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Pujolassos M, Susín A, Calle M. Microbiome compositional data analysis for survival studies. NAR Genom Bioinform 2024; 6:lqae038. [PMID: 38666212 PMCID: PMC11044448 DOI: 10.1093/nargab/lqae038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/23/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024] Open
Abstract
The growing interest in studying the relationship between the human microbiome and our health has also extended to time-to-event studies where researchers explore the connection between the microbiome and the occurrence of a specific event of interest. The analysis of microbiome obtained through high throughput sequencing techniques requires the use of specialized Compositional Data Analysis (CoDA) methods designed to accommodate its compositional nature. There is a limited availability of statistical tools for microbiome analysis that incorporate CoDA, and this is even more pronounced in the context of survival analysis. To fill this methodological gap, we present coda4microbiome for survival studies, a new methodology for the identification of microbial signatures in time-to-event studies. The algorithm implements an elastic-net penalized Cox regression model adapted to compositional covariates. We illustrate coda4microbiome algorithm for survival studies with a case study about the time to develop type 1 diabetes for non-obese diabetic mice. Our algorithm identified a bacterial signature composed of 21 genera associated with diabetes development. coda4microbiome for survival studies is integrated in the R package coda4microbiome as an extension of the existing functions for cross-sectional and longitudinal studies.
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Affiliation(s)
- Meritxell Pujolassos
- Bioscience Department, Faculty of Sciences, Technology and Engineering, University of Vic – Central University of Catalunya, Vic 08500, Spain
| | - Antoni Susín
- Mathematical Department, UPC-Barcelona Tech, Barcelona 08034, Spain
| | - M.Luz Calle
- Bioscience Department, Faculty of Sciences, Technology and Engineering, University of Vic – Central University of Catalunya, Vic 08500, Spain
- Institut de Recerca i Innovació en Ciències de la Vida i de la Salut a la Catalunya Central (IRIS-CC), Vic 08500, Spain
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2
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Simeon A, Radovanović M, Lončar-Turukalo T, Ceci M, Brdar S, Pio G. Multi-class boosting for the analysis of multiple incomplete views on microbiome data. BMC Bioinformatics 2024; 25:188. [PMID: 38745112 PMCID: PMC11092168 DOI: 10.1186/s12859-024-05767-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 04/04/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Microbiome dysbiosis has recently been associated with different diseases and disorders. In this context, machine learning (ML) approaches can be useful either to identify new patterns or learn predictive models. However, data to be fed to ML methods can be subject to different sampling, sequencing and preprocessing techniques. Each different choice in the pipeline can lead to a different view (i.e., feature set) of the same individuals, that classical (single-view) ML approaches may fail to simultaneously consider. Moreover, some views may be incomplete, i.e., some individuals may be missing in some views, possibly due to the absence of some measurements or to the fact that some features are not available/applicable for all the individuals. Multi-view learning methods can represent a possible solution to consider multiple feature sets for the same individuals, but most existing multi-view learning methods are limited to binary classification tasks or cannot work with incomplete views. RESULTS We propose irBoost.SH, an extension of the multi-view boosting algorithm rBoost.SH, based on multi-armed bandits. irBoost.SH solves multi-class classification tasks and can analyze incomplete views. At each iteration, it identifies one winning view using adversarial multi-armed bandits and uses its predictions to update a shared instance weight distribution in a learning process based on boosting. In our experiments, performed on 5 multi-view microbiome datasets, the model learned by irBoost.SH always outperforms the best model learned from a single view, its closest competitor rBoost.SH, and the model learned by a multi-view approach based on feature concatenation, reaching an improvement of 11.8% of the F1-score in the prediction of the Autism Spectrum disorder and of 114% in the prediction of the Colorectal Cancer disease. CONCLUSIONS The proposed method irBoost.SH exhibited outstanding performances in our experiments, also compared to competitor approaches. The obtained results confirm that irBoost.SH can fruitfully be adopted for the analysis of microbiome data, due to its capability to simultaneously exploit multiple feature sets obtained through different sequencing and preprocessing pipelines.
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Affiliation(s)
- Andrea Simeon
- BioSense Institute, University of Novi Sad, dr Zorana Djindjića 1, Novi Sad, 21000, Serbia.
| | - Miloš Radovanović
- Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovića 3, Novi Sad, 21000, Serbia
| | - Tatjana Lončar-Turukalo
- Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, Novi Sad, 21000, Serbia
| | - Michelangelo Ceci
- Department of Computer Science, University Bari Aldo Moro, Via Orabona 4, 70125, Bari, Italy
- Big Data Laboratory, National Interuniversity Consortium for Informatics (CINI), Via Ariosto 25, 00185, Rome, Italy
- Department of Knowledge Technologies, Jožef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia
| | - Sanja Brdar
- BioSense Institute, University of Novi Sad, dr Zorana Djindjića 1, Novi Sad, 21000, Serbia
| | - Gianvito Pio
- Department of Computer Science, University Bari Aldo Moro, Via Orabona 4, 70125, Bari, Italy.
- Big Data Laboratory, National Interuniversity Consortium for Informatics (CINI), Via Ariosto 25, 00185, Rome, Italy.
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Asnicar F, Thomas AM, Passerini A, Waldron L, Segata N. Machine learning for microbiologists. Nat Rev Microbiol 2024; 22:191-205. [PMID: 37968359 DOI: 10.1038/s41579-023-00984-1] [Citation(s) in RCA: 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.
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Affiliation(s)
- Francesco Asnicar
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy
| | - Andrew Maltez Thomas
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy
| | - Andrea Passerini
- Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
| | - Levi Waldron
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy.
- Department of Epidemiology and Biostatistics, City University of New York, New York, NY, USA.
| | - Nicola Segata
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy.
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy.
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Gradisteanu Pircalabioru G, Raileanu M, Dionisie MV, Lixandru-Petre IO, Iliescu C. Fast detection of bacterial gut pathogens on miniaturized devices: an overview. Expert Rev Mol Diagn 2024; 24:201-218. [PMID: 38347807 DOI: 10.1080/14737159.2024.2316756] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 02/06/2024] [Indexed: 03/23/2024]
Abstract
INTRODUCTION Gut microbes pose challenges like colon inflammation, deadly diarrhea, antimicrobial resistance dissemination, and chronic disease onset. Development of early, rapid and specific diagnosis tools is essential for improving infection control. Point-of-care testing (POCT) systems offer rapid, sensitive, low-cost and sample-to-answer methods for microbe detection from various clinical and environmental samples, bringing the advantages of portability, automation, and simple operation. AREAS COVERED Rapid detection of gut microbes can be done using a wide array of techniques including biosensors, immunological assays, electrochemical impedance spectroscopy, mass spectrometry and molecular biology. Inclusion of Internet of Things, machine learning, and smartphone-based point-of-care applications is an important aspect of POCT. In this review, the authors discuss various fast diagnostic platforms for gut pathogens and their main challenges. EXPERT OPINION Developing effective assays for microbe detection can be complex. Assay design must consider factors like target selection, real-time and multiplex detection, sample type, reagent stability and storage, primer/probe design, and optimizing reaction conditions for accuracy and sensitivity. Mitigating these challenges requires interdisciplinary collaboration among scientists, clinicians, engineers, and industry partners. Future efforts are essential to enhance sensitivity, specificity, and versatility of POCT systems for gut microbe detection and quantification, advancing infectious disease diagnostics and management.
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Affiliation(s)
- Gratiela Gradisteanu Pircalabioru
- eBio-hub Research Centre, National University of Science and Technology "Politehnica" Bucharest, Bucharest, Romania
- Division of Earth, Environmental and Life Sciences, The Research Institute of University of Bucharest (ICUB), Bucharest, Romania
- Academy of Romanian Scientists, Bucharest, Romania
| | - Mina Raileanu
- eBio-hub Research Centre, National University of Science and Technology "Politehnica" Bucharest, Bucharest, Romania
- Department of Life and Environmental Physics, Horia Hulubei National Institute of Physics and Nuclear Engineering, Magurele, Romania
| | - Mihai Viorel Dionisie
- eBio-hub Research Centre, National University of Science and Technology "Politehnica" Bucharest, Bucharest, Romania
| | - Irina-Oana Lixandru-Petre
- eBio-hub Research Centre, National University of Science and Technology "Politehnica" Bucharest, Bucharest, Romania
| | - Ciprian Iliescu
- eBio-hub Research Centre, National University of Science and Technology "Politehnica" Bucharest, Bucharest, Romania
- Academy of Romanian Scientists, Bucharest, Romania
- Microsystems in Biomedical and Environmental Applications, National Research and Development Institute for Microtechnology, Bucharest, Romania
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Giacobbe DR, Zhang Y, de la Fuente J. Explainable artificial intelligence and machine learning: novel approaches to face infectious diseases challenges. Ann Med 2023; 55:2286336. [PMID: 38010090 PMCID: PMC10836268 DOI: 10.1080/07853890.2023.2286336] [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: 08/16/2023] [Accepted: 11/16/2023] [Indexed: 11/29/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) are revolutionizing human activities in various fields, with medicine and infectious diseases being not exempt from their rapid and exponential growth. Furthermore, the field of explainable AI and ML has gained particular relevance and is attracting increasing interest. Infectious diseases have already started to benefit from explainable AI/ML models. For example, they have been employed or proposed to better understand complex models aimed at improving the diagnosis and management of coronavirus disease 2019, in the field of antimicrobial resistance prediction and in quantum vaccine algorithms. Although some issues concerning the dichotomy between explainability and interpretability still require careful attention, an in-depth understanding of how complex AI/ML models arrive at their predictions or recommendations is becoming increasingly essential to properly face the growing challenges of infectious diseases in the present century.
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Affiliation(s)
- Daniele Roberto Giacobbe
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
- Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Italy
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK
- School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - José de la Fuente
- SaBio (Health and Biotechnology), Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM, Ciudad Real, Spain
- Department of Veterinary Pathobiology, Center for Veterinary Health Sciences, Oklahoma State University, Stillwater, OK, USA
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Lathe R, Schultek NM, Balin BJ, Ehrlich GD, Auber LA, Perry G, Breitschwerdt EB, Corry DB, Doty RL, Rissman RA, Nara PL, Itzhaki R, Eimer WA, Tanzi RE. Establishment of a consensus protocol to explore the brain pathobiome in patients with mild cognitive impairment and Alzheimer's disease: Research outline and call for collaboration. Alzheimers Dement 2023; 19:5209-5231. [PMID: 37283269 PMCID: PMC10918877 DOI: 10.1002/alz.13076] [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: 02/08/2023] [Accepted: 03/06/2023] [Indexed: 06/08/2023]
Abstract
Microbial infections of the brain can lead to dementia, and for many decades microbial infections have been implicated in Alzheimer's disease (AD) pathology. However, a causal role for infection in AD remains contentious, and the lack of standardized detection methodologies has led to inconsistent detection/identification of microbes in AD brains. There is a need for a consensus methodology; the Alzheimer's Pathobiome Initiative aims to perform comparative molecular analyses of microbes in post mortem brains versus cerebrospinal fluid, blood, olfactory neuroepithelium, oral/nasopharyngeal tissue, bronchoalveolar, urinary, and gut/stool samples. Diverse extraction methodologies, polymerase chain reaction and sequencing techniques, and bioinformatic tools will be evaluated, in addition to direct microbial culture and metabolomic techniques. The goal is to provide a roadmap for detecting infectious agents in patients with mild cognitive impairment or AD. Positive findings would then prompt tailoring of antimicrobial treatments that might attenuate or remit mounting clinical deficits in a subset of patients.
