiota of many MSM with Western origin resembles gut microbial communities of non-Westernized populations. Specifically, MSM gut microbiomes are frequently dominated by members of the Prevotellaceae family, including co-colonization of species from the Segatella copri complex and unknown Prevotellaceae members. Questionnaire-based analysis exploring inter-individual differences in MSM links specific sexual practices to microbiota composition. Moreover, machine learning identifies microbial features associated with sexual activities in MSM. Together, this study shows associations of sexual activities with gut microbiome alterations in MSM, which may have a large impact on population-based microbiota studies.
Collapse
Affiliation(s)
- Kun D Huang
- Department of Microbial Immune Regulation, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Lena Amend
- Department of Microbial Immune Regulation, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Eric J C Gálvez
- Department of Microbial Immune Regulation, Helmholtz Centre for Infection Research, Braunschweig, Germany; Hannover Medical School, Hannover, Germany; Roche Pharma Research and Early Development, Roche Innovation Center, Basel, Switzerland
| | - Till-Robin Lesker
- Department of Microbial Immune Regulation, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Romulo de Oliveira
- Department of Microbial Immune Regulation, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Agata Bielecka
- Department of Microbial Immune Regulation, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | | | - Mireia Valles-Colomer
- Department CIBIO, University of Trento, Trento, Italy; Department of Medicine and Life Sciences, University Pompeu Fabra, Barcelona, Spain
| | - Isabel Ruf
- Institute of Medical Microbiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Edoardo Pasolli
- Department of Agricultural Sciences, University of Naples, Naples, Italy
| | - Jan Buer
- Institute of Medical Microbiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Nicola Segata
- Department CIBIO, University of Trento, Trento, Italy
| | - Stefan Esser
- Department of Dermatology and Venerology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Till Strowig
- Department of Microbial Immune Regulation, Helmholtz Centre for Infection Research, Braunschweig, Germany; Hannover Medical School, Hannover, Germany; Centre for Individualized Infection Medicine, Hannover, Germany.
| | - Jan Kehrmann
- Institute of Medical Microbiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
| |
Collapse
|
20
|
Gunjur A, Shao Y, Rozday T, Klein O, Mu A, Haak BW, Markman B, Kee D, Carlino MS, Underhill C, Frentzas S, Michael M, Gao B, Palmer J, Cebon J, Behren A, Adams DJ, Lawley TD. A gut microbial signature for combination immune checkpoint blockade across cancer types. Nat Med 2024; 30:797-809. [PMID: 38429524 PMCID: PMC10957475 DOI: 10.1038/s41591-024-02823-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 01/12/2024] [Indexed: 03/03/2024]
Abstract
Immune checkpoint blockade (ICB) targeting programmed cell death protein 1 (PD-1) and cytotoxic T lymphocyte protein 4 (CTLA-4) can induce remarkable, yet unpredictable, responses across a variety of cancers. Studies suggest that there is a relationship between a cancer patient's gut microbiota composition and clinical response to ICB; however, defining microbiome-based biomarkers that generalize across cohorts has been challenging. This may relate to previous efforts quantifying microbiota to species (or higher taxonomic rank) abundances, whereas microbial functions are often strain specific. Here, we performed deep shotgun metagenomic sequencing of baseline fecal samples from a unique, richly annotated phase 2 trial cohort of patients with diverse rare cancers treated with combination ICB (n = 106 discovery cohort). We demonstrate that strain-resolved microbial abundances improve machine learning predictions of ICB response and 12-month progression-free survival relative to models built using species-rank quantifications or comprehensive pretreatment clinical factors. Through a meta-analysis of gut metagenomes from a further six comparable studies (n = 364 validation cohort), we found cross-cancer (and cross-country) validity of strain-response signatures, but only when the training and test cohorts used concordant ICB regimens (anti-PD-1 monotherapy or combination anti-PD-1 plus anti-CTLA-4). This suggests that future development of gut microbiome diagnostics or therapeutics should be tailored according to ICB treatment regimen rather than according to cancer type.
Collapse
Affiliation(s)
- Ashray Gunjur
- Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK.
- Experimental Cancer Genetics, Wellcome Sanger Institute, Hinxton, UK.
| | - Yan Shao
- Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK
| | - Timothy Rozday
- Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK
| | - Oliver Klein
- Olivia Newton-John Cancer Research Institute, La Trobe University School of Cancer Medicine, Melbourne, Victoria, Australia
- Department of Medical Oncology, Austin Health, Melbourne, Victoria, Australia
- Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Andre Mu
- Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Bastiaan W Haak
- Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK
- Center for Experimental and Molecular Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Ben Markman
- Department of Medical Oncology, Monash Health, Melbourne, Victoria, Australia
- Department of Medical Oncology, Alfred Health, Melbourne, Victoria, Australia
- School of Clinical Sciences, Monash University, Melbourne, Victoria, Australia
| | - Damien Kee
- Department of Medical Oncology, Austin Health, Melbourne, Victoria, Australia
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Rare Cancer Laboratory, Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia
| | - Matteo S Carlino
- Department of Medical Oncology, Blacktown and Westmead Hospitals, Sydney, New South Wales, Australia
- Melanoma Institute of Australia, University of Sydney, Sydney, New South Wales, Australia
| | - Craig Underhill
- Border Medical Oncology and Haematology Research Unit, Albury-Wodonga Regional Cancer Centre, Albury-Wodonga, New South Wales, Australia
- Rural Medical School, University of New South Wales, Albury, New South Wales, Australia
| | - Sophia Frentzas
- Department of Medical Oncology, Monash Health, Melbourne, Victoria, Australia
| | - Michael Michael
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Bo Gao
- Department of Medical Oncology, Blacktown and Westmead Hospitals, Sydney, New South Wales, Australia
| | - Jodie Palmer
- Olivia Newton-John Cancer Research Institute, La Trobe University School of Cancer Medicine, Melbourne, Victoria, Australia
| | - Jonathan Cebon
- Olivia Newton-John Cancer Research Institute, La Trobe University School of Cancer Medicine, Melbourne, Victoria, Australia
- Department of Medical Oncology, Austin Health, Melbourne, Victoria, Australia
| | - Andreas Behren
- Olivia Newton-John Cancer Research Institute, La Trobe University School of Cancer Medicine, Melbourne, Victoria, Australia
| | - David J Adams
- Experimental Cancer Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Trevor D Lawley
- Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, UK.
| |
Collapse
|
21
|
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.
Collapse
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
| |
Collapse
|
22
|
Bazzani D, Heidrich V, Manghi P, Blanco-Miguez A, Asnicar F, Armanini F, Cavaliere S, Bertelle A, Dell'Acqua F, Dellasega E, Waldner R, Vicentini D, Bolzan M, Tomasi C, Segata N, Pasolli E, Ghensi P. Favorable subgingival plaque microbiome shifts are associated with clinical treatment for peri-implant diseases. NPJ Biofilms Microbiomes 2024; 10:12. [PMID: 38374114 PMCID: PMC10876967 DOI: 10.1038/s41522-024-00482-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 01/31/2024] [Indexed: 02/21/2024] Open
Abstract
We performed a longitudinal shotgun metagenomic investigation of the plaque microbiome associated with peri-implant diseases in a cohort of 91 subjects with 320 quality-controlled metagenomes. Through recently improved taxonomic profiling methods, we identified the most discriminative species between healthy and diseased subjects at baseline, evaluated their change over time, and provided evidence that clinical treatment had a positive effect on plaque microbiome composition in patients affected by mucositis and peri-implantitis.
