Decoding the chicken gastrointestinal microbiome

Decoding the chicken gastrointestinal microbiome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Decoding the chicken gastrointestinal microbiome PB Burrows, Fernanda Godoy Santos, Lawther KJ, Anne Richmond, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4969804/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jan, 2025 Read the published version in BMC Microbiology → Version 1 posted 4 You are reading this latest preprint version Abstract Metataxonomic studies have underpinned a vast understanding of microbial communities residing within livestock gastrointestinal tracts, albeit studies have often not been combined to provide a global census. Consequently, in this study we characterised the overall and common ‘core’ chicken microbiota across the gastrointestinal tract (GIT), whilst assessing the effects of GIT location, bird breed, age and geographical location on the GIT resident microbes using metataxonomic data compiled from studies completed across the world. Specifically, bacterial 16S ribosomal DNA sequences from GIT samples associated with various breeds, differing in age, diet, GIT (caecum, faeces, ileum and jejunum) and geographical location were obtained from the Short Read Archive and analysed using the MGnify pipeline. Metataxonomic profiles produced across the 602 datasets illustrated the presence of 3 phyla, 25 families and 30 genera, of which core genera (defined by presence in over 90% of datasets) belonged to Lactobacillus , Faecalibacterium , Butyricicoccus , Eisenbergiella , Subdoligranulum , Oscillibacter , Clostridium & Blautia . PERMANOVA analysis also showed that GIT location, bird breed, age and geographical location all had a significant effect on GIT microbial diversity. On a genus level, Faecalibacterium was most abundant in the caeca, Lactobacillus was most abundant in the faeces, ileum and jejunum, with the data showing that the caeca and faeces were most diverse. AIL F8 progeny, Ross 308 and Cobb 500 breeds GIT bacteria were dominated by Lactobacillus , and Eisenbergiella , Megamonas and Bacteroides were most abundant amongst Sasso-T451A and Tibetan chicken breeds. Microbial communities within each GIT region develop with age, from a Lactobacillus and Streptococcus dominated community during the earlier stages of growth, towards a Faecalibacterium , Eisenbergiella , Bacteroides , Megamonas , and Lactobacillus dominated community during the later stages of life. Geographical locations, and thus environmental effectors, also impacted upon gastrointestinal tract microbiota, with Canadian and European datasets being dominated by Lactobacillus , whilst UK and Chinese datasets were dominated by Eisenbergiella and Bacteroides respectively. This study aids in defining what ‘normal’ is within poultry gastrointestinal tract microbiota globally, which is imperative to enhancing the microbiome for productive and environmental improvements. Chicken gastrointestinal microbiota 16S rDNA metataxonomy gut diversity core microbiome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction The domesticated broiler chicken, Gallus gallus domesticus , is amongst the leading global food source, attributed to the rich protein content and micronutrients, coupled with low production cost of broiler chicken (Kim et al., 2019 ). Indeed, broiler chicken production has reached an impressive scale of 74 billion chicken slaughtered globally (Torrella, 2023 ), with expectations of reaching 121% of production rates by 2050 as compared with 2005 levels (Ayalew et al., 2022 ). In this context, it is vital to recognise the importance of sustainably enhancing chicken production in order to address the challenges of accommodating the globally increasing demand (Oakley et al., 2014b ; Ahmad et al., 2018 ; Gilroy et al., 2021 ). The productive capabilities of chicken are affected by many factors related to the host (genetics, immune response, general gut health) and environment (farm management, chicken welfare, feed), which all have a direct impact on the gastrointestinal tract (GIT) microbiota (Diaz Carrasco et al., 2019 ). Simultaneously, GIT microbiota significantly influence the health and metabolism of broilers (Ocejo et al., 2019 ), highlighting the importance of having a comprehensive understanding of GIT associated microbiomes in order to sustainably enhance broiler chicken production. Poultry are monogastric animals with a sophisticated physiology, whereby the digestive system consists of the crop, proventriculus, ventriculus (gizzard), small intestine (duodenum, jejunum, ileum), large intestine, caeca, colon and cloaca. Proximal gut (crop to ventriculus) is highly involved with softening feed through enzymatic and mechanical breakdown, within an acidic environment (Borda-Molina et al., 2018 ). The distal gut (small intestine, caeca and colon) further extracts nutrients from digested feed and non-starch polysaccharides (NSP) present in the feed (Borda-Molina et al., 2018 ). The complex microbial ecosystems within the chicken’s GIT are often dominated by lactic acid bacteria within the proximal gut, including the genera Lactobacillus , Bifidobacterium , Enterobacteriaceae and Klebsiella which initiate the digestion of feed (Shang et al., 2018 ; Yadav and Jha, 2019 ; Fathima et al., 2022 ); whereas Lactobacillus , Enterococcus , Clostridia , Streptococcus , Bacteroides , coliforms, Faecalibacterium , Ruminococcus are often abundant within the distal gut microbial communities (Knarreborg et al., 2002 ; Choi et al., 2014 ; Pan and Yu, 2014 ; Kumar et al., 2018 ; Yadav and Jha, 2019 ; Fathima et al., 2022 ). A bacterial census of poultry intestinal microbiome published in 2013, primarily utilising data already deposited from past metataxonomic studies sourced from three public databases (GenBank, Silva comprehensive ribosomal RNA database, and Ribosomal Database Project) and made available on the MG-RAST server ( https://www.mg-rast.org/mgmain.html?mgpage=search&search=Poultry_Gut_DB ) (Wei et al., 2013 ). This study used a total of 3,184 16S rDNA gene sequences, obtained using a range of primers and therefore hypervariable 16S rDNA regions, from the chicken caeca and from intestinal samples (without specification of the local intestine section used such as duodenum, ileum or jejunum). The study identified 12 phyla from intestinal sequences, whereby Firmicutes dominated (70%), followed by Bacteroidetes (12.3%) and Proteobacteria (9.3%); meanwhile 10 phyla were identified from the caecal data, where again, Firmicutes (78%) and Bacteroidetes (11%) were the dominating phyla. Intestinal sequences highlighted the presence of numerous genera belonging to Firmicutes accounting for > 1% of total sequences ( Clostridium , Ruminococcus , Lactobacillus , Eubacterium , Fecalibacterium , Butyrivibrio , Ethanoligenens , Alkaliphilus , Butyricicoccus , Blautia , Hespellia , Roseburia , & Megamonas ) (Wei et al., 2013 ); likewise Bacteroidetes were represented by four genera ( Bacteroides , Prevotella , Parabacteroides & Alistipes ) composing > 1% of total sequences. With respect to other phyla, only one genus from Actinobacteria ( Bifidobacterium ) and Proteobacteria ( Desulfohalobium ) presented a notable abundance within the intestinal microbiome at > 1% and 0.7% of total sequences respectively (Wei et al., 2013 ). Amongst the caecal samples, 31 genera belonged to Firmicutes, of which three represent > 5% of read abundance ( Ruminococcus , Clostridium & Eubacterium ), and ten represented > 1% of read abundances ( Fecalibacterium , Blautia , Butyrivibrio , Lactobacillus , Megamonas, Roseburia , Ethanoligenes , Hespellia , Veillonella , & Anaerostipes ). Bacteroides was the most abundant of the Bacteroidetes phylum accounting for 4% of total caecal sequences with other genera present ( Prevotella , Paraprevotella , Tanneralla and Riemeralla ). Proteobacteria were low in abundance and represented mainly by three genera ( Desulfohalobium , Escherichia / Shigella & Neisseria ) (Wei et al., 2013 ). In addition, Chica Cardenas et al. ( 2021 ) performed a similar study in 2021, producing a meta-analysis of chicken caeca microbial communities using data targeting the V3, V4 and V3-V4 hypervariable regions from 9 studies accounting for 324 total samples (Chica Cardenas et al., 2021 ). Upon comparing each of the hypervariable regions, they identified Oscillospira amongst all 3 using an 80% abundance cut-off, after the cut-off was reduced to 50%, 5 genera ( Oscillospira , Lactobacillus , Faecalibacterium , Clostridium and Ruminococcus ) were identified (Chica Cardenas et al., 2021 ). Overall, the selection of hypervariable region, in relation to assessing metataxonomic data, affects the evaluation of microbiomes and Chica Cardenas et al. ( 2021 ) found hypervariable region V4 presents the most diverse and most unique genera when compared with the other regions. Given that only 2 poultry GIT microbiota census are available (Wei et al., 2013 ; Chica Cardenas et al., 2021 ), with Wei et al. ( 2013 ) utilising sequences from different variable regions, which in itself has been shown to be a variable affecting bacterial metataxonomic results, and the recent study by Chica Cardenas et al. ( 2021 ) used a systematic approach of literature focused solely on caecal data to find the 16S rDNA sequences, resulting in only 324 sequence datasets being used in their analysis, it is now timely to re-visit the concept of the core poultry microbiome and factors which affect GIT bacterial colonisation using more comprehensive data. It should also be noted that neither study investigated the effect of geographical and GIT location or bird age either, although Chica Cardenas et al. ( 2021 ) did investigate breed and noted that there was a breed effect on the caecal bacteria present. Therefore, our aim in this study is to provide a comprehensive up to date study of our current understanding of the composition and diversity of chicken GIT microbiomes and factors which control this (GIT location, breed, bird age and geographical location) using all publicly available 16S rDNA sequences targeting the V3-V4 hypervariable region only, allowing an enhanced understanding of these microbiomes and factors which effect their development in the chicken GIT on a global scale. Methods and Materials Selection of sequence read archive data and bioprojects Sequence read archive (SRA) data relating to chicken GIT 16S rDNA sequences were selected from the SRA database between the months of February and July 2020, whilst using and combining search terms “chicken”, “broiler”, “hen”, “gastrointestinal tract”, “GIT”, “microbiome”, “microbiota”, “caecum”, “intestine” and “faeces”. Bioprojects were selected containing substantial associated metadata and excluding studies exhibiting any additional factors, such as intentional infection with Campylobacter spp., were excluded to prevent unnecessarily affecting results when analysing the core microbiota, although any controls produced were taken into consideration. A total of 114 bioprojects were selected pertaining to 6,742 individual sequencing datasets. These bioprojects were further refined by removing those which excluded important information such as GIT section sampled; age of birds; geographical location. Subsequently, only bioprojects which were obtained using the 16S rDNA gene V3-V4 hypervariable regions were chosen as this was the most common hypervariable region analysed. The choice of using data obtained using the same primers targeting the V3-V4 hypervariable region was in order to reduce the non-biological variability in the results. After refining our initially downloaded 114 bioprojects, 11 bioprojects were selected, of which 602 sequencing datasets were identified. The full metadata collected from these sequence datasets can be found in Supplementary File 1. Microsoft Excel Document. A summary breakdown of the studies involved can be found in Supplementary Table 1 and Supplementary Table 2. Subsequently, the 11 bioprojects datasets were submitted to the MGnify pipeline ( https://www.ebi.ac.uk/metagenomics ) for analysis. In brief, MGnify is an updated EBI Metagenomic platform where microbiome data can be analysed, explored as well as archived. Amplicons with paired end sequences are merged using SeqPrep (v1.2) and are subject to quality control assessment (QC). All outputs from this pipeline are presented as a Krona plot, bar charts and tables, and include MAPseq analysis (Mitchell et al., 2020 ). Data processing Outputs from the MGnify pipeline contained OTU count data at each taxon level; phylum, family and genus (Supplementary File 1. Microsoft Excel Document). Any taxonomic identifications as “unclassified,” “unidentified,” “group,” or “uncultured” were relabelled as ‘Unknown’ to facilitate estimation of the taxonomic assignment. Relative abundances were calculated by the application of the Total Sum Scaling (TSS) method using the following formula: OTU read count divided by (the total ASV read counts of a sample divided by the minimum total OTU read counts across the dataset). Relative abundances were calculated at phylum, family and genus taxon levels. OTU abundances less than 95% were categorised as ‘Other’. Relative abundances were subsequently grouped into GIT location (caecum, faeces and small intestine). Within each GIT location, data was grouped pertaining to each parameter (GIT location, breed, bird age and geographic location) to identify the prominent taxa in each parameter along with common core community members. Computational Statistical Analysis Data was tabulated into bar and pie charts using Excel, and Principal Component Analysis (PCA) plots were plotted using R (ver. 4.3.2). Venn diagrams were produced based on the common core microbiome across data groups using the website http://www.interactivenn.net/index.html (Heberle et al., 2015 ). Alpha and Beta diversity was performed using ‘vegan’ in R (ver. 2.6-4) (Oksanen J et al. , 2022a). Scripts can be found in Supplementary File 2. R Script Document. The effect of each parameter, as well as their interactions on OTU read counts, was assessed using Permutational multivariate analysis of variance (PERMANOVA). This analysis was based on Bray-Curtis dissimilarity and was conducted via the ‘adonis2’ function in the vegan package, employing 1000 permutations. Normalised data through a variance-stabilising transformation (VST) implemented by DESeq2 (version 1.42.0) (Love et al., 2014 ) was visualised using PCA. Results Summary of chicken GIT bacterial microbiome Principle component analysis (PCA) of normalised OTU read counts demonstrate clustering of communities according to each variable examined (Fig. 1 ). Regardless of variable, the principle component 1 and 2 explained 35% and 16% variance respectively. The communities derived from the different GIT locations (caecum, ileum and jejunum) resemble each other and cluster together, with the caecum presenting most deviation (Fig. 1 A). However, the faecal communities are separately clustered, with some deviating to resemble the caecum (Fig. 1 A). GIT OTUs across bird ages distinctly cluster together according to bird age, and OTUs across breeds Ross 308, AIL F8 and Sasso T451A also cluster together, whilst Cobb 500 OTUs are distinctly different from the other breeds and cluster away from the other sample OTUs (Fig. 1 B & C). Likewise, when comparing geographical locations, the European, UK and China-derived chicken GIT samples present most similarity, with OTUs generated from the GIT of Canadian clustering separately, indicating different taxonomic diversity within (Fig. 1 D). A total of 65,186,954 reads were identified across all datasets; 638,991 (0.98%) belonging to Archaea, and 64,547,963 (99.02%) belonging to Bacteria. Amongst the top 95% of sequences across all 602 datasets available, we found three phyla, twenty-three family and twenty-eight genera (Supplementary File 1. Microsoft Excel Document). Dominant phyla include: Firmicutes, Proteobacteria and Bacteroidetes, accounting for 80.62%, 7.89% and 5.91% of total read abundances respectively. On a family level Ruminococcaceae , Lactobacillaceae and Lachnospiraceae are the most abundant, accounting for 23.10%, 16.53% and 10.80% of read abundances respectively. Ten families ranged between 5% and > 1% of sequence read abundances, of these the most abundant were Enterobacteriaceae , Oscillospiraceae and Clostridiaceae , accounting for 4.87%, 2.76% and 2.53% respectively. On a genus level, Lactobacillus , Faecalibacterium , Eisenbergiella and Oscillibacter are the most abundant, accounting for 16.40%, 5.10%, 3.30% and 2.74% of total read abundances respectively (Fig. 2 ), and eight other genera represented > 1% of sequence read abundances ( Streptococcus , Bacteroides , Butyricicoccus , Alistipes , Enterococcus , Megamonas , Ruminiclostridium and Romboutsia ). PERMANOVA analyses identified significant differences in the microbial communities based on bird age, breed, GIT and geographical location (P < 0.001). Age as a variable was highlighted as being the main contributor in affecting the microbiome when associated with the gastrointestinal tract location and geographic location (P 0.05). This may be the result of the unequal dispersion of data across breeds (Table 1 ). Likewise, when explaining alpha diversity indices (Table 2 ), there were significant differences identified amongst each alpha diversity indices, where each independent variable (bird age, breed, GIT region and geographical location) were significant (P ≤ 0.001). Otherwise, bird age was the only variable compared with others, where significant differences were identified across each index when bird age and breed were compared, meanwhile, age and geographical location presented significant differences in inverse Simpson diversity (P = 0.000199). Table 1 Permutational multivariate analysis of variance (PERMANOVA) analysis of OTU read counts after TSS scaling to evaluate effects of independent factors including gastrointestinal tract location, bird breed, bird age and geographical location at genus level. PERMANOVA highlights significant differences between each of the independent variables (P < 0.001). Age, however, is the only variable that has been assessed alongside the variables presenting significant differences in GIT regions and Location (P 0.05). R 2 F P-value GIT region 0.3894 277.4005 1.00E-04 *** Breed 0.1576 84.2206 1.00E-04 *** Location 0.0198 42.3992 1.00E-04 *** Age 0.1509 8.2701 1.00E-04 *** GIT region: Age 0.0167 3.9654 1.00E-04 *** Breed: Age 0.0009 1.8415 0.08849 . Location: Age 0.0111 11.8616 1.00E-04 *** Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Table 2 ANOVA analysis of alpha diversity indices using OTU read counts after TSS scaling to evaluate effects of independent factors including gastrointestinal tract location, bird breed, bird age and geographical location at genus level . Analysis of Variance (ANOVA) was performed on alpha diversity indices including Chao1 Richness, Pielou’s Evenness, Shannon Diversity and Inverse Simpson Diversity measuring the impact each independent factor of GIT region, breed, location and age have on tested indices. The codes ‘*’, ‘ * *’, ‘***’, denotes levels of significance (0.05, 0.01 & 0.001 respectively) in differences amongst factors. Chao1 Evenness Shannon Diversity Inverse Simpson Diversity F P-value F P-value F P-value F P-value GIT region 603.065 2.00E-16 *** 176.491 2.00E-16 *** 187.380 2.00E-16 *** 86.605 2.00E-16 *** Breed 121.110 2.00E-16 *** 42.473 2.00E-16 *** 65.196 2.00E-16 *** 35.852 2.00E-16 *** Location 10.523 0.001 ** 31.246 3.48E-08 *** 40.382 4.19E-10 *** 22.243 3.00E-06 *** Age 73.244 2.00E-16 *** 30.282 5.58E-08 *** 74.332 2.00E-16 *** 56.381 2.23E-13 *** GIT region: Age 3.484 0.062 . 0.189 0.664 0.742 0.389 0.021 0.885 Breed: Age 74.956 2.00E-16 *** 18.479 1.65E-08 *** 35.817 2.10E-15 *** 26.604 8.70E-12 *** Location: Age 2.265 0.133 0.632 0.427 1.951 0.163 14.016 0.000199 *** Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Effect of gastrointestinal tract location on the microbial diversity Caecal and faecal samples were most diverse of the GIT locations, the latter being most species rich, whereas the small intestine regions are similar in richness, evenness and diversity (P < 0.001; Supplementary Figs. 1; Table 2 ). Following grouping into associated GIT locations, 375 datasets were associated with the caecum, and were composed of three phyla, fourteen families and twenty genera based on the top 95% of reads; 152 datasets were obtained for faeces, and were composed of four phyla, eighteen families and twenty genera; 38 datasets were obtained for the ileum, and were composed of two phyla, nine family and four genera; 37 datasets were obtained for the jejunum, and were composed of three phyla, thirteen family and seven genera. At the genus level, abundances across each GIT section vary substantially (Fig. 3 ), whereas Faecalibacterium is most abundant in the caecum followed by Eisenbergiella and Oscillibacter (7.22%, 5.07% & 4.30% of total read abundances respectively); meanwhile Streptococcus and Enterococcus follow Lactobacillus in terms of faecal sample dominance (8.27% & 5.05% of total read abundances respectively). This suggests there is little similarity between caecal and faecal samples. Ileal and jejunal datasets present slightly different abundances; following Lactobacillus in abundance are Candidatus arthromitus and Faecalibacterium (7.40% & 2.37% of total read abundances respectively) in ileal samples, while Faecalibacterium and Stenotrophomonas (4.53% & 2.88% of total read abundances respectively) follow in abundance in jejunal samples. Data and information regarding each taxonomic level can be found in Supplementary File 1. Microsoft Excel Document. Effects of breed on the gastrointestinal tract microbial diversity Five breeds were identified across all datasets, all have a high abundance of unknown genera from 26.40% (AIL F8) to 62.10% (Ross 308) (Fig. 4 ). Lactobacillus is most abundant of the known genera in the breeds AIL F8, Cobb 500 and Ross 308. Lactobacillus was followed by Faecalibacterium in AIL F8 (39.35% & 13.65% of total read abundances respectively). Cobb 500 highlights Lactobacillus (28.79% of total read abundances) and Streptococcus (10.54% of total read abundances) as most abundant respectively, accounting for 39.32% of total relative abundances. Likewise, Lactobacillus is followed by Eisenbergiella amongst Ross 308 (8.25% & 5.32% of total relative abundances respectively). Sasso T451A presents Megamonas and Bacteroides as most abundant (9.91% & 8.89% of total relative abundances respectively). Meanwhile, Bacteroides and Parabacteroides are the most abundant genera of the Tibetan breeds (28.08% & 1.62% of total relative abundances). In terms of diversity, all diversity indices were significantly different (P < 0.001), Cobb 500, Ross 308 and Sasso T451A are amongst the most diverse, as Tibetan chicken breeds follow. Cobb 500 is most rich, as Ross 308 and Sasso T451A are most even (Supplementary Figs. 2; Table 2 ). Effect of bird age on the gastrointestinal tract microbial diversity Datasets relating to forty-one separate ages were grouped together as follows: Week 1: 0–7 days old (130 datasets), Week 2: 8–14 days old (45 datasets), Week 3: 15–21 days old (78 datasets), Week 4: 22–28 days old (227 datasets), Week 5: 29–35 days old (64 datasets), Week 6: 39–42 days old (18 datasets) and Week 9 onwards: 58-‘>300’ days old (40 datasets) (Fig. 5 ). Week 9 onwards contains the dataset produced by Zhou et al. ( 2016 ) who identified the Tibetan breeds as being older than 300 days. During week 1, Lactobacillus dominated (40.72% of total relative abundances), followed by Streptococcus (10.19% of total relative abundances) and Eisenbergiella (10.13% of total relative abundances). There are minimal localised differences between days; on days 3, 5, 6 and 7, Eisenbergiella follows Lactobacillus in terms of abundance (9.72%, 13.32%, 7.23% & 6.12% of total relative abundances) but conversely, Eisenbergiella is more abundant than Lactobacillus on day 4 (9.63% of total relative abundances). On week 2, Eisenbergiella and Oscillibacter together dominate except for days 10 and 14, whereby Lactobacillus dominates (20.23% & 24.20% of total relative abundances). In terms of week 3 bacterial GIT diversity, Butyricicoccus is most abundant during days 15 and 16 (7.09% & 5.23% of total relative abundances), Faecalibacterium during days 18 and 19 (8.88% & 6.08% of total relative abundances), Oscillibacter during days 17 and 20 (5.59% & 4.95% of total relative abundances), whilst Lactobacillus dominates during day 21 (18.96% of total relative abundances). However, in week 3 as whole Eisenbergiella , Oscillibacter and Faecalibacterium are the most abundant genera, developing to Eisenbergiella , Oscillibacter and Lactobacillus dominated bacterial community during week 4. Days 22 and 24 are dominated by Eisenbergiella (5.99% & 6.74% of total relative abundances), whilst Oscillibacter dominates days 23, 25 and 26 (5.12%, 6.41% & 7.76% of total relative abundances), and Lactobacillus dominates on days 27 and 28 (37.08% & 24.98% of total relative abundances). By week 5, Lactobacillus is no longer amongst the dominating genera, except for day 35 where it is most abundant (10.22% of total relative abundances). A mix of Faecalibacterium , Eisenbergiella , Bacteroides and Oscillibacter are the most abundant during this week. Faecalibacterium dominates during days 30, 31, 32 and 34 (4.88%, 6.43%, 8.35% & 9.56% of total relative abundances); Bacteroides dominates day 29 (6.79% of total relative abundances) followed by Faecalibacterium (5.97% of total relative abundances), Eisenbergiella dominates during day 33 (8.52% of total relative abundances). These four genera comprise 13.88–23.20% relative abundances during this week. From week 6 onwards, abundant genera shift occur with Megamonas and Bacteroides being the dominating genera during day 39 (10.50% & 10.33% of total relative abundances), followed by Helicobacter and Campylobacter (8.17% & 7.83% of total relative abundances). Whereby by day 42, Bacteroides and Parabacteroides are the domaining genera (35.74% & 10.23% of total relative abundances). In week 9, day 58 is dominated Megamonas (23.10% of total relative abundances), while days 81 and > 300 are both dominated by Bacteroides (20.99% & 28.08% of total relative abundances), each followed by Alistipes (12.15% of total relative abundances), Methanocorpusculum (17.09% of total relative abundances) and Parabacteroides (1.62% of total relative abundances) respectively. These three dominating genera account for 35.25%, 38.08% & 29.71% of each age abundance respectively. Therefore, it can be concluded that as bird age, the community appears to shift away from Lactobacillus dominated as diversity increases (P < 0.001) (Supplementary Figs. 3; Table 2 ). Geographical location as an effect on the gastrointestinal tract microbiota The datasets represent various geographical locations, including China, Canada, Netherlands, France, Spain and the United Kingdom (UK). For simplicity when discussing the mainland European regions (France, Netherlands and Spain), they have been grouped together. Chinese datasets are the same Tibetan breed datasets discussed previously. Unknown genera are most abundant ranging from 28.48% (Canada) to 68.29% (UK) of total relative abundances (Fig. 6 ). Lactobacillus dominates in Canadian and European datasets (28.79% & 27.48% of total relative abundances), as Streptococcus and Faecalibacterium (10.54% & 7.21% of total relative abundances respectively) follow in abundance respectively. Meanwhile, UK datasets are more varied, with Eisenbergiella being most abundant, closely followed by Oscillibacter and Faecalibacterium (6.46%, 5.84% & 4.90% of total relative abundances respectively). Ruminiclostridium and Butyricicoccus are also more abundant in the UK (2.68% & 2.64% of total relative abundances) compared with Canadian and European datasets; with Romboutsia and Enterococcus (7.41% & 6.42% of total relative abundances) being abundant in Canadian dataset, and Alistipes and Bacteroides (3.52% & 3.50% of total relative abundances) being abundant in European datasets. Conversely, Bacteroides and Parabacteroides dominate Chinese datasets (28.08% & 1.62% of total relative abundances). Overall, the median diversity is similar across each location, although UK chicken datasets are the most diverse closely followed by Canada, likewise in terms of evenness. Meanwhile Canadian and Chinese datasets present the most richness across all datasets; significant differences were observed across each alpha diversity index examined (P ≤ 0.001) (Supplementary Figs. 4; Table 2 ). Common Core Microbiome Only Faecalibacterium and Lactobacillus were identified as common core microbiota members, across all GIT locations (Table 3 ; Fig. 7 ). Seventeen genera were identified as caecum common core microbiome, including Faecalibacterium , Lactobacillus , Blautia , Butyricicoccus and Eisenbergiella . Likewise, 14 genera were identified as members of the common core microbiome of faeces, including Enterococcus, Lactobacillus, Streptococcus, Clostridium, Blautia, Erysipelatoclostridium, Faecalibacterium and Butyricicoccus . Amongst the small intestine, five and four genera respectively