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Affiliation(s)
- Richard Lathe
- Division of Infection Medicine, Chancellor's Building, University of Edinburgh Medical School, Edinburgh, UK
| | | | - Brian J. Balin
- Department of Bio-Medical Sciences, Philadelphia College of Osteopathic Medicine, Philadelphia, PA 19131, USA
| | - Garth D. Ehrlich
- Center for Genomic Sciences, Institute for Molecular Medicine and Infectious Disease, Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA 19102, USA
| | | | - George Perry
- Department of Biology, The University of Texas at San Antonio, San Antonio, TX, USA
| | - Edward B. Breitschwerdt
- Intracellular Pathogens Research Laboratory, Comparative Medicine Institute, College of Veterinary Medicine, North Carolina State University, Raleigh, NC 27607, USA
| | - David B. Corry
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Richard L. Doty
- Smell and Taste Center, Department of Otorhinolaryngology: Head and Neck Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Robert A. Rissman
- Department of Neurosciences, University of California, San Diego and VA San Diego Healthcare System, La Jolla, CA
| | | | - Ruth Itzhaki
- Institute of Population Ageing, University of Oxford, Oxford, UK
| | - William A. Eimer
- Genetics and Aging Research Unit, Mass General Institute for Neurodegenerative Disease, Charlestown, MA 02129, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
- McCance Cancer Center for Brain Health, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Rudolph E. Tanzi
- Genetics and Aging Research Unit, Mass General Institute for Neurodegenerative Disease, Charlestown, MA 02129, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
- McCance Cancer Center for Brain Health, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Intracell Research Group Consortium Collaborators
- David L. Hahn (Intracell Research Group, USA), Benedict C. Albensi (Nova Southeastern, USA), James St John (Griffith University, Australia), Jenny Ekberg (Griffith University, Australia), Mark L. Nelson (Intracell Research Group, USA), Gerald McLaughlin (National Institutes of Health, USA), Christine Hammond (Philadelphia College of Osteopathic Medicine, USA), Judith Whittum-Hudson (Wayne State University, USA), Alan P. Hudson (Wayne State University, USA), Guillaume Sacco (Université Cote d’Azur, Centre Hospitalier Universitaire de Nice, CoBTek, France), Alexandra Konig (Université Cote d’Azur and CoBTek, France), Bruno Pietro Imbimbo (Chiesi Farmaceutici, Parma, Italy), Nicklas Linz (Ki Elements Ltd, Saarbrücken, Germany), Nicole Danielle Bell (Author, 'What Lurks in the Woods'), Shima T. Moein (Smell and Taste Center, Department of Otorhinolaryngology, Perelman School of Medicine, University of Philadelphia, USA), Jürgen G. Haas (Infection Medicine, University of Edinburgh Medical School, UK)
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7
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Ibrahimi E, Lopes MB, Dhamo X, Simeon A, Shigdel R, Hron K, Stres B, D’Elia D, Berland M, Marcos-Zambrano LJ. Overview of data preprocessing for machine learning applications in human microbiome research. Front Microbiol 2023; 14:1250909. [PMID: 37869650 PMCID: PMC10588656 DOI: 10.3389/fmicb.2023.1250909] [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: 06/30/2023] [Accepted: 09/22/2023] [Indexed: 10/24/2023] Open
Abstract
Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.
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Affiliation(s)
- Eliana Ibrahimi
- Department of Biology, Faculty of Natural Sciences, University of Tirana, Tirana, Albania
| | - Marta B. Lopes
- Department of Mathematics, 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
| | - Xhilda Dhamo
- Department of Applied Mathematics, Faculty of Natural Sciences, University of Tirana, Tirana, Albania
| | - Andrea Simeon
- BioSense Institute, University of Novi Sad, Novi Sad, Serbia
| | - Rajesh Shigdel
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Karel Hron
- Department of Mathematical Analysis and Applications of Mathematics, Faculty of Science, Palacký University Olomouc, Olomouc, Czechia
| | - Blaž Stres
- Department of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, Institute of Sanitary Engineering, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jožef Stefan Institute, Ljubljana, Slovenia
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Domenica D’Elia
- Department of Biomedical Sciences, National Research Council, Institute for Biomedical Technologies, Bari, Italy
| | - Magali Berland
- INRAE, MetaGenoPolis, Université Paris-Saclay, Jouy-en-Josas, France
| | - Laura Judith Marcos-Zambrano
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, Madrid, Spain
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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.
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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
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Papoutsoglou G, Tarazona S, Lopes MB, Klammsteiner T, Ibrahimi E, Eckenberger J, Novielli P, Tonda A, Simeon A, Shigdel R, Béreux S, Vitali G, Tangaro S, Lahti L, Temko A, Claesson MJ, Berland M. Machine learning approaches in microbiome research: challenges and best practices. Front Microbiol 2023; 14:1261889. [PMID: 37808286 PMCID: PMC10556866 DOI: 10.3389/fmicb.2023.1261889] [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: 07/19/2023] [Accepted: 09/04/2023] [Indexed: 10/10/2023] Open
Abstract
Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications.
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Affiliation(s)
- Georgios Papoutsoglou
- Department of Computer Science, University of Crete, Heraklion, Greece
- JADBio Gnosis DA S.A., Science and Technology Park of Crete, Heraklion, Greece
| | - Sonia Tarazona
- Department of Applied Statistics and Operations Research and Quality, Polytechnic University of Valencia, Valencia, Spain
| | - Marta B. Lopes
- Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, Caparica, Portugal
- Research and Development Unit for Mechanical and Industrial Engineering (UNIDEMI), Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Caparica, Portugal
| | - Thomas Klammsteiner
- Department of Ecology, Universität Innsbruck, Innsbruck, Austria
- Department of Microbiology, Universität Innsbruck, Innsbruck, Austria
| | - Eliana Ibrahimi
- Department of Biology, University of Tirana, Tirana, Albania
| | - Julia Eckenberger
- School of Microbiology, University College Cork, Cork, Ireland
- APC Microbiome Ireland, Cork, Ireland
| | - Pierfrancesco Novielli
- Department of Soil, Plant, and Food Sciences, University of Bari Aldo Moro, Bari, Italy
- National Institute for Nuclear Physics, Bari Division, Bari, Italy
| | - Alberto Tonda
- UMR 518 MIA-PS, INRAE, Paris-Saclay University, Palaiseau, France
- Complex Systems Institute of Paris Ile-de-France (ISC-PIF) - UAR 3611 CNRS, Paris, France
| | - Andrea Simeon
- BioSense Institute, University of Novi Sad, Novi Sad, Serbia
| | - Rajesh Shigdel
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Stéphane Béreux
- MetaGenoPolis, INRAE, Paris-Saclay University, Jouy-en-Josas, France
- MaIAGE, INRAE, Paris-Saclay University, Jouy-en-Josas, France
| | - Giacomo Vitali
- MetaGenoPolis, INRAE, Paris-Saclay University, Jouy-en-Josas, France
| | - Sabina Tangaro
- Department of Soil, Plant, and Food Sciences, University of Bari Aldo Moro, Bari, Italy
- National Institute for Nuclear Physics, Bari Division, Bari, Italy
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Andriy Temko
- Department of Electrical and Electronic Engineering, University College Cork, Cork, Ireland
| | - Marcus J. Claesson
- School of Microbiology, University College Cork, Cork, Ireland
- APC Microbiome Ireland, Cork, Ireland
| | - Magali Berland
- MetaGenoPolis, INRAE, Paris-Saclay University, Jouy-en-Josas, France
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10
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Cooper K, Clarke M, Clayton JB. Informatics for your Gut: at the Interface of Nutrition, the Microbiome, and Technology. Yearb Med Inform 2023; 32:89-98. [PMID: 37414029 PMCID: PMC10751132 DOI: 10.1055/s-0043-1768723] [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] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND A significant portion of individuals in the United States and worldwide experience diseases related to or driven by diet. As research surrounding user-centered design and the microbiome grows, movement of the spectrum of translational science from bench to bedside for improvement of human health through nutrition becomes more accessible. In this literature survey, we examined recent literature examining informatics research at the interface of nutrition and the microbiome. OBJECTIVES The objective of this survey was to synthesize recent literature describing how technology is being applied to understand health at the interface of nutrition and the microbiome focusing on the perspective of the consumer. METHODS A survey of the literature published between January 1, 2021 and October 10, 2022 was performed using the PubMed database and resulting literature was evaluated against inclusion and exclusion criteria. RESULTS A total of 139 papers were retrieved and evaluated against inclusion and exclusion criteria. After evaluation, 45 papers were reviewed in depth revealing four major themes: (1) microbiome and diet, (2) usability,(3) reproducibility and rigor, and (4) precision medicine and precision nutrition. CONCLUSIONS A review of the relationships between current literature on technology, nutrition and the microbiome, and self-management of dietary patterns was performed. Major themes that emerged from this survey revealed exciting new horizons for consumer management of diet and disease, as well as progress towards elucidating the relationship between diet, the microbiome, and health outcomes. The survey revealed continuing interest in the study of diet-related disease and the microbiome and acknowledgement of needs for data re-use, sharing, and unbiased and rigorous measurement of the microbiome. The literature also showed trends toward enhancing the usability of digital interventions to support consumer health and home management, and consensus building around how precision medicine and precision nutrition may be applied in the future to improve human health outcomes and prevent diet-related disease.
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Affiliation(s)
- Kate Cooper
- School of Interdisciplinary Informatics, College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, USA
| | - Martina Clarke
- School of Interdisciplinary Informatics, College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, USA
| | - Jonathan B. Clayton
- Department of Biology, University of Nebraska at Omaha, Omaha, NE, USA
- Department of Food Science and Technology, University of Nebraska—Lincoln, Lincoln, NE, USA
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
- Nebraska Food for Health Center, University of Nebraska—Lincoln, Lincoln, NE, USA
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11
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Christinaki AC, Theelen B, Zania A, Coutinho SDA, Cabañes JF, Boekhout T, Kouvelis VN. Co-evolution of large inverted repeats and G-quadruplex DNA in fungal mitochondria may facilitate mitogenome stability: the case of Malassezia. Sci Rep 2023; 13:6308. [PMID: 37072481 PMCID: PMC10113387 DOI: 10.1038/s41598-023-33486-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 04/13/2023] [Indexed: 05/03/2023] Open
Abstract
Mitogenomes are essential due to their contribution to cell respiration. Recently they have also been implicated in fungal pathogenicity mechanisms. Members of the basidiomycetous yeast genus Malassezia are an important fungal component of the human skin microbiome, linked to various skin diseases, bloodstream infections, and they are increasingly implicated in gut diseases and certain cancers. In this study, the comparative analysis of Malassezia mitogenomes contributed to phylogenetic tree construction for all species. The mitogenomes presented significant size and gene order diversity which correlates to their phylogeny. Most importantly, they showed the inclusion of large inverted repeats (LIRs) and G-quadruplex (G4) DNA elements, rendering Malassezia mitogenomes a valuable test case for elucidating the evolutionary mechanisms responsible for this genome diversity. Both LIRs and G4s coexist and convergently evolved to provide genome stability through recombination. This mechanism is common in chloroplasts but, hitherto, rarely found in mitogenomes.