Collapse
Affiliation(s)
| | | | - Paolo Manghi
- Department CIBIO, University of Trento, Trento, Italy
| | | | | | | | - Sara Cavaliere
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy
| | | | | | | | | | | | | | - Cristiano Tomasi
- PreBiomics S.r.l., Trento, Italy
- Department of Periodontology, Institute of Odontology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Nicola Segata
- Department CIBIO, University of Trento, Trento, Italy.
| | - Edoardo Pasolli
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy.
| | - Paolo Ghensi
- PreBiomics S.r.l., Trento, Italy.
- Department CIBIO, University of Trento, Trento, Italy.
| |
Collapse
|
23
|
Kumar B, Lorusso E, Fosso B, Pesole G. A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions. Front Microbiol 2024; 15:1343572. [PMID: 38419630 PMCID: PMC10900530 DOI: 10.3389/fmicb.2024.1343572] [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: 11/23/2023] [Accepted: 01/29/2024] [Indexed: 03/02/2024] Open
Abstract
Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.
Collapse
Affiliation(s)
- Bablu Kumar
- Università degli Studi di Milano, Milan, Italy
- Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro, Bari, Italy
| | - Erika Lorusso
- Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro, Bari, Italy
- National Research Council, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Bari, Italy
| | - Bruno Fosso
- Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro, Bari, Italy
| | - Graziano Pesole
- Department of Biosciences, Biotechnology and Environment, University of Bari A. Moro, Bari, Italy
- National Research Council, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, Bari, Italy
| |
Collapse
|
24
|
Wang Q, Fan X, Wu S, Su X. PM-CNN: microbiome status recognition and disease detection model based on phylogeny and multi-path neural network. BIOINFORMATICS ADVANCES 2024; 4:vbae013. [PMID: 38371919 PMCID: PMC10873578 DOI: 10.1093/bioadv/vbae013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 02/15/2024] [Accepted: 02/15/2024] [Indexed: 02/20/2024]
Abstract
Motivation The human microbiome, found throughout various body parts, plays a crucial role in health dynamics and disease development. Recent research has highlighted microbiome disparities between patients with different diseases and healthy individuals, suggesting the microbiome's potential in recognizing health states. Traditionally, microbiome-based status classification relies on pre-trained machine learning (ML) models. However, most ML methods overlook microbial relationships, limiting model performance. Results To address this gap, we propose PM-CNN (Phylogenetic Multi-path Convolutional Neural Network), a novel phylogeny-based neural network model for multi-status classification and disease detection using microbiome data. PM-CNN organizes microbes based on their phylogenetic relationships and extracts features using a multi-path convolutional neural network. An ensemble learning method then fuses these features to make accurate classification decisions. We applied PM-CNN to human microbiome data for status and disease detection, demonstrating its significant superiority over existing ML models. These results provide a robust foundation for microbiome-based state recognition and disease prediction in future research and applications. Availability and implementation PM-CNN software is available at https://github.com/qdu-bioinfo/PM_CNN.
Collapse
Affiliation(s)
- Qiangqiang Wang
- College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
| | - Xiaoqian Fan
- Department of Gastroenterology, Shouguang Hospital of Traditional Chinese Medicine, Weifang 262700, China
| | - Shunyao Wu
- College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
| | - Xiaoquan Su
- College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
| |
Collapse
|
25
|
Bauer KC, Trehan R, Ruf B, Myojin Y, Benmebarek MR, Ma C, Seifert M, Nur A, Qi J, Huang P, Soliman M, Green BL, Wabitsch S, Springer DA, Rodriguez-Matos FJ, Ghabra S, Gregory SN, Matta J, Dawson B, Golino J, Xie C, Dzutsev A, Trinchieri G, Korangy F, Greten TF. The Gut Microbiome Controls Liver Tumors via the Vagus Nerve. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576951. [PMID: 38328040 PMCID: PMC10849697 DOI: 10.1101/2024.01.23.576951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Liver cancer ranks amongst the deadliest cancers. Nerves have emerged as an understudied regulator of tumor progression. The parasympathetic vagus nerve influences systemic immunity via acetylcholine (ACh). Whether cholinergic neuroimmune interactions influence hepatocellular carcinoma (HCC) remains uncertain. Liver denervation via hepatic vagotomy (HV) significantly reduced liver tumor burden, while pharmacological enhancement of parasympathetic tone promoted tumor growth. Cholinergic disruption in Rag1KO mice revealed that cholinergic regulation requires adaptive immunity. Further scRNA-seq and in vitro studies indicated that vagal ACh dampens CD8+ T cell activity via muscarinic ACh receptor (AChR) CHRM3. Depletion of CD8+ T cells abrogated HV outcomes and selective deletion of Chrm3 on CD8 + T cells inhibited liver tumor growth. Beyond tumor-specific outcomes, vagotomy improved cancer-associated fatigue and anxiety-like behavior. As microbiota transplantation from HCC donors was sufficient to impair behavior, we investigated putative microbiota-neuroimmune crosstalk. Tumor, rather than vagotomy, robustly altered fecal bacterial composition, increasing Desulfovibrionales and Clostridial taxa. Strikingly, in tumor-free mice, vagotomy permitted HCC-associated microbiota to activate hepatic CD8+ T cells. These findings reveal that gut bacteria influence behavior and liver anti-tumor immunity via a dynamic and pharmaceutically targetable, vagus-liver axis.
Collapse
|
26
|
Zulfiqar M, Singh V, Steinbeck C, Sorokina M. Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities. Crit Rev Microbiol 2024:1-40. [PMID: 38270170 DOI: 10.1080/1040841x.2024.2306465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.
Collapse
Affiliation(s)
- Mahnoor Zulfiqar
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Vinay Singh
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Maria Sorokina
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Data Science and Artificial Intelligence, Research and Development, Pharmaceuticals, Bayer, Berlin, Germany
| |
Collapse
|
27
|
Muller E, Shiryan I, Borenstein E. Multi-omic integration of microbiome data for identifying disease-associated modules. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.03.547607. [PMID: 37461534 PMCID: PMC10349976 DOI: 10.1101/2023.07.03.547607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
The human gut microbiome is a complex ecosystem with profound implications for health and disease. This recognition has led to a surge in multi-omic microbiome studies, employing various molecular assays to elucidate the microbiome's role in diseases across multiple functional layers. However, despite the clear value of these multi-omic datasets, rigorous integrative analysis of such data poses significant challenges, hindering a comprehensive understanding of microbiome-disease interactions. Perhaps most notably, multiple approaches, including univariate and multivariate analyses, as well as machine learning, have been applied to such data to identify disease-associated markers, namely, specific features (e.g., species, pathways, metabolites) that are significantly altered in disease state. These methods, however, often yield extensive lists of features associated with the disease without effectively capturing the multi-layered structure of multi-omic data or offering clear, interpretable hypotheses about underlying microbiome-disease mechanisms. Here, we address this challenge by introducing MintTea - an intermediate integration-based method for analyzing multi-omic microbiome data. MintTea combines a canonical correlation analysis (CCA) extension, consensus analysis, and an evaluation protocol to robustly identify disease-associated multi-omic modules. Each such module consists of a set of features from the various omics that both shift in concord, and collectively associate with the disease. Applying MintTea to diverse case-control cohorts with multi-omic data, we show that this framework is able to capture modules with high predictive power for disease, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome (MS) study, we found a MS-associated module comprising of a highly correlated cluster of serum glutamate- and TCA cycle-related metabolites, as well as bacterial species previously implicated in insulin resistance. In another cohort, we identified a module associated with late-stage colorectal cancer, featuring Peptostreptococcus and Gemella species and several fecal amino acids, in agreement with these species' reported role in the metabolism of these amino acids and their coordinated increase in abundance during disease development. Finally, comparing modules identified in different datasets, we detected multiple significant overlaps, suggesting common interactions between microbiome features. Combined, this work serves as a proof of concept for the potential benefits of advanced integration methods in generating integrated multi-omic hypotheses underlying microbiome-disease interactions and a promising avenue for researchers seeking systems-level insights into coherent mechanisms governing microbiome-related diseases.
