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      Host and Environmental Factors Affecting the Intestinal Microbiota in Chickens

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          Abstract

          The initial development of intestinal microbiota in poultry plays an important role in production performance, overall health and resistance against microbial infections. Multiplexed sequencing of 16S ribosomal RNA gene amplicons is often used in studies, such as feed intervention or antimicrobial drug trials, to determine corresponding effects on the composition of intestinal microbiota. However, considerable variation of intestinal microbiota composition has been observed both within and across studies. Such variation may in part be attributed to technical factors, such as sampling procedures, sample storage, DNA extraction, the choice of PCR primers and corresponding region to be sequenced, and the sequencing platforms used. Furthermore, part of this variation in microbiota composition may also be explained by different host characteristics and environmental factors. To facilitate the improvement of design, reproducibility and interpretation of poultry microbiota studies, we have reviewed the literature on confounding factors influencing the observed intestinal microbiota in chickens. First, it has been identified that host-related factors, such as age, sex, and breed, have a large effect on intestinal microbiota. The diversity of chicken intestinal microbiota tends to increase most during the first weeks of life, and corresponding colonization patterns seem to differ between layer- and meat-type chickens. Second, it has been found that environmental factors, such as biosecurity level, housing, litter, feed access and climate also have an effect on the composition of the intestinal microbiota. As microbiota studies have to deal with many of these unknown or hidden host and environmental variables, the choice of study designs can have a great impact on study outcomes and interpretation of the data. Providing details on a broad range of host and environmental factors in articles and sequence data repositories is highly recommended. This creates opportunities to combine data from different studies for meta-analysis, which will facilitate scientific breakthroughs toward nutritional and husbandry associated strategies to improve animal health and performance.

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          Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors.

          In vertebrates, including humans, individuals harbor gut microbial communities whose species composition and relative proportions of dominant microbial groups are tremendously varied. Although external and stochastic factors clearly contribute to the individuality of the microbiota, the fundamental principles dictating how environmental factors and host genetic factors combine to shape this complex ecosystem are largely unknown and require systematic study. Here we examined factors that affect microbiota composition in a large (n = 645) mouse advanced intercross line originating from a cross between C57BL/6J and an ICR-derived outbred line (HR). Quantitative pyrosequencing of the microbiota defined a core measurable microbiota (CMM) of 64 conserved taxonomic groups that varied quantitatively across most animals in the population. Although some of this variation can be explained by litter and cohort effects, individual host genotype had a measurable contribution. Testing of the CMM abundances for cosegregation with 530 fully informative SNP markers identified 18 host quantitative trait loci (QTL) that show significant or suggestive genome-wide linkage with relative abundances of specific microbial taxa. These QTL affect microbiota composition in three ways; some loci control individual microbial species, some control groups of related taxa, and some have putative pleiotropic effects on groups of distantly related organisms. These data provide clear evidence for the importance of host genetic control in shaping individual microbiome diversity in mammals, a key step toward understanding the factors that govern the assemblages of gut microbiota associated with complex diseases.
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            Microbiota of the chicken gastrointestinal tract: influence on health, productivity and disease.

            Recent advances in the technology available for culture-independent methods for identification and enumeration of environmental bacteria have invigorated interest in the study of the role of chicken intestinal microbiota in health and productivity. Chickens harbour unique and diverse bacterial communities that include human and animal pathogens. Increasing public concern about the use of antibiotics in the poultry industry has influenced the ways in which poultry producers are working towards improving birds' intestinal health. Effective means of antibiotic-independent pathogen control through competitive exclusion and promotion of good protective microbiota are being actively investigated. With the realisation that just about any change in environment influences the highly responsive microbial communities and with the abandonment of the notion that we can isolate and investigate a single species of interest outside of the community, came a flood of studies that have attempted to profile the intestinal microbiota of chickens under numerous conditions. This review aims to address the main issues in investigating chicken microbiota and to summarise the data acquired to date.
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              The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies

              Background Characterizing microbial communities via next-generation sequencing is subject to a number of pitfalls involving sample processing. The observed community composition can be a severe distortion of the quantities of bacteria actually present in the microbiome, hampering analysis and threatening the validity of conclusions from metagenomic studies. We introduce an experimental protocol using mock communities for quantifying and characterizing bias introduced in the sample processing pipeline. We used 80 bacterial mock communities comprised of prescribed proportions of cells from seven vaginally-relevant bacterial strains to assess the bias introduced in the sample processing pipeline. We created two additional sets of 80 mock communities by mixing prescribed quantities of DNA and PCR product to quantify the relative contribution to bias of (1) DNA extraction, (2) PCR amplification, and (3) sequencing and taxonomic classification for particular choices of protocols for each step. We developed models to predict the “true” composition of environmental samples based on the observed proportions, and applied them to a set of clinical vaginal samples from a single subject during four visits. Results We observed that using different DNA extraction kits can produce dramatically different results but bias is introduced regardless of the choice of kit. We observed error rates from bias of over 85% in some samples, while technical variation was very low at less than 5% for most bacteria. The effects of DNA extraction and PCR amplification for our protocols were much larger than those due to sequencing and classification. The processing steps affected different bacteria in different ways, resulting in amplified and suppressed observed proportions of a community. When predictive models were applied to clinical samples from a subject, the predicted microbiome profiles were better reflections of the physiology and diagnosis of the subject at the visits than the observed community compositions. Conclusions Bias in 16S studies due to DNA extraction and PCR amplification will continue to require attention despite further advances in sequencing technology. Analysis of mock communities can help assess bias and facilitate the interpretation of results from environmental samples. Electronic supplementary material The online version of this article (doi:10.1186/s12866-015-0351-6) contains supplementary material, which is available to authorized users.
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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
                16 February 2018
                2018
                : 9
                : 235
                Affiliations
                [1] 1Department of Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University , Utrecht, Netherlands
                [2] 2Laboratory of Microbiology, Wageningen University & Research , Wageningen, Netherlands
                Author notes

                Edited by: Francesca Turroni, Università degli Studi di Parma, Italy

                Reviewed by: Robert J. Moore, RMIT University, Australia; Cristiano Bortoluzzi, University of Georgia, Georgia

                *Correspondence: Jannigje G. Kers, j.g.kers@ 123456uu.nl

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

                Article
                10.3389/fmicb.2018.00235
                5820305
                29503637
                cd8e6521-874d-4443-b7a1-75e868a22baf
                Copyright © 2018 Kers, Velkers, Fischer, Hermes, Stegeman and Smidt.

                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 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
                : 23 October 2017
                : 31 January 2018
                Page count
                Figures: 5, Tables: 0, Equations: 0, References: 150, Pages: 14, Words: 0
                Funding
                Funded by: Nederlandse Organisatie voor Wetenschappelijk Onderzoek 10.13039/501100003246
                Award ID: 86815020
                Categories
                Microbiology
                Review

                Microbiology & Virology
                gut microbiota,poultry,confounding factors,microbiome,gut health,16s rrna
                Microbiology & Virology
                gut microbiota, poultry, confounding factors, microbiome, gut health, 16s rrna

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