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Affiliation(s)
- Anastasia C Christinaki
- Section of Genetics and Biotechnology, Department of Biology, National and Kapodistrian University of Athens, Panepistimiopolis, 15771, Athens, Greece
- Westerdijk Fungal Biodiversity Institute, Uppsalalaan 8, 3584 CT, Utrecht, The Netherlands
| | - Bart Theelen
- Westerdijk Fungal Biodiversity Institute, Uppsalalaan 8, 3584 CT, Utrecht, The Netherlands
| | - Alkmini Zania
- Section of Genetics and Biotechnology, Department of Biology, National and Kapodistrian University of Athens, Panepistimiopolis, 15771, Athens, Greece
- Westerdijk Fungal Biodiversity Institute, Uppsalalaan 8, 3584 CT, Utrecht, The Netherlands
| | | | - Javier F Cabañes
- Veterinary Mycology Group, Department of Animal Health and Anatomy, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain
| | - Teun Boekhout
- Westerdijk Fungal Biodiversity Institute, Uppsalalaan 8, 3584 CT, Utrecht, The Netherlands
- College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Vassili N Kouvelis
- Section of Genetics and Biotechnology, Department of Biology, National and Kapodistrian University of Athens, Panepistimiopolis, 15771, Athens, Greece.
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12
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Chung T, Yan R, Weller DL, Kovac J. Conditional Forest Models Built Using Metagenomic Data Accurately Predicted Salmonella Contamination in Northeastern Streams. Microbiol Spectr 2023; 11:e0038123. [PMID: 36946722 PMCID: PMC10100987 DOI: 10.1128/spectrum.00381-23] [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: 01/25/2023] [Accepted: 02/27/2023] [Indexed: 03/23/2023] Open
Abstract
The use of water contaminated with Salmonella for produce production contributes to foodborne disease burden. To reduce human health risks, there is a need for novel, targeted approaches for assessing the pathogen status of agricultural water. We investigated the utility of water microbiome data for predicting Salmonella contamination of streams used to source water for produce production. Grab samples were collected from 60 New York streams in 2018 and tested for Salmonella. Separately, DNA was extracted from the samples and used for Illumina shotgun metagenomic sequencing. Reads were trimmed and used to assign taxonomy with Kraken2. Conditional forest (CF), regularized random forest (RRF), and support vector machine (SVM) models were implemented to predict Salmonella contamination. Model performance was assessed using 10-fold cross-validation repeated 10 times to quantify area under the curve (AUC) and Kappa score. CF models outperformed the other two algorithms based on AUC (0.86, CF; 0.81, RRF; 0.65, SVM) and Kappa score (0.53, CF; 0.41, RRF; 0.12, SVM). The taxa that were most informative for accurately predicting Salmonella contamination based on CF were compared to taxa identified by ALDEx2 as being differentially abundant between Salmonella-positive and -negative samples. CF and differential abundance tests both identified Aeromonas salmonicida (variable importance [VI] = 0.012) and Aeromonas sp. strain CA23 (VI = 0.025) as the two most informative taxa for predicting Salmonella contamination. Our findings suggest that microbiome-based models may provide an alternative to or complement existing water monitoring strategies. Similarly, the informative taxa identified in this study warrant further investigation as potential indicators of Salmonella contamination of agricultural water. IMPORTANCE Understanding the associations between surface water microbiome composition and the presence of foodborne pathogens, such as Salmonella, can facilitate the identification of novel indicators of Salmonella contamination. This study assessed the utility of microbiome data and three machine learning algorithms for predicting Salmonella contamination of Northeastern streams. The research reported here both expanded the knowledge on the microbiome composition of surface waters and identified putative novel indicators (i.e., Aeromonas species) for Salmonella in Northeastern streams. These putative indicators warrant further research to assess whether they are consistent indicators of Salmonella contamination across regions, waterways, and years not represented in the data set used in this study. Validated indicators identified using microbiome data may be used as targets in the development of rapid (e.g., PCR-based) detection assays for the assessment of microbial safety of agricultural surface waters.
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Affiliation(s)
- Taejung Chung
- Department of Food Science, The Pennsylvania State University, University Park, Pennsylvania, USA
- Microbiome Center, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Runan Yan
- Department of Food Science, The Pennsylvania State University, University Park, Pennsylvania, USA
- Microbiome Center, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Daniel L. Weller
- Department of Statistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA
| | - Jasna Kovac
- Department of Food Science, The Pennsylvania State University, University Park, Pennsylvania, USA
- Microbiome Center, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
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13
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Feldner-Busztin D, Firbas Nisantzis P, Edmunds SJ, Boza G, Racimo F, Gopalakrishnan S, Limborg MT, Lahti L, de Polavieja GG. Dealing with dimensionality: the application of machine learning to multi-omics data. Bioinformatics 2023; 39:6986971. [PMID: 36637211 PMCID: PMC9907220 DOI: 10.1093/bioinformatics/btad021] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 12/02/2022] [Accepted: 01/11/2023] [Indexed: 01/14/2023] Open
Abstract
MOTIVATION Machine learning (ML) methods are motivated by the need to automate information extraction from large datasets in order to support human users in data-driven tasks. This is an attractive approach for integrative joint analysis of vast amounts of omics data produced in next generation sequencing and other -omics assays. A systematic assessment of the current literature can help to identify key trends and potential gaps in methodology and applications. We surveyed the literature on ML multi-omic data integration and quantitatively explored the goals, techniques and data involved in this field. We were particularly interested in examining how researchers use ML to deal with the volume and complexity of these datasets. RESULTS Our main finding is that the methods used are those that address the challenges of datasets with few samples and many features. Dimensionality reduction methods are used to reduce the feature count alongside models that can also appropriately handle relatively few samples. Popular techniques include autoencoders, random forests and support vector machines. We also found that the field is heavily influenced by the use of The Cancer Genome Atlas dataset, which is accessible and contains many diverse experiments. AVAILABILITY AND IMPLEMENTATION All data and processing scripts are available at this GitLab repository: https://gitlab.com/polavieja_lab/ml_multi-omics_review/ or in Zenodo: https://doi.org/10.5281/zenodo.7361807. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dylan Feldner-Busztin
- Champalimaud Centre for the Unknown, Champalimaud Foundation, 1400-038 Lisbon, Portugal
| | | | - Shelley Jane Edmunds
- Center for Evolutionary Hologenomics, GLOBE Institute, Faculty of Health and Medical Sciences, University of Copenhagen, 1353 Copenhagen, Denmark
| | - Gergely Boza
- Centre for Ecological Research, 1113 Budapest, Hungary
| | - Fernando Racimo
- Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Shyam Gopalakrishnan
- Center for Evolutionary Hologenomics, GLOBE Institute, Faculty of Health and Medical Sciences, University of Copenhagen, 1353 Copenhagen, Denmark
| | - Morten Tønsberg Limborg
- Center for Evolutionary Hologenomics, GLOBE Institute, Faculty of Health and Medical Sciences, University of Copenhagen, 1353 Copenhagen, Denmark
| | - Leo Lahti
- Department of Computing, University of Turku, 20014 Turku, Finland
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14
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Busato S, Gordon M, Chaudhari M, Jensen I, Akyol T, Andersen S, Williams C. Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies. CURRENT OPINION IN PLANT BIOLOGY 2023; 71:102326. [PMID: 36538837 PMCID: PMC9925409 DOI: 10.1016/j.pbi.2022.102326] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 11/08/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
The plant-associated microbiome is a key component of plant systems, contributing to their health, growth, and productivity. The application of machine learning (ML) in this field promises to help untangle the relationships involved. However, measurements of microbial communities by high-throughput sequencing pose challenges for ML. Noise from low sample sizes, soil heterogeneity, and technical factors can impact the performance of ML. Additionally, the compositional and sparse nature of these datasets can impact the predictive accuracy of ML. We review recent literature from plant studies to illustrate that these properties often go unmentioned. We expand our analysis to other fields to quantify the degree to which mitigation approaches improve the performance of ML and describe the mathematical basis for this. With the advent of accessible analytical packages for microbiome data including learning models, researchers must be familiar with the nature of their datasets.
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Affiliation(s)
- Sebastiano Busato
- Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, USA; NC Plant Sciences Initiative, North Carolina State University, Raleigh, USA
| | - Max Gordon
- Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, USA; NC Plant Sciences Initiative, North Carolina State University, Raleigh, USA
| | - Meenal Chaudhari
- Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, USA; NC Plant Sciences Initiative, North Carolina State University, Raleigh, USA
| | - Ib Jensen
- Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Turgut Akyol
- Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Stig Andersen
- Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Cranos Williams
- Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, USA; NC Plant Sciences Initiative, North Carolina State University, Raleigh, USA; Department of Plant and Microbial Biology, North Carolina State University, Raleigh, USA.
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15
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Nedyalkova M, Vasighi M, Azmoon A, Naneva L, Simeonov V. Sequence-Based Prediction of Plant Allergenic Proteins: Machine Learning Classification Approach. ACS OMEGA 2023; 8:3698-3704. [PMID: 36743013 PMCID: PMC9893444 DOI: 10.1021/acsomega.2c02842] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 11/21/2022] [Indexed: 06/18/2023]
Abstract
This Article proposes a novel chemometric approach to understanding and exploring the allergenic nature of food proteins. Using machine learning methods (supervised and unsupervised), this work aims to predict the allergenicity of plant proteins. The strategy is based on scoring descriptors and testing their classification performance. Partitioning was based on support vector machines (SVM), and a k-nearest neighbor (KNN) classifier was applied. A fivefold cross-validation approach was used to validate the KNN classifier in the variable selection step as well as the final classifier. To overcome the problem of food allergies, a robust and efficient method for protein classification is needed.