Collapse
|
28
|
Peralta-Marzal LN, Rojas-Velazquez D, Rigters D, Prince N, Garssen J, Kraneveld AD, Perez-Pardo P, Lopez-Rincon A. A robust microbiome signature for autism spectrum disorder across different studies using machine learning. Sci Rep 2024; 14:814. [PMID: 38191575 PMCID: PMC10774349 DOI: 10.1038/s41598-023-50601-7] [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: 07/04/2023] [Accepted: 12/21/2023] [Indexed: 01/10/2024] Open
Abstract
Autism spectrum disorder (ASD) is a highly complex neurodevelopmental disorder characterized by deficits in sociability and repetitive behaviour, however there is a great heterogeneity within other comorbidities that accompany ASD. Recently, gut microbiome has been pointed out as a plausible contributing factor for ASD development as individuals diagnosed with ASD often suffer from intestinal problems and show a differentiated intestinal microbial composition. Nevertheless, gut microbiome studies in ASD rarely agree on the specific bacterial taxa involved in this disorder. Regarding the potential role of gut microbiome in ASD pathophysiology, our aim is to investigate whether there is a set of bacterial taxa relevant for ASD classification by using a sibling-controlled dataset. Additionally, we aim to validate these results across two independent cohorts as several confounding factors, such as lifestyle, influence both ASD and gut microbiome studies. A machine learning approach, recursive ensemble feature selection (REFS), was applied to 16S rRNA gene sequencing data from 117 subjects (60 ASD cases and 57 siblings) identifying 26 bacterial taxa that discriminate ASD cases from controls. The average area under the curve (AUC) of this specific set of bacteria in the sibling-controlled dataset was 81.6%. Moreover, we applied the selected bacterial taxa in a tenfold cross-validation scheme using two independent cohorts (a total of 223 samples-125 ASD cases and 98 controls). We obtained average AUCs of 74.8% and 74%, respectively. Analysis of the gut microbiome using REFS identified a set of bacterial taxa that can be used to predict the ASD status of children in three distinct cohorts with AUC over 80% for the best-performing classifiers. Our results indicate that the gut microbiome has a strong association with ASD and should not be disregarded as a potential target for therapeutic interventions. Furthermore, our work can contribute to use the proposed approach for identifying microbiome signatures across other 16S rRNA gene sequencing datasets.
Collapse
Affiliation(s)
- Lucia N Peralta-Marzal
- Division of Pharmacology, Faculty of Science, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands
| | - David Rojas-Velazquez
- Division of Pharmacology, Faculty of Science, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands
- Department of Data Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Douwe Rigters
- Division of Pharmacology, Faculty of Science, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands
| | - Naika Prince
- Division of Pharmacology, Faculty of Science, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands
| | - Johan Garssen
- Division of Pharmacology, Faculty of Science, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands
- Global Centre of Excellence Immunology, Danone Nutricia Research, Utrecht, The Netherlands
| | - Aletta D Kraneveld
- Division of Pharmacology, Faculty of Science, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands
- Department of Neuroscience, Faculty of Science, VU University, Amsterdam, The Netherlands
| | - Paula Perez-Pardo
- Division of Pharmacology, Faculty of Science, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands.
| | - Alejandro Lopez-Rincon
- Division of Pharmacology, Faculty of Science, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands
- Department of Data Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| |
Collapse
|
29
|
Valles-Colomer M, Manghi P, Cumbo F, Masetti G, Armanini F, Asnicar F, Blanco-Miguez A, Pinto F, Punčochář M, Garaventa A, Amoroso L, Ponzoni M, Corrias MV, Segata N. Neuroblastoma is associated with alterations in gut microbiome composition subsequent to maternal microbial seeding. EBioMedicine 2024; 99:104917. [PMID: 38104504 PMCID: PMC10731604 DOI: 10.1016/j.ebiom.2023.104917] [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: 07/12/2023] [Revised: 11/21/2023] [Accepted: 12/01/2023] [Indexed: 12/19/2023] Open
Abstract
BACKGROUND Neuroblastoma is the most frequent extracranial solid tumour in children, accounting for ∼15% of deaths due to cancer in childhood. The most common clinical presentation are abdominal tumours. An altered gut microbiome composition has been linked to multiple cancer types, and reported in murine models of neuroblastoma. Whether children with neuroblastoma display alterations in gut microbiome composition remains unexplored. METHODS We assessed gut microbiome composition by shotgun metagenomic profiling in an observational cross-sectional study on 288 individuals, consisting of patients with a diagnosis of neuroblastoma at disease onset (N = 63), healthy controls matching the patients on the main covariates of microbiome composition (N = 94), healthy siblings of the patients (N = 13), mothers of patients (N = 59), and mothers of the controls (N = 59). We examined taxonomic and functional microbiome composition and mother-infant strain transmission patterns. FINDINGS Patients with neuroblastoma displayed alterations in gut microbiome composition characterised by reduced microbiome richness, decreased relative abundances of 18 species (including Phocaeicola dorei and Bifidobacterium bifidum), enriched protein fermentation and reduced carbohydrate fermentation potential. Using machine learning, we could successfully discriminate patients from controls (AUC = 82%). Healthy siblings did not display such alterations but resembled the healthy control group. No significant differences in maternal microbiome composition nor mother-to-offspring transmission were detected. INTERPRETATION Patients with neuroblastoma display alterations in taxonomic and functional gut microbiome composition, which cannot be traced to differential maternal seeding. Follow-up research should include investigating potential causal links. FUNDING Italian Ministry of Health Ricerca Corrente and Ricerca Finalizzata 5 per mille (to MPonzoni); Fondazione Italiana Neuroblastoma (to MPonzoni); European Research Council (ERC-StG project MetaPG-716575 and ERC-CoG microTOUCH-101045015 to NS); the European H2020 program ONCOBIOME-825410 project (to NS); the National Cancer Institute of the National Institutes of Health 1U01CA230551 (to NS); the Premio Internazionale Lombardia e Ricerca 2019 (to NS); the MIUR Progetti di Ricerca di Rilevante Interesse Nazionale (PRIN) Bando 2017 Grant 2017J3E2W2 (to NS); EMBO ALTF 593-2020 and Knowledge Generation Project from the Spanish Ministry of Science and Innovation (PID2022-139328OA-I00) (to MV-C).
Collapse
Affiliation(s)
- Mireia Valles-Colomer
- Department CIBIO, University of Trento, Trento, Italy; MELIS Department, Pompeu Fabra University, Barcelona, Spain.
| | - Paolo Manghi
- Department CIBIO, University of Trento, Trento, Italy
| | - Fabio Cumbo
- Department CIBIO, University of Trento, Trento, Italy
| | | | | | | | | | | | | | | | | | - Mirco Ponzoni
- Laboratory of Experimental Therapies in Oncology, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Maria Valeria Corrias
- Laboratory of Experimental Therapies in Oncology, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Nicola Segata
- Department CIBIO, University of Trento, Trento, Italy; Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy.
| |
Collapse
|
30
|
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.
Collapse
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
| |
Collapse
|
31
|
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.
