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      Maturation of the gut microbiome and risk of asthma in childhood

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          Abstract

          The composition of the human gut microbiome matures within the first years of life. It has been hypothesized that microbial compositions in this period can cause immune dysregulations and potentially cause asthma. Here we show, by associating gut microbial composition from 16S rRNA gene amplicon sequencing during the first year of life with subsequent risk of asthma in 690 participants, that 1-year-old children with an immature microbial composition have an increased risk of asthma at age 5 years. This association is only apparent among children born to asthmatic mothers, suggesting that lacking microbial stimulation during the first year of life can trigger their inherited asthma risk. Conversely, adequate maturation of the gut microbiome in this period may protect these pre-disposed children.

          Abstract

          Colonization of commensal bacteria is thought to impact immune development, especially in the earliest years of life. Here, the authors show, by analyzing the development of the gut microbiome of 690 children, that microbial composition at the age of 1 year is associated with asthma diagnosed in the first 5 years of life.

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          mixOmics: An R package for ‘omics feature selection and multiple data integration

          Florian Rohart, Benoît Gautier, Amrit Singh … (2017)
          The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.
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            Exposure to environmental microorganisms and childhood asthma.

            Markus J Ege, Melanie L. Mayer, Anne-Cécile Normand … (2011)
            Children who grow up in environments that afford them a wide range of microbial exposures, such as traditional farms, are protected from childhood asthma and atopy. In previous studies, markers of microbial exposure have been inversely related to these conditions. In two cross-sectional studies, we compared children living on farms with those in a reference group with respect to the prevalence of asthma and atopy and to the diversity of microbial exposure. In one study--PARSIFAL (Prevention of Allergy-Risk Factors for Sensitization in Children Related to Farming and Anthroposophic Lifestyle)--samples of mattress dust were screened for bacterial DNA with the use of single-strand conformation polymorphism (SSCP) analyses to detect environmental bacteria that cannot be measured by means of culture techniques. In the other study--GABRIELA (Multidisciplinary Study to Identify the Genetic and Environmental Causes of Asthma in the European Community [GABRIEL] Advanced Study)--samples of settled dust from children's rooms were evaluated for bacterial and fungal taxa with the use of culture techniques. In both studies, children who lived on farms had lower prevalences of asthma and atopy and were exposed to a greater variety of environmental microorganisms than the children in the reference group. In turn, diversity of microbial exposure was inversely related to the risk of asthma (odds ratio for PARSIFAL, 0.62; 95% confidence interval [CI], 0.44 to 0.89; odds ratio for GABRIELA, 0.86; 95% CI, 0.75 to 0.99). In addition, the presence of certain more circumscribed exposures was also inversely related to the risk of asthma; this included exposure to species in the fungal taxon eurotium (adjusted odds ratio, 0.37; 95% CI, 0.18 to 0.76) and to a variety of bacterial species, including Listeria monocytogenes, bacillus species, corynebacterium species, and others (adjusted odds ratio, 0.57; 95% CI, 0.38 to 0.86). Children living on farms were exposed to a wider range of microbes than were children in the reference group, and this exposure explains a substantial fraction of the inverse relation between asthma and growing up on a farm. (Funded by the Deutsche Forschungsgemeinschaft and the European Commission.).
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              Group-specific primer and probe sets to detect methanogenic communities using quantitative real-time polymerase chain reaction.

              Youngseob Yu, Changsoo Lee, Jaai Kim … (2005)
              Real-time polymerase chain reaction (PCR) is a highly sensitive method that can be used for the detection and quantification of microbial populations without cultivating them in anaerobic processes and environmental samples. This work was conducted to design primer and probe sets for the detection of methanogens using a real-time PCR with the TaqMan system. Six group-specific methanogenic primer and probe sets were designed. These sets separately detect four orders (Methanococcales, Methanobacteriales, Methanomicrobiales, and Methanosarcinales) along with two families (Methanosarcinaceae and Methanosaetaceae) of the order Methanosarcinales. We also designed the universal primer and probe sets that specifically detect the 16S rDNA of prokaryotes and of the domain Bacteria and Archaea, and which are fully compatible with the TaqMan real-time PCR system. Target-group specificity of each primer and probe set was empirically verified by testing DNA isolated from 28 archaeal cultures and by analyzing potential false results. In general, each primer and probe set was very specific to the target group. The primer and probe sets designed in this study can be used to detect and quantify the order-level (family-level in the case of Methanosarcinales) methanogenic groups in anaerobic biological processes and various environments.
              Article 0 comments Cited 390 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Søren J. Sørensen: sjs@bio.ku.dk
                Hans Bisgaard: bisgaard@copsac.com
                Journal
                Journal ID (nlm-ta): Nat Commun
                Journal ID (iso-abbrev): Nat Commun
                Title: Nature Communications
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2041-1723
                Publication date (Electronic): 10 January 2018
                Publication date PMC-release: 10 January 2018
                Publication date Collection: 2018
                Volume: 9
                Electronic Location Identifier: 141
                Affiliations
                [1 ]ISNI 0000 0001 0674 042X, GRID grid.5254.6, COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, , University of Copenhagen, ; Ledreborg Alle 34, 2820 Gentofte, Denmark
                [2 ]ISNI 0000 0001 2109 4251, GRID grid.240324.3, Departments of Medicine and Microbiology, and the Human Microbiome Program, , New York University Langone Medical Center, ; 550 First Avenue, New York, NY 10016 USA
                [3 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Biostatistics, , Harvard School of Public Health, ; 655 Huntington Avenue, Boston, MA 02115 USA
                [4 ]ISNI 0000 0001 0674 042X, GRID grid.5254.6, Section of Chemometrics and Analytical Technologies, Department of Food Science, , University of Copenhagen, ; Rolighedsvej 30, 1958 Frederiksberg C, Denmark
                [5 ]ISNI 0000 0001 0674 042X, GRID grid.5254.6, Section of Microbiology, Department of Biology, , University of Copenhagen, ; Universitetsparken 15, 2100 Copenhagen, Denmark
                Author information
                http://orcid.org/0000-0003-2447-2443
                http://orcid.org/0000-0001-5483-7533
                Article
                Publisher ID: 2573
                DOI: 10.1038/s41467-017-02573-2
                PMC ID: 5762761
                SO-VID: 71daa10b-7e8b-4ca5-8bb3-9c1cbf5d0479
                Copyright © © The Author(s) 2018
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 7 July 2017
                Date accepted : 7 December 2017
                Categories
                Subject: Article
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                issue-copyright-statement © The Author(s) 2018

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