Intestinal microbiota plays a crucial role in immune development and disease progression in mammals from birth onwards. The gastrointestinal tract of newborn mammals is rapidly colonized by microbes with tremendous biomass and diversity. Understanding how this complex of segmental communities evolves in different gastrointestinal sites over time has great biological significance and medical implications. However, most previous reports examining intestinal microbiota have focused on fecal samples, a strategy that overlooks the spatial microbial dynamics in different intestinal segments. Using intestinal digesta from six intestinal segments (duodenum, jejunum, ileum, cecum, colon and rectum) of newborn piglets, we herein conducted a large-scale 16S rRNA gene sequencing-based study to characterize the segmental dynamics of porcine gut microbiota at eight postnatal intervals (days 1, 7, 14, 21, 28, 35, 120 and 180). A total of 4,465 OTUs were obtained and showed that the six intestinal segments could be divided into three parts; in the duodenum-jejunum section, the most abundant genera included Lactobacillus and Bacteroides ; in the ileum, Fusobacterium and Escherichia ; and in the cecum-rectum section, Prevotella . Although the microbial communities of the piglets were similar among the six intestinal segments on postnatal day 1, they evolved and quickly differentiated at later intervals. An examination of time-dependent alterations in the dominant microbes revealed that the microbiome in the large intestine was very different from and much more stable than that in the small intestine. The gut microbiota in newborn piglets exhibited apparent temporal and spatial variations in different intestinal segments. The database of gut microbes in piglets could be a referable resource for future studies on mammalian gut microbiome development in early host growth phases.

No abstract

BackgroundCopy number variations (CNVs) confer significant effects on genetic innovation and phenotypic variation. Previous CNV studies in swine seldom focused on in-depth characterization of global CNVs.ResultsUsing whole-genome assembly comparison (WGAC) and whole-genome shotgun sequence detection (WSSD) approaches by next generation sequencing (NGS), we probed formation signatures of both segmental duplications (SDs) and individualized CNVs in an integrated fashion, building the finest resolution CNV and SD maps of pigs so far. We obtained copy number estimates of all protein-coding genes with copy number variation carried by individuals, and further confirmed two genes with high copy numbers in Meishan pigs through an enlarged population. We determined genome-wide CNV hotspots, which were significantly enriched in SD regions, suggesting evolution of CNV hotspots may be affected by ancestral SDs. Through systematically enrichment analyses based on simulations and bioinformatics analyses, we revealed CNV-related genes undergo a different selective constraint from those CNV-unrelated regions, and CNVs may be associated with or affect pig health and production performance under recent selection.ConclusionsOur studies lay out one way for characterization of CNVs in the pig genome, provide insight into the pig genome variation and prompt CNV mechanisms studies when using pigs as biomedical models for human diseases.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-593) contains supplementary material, which is available to authorized users.

The genetic structure and diversity of 15 Chinese indigenous chicken breeds was investigated using 29 microsatellite markers. The total number of birds examined was 542, on average 36 birds per breed. A total of 277 alleles (mean number 9.55 alleles per locus, ranging from 2 to 25) was observed. All populations showed high levels of heterozygosity with the lowest estimate of 0.440 for the Gushi chickens, and the highest one of 0.644 observed for Wannan Three-yellow chickens. The global heterozygote deficit across all populations (F IT ) amounted to 0.180 (p<0.001). About 16% of the total genetic variability originated from differences between breeds, with all loci contributing significantly to this differentiation. An unrooted consensus tree was constructed using the Neighbour-Joining method and pair-wise distances based on marker estimated kinships. Two main groups were found. The heavy-body type populations grouped together in one cluster while the light-body type populations formed the second cluster. The STRUCTURE software was used to assess genetic clustering of these chicken breeds. Similar to the phylogenetic analysis, the heavy-body type and light-body type populations separated first. Clustering analysis provided an accurate representation of the current genetic relations among the breeds. Remarkably similar breed rankings were obtained with all methods.

No abstract