Resequencing Analyses Revealed Genetic Diversity and Selection Signatures during Rabbit Breeding and Improvement.

Domestication has shaped the diverse characteristics of rabbits, including coat color, fur structure, body size, and various physiological traits. Utilizing whole-genome resequencing (DNBSEQ-T7), we analyzed the genetic diversity, population structure, and genomic selection across 180 rabbits from 17 distinct breeds to uncover the genetic basis of these traits. We conducted whole-genome sequencing on 17 rabbit breeds, identifying 17,430,184 high-quality SNPs and analyzing genomic diversity, patterns of genomic variation, population structure, and selection signatures related to coat color, coat structure, long hair, body size, reproductive capacity, and disease resistance. Through PCA and NJ tree analyses, distinct clusters emerged among Chinese indigenous rabbits, suggesting varied origins and domestication histories. Selective sweep testing pinpointed regions and genes linked to domestication and key morphological and economic traits, including those affecting coat color (TYR, ASIP), structure (LIPH), body size (INSIG2, GLI3), fertility (EDNRA, SRD5A2), heat stress adaptation (PLCB1), and immune response (SEC31A, CD86, LAP3). Our study identified key genomic signatures of selection related to traits such as coat color, fur structure, body size, and fertility; these findings highlight the genetic basis underlying phenotypic diversification in rabbits and have implications for breeding programs aiming to improve productive, reproductive, and adaptive traits. The detected genomic signatures of selection also provide insights into rabbit domestication and can aid conservation efforts for indigenous breeds.

Marker Density and Models to Improve the Accuracy of Genomic Selection for Growth and Slaughter Traits in Meat Rabbits.

The selection and breeding of good meat rabbit breeds are fundamental to their industrial development, and genomic selection (GS) can employ genomic information to make up for the shortcomings of traditional phenotype-based breeding methods. For the practical implementation of GS in meat rabbit breeding, it is necessary to assess different marker densities and GS models. Here, we obtained low-coverage whole-genome sequencing (lcWGS) data from 1515 meat rabbits (including parent herd and half-sibling offspring). The specific objectives were (1) to derive a baseline for heritability estimates and genomic predictions based on randomly selected marker densities and (2) to assess the accuracy of genomic predictions for single- and multiple-trait linear mixed models. We found that a marker density of 50 K can be used as a baseline for heritability estimation and genomic prediction. For GS, the multi-trait genomic best linear unbiased prediction (GBLUP) model results in more accurate predictions for virtually all traits compared to the single-trait model, with improvements greater than 15% for all of them, which may be attributed to the use of information on genetically related traits. In addition, we discovered a positive correlation between the performance of the multi-trait GBLUP and the genetic correlation between the traits. We anticipate that this approach will provide solutions for GS, as well as optimize breeding programs, in meat rabbits.

Cost-effectively dissecting the genetic architecture of complex wool traits in rabbits by low-coverage sequencing.

BACKGROUND: Rabbit wool traits are important in fiber production and for model organism research on hair growth, but their genetic architecture remains obscure. In this study, we focused on wool characteristics in Angora rabbits, a breed well-known for the quality of its wool. Considering the cost to generate population-scale sequence data and the biased detection of variants using chip data, developing an effective genotyping strategy using low-coverage whole-genome sequencing (LCS) data is necessary to conduct genetic analyses. RESULTS: Different genotype imputation strategies (BaseVar + STITCH, Bcftools + Beagle4, and GATK + Beagle5), sequencing coverages (0.1X, 0.5X, 1.0X, 1.5X, and 2.0X), and sample sizes (100, 200, 300, 400, 500, and 600) were compared. Our results showed that using BaseVar + STITCH at a sequencing depth of 1.0X with a sample size larger than 300 resulted in the highest genotyping accuracy, with a genotype concordance higher than 98.8% and genotype accuracy higher than 0.97. We performed multivariate genome-wide association studies (GWAS), followed by conditional GWAS and estimation of the confidence intervals of quantitative trait loci (QTL) to investigate the genetic architecture of wool traits. Six QTL were detected, which explained 0.4 to 7.5% of the phenotypic variation. Gene-level mapping identified the fibroblast growth factor 10 (FGF10) gene as associated with fiber growth and diameter, which agrees with previous results from functional data analyses on the FGF gene family in other species, and is relevant for wool rabbit breeding. CONCLUSIONS: We suggest that LCS followed by imputation can be a cost-effective alternative to array and high-depth sequencing for assessing common variants. GWAS combined with LCS can identify new QTL and candidate genes that are associated with quantitative traits. This study provides a cost-effective and powerful method for investigating the genetic architecture of complex traits, which will be useful for genomic breeding applications.

