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Moreno-Indias I, Lahti L, Nedyalkova M, Elbere I, Roshchupkin G, Adilovic M, Aydemir O, Bakir-Gungor B, Santa Pau ECD, D’Elia D, Desai MS, Falquet L, Gundogdu A, Hron K, Klammsteiner T, Lopes MB, Marcos-Zambrano LJ, Marques C, Mason M, May P, Pašić L, Pio G, Pongor S, Promponas VJ, Przymus P, Saez-Rodriguez J, Sampri A, Shigdel R, Stres B, Suharoschi R, Truu J, Truică CO, Vilne B, Vlachakis D, Yilmaz E, Zeller G, Zomer AL, Gómez-Cabrero D, Claesson MJ. Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions. Front Microbiol 2021; 12:635781. [PMID: 33692771 PMCID: PMC7937616 DOI: 10.3389/fmicb.2021.635781] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 01/28/2021] [Indexed: 12/23/2022] Open
Abstract
The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.
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Affiliation(s)
- Isabel Moreno-Indias
- Instituto de Investigación Biomédica de Málaga (IBIMA), Unidad de Gestión Clìnica de Endocrinologìa y Nutrición, Hospital Clìnico Universitario Virgen de la Victoria, Universidad de Málaga, Málaga, Spain
- Centro de Investigación Biomeìdica en Red de Fisiopatologtìa de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Miroslava Nedyalkova
- Human Genetics and Disease Mechanisms, Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Ilze Elbere
- Latvian Biomedical Research and Study Centre, Riga, Latvia
| | | | - Muhamed Adilovic
- Department of Genetics and Bioengineering, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Onder Aydemir
- Department of Electrical and Electronics Engineering, Karadeniz Technical University, Trabzon, Turkey
| | - Burcu Bakir-Gungor
- Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey
| | | | - Domenica D’Elia
- Department for Biomedical Sciences, Institute for Biomedical Technologies, National Research Council, Bari, Italy
| | - Mahesh S. Desai
- Department of Infection and Immunity, Luxembourg Institute of Health, Esch-sur-Alzette, Luxembourg
- Odense Research Center for Anaphylaxis, Department of Dermatology and Allergy Center, Odense University Hospital, University of Southern Denmark, Odense, Denmark
| | - Laurent Falquet
- Department of Biology, University of Fribourg, Fribourg, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Aycan Gundogdu
- Department of Microbiology and Clinical Microbiology, Faculty of Medicine, Erciyes University, Kayseri, Turkey
- Metagenomics Laboratory, Genome and Stem Cell Center (GenKök), Erciyes University, Kayseri, Turkey
| | - Karel Hron
- Department of Mathematical Analysis and Applications of Mathematics, Palacký University, Olomouc, Czechia
| | | | - 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
| | - Laura Judith Marcos-Zambrano
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, Madrid, Spain
| | - Cláudia Marques
- CINTESIS, NOVA Medical School, NMS, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Michael Mason
- Computational Oncology, Sage Bionetworks, Seattle, WA, United States
| | - Patrick May
- Bioinformatics Core, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Lejla Pašić
- Sarajevo Medical School, University Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina
| | - Gianvito Pio
- Department of Computer Science, University of Bari Aldo Moro, Bari, Italy
| | - Sándor Pongor
- Faculty of Information Tehnology and Bionics, Pázmány University, Budapest, Hungary
| | - Vasilis J. Promponas
- Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus
| | - Piotr Przymus
- Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Toruñ, Poland
| | - Julio Saez-Rodriguez
- Institute of Computational Biomedicine, Heidelberg University, Faculty of Medicine and Heidelberg University Hospital, Heidelberg, Germany
| | - Alexia Sampri
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, University of Manchester, Manchester, United Kingdom
| | - Rajesh Shigdel
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Blaz Stres
- Jozef Stefan Institute, Ljubljana, Slovenia
- Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
| | - Ramona Suharoschi
- Molecular Nutrition and Proteomics Lab, Faculty of the Food Science and Technology, Institute of Life Sciences, University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, Cluj-Napoca, Romania
| | - Jaak Truu
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Ciprian-Octavian Truică
- Department of Computer Science and Engineering, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
| | - Baiba Vilne
- Bioinformatics Research Unit, Riga Stradins University, Riga, Latvia
