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      Distinct Changes Occur in the Human Breast Milk Microbiome Between Early and Established Lactation in Breastfeeding Guatemalan Mothers

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          Abstract

          Human breast milk contains a diverse community of bacteria, but as breast milk microbiome studies have largely focused on mothers from high income countries where few women breastfeed to 6 months, the temporal changes in the breast milk microbiome that occur during later lactation stages have not been explored. For this cross-sectional study, microbiota from breast milk samples of Mam-Mayan mothers living in eight remote rural communities in the Western Highlands of Guatemala were analyzed. All mothers delivered vaginally and breastfed their infants for 6 months. Breast milk from 76 unrelated mothers was used to compare two lactation stages, either “early” (6–46 days post-partum, n = 33) or “late” (109–184 days post-partum, n = 43). Breast milk microbial communities were assessed using 16S ribosomal RNA gene sequencing and lactation stages were compared using DESeq2 differential abundance analysis. A total of 1,505 OTUs were identified, including 287 which could be annotated as putative species. Among several maternal factors, lactation stage explained microbiome variance and inertia in ordination with the most significance ( p < 0.001). Differential abundance analysis identified 137 OTUs as significantly higher in either early or late lactation. These included a general shift from Staphylococcus and Streptococcus species in early lactation to Sphingobium and Pseudomonas species in late lactation. Species enriched in early lactation included putative commensal bacteria known to colonize the infant oral and intestinal tracts whereas species enriched in late lactation had a uniform functional trait associated with aromatic compound degradation. Differentially abundant species also included several species which have not previously been reported within breast milk, such as Janthinobacterium agaricidamnosum, Novosphingobium clariflavum, Ottowia beijingensis, and Flavobacterium cucumis. These discoveries describe temporal changes to the breast milk microbiome of healthy Guatemalan mothers from early to late lactation. Collectively, these findings illustrate how studying under-represented human populations might advance our understanding of factors that modulate the human milk microbiome in low and middle income countries (LMIC).

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

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              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.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                12 February 2021
                2021
                : 12
                : 557180
                Affiliations
                [1] 1Canadian Centre for Computational Genomics (C3G), Department of Human Genetics, McGill University, Montréal , QC, Canada
                [2] 2Microbiome Research Platform, McGill Interdisciplinary Initiative in Infection and Immunity (MI4), Genome Centre, McGill University, Montréal , QC, Canada
                [3] 3Institut de Recherche en Biologie Végétale, Université de Montréal, Montréal , QC, Canada
                [4] 4School of Human Nutrition, McGill University, Ste-Anne de Bellevue , QC, Canada
                [5] 5Center for Studies of Sensory Impairment, Aging and Metabolism (CeSSIAM) , Guatemala City, Guatemala
                [6] 6Institute of Parasitology, McGill University, Ste-Anne de Bellevue , QC, Canada
                Author notes

                Edited by: Ana Rivas, University of Granada, Spain

                Reviewed by: Rosa Del Campo, Ramón y Cajal Institute for Health Research, Spain; Margarita Aguilera, University of Granada, Spain

                *Correspondence: Kristine G. Koski, kristine.koski@ 123456mcgill.ca

                This article was submitted to Food Microbiology, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2021.557180
                7907006
                33643228
                d7b520af-e0db-4459-ad97-d058d8564d40
                Copyright © 2021 Gonzalez, Brereton, Li, Lopez Leyva, Solomons, Agellon, Scott and Koski.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 29 April 2020
                : 21 January 2021
                Page count
                Figures: 3, Tables: 1, Equations: 0, References: 110, Pages: 13, Words: 0
                Funding
                Funded by: Natural Sciences and Engineering Research Council of Canada 10.13039/501100000038
                Award ID: RGPIN-2015-04390
                Award ID: RGPIN-2016-04961
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                16s rrna gene,human breast milk,microbiome,lactation stage,metagenomics 16s

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