Effects of genomic selection for intramuscular fat content in breast muscle in Chinese local chickens

GC-IMS and multivariate analyses of volatile organic components in different Chinese breeds of chickens

This study examined the difference in volatile flavor characteristics among four different local breeds of chicken by headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) combined with multivariate analysis. In total, 65 volatile organic compounds (VOCs) were identified (17 aldehydes, 12 alcohols, 7 ketones, 5 esters, 2 acids, and 22 unidentified, i.e., 26.15% aldehydes, 18.46% alcohols, 10.77% ketones, 7.69% esters, 3.08% acids, and 33.84% unidentified), of which 43 were annotated. The chicken meats from the four breeds exhibited good separation in topographic plots, VOC fingerprinting, and multivariate analysis. Meanwhile, 20 different volatile components, with variable importance in projection value > 1, were selected as potential markers to distinguish different breeds of chicken by partial least squares discriminant analysis (PLS-DA). These findings provide insights into the flavor traits of chicken meat. Also, HS-GC-IMS combined with multivariate analysis can be a convenient and powerful method for characterizing different meats.

Identification of characteristic aroma compounds in chicken meat and their metabolic mechanisms using gas chromatography-olfactometry, odor activity values, and metabolomics

Aroma has an important influence on the aroma quality of chicken meat. This study aimed to identify the characteristic aroma substances in chicken meat and elucidate their metabolic mechanisms. Using gas chromatography-olfactometry and odor activity values, we identified nonanal, octanal, and dimethyl tetrasulfide as the basic characteristic aroma compounds in chicken meat, present in several breeds. Hexanal, 1-octen-3-ol, (E)-2-nonenal, heptanal, and (E,E)-2,4-decadienal were breed-specific aroma compounds found in native Chinese chickens but not in the meat of white-feathered broilers. Metabolomics analysis showed that L-glutamine was an important metabolic marker of nonanal, hexanal, heptanal, octanal, and 1-octen-3-ol. Exogenous supplementation experiments found that L-glutamine increased the content of D-glucosamine-6-P and induced the degradation of L-proline, L-arginine, and L-lysine to enhance the Maillard reaction and promote the formation of nonanal, hexanal, heptanal, octanal, and 1-octen-3-ol, thus improving the aroma profile of chicken meat.

Emerging perspectives in the gut-muscle axis: The gut microbiota and its metabolites as important modulators of meat quality

Animal breeding has made great genetic progress in increasing carcass weight and meat yield in recent decades. However, these improvements have come at the expense of meat quality. As the demand for meat quantity continues to rise, the meat industry faces the great challenge of maintaining and even increasing product quality. Recent research, including traditional statistical analyses and gut microbiota regulation research, has demonstrated that the gut microbiome exerts a considerable effect on meat quality, which has become increasingly intriguing in farm animals. Microbial metabolites play crucial roles as substrates or signalling factors to distant organs, influencing meat quality either beneficially or detrimentally. Interventions targeting the gut microbiota exhibit excellent potential as natural ways to foster the conversion of myofibres and promote intramuscular fat deposition. Here, we highlight the emerging roles of the gut microbiota in various dimensions of meat quality. We focus particularly on the effects of the gut microbiota and gut-derived molecules on muscle fibre metabolism and intramuscular fat deposition and attempt to summarize the potential underlying mechanisms.

The cecal ecosystem is a great contributor to intramuscular fat deposition in broilers

Intramuscular fat (IMF) content is a meat quality trait of major economic importance in animal production. Emerging evidence has demonstrated that meat quality can be improved by regulating the gut microbiota. However, the organization and ecological properties of the gut microbiota and its relationship with the IMF content remain unclear in chickens. Here, we investigated the microbial communities of 206 cecal samples from broilers with excellent meat quality. We noted that the cecal microbial ecosystem obtained from hosts reared under the same management and dietary conditions showed clear compositional stratification. Two enterotypes, in which the ecological properties, including diversity and interaction strengths, were significantly different, described the microbial composition pattern. Compared with enterotype 2, enterotype 1, distinguished by the Clostridia_vadinBB60_group, had a higher fat deposition, although no discrepancy was found in growth performance and meat yield. A moderate correlation was observed in the IMF content between 2 muscle tissues, despite the IMF content of thigh muscle was 42.76% greater than that of breast muscle. Additionally, the lower abundance of cecal vadinBE97 was related to higher IMF levels in both muscle tissues. Although vadinBE97 accounted for 0.40% of the total abundance of genera in the cecum, it exhibited significant and positive correlations with other genera (accounting for 25.3% of the tested genera). Our results highlight important insights into the cecal microbial ecosystem and its association with meat quality. Microbial interactions should be carefully considered when developing approaches to improve the IMF content by regulating the gut microbiota in broilers.