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Affiliation(s)
- Miroslava Nedyalkova
- Faculty
of Chemistry and Pharmacy, Inorganic Chemistry, University of Sofia, 1172Sofia, Bulgaria
- Department
of Chemistry, University of Fribourg, Chemin de Muse 9, CH-1700Fribourg, Switzerland
| | - Mahdi Vasighi
- Department
of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan45137, Iran
| | - Amirreza Azmoon
- Department
of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan45137, Iran
| | | | - Vasil Simeonov
- Department
of Inorganic Chemistry, University of Sofia, 1172Sofia, Bulgaria
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16
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Hu X, Haas JG, Lathe R. The electronic tree of life (eToL): a net of long probes to characterize the microbiome from RNA-seq data. BMC Microbiol 2022; 22:317. [PMID: 36550399 PMCID: PMC9773549 DOI: 10.1186/s12866-022-02671-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 10/11/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Microbiome analysis generally requires PCR-based or metagenomic shotgun sequencing, sophisticated programs, and large volumes of data. Alternative approaches based on widely available RNA-seq data are constrained because of sequence similarities between the transcriptomes of microbes/viruses and those of the host, compounded by the extreme abundance of host sequences in such libraries. Current approaches are also limited to specific microbial groups. There is a need for alternative methods of microbiome analysis that encompass the entire tree of life. RESULTS We report a method to specifically retrieve non-human sequences in human tissue RNA-seq data. For cellular microbes we used a bioinformatic 'net', based on filtered 64-mer sequences designed from small subunit ribosomal RNA (rRNA) sequences across the Tree of Life (the 'electronic tree of life', eToL), to comprehensively (98%) entrap all non-human rRNA sequences present in the target tissue. Using brain as a model, retrieval of matching reads, re-exclusion of human-related sequences, followed by contig building and species identification, is followed by confirmation of the abundance and identity of the corresponding species groups. We provide methods to automate this analysis. The method reduces the computation time versus metagenomics by a factor of >1000. A variant approach is necessary for viruses. Again, because of significant matches between viral and human sequences, a 'stripping' approach is essential. Contamination during workup is a potential problem, and we discuss strategies to circumvent this issue. To illustrate the versatility of the method we report the use of the eToL methodology to unambiguously identify exogenous microbial and viral sequences in human tissue RNA-seq data across the entire tree of life including Archaea, Bacteria, Chloroplastida, basal Eukaryota, Fungi, and Holozoa/Metazoa, and discuss the technical and bioinformatic challenges involved. CONCLUSIONS This generic methodology is likely to find wide application in microbiome analysis including diagnostics.
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Affiliation(s)
- Xinyue Hu
- Program in Bioinformatics, School of Biological Sciences, King's Buildings, University of Edinburgh, Edinburgh, EH9 3FD, UK
| | - Jürgen G Haas
- Division of Infection Medicine, University of Edinburgh, Little France, Edinburgh, EH16 4SB, UK
| | - Richard Lathe
- Division of Infection Medicine, University of Edinburgh, Little France, Edinburgh, EH16 4SB, UK.
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17
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Eggers S, Bixby M, Renzetti S, Curtin P, Gennings C. Human Microbiome Mixture Analysis Using Weighted Quantile Sum Regression. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:ijerph20010094. [PMID: 36612415 PMCID: PMC9819204 DOI: 10.3390/ijerph20010094] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/13/2022] [Accepted: 12/16/2022] [Indexed: 05/30/2023]
Abstract
Studies of the health effects of the microbiome often measure overall associations by using diversity metrics, and individual taxa associations in separate analyses, but do not consider the correlated relationships between taxa in the microbiome. In this study, we applied random subset weighted quantile sum regression with repeated holdouts (WQSRSRH), a mixture method successfully applied to 'omic data to account for relationships between many predictors, to processed amplicon sequencing data from the Human Microbiome Project. We simulated a binary variable associated with 20 operational taxonomic units (OTUs). WQSRSRH was used to test for the association between the microbiome and the simulated variable, adjusted for sex, and sensitivity and specificity were calculated. The WQSRSRH method was also compared to other standard methods for microbiome analysis. The method was further illustrated using real data from the Growth and Obesity Cohort in Chile to assess the association between the gut microbiome and body mass index. In the analysis with simulated data, WQSRSRH predicted the correct directionality of association between the microbiome and the simulated variable, with an average sensitivity and specificity of 75% and 70%, respectively, in identifying the 20 associated OTUs. WQSRSRH performed better than all other comparison methods. In the illustration analysis of the gut microbiome and obesity, the WQSRSRH analysis identified an inverse association between body mass index and the gut microbe mixture, identifying Bacteroides, Clostridium, Prevotella, and Ruminococcus as important genera in the negative association. The application of WQSRSRH to the microbiome allows for analysis of the mixture effect of all the taxa in the microbiome, while simultaneously identifying the most important to the mixture, and allowing for covariate adjustment. It outperformed other methods when using simulated data, and in analysis with real data found results consistent with other study findings.
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Affiliation(s)
- Shoshannah Eggers
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1057, New York, NY 10029, USA
| | - Moira Bixby
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1057, New York, NY 10029, USA
| | - Stefano Renzetti
- Department of Medical-Surgical Specialties, Radiological Sciences and Public Health, Università degli Studi di Brescia, Piazza del Mercato, 15, 25121 Brescia, Italy
| | - Paul Curtin
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1057, New York, NY 10029, USA
| | - Chris Gennings
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1057, New York, NY 10029, USA
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18
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Zhan C, Matsumoto H, Liu Y, Wang M. Pathways to engineering the phyllosphere microbiome for sustainable crop production. NATURE FOOD 2022; 3:997-1004. [PMID: 37118297 DOI: 10.1038/s43016-022-00636-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/12/2022] [Indexed: 04/30/2023]
Abstract
Current disease resistance breeding, which is largely dependent on the exploitation of resistance genes in host plants, faces the serious challenges of rapidly evolving phytopathogens. The phyllosphere is the largest biological surface on Earth and an untapped reservoir of functional microbiomes. The phyllosphere microbiome has the potential to defend against plant diseases. However, the mechanisms of how the microbiota assemble and function in the phyllosphere remain largely elusive, and this restricts the exploitation of the targeted beneficial microbes in the field. Here we review the endogenous and exogenous cues impacting microbiota assembly in the phyllosphere and how the phyllosphere microbiota in turn facilitate the disease resistance of host plants. We further construct a holistic framework by integrating of holo-omics, genetic manipulation, culture-dependent characterization and emerging artificial intelligence techniques, such as deep learning, to engineer the phyllosphere microbiome for sustainable crop production.
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Affiliation(s)
- Chengfang Zhan
- State Key Laboratory of Rice Biology & Ministry of Agricultural and Rural Affairs Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Hangzhou, China
- Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Institute of Pesticide and Environmental Toxicology, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Haruna Matsumoto
- State Key Laboratory of Rice Biology & Ministry of Agricultural and Rural Affairs Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Hangzhou, China
- Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Institute of Pesticide and Environmental Toxicology, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Yufei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Mengcen Wang
- State Key Laboratory of Rice Biology & Ministry of Agricultural and Rural Affairs Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Hangzhou, China.
- Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Institute of Pesticide and Environmental Toxicology, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China.
- Global Education Program for AgriScience Frontiers, Graduate School of Agriculture, Hokkaido University, Sapporo, Japan.
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19
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Diakou I, Papakonstantinou E, Papageorgiou L, Pierouli K, Dragoumani K, Spandidos DA, Bacopoulou F, Chrousos GP, Goulielmos GΝ, Eliopoulos E, Vlachakis D. Multiple sclerosis and computational biology (Review). Biomed Rep 2022; 17:96. [PMID: 36382258 PMCID: PMC9634047 DOI: 10.3892/br.2022.1579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/27/2022] [Indexed: 12/02/2022] Open
Abstract
Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased worldwide. The resultant symptoms may be debilitating and can substantially reduce the of patients. Computational biology, which involves the use of computational tools to answer biomedical questions, may provide the basis for novel healthcare approaches in the context of MS. The rapid accumulation of health data, and the ever-increasing computational power and evolving technology have helped to modernize and refine MS research. From the discovery of novel biomarkers to the optimization of treatment and a number of quality-of-life enhancements for patients, computational biology methods and tools are shaping the field of MS diagnosis, management and treatment. The final goal in such a complex disease would be personalized medicine, i.e., providing healthcare services that are tailored to the individual patient, in accordance to the particular biology of their disease and the environmental factors to which they are subjected. The present review article summarizes the current knowledge on MS, modern computational biology and the impact of modern computational approaches of MS.
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Affiliation(s)
- Io Diakou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Eleni Papakonstantinou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Louis Papageorgiou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Katerina Pierouli
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Konstantina Dragoumani
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Demetrios A. Spandidos
- Laboratory of Clinical Virology, School of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Flora Bacopoulou
- University Research Institute of Maternal and Child Health and Precision Medicine, and UNESCO Chair on Adolescent Health Care, National and Kapodistrian University of Athens, ‘Aghia Sophia’ Children's Hospital, 11527 Athens, Greece
| | - George P. Chrousos
- University Research Institute of Maternal and Child Health and Precision Medicine, and UNESCO Chair on Adolescent Health Care, National and Kapodistrian University of Athens, ‘Aghia Sophia’ Children's Hospital, 11527 Athens, Greece
| | - Georges Ν. Goulielmos
- Section of Molecular Pathology and Human Genetics, Department of Internal Medicine, School of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Elias Eliopoulos
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
| | - Dimitrios Vlachakis
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
- University Research Institute of Maternal and Child Health and Precision Medicine, and UNESCO Chair on Adolescent Health Care, National and Kapodistrian University of Athens, ‘Aghia Sophia’ Children's Hospital, 11527 Athens, Greece
- Division of Endocrinology and Metabolism, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of The Academy of Athens, 11527 Athens, Greece
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20
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Arcila-Galvis JE, Loria-Kohen V, Ramírez de Molina A, Carrillo de Santa Pau E, Marcos-Zambrano LJ. A comprehensive map of microbial biomarkers along the gastrointestinal tract for celiac disease patients. Front Microbiol 2022; 13:956119. [PMID: 36177469 PMCID: PMC9513315 DOI: 10.3389/fmicb.2022.956119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 08/11/2022] [Indexed: 11/13/2022] Open
Abstract
Dysbiosis of the microbiome has been related to Celiac disease (CeD) progress, an autoimmune disease characterized by gluten intolerance developed in genetically susceptible individuals under certain environmental factors. The microbiome contributes to CeD pathophysiology, modulating the immune response by the action of short-chain fatty acids (SCFA), affecting gut barrier integrity allowing the entrance of gluten-derived proteins, and degrading immunogenic peptides of gluten through endoprolyl peptidase enzymes. Despite the evidence suggesting the implication of gut microbiome over CeD pathogenesis, there is no consensus about the specific microbial changes observed in this pathology. Here, we compiled the largest dataset of 16S prokaryotic ribosomal RNA gene high-throughput sequencing for consensus profiling. We present for the first time an integrative analysis of metataxonomic data from patients with CeD, including samples from different body sites (saliva, pharynx, duodenum, and stool). We found the presence of coordinated changes through the gastrointestinal tract (GIT) characterized by an increase in Actinobacteria species in the upper GIT (pharynx and duodenum) and an increase in Proteobacteria in the lower GIT (duodenum and stool), as well as site-specific changes evidencing a dysbiosis in patients with CeD' microbiota. Moreover, we described the effect of adherence to a gluten-free diet (GFD) evidenced by an increase in beneficial bacteria and a decrease in some Betaproteobacteriales but not fully restoring CeD-related dysbiosis. Finally, we built a Random Forest model to classify patients based on the lower GIT composition achieving good performance.
