Transcriptome Analysis Reveals the Age-Related Developmental Dynamics Pattern of the Longissimus Dorsi Muscle in Ningxiang Pigs

The growth and development of the Longissimus Dorsi muscle are complex, playing an important role in the determination of pork quality. The study of the Longissimus Dorsi muscle at the mRNA level is particularly crucial for finding molecular approaches to improving meat quality in pig breeding. The current study utilized transcriptome technology to explore the regulatory mechanisms of muscle growth and intramuscular fat (IMF) deposition in the Longissimus Dorsi muscle at three core developmental stages (natal stage on day 1, growing stage on day 60, and finishing stage on day 210) in Ningxiang pigs. Our results revealed 441 differentially expressed genes (DEGs) in common for day 1 vs. day 60 and day 60 vs. day 210, and GO (Gene Ontology) analysis showed that candidate genes RIPOR2, MEGF10, KLHL40, PLEC, TBX3, FBP2, and HOMER1 may be closely related to muscle growth and development, while KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis showed that DEGs (UBC, SLC27A5, RXRG, PRKCQ, PRKAG2, PPARGC1A, PLIN5, PLIN4, IRS2, and CPT1B) involved the PPAR (Peroxisome Proliferator-Activated Receptor) signaling pathway and adipocytokine signaling pathway, which might play a pivotal role in the regulation of IMF deposition. PPI (Protein-Protein Interaction Networks) analysis found that the STAT1 gene was the top hub gene. Taken together, our results provide evidence for the molecular mechanisms of growth and development and IMF deposition in Longissimus Dorsi muscle to optimize carcass mass.

Identifying miRNA-mRNA regulatory networks on extreme n-6/n-3 polyunsaturated fatty acid ratio expression profiles in porcine skeletal muscle

Omega-3 (n-3) and omega-6 (n-6) polyunsaturated fatty acids (PUFAs) are essential fatty acids with antagonistic inflammatory functions that play vital roles in metabolic health and immune response. Current commercial swine diets tend to over-supplement with n-6 PUFAs, which may increase the likelihood of developing inflammatory diseases and affect the overall well-being of the animals. However, it is still poorly understood how n-6/n-3 PUFA ratios affect the porcine transcriptome expression and how messenger RNAs (mRNAs) and microRNAs (miRNAs) might regulate biological processes related to PUFA metabolism. On account of this, we selected a total of 20 Iberian × Duroc crossbred pigs with extreme values for n-6/n-3 FA ratio (10 high vs 10 low), and longissimus dorsi muscle samples were used to identify differentially expressed mRNAs and miRNAs. The observed differentially expressed mRNAs were associated to biological pathways related to muscle growth and immunomodulation, while the differentially expressed microRNAs (ssc-miR-30a-3p, ssc-miR-30e-3p, ssc-miR-15b and ssc-miR-7142-3p) were correlated to adipogenesis and immunity. Relevant miRNA-to-mRNA regulatory networks were also predicted (i.e., mir15b to ARRDC3; mir-7142-3p to METTL21C), and linked to lipolysis, obesity, myogenesis, and protein degradation. The n-6/n-3 PUFA ratio differences in pig skeletal muscle revealed genes, miRNAs and enriched pathways involved in lipid metabolism, cell proliferation and inflammation.

Application of Genetic, Genomic and Biological Pathways in Improvement of Swine Feed Efficiency

Despite the significant improvement of feed efficiency (FE) in pigs over the past decades, feed costs remain a major challenge for producers profitability. Improving FE is a top priority for the global swine industry. A deeper understanding of the biology underlying FE is crucial for making progress in genetic improvement of FE traits. This review comprehensively discusses the topics related to the FE in pigs including: measurements, genetics, genomics, biological pathways and the advanced technologies and methods involved in FE improvement. We first provide an update of heritability for different FE indicators and then characterize the correlations of FE traits with other economically important traits. Moreover, we present the quantitative trait loci (QTL) and possible candidate genes associated with FE in pigs and outline the most important biological pathways related to the FE traits in pigs. Finally, we present possible ways to improve FE in swine including the implementation of genomic selection, new technologies for measuring the FE traits, and the potential use of genome editing and omics technologies.

Metagenomic analysis reveals linkages between cecal microbiota and feed efficiency in Xiayan chickens

The cecal microbiota plays a critical role in energy harvest and nutrient digestion, influencing intestinal health and the performance of chickens. Feed efficiency (FE) is essential for improving economic efficiency and saving social resources in chicken production and may be affected by the cecal microbiota. Therefore, to investigate the composition and functional capacity of cecum microbes related to FE in Xiayan chicken, an indigenous breed in Guangxi province, metagenome sequencing was performed on chicken cecal contents. 173 male and 167 female chickens were divided into high and low FE groups according to the residual feed intake. The cecal microbial genome was extracted and sequenced. The results showed that the genera Bacteroides, Prevotella, and Alistipes were the 3 most abundant in each cecal microbiome. The linear discriminant analysis effect size revealed 6 potential biomarkers in male and 14 in female chickens. Notably, the relative abundance of Lactobacillus in the high FE group was higher than that of the low FE group both in the male and female chickens, and the species Limosilactobacillus oris has a higher score in the high FE group of male chickens. In contrast, some potentially pathogenic microorganisms such as Campylobacter avium in females and Helicobacter pullorum in males were enriched in the low FE group. Predictive functional analysis showed that the high FE group in male chickens had a greater ability of xenobiotics biodegradation and metabolism and signaling molecules and interaction. In addition, the host sex was found to exert effects on the cecal microbial composition and function associated with FE. These results increased our understanding of the cecal microbial composition and identified many potential biomarkers related to FE, which may be used to improve the FE of the chickens.

