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      Stunted microbiota and opportunistic pathogen colonisation in caesarean section birth

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          Abstract

          Immediately after birth, newborn babies experience rapid colonisation by microorganisms from their mothers and the surrounding environment 1 . Diseases in childhood and later in life are potentially mediated through perturbation of the infant gut microbiota colonisations 2 . However, the impact of modern clinical practices, such as caesarean section delivery and antibiotic usage, on the earliest stages of gut microbiota acquisition and development during the neonatal period (≤1 month) remains controversial 3, 4 . Here we report disrupted maternal transmission of Bacteroides strains and high-level colonisation by healthcare-associated opportunistic pathogens, including Enterococcus, Enterobacter and Klebsiella species, in babies delivered by caesarean section (C-section), and to a lesser extent, in those delivered vaginally with maternal antibiotic prophylaxis or not breastfed during the neonatal period. Applying longitudinal sampling and whole-genome shotgun metagenomic analysis on 1,679 gut microbiotas of 771 full term, UK-hospital born babies and mothers, we demonstrate that the mode of delivery is a significant factor impacting gut microbiota composition during the neonatal period that persists into infancy (1 month - 1 year). Matched large-scale culturing and whole-genome sequencing (WGS) of over 800 bacterial strains cultured from these babies identified virulence factors and clinically relevant antimicrobial resistance (AMR) in opportunistic pathogens that may predispose to opportunistic infections. Our findings highlight the critical early roles of the local environment (i.e. mother and hospital) in establishing the gut microbiota in very early life, and identifies colonisation with AMR carrying, healthcare-associated opportunistic pathogens as a previously unappreciated risk factor.

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          BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data.

          O. Gascuel (1997)
          We propose an improved version of the neighbor-joining (NJ) algorithm of Saitou and Nei. This new algorithm, BIONJ, follows the same agglomerative scheme as NJ, which consists of iteratively picking a pair of taxa, creating a new mode which represents the cluster of these taxa, and reducing the distance matrix by replacing both taxa by this node. Moreover, BIONJ uses a simple first-order model of the variances and covariances of evolutionary distance estimates. This model is well adapted when these estimates are obtained from aligned sequences. At each step it permits the selection, from the class of admissible reductions, of the reduction which minimizes the variance of the new distance matrix. In this way, we obtain better estimates to choose the pair of taxa to be agglomerated during the next steps. Moreover, in comparison with NJ's estimates, these estimates become better and better as the algorithm proceeds. BIONJ retains the good properties of NJ--especially its low run time. Computer simulations have been performed with 12-taxon model trees to determine BIONJ's efficiency. When the substitution rates are low (maximum pairwise divergence approximately 0.1 substitutions per site) or when they are constant among lineages, BIONJ is only slightly better than NJ. When the substitution rates are higher and vary among lineages,BIONJ clearly has better topological accuracy. In the latter case, for the model trees and the conditions of evolution tested, the topological error reduction is on the average around 20%. With highly-varying-rate trees and with high substitution rates (maximum pairwise divergence approximately 1.0 substitutions per site), the error reduction may even rise above 50%, while the probability of finding the correct tree may be augmented by as much as 15%.
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            PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing?

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              Is Open Access

              Bracken: estimating species abundance in metagenomics data

              Metagenomic experiments attempt to characterize microbial communities using high-throughput DNA sequencing. Identification of the microorganisms in a sample provides information about the genetic profile, population structure, and role of microorganisms within an environment. Until recently, most metagenomics studies focused on high-level characterization at the level of phyla, or alternatively sequenced the 16S ribosomal RNA gene that is present in bacterial species. As the cost of sequencing has fallen, though, metagenomics experiments have increasingly used unbiased shotgun sequencing to capture all the organisms in a sample. This approach requires a method for estimating abundance directly from the raw read data. Here we describe a fast, accurate new method that computes the abundance at the species level using the reads collected in a metagenomics experiment. Bracken (Bayesian Reestimation of Abundance after Classification with KrakEN) uses the taxonomic assignments made by Kraken, a very fast read-level classifier, along with information about the genomes themselves to estimate abundance at the species level, the genus level, or above. We demonstrate that Bracken can produce accurate species- and genus-level abundance estimates even when a sample contains multiple near-identical species.
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                Author and article information

                Journal
                0410462
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                17 August 2019
                18 September 2019
                October 2019
                18 March 2020
                : 574
                : 7776
                : 117-121
                Affiliations
                [1 ]Host-Microbiota Interactions Laboratory, Wellcome Sanger Institute, Hinxton, United Kingdom
                [2 ]Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, Victoria, Australia
                [3 ]Department of Molecular and Translational Sciences, Monash University, Clayton, Victoria, Australia
                [4 ]Institute for Global Health, University College London, London, United Kingdom
                [5 ]Birmingham Clinical Trials Unit, University of Birmingham, Birmingham, United Kingdom
                Author notes
                [§ ] Corresponding authors: Trevor D. Lawley: Wellcome Sanger Institute, Hinxton, United Kingdom, CB10 1SA, Phone 01223 495 391, Fax 01223 495 239, tl2@ 123456sanger.ac.uk ; Nigel Field: Institute for Global Health, University College London, London, United Kingdom, WC1N 1EH, nigel.field@ 123456ucl.ac.uk
                Article
                EMS84123
                10.1038/s41586-019-1560-1
                6894937
                31534227
                4721cee8-b89f-4f11-9698-7e8166108687

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                gastrointestinal microbiota,early-life microbiota colonisation,clinical metagenomics,neonatal,c-section,intrapartum antibiotic prophylaxis,paediatric,opportunistic pathogens,antimicrobial resistance (amr),enterococcus,klebsiella

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