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Affiliation(s)
- Juliana Estefanía Arcila-Galvis
- Computational Biology Group, Precision Nutrition, and Cancer Research Program, IMDEA Food Institute, Madrid, Spain.,Computational Epigenomics Laboratory, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom
| | - Viviana Loria-Kohen
- Nutrition and Clinical Trials Unit, GENYAL Platform IMDEA-Food Institute, Madrid, Spain.,Departamento de Nutrición y Ciencia de los Alimentos, Faculty of Pharmacy, Universidad Complutense de Madrid, Madrid, Spain
| | - Ana Ramírez de Molina
- Nutrition and Clinical Trials Unit, GENYAL Platform IMDEA-Food Institute, Madrid, Spain
| | | | - Laura Judith Marcos-Zambrano
- Computational Biology Group, Precision Nutrition, and Cancer Research Program, IMDEA Food Institute, Madrid, Spain
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21
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Matthewman C, Narin A, Huston H, Hopkins CE. Systems to model the personalized aspects of microbiome health and gut dysbiosis. Mol Aspects Med 2022; 91:101115. [PMID: 36104261 DOI: 10.1016/j.mam.2022.101115] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 08/03/2022] [Indexed: 01/17/2023]
Abstract
The human gut microbiome is a complex and dynamic microbial entity that interacts with the environment and other parts of the body including the brain, heart, liver, and immune system. These multisystem interactions are highly conserved from invertebrates to humans, however the complexity and diversity of human microbiota compositions often yield a context that is unique to each individual. Yet commonalities remain across species, where a healthy gut microbiome will be rich in symbiotic commensal biota while an unhealthy gut microbiota will be experiencing abnormal blooms of pathobiont bacteria. In this review we discuss how omics technologies can be applied in a personalized approach to understand the microbial crosstalk and microbial-host interactions that affect the delicate balance between eubiosis and dysbiosis in an individual gut microbiome. We further highlight the strengths of model organisms in identifying and characterizing these conserved synergistic and/or pathogenic host-microbe interactions. And finally, we touch upon the growing area of personalized therapeutic interventions targeting gut microbiome.
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22
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Ruuskanen MO, Vats D, Potbhare R, RaviKumar A, Munukka E, Ashma R, Lahti L. Towards standardized and reproducible research in skin microbiomes. Environ Microbiol 2022; 24:3840-3860. [PMID: 35229437 PMCID: PMC9790573 DOI: 10.1111/1462-2920.15945] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 02/15/2022] [Accepted: 02/16/2022] [Indexed: 12/30/2022]
Abstract
Skin is a complex organ serving a critical role as a barrier and mediator of interactions between the human body and its environment. Recent studies have uncovered how resident microbial communities play a significant role in maintaining the normal healthy function of the skin and the immune system. In turn, numerous host-associated and environmental factors influence these communities' composition and diversity across the cutaneous surface. In addition, specific compositional changes in skin microbiota have also been connected to the development of several chronic diseases. The current era of microbiome research is characterized by its reliance on large data sets of nucleotide sequences produced with high-throughput sequencing of sample-extracted DNA. These approaches have yielded new insights into many previously uncharacterized microbial communities. Application of standardized practices in the study of skin microbial communities could help us understand their complex structures, functional capacities, and health associations and increase the reproducibility of the research. Here, we overview the current research in human skin microbiomes and outline challenges specific to their study. Furthermore, we provide perspectives on recent advances in methods, analytical tools and applications of skin microbiomes in medicine and forensics.
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Affiliation(s)
- Matti O. Ruuskanen
- Department of Computing, Faculty of TechnologyUniversity of TurkuTurkuFinland
| | - Deepti Vats
- Department of Zoology, Centre of Advanced StudySavitribai Phule Pune UniversityPuneIndia
| | - Renuka Potbhare
- Department of Zoology, Centre of Advanced StudySavitribai Phule Pune UniversityPuneIndia
| | - Ameeta RaviKumar
- Institute of Bioinformatics and BiotechnologySavitribai Phule Pune UniversityPuneIndia
| | - Eveliina Munukka
- Microbiome Biobank, Institute of BiomedicineUniversity of TurkuTurkuFinland
| | - Richa Ashma
- Department of Zoology, Centre of Advanced StudySavitribai Phule Pune UniversityPuneIndia
| | - Leo Lahti
- Department of Computing, Faculty of TechnologyUniversity of TurkuTurkuFinland
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23
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Costantini C, Nunzi E, Romani L. From the nose to the lungs: the intricate journey of airborne pathogens amidst commensal bacteria. Am J Physiol Cell Physiol 2022; 323:C1036-C1043. [PMID: 36036448 PMCID: PMC9529274 DOI: 10.1152/ajpcell.00287.2022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The recent COVID-19 pandemic has dramatically brought the pitfalls of airborne pathogens to the attention of the scientific community. Not only viruses but also bacteria and fungi may exploit air transmission to colonize and infect potential hosts and be the cause of significant morbidity and mortality in susceptible populations. The efforts to decipher the mechanisms of pathogenicity of airborne microbes have brought to light the delicate equilibrium that governs the homeostasis of mucosal membranes. The microorganisms already thriving in the permissive environment of the respiratory tract represent a critical component of this equilibrium and a potent barrier to infection by means of direct competition with airborne pathogens or indirectly via modulation of the immune response. Moving down the respiratory tract, physicochemical and biological constraints promote site-specific expansion of microbes that engage in cross talk with the local immune system to maintain homeostasis and promote protection. In this review, we critically assess the site-specific microbial communities that an airborne pathogen encounters in its hypothetical travel along the respiratory tract and discuss the changes in the composition and function of the microbiome in airborne diseases by taking fungal and SARS-CoV-2 infections as examples. Finally, we discuss how technological and bioinformatics advancements may turn microbiome analysis into a valuable tool in the hands of clinicians to predict the risk of disease onset, the clinical course, and the response to treatment of individual patients in the direction of personalized medicine implementation.
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Affiliation(s)
- Claudio Costantini
- Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Emilia Nunzi
- Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Luigina Romani
- Department of Medicine and Surgery, University of Perugia, Perugia, Italy
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24
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Boyraz A, Pawlowsky-Glahn V, Egozcue JJ, Acar AC. Principal microbial groups: compositional alternative to phylogenetic grouping of microbiome data. Brief Bioinform 2022; 23:6675749. [PMID: 36007229 DOI: 10.1093/bib/bbac328] [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: 02/01/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 11/13/2022] Open
Abstract
Statistical and machine learning techniques based on relative abundances have been used to predict health conditions and to identify microbial biomarkers. However, high dimensionality, sparsity and the compositional nature of microbiome data represent statistical challenges. On the other hand, the taxon grouping allows summarizing microbiome abundance with a coarser resolution in a lower dimension, but it presents new challenges when correlating taxa with a disease. In this work, we present a novel approach that groups Operational Taxonomical Units (OTUs) based only on relative abundances as an alternative to taxon grouping. The proposed procedure acknowledges the compositional data making use of principal balances. The identified groups are called Principal Microbial Groups (PMGs). The procedure reduces the need for user-defined aggregation of $\textrm{OTU}$s and offers the possibility of working with coarse group of $\textrm{OTU}$s, which are not present in a phylogenetic tree. PMGs can be used for two different goals: (1) as a dimensionality reduction method for compositional data, (2) as an aggregation procedure that provides an alternative to taxon grouping for construction of microbial balances afterward used for disease prediction. We illustrate the procedure with a cirrhosis study data. PMGs provide a coherent data analysis for the search of biomarkers in human microbiota. The source code and demo data for PMGs are available at: https://github.com/asliboyraz/PMGs.
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Affiliation(s)
- Aslı Boyraz
- Department of Computer Programming, Recep Tayyip Erdoğan University, Ardeşen Vocational School, Rize, 53400, Turkey
| | - Vera Pawlowsky-Glahn
- Department of Computer Sciences, Applied Mathematics and Statistics, University of Girona, Campus Montilivi, 17003 Girona, Spain
| | - Juan José Egozcue
- Department of Civil and Environmental Engineering, Universitat Politécnica de Catalunya, Barcelona, 08034, Spain
| | - Aybar Can Acar
- Department of Medical Informatics, Middle East Technical University, Ankara Turkey
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25
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Abstract
![]()
AlphaFold has burst into our lives. A powerful algorithm
that underscores
the strength of biological sequence data and artificial intelligence
(AI). AlphaFold has appended projects and research directions. The
database it has been creating promises an untold number of applications
with vast potential impacts that are still difficult to surmise. AI
approaches can revolutionize personalized treatments and usher in
better-informed clinical trials. They promise to make giant leaps
toward reshaping and revamping drug discovery strategies, selecting
and prioritizing combinations of drug targets. Here, we briefly overview
AI in structural biology, including in molecular dynamics simulations
and prediction of microbiota–human protein–protein interactions.
We highlight the advancements accomplished by the deep-learning-powered
AlphaFold in protein structure prediction and their powerful impact
on the life sciences. At the same time, AlphaFold does not resolve
the decades-long protein folding challenge, nor does it identify the
folding pathways. The models that AlphaFold provides do not capture
conformational mechanisms like frustration and allostery, which are
rooted in ensembles, and controlled by their dynamic distributions.
Allostery and signaling are properties of populations. AlphaFold also
does not generate ensembles of intrinsically disordered proteins and
regions, instead describing them by their low structural probabilities.
Since AlphaFold generates single ranked structures, rather than conformational
ensembles, it cannot elucidate the mechanisms of allosteric activating
driver hotspot mutations nor of allosteric drug resistance. However,
by capturing key features, deep learning techniques can use the single
predicted conformation as the basis for generating a diverse ensemble.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, United States.,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Mingzhen Zhang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, United States
| | - Yonglan Liu
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, Maryland 21702, United States
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, United States
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26
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Liu T, Klammsteiner T, Dregulo AM, Kumar V, Zhou Y, Zhang Z, Awasthi MK. Black soldier fly larvae for organic manure recycling and its potential for a circular bioeconomy: A review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 833:155122. [PMID: 35405225 DOI: 10.1016/j.scitotenv.2022.155122] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 03/30/2022] [Accepted: 04/01/2022] [Indexed: 06/14/2023]
Abstract
Livestock farming and its products provide a diverse range of benefits for our day-to-day life. However, the ever-increasing demand for farmed animals has raised concerns about waste management and its impact on the environment. Worldwide, cattle produce enormous amounts of manure, which is detrimental to soil properties if poorly managed. Waste management with insect larvae is considered one of the most efficient techniques for resource recovery from manure. In recent years, the use of black soldier fly larvae (BSFL) for resource recovery has emerged as an effective method. Using BSFL has several advantages over traditional methods, as the larvae produce a safe compost and extract trace elements like Cu and Zn. This paper is a comprehensive review of the potential of BSFL for recycling organic wastes from livestock farming, manure bioconversion, parameters affecting the BSFL application on organic farming, and process performance of biomolecule degradation. The last part discusses the economic feasibility, lifecycle assessment, and circular bioeconomy of the BSFL in manure recycling. Moreover, it discusses the future perspectives associated with the application of BSFL. Specifically, this review discusses BSFL cultivation and its impact on the larvae's physiology, gut biochemical physiology, gut microbes and metabolic pathways, nutrient conservation and global warming potential, microbial decomposition of organic nutrients, total and pathogenic microbial dynamics, and recycling of rearing residues as fertilizer.
