10
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Community use of oral antibiotics transiently reprofiles the intestinal microbiome in young Bangladeshi children

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Antibiotics may alter the gut microbiome, and this is one of the mechanisms by which antimicrobial resistance may be promoted. Suboptimal antimicrobial stewardship in Asia has been linked to antimicrobial resistance. We aim to examine the relationship between oral antibiotic use and composition and antimicrobial resistance in the gut microbiome in 1093 Bangladeshi infants. We leverage a trial of 8-month-old infants in rural Bangladesh: 61% of children were cumulatively exposed to antibiotics (most commonly cephalosporins and macrolides) over the 12-month study period, including 47% in the first 3 months of the study, usually for fever or respiratory infection. 16S rRNA amplicon sequencing in 11-month-old infants reveals that alpha diversity of the intestinal microbiome is reduced in children who received antibiotics within the previous 7 days; these samples also exhibit enrichment for Enterococcus and Escherichia/Shigella genera. No effect is seen in children who received antibiotics earlier. Using shotgun metagenomics, overall abundance of antimicrobial resistance genes declines over time. Enrichment for an Enterococcus-related antimicrobial resistance gene is observed in children receiving antibiotics within the previous 7 days, but not earlier. Presence of antimicrobial resistance genes is correlated to microbiome composition. In Bangladeshi children, community use of antibiotics transiently reprofiles the gut microbiome.

          Abstract

          In this study, the authors examined the relationship between oral antibiotic use and composition and antimicrobial resistance in the gut microbiome in Bangladeshi infants with a high exposure to community-administered antibiotics.

          Related collections

          Most cited references43

          • Record: found
          • Abstract: not found
          • Article: not found

          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            DADA2: High resolution sample inference from Illumina amplicon data

            We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data

              Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
                Bookmark

                Author and article information

                Contributors
                Baldi.a@wehi.edu.au
                Pasricha.s@wehi.edu.au
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                14 August 2024
                14 August 2024
                2024
                : 15
                : 6980
                Affiliations
                [1 ]Population Health and Immunity Division, Walter and Eliza Hall Institute of Medical Research, ( https://ror.org/01b6kha49) Parkville, VIC Australia
                [2 ]Department of Medical Biology, The University of Melbourne, ( https://ror.org/01ej9dk98) Parkville, VIC Australia
                [3 ]Centre for Epidemiology and Biostatistics, University of Melbourne School of Population and Global Health, ( https://ror.org/01ej9dk98) Carlton, VIC Australia
                [4 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Department of Infectious Diseases at the Peter Doherty Institute of Infection and Immunity, , The University of Melbourne, ; Melbourne, VIC Australia
                [5 ]International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), ( https://ror.org/04vsvr128) Dhaka, Bangladesh
                [6 ]Advanced Technology and Biology Division, Walter and Eliza Hall Institute of Medical Research, ( https://ror.org/01b6kha49) Parkville, VIC Australia
                [7 ]Diagnostic Haematology, The Royal Melbourne Hospital, ( https://ror.org/005bvs909) Parkville, VIC Australia
                [8 ]Victorian Infectious Diseases Service, Royal Melbourne Hospital, ( https://ror.org/005bvs909) Parkville, VIC Australia
                [9 ]Faculty of Science, University of Melbourne, ( https://ror.org/01ej9dk98) Melbourne, VIC Australia
                [10 ]GRID grid.1055.1, ISNI 0000000403978434, Clinical Haematology at The Royal Melbourne Hospital and the Peter MacCallum Cancer Centre, ; Parkville, VIC Australia
                Author information
                http://orcid.org/0000-0003-1997-3999
                http://orcid.org/0000-0002-9796-9381
                http://orcid.org/0000-0002-1367-7163
                http://orcid.org/0000-0002-1661-1872
                http://orcid.org/0000-0002-2500-8944
                http://orcid.org/0000-0001-8596-0366
                http://orcid.org/0000-0002-5502-0434
                Article
                51326
                10.1038/s41467-024-51326-5
                11324872
                39143045
                81c01e8d-e075-4840-b6bb-914cdc854dae
                © The Author(s) 2024

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 January 2024
                : 5 August 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000925, Department of Health | National Health and Medical Research Council (NHMRC);
                Award ID: GNT1103262
                Award ID: GNT1158696
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                epidemiology,antimicrobial resistance,microbiota
                Uncategorized
                epidemiology, antimicrobial resistance, microbiota

                Comments

                Comment on this article