Collapse
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
| |
Collapse
|
32
|
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.
Collapse
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
| |
Collapse
|
33
|
Afonso CL, Afonso AM. Next-Generation Sequencing for the Detection of Microbial Agents in Avian Clinical Samples. Vet Sci 2023; 10:690. [PMID: 38133241 PMCID: PMC10747646 DOI: 10.3390/vetsci10120690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/24/2023] [Accepted: 11/29/2023] [Indexed: 12/23/2023] Open
Abstract
Direct-targeted next-generation sequencing (tNGS), with its undoubtedly superior diagnostic capacity over real-time PCR (RT-PCR), and direct-non-targeted NGS (ntNGS), with its higher capacity to identify and characterize multiple agents, are both likely to become diagnostic methods of choice in the future. tNGS is a rapid and sensitive method for precise characterization of suspected agents. ntNGS, also known as agnostic diagnosis, does not require a hypothesis and has been used to identify unsuspected infections in clinical samples. Implemented in the form of multiplexed total DNA metagenomics or as total RNA sequencing, the approach produces comprehensive and actionable reports that allow semi-quantitative identification of most of the agents present in respiratory, cloacal, and tissue samples. The diagnostic benefits of the use of direct tNGS and ntNGS are high specificity, compatibility with different types of clinical samples (fresh, frozen, FTA cards, and paraffin-embedded), production of nearly complete infection profiles (viruses, bacteria, fungus, and parasites), production of "semi-quantitative" information, direct agent genotyping, and infectious agent mutational information. The achievements of NGS in terms of diagnosing poultry problems are described here, along with future applications. Multiplexing, development of standard operating procedures, robotics, sequencing kits, automated bioinformatics, cloud computing, and artificial intelligence (AI) are disciplines converging toward the use of this technology for active surveillance in poultry farms. Other advances in human and veterinary NGS sequencing are likely to be adaptable to avian species in the future.
Collapse
|
34
|
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.
Collapse
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
| |
Collapse
|
35
|
Ayoola MB, Pillai N, Nanduri B, Rothrock MJ, Ramkumar M. Predicting foodborne pathogens and probiotics taxa within poultry-related microbiomes using a machine learning approach. Anim Microbiome 2023; 5:57. [PMID: 37968727 PMCID: PMC10648331 DOI: 10.1186/s42523-023-00260-w] [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: 02/18/2023] [Accepted: 08/23/2023] [Indexed: 11/17/2023] Open
Abstract
BACKGROUND Microbiomes that can serve as an indicator of gut, intestinal, and general health of humans and animals are largely influenced by food consumed and contaminant bioagents. Microbiome studies usually focus on estimating the alpha (within sample) and beta (similarity/dissimilarity among samples) diversities. This study took a combinatorial approach and applied machine learning to microbiome data to predict the presence of disease-causing pathogens and their association with known/potential probiotic taxa. Probiotics are beneficial living microorganisms capable of improving the host organism's digestive system, immune function and ultimately overall health. Here, 16 S rRNA gene high-throughput Illumina sequencing of temporal pre-harvest (feces, soil) samples of 42 pastured poultry flocks (poultry in this entire work solely refers to chickens) from southeastern U.S. farms was used to generate the relative abundance of operational taxonomic units (OTUs) as machine learning input. Unique genera from the OTUs were used as predictors of the prevalence of foodborne pathogens (Salmonella, Campylobacter and Listeria) at different stages of poultry growth (START (2-4 weeks old), MID (5-7 weeks old), END (8-11 weeks old)), association with farm management practices and physicochemical properties. RESULT While we did not see any significant associations between known probiotics and Salmonella or Listeria, we observed significant negative correlations between known probiotics (Bacillus and Clostridium) and Campylobacter at the mid-time point of sample collection. Our data indicates a negative correlation between potential probiotics and Campylobacter at both early and end-time points of sample collection. Furthermore, our model prediction shows that changes in farm operations such as how often the houses are moved on the pasture, age at which chickens are introduced to the pasture, diet composition and presence of other animals on the farm could favorably increase the abundance and activity of probiotics that could reduce Campylobacter prevalence. CONCLUSION Integration of microbiome data with farm management practices using machine learning provided insights on how to reduce Campylobacter prevalence and transmission along the farm-to-fork continuum. Altering management practices to support proliferation of beneficial probiotics to reduce pathogen prevalence identified here could constitute a complementary method to the existing but ineffective interventions such as vaccination and bacteriophage cocktails usage. Study findings also corroborate the presence of bacterial genera such as Caloramator, DA101, Parabacteroides and Faecalibacterium as potential probiotics.
Collapse
Affiliation(s)
- Moses B Ayoola
- Geosystems Research Institute, Mississippi State University, Starkville, MS, 39762, USA
- Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University, Starkville, MS, 39762, USA
| | - Nisha Pillai
- Department of Computer Science and Engineering, Mississippi State University, Starkville, MS, 39762, USA
| | - Bindu Nanduri
- Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University, Starkville, MS, 39762, USA
| | - Michael J Rothrock
- Egg Safety and Quality Research Unit, USDA-ARS U.S. National Poultry Research Center, Athens, GA 30605, USA
| | - Mahalingam Ramkumar
- Department of Computer Science and Engineering, Mississippi State University, Starkville, MS, 39762, USA.
| |
Collapse
|
36
|
Chen HC, Liu YW, Chang KC, Wu YW, Chen YM, Chao YK, You MY, Lundy DJ, Lin CJ, Hsieh ML, Cheng YC, Prajnamitra RP, Lin PJ, Ruan SC, Chen DHK, Shih ESC, Chen KW, Chang SS, Chang CMC, Puntney R, Moy AW, Cheng YY, Chien HY, Lee JJ, Wu DC, Hwang MJ, Coonen J, Hacker TA, Yen CLE, Rey FE, Kamp TJ, Hsieh PCH. Gut butyrate-producers confer post-infarction cardiac protection. Nat Commun 2023; 14:7249. [PMID: 37945565 PMCID: PMC10636175 DOI: 10.1038/s41467-023-43167-5] [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: 07/30/2023] [Accepted: 11/02/2023] [Indexed: 11/12/2023] Open
Abstract
The gut microbiome and its metabolites are increasingly implicated in several cardiovascular diseases, but their role in human myocardial infarction (MI) injury responses have yet to be established. To address this, we examined stool samples from 77 ST-elevation MI (STEMI) patients using 16 S V3-V4 next-generation sequencing, metagenomics and machine learning. Our analysis identified an enriched population of butyrate-producing bacteria. These findings were then validated using a controlled ischemia/reperfusion model using eight nonhuman primates. To elucidate mechanisms, we inoculated gnotobiotic mice with these bacteria and found that they can produce beta-hydroxybutyrate, supporting cardiac function post-MI. This was further confirmed using HMGCS2-deficient mice which lack endogenous ketogenesis and have poor outcomes after MI. Inoculation increased plasma ketone levels and provided significant improvements in cardiac function post-MI. Together, this demonstrates a previously unknown role of gut butyrate-producers in the post-MI response.