were identified as members of the core microbiome of the ileum and jejunum and only Faecalibacterium , Lactobacillus and Microbacterium were common in both small intestine regions (Table 3 ; Fig. 7 ). In conclusion, 17, 14 and 3 genera were identified as members of the common core microbiome across datasets associated with the caecum, faeces and small intestine respectively, as 8 genera were common in the caecal and faecal communities, where Faecalibacterium and Lactobacillus were common across each GIT region (Fig. 7 ). Table 3 Members of the common core microbiome across each GIT region at Genus level. Listed below are the common core genera present across > 90% of datasets associated with each GIT region. Caecal Faecal Small Intestine Anaerostipes Blautia Faecalibacterium Anaerotruncus Butyricicoccus Lactobacillus Blautia Clostridium Microbacterium Butyricicoccus Eisenbergiella Clostridium Enterococcus Eisenbergiella Erysipelatoclostridium Erysipelatoclostridium Faecalibacterium Faecalibacterium Lachnoclostridium Fusicatenibacter Lactobacillus Intestinimonas Paucibacter Lachnoclostridium Romboutsia Lactobacillus Roseburia Negativibacillus Streptococcus Oscillibacter Subdoligranulum Roseburia Ruminiclostridium Subdoligranulum Discussion Our aim was to identify overall and the ‘core’ gastrointestinal tract (GIT) microbiota of broiler chickens and assess the effects of GIT location, breed, age and geographical location on the microbial diversity, using publicly available data from the Sequence Read Archive (SRA) database. A total of 602 sequence datasets pertaining to 16S rDNA gene V3-V4 hypervariable regions were distributed into groups according to the metadata provided (GIT location tested, breed and age of birds as well as their geographical origin). Firmicutes dominated followed by Proteobacteria and Bacteroidetes; Ruminococcaceae , Lactobacillaceae and Lachnospiraceae predominated composing half of all sequencing reads. At genus level most reads are unknown, although Lactobacillus is most abundant of those identified, followed by Faecalibacterium and Eisenbergiella . Of which, we suggest Lactobacillus , Faecalibacterium , Butyricicoccus , Eisenbergiella , Subdoligranulum , Oscillibacter , Clostridium & Blautia the common core microbiome of the gastrointestinal tract. These genera have been described as commensal and beneficial to broilers, aiding in producing short chain fatty acids (SCFA) from non-starch polysaccharides (NSP), alongside contributing to immune response regulation and overall benefiting bird’s health (Eeckhaut et al., 2008 ; Goldstein et al., 2015 ; Wang et al., 2018 ; Khan et al., 2020 ; Le Roy et al., 2020 ; Gong et al., 2021 ). Analysis by PERMANOVA suggests each variable presents significant impacts on microbiota diversity, meanwhile, age had a greater effect on GIT location and geographical location than on breed. Gastrointestinal Tract Location When discussing the most prominent genera per GIT location (small intestine, caeca and faeces), microbial communities differ dependent on function (Aruwa et al., 2021 ). The small intestine facilitate digestate transportation to the caeca, evident by reduced enzymatic activity; while the caeca’s anaerobic environment facilitates nitrogen recycling, producing SCFAs from NSPs (Aruwa et al., 2021 ; Choi et al., 2021 ). Otherwise, the large intestine is short and does not retain digestate for an extensive period of time, but is involved with nutrient absorption (Aruwa et al., 2021 ). The nature of faecal communities, as an ethically viable approach as a reference point for quantifying and identifying most members of the gut microbiota (Claesson et al., 2011 ; Yan et al., 2019 ), is reflected in our results as microbial communities from the small intestine (jejunum and ileum) and faeces resemble each other most. Of course, faecal community should not be reported as a ‘true’ representation of the intestinal or caecal microbiota (Pauwels et al., 2015 ; Tang et al., 2020 ). This is in contrast to Richards-Rios et al. ( 2020 ) who highlight similarity in the caecal and ileal microbiota, especially during the earlier days of a broiler’s life. Alpha diversities also suggest differences, faecal communities have greater richness, as the caecal community is most even and is most diverse, closely followed by faecal. Diversity, furthermore, was reported by Wei et al. ( 2013 ). Excluding the caecal community, Lactobacillus is most prominent in each GIT location (reaching 64.35% of total abundances). Remaining (Diaz Carrasco et al., 2019 ) community members deviate. Small intestine communities present Candidatus arthromitus and Faecalibacterium in the ileum (7.40% & 2.37% of total relative abundances respectively), Faecalibacterium and Stenotrophomonas in the jejunum (4.53% & 2.88% of total relative abundances respectively) as the following genera. Stenotrophomonas is an environmental bacteria found in water, a bacterium which is rapidly emerging as a multi-drug resistant pathogen (Adegoke et al., 2017 ). Meanwhile, Streptococcus , Enterococcus and Romboutsia (8.27%, 5.05% & 4.93% of total abundances respectively) follow in the faecal community. Faecalibacterium , Eisenbergiella and Oscillibacter (7.22%, 5.07% & 4.30% of total abundances respectively) are most prominent in the caecal community. In doing so, the caeca present greatest levels of unclassified, or ‘unknown’, genera (59.58% of total abundances), compared to the remaining GIT locations (reaching 32.83% of total abundances). As the small intestinal and caecal microbiomes differ, previous indications suggest there should be some similarity due to the passage of contents and excreta (Aruwa et al., 2021 ). Furthermore, in identifying the core microbiota, Lactobacillus , Faecalibacterium , Butyricicoccus , Eisenbergiella , Subdoligranulum , Erysipelatoclostridium , Lachnoclostridium , Roseburia , Clostridium & Blautia were present across 90% of datasets in caecal and faecal communities, which has been previously reported (Yan et al., 2019 ); although only Faecalibacterium and Lactobacillus were present across 90% of datasets in each GIT location. Our small intestine and caecal communities present differences from Wei et al. ( 2013 ) as we see Clostridium and Ruminococcus as dominating (18% & 14% respectively) within the ‘intestinal microbiome’, followed by Lactobacillus , Bacteroides , Faecalibacterium and Eubacterium (8%, 6%, 5% & 5% respectively). The jejunum and ileum are expected to be similar, as is seen in our results, due to their proximity and related functions (Aruwa et al., 2021 ). Ruminococcus , Clostridium and Eubacterium are most abundant amongst the caecal community according to Wei et al. ( 2013 ), which differs from our results which resemble those found be Chica Cardenas et al. ( 2021 ) more as they also show that Gram-positive cocci, Bifidobacterium , Clostridium , E. coli , Lactobacillus , Streptococcus , Bacteroidetes are present. Of which, they also show that Oscillospira , Lactobacillus , Faecalibacterium , Clostridium and Ruminococcus were deemed as core microbiome members (Chica Cardenas et al., 2021 ), which is what we note with the exception of Clostridium . Nonetheless, like our results, Wei et al. ( 2013 ) identified the caeca as containing the most diverse communities. Meanwhile, our faecal community resemble the data published by Videnska et al. ( 2014 ), being comprised of Firmicutes, Proteobacteria, Bacteroidetes and Actinobacteria (76.2%, 14%, 6.5% & 3.8% respectively). Furthermore, Hou et al. ( 2016 ) identified Clostridium (23.44%), Bacteroides (18.78%), Lactobacillus (8.77%), Ruminococcus (3.97%), Hallella (1.61%), Subdoligranulum (1.07%), Faecalibacterium (1.04%), Roseburia (0.98%), and Eubacterium (0.31%) as the common core, matching our global data. Breed With regards to differences in GIT microbial communities between breeds, we identified similar trends, whereby the main commercial breeds have similarity, with AIL F8 Progeny, Cobb 500 and Ross 308 being dominated by Lactobacillus (39.35%, 28.79% & 8.25% of total abundance respectively); with differing genera then dominating, with Faecalibacterium being the next dominant in AIL F8 progeny (13.65% of total abundances), Streptococcus in Cobb 500 (10.54% of total abundances), and Eisenbergiella , Oscillibacter and Faecalibacterium in Ross 308 (5.32%, 4.60% & 4.10% of total abundances respectively). Sasso T451A and Tibetan breeds represent non-commercial breeds with, Sasso T451A being the most diverse, followed by the Tibetan breeds, AIL F8 progeny and Cobb 500 were similar in diversity, with Ross 308 having the least GIT bacterial diversity. Other publications also suggest breed influences the overall GIT microbiome, for example Pandit et al. ( 2018 ) noted a domination of Bacteroidetes/Firmicutes in Ross 308 and Kadaknath breeds, as opposed to Cobb 400 and Aseel breeds whereby caecal bacteria communities had an overall combined abundance ranging between 76.6% and 90.8%. Ghagus and Nicobari breeds also present Bacteroidetes/Firmicutes as most abundant (Paul et al., 2021 ). Bird Age As animals age, the GIT microbiota develops due to changing metabolic function, immune interactions, feed introduction, environmental changes amongst other factors (Shang et al., 2018 ). Our findings suggest a development of the microbiota by increased of diversity and development of common genera. We note that Lactobacillus dominates during 1 week of age and 4 weeks of age, although, the microbiome stabilises with age to contain Streptococcus , Eisenbergiella , Oscillibacter , Faecalibacterium , Butyricicoccus , Subdoligranulum, Faecalibacterium and Bacteroides . Eventually a combination of Bacteroides , Megamonas , Faecalibacterium , Eisenbergiella , Alistipes , Lactobacillus , Methanocorpusculum and Parabacteroides are most prominent during the latter ages of this study. This is indicative of previous publications, which show that bacteria are present upon hatch and undergo successional development where the bacterial taxa stabilise with age (Ballou et al., 2016 ; Shang et al., 2018 ; Ocejo et al., 2019 ). For example, Glendinning et al. ( 2019 ) note the development from a Clostridium sensu stricto 1 dominating community within the cereal and intestinal communities during the earlier ages, towards a community containing Enterococcus , Escherichia/Shigella and Lactobacillus , with age. Xiao et al. ( 2021 ) identify Escherichia and Clostridium as the dominating genera at day 0 in laying hens, developing towards Lactobacillus domination, finally to a community persisting of Bacteroides , Odoribacter and Clostridiales vadin BB60 group by day 50. Importantly, like our global data, Glendinning et al. ( 2019 ) and Xiao et al. ( 2021 ) both highlight the increase of diversity as the birds age. Geographical Location It is evident that the microbial composition of GIT microbiota from different locations is highly varied (Pin Viso et al., 2021 ). We found Lactobacillus is similar in abundance in both the Canadian and European (28.79% & 27.48% of total abundances respectively) datasets, and Eisenbergiella is most abundant in the UK (6.46% of total abundances), with only the Chinese datasets showing a significant abundance of Bacteroides (28.08% of total abundances). The variation amongst geographical locations is recorded by Pin Viso et al. ( 2021 ) who highlight a high rate of variation amongst regions included in their study: Argentina, Australia, Croatia, Germany, Hungary, Malaysia and the United States. They highlight Lactobacillaceae dominated within European countries (Croatia, Germany, Hungary and Slovakia), except for Germany where Bacteroidaceae dominated (Pin Viso et al., 2021 ). There was high variation of Bacteroidaceae , Lactobacillaceae , Lachnospiraceae , Ruminococcaceae and Clostridiaceae (Pin Viso et al., 2021 ). Pandit et al. ( 2018 ) compared caecal microbiota of Aseel and Kadaknath breeds sourced from two separate farms and identified that both breed and geographical locations affect GIT bacterial diversity. They found that Bacteroides dominated in both locations (> 20% of total abundances), except for Kadaknath breeds at farm 2 where Fusobacteria dominated (44.1% of total abundances). Farm 1 showed that the Aseel breed were subsequently dominated by Clostridium (6.5%), whilst Alistipes (23.1% of total abundances) followed in abundance in the Kadaknath bred; this differs at Farm 2 as Aseel breeds on this farm had Alistipes as the second most abundant GIT bacteria (6.4% of total abundances), while the Kadaknath breed had Bacteroides as the second most abundant GIT bacteria (21.6% of total abundances) (Pandit et al., 2018 ). Ultimately this highlights that there are environmental impacts which have a role in shaping the GIT microbiome. Conclusion The aim of this study was to identify the core microbiota of a chicken gastrointestinal tract using all data made available through the SRA database and to define the main variables (gastrointestinal tract location, breed, age and geographical location) which affect microbiota gastrointestinal tract diversity. We identified significant influences by each variable examined including the gastrointestinal tract location, geographical location, breed and age of birds. It was apparent that irrespective of gastrointestinal tract location, breed, age and geographical location, Lactobacillus is the prominent genus of the chicken GIT bacterial community. Identifying the common core microbiome of the caecal and faecal communities suggest Lactobacillus , Faecalibacterium , Butyricicoccus , Eisenbergiella , Subdoligranulum , Oscillibacter , Clostridium & Blautia are core, meanwhile, only Faecalibacterium and Lactobacillus were present across 90% of datasets for all gastrointestinal tract locations. In terms of breed, differences in the microbial communities were found with Ross 308, Cobb 500 and Sasso T451A having the most diverse bacterial GIT communities. We also note a successional development of the microbial community as birds age. The caecal microbial communities develop from a Lactobacillus dominated community towards a stabilised Faecalibacterium , Eisenbergiella , Bacteroides , Megamonas , and Lactobacillus community as birds age. 