| | - Dimitrios Vlachakis
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Ercument Yilmaz
- Department of Computer Technologies, Karadeniz Technical University, Trabzon, Turkey
| | - Georg Zeller
- European Molecular Biology Laboratory, Structural and Computational Biology Unit, Heidelberg, Germany
| | - Aldert L. Zomer
- Department of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
| | - David Gómez-Cabrero
- Navarrabiomed, Complejo Hospitalario de Navarra (CHN), IdiSNA, Universidad Pública de Navarra (UPNA), Pamplona, Spain
| | - Marcus J. Claesson
- School of Microbiology and APC Microbiome Ireland, University College Cork, Cork, Ireland
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154
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Vari HK, Roslund MI, Oikarinen S, Nurminen N, Puhakka R, Parajuli A, Grönroos M, Siter N, Laitinen OH, Hyöty H, Rajaniemi J, Rantalainen AL, Sinkkonen A. Associations between land cover categories, gaseous PAH levels in ambient air and endocrine signaling predicted from gut bacterial metagenome of the elderly. CHEMOSPHERE 2021; 265:128965. [PMID: 33248729 DOI: 10.1016/j.chemosphere.2020.128965] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/05/2020] [Accepted: 11/10/2020] [Indexed: 06/12/2023]
Abstract
There is evidence that polycyclic aromatic hydrocarbons (PAHs) and human gut microbiota are associated with the modulation of endocrine signaling pathways. Independently, studies have found associations between air pollution, land cover and commensal microbiota. We are the first to estimate the interaction between land cover categories associated with air pollution or purification, PAH levels and endocrine signaling predicted from gut metagenome among urban and rural populations. The study participants were elderly people (65-79 years); 30 lived in rural and 32 in urban areas. Semi-Permeable Membrane devices were utilized to measure air PAH concentrations as they simulate the process of bioconcentration in the fatty tissues. Land cover categories were estimated using CORINE database and geographic information system. Functional orthologues for peroxisome proliferator-activated receptor (PPAR) pathway in endocrine system were analyzed from gut bacterial metagenome with Kyoto Encyclopaedia of Genes and Genomes. High coverage of broad-leaved and mixed forests around the homes were associated with decreased PAH levels in ambient air, while gut functional orthologues for PPAR pathway increased along with these forest types. The difference between urban and rural PAH concentrations was not notable. However, some rural measurements were higher than the urban average, which was due to the use of heavy equipment on active farms. The provision of air purification by forests might be an important determining factor in the context of endocrine disruption potential of PAHs. Particularly broad-leaved forests around homes may reduce PAH levels in ambient air and balance pollution-induced disturbances within commensal gut microbiota.
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Affiliation(s)
- Heli K Vari
- Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Niemenkatu 73, Lahti, Finland
| | - Marja I Roslund
- Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Niemenkatu 73, Lahti, Finland
| | - Sami Oikarinen
- Tampere University, Faculty of Medicine and Health Technology, Arvo Ylpönkatu 34, Tampere, Finland
| | - Noora Nurminen
- Tampere University, Faculty of Medicine and Health Technology, Arvo Ylpönkatu 34, Tampere, Finland
| | - Riikka Puhakka
- Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Niemenkatu 73, Lahti, Finland
| | - Anirudra Parajuli
- Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Niemenkatu 73, Lahti, Finland
| | - Mira Grönroos
- Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Niemenkatu 73, Lahti, Finland
| | - Nathan Siter
- Tampere University, Faculty of Built Environment, Korkeakoulunkatu 5, Tampere, Finland
| | - Olli H Laitinen
- Tampere University, Faculty of Medicine and Health Technology, Arvo Ylpönkatu 34, Tampere, Finland
| | - Heikki Hyöty
- Tampere University, Faculty of Medicine and Health Technology, Arvo Ylpönkatu 34, Tampere, Finland
| | - Juho Rajaniemi
- Tampere University, Faculty of Built Environment, Korkeakoulunkatu 5, Tampere, Finland
| | - Anna-Lea Rantalainen
- Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Niemenkatu 73, Lahti, Finland
| | - Aki Sinkkonen
- Natural Resources Institute Finland, Horticulture Technologies, Itäinen Pitkäkatu 4, Turku, Finland.