Collapse
Affiliation(s)
- Hung-Chih Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Yen-Wen Liu
- Division of Cardiology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, 704, Taiwan
| | - Kuan-Cheng Chang
- Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, 40447, Taiwan
- School of Medicine, China Medical University, Taichung, 40402, Taiwan
| | - Yen-Wen Wu
- Cardiovascular Medical Center, Far Eastern Memorial Hospital, New Taipei City, 220, Taiwan
| | - Yi-Ming Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Yu-Kai Chao
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Min-Yi You
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - David J Lundy
- Graduate Institute of Biomedical Materials and Tissue Engineering, Taipei Medical University, Taipei, 110, Taiwan
| | - Chen-Ju Lin
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Marvin L Hsieh
- Model Organisms Research Core, Department of Medicine, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Yu-Che Cheng
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Ray P Prajnamitra
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Po-Ju Lin
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Shu-Chian Ruan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | | | - Edward S C Shih
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Ke-Wei Chen
- Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Shih-Sheng Chang
- Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, 40447, Taiwan
- School of Medicine, China Medical University, Taichung, 40402, Taiwan
| | - Cindy M C Chang
- Model Organisms Research Core, Department of Medicine, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Riley Puntney
- Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, 53715, USA
| | - Amy Wu Moy
- Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, 53715, USA
| | - Yuan-Yuan Cheng
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Hsin-Yuan Chien
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Jia-Jung Lee
- Division of Nephrology, Department of Medicine, Kaohsiung Medical University & Hospital, Kaohsiung, 807, Taiwan
| | - Deng-Chyang Wu
- Division of Gastroenterology, Department of Medicine, Kaohsiung Medical University & Hospital, Kaohsiung, 807, Taiwan
| | - Ming-Jing Hwang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
| | - Jennifer Coonen
- Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, 53715, USA
| | - Timothy A Hacker
- Model Organisms Research Core, Department of Medicine, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - C-L Eric Yen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan
- Department of Nutritional Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Federico E Rey
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Timothy J Kamp
- Department of Medicine and Stem Cell and Regenerative Medicine Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Patrick C H Hsieh
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan.
- Department of Medicine and Stem Cell and Regenerative Medicine Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Institute of Medical Genomics and Proteomics and Institute of Clinical Medicine, National Taiwan University College of Medicine, Taipei, 100, Taiwan.
| |
Collapse
|
37
|
Huttenhower C, Finn RD, McHardy AC. Challenges and opportunities in sharing microbiome data and analyses. Nat Microbiol 2023; 8:1960-1970. [PMID: 37783751 DOI: 10.1038/s41564-023-01484-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 08/28/2023] [Indexed: 10/04/2023]
Abstract
Microbiome data, metadata and analytical workflows have become 'big' in terms of volume and complexity. Although the infrastructure and technologies to share data have been established, the interdisciplinary and multi-omic nature of the field can make resources difficult to identify and use. Following best practices for data deposition requires substantial effort, with sometimes little obvious reward. Gaps remain where microbiome-specific resources for data sharing or reproducibility do not yet exist. We outline available best practices, challenges to their adoption and opportunities in data sharing in microbiome research. We showcase examples of best practices and advocate for their enforcement and incentivization for data sharing. This includes recognition of data curation and sharing endeavours by individuals, institutions, journals and funders. Opportunities for progress include enabling microbiome-specific databases to incorporate future methods for data analysis, integration and reuse.
Collapse
Affiliation(s)
- Curtis Huttenhower
- Harvard Chan Microbiome in Public Health Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Departments of Biostatistics and Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Robert D Finn
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Alice Carolyn McHardy
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany.
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany.
| |
Collapse
|
38
|
Feng J, Yang K, Liu X, Song M, Zhan P, Zhang M, Chen J, Liu J. Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types. PeerJ 2023; 11:e16304. [PMID: 37901464 PMCID: PMC10601900 DOI: 10.7717/peerj.16304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 09/26/2023] [Indexed: 10/31/2023] Open
Abstract
Machine learning (ML) includes a broad class of computer programs that improve with experience and shows unique strengths in performing tasks such as clustering, classification and regression. Over the past decade, microbial communities have been implicated in influencing the onset, progression, metastasis, and therapeutic response of multiple cancers. Host-microbe interaction may be a physiological pathway contributing to cancer development. With the accumulation of a large number of high-throughput data, ML has been successfully applied to the study of human cancer microbiomics in an attempt to reveal the complex mechanism behind cancer. In this review, we begin with a brief overview of the data sources included in cancer microbiomics studies. Then, the characteristics of the ML algorithm are briefly introduced. Secondly, the application progress of ML in cancer microbiomics is also reviewed. Finally, we highlight the challenges and future prospects facing ML in cancer microbiomics. On this basis, we conclude that the development of cancer microbiomics can not be achieved without ML, and that ML can be used to develop tumor-targeting microbial therapies, ultimately contributing to personalized and precision medicine.
Collapse
Affiliation(s)
- Jia Feng
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Kailan Yang
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Xuexue Liu
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Min Song
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Ping Zhan
- Department of Obstetrics, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Mi Zhang
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Jinsong Chen
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Jinbo Liu
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| |
Collapse
|
39
|
Wassan JT, Wang H, Zheng H. Developing a New Phylogeny-Driven Random Forest Model for Functional Metagenomics. IEEE Trans Nanobioscience 2023; 22:763-770. [PMID: 37279136 DOI: 10.1109/tnb.2023.3283462] [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: 06/08/2023]
Abstract
Metagenomics is an unobtrusive science linking microbial genes to biological functions or environmental states. Classifying microbial genes into their functional repertoire is an important task in the downstream analysis of Metagenomic studies. The task involves Machine Learning (ML) based supervised methods to achieve good classification performance. Random Forest (RF) has been applied rigorously to microbial gene abundance profiles, mapping them to functional phenotypes. The current research targets tuning RF by the evolutionary ancestry of microbial phylogeny, developing a Phylogeny-RF model for functional classification of metagenomes. This method facilitates capturing the effects of phylogenetic relatedness in an ML classifier itself rather than just applying a supervised classifier over the raw abundances of microbial genes. The idea is rooted in the fact that closely related microbes by phylogeny are highly correlated and tend to have similar genetic and phenotypic traits. Such microbes behave similarly; and hence tend to be selected together, or one of these could be dropped from the analysis, to improve the ML process. The proposed Phylogeny-RF algorithm has been compared with state-of-the-art classification methods including RF and the phylogeny-aware methods of MetaPhyl and PhILR, using three real-world 16S rRNA metagenomic datasets. It has been observed that the proposed method not only achieved significantly better performance than the traditional RF model but also performed better than the other phylogeny-driven benchmarks (p < 0.05). For example, Phylogeny-RF attained a highest AUC of 0.949 and Kappa of 0.891 over soil microbiomes in comparison to other benchmarks.
Collapse
|
40
|
Liu Z, Wang Q, Ma A, Feng S, Chung D, Zhao J, Ma Q, Liu B. Inference of disease-associated microbial gene modules based on metagenomic and metatranscriptomic data. Comput Biol Med 2023; 165:107458. [PMID: 37703713 DOI: 10.1016/j.compbiomed.2023.107458] [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: 07/10/2023] [Revised: 08/22/2023] [Accepted: 09/04/2023] [Indexed: 09/15/2023]
Abstract
The identification of microbial characteristics associated with diseases is crucial for disease diagnosis and therapy. However, the presence of heterogeneity, high dimensionality, and large amounts of microbial data presents tremendous challenges in discovering key microbial features. In this paper, we present IDAM, a novel computational method for inferring disease-associated gene modules from metagenomic and metatranscriptomic data. This method integrates gene context conservation (uber-operons) and regulatory mechanisms (gene co-expression patterns) within a mathematical graph model to explore gene modules associated with specific diseases. It alleviates reliance on prior meta-data. We applied IDAM to publicly available datasets from inflammatory bowel disease, melanoma, type 1 diabetes mellitus, and irritable bowel syndrome. The results demonstrated the superior performance of IDAM in inferring disease-associated characteristics compared to existing popular tools. Furthermore, we showcased the high reproducibility of the gene modules inferred by IDAM using independent cohorts with inflammatory bowel disease. We believe that IDAM can be a highly advantageous method for exploring disease-associated microbial characteristics. The source code of IDAM is freely available at https://github.com/OSU-BMBL/IDAM, and the web server can be accessed at https://bmblx.bmi.osumc.edu/idam/.
Collapse
Affiliation(s)
- Zhaoqian Liu
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China
| | - Qi Wang
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China
| | - Anjun Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Shaohong Feng
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Dongjun Chung
- 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
| | - Jing Zhao
- Department of Biomedical Informatics, 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, 250100, China.
| |
Collapse
|
41
|
Chang CC, Liu TC, Lu CJ, Chiu HC, Lin WN. Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis. Front Microbiol 2023; 14:1227300. [PMID: 37829445 PMCID: PMC10565662 DOI: 10.3389/fmicb.2023.1227300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 09/06/2023] [Indexed: 10/14/2023] Open
Abstract
Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of patients with MG would differ from those without, and supervised machine learning (ML) analysis strategy could be trained using data from gut microbiota for diagnostic screening of MG. Genomic DNA from the stool samples of MG and those without were collected and established a sequencing library by constructing amplicon sequence variants (ASVs) and completing taxonomic classification of each representative DNA sequence. Four ML methods, namely least absolute shrinkage and selection operator, extreme gradient boosting (XGBoost), random forest, and classification and regression trees with nested leave-one-out cross-validation were trained using ASV taxon-based data and full ASV-based data to identify key ASVs in each data set. The results revealed XGBoost to have the best predicted performance. Overlapping key features extracted when XGBoost was trained using the full ASV-based and ASV taxon-based data were identified, and 31 high-importance ASVs (HIASVs) were obtained, assigned importance scores, and ranked. The most significant difference observed was in the abundance of bacteria in the Lachnospiraceae and Ruminococcaceae families. The 31 HIASVs were used to train the XGBoost algorithm to differentiate individuals with and without MG. The model had high diagnostic classification power and could accurately predict and identify patients with MG. In addition, the abundance of Lachnospiraceae was associated with limb weakness severity. In this study, we discovered that the composition of gut microbiomes differed between MG and non-MG subjects. In addition, the proposed XGBoost model trained using 31 HIASVs had the most favorable performance with respect to analyzing gut microbiomes. These HIASVs selected by the ML model may serve as biomarkers for clinical use and mechanistic study in the future. Our proposed ML model can identify several taxonomic markers and effectively discriminate patients with MG from those without with a high accuracy, the ML strategy can be applied as a benchmark to conduct noninvasive screening of MG.
Collapse
Affiliation(s)
- Che-Cheng Chang
- PhD Program in Nutrition and Food Science, Fu Jen Catholic University, New Taipei City, Taiwan
- Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City, Taiwan
- Graduate Institute of Biomedical and Pharmaceutical Science, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Tzu-Chi Liu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Hou-Chang Chiu
- School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
- Department of Neurology, Taipei Medical University, Shuang-Ho Hospital, New Taipei City, Taiwan
| | - Wei-Ning Lin
- Graduate Institute of Biomedical and Pharmaceutical Science, Fu Jen Catholic University, New Taipei City, Taiwan
| |
Collapse
|
42
|
Huang C, Gin C, Fettweis J, Foxman B, Gelaye B, MacIntyre DA, Subramaniam A, Fraser W, Tabatabaei N, Callahan B. Meta-analysis reveals the vaginal microbiome is a better predictor of earlier than later preterm birth. BMC Biol 2023; 21:199. [PMID: 37743497 PMCID: PMC10518966 DOI: 10.1186/s12915-023-01702-2] [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: 04/27/2023] [Accepted: 09/12/2023] [Indexed: 09/26/2023] Open
Abstract
BACKGROUND High-throughput sequencing measurements of the vaginal microbiome have yielded intriguing potential relationships between the vaginal microbiome and preterm birth (PTB; live birth prior to 37 weeks of gestation). However, results across studies have been inconsistent. RESULTS Here, we perform an integrated analysis of previously published datasets from 12 cohorts of pregnant women whose vaginal microbiomes were measured by 16S rRNA gene sequencing. Of 2039 women included in our analysis, 586 went on to deliver prematurely. Substantial variation between these datasets existed in their definition of preterm birth, characteristics of the study populations, and sequencing methodology. Nevertheless, a small group of taxa comprised a vast majority of the measured microbiome in all cohorts. We trained machine learning (ML) models to predict PTB from the composition of the vaginal microbiome, finding low to modest predictive accuracy (0.28-0.79). Predictive accuracy was typically lower when ML models trained in one dataset predicted PTB in another dataset. Earlier preterm birth (< 32 weeks, < 34 weeks) was more predictable from the vaginal microbiome than late preterm birth (34-37 weeks), both within and across datasets. Integrated differential abundance analysis revealed a highly significant negative association between L. crispatus and PTB that was consistent across almost all studies. The presence of the majority (18 out of 25) of genera was associated with a higher risk of PTB, with L. iners, Prevotella, and Gardnerella showing particularly consistent and significant associations. Some example discrepancies between studies could be attributed to specific methodological differences but not most study-to-study variations in the relationship between the vaginal microbiome and preterm birth. CONCLUSIONS We believe future studies of the vaginal microbiome and PTB will benefit from a focus on earlier preterm births and improved reporting of specific patient metadata shown to influence the vaginal microbiome and/or birth outcomes.
Collapse
Affiliation(s)
- Caizhi Huang
- Bioinformatics Research Center, North Carolina State University, Raleigh, 27606, USA
| | - Craig Gin
- Department of Population Health and Pathobiology, North Carolina State University, Raleigh, 27607, USA
| | - Jennifer Fettweis
- Department of Obstetrics and Gynecology, Virginia Commonwealth University, Richmond, 23284, USA
| | - Betsy Foxman
- Thomas Francis School of Public Health, University of Michigan, Raleigh, 27606, USA
| | - Bizu Gelaye
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, 02115, USA
| | - David A MacIntyre
- March of Dimes Prematurity Research Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, SW7 2AZ, USA
| | - Akila Subramaniam
- Obstetrics & Gynecology and Maternal-Fetal Medicine, University of Alabama at Birmingham, Birmingham, 35294, USA
| | - William Fraser
- Departments of Obstetrics and Gynecology, University of Sherbrooke, Sherbrooke, J1K 2R1, USA
| | - Negar Tabatabaei
- Departments of Obstetrics and Gynecology, University of Sherbrooke, Sherbrooke, J1K 2R1, USA
- Department of Pharmacology and Regenerative Medicine, University of Illinois College of Medicine, Chicago, 60612, USA
| | - Benjamin Callahan
- Bioinformatics Research Center, North Carolina State University, Raleigh, 27606, USA.
- Department of Population Health and Pathobiology, North Carolina State University, Raleigh, 27607, USA.
| |
Collapse
|
43
|
D’Elia D, Truu J, Lahti L, Berland M, Papoutsoglou G, Ceci M, Zomer A, Lopes MB, Ibrahimi E, Gruca A, Nechyporenko A, Frohme M, Klammsteiner T, Pau ECDS, Marcos-Zambrano LJ, Hron K, Pio G, Simeon A, Suharoschi R, Moreno-Indias I, Temko A, Nedyalkova M, Apostol ES, Truică CO, Shigdel R, Telalović JH, Bongcam-Rudloff E, Przymus P, Jordamović NB, Falquet L, Tarazona S, Sampri A, Isola G, Pérez-Serrano D, Trajkovik V, Klucar L, Loncar-Turukalo T, Havulinna AS, Jansen C, Bertelsen RJ, Claesson MJ. Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action. Front Microbiol 2023; 14:1257002. [PMID: 37808321 PMCID: PMC10558209 DOI: 10.3389/fmicb.2023.1257002] [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: 07/11/2023] [Accepted: 09/05/2023] [Indexed: 10/10/2023] Open
Abstract
The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.