Marker density and statistical model designs to increase accuracy of genomic selection for wool traits in Angora rabbits.

The Angora rabbit, a well-known breed for fiber production, has been undergoing traditional breeding programs relying mainly on phenotypes. Genomic selection (GS) uses genomic information and promises to accelerate genetic gain. Practically, to implement GS in Angora rabbit breeding, it is necessary to evaluate different marker densities and GS models to develop suitable strategies for an optimized breeding pipeline. Considering a lack in microarray, low-coverage sequencing combined with genotype imputation was used to boost the number of SNPs across the rabbit genome. Here, in a population of 629 Angora rabbits, a total of 18,577,154 high-quality SNPs were imputed (imputation accuracy above 98%) based on low-coverage sequencing of 3.84X genomic coverage, and wool traits and body weight were measured at 70, 140 and 210 days of age. From the original markers, 0.5K, 1K, 3K, 5K, 10K, 50K, 100K, 500K, 1M and 2M were randomly selected and evaluated, resulting in 50K markers as the baseline for the heritability estimation and genomic prediction. Comparing to the GS performance of single-trait models, the prediction accuracy of nearly all traits could be improved by multi-trait models, which might because multiple-trait models used information from genetically correlated traits. Furthermore, we observed high significant negative correlation between the increased prediction accuracy from single-trait to multiple-trait models and estimated heritability. The results indicated that low-heritability traits could borrow more information from correlated traits and hence achieve higher prediction accuracy. The research first reported heritability estimation in rabbits by using genome-wide markers, and provided 50K as an optimal marker density for further microarray design, genetic evaluation and genomic selection in Angora rabbits. We expect that the work could provide strategies for GS in early selection, and optimize breeding programs in rabbits.

Fat-1 expression alleviates atherosclerosis in transgenic rabbits.

Atherosclerosis is the main cause of cardiovascular diseases. The Fat-1 gene can express the n-3 fatty acid desaturase, which converts n-6 polyunsaturated fatty acids (PUFA) to n-3 PUFAs. The role of n-3 PUFAs in atherosclerosis is widely debated. This study explored the effect of n-3 PUFAs on atherosclerosis in rabbits. In this study, atherosclerosis was induced in Fat-1 transgenic rabbits and their littermate (WT) rabbits by feeding a high-cholesterol diet containing 0.3% cholesterol and 3% soybean oil for 16 weeks. Plasma lipid, fatty acid and pathological analyses of atherosclerotic lesions were conducted. Fatty acid composition in the liver and muscle showed that n-3 PUFAs increased and n-6 PUFAs decreased in the Fat-1 group. Plasma high-density lipoprotein cholesterol (HDL-C) levels were significantly increased in the Fat-1 group, and the atherosclerotic lesion area of the aortic arch in Fat-1 transgenic rabbits was significantly reduced. Histological analysis showed that smooth muscle cells (SMCs) in atherosclerotic lesions decreased significantly. In conclusion, n-3 PUFAs improve atherosclerosis in Fat-1 transgenic rabbits, and this process may depend on the increase in plasma HDL-C and the decrease in the amount of SMCs in atherosclerotic plaques.

Characterization of microRNA Profiles in Pasteurella multocida-Infected Rabbits and Identification of miR-29-5p as a Regulator of Antibacterial Immune Response.

Pasteurella multocida is the pathogenic agent for a variety of severe diseases in livestock, including rabbits. MicroRNAs (miRNAs) participate in the immune response to the pathogen. Distinct miRNA expression patterns were explored in rabbit lung by small-RNA deep sequencing to assess dysregulated miRNAs during P. multocida infection. Totally, 571 miRNAs were screened, of which, 62 were novel, and 32 exhibited differential expression (DE). Of the 32 known DE-miRNAs, 13 and 15 occurred at 1 day and 3 days post-infection (dpi); and ocu-miR-107-3p and ocu-miR-29b-5p were shared between the two time points. Moreover, 7,345 non-redundant target genes were predicted for the 32 DE-miRNAs. Putative target genes were enriched in diverse GO and KEGG pathways and might be crucial for disease resistance. Interestingly, upregulation of ocu-miR-29-5p suppresses P. multocida propagation and downregulates expression of epithelial membrane protein-2 (EMP2) and T-box 4 (TBX4) genes by binding to their 3' untranslated region in RK13 cells. Thus, ocu-miR-29-5p may indirectly inhibit P. multocida invasion by modulating genes related to the host immune response, such as EMP2 and TBX4.

The Profiles of Long Non-coding RNA and mRNA Transcriptome Reveals the Genes and Pathway Potentially Involved in Pasteurella multocida Infection of New Zealand Rabbits.

Infection with Pasteurella multocida (P. multocida) causes severe epidemic diseases in rabbits and is responsible for the pronounced economic losses in the livestock industry. Long non-coding RNAs (lncRNAs) have been proven to exert vital functions in regulating the host immune responses to bacterial attacks. However, little is known about how lncRNAs participate in the rabbit's immune response against P. multocida infection in the lungs. LncRNA and mRNA expression profiles were analyzed by transcriptomics and bioinformatics during P. multocida infection. A total of 336 lncRNAs and 7,014 mRNAs were differentially regulated at 1 day and 3 days post infection (dpi). Nearly 80% of the differentially expressed lncRNAs exhibited an increased expression at 3 dpi suggesting that the P. multocida genes are responsible for regulation. Moreover, GO and KEGG enriched analysis indicated that the immune-related pathways including pattern recognition receptors (PRRs), cytokines, and chemokines were significantly enriched at 3 dpi. These results indicate that the dysregulated immune-related genes may play crucial roles in defending against P. multocida attacks. Overall, these results advance our cognition of the role of lncRNAs and mRNAs in modulating the rabbit's innate immune response against P. multocida attacks, which will offer a valuable clue for further studies into exploring P. multocida-related diseases in human.

Chicken cecal DNA methylome alteration in the response to Salmonella enterica serovar Enteritidis inoculation.

BACKGROUND: Salmonella enterica serovar Enteritidis (SE) is one of the pathogenic bacteria, which affects poultry production and poses a severe threat to public health. Chicken meat and eggs are the main sources of human salmonellosis. DNA methylation is involved in regulatory processes including gene expression, chromatin structure and genomic imprinting. To understand the methylation regulation in the response to SE inoculation in chicken, the genome-wide DNA methylation profile following SE inoculation was analyzed through whole-genome bisulfite sequencing in the current study. RESULTS: There were 185,362,463 clean reads and 126,098,724 unique reads in the control group, and 180,530,750 clean reads and 126,782,896 unique reads in the inoculated group. The methylation density in the gene body was higher than that in the upstream and downstream regions of the gene. There were 8946 differentially methylated genes (3639 hypo-methylated genes, 5307 hyper-methylated genes) obtained between inoculated and control groups. Methylated genes were mainly enriched in immune-related Gene Ontology (GO) terms and metabolic process terms. Cytokine-cytokine receptor interaction, TGF-beta signaling pathway, FoxO signaling pathway, Wnt signaling pathway and several metabolism-related pathways were significantly enriched. The density of differentially methylated cytosines in miRNAs was the highest. HOX genes were widely methylated. CONCLUSIONS: The genome-wide DNA methylation profile in the response to SE inoculation in chicken was analyzed. SE inoculation promoted the DNA methylation in the chicken cecum and caused methylation alteration in immune- and metabolic- related genes. Wnt signal pathway, miRNAs and HOX gene family may play crucial roles in the methylation regulation of SE inoculation in chicken. The findings herein will deepen the understanding of epigenetic regulation in the response to SE inoculation in chicken.

Cecal CircRNAs Are Associated With the Response to Salmonella Enterica Serovar Enteritidis Inoculation in the Chicken.

Circular RNAs (circRNAs) are a class of endogenous noncoding RNA, which is different from linear RNA. CircRNA is an RNA molecule with a closed loop structure formed by reverse splicing. CircRNAs have been studied in several organisms, however, the circRNAs associated with the response to Salmonella enterica serovar Enteritidis (SE) inoculation in chickens are still unclear. In the current study, Jining Bairi chickens were inoculated with SE. CircRNAs involved in the response to SE inoculation were identified through next-generation sequencing. Our results showed that there were 5,118 circRNAs identified in the control and treated groups. There were 62 circRNAs significantly differentially expressed following SE inoculation. Functional classification revealed that those significantly differentially expressed circRNAs were associated with immune system process, rhythmic process and signaling following SE inoculation. CircRNAs NC_006091.4: 65510578|65515090, NC_006099.4: 16132825|16236906, and NC_006099.4: 15993284|16006290 play important roles in the response to SE inoculation. The findings in the current study provide evidence that circRNA alterations are involved in the response to SE inoculation in the chicken.

Cecal microbiome profile altered by Salmonella enterica, serovar Enteritidis inoculation in chicken.

BACKGROUND: Salmonella enterica, serovar Enteritidis (S. Enteritidis), an important zoonotic foodborne pathogen, can affect the microbiota of the chicken intestine and cause many enteric diseases, such as acute gastroenteritis. The gut microbiota contributes to the development and function of the host immune system and competes with pathogenic microbes. The interaction between S. Enteritidis and the host cecal microbiota is still not fully understood. We investigated the microbiome composition in both treated and control groups through 16S ribosomal RNA (rRNA) gene sequencing at 1, 3, 7, 14, 21, 28, and 35 days post-S. Enteritidis inoculation (dpi) in the current study. RESULTS: Chao1 richness and Shannon diversity significantly increased with chicken development in both the treated and control groups (P < 0.05). The Chao1 index was significantly lower in the treated group than that in the control group at 14 dpi (P < 0.05). Phyla Proteobacteria and Firmicutes were most dominant at 1 and 3 dpi. S. Enteritidis inoculation influenced cecal microbiota mainly at 7 and 14 dpi. S. Enteritidis inoculation significantly altered the relative abundance of 18 genera at different time points (P < 0.05) with relative abundance significantly changed after 14 dpi. The abundance of those genera changed dramatically between 28 and 35 dpi in the treated group compared to control group. Positive correlations existed between Bacillus and Blautia and between Coprococcus and Flavonifractor following S. Enteritidis inoculation. CONCLUSIONS: Our results indicated that both development and S. Enteritidis have effect on chicken cecal microbiota profiles. S. Enteritidis inoculation in young chicks influences the cecal microbiota mainly at 7 and 14 dpi. The cecal microbiota exhibited immunity to S. Enteritidis inoculation at 28 dpi. These findings will provide basic knowledge of the role that chicken cecal microbiota play in response to S. Enteritidis inoculation.

The desaturase OPIN17 from Phytophthora infestans converts arachidonic acid to eicosapentaenoic acid in CHO cells.

We demonstrate the ability to increase the amount of eicosapentaenoic acid (EPA, 20:5n-3) in mammalian cells using OPIN17 desaturase gene. This gene was codon optimized based on genomic sequence of Δ17 from Phytophthora infestans and introduced into Chinese hamster ovary cells using liposome-mediated transfection protocol. Reverse transcription polymerase chain reaction was utilized to evaluate co-expression of AcGFP1 and OPIN17. Our results indicate that the OPIN17 gene can be expressed in mammalian cells. Heterologous expression of this gene was evaluated by assessing the fatty acid content of OPIN17-transfected cells. A total cellular lipid analysis of transfected cells which were fed with arachidonic acid (AA, 20:4n-6) as a substrate resulted in an 86.5-246 % (p < 0.05) increase in the amount of EPA in transfected cells compared with that in control cells. The ratio of AA to EPA was reduced from approximately 4.07:1 in control cells to 2.2:1 in transfected cells (p < 0.05), which indicates an EPA percent conversion of 30.94 %. Our study demonstrates that the codon-optimized OPIN17 gene can be functionally expressed in mammalian cells, converting AA into EPA and elevating the level of ω-3 polyunsaturated fatty acids efficiently. These results provide an additional support for the use of this gene in generating transgenic livestock.

[Relationship of genetic polymorphism of PRLR and RBP4 genes with litter size traits in pig].

The polymorphism of prolactin receptor (PRLR) and retinol-binding protein 4 (RBP4) genes was detected by PCR-RFLP method, and the effects of PRLR and RBP4 genes on litter size traits in pig were analyzed by the least square analysis. Data of total number born (TNB) and number born alive (NBA) from 323 sows, including five Shandong local pig breeds and three foreign pig breeds, were collected. The results showed that the polymorphic sites of both PRLR and RBP4 genes were found in all populations tested. The genotype distribution, however, revealed great differences between Shandong local pig breeds and foreign pig breeds. The effects of genotypes on TNB and NBA were significant (P<0.05). The homozygote AA was the most prolific genotype. For PRLR gene, AA genotypic sows of Shandong local pig breeds and foreign pig breeds produced 1.03 TNB, 0.89 NBA, and 1.26 TNB, 1.11 NBA more than BB genotypic sows, respectively. For RBP4 gene, AA genotypic sows of Shandong local pig breeds and foreign pig breeds produced 0.59 TNB, 0.51 NBA and 0.72 TNB, 0.64 NBA more than BB genotypic sows, respectively.

[Analysis of genetic diversity of Shandong indigenous chicken breeds using microsatellite marker].

Microsatellite marker is one of the frequently used molecular markers. It has been used in the genotype identification, pedigree analysis and estimation of genetic distance. In this paper, five microsatellite markers with high polymorphisms were selected to detect the genetic diversity of seven chicken breeds. The alleles frequencies, polymorphism information content (PIC) and average heterozygosity within each population, and DA genetic distance among breeds were analyzed. The application of microsatellite polymorphisms to the detection of genetic variability and relationship among populations was discussed. Altogether, forty alleles were found in this experiment, and among them the most alleles (10) were detected by ADL0136 and the least (5) were detected by ADL0146. The distribution of alleles was not balanced, each locus having one or more dominant alleles. The average heterozygosity in the Shouguang chicken was the lowest (0.3327), and that in other breeds was also less than 0.4. It can be seen then that microsatellite polymorphisms can be used to reveal the variability within population through calculation of average heterozygosity. The PIC values ranged from 0.6169 (Shouguang chicken) to 0.7027 (Laiwu Black chicken). UPGMA tree was completed through analysis of DA genetic distance. In the tree, the Rizhao Pockmarked and the Jining Hundred chicken were first grouped together with a bootstrap value of 92%, before they were grouped with the Laiwu Black and the Shouguang chickens. The Anoka Yellow and the Guangxi Yellow chicken were grouped together with a bootstrap value of 80%, but the Luxi Fighting chicken had its own branch. In summary, the UPGMA tree well reflected the evolutionary and breeding history of the seven breeds.