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155
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Arnesen H, Hitch TCA, Steppeler C, Müller MHB, Knutsen LE, Gunnes G, Angell IL, Ormaasen I, Rudi K, Paulsen JE, Clavel T, Carlsen H, Boysen P. Naturalizing laboratory mice by housing in a farmyard-type habitat confers protection against colorectal carcinogenesis. Gut Microbes 2021; 13:1993581. [PMID: 34751603 PMCID: PMC8583187 DOI: 10.1080/19490976.2021.1993581] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 09/27/2021] [Accepted: 10/08/2021] [Indexed: 02/04/2023] Open
Abstract
Living in a farm environment in proximity to animals is associated with reduced risk of developing allergies and asthma, and has been suggested to protect against other diseases, such as inflammatory bowel disease and cancer. Despite epidemiological evidence, experimental disease models that recapitulate such environments are needed to understand the underlying mechanisms. In this study, we show that feralizing conventional inbred mice by continuous exposure to a livestock farmyard-type environment conferred protection toward colorectal carcinogenesis. Two independent experimental approaches for colorectal cancer induction were used; spontaneous (Apc Min/+ mice on an A/J background) or chemical (AOM/DSS). In contrast to conventionally reared laboratory mice, the feralized mouse gut microbiota structure remained stable and resistant to mutagen- and colitis-induced neoplasia. Moreover, the feralized mice exhibited signs of a more mature immunophenotype, indicated by increased expression of NK and T-cell maturation markers, and a more potent IFN-γ response to stimuli. In our study, hygienically born and raised mice subsequently feralized post-weaning were protected to a similar level as life-long exposed mice, although the greatest effect was seen upon neonatal exposure. Collectively, we show protective implications of a farmyard-type environment on colorectal cancer development and demonstrate the utility of a novel animal modeling approach that recapitulates realistic disease responses in a naturalized mammal.
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Affiliation(s)
- Henriette Arnesen
- Department of Preclinical Sciences and Pathology, Faculty of Veterinary Medicine, Norwegian University of Life Sciences (NMBU), Aas, Norway
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Norway
| | - Thomas C A Hitch
- Functional Microbiome Research Group, Institute of Medical Microbiology, University Hospital of RWTH Aachen, Aachen, Germany
| | - Christina Steppeler
- Department of Paraclinical Sciences, Faculty of Veterinary Medicine, Norwegian University of Life Sciences (NMBU), Aas, Oslo, Norway
| | - Mette Helen Bjørge Müller
- Department of Paraclinical Sciences, Faculty of Veterinary Medicine, Norwegian University of Life Sciences (NMBU), Aas, Oslo, Norway
| | - Linn Emilie Knutsen
- Department of Preclinical Sciences and Pathology, Faculty of Veterinary Medicine, Norwegian University of Life Sciences (NMBU), Aas, Norway
| | - Gjermund Gunnes
- Department of Preclinical Sciences and Pathology, Faculty of Veterinary Medicine, Norwegian University of Life Sciences (NMBU), Aas, Norway
| | - Inga Leena Angell
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Norway
| | - Ida Ormaasen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Norway
| | - Knut Rudi
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Norway
| | - Jan Erik Paulsen
- Department of Paraclinical Sciences, Faculty of Veterinary Medicine, Norwegian University of Life Sciences (NMBU), Aas, Oslo, Norway
| | - Thomas Clavel
- Functional Microbiome Research Group, Institute of Medical Microbiology, University Hospital of RWTH Aachen, Aachen, Germany
| | - Harald Carlsen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Norway
| | - Preben Boysen
- Department of Preclinical Sciences and Pathology, Faculty of Veterinary Medicine, Norwegian University of Life Sciences (NMBU), Aas, Norway
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