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      Characterization of the Intestinal Microbiota of Broiler Breeders With Different Egg Laying Rate

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          Abstract

          The gastrointestinal microbiota plays a pivotal role in maintaining animal health, immunity and reproductive performances. However, literature about the relationship between microbiota and reproductive performance is limited. The aim of the present study was to determine differences in the intestinal microbiota of broiler breeders with different egg laying rate. A total of 200 AA+ parent broiler breeders (41-week-old) were separated into two groups according to their different egg laying rate [average egg laying rate group (AR: 78.57 ± 0.20%) and high egg laying rate group (HR: 90.79 ± 0.43%). Feed conversion ratio (FCR), ovary cell apoptosis rate (ApoCR) and relative abdominal fat weight were lower ( p = 0.01), while the hatchability rate of qualified egg was higher ( p = 0.04) in HR group than that in AR group. Phascolarctobacterium abundance were lower ( p = 0.012) in ileum of HR birds. Romboutsia (genus) in ileum was negatively related to the feed efficiency ( r = −0.58, p < 0.05), Firmicutes (phylum) and Lactobacillus (genus) abundances in cecum were positively related to the egg laying rate (ELR) ( r = 0.35 and 0.48, p < 0.05), feed efficiency ( r = 0.42 and 0.43, p < 0.05), while Spirochaetes (phylum) and Sphaerochaeta (genus) abundances in cecum were negatively related to the ELR ( r = −0.43 and −0.70, p < 0.05), feed efficiency ( r = 0.54 and 0.48, p < 0.05), and positively related to ApoCR ( r = 0.46 and 0.47, p < 0.05). Our results suggested that microbiota, such as Firmicutes (phylum) and Lactobacillus (genus) have positive relationship, while Spirochaetes (phylum) and Romboutsia (genus) abundances exert negative relationship with broiler breeders' reproductive performances.

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          Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

          The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.
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            UCHIME improves sensitivity and speed of chimera detection

            Motivation: Chimeric DNA sequences often form during polymerase chain reaction amplification, especially when sequencing single regions (e.g. 16S rRNA or fungal Internal Transcribed Spacer) to assess diversity or compare populations. Undetected chimeras may be misinterpreted as novel species, causing inflated estimates of diversity and spurious inferences of differences between populations. Detection and removal of chimeras is therefore of critical importance in such experiments. Results: We describe UCHIME, a new program that detects chimeric sequences with two or more segments. UCHIME either uses a database of chimera-free sequences or detects chimeras de novo by exploiting abundance data. UCHIME has better sensitivity than ChimeraSlayer (previously the most sensitive database method), especially with short, noisy sequences. In testing on artificial bacterial communities with known composition, UCHIME de novo sensitivity is shown to be comparable to Perseus. UCHIME is >100× faster than Perseus and >1000× faster than ChimeraSlayer. Contact: robert@drive5.com Availability: Source, binaries and data: http://drive5.com/uchime. Supplementary information: Supplementary data are available at Bioinformatics online.
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              Obesity alters gut microbial ecology.

              We have analyzed 5,088 bacterial 16S rRNA gene sequences from the distal intestinal (cecal) microbiota of genetically obese ob/ob mice, lean ob/+ and wild-type siblings, and their ob/+ mothers, all fed the same polysaccharide-rich diet. Although the majority of mouse gut species are unique, the mouse and human microbiota(s) are similar at the division (superkingdom) level, with Firmicutes and Bacteroidetes dominating. Microbial-community composition is inherited from mothers. However, compared with lean mice and regardless of kinship, ob/ob animals have a 50% reduction in the abundance of Bacteroidetes and a proportional increase in Firmicutes. These changes, which are division-wide, indicate that, in this model, obesity affects the diversity of the gut microbiota and suggest that intentional manipulation of community structure may be useful for regulating energy balance in obese individuals. The sequences reported in this paper have been deposited in the GenBank database [accession nos. DQ 014552--DQ 015671 (mothers) and AY 989911--AY 993908 (offspring)].
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                Author and article information

                Contributors
                Journal
                Front Vet Sci
                Front Vet Sci
                Front. Vet. Sci.
                Frontiers in Veterinary Science
                Frontiers Media S.A.
                2297-1769
                24 November 2020
                2020
                : 7
                Affiliations
                [1] 1Key Laboratory of Animal Disease-Resistance Nutrition, Ministry of Education, Animal Nutrition Institute, Sichuan Agricultural University , Chengdu, China
                [2] 2College of Agriculture and Forestry, Pu'er University , Pu'er City, China
                [3] 3Faculty of Veterinary and Agricultural Sciences, The University of Melbourne , Parkville, VIC, Australia
                Author notes

                Edited by: Xiangfeng Kong, Chinese Academy of Sciences, China

                Reviewed by: Mojtaba Zaghari, University of Tehran, Iran; Jing Wang, Feed Research Institute (CAAS), China

                *Correspondence: Jianping Wang wangjianping@ 123456sicau.edu.cn

                This article was submitted to Animal Nutrition and Metabolism, a section of the journal Frontiers in Veterinary Science

                †These authors have contributed equally to this work

                Article
                10.3389/fvets.2020.599337
                7732610
                3b74bc76-ebcb-4702-8ce9-a16c595d346c
                Copyright © 2020 Yang, Zhang, Wang, Celi, Ding, Bai, Zeng, Mao, Zhuo, Xu, Yan, Zhang and Shan.

                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.

                Page count
                Figures: 4, Tables: 4, Equations: 0, References: 40, Pages: 10, Words: 5646
                Funding
                Funded by: National Key Research and Development Program of China Stem Cell and Translational Research 10.13039/501100013290
                Categories
                Veterinary Science
                Original Research

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