Estimation of genetic variability and identification of regions under selection based on runs of homozygosity in Beijing-You Chickens

The genetic composition of populations is the result of a long-term process of selection and adaptation to specific environments and ecosystems. Runs of homozygosity (ROHs) are homozygous segments of the genome where the 2 haplotypes inherited from the parents are identical. The detection of ROH can be used to describe the genetic variability and quantify the level of inbreeding in an individual. Here, we investigated the occurrence and distribution of ROHs in 40 Beijing-You Chickens from the random breeding population (BJY_C) and 40 Beijing-You Chickens from the intramuscular fat (IMF) selection population (BJY_S). Principal component analysis (PCA) and maximum likelihood (ML) analyses showed that BJY_C was completely separated from the BJY_S. The nucleotide diversity of BJY_C was higher than that of BJY_S, and the decay rate of LD of BJY_C was faster. The ROHs were identified for a total of 7,101 in BJY_C and 9,273 in BJY_S, respectively. The ROH-based inbreeding estimate (F_ROH) of BJY_C was 0.079, which was significantly lower than that of BJY_S (F_ROH = 0.114). The results were the same as the estimates of the inbreeding coefficients calculated based on homozygosity (F_HOM), the correlation between uniting gametes (F_UNI), and the genomic relationship matrix (F_GRM). Additionally, the distribution and number of ROH islands in chromosomes of BJY_C and BJY_S were significantly different. The ROH islands of BJY_S that included genes associated with lipid metabolism and fat deposition, such as CIDEA and S1PR1, were absent in BJY_C. However, GPR161 was detected in both populations, which is a candidate gene for the formation of the unique five-finger trait in Beijing-You chickens. Our findings contributed to the understanding of the genetic diversity of random or artificially selected populations, and allowed the accurate monitoring of population inbreeding using genomic information, as well as the detection of genomic regions that affect traits under selection.

SLC16A7 Promotes Triglyceride Deposition by De Novo Lipogenesis in Chicken Muscle Tissue

Triglyceride (TG) content in chicken muscle tissue signifies intramuscular fat (IMF) content, which is important for improving meat quality. However, the genetic basis of TG deposition in chicken is still unclear. Using 520 chickens from an artificially selected line with significantly increased IMF content and a control line, a genome-wide association study (GWAS) with TG content reports a region of 802 Kb located in chromosome 1. The XP-EHH and gene expression analysis together reveal that the solute carrier family 16 member A7 (SLC16A7) gene is the key candidate gene associated with TG content in chicken muscle tissue. Furthermore, the weighted gene co-expression network analysis (WGCNA) confirmed the regulatory effects of SLC16A7 on promoting TG deposition by de novo lipogenesis (DNL). Functional verification of SLC16A7 in vitro also supports this view, and reveals that this effect mainly occurs in myocytes. Our data highlight a potential IMF deposition pathway by DNL, induced by SLC16A7 in chicken myocytes. These findings will improve the understanding of IMF regulation in chicken and guide the formulation of breeding strategies for high-quality chicken.

ELOVL gene family plays a virtual role in response to breeding selection and lipid deposition in different tissues in chicken (Gallus gallus)

Background

Elongases of very long chain fatty acids (ELOVLs), a family of first rate-limiting enzymes in the synthesis of long-chain fatty acids, play an essential role in the biosynthesis of complex lipids. Disrupting any of ELOVLs affects normal growth and development in mammals. Genetic variations in ELOVLs are associated with backfat or intramuscular fatty acid composition in livestock. However, the effects of ELOVL gene family on breeding selection and lipid deposition in different tissues are still unknown in chickens.

Results

Genetic variation patterns and genetic associations analysis showed that the genetic variations of ELOVL genes were contributed to breeding selection of commercial varieties in chicken, and 14 SNPs in ELOVL2-6 were associated with body weight, carcass or fat deposition traits. Especially, one SNP rs17631638T > C in the promoter of ELOVL3 was associated with intramuscular fat content (IMF), and its allele frequency was significantly higher in native and layer breeds compared to that in commercial broiler breeds. Quantitative real-time PCR (qRT-PCR) determined that the ELOVL3 expressions in pectoralis were affected by the genotypes of rs17631638T > C. In addition, the transcription levels of ELOVL genes except ELOVL5 were regulated by estrogen in chicken liver and hypothalamus with different regulatory pathways. The expression levels of ELOVL1-6 in hypothalamus, liver, abdominal fat and pectoralis were correlated with abdominal fat weight, abdominal fat percentage, liver lipid content and IMF. Noteworthily, expression of ELOVL3 in pectoralis was highly positively correlated with IMF and glycerophospholipid molecules, including phosphatidyl choline, phosphatidyl ethanolamine, phosphatidyl glycerol and phospholipids inositol, rich in ω-3 and ω-6 long-chain unsaturated fatty acids, suggesting ELOVL3 could contribute to intramuscular fat deposition by increasing the proportion of long-chain unsaturated glycerophospholipid molecules in pectoralis.

Conclusions

In summary, we demonstrated the genetic contribution of ELOVL gene family to breeding selection for specialized varieties, and revealed the expression regulation of ELOVL genes and their potential roles in regulating lipid deposition in different tissues. This study provides new insights into understanding the functions of ELOVL family on avian growth and lipid deposition in different tissues and the genetic variation in ELOVL3 may aid the marker-assisted selection of meat quality in chicken.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08932-8.