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Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 20 Jan, 2025 Read the published version in BMC Microbiology → Version 1 posted Editorial decision: Revision requested 03 Sep, 2024 Editor assigned by journal 30 Aug, 2024 Submission checks completed at journal 30 Aug, 2024 First submitted to journal 24 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4969804","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":348729828,"identity":"57023cda-141d-476e-be82-cfdf90b8aac2","order_by":0,"name":"PB Burrows","email":"","orcid":"","institution":"Queen’s University Belfast","correspondingAuthor":false,"prefix":"","firstName":"PB","middleName":"","lastName":"Burrows","suffix":""},{"id":348729831,"identity":"7dbaf4f8-0cb4-4ad5-aebe-78d69f4b444d","order_by":1,"name":"Fernanda Godoy Santos","email":"","orcid":"","institution":"Queen’s University Belfast","correspondingAuthor":false,"prefix":"","firstName":"Fernanda","middleName":"Godoy","lastName":"Santos","suffix":""},{"id":348729832,"identity":"caedc8c0-9d04-4ffb-acaf-466abcdb1b77","order_by":2,"name":"Lawther KJ","email":"","orcid":"","institution":"Queen’s University Belfast","correspondingAuthor":false,"prefix":"","firstName":"Lawther","middleName":"","lastName":"KJ","suffix":""},{"id":348729833,"identity":"68904c58-4b2c-49b2-9ade-e45387d94f7b","order_by":3,"name":"Anne Richmond","email":"","orcid":"","institution":"moy park","correspondingAuthor":false,"prefix":"","firstName":"Anne","middleName":"","lastName":"Richmond","suffix":""},{"id":348729834,"identity":"c696702a-c6b5-4a24-9691-7c23e24af7cb","order_by":4,"name":"N Corcionivoschi","email":"","orcid":"","institution":"Agri Food and Biosciences Institute","correspondingAuthor":false,"prefix":"","firstName":"N","middleName":"","lastName":"Corcionivoschi","suffix":""},{"id":348729835,"identity":"93760bb5-8a98-405b-ad6f-8b0be5323e73","order_by":5,"name":"Sharon Huws","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYBAC+QYeAyBlw8AgAREwIKjF4ABYSxopWhjAWg6TpsVMmufPedkNtxsYP/xgOGxMUIt8A/83ad6228Yb7hxgluxhOGxGUAvDAaAtuQ23EzfcSGCQBrrQhjgtOX/OgbQw/yZBC9sBkBY2kC2EHWZwmMfY+m9bsvHMOwfbLHsM0onwfnuP4c0Zf+xk+243H77xo8LasIGgHmYIxdgAQkTECgIwEjZ8FIyCUTAKRiwAAN6vOpGcHBIHAAAAAElFTkSuQmCC","orcid":"","institution":"Queen’s University Belfast","correspondingAuthor":true,"prefix":"","firstName":"Sharon","middleName":"","lastName":"Huws","suffix":""}],"badges":[],"createdAt":"2024-08-24 15:21:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4969804/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4969804/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12866-024-03690-x","type":"published","date":"2025-01-20T15:57:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65600130,"identity":"bcd102f0-f17d-4a4d-bb30-aaa68a560656","added_by":"auto","created_at":"2024-09-30 11:52:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":446966,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrinciple component analysis of OTU read counts of all datasets at genus level.\u003c/strong\u003e The PCA plots highlight the variability in OTU read counts subsequent to normalisation using variance-stabilising transformation (VST). The plots are organised to present: \u003cstrong\u003eA\u003c/strong\u003e. GIT organ; \u003cstrong\u003eB\u003c/strong\u003e. bird breed; \u003cstrong\u003eC\u003c/strong\u003e. bird age; \u003cstrong\u003eD\u003c/strong\u003e. geographical location. Principle component 1 explains 33% variance, as principle component 2 highlights 16% variance.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4969804/v1/d47451531bb34e192f69d90c.png"},{"id":65600129,"identity":"9df6a2d6-d9e1-4ebe-876c-9f9c42217332","added_by":"auto","created_at":"2024-09-30 11:52:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91008,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative abundances of total datasets at genus level. \u003c/strong\u003eRelative abundances of total datasets highlight 48.30% are of unknown genus with the remaining 51.70% as being identified. 31.84% relative abundance of which are labelled above whilst the remaining 19.86% consists of 687 genera comprising of \u0026gt;2%. Pie chart was produced in Microsoft Excel. Pie charts of the total relative abundances at phylum and family level can be found in Supplementary File 1. Microsoft Excel Document.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4969804/v1/210a1f832f7680cf70bb8084.png"},{"id":65601283,"identity":"e6f1daff-d1d3-46ab-a7db-e51b1d798ade","added_by":"auto","created_at":"2024-09-30 12:00:17","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":394218,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial communities of each GIT section at genus level.\u003c/strong\u003e Relative abundance bar chart showing the abundances of genera within caecal, faecal, ileal and jejunal samples. Up to 59.58% of genera are unknown in caecal samples, while up to 32.83% are unknown in other sample types, where \u003cem\u003eLactobacillus\u003c/em\u003e dominate reaching 64.35%. Relative abundance graphs have been produced using normalised data presenting abundances across all datasets but grouped into each variable.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4969804/v1/d5e6d1df8e09e12b6d4fee70.jpeg"},{"id":65601287,"identity":"4d06cbe6-e8a9-4dc8-9708-ca31aeb6c883","added_by":"auto","created_at":"2024-09-30 12:00:17","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":415298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial communities of each breed at genus level.\u003c/strong\u003eRelative abundance bar chart showing the abundances of genera within each breed (AIL F8, Cobb 500, Ross 308, Sasso T451A and Tibetan chicken samples). 26.40% - 62.10% of genera are unknown across all variables, where \u003cem\u003eLactobacillus\u003c/em\u003edominate AIL F8, Ross 308 and Cobb 500 reaching 39.35% of total relative abundance, and \u003cem\u003eBacteroides\u003c/em\u003e dominates Tibetan chicken with 28.08% of total relative abundances. Sasso T451A is dominated by \u003cem\u003eMegamonas\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e(9.91% \u0026amp; 8.89%) respectively. Relative abundance graphs have been produced using scaled data presenting abundances across all datasets but grouped into each variable.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4969804/v1/0ec3a6691a5f2842209ea82e.jpeg"},{"id":65602483,"identity":"195f7753-83d8-4458-83c0-efec0b0ff6eb","added_by":"auto","created_at":"2024-09-30 12:08:17","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":737162,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial communities of each age at genus level.\u003c/strong\u003eRelative abundance bar chart showing the abundances of genera during weeks 1 to beyond week 9. 26.87% - 73.94% of genera are unknown across all variables, where \u003cem\u003eLactobacillus\u003c/em\u003e is amongst the most abundant during the earlier ages reaching 35.80%, where \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eEisenbergiella\u003c/em\u003e and \u003cem\u003eOscillibacter\u003c/em\u003eare amongst the dominating genera from the earlier ages to approaching day 35 (reaching 13.47%, 9.62% and 13.65% respectively). Relative abundance graphs have been produced using scaled data presenting abundances across all datasets but grouped into each variable.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4969804/v1/0ae832f1b499031d3d631306.jpeg"},{"id":65600134,"identity":"44102ed5-6dff-4f27-980f-7336a5b58f94","added_by":"auto","created_at":"2024-09-30 11:52:17","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":382980,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial communities of each geographical location at genus level.\u003c/strong\u003e Relative abundance bar chart showing the abundances of genera within the Canadian, Chinese, European and UK datasets. Unknown genera range between 28.48% - 68.29% across all variables, where \u003cem\u003eLactobacillus\u003c/em\u003e dominate the Canadian and European datasets reaching 28.79%, and \u003cem\u003eBacteroides\u003c/em\u003e dominates Chinese datasets with 28.08%; \u003cem\u003eEisenbergiella\u003c/em\u003e, \u003cem\u003eOscillibacter\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e are amongst the most abundant in the UK datasets with 6.46%, 5.84% and 4.90% respectively. Relative abundance graphs have been produced using scaled data presenting abundances across all datasets but grouped into each variable.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4969804/v1/d2f7fb33a93e70aaf2ee5640.jpeg"},{"id":65600132,"identity":"7d703f0d-b38d-41ca-8f27-549a84f31795","added_by":"auto","created_at":"2024-09-30 11:52:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":34976,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNumber of genera present amongst each GIT region as common core members.\u003c/strong\u003e Venn diagram showing the number of genera identified as being common core across the caecum, faeces and small intestine datasets irrespective of variables. The graph highlights two genera (\u003cem\u003eFaecalibacterium\u003c/em\u003e\u0026amp; \u003cem\u003eLactobacillus\u003c/em\u003e) are shared between each GIT region datasets. Graph was produced using the website \u003ca href=\"http://www.interactivenn.net/index.html\"\u003ehttp://www.interactivenn.net/index.html\u003c/a\u003e (Heberle\u003cem\u003e et al.\u003c/em\u003e, 2015).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4969804/v1/aa81dc7b9cbd4df76390677d.png"},{"id":74859393,"identity":"9daa97db-3f33-4920-a530-3ec3d47d81e7","added_by":"auto","created_at":"2025-01-27 16:14:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3987178,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4969804/v1/3d3d56df-eec8-4943-96e0-cc8ff99c8124.pdf"},{"id":65601285,"identity":"5f13caa2-8d05-4fac-937c-cb7a02d7548e","added_by":"auto","created_at":"2024-09-30 12:00:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":94047,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4969804/v1/7d379898efad3b90a9e50e72.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Decoding the chicken gastrointestinal microbiome","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe domesticated broiler chicken, \u003cem\u003eGallus gallus domesticus\u003c/em\u003e, is amongst the leading global food source, attributed to the rich protein content and micronutrients, coupled with low production cost of broiler chicken (Kim et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Indeed, broiler chicken production has reached an impressive scale of 74\u0026nbsp;billion chicken slaughtered globally (Torrella, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with expectations of reaching 121% of production rates by 2050 as compared with 2005 levels (Ayalew et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this context, it is vital to recognise the importance of sustainably enhancing chicken production in order to address the challenges of accommodating the globally increasing demand (Oakley et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e; Ahmad et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gilroy et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The productive capabilities of chicken are affected by many factors related to the host (genetics, immune response, general gut health) and environment (farm management, chicken welfare, feed), which all have a direct impact on the gastrointestinal tract (GIT) microbiota (Diaz Carrasco et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Simultaneously, GIT microbiota significantly influence the health and metabolism of broilers (Ocejo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), highlighting the importance of having a comprehensive understanding of GIT associated microbiomes in order to sustainably enhance broiler chicken production.\u003c/p\u003e \u003cp\u003ePoultry are monogastric animals with a sophisticated physiology, whereby the digestive system consists of the crop, proventriculus, ventriculus (gizzard), small intestine (duodenum, jejunum, ileum), large intestine, caeca, colon and cloaca. Proximal gut (crop to ventriculus) is highly involved with softening feed through enzymatic and mechanical breakdown, within an acidic environment (Borda-Molina et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The distal gut (small intestine, caeca and colon) further extracts nutrients from digested feed and non-starch polysaccharides (NSP) present in the feed (Borda-Molina et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The complex microbial ecosystems within the chicken\u0026rsquo;s GIT are often dominated by lactic acid bacteria within the proximal gut, including the genera \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eEnterobacteriaceae\u003c/em\u003e and \u003cem\u003eKlebsiella\u003c/em\u003e which initiate the digestion of feed (Shang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yadav and Jha, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fathima et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); whereas \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eClostridia\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, coliforms, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e are often abundant within the distal gut microbial communities (Knarreborg et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Choi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pan and Yu, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yadav and Jha, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fathima et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA bacterial census of poultry intestinal microbiome published in 2013, primarily utilising data already deposited from past metataxonomic studies sourced from three public databases (GenBank, Silva comprehensive ribosomal RNA database, and Ribosomal Database Project) and made available on the MG-RAST server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mg-rast.org/mgmain.html?mgpage=search\u0026amp;search=Poultry_Gut_DB\u003c/span\u003e\u003cspan address=\"https://www.mg-rast.org/mgmain.html?mgpage=search\u0026amp;search=Poultry_Gut_DB\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Wei et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This study used a total of 3,184 16S rDNA gene sequences, obtained using a range of primers and therefore hypervariable 16S rDNA regions, from the chicken caeca and from intestinal samples (without specification of the local intestine section used such as duodenum, ileum or jejunum). The study identified 12 phyla from intestinal sequences, whereby Firmicutes dominated (70%), followed by Bacteroidetes (12.3%) and Proteobacteria (9.3%); meanwhile 10 phyla were identified from the caecal data, where again, Firmicutes (78%) and Bacteroidetes (11%) were the dominating phyla. Intestinal sequences highlighted the presence of numerous genera belonging to Firmicutes accounting for \u0026gt;\u0026thinsp;1% of total sequences (\u003cem\u003eClostridium\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eEubacterium\u003c/em\u003e, \u003cem\u003eFecalibacterium\u003c/em\u003e, \u003cem\u003eButyrivibrio\u003c/em\u003e, \u003cem\u003eEthanoligenens\u003c/em\u003e, \u003cem\u003eAlkaliphilus\u003c/em\u003e, \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eHespellia\u003c/em\u003e, \u003cem\u003eRoseburia\u003c/em\u003e, \u0026amp; \u003cem\u003eMegamonas\u003c/em\u003e) (Wei et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); likewise Bacteroidetes were represented by four genera (\u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eParabacteroides\u003c/em\u003e \u0026amp; \u003cem\u003eAlistipes\u003c/em\u003e) composing\u0026thinsp;\u0026gt;\u0026thinsp;1% of total sequences. With respect to other phyla, only one genus from Actinobacteria (\u003cem\u003eBifidobacterium\u003c/em\u003e) and Proteobacteria (\u003cem\u003eDesulfohalobium\u003c/em\u003e) presented a notable abundance within the intestinal microbiome at \u0026gt;\u0026thinsp;1% and 0.7% of total sequences respectively (Wei et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Amongst the caecal samples, 31 genera belonged to Firmicutes, of which three represent\u0026thinsp;\u0026gt;\u0026thinsp;5% of read abundance (\u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e \u0026amp; \u003cem\u003eEubacterium\u003c/em\u003e), and ten represented\u0026thinsp;\u0026gt;\u0026thinsp;1% of read abundances (\u003cem\u003eFecalibacterium\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eButyrivibrio\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eMegamonas, Roseburia\u003c/em\u003e, \u003cem\u003eEthanoligenes\u003c/em\u003e, \u003cem\u003eHespellia\u003c/em\u003e, \u003cem\u003eVeillonella\u003c/em\u003e, \u0026amp; \u003cem\u003eAnaerostipes\u003c/em\u003e). \u003cem\u003eBacteroides\u003c/em\u003e was the most abundant of the Bacteroidetes phylum accounting for 4% of total caecal sequences with other genera present (\u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eParaprevotella\u003c/em\u003e, \u003cem\u003eTanneralla\u003c/em\u003e and \u003cem\u003eRiemeralla\u003c/em\u003e). Proteobacteria were low in abundance and represented mainly by three genera (\u003cem\u003eDesulfohalobium\u003c/em\u003e, \u003cem\u003eEscherichia\u003c/em\u003e/\u003cem\u003eShigella\u003c/em\u003e \u0026amp; \u003cem\u003eNeisseria\u003c/em\u003e) (Wei et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition, Chica Cardenas et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) performed a similar study in 2021, producing a meta-analysis of chicken caeca microbial communities using data targeting the V3, V4 and V3-V4 hypervariable regions from 9 studies accounting for 324 total samples (Chica Cardenas et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Upon comparing each of the hypervariable regions, they identified \u003cem\u003eOscillospira\u003c/em\u003e amongst all 3 using an 80% abundance cut-off, after the cut-off was reduced to 50%, 5 genera (\u003cem\u003eOscillospira\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e and \u003cem\u003eRuminococcus\u003c/em\u003e) were identified (Chica Cardenas et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Overall, the selection of hypervariable region, in relation to assessing metataxonomic data, affects the evaluation of microbiomes and Chica Cardenas et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found hypervariable region V4 presents the most diverse and most unique genera when compared with the other regions.\u003c/p\u003e \u003cp\u003eGiven that only 2 poultry GIT microbiota census are available (Wei et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Chica Cardenas et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with Wei et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) utilising sequences from different variable regions, which in itself has been shown to be a variable affecting bacterial metataxonomic results, and the recent study by Chica Cardenas et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) used a systematic approach of literature focused solely on caecal data to find the 16S rDNA sequences, resulting in only 324 sequence datasets being used in their analysis, it is now timely to re-visit the concept of the core poultry microbiome and factors which affect GIT bacterial colonisation using more comprehensive data. It should also be noted that neither study investigated the effect of geographical and GIT location or bird age either, although Chica Cardenas et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) did investigate breed and noted that there was a breed effect on the caecal bacteria present. Therefore, our aim in this study is to provide a comprehensive up to date study of our current understanding of the composition and diversity of chicken GIT microbiomes and factors which control this (GIT location, breed, bird age and geographical location) using all publicly available 16S rDNA sequences targeting the V3-V4 hypervariable region only, allowing an enhanced understanding of these microbiomes and factors which effect their development in the chicken GIT on a global scale.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSelection of sequence read archive data and bioprojects\u003c/h2\u003e \u003cp\u003eSequence read archive (SRA) data relating to chicken GIT 16S rDNA sequences were selected from the SRA database between the months of February and July 2020, whilst using and combining search terms \u0026ldquo;chicken\u0026rdquo;, \u0026ldquo;broiler\u0026rdquo;, \u0026ldquo;hen\u0026rdquo;, \u0026ldquo;gastrointestinal tract\u0026rdquo;, \u0026ldquo;GIT\u0026rdquo;, \u0026ldquo;microbiome\u0026rdquo;, \u0026ldquo;microbiota\u0026rdquo;, \u0026ldquo;caecum\u0026rdquo;, \u0026ldquo;intestine\u0026rdquo; and \u0026ldquo;faeces\u0026rdquo;. Bioprojects were selected containing substantial associated metadata and excluding studies exhibiting any additional factors, such as intentional infection with \u003cem\u003eCampylobacter\u003c/em\u003e spp., were excluded to prevent unnecessarily affecting results when analysing the core microbiota, although any controls produced were taken into consideration. A total of 114 bioprojects were selected pertaining to 6,742 individual sequencing datasets. These bioprojects were further refined by removing those which excluded important information such as GIT section sampled; age of birds; geographical location. Subsequently, only bioprojects which were obtained using the 16S rDNA gene V3-V4 hypervariable regions were chosen as this was the most common hypervariable region analysed. The choice of using data obtained using the same primers targeting the V3-V4 hypervariable region was in order to reduce the non-biological variability in the results. After refining our initially downloaded 114 bioprojects, 11 bioprojects were selected, of which 602 sequencing datasets were identified. The full metadata collected from these sequence datasets can be found in Supplementary File 1. Microsoft Excel Document. A summary breakdown of the studies involved can be found in Supplementary Table\u0026nbsp;1 and Supplementary Table\u0026nbsp;2. Subsequently, the 11 bioprojects datasets were submitted to the MGnify pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/metagenomics\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/metagenomics\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for analysis. In brief, MGnify is an updated EBI Metagenomic platform where microbiome data can be analysed, explored as well as archived. Amplicons with paired end sequences are merged using SeqPrep (v1.2) and are subject to quality control assessment (QC). All outputs from this pipeline are presented as a Krona plot, bar charts and tables, and include MAPseq analysis (Mitchell et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData processing\u003c/h2\u003e \u003cp\u003eOutputs from the MGnify pipeline contained OTU count data at each taxon level; phylum, family and genus (Supplementary File 1. Microsoft Excel Document). Any taxonomic identifications as \u0026ldquo;unclassified,\u0026rdquo; \u0026ldquo;unidentified,\u0026rdquo; \u0026ldquo;group,\u0026rdquo; or \u0026ldquo;uncultured\u0026rdquo; were relabelled as \u0026lsquo;Unknown\u0026rsquo; to facilitate estimation of the taxonomic assignment. Relative abundances were calculated by the application of the Total Sum Scaling (TSS) method using the following formula: OTU read count divided by (the total ASV read counts of a sample divided by the minimum total OTU read counts across the dataset). Relative abundances were calculated at phylum, family and genus taxon levels. OTU abundances less than 95% were categorised as \u0026lsquo;Other\u0026rsquo;. Relative abundances were subsequently grouped into GIT location (caecum, faeces and small intestine). Within each GIT location, data was grouped pertaining to each parameter (GIT location, breed, bird age and geographic location) to identify the prominent taxa in each parameter along with common core community members.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eComputational Statistical Analysis\u003c/h2\u003e \u003cp\u003eData was tabulated into bar and pie charts using Excel, and Principal Component Analysis (PCA) plots were plotted using R (ver. 4.3.2). Venn diagrams were produced based on the common core microbiome across data groups using the website \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.interactivenn.net/index.html\u003c/span\u003e\u003cspan address=\"http://www.interactivenn.net/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Heberle et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Alpha and Beta diversity was performed using \u0026lsquo;vegan\u0026rsquo; in R (ver. 2.6-4) (Oksanen J \u003cem\u003eet al.\u003c/em\u003e, 2022a). Scripts can be found in Supplementary File 2. R Script Document. The effect of each parameter, as well as their interactions on OTU read counts, was assessed using Permutational multivariate analysis of variance (PERMANOVA). This analysis was based on Bray-Curtis dissimilarity and was conducted via the \u0026lsquo;adonis2\u0026rsquo; function in the vegan package, employing 1000 permutations. Normalised data through a variance-stabilising transformation (VST) implemented by DESeq2 (version 1.42.0) (Love et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) was visualised using PCA.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSummary of chicken GIT bacterial microbiome\u003c/h2\u003e \u003cp\u003ePrinciple component analysis (PCA) of normalised OTU read counts demonstrate clustering of communities according to each variable examined (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Regardless of variable, the principle component 1 and 2 explained 35% and 16% variance respectively. The communities derived from the different GIT locations (caecum, ileum and jejunum) resemble each other and cluster together, with the caecum presenting most deviation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). However, the faecal communities are separately clustered, with some deviating to resemble the caecum (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). GIT OTUs across bird ages distinctly cluster together according to bird age, and OTUs across breeds Ross 308, AIL F8 and Sasso T451A also cluster together, whilst Cobb 500 OTUs are distinctly different from the other breeds and cluster away from the other sample OTUs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB \u0026amp; C). Likewise, when comparing geographical locations, the European, UK and China-derived chicken GIT samples present most similarity, with OTUs generated from the GIT of Canadian clustering separately, indicating different taxonomic diversity within (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of 65,186,954 reads were identified across all datasets; 638,991 (0.98%) belonging to Archaea, and 64,547,963 (99.02%) belonging to Bacteria. Amongst the top 95% of sequences across all 602 datasets available, we found three phyla, twenty-three family and twenty-eight genera (Supplementary File 1. Microsoft Excel Document). Dominant phyla include: Firmicutes, Proteobacteria and Bacteroidetes, accounting for 80.62%, 7.89% and 5.91% of total read abundances respectively. On a family level \u003cem\u003eRuminococcaceae\u003c/em\u003e, \u003cem\u003eLactobacillaceae\u003c/em\u003e and \u003cem\u003eLachnospiraceae\u003c/em\u003e are the most abundant, accounting for 23.10%, 16.53% and 10.80% of read abundances respectively. Ten families ranged between 5% and \u0026gt;\u0026thinsp;1% of sequence read abundances, of these the most abundant were \u003cem\u003eEnterobacteriaceae\u003c/em\u003e, \u003cem\u003eOscillospiraceae\u003c/em\u003e and \u003cem\u003eClostridiaceae\u003c/em\u003e, accounting for 4.87%, 2.76% and 2.53% respectively. On a genus level, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eEisenbergiella\u003c/em\u003e and \u003cem\u003eOscillibacter\u003c/em\u003e are the most abundant, accounting for 16.40%, 5.10%, 3.30% and 2.74% of total read abundances respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and eight other genera represented\u0026thinsp;\u0026gt;\u0026thinsp;1% of sequence read abundances (\u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eAlistipes\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eMegamonas\u003c/em\u003e, \u003cem\u003eRuminiclostridium\u003c/em\u003e and \u003cem\u003eRomboutsia\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePERMANOVA analyses identified significant differences in the microbial communities based on bird age, breed, GIT and geographical location (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Age as a variable was highlighted as being the main contributor in affecting the microbiome when associated with the gastrointestinal tract location and geographic location (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); age was less affected by breed however (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This may be the result of the unequal dispersion of data across breeds (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Likewise, when explaining alpha diversity indices (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), there were significant differences identified amongst each alpha diversity indices, where each independent variable (bird age, breed, GIT region and geographical location) were significant (P\u0026thinsp;\u0026le;\u0026thinsp;0.001). Otherwise, bird age was the only variable compared with others, where significant differences were identified across each index when bird age and breed were compared, meanwhile, age and geographical location presented significant differences in inverse Simpson diversity (P\u0026thinsp;=\u0026thinsp;0.000199).