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Affiliation(s)
- Tao Liu
- College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, PR China
| | - Thomas Klammsteiner
- Department of Microbiology, University of Innsbruck, Technikerstrasse 25d, 6020 Innsbruck, Austria
| | - Andrei Mikhailovich Dregulo
- Federal State Budgetary Educational Institution of Higher Education "Saint-Petersburg State University" 7-9 Universitetskaya emb., 199034, Saint- Petersburg, Russia.
| | - Vinay Kumar
- Department of Biotechnology, Indian Institute of Technology (IIT) Roorkee, Roorkee 247667, Uttarakhand, India
| | - Yuwen Zhou
- College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, PR China
| | - Zengqiang Zhang
- College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, PR China
| | - Mukesh Kumar Awasthi
- College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, PR China.
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27
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Morgan EW, Perdew GH, Patterson AD. Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research. Toxicol Sci 2022; 187:189-213. [PMID: 35285497 PMCID: PMC9154275 DOI: 10.1093/toxsci/kfac029] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.
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Affiliation(s)
- Ethan W Morgan
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Gary H Perdew
- Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Andrew D Patterson
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.,Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
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28
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Vilne B, Ķibilds J, Siksna I, Lazda I, Valciņa O, Krūmiņa A. Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease. Front Microbiol 2022; 13:627892. [PMID: 35479632 PMCID: PMC9036178 DOI: 10.3389/fmicb.2022.627892] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 02/24/2022] [Indexed: 12/14/2022] Open
Abstract
Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and the main leading cause of morbidity and mortality worldwide, posing a huge socio-economic burden to the society and health systems. Therefore, timely and precise identification of people at high risk of CAD is urgently required. Most current CAD risk prediction approaches are based on a small number of traditional risk factors (age, sex, diabetes, LDL and HDL cholesterol, smoking, systolic blood pressure) and are incompletely predictive across all patient groups, as CAD is a multi-factorial disease with complex etiology, considered to be driven by both genetic, as well as numerous environmental/lifestyle factors. Diet is one of the modifiable factors for improving lifestyle and disease prevention. However, the current rise in obesity, type 2 diabetes (T2D) and CVD/CAD indicates that the “one-size-fits-all” approach may not be efficient, due to significant variation in inter-individual responses. Recently, the gut microbiome has emerged as a potential and previously under-explored contributor to these variations. Hence, efficient integration of dietary and gut microbiome information alongside with genetic variations and clinical data holds a great promise to improve CAD risk prediction. Nevertheless, the highly complex nature of meals combined with the huge inter-individual variability of the gut microbiome poses several Big Data analytics challenges in modeling diet-gut microbiota interactions and integrating these within CAD risk prediction approaches for the development of personalized decision support systems (DSS). In this regard, the recent re-emergence of Artificial Intelligence (AI) / Machine Learning (ML) is opening intriguing perspectives, as these approaches are able to capture large and complex matrices of data, incorporating their interactions and identifying both linear and non-linear relationships. In this Mini-Review, we consider (1) the most used AI/ML approaches and their different use cases for CAD risk prediction (2) modeling of the content, choice and impact of dietary factors on CAD risk; (3) classification of individuals by their gut microbiome composition into CAD cases vs. controls and (4) modeling of the diet-gut microbiome interactions and their impact on CAD risk. Finally, we provide an outlook for putting it all together for improved CAD risk predictions.
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Affiliation(s)
- Baiba Vilne
- Bioinformatics Lab, Riga Stradins University, Riga, Latvia
- COST Action CA18131 - Statistical and Machine Learning Techniques in Human Microbiome Studies, Brussels, Belgium
- *Correspondence: Baiba Vilne
| | - Juris Ķibilds
- Institute of Food Safety, Animal Health and Environment BIOR, Riga, Latvia
| | - Inese Siksna
- Institute of Food Safety, Animal Health and Environment BIOR, Riga, Latvia
| | - Ilva Lazda
- Institute of Food Safety, Animal Health and Environment BIOR, Riga, Latvia
| | - Olga Valciņa
- Institute of Food Safety, Animal Health and Environment BIOR, Riga, Latvia
| | - Angelika Krūmiņa
- Institute of Food Safety, Animal Health and Environment BIOR, Riga, Latvia
- Department of Infectology and Dermatology, Riga Stradins University, Riga, Latvia
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29
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Grazioli F, Siarheyeu R, Alqassem I, Henschel A, Pileggi G, Meiser A. Microbiome-based disease prediction with multimodal variational information bottlenecks. PLoS Comput Biol 2022; 18:e1010050. [PMID: 35404958 PMCID: PMC9022840 DOI: 10.1371/journal.pcbi.1010050] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 04/21/2022] [Accepted: 03/22/2022] [Indexed: 01/12/2023] Open
Abstract
Scientific research is shedding light on the interaction of the gut microbiome with the human host and on its role in human health. Existing machine learning methods have shown great potential in discriminating healthy from diseased microbiome states. Most of them leverage shotgun metagenomic sequencing to extract gut microbial species-relative abundances or strain-level markers. Each of these gut microbial profiling modalities showed diagnostic potential when tested separately; however, no existing approach combines them in a single predictive framework. Here, we propose the Multimodal Variational Information Bottleneck (MVIB), a novel deep learning model capable of learning a joint representation of multiple heterogeneous data modalities. MVIB achieves competitive classification performance while being faster than existing methods. Additionally, MVIB offers interpretable results. Our model adopts an information theoretic interpretation of deep neural networks and computes a joint stochastic encoding of different input data modalities. We use MVIB to predict whether human hosts are affected by a certain disease by jointly analysing gut microbial species-relative abundances and strain-level markers. MVIB is evaluated on human gut metagenomic samples from 11 publicly available disease cohorts covering 6 different diseases. We achieve high performance (0.80 < ROC AUC < 0.95) on 5 cohorts and at least medium performance on the remaining ones. We adopt a saliency technique to interpret the output of MVIB and identify the most relevant microbial species and strain-level markers to the model’s predictions. We also perform cross-study generalisation experiments, where we train and test MVIB on different cohorts of the same disease, and overall we achieve comparable results to the baseline approach, i.e. the Random Forest. Further, we evaluate our model by adding metabolomic data derived from mass spectrometry as a third input modality. Our method is scalable with respect to input data modalities and has an average training time of < 1.4 seconds. The source code and the datasets used in this work are publicly available. The gut microbiome can be an indicator of various diseases due to its interaction with the human system. Our main objective is to improve on the current state of the art in microbiome classification for diagnostic purposes. A rich body of literature evidences the clinical value of microbiome predictive models. Here, we propose the Multimodal Variational Information Bottleneck (MVIB), a novel deep learning model for microbiome-based disease prediction. MVIB learns a joint stochastic encoding of different input data modalities to predict the output class. We use MVIB to predict whether human hosts are affected by a certain disease by jointly analysing gut microbial species-relative abundance and strain-level marker profiles. Both of these gut microbial features showed diagnostic potential when tested separately in previous studies; however, no research has combined them in a single predictive tool. We evaluate MVIB on various human gut metagenomic samples from 11 publicly available disease cohorts. MVIB achieves competitive performance compared to state-of-the-art methods. Additionally, we evaluate our model by adding metabolomic data as a third input modality and we show that MVIB is scalable with respect to input feature modalities. Further, we adopt a saliency technique to interpret the output of MVIB and identify the most relevant microbial species and strain-level markers to our model predictions.
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Affiliation(s)
| | | | | | - Andreas Henschel
- Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, UAE
- Research and Data Intelligence Support Center, Khalifa University, Abu Dhabi, UAE
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30
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Lim H, Cankara F, Tsai CJ, Keskin O, Nussinov R, Gursoy A. Artificial intelligence approaches to human-microbiome protein–protein interactions. Curr Opin Struct Biol 2022; 73:102328. [DOI: 10.1016/j.sbi.2022.102328] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 12/01/2021] [Accepted: 12/31/2021] [Indexed: 02/08/2023]
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31
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Host phenotype classification from human microbiome data is mainly driven by the presence of microbial taxa. PLoS Comput Biol 2022; 18:e1010066. [PMID: 35446845 PMCID: PMC9064115 DOI: 10.1371/journal.pcbi.1010066] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 05/03/2022] [Accepted: 03/29/2022] [Indexed: 12/14/2022] Open
Abstract
Machine learning-based classification approaches are widely used to predict host phenotypes from microbiome data. Classifiers are typically employed by considering operational taxonomic units or relative abundance profiles as input features. Such types of data are intrinsically sparse, which opens the opportunity to make predictions from the presence/absence rather than the relative abundance of microbial taxa. This also poses the question whether it is the presence rather than the abundance of particular taxa to be relevant for discrimination purposes, an aspect that has been so far overlooked in the literature. In this paper, we aim at filling this gap by performing a meta-analysis on 4,128 publicly available metagenomes associated with multiple case-control studies. At species-level taxonomic resolution, we show that it is the presence rather than the relative abundance of specific microbial taxa to be important when building classification models. Such findings are robust to the choice of the classifier and confirmed by statistical tests applied to identifying differentially abundant/present taxa. Results are further confirmed at coarser taxonomic resolutions and validated on 4,026 additional 16S rRNA samples coming from 30 public case-control studies. The composition of the human microbiome has been linked to a large number of different diseases. In this context, classification methodologies based on machine learning approaches have represented a promising tool for diagnostic purposes from metagenomics data. The link between microbial population composition and host phenotypes has been usually performed by considering taxonomic profiles represented by relative abundances of microbial species. In this study, we show that it is more the presence rather than the relative abundance of microbial taxa to be relevant to maximize classification accuracy. This is accomplished by conducting a meta-analysis on more than 4,000 shotgun metagenomes coming from 25 case-control studies and in which original relative abundance data are degraded to presence/absence profiles. Findings are also extended to 16S rRNA data and advance the research field in building prediction models directly from human microbiome data.
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Potbhare R, RaviKumar A, Munukka E, Lahti L, Ashma R. Skin microbiota diversity among genetically unrelated individuals of Indian origin. PeerJ 2022; 10:e13075. [PMID: 35313523 PMCID: PMC8934042 DOI: 10.7717/peerj.13075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Accepted: 02/16/2022] [Indexed: 01/12/2023] Open
Abstract
Background Human skin harbors complex transient and resident microbial communities that show intra- & inter-individual variation due to various environmental and host-associated factors such as skin site, diet, age, gender, genetics, or the type and use of cosmetics. This variation remains largely uncharacterized in the Indian population; hence, the present study aims to characterize the variation in skin microbiota among individuals of Indian origin and quantify associations with age, diet, and geography. Methods Axillary sweat samples from genetically unrelated individuals (N = 58) residing in the three geographical locations of Maharashtra, India, were collected using a sterile cotton swab. Bacterial DNA was extracted using a standard protocol and checked for quality. Variable regions (V3-V4) of the 16S rRNA gene were sequenced using the Illumina platform. We used standard methods from microbiota bioinformatics, including alpha and beta diversity, community typing, and differential abundance, to quantify the association of skin microbiota with age, diet, and geographical location. Results Our study indicated the prevalence of phyla- Firmicutes, Proteobacteria, and Actinobacteria, consistent with previous reports on skin microbiota composition of the world population level. The alpha diversity (Shannon index) was significantly associated with the age group (Kruskal-Wallis test, p = 0.02), but not with geography (p = 0.62) or diet (p = 0.74). The overall skin microbiota community composition was significantly associated with geographical location based on Community State Types (CST) analysis and PERMANOVA (R2 = 0.07, p = 0.01). Differential abundance analysis at the genus level indicated a distinctively high abundance of Staphylococcus and Corynebacterium among individuals of the Pune district. Pseudomonas and Anaerococcus were abundant in individuals from Ahmednagar whereas, Paenibacillus, Geobacillus, Virgibacillus, Jeotgalicoccus, Pullulanibacillus, Delsulfosporomusa, Citinovibrio, and Calditerricola were abundant in individuals from Nashik district. Conclusion Our work provides one of the first characterizations of skin microbiota variation in different sub-populations in India. The analysis quantifies the level of individuality, as contrasted to the other factors of age, geography, and diet, thus helping to evaluate the applicability of skin microbiota profiles as a potential biomarker to stratify individuals.