Collapse
Affiliation(s)
- Domenica D’Elia
- Department of Biomedical Sciences, National Research Council, Institute for Biomedical Technologies, Bari, Italy
| | - Jaak Truu
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Magali Berland
- Université Paris-Saclay, INRAE, MetaGenoPolis, Jouy-en-Josas, France
| | - Georgios Papoutsoglou
- JADBio Gnosis DA S.A., Science and Technology Park of Crete, Heraklion, Greece
- Department of Computer Science, University of Crete, Heraklion, Greece
| | - Michelangelo Ceci
- Department of Computer Science, University of Bari Aldo Moro, Bari, Italy
| | - Aldert Zomer
- Department of Biomolecular Health Sciences (Infectious Diseases and Immunology), Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
| | - Marta B. Lopes
- Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, Caparica, Portugal
- UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Caparica, Portugal
| | - Eliana Ibrahimi
- Department of Biology, University of Tirana, Tirana, Albania
| | - Aleksandra Gruca
- Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland
| | - Alina Nechyporenko
- Systems Engineering Department, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine
- Department of Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, Wildau, Germany
| | - Marcus Frohme
- Department of Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, Wildau, Germany
| | - Thomas Klammsteiner
- Department of Microbiology, Universität Innsbruck, Innsbruck, Austria
- Department of Ecology, Universität Innsbruck, Innsbruck, Austria
| | - Enrique Carrillo-de Santa Pau
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Madrid, Spain
| | - Laura Judith Marcos-Zambrano
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Madrid, Spain
| | - Karel Hron
- Department of Mathematical Analysis and Applications of Mathematics, Faculty of Science, Palacký University, Olomouc, Czechia
| | - Gianvito Pio
- Department of Computer Science, University of Bari Aldo Moro, Bari, Italy
| | - Andrea Simeon
- BioSense Institute, University of Novi Sad, Novi Sad, Serbia
| | - Ramona Suharoschi
- Molecular Nutrition and Proteomics Research Laboratory, Department of Food Science, University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, Cluj-Napoca, Romania
| | - Isabel Moreno-Indias
- Department of Endocrinology and Nutrition, Virgen de la Victoria University Hospital, the Biomedical Research Institute of Malaga and Platform in Nanomedicine (IBIMA-BIONAND Platform), University of Malaga, Malaga, Spain
| | - Andriy Temko
- Department of Electrical and Electronic Engineering, University College Cork, Cork, Ireland
| | | | - Elena-Simona Apostol
- Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
| | - Ciprian-Octavian Truică
- Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
| | - Rajesh Shigdel
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Jasminka Hasić Telalović
- Department of Computer Science, University Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina
| | - Erik Bongcam-Rudloff
- Swedish University of Agricultural Sciences, Department of Animal Breeding and Genetics, Uppsala, Sweden
| | | | - Naida Babić Jordamović
- Computational Biology, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy
- Verlab Research Institute for BIomedical Engineering, Medical Devices and Artificial Intelligence, Sarajevo, Bosnia and Herzegovina
| | - Laurent Falquet
- University of Fribourg and Swiss Institute of Bioinformatics, Fribourg, Switzerland
| | - Sonia Tarazona
- Department of Applied Statistics and Operations Research and Quality, Universitat Politècnica de València, València, Spain
| | - Alexia Sampri
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
| | - Gaetano Isola
- Department of General Surgery and Surgical-Medical Specialties, School of Dentistry, University of Catania, Catania, Italy
| | - David Pérez-Serrano
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Madrid, Spain
| | | | - Lubos Klucar
- Institute of Molecular Biology, Slovak Academy of Sciences, Bratislava, Slovakia
| | | | - Aki S. Havulinna
- Finnish Institute for Health and Welfare, Helsinki, Finland
- Institute for Molecular Medicine Finland, FIMM-HiLIFE, Helsinki, Finland
| | - Christian Jansen
- Biome Diagnostics GmbH, Vienna, Austria
- Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
| | | | | |
Collapse
|
44
|
Paraskevaidis I, Xanthopoulos A, Tsougos E, Triposkiadis F. Human Gut Microbiota in Heart Failure: Trying to Unmask an Emerging Organ. Biomedicines 2023; 11:2574. [PMID: 37761015 PMCID: PMC10526035 DOI: 10.3390/biomedicines11092574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/08/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023] Open
Abstract
There is a bidirectional relationship between the heart and the gut. The gut microbiota, the community of gut micro-organisms themselves, is an excellent gut-homeostasis keeper since it controls the growth of potentially harmful bacteria and protects the microbiota environment. There is evidence suggesting that a diet rich in fatty acids can be metabolized and converted by gut microbiota and hepatic enzymes to trimethyl-amine N-oxide (TMAO), a product that is associated with atherogenesis, platelet dysfunction, thrombotic events, coronary artery disease, stroke, heart failure (HF), and, ultimately, death. HF, by inducing gut ischemia, congestion, and, consequently, gut barrier dysfunction, promotes the intestinal leaking of micro-organisms and their products, facilitating their entrance into circulation and thus stimulating a low-grade inflammation associated with an immune response. Drugs used for HF may alter the gut microbiota, and, conversely, gut microbiota may modify the pharmacokinetic properties of the drugs. The modification of lifestyle based mainly on exercise and a Mediterranean diet, along with the use of pre- or probiotics, may be beneficial for the gut microbiota environment. The potential role of gut microbiota in HF development and progression is the subject of this review.
Collapse
Affiliation(s)
| | - Andrew Xanthopoulos
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece; (A.X.); (F.T.)
| | - Elias Tsougos
- 6th Department of Cardiology, Hygeia Hospital, 15123 Athens, Greece
| | - Filippos Triposkiadis
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece; (A.X.); (F.T.)
| |
Collapse
|
45
|
Niazi SK. The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives. Drug Des Devel Ther 2023; 17:2691-2725. [PMID: 37701048 PMCID: PMC10493153 DOI: 10.2147/dddt.s424991] [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: 06/28/2023] [Accepted: 08/24/2023] [Indexed: 09/14/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.
Collapse
|
46
|
Gupta R, Kumari S, Senapati A, Ambasta RK, Kumar P. New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease. Ageing Res Rev 2023; 90:102013. [PMID: 37429545 DOI: 10.1016/j.arr.2023.102013] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/26/2023] [Accepted: 07/06/2023] [Indexed: 07/12/2023]
Abstract
Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.