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003ePermutational multivariate analysis of variance (PERMANOVA) analysis of OTU read counts after TSS scaling to evaluate effects of independent factors including gastrointestinal tract location, bird breed, bird age and geographical location at genus level.\u003c/b\u003e PERMANOVA highlights significant differences between each of the independent variables (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Age, however, is the only variable that has been assessed alongside the variables presenting significant differences in GIT regions and Location (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as breed is less affected by age (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIT region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e277.4005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.2206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.3992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.2701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIT region: Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreed: Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation: Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCodes: 0 \u0026lsquo;***\u0026rsquo; 0.001 \u0026lsquo;**\u0026rsquo; 0.01 \u0026lsquo;*\u0026rsquo; 0.05 \u0026lsquo;.\u0026rsquo; 0.1 \u0026lsquo; \u0026rsquo; 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eANOVA analysis of alpha diversity indices using OTU read counts after TSS scaling to evaluate effects of independent factors including gastrointestinal tract location, bird breed, bird age and geographical location at genus level\u003c/b\u003e. Analysis of Variance (ANOVA) was performed on alpha diversity indices including Chao1 Richness, Pielou\u0026rsquo;s Evenness, Shannon Diversity and Inverse Simpson Diversity measuring the impact each independent factor of GIT region, breed, location and age have on tested indices. The codes \u0026lsquo;*\u0026rsquo;, \u0026lsquo;\u003cem\u003e*\u003c/em\u003e*\u0026rsquo;, \u0026lsquo;***\u0026rsquo;, denotes levels of significance (0.05, 0.01 \u0026amp; 0.001 respectively) in differences amongst factors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eChao1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eEvenness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eShannon Diversity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eInverse Simpson Diversity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIT region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e603.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e176.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e187.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e86.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.00E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e65.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e35.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.00E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.48E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.19E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.00E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.58E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e74.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e56.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.23E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIT region: Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreed: Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.65E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.10E-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8.70E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation: Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003eCodes: 0 \u0026lsquo;***\u0026rsquo; 0.001 \u0026lsquo;**\u0026rsquo; 0.01 \u0026lsquo;*\u0026rsquo; 0.05 \u0026lsquo;.\u0026rsquo; 0.1 \u0026lsquo; \u0026rsquo; 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEffect of gastrointestinal tract location on the microbial diversity\u003c/h2\u003e \u003cp\u003eCaecal and faecal samples were most diverse of the GIT locations, the latter being most species rich, whereas the small intestine regions are similar in richness, evenness and diversity (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Supplementary Figs.\u0026nbsp;1; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Following grouping into associated GIT locations, 375 datasets were associated with the caecum, and were composed of three phyla, fourteen families and twenty genera based on the top 95% of reads; 152 datasets were obtained for faeces, and were composed of four phyla, eighteen families and twenty genera; 38 datasets were obtained for the ileum, and were composed of two phyla, nine family and four genera; 37 datasets were obtained for the jejunum, and were composed of three phyla, thirteen family and seven genera. At the genus level, abundances across each GIT section vary substantially (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), whereas \u003cem\u003eFaecalibacterium\u003c/em\u003e is most abundant in the caecum followed by \u003cem\u003eEisenbergiella\u003c/em\u003e and \u003cem\u003eOscillibacter\u003c/em\u003e (7.22%, 5.07% \u0026amp; 4.30% of total read abundances respectively); meanwhile \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eEnterococcus\u003c/em\u003e follow \u003cem\u003eLactobacillus\u003c/em\u003e in terms of faecal sample dominance (8.27% \u0026amp; 5.05% of total read abundances respectively). This suggests there is little similarity between caecal and faecal samples. Ileal and jejunal datasets present slightly different abundances; following \u003cem\u003eLactobacillus\u003c/em\u003e in abundance are \u003cem\u003eCandidatus arthromitus\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e (7.40% \u0026amp; 2.37% of total read abundances respectively) in ileal samples, while \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eStenotrophomonas\u003c/em\u003e (4.53% \u0026amp; 2.88% of total read abundances respectively) follow in abundance in jejunal samples. Data and information regarding each taxonomic level can be found in Supplementary File 1. Microsoft Excel Document.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eEffects of breed on the gastrointestinal tract microbial diversity\u003c/h2\u003e \u003cp\u003eFive breeds were identified across all datasets, all have a high abundance of unknown genera from 26.40% (AIL F8) to 62.10% (Ross 308) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eLactobacillus\u003c/em\u003e is most abundant of the known genera in the breeds AIL F8, Cobb 500 and Ross 308. \u003cem\u003eLactobacillus\u003c/em\u003e was followed by \u003cem\u003eFaecalibacterium\u003c/em\u003e in AIL F8 (39.35% \u0026amp; 13.65% of total read abundances respectively). Cobb 500 highlights \u003cem\u003eLactobacillus\u003c/em\u003e (28.79% of total read abundances) and \u003cem\u003eStreptococcus\u003c/em\u003e (10.54% of total read abundances) as most abundant respectively, accounting for 39.32% of total relative abundances. Likewise, \u003cem\u003eLactobacillus\u003c/em\u003e is followed by \u003cem\u003eEisenbergiella\u003c/em\u003e amongst Ross 308 (8.25% \u0026amp; 5.32% of total relative abundances respectively). Sasso T451A presents \u003cem\u003eMegamonas\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e as most abundant (9.91% \u0026amp; 8.89% of total relative abundances respectively). Meanwhile, \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eParabacteroides\u003c/em\u003e are the most abundant genera of the Tibetan breeds (28.08% \u0026amp; 1.62% of total relative abundances). In terms of diversity, all diversity indices were significantly different (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Cobb 500, Ross 308 and Sasso T451A are amongst the most diverse, as Tibetan chicken breeds follow. Cobb 500 is most rich, as Ross 308 and Sasso T451A are most even (Supplementary Figs.\u0026nbsp;2; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEffect of bird age on the gastrointestinal tract microbial diversity\u003c/h2\u003e \u003cp\u003eDatasets relating to forty-one separate ages were grouped together as follows: Week 1: 0\u0026ndash;7 days old (130 datasets), Week 2: 8\u0026ndash;14 days old (45 datasets), Week 3: 15\u0026ndash;21 days old (78 datasets), Week 4: 22\u0026ndash;28 days old (227 datasets), Week 5: 29\u0026ndash;35 days old (64 datasets), Week 6: 39\u0026ndash;42 days old (18 datasets) and Week 9 onwards: 58-\u0026lsquo;\u0026gt;300\u0026rsquo; days old (40 datasets) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Week 9 onwards contains the dataset produced by Zhou et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) who identified the Tibetan breeds as being older than 300 days.\u003c/p\u003e \u003cp\u003eDuring week 1, \u003cem\u003eLactobacillus\u003c/em\u003e dominated (40.72% of total relative abundances), followed by \u003cem\u003eStreptococcus\u003c/em\u003e (10.19% of total relative abundances) and \u003cem\u003eEisenbergiella\u003c/em\u003e (10.13% of total relative abundances). There are minimal localised differences between days; on days 3, 5, 6 and 7, \u003cem\u003eEisenbergiella\u003c/em\u003e follows \u003cem\u003eLactobacillus\u003c/em\u003e in terms of abundance (9.72%, 13.32%, 7.23% \u0026amp; 6.12% of total relative abundances) but conversely, \u003cem\u003eEisenbergiella\u003c/em\u003e is more abundant than \u003cem\u003eLactobacillus\u003c/em\u003e on day 4 (9.63% of total relative abundances). On week 2, \u003cem\u003eEisenbergiella\u003c/em\u003e and \u003cem\u003eOscillibacter\u003c/em\u003e together dominate except for days 10 and 14, whereby \u003cem\u003eLactobacillus\u003c/em\u003e dominates (20.23% \u0026amp; 24.20% of total relative abundances). In terms of week 3 bacterial GIT diversity, \u003cem\u003eButyricicoccus\u003c/em\u003e is most abundant during days 15 and 16 (7.09% \u0026amp; 5.23% of total relative abundances), \u003cem\u003eFaecalibacterium\u003c/em\u003e during days 18 and 19 (8.88% \u0026amp; 6.08% of total relative abundances), \u003cem\u003eOscillibacter\u003c/em\u003e during days 17 and 20 (5.59% \u0026amp; 4.95% of total relative abundances), whilst \u003cem\u003eLactobacillus\u003c/em\u003e dominates during day 21 (18.96% of total relative abundances). However, in week 3 as whole \u003cem\u003eEisenbergiella\u003c/em\u003e, \u003cem\u003eOscillibacter\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e are the most abundant genera, developing to \u003cem\u003eEisenbergiella\u003c/em\u003e, \u003cem\u003eOscillibacter\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e dominated bacterial community during week 4. Days 22 and 24 are dominated by \u003cem\u003eEisenbergiella\u003c/em\u003e (5.99% \u0026amp; 6.74% of total relative abundances), whilst \u003cem\u003eOscillibacter\u003c/em\u003e dominates days 23, 25 and 26 (5.12%, 6.41% \u0026amp; 7.76% of total relative abundances), and \u003cem\u003eLactobacillus\u003c/em\u003e dominates on days 27 and 28 (37.08% \u0026amp; 24.98% of total relative abundances). By week 5, \u003cem\u003eLactobacillus\u003c/em\u003e is no longer amongst the dominating genera, except for day 35 where it is most abundant (10.22% of total relative abundances). A mix of \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eEisenbergiella\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eOscillibacter\u003c/em\u003e are the most abundant during this week. \u003cem\u003eFaecalibacterium\u003c/em\u003e dominates during days 30, 31, 32 and 34 (4.88%, 6.43%, 8.35% \u0026amp; 9.56% of total relative abundances); \u003cem\u003eBacteroides\u003c/em\u003e dominates day 29 (6.79% of total relative abundances) followed by \u003cem\u003eFaecalibacterium\u003c/em\u003e (5.97% of total relative abundances), \u003cem\u003eEisenbergiella\u003c/em\u003e dominates during day 33 (8.52% of total relative abundances). These four genera comprise 13.88\u0026ndash;23.20% relative abundances during this week. From week 6 onwards, abundant genera shift occur with \u003cem\u003eMegamonas\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e being the dominating genera during day 39 (10.50% \u0026amp; 10.33% of total relative abundances), followed by \u003cem\u003eHelicobacter\u003c/em\u003e and \u003cem\u003eCampylobacter\u003c/em\u003e (8.17% \u0026amp; 7.83% of total relative abundances). Whereby by day 42, \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eParabacteroides\u003c/em\u003e are the domaining genera (35.74% \u0026amp; 10.23% of total relative abundances). In week 9, day 58 is dominated \u003cem\u003eMegamonas\u003c/em\u003e (23.10% of total relative abundances), while days 81 and \u0026gt;\u0026thinsp;300 are both dominated by \u003cem\u003eBacteroides\u003c/em\u003e (20.99% \u0026amp; 28.08% of total relative abundances), each followed by \u003cem\u003eAlistipes\u003c/em\u003e (12.15% of total relative abundances), \u003cem\u003eMethanocorpusculum\u003c/em\u003e (17.09% of total relative abundances) and \u003cem\u003eParabacteroides\u003c/em\u003e (1.62% of total relative abundances) respectively. These three dominating genera account for 35.25%, 38.08% \u0026amp; 29.71% of each age abundance respectively. Therefore, it can be concluded that as bird age, the community appears to shift away from \u003cem\u003eLactobacillus\u003c/em\u003e dominated as diversity increases (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Supplementary Figs.