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Affiliation(s)
- Renuka Potbhare
- Department of Zoology, Savitribai Phule Pune University, Pune, Maharashtra, India
| | - Ameeta RaviKumar
- Institute of Bioinformatics and Biotechnology, Savitribai Phule Pune University, Pune, Maharashtra, India
| | - Eveliina Munukka
- Microbiome Biobank, Institute of Biomedicine, University of Turku, Turku, Finland,Biocodex Nordics, Finland
| | - Leo Lahti
- Department of Computing, Faculty of Technology, University of Turku, Turku, Finland
| | - Richa Ashma
- Department of Zoology, Savitribai Phule Pune University, Pune, Maharashtra, India
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Eckermann HA, Ou Y, Lahti L, Weerth C. Can gut microbiota throughout the first 10 years of life predict executive functioning in childhood? Dev Psychobiol 2022; 64:e22226. [DOI: 10.1002/dev.22226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/12/2021] [Accepted: 11/15/2021] [Indexed: 11/08/2022]
Affiliation(s)
- Henrik Andreas Eckermann
- Department of Cognitive Neuroscience Cognition and Behavior Radboud University Medical Center Donders Institute for Brain Nijmegen The Netherlands
| | - Yangwenshan Ou
- Laboratory of Microbiology Wageningen University Wageningen The Netherlands
| | - Leo Lahti
- Department of Computing University of Turku Turku Finland
| | - Carolina Weerth
- Department of Cognitive Neuroscience Cognition and Behavior Radboud University Medical Center Donders Institute for Brain Nijmegen The Netherlands
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Hillestad EMR, van der Meeren A, Nagaraja BH, Bjørsvik BR, Haleem N, Benitez-Paez A, Sanz Y, Hausken T, Lied GA, Lundervold A, Berentsen B. Gut bless you: The microbiota-gut-brain axis in irritable bowel syndrome. World J Gastroenterol 2022; 28:412-431. [PMID: 35125827 PMCID: PMC8790555 DOI: 10.3748/wjg.v28.i4.412] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 06/24/2021] [Accepted: 01/13/2022] [Indexed: 12/16/2022] Open
Abstract
Irritable bowel syndrome (IBS) is a common clinical label for medically unexplained gastrointestinal symptoms, recently described as a disturbance of the microbiota-gut-brain axis. Despite decades of research, the pathophysiology of this highly heterogeneous disorder remains elusive. However, a dramatic change in the understanding of the underlying pathophysiological mechanisms surfaced when the importance of gut microbiota protruded the scientific picture. Are we getting any closer to understanding IBS' etiology, or are we drowning in unspecific, conflicting data because we possess limited tools to unravel the cluster of secrets our gut microbiota is concealing? In this comprehensive review we are discussing some of the major important features of IBS and their interaction with gut microbiota, clinical microbiota-altering treatment such as the low FODMAP diet and fecal microbiota transplantation, neuroimaging and methods in microbiota analyses, and current and future challenges with big data analysis in IBS.
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Affiliation(s)
- Eline Margrete Randulff Hillestad
- Department of Clinical Medicine, University of Bergen, Bergen 5021, Norway
- National Center for Functional Gastrointestinal Disorders, Department of Medicine, Haukeland University Hospital, Bergen 5021, Norway
| | - Aina van der Meeren
- National Center for Functional Gastrointestinal Disorders, Department of Medicine, Haukeland University Hospital, Bergen 5021, Norway
| | - Bharat Halandur Nagaraja
- Mohn Medical Imaging and Visualization Center, Department of Radiology, Haukeland University Hospital, Bergen 5021, Norway
| | - Ben René Bjørsvik
- National Center for Functional Gastrointestinal Disorders, Department of Medicine, Haukeland University Hospital, Bergen 5021, Norway
- Mohn Medical Imaging and Visualization Center, Department of Radiology, Haukeland University Hospital, Bergen 5021, Norway
| | - Noman Haleem
- National Center for Functional Gastrointestinal Disorders, Department of Medicine, Haukeland University Hospital, Bergen 5021, Norway
- Mohn Medical Imaging and Visualization Center, Department of Radiology, Haukeland University Hospital, Bergen 5021, Norway
| | - Alfonso Benitez-Paez
- Host-Microbe Interactions in Metabolic Health Laboratory, Principe Felipe Research Center, Valencia 46012, Spain
| | - Yolanda Sanz
- Microbial Ecology, Nutrition and Health Research Unit, Institute of Agrochemistry and Food Technology, National Research Council, Paterna-Valencia 46980, Spain
| | - Trygve Hausken
- Department of Clinical Medicine, University of Bergen, Bergen 5021, Norway
- National Center for Functional Gastrointestinal Disorders, Department of Medicine, Haukeland University Hospital, Bergen 5021, Norway
| | - Gülen Arslan Lied
- National Center for Functional Gastrointestinal Disorders, Department of Medicine, Haukeland University Hospital, Bergen 5021, Norway
- Center for Nutrition, Department of Clinical Medicine, University of Bergen, Bergen 5021, Norway
| | - Arvid Lundervold
- Mohn Medical Imaging and Visualization Center, Department of Radiology, Haukeland University Hospital, Bergen 5021, Norway
- Department of Biomedicine, University of Bergen, Bergen 5021, Norway
| | - Birgitte Berentsen
- Department of Clinical Medicine, University of Bergen, Bergen 5021, Norway
- National Center for Functional Gastrointestinal Disorders, Department of Medicine, Haukeland University Hospital, Bergen 5021, Norway
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D'Amico F, Barone M, Tavella T, Rampelli S, Brigidi P, Turroni S. Host microbiomes in tumor precision medicine: how far are we? Curr Med Chem 2022; 29:3202-3230. [PMID: 34986765 DOI: 10.2174/0929867329666220105121754] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 11/13/2021] [Accepted: 11/22/2021] [Indexed: 11/22/2022]
Abstract
The human gut microbiome has received a crescendo of attention in recent years, due to the countless influences on human pathophysiology, including cancer. Research on cancer and anticancer therapy is constantly looking for new hints to improve the response to therapy while reducing the risk of relapse. In this scenario, the gut microbiome and the plethora of microbial-derived metabolites are considered a new opening in the development of innovative anticancer treatments for a better prognosis. This narrative review summarizes the current knowledge on the role of the gut microbiome in the onset and progression of cancer, as well as in response to chemo-immunotherapy. Recent findings regarding the tumor microbiome and its implications for clinical practice are also commented on. Current microbiome-based intervention strategies (i.e., prebiotics, probiotics, live biotherapeutics and fecal microbiota transplantation) are then discussed, along with key shortcomings, including a lack of long-term safety information in patients who are already severely compromised by standard treatments. The implementation of bioinformatic tools applied to microbiomics and other omics data, such as machine learning, has an enormous potential to push research in the field, enabling the prediction of health risk and therapeutic outcomes, for a truly personalized precision medicine.
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Affiliation(s)
- Federica D'Amico
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
| | - Monica Barone
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
| | - Teresa Tavella
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
| | - Simone Rampelli
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
| | - Patrizia Brigidi
- Microbiome Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Italy
| | - Silvia Turroni
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
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Curry KD, Nute MG, Treangen TJ. It takes guts to learn: machine learning techniques for disease detection from the gut microbiome. Emerg Top Life Sci 2021; 5:815-827. [PMID: 34779841 PMCID: PMC8786294 DOI: 10.1042/etls20210213] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/29/2021] [Accepted: 10/06/2021] [Indexed: 02/01/2023]
Abstract
Associations between the human gut microbiome and expression of host illness have been noted in a variety of conditions ranging from gastrointestinal dysfunctions to neurological deficits. Machine learning (ML) methods have generated promising results for disease prediction from gut metagenomic information for diseases including liver cirrhosis and irritable bowel disease, but have lacked efficacy when predicting other illnesses. Here, we review current ML methods designed for disease classification from microbiome data. We highlight the computational challenges these methods have effectively overcome and discuss the biological components that have been overlooked to offer perspectives on future work in this area.
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Affiliation(s)
- Kristen D. Curry
- Department of Computer Science, Rice University, Houston, TX 77005, USA
| | - Michael G. Nute
- Department of Computer Science, Rice University, Houston, TX 77005, USA
| | - Todd J. Treangen
- Department of Computer Science, Rice University, Houston, TX 77005, USA
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37
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Sabater C, Calvete-Torre I, Villamiel M, Moreno FJ, Margolles A, Ruiz L. Vegetable waste and by-products to feed a healthy gut microbiota: Current evidence, machine learning and computational tools to design novel microbiome-targeted foods. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.10.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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38
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Exploring Machine Learning Techniques to Predict the Response to Omalizumab in Chronic Spontaneous Urticaria. Diagnostics (Basel) 2021; 11:diagnostics11112150. [PMID: 34829497 PMCID: PMC8623518 DOI: 10.3390/diagnostics11112150] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 11/08/2021] [Accepted: 11/15/2021] [Indexed: 11/23/2022] Open
Abstract
Background: Omalizumab is the best treatment for patients with chronic spontaneous urticaria (CSU). Machine learning (ML) approaches can be used to predict response to therapy and the effectiveness of a treatment. No studies are available on the use of ML techniques to predict the response to Omalizumab in CSU. Methods: Data from 132 CSU outpatients were analyzed. Urticaria Activity Score over 7 days (UAS7) and treatment efficacy were assessed. Clinical and demographic characteristics were used for training and validating ML models to predict the response to treatment. Two methodologies were used to label the data based on the response to treatment (UAS7 ≥ 6): (A) at 1, 3 and 5 months; (B) classifying the patients as early responders (ER), late responders (LR) or non-responders (NR) (ER: UAS 7 ≥ 6 at first month, LR: UAS 7 ≥ 6 at third month, NR: if none of the previous conditions occurred). Results: ER were predominantly characterized by hypertension, while LR mainly suffered from asthma and hypothyroidism. A slight positive correlation (R2 = 0.21) was found between total IgE levels and UAS7 at 1 month. Variable Importance Analysis (VIA) reported D-dimer and C-reactive proteins as the key blood tests for the performance of learning techniques. Using methodology (A), SVM (specificity of 0.81) and k-NN (sensitivity of 0.8) are the best models to predict LR at the third month. Conclusion: k-NN plus the SVM model could be used to identify the response to treatment. D-dimer and C-reactive proteins have greater predictive power in training ML models.
