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      Layer chicken microbiota: a comprehensive analysis of spatial and temporal dynamics across all major gut sections

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          Abstract

          Background

          The gut microbiota influences chicken health, welfare, and productivity. A diverse and balanced microbiota has been associated with improved growth, efficient feed utilisation, a well-developed immune system, disease resistance, and stress tolerance in chickens. Previous studies on chicken gut microbiota have predominantly focused on broiler chickens and have usually been limited to one or two sections of the digestive system, under controlled research environments, and often sampled at a single time point. To extend these studies, this investigation examined the microbiota of commercially raised layer chickens across all major gut sections of the digestive system and with regular sampling from rearing to the end of production at 80 weeks. The aim was to build a detailed picture of microbiota development across the entire digestive system of layer chickens and study spatial and temporal dynamics.

          Results

          The taxonomic composition of gut microbiota differed significantly between birds in the rearing and production stages, indicating a shift after laying onset. Similar microbiota compositions were observed between proventriculus and gizzard, as well as between jejunum and ileum, likely due to their anatomical proximity. Lactobacillus dominated the upper gut in pullets and the lower gut in older birds. The oesophagus had a high proportion of Proteobacteria, including opportunistic pathogens such as Gallibacterium. Relative abundance of Gallibacterium increased after peak production in multiple gut sections. Aeriscardovia was enriched in the late-lay phase compared to younger birds in multiple gut sections. Age influenced microbial richness and diversity in different organs. The upper gut showed decreased diversity over time, possibly influenced by dietary changes, while the lower gut, specifically cecum and colon, displayed increased richness as birds matured. However, age-related changes were inconsistent across all organs, suggesting the influence of organ-specific factors in microbiota maturation.

          Conclusion

          Addressing a gap in previous research, this study explored the microbiota across all major gut sections and tracked their dynamics from rearing to the end of the production cycle in commercially raised layer chickens. This study provides a comprehensive understanding of microbiota structure and development which help to develop targeted strategies to optimise gut health and overall productivity in poultry production.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s40104-023-00979-1.

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          Most cited references34

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          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.
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            Cutadapt removes adapter sequences from high-throughput sequencing reads

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              The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

              SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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                Author and article information

                Contributors
                y.sharmabajagai@cqu.edu.au
                Journal
                J Anim Sci Biotechnol
                J Anim Sci Biotechnol
                Journal of Animal Science and Biotechnology
                BioMed Central (London )
                1674-9782
                2049-1891
                5 February 2024
                5 February 2024
                2024
                : 15
                : 20
                Affiliations
                [1 ]Institute for Future Farming Systems, Central Queensland University, ( https://ror.org/023q4bk22) Rockhampton, QLD 4701 Australia
                [2 ]School of Science, RMIT University, ( https://ror.org/04ttjf776) Bundoora, VIC 3083 Australia
                [3 ]School of Animal and Veterinary Sciences, The University of Adelaide, ( https://ror.org/00892tw58) Roseworthy, South Australia 5371 Australia
                Author information
                http://orcid.org/0000-0002-3043-071X
                Article
                979
                10.1186/s40104-023-00979-1
                10840231
                38317171
                58c7f4e9-925e-4149-b244-06f84f3d0eb7
                © 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 10 August 2023
                : 17 December 2023
                Funding
                Funded by: Australian Eggs
                Award ID: 18AEC
                Award ID: 18AEC
                Award ID: 18AEC
                Award Recipient :
                Categories
                Research
                Custom metadata
                © Chinese Association of Animal Science and Veterinary Medicine 2024

                Animal science & Zoology
                chicken microbiota,gut microbiota,layers,spatial variation,temporal variation

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