\u0026nbsp;3; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGeographical location as an effect on the gastrointestinal tract microbiota\u003c/h2\u003e \u003cp\u003eThe datasets represent various geographical locations, including China, Canada, Netherlands, France, Spain and the United Kingdom (UK). For simplicity when discussing the mainland European regions (France, Netherlands and Spain), they have been grouped together. Chinese datasets are the same Tibetan breed datasets discussed previously. Unknown genera are most abundant ranging from 28.48% (Canada) to 68.29% (UK) of total relative abundances (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). \u003cem\u003eLactobacillus\u003c/em\u003e dominates in Canadian and European datasets (28.79% \u0026amp; 27.48% of total relative abundances), as \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e (10.54% \u0026amp; 7.21% of total relative abundances respectively) follow in abundance respectively. Meanwhile, UK datasets are more varied, with \u003cem\u003eEisenbergiella\u003c/em\u003e being most abundant, closely followed by \u003cem\u003eOscillibacter\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e (6.46%, 5.84% \u0026amp; 4.90% of total relative abundances respectively). \u003cem\u003eRuminiclostridium\u003c/em\u003e and \u003cem\u003eButyricicoccus\u003c/em\u003e are also more abundant in the UK (2.68% \u0026amp; 2.64% of total relative abundances) compared with Canadian and European datasets; with \u003cem\u003eRomboutsia\u003c/em\u003e and \u003cem\u003eEnterococcus\u003c/em\u003e (7.41% \u0026amp; 6.42% of total relative abundances) being abundant in Canadian dataset, and \u003cem\u003eAlistipes\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e (3.52% \u0026amp; 3.50% of total relative abundances) being abundant in European datasets. Conversely, \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eParabacteroides\u003c/em\u003e dominate Chinese datasets (28.08% \u0026amp; 1.62% of total relative abundances). Overall, the median diversity is similar across each location, although UK chicken datasets are the most diverse closely followed by Canada, likewise in terms of evenness. Meanwhile Canadian and Chinese datasets present the most richness across all datasets; significant differences were observed across each alpha diversity index examined (P\u0026thinsp;\u0026le;\u0026thinsp;0.001) (Supplementary Figs.\u0026nbsp;4; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCommon Core Microbiome\u003c/h2\u003e \u003cp\u003eOnly \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e were identified as common core microbiota members, across all GIT locations (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Seventeen genera were identified as caecum common core microbiome, including \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eButyricicoccus\u003c/em\u003e and \u003cem\u003eEisenbergiella\u003c/em\u003e. Likewise, 14 genera were identified as members of the common core microbiome of faeces, including \u003cem\u003eEnterococcus, Lactobacillus, Streptococcus, Clostridium, Blautia, Erysipelatoclostridium, Faecalibacterium\u003c/em\u003e and \u003cem\u003eButyricicoccus\u003c/em\u003e. Amongst the small intestine, five and four genera respectively were identified as members of the core microbiome of the ileum and jejunum and only \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eMicrobacterium\u003c/em\u003e were common in both small intestine regions (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In conclusion, 17, 14 and 3 genera were identified as members of the common core microbiome across datasets associated with the caecum, faeces and small intestine respectively, as 8 genera were common in the caecal and faecal communities, where \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e were common across each GIT region (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eMembers of the common core microbiome across each GIT region at Genus level.\u003c/b\u003e Listed below are the common core genera present across \u0026gt;\u0026thinsp;90% of datasets associated with each GIT region.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaecal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaecal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmall Intestine\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAnaerostipes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBlautia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFaecalibacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAnaerotruncus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eButyricicoccus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLactobacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBlautia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eClostridium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMicrobacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eButyricicoccus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEisenbergiella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eClostridium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEnterococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEisenbergiella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eErysipelatoclostridium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eErysipelatoclostridium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFaecalibacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFaecalibacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLachnoclostridium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFusicatenibacter\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLactobacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIntestinimonas\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePaucibacter\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLachnoclostridium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRomboutsia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLactobacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRoseburia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNegativibacillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eStreptococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOscillibacter\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSubdoligranulum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRoseburia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRuminiclostridium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSubdoligranulum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur aim was to identify overall and the \u0026lsquo;core\u0026rsquo; gastrointestinal tract (GIT) microbiota of broiler chickens and assess the effects of GIT location, breed, age and geographical location on the microbial diversity, using publicly available data from the Sequence Read Archive (SRA) database. A total of 602 sequence datasets pertaining to 16S rDNA gene V3-V4 hypervariable regions were distributed into groups according to the metadata provided (GIT location tested, breed and age of birds as well as their geographical origin).\u003c/p\u003e \u003cp\u003eFirmicutes dominated followed by Proteobacteria and Bacteroidetes; \u003cem\u003eRuminococcaceae\u003c/em\u003e, \u003cem\u003eLactobacillaceae\u003c/em\u003e and \u003cem\u003eLachnospiraceae\u003c/em\u003e predominated composing half of all sequencing reads. At genus level most reads are unknown, although \u003cem\u003eLactobacillus\u003c/em\u003e is most abundant of those identified, followed by \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eEisenbergiella\u003c/em\u003e. Of which, we suggest \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eEisenbergiella\u003c/em\u003e, \u003cem\u003eSubdoligranulum\u003c/em\u003e, \u003cem\u003eOscillibacter\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e \u0026amp; \u003cem\u003eBlautia\u003c/em\u003e the common core microbiome of the gastrointestinal tract. These genera have been described as commensal and beneficial to broilers, aiding in producing short chain fatty acids (SCFA) from non-starch polysaccharides (NSP), alongside contributing to immune response regulation and overall benefiting bird\u0026rsquo;s health (Eeckhaut et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Goldstein et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Khan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Le Roy et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gong et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Analysis by PERMANOVA suggests each variable presents significant impacts on microbiota diversity, meanwhile, age had a greater effect on GIT location and geographical location than on breed.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGastrointestinal Tract Location\u003c/h2\u003e \u003cp\u003eWhen discussing the most prominent genera per GIT location (small intestine, caeca and faeces), microbial communities differ dependent on function (Aruwa et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The small intestine facilitate digestate transportation to the caeca, evident by reduced enzymatic activity; while the caeca\u0026rsquo;s anaerobic environment facilitates nitrogen recycling, producing SCFAs from NSPs (Aruwa et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Choi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Otherwise, the large intestine is short and does not retain digestate for an extensive period of time, but is involved with nutrient absorption (Aruwa et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The nature of faecal communities, as an ethically viable approach as a reference point for quantifying and identifying most members of the gut microbiota (Claesson et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), is reflected in our results as microbial communities from the small intestine (jejunum and ileum) and faeces resemble each other most. Of course, faecal community should not be reported as a \u0026lsquo;true\u0026rsquo; representation of the intestinal or caecal microbiota (Pauwels et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This is in contrast to Richards-Rios et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) who highlight similarity in the caecal and ileal microbiota, especially during the earlier days of a broiler\u0026rsquo;s life. Alpha diversities also suggest differences, faecal communities have greater richness, as the caecal community is most even and is most diverse, closely followed by faecal. Diversity, furthermore, was reported by Wei et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExcluding the caecal community, \u003cem\u003eLactobacillus\u003c/em\u003e is most prominent in each GIT location (reaching 64.35% of total abundances). Remaining (Diaz Carrasco et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) community members deviate. Small intestine communities present \u003cem\u003eCandidatus arthromitus\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e in the ileum (7.40% \u0026amp; 2.37% of total relative abundances respectively), \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eStenotrophomonas\u003c/em\u003e in the jejunum (4.53% \u0026amp; 2.88% of total relative abundances respectively) as the following genera. \u003cem\u003eStenotrophomonas\u003c/em\u003e is an environmental bacteria found in water, a bacterium which is rapidly emerging as a multi-drug resistant pathogen (Adegoke et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Meanwhile, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e and \u003cem\u003eRomboutsia\u003c/em\u003e (8.27%, 5.05% \u0026amp; 4.93% of total abundances respectively) follow in the faecal community. \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eEisenbergiella\u003c/em\u003e and \u003cem\u003eOscillibacter\u003c/em\u003e (7.22%, 5.07% \u0026amp; 4.30% of total abundances respectively) are most prominent in the caecal community. In doing so, the caeca present greatest levels of unclassified, or \u0026lsquo;unknown\u0026rsquo;, genera (59.58% of total abundances), compared to the remaining GIT locations (reaching 32.83% of total abundances). As the small intestinal and caecal microbiomes differ, previous indications suggest there should be some similarity due to the passage of contents and excreta (Aruwa et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, in identifying the core microbiota, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eEisenbergiella\u003c/em\u003e, \u003cem\u003eSubdoligranulum\u003c/em\u003e, \u003cem\u003eErysipelatoclostridium\u003c/em\u003e, \u003cem\u003eLachnoclostridium\u003c/em\u003e, \u003cem\u003eRoseburia\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e \u0026amp; \u003cem\u003eBlautia\u003c/em\u003e were present across 90% of datasets in caecal and faecal communities, which has been previously reported (Yan et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); although only \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e were present across 90% of datasets in each GIT location.\u003c/p\u003e \u003cp\u003eOur small intestine and caecal communities present differences from Wei et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) as we see \u003cem\u003eClostridium\u003c/em\u003e and \u003cem\u003eRuminococcus\u003c/em\u003e as dominating (18% \u0026amp; 14% respectively) within the \u0026lsquo;intestinal microbiome\u0026rsquo;, followed by \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eEubacterium\u003c/em\u003e (8%, 6%, 5% \u0026amp; 5% respectively). The jejunum and ileum are expected to be similar, as is seen in our results, due to their proximity and related functions (Aruwa et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e and \u003cem\u003eEubacterium\u003c/em\u003e are most abundant amongst the caecal community according to Wei et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which differs from our results which resemble those found be Chica Cardenas et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) more as they also show that Gram-positive cocci, \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e, \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eBacteroidetes\u003c/em\u003e are present. Of which, they also show that \u003cem\u003eOscillospira\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e and \u003cem\u003eRuminococcus\u003c/em\u003e were deemed as core microbiome members (Chica Cardenas et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which is what we note with the exception of \u003cem\u003eClostridium\u003c/em\u003e. Nonetheless, like our results, Wei et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) identified the caeca as containing the most diverse communities. Meanwhile, our faecal community resemble the data published by Videnska et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), being comprised of Firmicutes, Proteobacteria, Bacteroidetes and Actinobacteria (76.2%, 14%, 6.5% \u0026amp; 3.8% respectively). Furthermore, Hou et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) identified \u003cem\u003eClostridium\u003c/em\u003e (23.44%), \u003cem\u003eBacteroides\u003c/em\u003e (18.78%), \u003cem\u003eLactobacillus\u003c/em\u003e (8.77%), \u003cem\u003eRuminococcus\u003c/em\u003e (3.97%), \u003cem\u003eHallella\u003c/em\u003e (1.61%), \u003cem\u003eSubdoligranulum\u003c/em\u003e (1.07%), \u003cem\u003eFaecalibacterium\u003c/em\u003e (1.04%), \u003cem\u003eRoseburia\u003c/em\u003e (0.98%), and \u003cem\u003eEubacterium\u003c/em\u003e (0.31%) as the common core, matching our global data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBreed\u003c/h2\u003e \u003cp\u003eWith regards to differences in GIT microbial