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Rey JS, Li W, Bryer AJ, Beatson H, Lantz C, Engelman AN, Perilla JR. Deep-learning in situ classification of HIV-1 virion morphology. Comput Struct Biotechnol J 2021; 19:5688-5700. [PMID: 34765089 PMCID: PMC8554174 DOI: 10.1016/j.csbj.2021.10.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 09/30/2021] [Accepted: 10/01/2021] [Indexed: 01/04/2023] Open
Abstract
Efficient classifier capable of overcoming inherent problems of small-data training sets. Automated detection and classification of HIV-1 particle morphology from transmission electron micrographs. Three orders of magnitude speed increase in data processing with negligible loss in accuracy.
Transmission electron microscopy (TEM) has a multitude of uses in biomedical imaging due to its ability to discern ultrastructure morphology at the nanometer scale. Through its ability to directly visualize virus particles, TEM has for several decades been an invaluable tool in the virologist’s toolbox. As applied to HIV-1 research, TEM is critical to evaluate activities of inhibitors that block the maturation and morphogenesis steps of the virus lifecycle. However, both the preparation and analysis of TEM micrographs requires time consuming manual labor. Through the dedicated use of computer vision frameworks and machine learning techniques, we have developed a convolutional neural network backbone of a two-stage Region Based Convolutional Neural Network (RCNN) capable of identifying, segmenting and classifying HIV-1 virions at different stages of maturation and morphogenesis. Our results outperformed common RCNN backbones, achieving 80.0% mean Average Precision on a diverse set of micrographs comprising different experimental samples and magnifications. We expect that this tool will be of interest to a broad range of researchers.
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Affiliation(s)
- Juan S Rey
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, United States
| | - Wen Li
- Department of Medicine, Harvard Medical School, Boston, MA, United States.,Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, United States
| | - Alexander J Bryer
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, United States
| | - Hagan Beatson
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, United States
| | - Christian Lantz
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, United States
| | - Alan N Engelman
- Department of Medicine, Harvard Medical School, Boston, MA, United States.,Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, United States
| | - Juan R Perilla
- Department of Chemistry and Biochemistry, University of Delaware, Newark, DE, United States.,Data Science Institute, University of Delaware, Newark, DE, United States
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Hintikka J, Lensu S, Mäkinen E, Karvinen S, Honkanen M, Lindén J, Garrels T, Pekkala S, Lahti L. Xylo-Oligosaccharides in Prevention of Hepatic Steatosis and Adipose Tissue Inflammation: Associating Taxonomic and Metabolomic Patterns in Fecal Microbiomes with Biclustering. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:4049. [PMID: 33921370 PMCID: PMC8068902 DOI: 10.3390/ijerph18084049] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 03/26/2021] [Accepted: 04/08/2021] [Indexed: 12/15/2022]
Abstract
We have shown that prebiotic xylo-oligosaccharides (XOS) increased beneficial gut microbiota (GM) and prevented high fat diet-induced hepatic steatosis, but the mechanisms associated with these effects are not clear. We studied whether XOS affects adipose tissue inflammation and insulin signaling, and whether the GM and fecal metabolome explain associated patterns. XOS was supplemented or not with high (HFD) or low (LFD) fat diet for 12 weeks in male Wistar rats (n = 10/group). Previously analyzed GM and fecal metabolites were biclustered to reduce data dimensionality and identify interpretable groups of co-occurring genera and metabolites. Based on our findings, biclustering provides a useful algorithmic method for capturing such joint signatures. On the HFD, XOS-supplemented rats showed lower number of adipose tissue crown-like structures, increased phosphorylation of AKT in liver and adipose tissue as well as lower expression of hepatic miRNAs. XOS-supplemented rats had more fecal glycine and less hypoxanthine, isovalerate, branched chain amino acids and aromatic amino acids. Several bacterial genera were associated with the metabolic signatures. In conclusion, the beneficial effects of XOS on hepatic steatosis involved decreased adipose tissue inflammation and likely improved insulin signaling, which were further associated with fecal metabolites and GM.
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Affiliation(s)
- Jukka Hintikka
- Faculty of Sport and Health Sciences, University of Jyväskylä, FI-40014 Jyväskylä, Finland; (S.L.); (E.M.); (S.K.); (M.H.); (S.P.)
| | - Sanna Lensu
- Faculty of Sport and Health Sciences, University of Jyväskylä, FI-40014 Jyväskylä, Finland; (S.L.); (E.M.); (S.K.); (M.H.); (S.P.)
- Department of Psychology, University of Jyväskylä, FI-40014 Jyväskylä, Finland
| | - Elina Mäkinen
- Faculty of Sport and Health Sciences, University of Jyväskylä, FI-40014 Jyväskylä, Finland; (S.L.); (E.M.); (S.K.); (M.H.); (S.P.)
| | - Sira Karvinen
- Faculty of Sport and Health Sciences, University of Jyväskylä, FI-40014 Jyväskylä, Finland; (S.L.); (E.M.); (S.K.); (M.H.); (S.P.)
| | - Marjaana Honkanen
- Faculty of Sport and Health Sciences, University of Jyväskylä, FI-40014 Jyväskylä, Finland; (S.L.); (E.M.); (S.K.); (M.H.); (S.P.)
| | - Jere Lindén
- Veterinary Pathology and Parasitology and Finnish Centre for Laboratory Animal Pathology/HiLIFE, University of Helsinki, FIN-00014 Helsinki, Finland;
| | - Tim Garrels
- Department of Computing, University of Turku, FI-20014 Turku, Finland; (T.G.); (L.L.)
| | - Satu Pekkala
- Faculty of Sport and Health Sciences, University of Jyväskylä, FI-40014 Jyväskylä, Finland; (S.L.); (E.M.); (S.K.); (M.H.); (S.P.)
- Department of Clinical Microbiology, Turku University Hospital, FI-20521 Turku, Finland
| | - Leo Lahti
- Department of Computing, University of Turku, FI-20014 Turku, Finland; (T.G.); (L.L.)
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Ruuskanen MO, Sommeria-Klein G, Havulinna AS, Niiranen TJ, Lahti L. Modelling spatial patterns in host-associated microbial communities. Environ Microbiol 2021; 23:2374-2388. [PMID: 33734553 DOI: 10.1111/1462-2920.15462] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 03/02/2021] [Accepted: 03/11/2021] [Indexed: 12/12/2022]
Abstract
Microbial communities exhibit spatial structure at different scales, due to constant interactions with their environment and dispersal limitation. While this spatial structure is often considered in studies focusing on free-living environmental communities, it has received less attention in the context of host-associated microbial communities or microbiota. The wider adoption of methods accounting for spatial variation in these communities will help to address open questions in basic microbial ecology as well as realize the full potential of microbiome-aided medicine. Here, we first overview known factors affecting the composition of microbiota across diverse host types and at different scales, with a focus on the human gut as one of the most actively studied microbiota. We outline a number of topical open questions in the field related to spatial variation and patterns. We then review the existing methodology for the spatial modelling of microbiota. We suggest that methodology from related fields, such as systems biology and macro-organismal ecology, could be adapted to obtain more accurate models of spatial structure. We further posit that methodological developments in the spatial modelling and analysis of microbiota could in turn broadly benefit theoretical and applied ecology and contribute to the development of novel industrial and clinical applications.
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Affiliation(s)
- Matti O Ruuskanen
- Department of Internal Medicine, University of Turku, Turku, Finland.,Finnish Institute for Health and Welfare, Helsinki, Finland
| | | | - Aki S Havulinna
- Finnish Institute for Health and Welfare, Helsinki, Finland.,Institute for Molecular Medicine Finland, FIMM-HiLIFE, Helsinki, Finland
| | - Teemu J Niiranen
- Department of Internal Medicine, University of Turku, Turku, Finland.,Finnish Institute for Health and Welfare, Helsinki, Finland.,Division of Medicine, Turku University Hospital, Turku, Finland
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
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Marcos-Zambrano LJ, Karaduzovic-Hadziabdic K, Loncar Turukalo T, Przymus P, Trajkovik V, Aasmets O, Berland M, Gruca A, Hasic J, Hron K, Klammsteiner T, Kolev M, Lahti L, Lopes MB, Moreno V, Naskinova I, Org E, Paciência I, Papoutsoglou G, Shigdel R, Stres B, Vilne B, Yousef M, Zdravevski E, Tsamardinos I, Carrillo de Santa Pau E, Claesson MJ, Moreno-Indias I, Truu J. Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment. Front Microbiol 2021; 12:634511. [PMID: 33737920 PMCID: PMC7962872 DOI: 10.3389/fmicb.2021.634511] [Citation(s) in RCA: 113] [Impact Index Per Article: 37.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 02/01/2021] [Indexed: 12/19/2022] Open
Abstract
The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.
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Affiliation(s)
- Laura Judith Marcos-Zambrano
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, Madrid, Spain
| | | | | | - Piotr Przymus
- Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Toruń, Poland
| | - Vladimir Trajkovik
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, North Macedonia
| | - Oliver Aasmets
- Institute of Genomics, Estonian Genome Centre, University of Tartu, Tartu, Estonia
- Department of Biotechnology, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Magali Berland
- Université Paris-Saclay, INRAE, MGP, Jouy-en-Josas, France
| | - Aleksandra Gruca
- Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland
| | - Jasminka Hasic
- University Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina
| | - Karel Hron
- Department of Mathematical Analysis and Applications of Mathematics, Palacký University, Olomouc, Czechia
| | | | - Mikhail Kolev
- South West University “Neofit Rilski”, Blagoevgrad, Bulgaria
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Marta B. Lopes
- NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), FCT, UNL, Caparica, Portugal
- Centro de Matemática e Aplicações (CMA), FCT, UNL, Caparica, Portugal
| | - Victor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology (ICO)Barcelona, Spain
- Colorectal Cancer Group, Institut de Recerca Biomedica de Bellvitge (IDIBELL), Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Barcelona, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Irina Naskinova
- South West University “Neofit Rilski”, Blagoevgrad, Bulgaria
| | - Elin Org
- Institute of Genomics, Estonian Genome Centre, University of Tartu, Tartu, Estonia
| | - Inês Paciência
- EPIUnit – Instituto de Saúde Pública da Universidade do Porto, Porto, Portugal
| | | | - Rajesh Shigdel
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Blaz Stres
- Group for Microbiology and Microbial Biotechnology, Department of Animal Science, University of Ljubljana, Ljubljana, Slovenia
| | - Baiba Vilne
- Bioinformatics Research Unit, Riga Stradins University, Riga, Latvia
| | - Malik Yousef
- Department of Information Systems, Zefat Academic College, Zefat, Israel
- Galilee Digital Health Research Center (GDH), Zefat Academic College, Zefat, Israel
| | - Eftim Zdravevski
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, North Macedonia
| | | | | | - Marcus J. Claesson
- School of Microbiology & APC Microbiome Ireland, University College Cork, Cork, Ireland
| | - Isabel Moreno-Indias
- Unidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Clínico Universitario Virgen de la Victoria, Universidad de Málaga, Málaga, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Jaak Truu
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
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