Dataset Information

16S rRNA amplicon sequencing characterization of caecal microbiome composition of broilers and free-range slow-growing chickens throughout their productive lifespan.

ABSTRACT: Gut microbiota affects health, metabolism and immunity of the host, and in the case of livestock, also food-safety. Here, 16S rRNA gene high-throughput Illumina sequencing was used to describe the microbiome of chicken caeca in two different breeds and management systems throughout their whole productive lifespan. Broilers (Ross-308), as a fast-growing breed reared in an intensive system for 42-days, and a slow-growing breed of chicken (Sasso-T451A) reared in an extensive farming system with outdoor access for 86-days, were compared. The core microbiome and differentially abundant taxa, as well as taxa associated with age were identified. Age was identified as the strongest influencing factor in caecal microbiota composition, and, in general, each age-group showed an age-associated community profile, with a transition period at the middle of their lifespan. However, substantial differences were observed in the composition of caecal microbiota of both chicken breeds, microbiota being richer and more complex in free-range chicken than in broilers. Several taxa positively/negatively correlated with Campylobacter relative abundance were also identified. Especially noteworthy was the identification by microbial community comparison of microbiota profiles suggestive of dysbiosis in several free-range chickens, probably associated to the typhlitis observed in the lumen of their caeca.

SUBMITTER: Ocejo M

PROVIDER: S-EPMC6385345 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

16S rRNA amplicon sequencing characterization of caecal microbiome composition of broilers and free-range slow-growing chickens throughout their productive lifespan.

Ocejo Medelin M Oporto Beatriz B Hurtado Ana A

Scientific reports 20190221 1

Gut microbiota affects health, metabolism and immunity of the host, and in the case of livestock, also food-safety. Here, 16S rRNA gene high-throughput Illumina sequencing was used to describe the microbiome of chicken caeca in two different breeds and management systems throughout their whole productive lifespan. Broilers (Ross-308), as a fast-growing breed reared in an intensive system for 42-days, and a slow-growing breed of chicken (Sasso-T451A) reared in an extensive farming system with out ...[more]

PMID: 30792439

Similar Datasets

Project description:IntroductionGrowth traits are important in poultry production, however, little is known for its regulatory mechanism at epigenetic level. Therefore, in this study, we aim to compare DNA methylation profiles between fast- and slow-growing broilers in order to identify candidate genes for chicken growth. Methylated DNA immunoprecipitation-sequencing (MeDIP-seq) was used to investigate the genome-wide DNA methylation pattern in high and low tails of Recessive White Rock (WRR(h); WRR(l)) and that of Xinhua Chickens (XH(h); XH(l)) at 7 weeks of age. The results showed that the average methylation density was the lowest in CGIs followed by promoters. Within the gene body, the methylation density of introns was higher than that of UTRs and exons. Moreover, different methylation levels were observed in different repeat types with the highest in LINE/CR1. Methylated CGIs were prominently distributed in the intergenic regions and were enriched in the size ranging 200-300 bp. In total 13,294 methylated genes were found in four samples, including 4,085 differentially methylated genes of WRR(h) Vs. WRR(l), 5,599 of XH(h) Vs. XH(l), 4,204 of WRR(h) Vs. XH(h), as well as 7,301 of WRR(l) Vs. XH(l). Moreover, 132 differentially methylated genes related to growth and metabolism were observed in both inner contrasts (WRR(h) Vs. WRR(l) and XH(h) Vs. XH(l)), whereas 129 differentially methylated genes related to growth and metabolism were found in both across-breed contrasts (WRR(h) Vs. XH(h) and WRR(l) Vs. XH(l)). Further analysis showed that overall 75 genes exhibited altered DNA methylation in all four contrasts, which included some well-known growth factors of IGF1R, FGF12, FGF14, FGF18, FGFR2, and FGFR3. In addition, we validate the MeDIP-seq results by bisulfite sequencing in some regions.ConclusionsThis study revealed the global DNA methylation pattern of chicken muscle, and identified candidate genes that potentially regulate muscle development at 7 weeks of age at methylation level.

Project description:Chicken growth traits are important in poultry production, however, little is known for its regulatory mechanism at epigenetic level. Therefore, this study aims to compare DNA methylation profiles between fast- and slow-growing broilers in order to identify candidate genes underlying chicken growth. Methylated DNA immunoprecipitation-sequencing (MeDIP-seq) was used to investigate the genome-wide DNA methylation pattern in high and low tails of Recessive White Rock (WRRh, WRRl) and that of Xinhua Chickens (XHh, XHl). The results showed that the average of methylation density was the lowest in CGIs followed by promoters. Within the gene body, the methylation density of introns was higher than UTRs and exons. Moreover, different methylation levels were observed in different repeat types with the highest in LINE/CR1. Methylated CGIs were prominently distributed in the intergenic regions and were enriched in the size range of 200-300 bp. In total 13,294 methylated genes were found in four samples, including 4,085 differentially methylated genes between WRRh and WRRl, 5,599 between XHh and XHl, 4,204 between WRRh and XHh, as well as 7,301 between WRRl and XHl. Moreover, 132 differentially methylated genes related to growth and metabolism were observed in both inner contrasts (WRRh Vs. WRRl and XHh Vs. XHl), whereas 129 differentially methylated genes related to growth and metabolism were found in both across-breed contrasts (WRRh Vs. XHh and WRRl Vs. XHl). Further analysis showed that overall 75 genes exhibited altered DNA methylation in all four contrasts, which included some well-known growth factors of IGF1R, FGF12, FGF14, FGF18, FGFR2, and FGFR3. In addition, we further validate the MeDIP-seq results by bisulfite sequencing in some regions. 12 Breast muscle samples were collected from two breeds of different growth rate, Recessive White Rock (WRR) and Xinhua Chickens (XH). Each breed included low and high-weight groups and 3 samples from each group were pooled equally for methylated DNA immunoprecipitation-sequencing (MeDIP-seq).

Dataset Information

16S rRNA amplicon sequencing characterization of caecal microbiome composition of broilers and free-range slow-growing chickens throughout their productive lifespan.

Publications

16S rRNA amplicon sequencing characterization of caecal microbiome composition of broilers and free-range slow-growing chickens throughout their productive lifespan.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets