Heat stress (HS) negatively affects chicken performance. Agricultural expansion will happen in regions that experience high ambient temperatures, where fast-growing commercial chickens are vulnerable. Indigenous chickens of such regions, due to generations of exposure to environmental challenges, might have higher thermal tolerance. In this study, two indigenous chicken ecotypes, from the hot and humid Mombasa (lowland) and the colder Naivasha (highland) regions, were used to investigate the effects of acute (5 h, 35°C) and chronic (3 days of 35°C for 8 h/day) HS on the cardiac and skeletal muscle, through RNA sequencing. The rectal temperature gain and the number of differentially expressed genes (DEGs) [False Discovery Rate (FDR) < 0.05] were two times higher in the acute stage than in the chronic stage in both ecotypes, suggesting that cyclic exposure to HS can lead to adaptation. A tissue- and stage-specific difference in response to HS was observed, with peroxisome proliferator-activated-receptor (PPAR) signaling and mitogen-activate protein kinase (MAPK) signaling pathways, enriched in heart and skeletal muscle, respectively, and the p53 pathway enriched only in the acute stage in both tissues. The acute and chronic stage DEGs were integrated by a region-specific gene coexpression network (GCN), and genes with the highest number of connections (hub genes) were identified. The hub genes in the lowland network were CCNB2, Crb2, CHST9, SESN1, and NR4A3, while COMMD4, TTC32, H1F0, ACYP1, and RPS28 were the hub genes in the highland network. Pathway analysis of genes in the GCN showed that p53 and PPAR signaling pathways were enriched in both low and highland networks, while MAPK signaling and protein processing in endoplasmic reticulum were enriched only in the gene network of highland chickens. This shows that to dissipate the accumulated heat, to reduce heat induced apoptosis, and to promote DNA damage repair, the ecotypes activated or suppressed different genes, indicating the differences in thermal tolerance and HS response mechanisms between the ecotypes. This study provides information on the HS response of chickens, adapted to two different agro climatic environments, extending our understanding of the mechanisms of HS response and the effect of adaptation in counteracting HS.

Background: In this study, we assessed the accuracy of genomic prediction for carcass weight (CWT), marbling score (MS), eye muscle area (EMA) and back fat thickness (BFT) in Hanwoo cattle when using genomic best linear unbiased prediction (GBLUP), weighted GBLUP (wGBLUP), and a BayesR model. For these models, we investigated the potential gain from using pre-selected single nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) on imputed sequence data and from gene expression information. We used data on 13,717 animals with carcass phenotypes and imputed sequence genotypes that were split in an independent GWAS discovery set of varying size and a remaining set for validation of prediction. Expression data were used from a Hanwoo gene expression experiment based on 45 animals. Results: Using a larger number of animals in the reference set increased the accuracy of genomic prediction whereas a larger independent GWAS discovery dataset improved identification of predictive SNPs. Using pre-selected SNPs from GWAS in GBLUP improved accuracy of prediction by 0.02 for EMA and up to 0.05 for BFT, CWT, and MS, compared to a 50 k standard SNP array that gave accuracies of 0.50, 0.47, 0.58, and 0.47, respectively. Accuracy of prediction of BFT and CWT increased when BayesR was applied with the 50 k SNP array (0.02 and 0.03, respectively) and was further improved by combining the 50 k array with the top-SNPs (0.06 and 0.04, respectively). By contrast, using BayesR resulted in limited improvement for EMA and MS. wGBLUP did not improve accuracy but increased prediction bias. Based on the RNA-seq experiment, we identified informative expression quantitative trait loci, which, when used in GBLUP, improved the accuracy of prediction slightly, i.e. between 0.01 and 0.02. SNPs that were located in genes, the expression of which was associated with differences in trait phenotype, did not contribute to a higher prediction accuracy. Conclusions: Our results show that, in Hanwoo beef cattle, when SNPs are pre-selected from GWAS on imputed sequence data, the accuracy of prediction improves only slightly whereas the contribution of SNPs that are selected based on gene expression is not significant. The benefit of statistical models to prioritize selected SNPs for estimating genomic breeding values is trait-specific and depends on the genetic architecture of each trait.

Chicken is important livestock that serves as a vital food source which remain largely affected by heat stress. Therefore, we performed the transcriptome analysis to help understand the mechanisms of heat stress response in chickens. In the animal experiments, we grouped them into a normal and severe at 21 and 33 °C, with identified physiologic parameters for 2-weeks. Subsequently, RNA-seq analysis was performed to identify DEGs with a false discovery rate < 0.05 and a fold change ≥ 1.5. In the physiological parameters, we observed average daily gain was declined, rectal temperature and respiration rate was increased in severe group. Among total 245 DEGs, 230 and 15 genes were upregulated and downregulated, respectively. In upregulated DEGs, HSPs, MYLK2, and BDKRB1 genes were identified as key genes in heat stress. The KEGG pathway analysis showed involvement in the ATP metabolic process, MAPK signaling pathway and calcium signaling pathway with related protein processing and synthesis. In conclusion, with induced heat stress, such changes in physiologic parameters alter the neuroendocrine system, and we observed that the heat stress environment regulates such Heat shock protein genes to protect the cells and proteins from an altered metabolism. These findings provide a more comprehensive understanding of the heat stress response in poultry.

Meat from Korean native chickens (KNCs) has high consumer demand; however, slow growth performance and high variation in body weight (BW) of KNCs remain an issue. Genome-wide association study (GWAS) is a powerful method to identify quantitative trait-associated genomic loci. A GWAS, based on a large-scale KNC population, is needed to identify underlying genetic mechanisms related to its growth traits. To identify BW-associated genomic regions, we performed a GWAS using the chicken 60K single nucleotide polymorphism (SNP) panel for 1328 KNCs. BW was measured at 8 weeks of age, from 2018 to 2020. Twelve SNPs were associated with BW at the suggestive significance level (p < 2.95 × 10⁻⁵) and located near or within 11 candidate genes, including WDR37, KCNIP4, SLIT2, PPARGC1A, MYOCD and ADGRA3. Gene set enrichment analysis based on the GWAS results at p < 0.05 (1680 SNPs) showed that 32 Gene Ontology terms and two Kyoto Encyclopedia of Genes and Genomes pathways, including regulation of transcription, motor activity, the mitogen-activated protein kinase signaling pathway, and tight junction, were significantly enriched (p < 0.05) for BW-associated genes. These pathways are involved in cell growth and development, related to BW gain. The identified SNPs are potential biomarkers in KNC breeding.