Genetic parameters and expected responses to selection for components of feed efficiency in a Duroc pig line

Background

Improving feed efficiency (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FE}}$$\end{document}FE) is a key factor for any pig breeding company. Although this can be achieved by selection on an index of multi-trait best linear unbiased prediction of breeding values with optimal economic weights, considering deviations of feed intake from actual needs (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI) should be of value for further research on biological aspects of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FE}}$$\end{document}FE. Here, we present a random regression model that extends the classical definition of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI by including animal-specific needs in the model. Using this model, we explore the genetic determinism of several \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FE}}$$\end{document}FE components: use of feed for growth (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{WG}}$$\end{document}WG), use of feed for backfat deposition (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FG}}$$\end{document}FG), use of feed for maintenance (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{MW}}$$\end{document}MW), and unspecific efficiency in the use of feed (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI). Expected response to alternative selection indexes involving different components is also studied.

Results

Based on goodness-of-fit to the available feed intake (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FI}}$$\end{document}FI) data, the model that assumes individual (genetic and permanent) variation in the use of feed for maintenance, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{WG}}$$\end{document}WG and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FG}}$$\end{document}FG showed the best performance. Joint individual variation in feed allocation to maintenance, growth and backfat deposition comprised 37% of the individual variation of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FI}}$$\end{document}FI. The estimated heritabilities of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI using the model that accounts for animal-specific needs and the traditional \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI model were 0.12 and 0.18, respectively. The estimated heritabilities for the regression coefficients were 0.44, 0.39 and 0.55 for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{MW}}$$\end{document}MW, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{WG}}$$\end{document}WG and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FG}}$$\end{document}FG, respectively. Estimates of genetic correlations of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI were positive with amount of feed used for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{WG}}$$\end{document}WG and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FG}}$$\end{document}FG but negative for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{MW}}$$\end{document}MW. Expected response in overall efficiency, reducing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FI}}$$\end{document}FI without altering performance, was 2.5% higher when the model assumed animal-specific needs than when the traditional definition of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI was considered.

Conclusions

Expected response in overall efficiency, by reducing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FI}}$$\end{document}FI without altering performance, is slightly better with a model that assumes animal-specific needs instead of batch-specific needs to correct \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FI}}$$\end{document}FI. The relatively small difference between the traditional \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}$$\end{document}RFI model and our model is due to random intercepts (unspecific use of feed) accounting for the majority of variability in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FI}}$$\end{document}FI. Overall, a model that accounts for animal-specific needs for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{MW}}$$\end{document}MW, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{WG}}$$\end{document}WG and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FG}}$$\end{document}FG is statistically superior and allows for the possibility to act differentially on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{FE}}$$\end{document}FE components.

Unraveling the Fecal Microbiota and Metagenomic Functional Capacity Associated with Feed Efficiency in Pigs

Gut microbiota plays fundamental roles in energy harvest, nutrient digestion, and intestinal health, especially in processing indigestible components of polysaccharides in diet. Unraveling the microbial taxa and functional capacity of gut microbiome associated with feed efficiency can provide important knowledge to improve pig feed efficiency in swine industry. In the current research, we studied the association of fecal microbiota with feed efficiency in 280 commercial Duroc pigs. All experimental pigs could be clustered into two enterotype-like groups. Different enterotypes showed the tendency of association with the feed efficiency (P = 0.07). We further identified 31 operational taxonomic units (OTUs) showing the potential associations with porcine feed efficiency. These OTUs were mainly annotated to the bacteria related to the metabolisms of dietary polysaccharides. Although we did not identify the RFI-associated bacterial species at FDR < 0.05 level, metagenomic sequencing analysis did find the distinct function capacities of gut microbiome between the high and low RFI pigs (FDR < 0.05). The KEGG orthologies related to nitrogen metabolism, amino acid metabolism, and transport system, and eight KEGG pathways including glycine, serine, and threonine metabolism were positively associated with porcine feed efficiency. We inferred that gut microbiota might improve porcine feed efficiency through promoting intestinal health by the SCFAs produced by fermenting dietary polysaccharides and improving the utilization of dietary protein. The present results provided important basic knowledge for improving porcine feed efficiency through modulating gut microbiome.

Genetic relationships between boar feed efficiency and sow piglet production, body condition score, and stayability in Norwegian Landrace pigs

Both feed efficiency and sow production are economically important traits in pig breeding. One challenge in a maternal line such as Norwegian Landrace is to breed for highly feed efficient fattening pigs and, at the same time, produce sows with high daily feed intake to maintain their BCS in multiple parities. The aim of this study was to estimate genetic correlations among novel feed efficiency measurements on Norwegian Landrace boars and piglet production, stayability, and body condition in Norwegian Landrace sows. The feed efficiency measurements were lean meat and fat efficiency. These measurements were calculated using an extended residual feed intake model where total feed intake in the test period was the response variable and fat (kg) and lean meat (kg) on the carcass were included as both fixed and random regressions. The random regression coefficients that resulted from this model were breeding values, which represented the amount of feed used to produce an extra kilogram of lean meat and fat. The sow traits were stayability of the sow from first to second parity, BCS at weaning, litter weight at 3 wk, and total number of piglets born. All traits were recorded on first parity purebred Norwegian Landrace and analyzed using multivariate animal models. All genetic correlations between fat efficiency and sow traits were low. Significant genetic correlations were found only between fat efficiency and stayability (0.21 ± 0.11) and between fat efficiency and total litter weight at 3 wk (0.21 ± 0.10). The results indicate that selection for efficient deposition of fat could give poor stayability and lower litter weight at 3 wk in first parity sows. The genetic correlations between lean meat efficiency and sow traits were not significantly different from 0 and signified no genetic relationships between these traits. Selection for efficient deposition of lean meat should not affect the sow traits and is, therefore, beneficial.