Assessment the effect of genomic selection and detection of selective signature in broilers

Due to high selection advances and shortened generation interval, genomic selection (GS) is now an effective animal breeding scheme. In broilers, many studies have compared the accuracy of different GS prediction methods, but few reports have demonstrated phenotypic or genetic changes using GS. In this study, the paternal chicken line B underwent continuous selection for 3 generations. The chicken 55 k SNP chip was used to estimate the genetic parameters and detect genomic response regions by selective sweep analysis. The heritability for body weight (BW), meat production, and abdominal fat traits were ranged from 0.12 to 0.38. A high genetic correlation was found between BW and meat production traits, while a low genetic correlation (<0.1) was found between meat production and abdominal fat traits. Selection resulted in an increase of about 516 g in BW and 140 g in breast muscle weight. Percentage of breast muscle and whole thigh were increased 0.8 to 1.5%. No change was observed in abdominal fat percentage. The genomic estimated breeding value advances was positive for BW and meat production (except whole thigh percentage), while negative for abdominal fat percentage. By selective sweep analysis, 39 common chromosomal regions and 102 protein coding genes were found to be influenced, including MYH1A, MYH1B, and MYH1D of the MYH gene family. Tight junction pathway as well as myosin complex related terms were enriched. This study demonstrates the effective use of GS for improvements in BW and meat production in chicken line B. Further, genomic regions, responsive to intensive genetic selection, were identified to contain genes of the MYH family.

UHPLC-QTOF/MS-based comparative metabolomics in pectoralis major of fast- and slow-growing chickens at market ages

The molecular regulatory mechanism underlying meat quality between different chicken genotypes remains elusive. This study aimed to identify the differences in metabolites and pathways in pectoralis major (breast muscle) between a commercial fast-growing chicken genotype (Cobb500) and a slow-growing Chinese native chicken genotype (Beijing-You chickens, BYC) at market ages respectively based on ultra-high-performance liquid chromatography-quadrupole/time of flight mass spectrometry (UHPLC-QTOF/MS). Eighteen metabolites were identified as potential biomarkers between BYC and Cobb500 at market ages. Among them, L-cysteine exhibited a higher relative intensity in BYC compared with Cobb500 and was enriched into 10 potential flavor-associated KEGG pathways. In addition, the glycerophospholipid metabolism pathway was found to be associated with chicken meat flavor and the accumulation of sn-glycerol 3-phosphate and acetylcholine was more predominant in BYC than that in Cobb500, which were catalyzed by glycerophosphocholine phosphodiesterase (GPCPD1, EC:3.1.4.2), choline O-acetyltransferase (CHAT, EC:2.3.1.6), and acetylcholinesterase (ACHE, EC:3.1.1.7). Overall, the present study provided some metabolites and pathways for further investigating the roles of the differences in meat flavor quality in breast muscle between Cobb500 and BYC at market ages.

High-Throughput Sequencing With the Preselection of Markers Is a Good Alternative to SNP Chips for Genomic Prediction in Broilers

The choice of a genetic marker genotyping platform is important for genomic prediction in livestock and poultry. High-throughput sequencing can produce more genetic markers, but the genotype quality is lower than that obtained with single nucleotide polymorphism (SNP) chips. The aim of this study was to compare the accuracy of genomic prediction between high-throughput sequencing and SNP chips in broilers. In this study, we developed a new SNP marker screening method, the pre-marker-selection (PMS) method, to determine whether an SNP marker can be used for genomic prediction. We also compared a method which preselection marker based results from genome-wide association studies (GWAS). With the two methods, we analysed body weight at the12^th week (BW) and feed conversion ratio (FCR) in a local broiler population. A total of 395 birds were selected from the F2 generation of the population, and 10X specific-locus amplified fragment sequencing (SLAF-seq) and the Illumina Chicken 60K SNP Beadchip were used for genotyping. The genomic best linear unbiased prediction method (GBLUP) was used to predict the genomic breeding values. The accuracy of genomic prediction was validated by the leave-one-out cross-validation method. Without SNP marker screening, the accuracies of the genomic estimated breeding value (GEBV) of BW and FCR were 0.509 and 0.249, respectively, when using SLAF-seq, and the accuracies were 0.516 and 0.232, respectively, when using the SNP chip. With SNP marker screening by the PMS method, the accuracies of GEBV of the two traits were 0.671 and 0.499, respectively, when using SLAF-seq, and 0.605 and 0.422, respectively, when using the SNP chip. Our SNP marker screening method led to an increase of prediction accuracy by 0.089-0.250. With SNP marker screening by the GWAS method, the accuracies of genomic prediction for the two traits were also improved, but the gains of accuracy were less than the gains with PMS method for all traits. The results from this study indicate that our PMS method can improve the accuracy of GEBV, and that more accurate genomic prediction can be obtained from an increased number of genomic markers when using high-throughput sequencing in local broiler populations. Due to its lower genotyping cost, high-throughput sequencing could be a good alternative to SNP chips for genomic prediction in breeding programmes of local broiler populations.