Genetic pattern and gene localization of polydactyly in Beijing fatty chicken.

Polydactyly, a common heritable limb malformation in vertebrates, is characterized by supernumerary digits. In chickens, basic characteristics and rough dominant genes have been explored in past decades; however, the elaborate pattern of inheritance and the determinant gene remain obscure. In this study, different types of polydactylism were classified by the numbers and the shapes of toes, including the newly defined subtypes of B' and G, for the Beijing fatty chicken, a native breed of chicken from China. Through experiments on hybridization, we demonstrated a complete dominant inheritance of polydactyly instead of an incomplete penetrance or genetic modification of the previous conjecture. In particular, by using the F2 population of the five-digit purebred line of Beijing fatty chicken backcrossed to Shiqiza chicken and by using restriction-site associated DNA based markers, we performed a genome-wide association study on the trait of polydactyly. Furthermore, whole genome resequencing strategy was applied to sweep SNPs across the whole genome. An outlier-based Fst approach was employed to search for signatures of selection, and results indicated that the determinant mutation was found in the region ranging from 8.3 Mb to 8.7 Mb, where the polydactyly candidate gene LMBR1 was located. The G/T mutation of rs80659072 was identified to be highly associated with polydactyly in our resequencing and was validated in random samples from an expanded population. Thus, we confirmed that LMBR1 was the causative gene of polydactyly in the Beijing fatty chicken by using GWAS with restriction-site associated DNA based markers and resequencing.

Divergent selection-induced obesity alters the composition and functional pathways of chicken gut microbiota.

BACKGROUND: The gastrointestinal tract is populated by a complex and vast microbial network, with a composition that reflects the relationships of the symbiosis, co-metabolism, and co-evolution of these microorganisms with their host. The mechanism that underlies such interactions between the genetics of the host and gut microbiota remains elusive. RESULTS: To understand how genetic variation of the host shapes the gut microbiota and interacts with it to affect the metabolic phenotype of the host, we compared the abundance of microbial taxa and their functional performance between two lines of chickens (fat and lean) that had undergone long-term divergent selection for abdominal fat pad weight, which resulted in a 4.5-fold increase in the fat line compared to the lean line. Our analysis revealed that the proportions of Fusobacteria and Proteobacteria differed significantly between the two lines (8 vs. 18% and 33 vs. 24%, respectively) at the phylum level. Eight bacterial genera and 11 species were also substantially influenced by the host genotype. Differences between the two lines in the frequency of host alleles at loci that influence accumulation of abdominal fat were associated with differences in the abundance and composition of the gut microbiota. Moreover, microbial genome functional analysis showed that the gut microbiota was involved in pathways that are associated with fat metabolism such as lipid and glycan biosynthesis, as well as amino acid and energy metabolism. Interestingly, citrate cycle and peroxisome proliferator activated receptor (PPAR) signaling pathways that play important roles in lipid storage and metabolism were more prevalent in the fat line than in the lean line. CONCLUSIONS: Our study demonstrates that long-term divergent selection not only alters the composition of the gut microbiota, but also influences its functional performance by enriching its relative abundance in microbial taxa. These results support the hypothesis that the host and gut microbiota interact at the genetic level and that these interactions result in their co-evolution.

Porcine Epidemic Diarrhea Virus Infection Induced the Unbalance of Gut Microbiota in Piglets.

Porcine epidemic diarrhea (PED) is a devastating disease in livestock industry. Most of the previous studies related to the PED were focused on the pathology and etiology of porcine epidemic diarrhea virus (PEDV). A little was known regarding the status of gut microbiota after piglets infected by PEDV. In this study, aided by metagenome sequencing technology, gut microbiota profiles in feces of viral diarrhea (VD) and viral control (VC) piglets were investigated. The results showed that the abundance of four dominant phyla (Fusobacteria, Actinobacteria, Verrucomicrobia, and Proteobacteria) in feces was affected greatly by porcine epidemic diarrhea. Especially, the abundance of Fusobacteria was higher in VD piglets (36%) than in VC piglets (5%). On the contrary, the Verrucomicrobia was detected in lower distribution proportion in VD piglets (around 0%) than in VC piglets (20%). Furthermore, 25 genera were significantly different between VC and VD piglets at the genus level. Among the 25 genera, Leptotrichia belonging to Fusobacteria was remarkably lower in VC piglets than in VD piglets. Akkermansia belonging to Verrucomicrobia was higher in VC piglets than in VD piglets. Our findings implicated that the gut microbiota associated with PED significantly provided an insight into the pathology and physiology of PED.

The dynamic distribution of porcine microbiota across different ages and gastrointestinal tract segments.

Metagenome of gut microbes has been implicated in metabolism, immunity, and health maintenance of its host. However, in most of previous studies, the microbiota was sampled from feces instead of gastrointestinal (GI) tract. In this study, we compared the microbial populations from feces at four different developmental stages and contents of four intestinal segments at maturity to examine the dynamic shift of microbiota in pigs and investigated whether adult porcine fecal samples could be used to represent samples of the GI tract. Analysis results revealed that the ratio of Firmicutes to Bacteroidetes from the feces of the older pigs (2-, 3-, 6- month) were 10 times higher compared to those from piglets (1-month). As the pigs matured, so did it seem that the composition of microbiome became more stable in feces. In adult pigs, there were significant differences in microbial profiles between the contents of the small intestine and large intestine. The dominant genera in the small intestine belonged to aerobe or facultative anaerobe categories, whereas the main genera in the large intestine were all anaerobes. Compared to the GI tract, the composition of microbiome was quite different in feces. The microbial profile in large intestine was more similar to feces than those in the small intestine, with the similarity of 0.75 and 0.38 on average, respectively. Microbial functions, predicted by metagenome profiles, showed the enrichment associated with metabolism pathway and metabolic disease in large intestine and feces while higher abundance of infectious disease, immune function disease, and cancer in small intestine. Fecal microbes also showed enriched function in metabolic pathways compared to microbes from pooled gut contents. Our study extended the understanding of dynamic shift of gut microbes during pig growth and also characterized the profiles of bacterial communities across GI tracts of mature pigs.

SNP discovery and genotyping using restriction-site-associated DNA sequencing in chickens.

Single nucleotide polymorphisms (SNPs) are essential to the understanding of population genetic variation and diversity. Here, we performed restriction-site-associated DNA sequencing (RAD-seq) on 72 individuals from 13 Chinese indigenous and three introduced chicken breeds. A total of 620 million reads were obtained using an Illumina Hiseq2000 sequencer. An average of 75,587 SNPs were identified from each individual. Further filtering strictly validated 28,895 SNPs candidates for all populations. When compared with the NCBI dbSNP (chicken_9031), 15,404 SNPs were new discoveries. In this study, RAD-seq was performed for the first time on chickens, implicating the remarkable effectiveness and potential applications on genetic analysis and breeding technique for whole-genome selection in chicken and other agricultural animals.

Analysis of horse genomes provides insight into the diversification and adaptive evolution of karyotype.

Karyotypic diversification is more prominent in Equus species than in other mammals. Here, using next generation sequencing technology, we generated and de novo assembled quality genomes sequences for a male wild horse (Przewalski's horse) and a male domestic horse (Mongolian horse), with about 93-fold and 91-fold coverage, respectively. Portion of Y chromosome from wild horse assemblies (3 M bp) and Mongolian horse (2 M bp) were also sequenced and de novo assembled. We confirmed a Robertsonian translocation event through the wild horse's chromosomes 23 and 24, which contained sequences that were highly homologous with those on the domestic horse's chromosome 5. The four main types of rearrangement, insertion of unknown origin, inserted duplication, inversion, and relocation, are not evenly distributed on all the chromosomes, and some chromosomes, such as the X chromosome, contain more rearrangements than others, and the number of inversions is far less than the number of insertions and relocations in the horse genome. Furthermore, we discovered the percentages of LINE_L1 and LTR_ERV1 are significantly increased in rearrangement regions. The analysis results of the two representative Equus species genomes improved our knowledge of Equus chromosome rearrangement and karyotype evolution.

Body weight selection affects quantitative genetic correlated responses in gut microbiota.

The abundance of gut microbiota can be viewed as a quantitative trait, which is affected by the genetics and environment of the host. To quantify the effects of host genetics, we calculated the heritability of abundance of specific microorganisms and genetic correlations among them in the gut microbiota of two lines of chickens maintained under the same husbandry and dietary regimes. The lines, which originated from a common founder population, had undergone >50 generations of selection for high (HW) or low (LW) 56-day body weight and now differ by more than 10-fold in body weight at selection age. We identified families of Paenibacillaceae, Streptococcaceae, Helicobacteraceae, and Burkholderiaceae that had moderate heritabilities. Although there were no obvious phenotypic correlations among gut microbiota, significant genetic correlations were observed. Moreover, the effects were modified by genetic selection for body weight, which altered the quantitative genetic background of the host. Heritabilities for Bacillaceae, Flavobacteriaceae, Helicobacteraceae, Comamonadaceae, Enterococcaceae, and Streptococcaceae were moderate in LW line and little to zero in the HW line. These results suggest that loci associated with these microbiota families, while exhibiting genetic variation in LW, have been fixed in HW line. Also, long term selection for body weight has altered the genetic correlations among gut microbiota. No microbiota families had significant heritabilities in both the LW and HW lines suggesting that the presence and/or absence of a particular microbiota family either has a strong growth promoting or inhibiting effect, but not both. These results demonstrate that the quantitative genetics of the host have considerable influence on the gut microbiota.

Quantitative genetic background of the host influences gut microbiomes in chickens.

Host genotype and gender are among the factors that influence the composition of gut microbiota. We studied the population structure of gut microbiota in two lines of chickens maintained under the same husbandry and dietary regimes. The lines, which originated from a common founder population, had undergone 54 generations of selection for high (HW) or low (LW) 56-day body weight, and now differ by more than 10-fold in body weight at selection age. Of 190 microbiome species, 68 were affected by genotype (line), gender, and genotype by gender interactions. Fifteen of the 68 species belong to Lactobacillus. Species affected by genotype, gender, and the genotype by gender interaction, were 29, 48, and 12, respectively. Species affected by gender were 30 and 17 in the HW and LW lines, respectively. Thus, under a common diet and husbandry host quantitative genotype and gender influenced gut microbiota composite.