Collapse
Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA.
| | - Smita Kumari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA
| | | | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA.
| |
Collapse
|
47
|
Deschênes T, Tohoundjona FWE, Plante PL, Di Marzo V, Raymond F. Gene-based microbiome representation enhances host phenotype classification. mSystems 2023; 8:e0053123. [PMID: 37404032 PMCID: PMC10469787 DOI: 10.1128/msystems.00531-23] [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: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 07/06/2023] Open
Abstract
With the concomitant advances in both the microbiome and machine learning fields, the gut microbiome has become of great interest for the potential discovery of biomarkers to be used in the classification of the host health status. Shotgun metagenomics data derived from the human microbiome is composed of a high-dimensional set of microbial features. The use of such complex data for the modeling of host-microbiome interactions remains a challenge as retaining de novo content yields a highly granular set of microbial features. In this study, we compared the prediction performances of machine learning approaches according to different types of data representations derived from shotgun metagenomics. These representations include commonly used taxonomic and functional profiles and the more granular gene cluster approach. For the five case-control datasets used in this study (Type 2 diabetes, obesity, liver cirrhosis, colorectal cancer, and inflammatory bowel disease), gene-based approaches, whether used alone or in combination with reference-based data types, allowed improved or similar classification performances as the taxonomic and functional profiles. In addition, we show that using subsets of gene families from specific functional categories of genes highlight the importance of these functions on the host phenotype. This study demonstrates that both reference-free microbiome representations and curated metagenomic annotations can provide relevant representations for machine learning based on metagenomic data. IMPORTANCE Data representation is an essential part of machine learning performance when using metagenomic data. In this work, we show that different microbiome representations provide varied host phenotype classification performance depending on the dataset. In classification tasks, untargeted microbiome gene content can provide similar or improved classification compared to taxonomical profiling. Feature selection based on biological function also improves classification performance for some pathologies. Function-based feature selection combined with interpretable machine learning algorithms can generate new hypotheses that can potentially be assayed mechanistically. This work thus proposes new approaches to represent microbiome data for machine learning that can potentiate the findings associated with metagenomic data.
Collapse
Affiliation(s)
- Thomas Deschênes
- Centre Nutrition, Santé et Société (NUTRISS) – Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Québec, Canada
- Canada Research Excellence Chair on the Microbiome-Endocannabinoidome Axis in Metabolic Health (CERC-MEND), Quebec City, Quebec, Canada
- Institut Intelligence et Données, Université Laval, Québec, Canada
| | - Fred Wilfried Elom Tohoundjona
- Centre Nutrition, Santé et Société (NUTRISS) – Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Québec, Canada
- Canada Research Excellence Chair on the Microbiome-Endocannabinoidome Axis in Metabolic Health (CERC-MEND), Quebec City, Quebec, Canada
| | - Pier-Luc Plante
- Centre Nutrition, Santé et Société (NUTRISS) – Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Québec, Canada
- Canada Research Excellence Chair on the Microbiome-Endocannabinoidome Axis in Metabolic Health (CERC-MEND), Quebec City, Quebec, Canada
- Institut Intelligence et Données, Université Laval, Québec, Canada
| | - Vincenzo Di Marzo
- Centre Nutrition, Santé et Société (NUTRISS) – Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Québec, Canada
- Canada Research Excellence Chair on the Microbiome-Endocannabinoidome Axis in Metabolic Health (CERC-MEND), Quebec City, Quebec, Canada
- École de nutrition, Faculté des sciences de l’agriculture et de l’alimentation (FSAA), Université Laval, Québec, Canada
- Centre de recherche de l’Institut universitaire de cardiologie et de pneumologie de Québec (IUCPQ), Québec, Canada
- Département de médecine, Faculté de Médecine, Université Laval, Québec, Canada
- Joint International Unit on Chemical and Biomolecular Research on the Microbiome and its Impact on Metabolic Health and Nutrition (UMI-MicroMeNu), Quebec City, Canada
| | - Frédéric Raymond
- Centre Nutrition, Santé et Société (NUTRISS) – Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Québec, Canada
- Canada Research Excellence Chair on the Microbiome-Endocannabinoidome Axis in Metabolic Health (CERC-MEND), Quebec City, Quebec, Canada
- Institut Intelligence et Données, Université Laval, Québec, Canada
- École de nutrition, Faculté des sciences de l’agriculture et de l’alimentation (FSAA), Université Laval, Québec, Canada
| |
Collapse
|
48
|
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.
Collapse
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
| | | |
Collapse
|
49
|
Liu Y, Zhang YZ, Imoto S. Microbial Gene Ontology informed deep neural network for microbe functionality discovery in human diseases. PLoS One 2023; 18:e0290307. [PMID: 37603579 PMCID: PMC10441785 DOI: 10.1371/journal.pone.0290307] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 08/04/2023] [Indexed: 08/23/2023] Open
Abstract
The human microbiome plays a crucial role in human health and is associated with a number of human diseases. Determining microbiome functional roles in human diseases remains a biological challenge due to the high dimensionality of metagenome gene features. However, existing models were limited in providing biological interpretability, where the functional role of microbes in human diseases is unexplored. Here we propose to utilize a neural network-based model incorporating Gene Ontology (GO) relationship network to discover the microbe functionality in human diseases. We use four benchmark datasets, including diabetes, liver cirrhosis, inflammatory bowel disease, and colorectal cancer, to explore the microbe functionality in the human diseases. Our model discovered and visualized the novel candidates' important microbiome genes and their functions by calculating the important score of each gene and GO term in the network. Furthermore, we demonstrate that our model achieves a competitive performance in predicting the disease by comparison with other non-Gene Ontology informed models. The discovered candidates' important microbiome genes and their functions provide novel insights into microbe functional contribution.
Collapse
Affiliation(s)
- Yunjie Liu
- Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Yao-zhong Zhang
- Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Seiya Imoto
- Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| |
Collapse
|
50
|
Cao C, Jin X, Ding Q, Zhu J, Yang D, Fan B. The altered composition of gut microbiota and biochemical features as well as dietary patterns in a southern Chinese population with recurrent renal calcium oxalate stones. Urolithiasis 2023; 51:95. [PMID: 37458823 DOI: 10.1007/s00240-023-01467-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 07/05/2023] [Indexed: 07/20/2023]
Abstract
The correlation among gut microbiota, biochemical features, and dietary patterns in recurrent stone formers has been inadequately investigated in the Chinese population. Forty-two patients with calcium oxalate stones (CaOxS group), including 34 recurrent stone formers (RS group), and 40 nonstone healthy subjects (NS group) from Changshu Hospital Affiliated with Soochow University, were prospectively recruited. Food frequency questionnaires were completed by participants, fasting vein blood was extracted, 24-h urine was collected for biochemical detection, and fecal samples were gathered for 16S ribosomal RNA (rRNA) gene sequencing. BMI; serum levels of triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), magnesium, and creatinine; and urine levels of magnesium in stone formers were significantly different from those of controls, and RS patients showed significantly low serum phosphate and high urine phosphate levels. Celery, bamboo shoots, and pickled food were the favored foods of local stone formers. Patients with recurrent stones had altered microbiota composition, with Escherichia, Fusobacterium, and Epulopiscium being the predominant pathogenic genera. The gut microbiota in RS patients had stronger functions in fatty acid and amino acid degradation but weaker functions in their biosynthesis. The pathogenic genera were positively correlated with BMI; serum levels of TGs and creatinine; urine levels of calcium, phosphate, and uric acid (UA); and celery, bamboo shoots, and pickled food intake. The abundance of Escherichia and Fusobacterium and the levels of serum magnesium and creatinine were the most relevant factors associated with stone recurrence and could be validated as biomarkers of recurrence. Our research provides a novel prevention strategy for the recurrence of renal calcium oxalate stones in the Han Chinese population of southern China.
Collapse
Affiliation(s)
- Cheng Cao
- Department of Urology, The Changshu Hospital Affiliated to Soochow University, Changshu, China
| | - Xiaohua Jin
- Department of Urology, The Changshu Hospital Affiliated to Soochow University, Changshu, China
| | - Qi Ding
- Department of Urology, The Changshu Hospital Affiliated to Soochow University, Changshu, China
| | - Jin Zhu
- Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Dongrong Yang
- Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Bo Fan
- Department of Urology, The Changshu Hospital Affiliated to Soochow University, Changshu, China.
| |
Collapse
|