communities between breeds, we identified similar trends, whereby the main commercial breeds have similarity, with AIL F8 Progeny, Cobb 500 and Ross 308 being dominated by \u003cem\u003eLactobacillus\u003c/em\u003e (39.35%, 28.79% \u0026amp; 8.25% of total abundance respectively); with differing genera then dominating, with \u003cem\u003eFaecalibacterium\u003c/em\u003e being the next dominant in AIL F8 progeny (13.65% of total abundances), \u003cem\u003eStreptococcus\u003c/em\u003e in Cobb 500 (10.54% of total abundances), and \u003cem\u003eEisenbergiella\u003c/em\u003e, \u003cem\u003eOscillibacter\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e in Ross 308 (5.32%, 4.60% \u0026amp; 4.10% of total abundances respectively). Sasso T451A and Tibetan breeds represent non-commercial breeds with, Sasso T451A being the most diverse, followed by the Tibetan breeds, AIL F8 progeny and Cobb 500 were similar in diversity, with Ross 308 having the least GIT bacterial diversity. Other publications also suggest breed influences the overall GIT microbiome, for example Pandit et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) noted a domination of Bacteroidetes/Firmicutes in Ross 308 and Kadaknath breeds, as opposed to Cobb 400 and Aseel breeds whereby caecal bacteria communities had an overall combined abundance ranging between 76.6% and 90.8%. Ghagus and Nicobari breeds also present Bacteroidetes/Firmicutes as most abundant (Paul et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBird Age\u003c/h2\u003e \u003cp\u003eAs animals age, the GIT microbiota develops due to changing metabolic function, immune interactions, feed introduction, environmental changes amongst other factors (Shang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Our findings suggest a development of the microbiota by increased of diversity and development of common genera. We note that \u003cem\u003eLactobacillus\u003c/em\u003e dominates during 1 week of age and 4 weeks of age, although, the microbiome stabilises with age to contain \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eEisenbergiella\u003c/em\u003e, \u003cem\u003eOscillibacter\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eSubdoligranulum, Faecalibacterium\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e. Eventually a combination of \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eMegamonas\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eEisenbergiella\u003c/em\u003e, \u003cem\u003eAlistipes\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eMethanocorpusculum\u003c/em\u003e and \u003cem\u003eParabacteroides\u003c/em\u003e are most prominent during the latter ages of this study. This is indicative of previous publications, which show that bacteria are present upon hatch and undergo successional development where the bacterial taxa stabilise with age (Ballou et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ocejo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, Glendinning et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) note the development from a \u003cem\u003eClostridium sensu stricto 1\u003c/em\u003e dominating community within the cereal and intestinal communities during the earlier ages, towards a community containing \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eEscherichia/Shigella\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e, with age. Xiao et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) identify \u003cem\u003eEscherichia\u003c/em\u003e and \u003cem\u003eClostridium\u003c/em\u003e as the dominating genera at day 0 in laying hens, developing towards \u003cem\u003eLactobacillus\u003c/em\u003e domination, finally to a community persisting of \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eOdoribacter\u003c/em\u003e and \u003cem\u003eClostridiales vadin BB60\u003c/em\u003e group by day 50. Importantly, like our global data, Glendinning et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Xiao et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) both highlight the increase of diversity as the birds age.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eGeographical Location\u003c/h2\u003e \u003cp\u003eIt is evident that the microbial composition of GIT microbiota from different locations is highly varied (Pin Viso et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We found \u003cem\u003eLactobacillus\u003c/em\u003e is similar in abundance in both the Canadian and European (28.79% \u0026amp; 27.48% of total abundances respectively) datasets, and \u003cem\u003eEisenbergiella\u003c/em\u003e is most abundant in the UK (6.46% of total abundances), with only the Chinese datasets showing a significant abundance of \u003cem\u003eBacteroides\u003c/em\u003e (28.08% of total abundances). The variation amongst geographical locations is recorded by Pin Viso et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) who highlight a high rate of variation amongst regions included in their study: Argentina, Australia, Croatia, Germany, Hungary, Malaysia and the United States. They highlight \u003cem\u003eLactobacillaceae\u003c/em\u003e dominated within European countries (Croatia, Germany, Hungary and Slovakia), except for Germany where \u003cem\u003eBacteroidaceae\u003c/em\u003e dominated (Pin Viso et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). There was high variation of \u003cem\u003eBacteroidaceae\u003c/em\u003e, \u003cem\u003eLactobacillaceae\u003c/em\u003e, \u003cem\u003eLachnospiraceae\u003c/em\u003e, \u003cem\u003eRuminococcaceae\u003c/em\u003e and \u003cem\u003eClostridiaceae\u003c/em\u003e (Pin Viso et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Pandit et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) compared caecal microbiota of Aseel and Kadaknath breeds sourced from two separate farms and identified that both breed and geographical locations affect GIT bacterial diversity. They found that \u003cem\u003eBacteroides\u003c/em\u003e dominated in both locations (\u0026gt;\u0026thinsp;20% of total abundances), except for Kadaknath breeds at farm 2 where \u003cem\u003eFusobacteria\u003c/em\u003e dominated (44.1% of total abundances). Farm 1 showed that the Aseel breed were subsequently dominated by \u003cem\u003eClostridium\u003c/em\u003e (6.5%), whilst \u003cem\u003eAlistipes\u003c/em\u003e (23.1% of total abundances) followed in abundance in the Kadaknath bred; this differs at Farm 2 as Aseel breeds on this farm had \u003cem\u003eAlistipes\u003c/em\u003e as the second most abundant GIT bacteria (6.4% of total abundances), while the Kadaknath breed had \u003cem\u003eBacteroides\u003c/em\u003e as the second most abundant GIT bacteria (21.6% of total abundances) (Pandit et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Ultimately this highlights that there are environmental impacts which have a role in shaping the GIT microbiome.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe aim of this study was to identify the core microbiota of a chicken gastrointestinal tract using all data made available through the SRA database and to define the main variables (gastrointestinal tract location, breed, age and geographical location) which affect microbiota gastrointestinal tract diversity. We identified significant influences by each variable examined including the gastrointestinal tract location, geographical location, breed and age of birds. It was apparent that irrespective of gastrointestinal tract location, breed, age and geographical location, \u003cem\u003eLactobacillus\u003c/em\u003e is the prominent genus of the chicken GIT bacterial community. Identifying the common core microbiome of the caecal and faecal communities suggest \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eEisenbergiella\u003c/em\u003e, \u003cem\u003eSubdoligranulum\u003c/em\u003e, \u003cem\u003eOscillibacter\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e \u0026amp; \u003cem\u003eBlautia\u003c/em\u003e are core, meanwhile, only \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e were present across 90% of datasets for all gastrointestinal tract locations. In terms of breed, differences in the microbial communities were found with Ross 308, Cobb 500 and Sasso T451A having the most diverse bacterial GIT communities. We also note a successional development of the microbial community as birds age. The caecal microbial communities develop from a \u003cem\u003eLactobacillus\u003c/em\u003e dominated community towards a stabilised \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eEisenbergiella\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eMegamonas\u003c/em\u003e, and \u003cem\u003eLactobacillus\u003c/em\u003e community as birds age. Meanwhile, geographical location also impacts the community, as \u003cem\u003eEisenbergiella\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e are dominate amongst China and UK, while \u003cem\u003eLactobacillus\u003c/em\u003e dominates in both the Canadian and European datasets. This study defined what \u0026lsquo;normal\u0026rsquo; is within poultry GIT microbiota globally, alongside defining the factors which affect the diversity present, which is imperative to enhancing the microbiome for productive and environmental improvements.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePB led the study but all co-authors contributed to the design and computational aspects. All authors contribued to writting the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdegoke, A. A., Stenstrom, T. A. \u0026amp; Okoh, A. I. 2017. Stenotrophomonas maltophilia as an Emerging Ubiquitous Pathogen: Looking Beyond Contemporary Antibiotic Therapy. \u003cem\u003eFront Microbiol,\u003c/em\u003e 8\u003cstrong\u003e,\u003c/strong\u003e 2276.\u003c/li\u003e\n\u003cli\u003eAhmad, S., Rehman, R., Haider, S., Batool, Z., Ahmed, F., Ahmed, S. 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Cecal microbiota of Tibetan Chickens from five geographic regions were determined by 16S rRNA sequencing. \u003cem\u003eMicrobiologyopen,\u003c/em\u003e 5\u003cstrong\u003e,\u003c/strong\u003e 753-762.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Chicken, gastrointestinal, microbiota, 16S rDNA, metataxonomy, gut, diversity, core microbiome","lastPublishedDoi":"10.21203/rs.3.rs-4969804/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4969804/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMetataxonomic studies have underpinned a vast understanding of microbial communities residing within livestock gastrointestinal tracts, albeit studies have often not been combined to provide a global census. Consequently, in this study we characterised the overall and common \u0026lsquo;core\u0026rsquo; chicken microbiota across the gastrointestinal tract (GIT), whilst assessing the effects of GIT location, bird breed, age and geographical location on the GIT resident microbes using metataxonomic data compiled from studies completed across the world. Specifically, bacterial 16S ribosomal DNA sequences from GIT samples associated with various breeds, differing in age, diet, GIT (caecum, faeces, ileum and jejunum) and geographical location were obtained from the Short Read Archive and analysed using the MGnify pipeline. Metataxonomic profiles produced across the 602 datasets illustrated the presence of 3 phyla, 25 families and 30 genera, of which core genera (defined by presence in over 90% of datasets) belonged to \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eButyricicoccus\u003c/em\u003e, \u003cem\u003eEisenbergiella\u003c/em\u003e, \u003cem\u003eSubdoligranulum\u003c/em\u003e, \u003cem\u003eOscillibacter\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e \u0026amp; \u003cem\u003eBlautia\u003c/em\u003e. PERMANOVA analysis also showed that GIT location, bird breed, age and geographical location all had a significant effect on GIT microbial diversity. On a genus level, \u003cem\u003eFaecalibacterium\u003c/em\u003e was most abundant in the caeca, \u003cem\u003eLactobacillus\u003c/em\u003e was most abundant in the faeces, ileum and jejunum, with the data showing that the caeca and faeces were most diverse. AIL F8 progeny, Ross 308 and Cobb 500 breeds GIT bacteria were dominated by \u003cem\u003eLactobacillus\u003c/em\u003e, and \u003cem\u003eEisenbergiella\u003c/em\u003e, \u003cem\u003eMegamonas\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e were most abundant amongst Sasso-T451A and Tibetan chicken breeds. Microbial communities within each GIT region develop with age, from a \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e dominated community during the earlier stages of growth, towards a \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eEisenbergiella\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eMegamonas\u003c/em\u003e, and \u003cem\u003eLactobacillus\u003c/em\u003e dominated community during the later stages of life. Geographical locations, and thus environmental effectors, also impacted upon gastrointestinal tract microbiota, with Canadian and European datasets being dominated by \u003cem\u003eLactobacillus\u003c/em\u003e, whilst UK and Chinese datasets were dominated by \u003cem\u003eEisenbergiella\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e respectively. This study aids in defining what \u0026lsquo;normal\u0026rsquo; is within poultry gastrointestinal tract microbiota globally, which is imperative to enhancing the microbiome for productive and environmental improvements.\u003c/p\u003e","manuscriptTitle":"Decoding the chicken gastrointestinal microbiome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-30 11:52:12","doi":"10.21203/rs.3.rs-4969804/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-03T05:51:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-30T06:42:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-30T06:41:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2024-08-24T15:19:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dfbe4f30-feee-4f65-852c-2d1420f5573e","owner":[],"postedDate":"September 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-27T16:12:24+00:00","versionOfRecord":{"articleIdentity":"rs-4969804","link":"https://doi.org/10.1186/s12866-024-03690-x","journal":{"identity":"bmc-microbiology","isVorOnly":false,"title":"BMC Microbiology"},"publishedOn":"2025-01-20 15:57:07","publishedOnDateReadable":"January 20th, 2025"},"versionCreatedAt":"2024-09-30 11:52:12","video":"","vorDoi":"10.1186/s12866-024-03690-x","vorDoiUrl":"https://doi.org/10.1186/s12866-024-03690-x","workflowStages":[]},"version":"v1","identity":"rs-4969804","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4969804","identity":"rs-4969804","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}