Emerging Roles of Non-Coding RNAs in the Feed Efficiency of Livestock Species

Long non-coding RNA (LncRNA) and epigenetic factors: their role in regulating the adipocytes in bovine

Investigating the involvement of long non-coding RNAs (lncRNAs) and epigenetic processes in bovine adipocytes can provide valuable new insights into controlling adipogenesis in livestock. Long non-coding RNAs have been associated with forming chromatin loops that facilitate enhancer-promoter interactions during adipogenesis, as well as regulating important adipogenic transcription factors like C/EBPα and PPARγ. They significantly influence gene expression regulation at the post-transcriptional level and are extensively researched for their diverse roles in cellular functions. Epigenetic modifications such as chromatin reorganization, histone alterations, and DNA methylation subsequently affect the activation of genes related to adipogenesis and the progression of adipocyte differentiation. By investigating how fat deposition is epigenetically regulated in beef cattle, scientists aim to unravel molecular mechanisms, identify key regulatory genes and pathways, and develop targeted strategies for modifying fat deposition to enhance desirable traits such as marbling and meat tenderness. This review paper delves into lncRNAs and epigenetic factors and their role in regulating bovine adipocytes while focusing on their potential as targets for genetic improvement to increase production efficiency. Recent genomics advancements, including molecular markers and genetic variations, can boost animal productivity, meeting global demands for high-quality meat products. This review establishes a foundation for future research on understanding regulatory networks linked to lncRNAs and epigenetic changes, contributing to both scholarly knowledge advancement and practical applications within animal agriculture.

Multi-Omics Analysis Reveals the Regulatory Mechanism of Probiotics on the Growth Performance of Fattening Sheep

Probiotics have been proven to improve the growth performance of livestock and poultry. The aim of this experiment was to investigate the effects of probiotic supplementation on the growth performance; rumen and intestinal microbiota; rumen fluid, serum, and urine metabolism; and rumen epithelial cell transcriptomics of fattening meat sheep. Twelve Hu sheep were selected and randomly divided into two groups. They were fed a basal diet (CON) or a basal diet supplemented with 1.5 × 108 CFU/g probiotics (PRB). The results show that the average daily weight gain, and volatile fatty acid and serum antioxidant capacity concentrations of the PRB group were significantly higher than those of the CON group (p < 0.05). Compared to the CON group, the thickness of the rumen muscle layer in the PRB group was significantly decreased (p < 0.01); the thickness of the duodenal muscle layer in the fattening sheep was significantly reduced; and the length of the duodenal villi, the thickness of the cecal and rectal mucosal muscle layers, and the thickness of the cecal, colon, and rectal mucosal layers (p < 0.05) were significantly increased. At the genus level, the addition of probiotics altered the composition of the rumen and intestinal microbiota, significantly upregulating the relative abundance of Subdivision5_genera_incertae_sedis and Acinetobacter in the rumen microbiota, and significantly downregulating the relative abundance of Butyrivibrio, Saccharofermentans, and Fibrobacter. The relative abundance of faecalicoccus was significantly upregulated in the intestinal microbiota, while the relative abundance of Coprococcus, Porphyromonas, and Anaerobacterium were significantly downregulated (p < 0.05). There were significant differences in the rumen, serum, and urine metabolites between the PRB group and the CON group, with 188, 138, and 104 metabolites (p < 0.05), mainly affecting pathways such as vitamin B2, vitamin B3, vitamin B6, and a series of amino acid metabolisms. The differential genes in the transcriptome sequencing were mainly enriched in protein modification regulation (especially histone modification), immune function regulation, and energy metabolism. Therefore, adding probiotics improved the growth performance of fattening sheep by altering the rumen and intestinal microbiota; the rumen, serum, and urine metabolome; and the transcriptome.

Comparison of muscle fiber characteristics and meat quality between newborn and adult Haimen goats

Goat meat is popular with consumers for its rich nutritional content. Muscle fiber characteristics have been shown to play a crucial role in determining the quantity and quality of meat. However, little is known about the temporal changes in muscle fiber characteristics and meat quality during growth in goats. In this study, muscle fiber type, fiber diameter, fiber cross-sectional area (CSA), glycolytic potential (GP), meat pH, and meat color were analyzed in the gastrocnemius (GAS), gluteus medius (GM), biceps brachii (BB), longissimus lumborum (LL) muscles from newborn (NHMG) and adult (AHMG) Haimen goats. The distribution of type I and type Π fiber in goats is not consistent across the four muscles and undergoes alterations with age. The diameter and CSA of the muscle fibers were similar among the four NHMG muscles. However, in AHMG, the LL muscle had the largest fiber in terms of both diameter and CSA, followed by BB, GM, and GAS muscles. Moreover, the CSA of type Π fibers was higher than that of type I fibers in both NHMG and AHMG. GP values ranged from 90 to 140 umol/g across the muscle and no significant differences were observed. AHMG had a higher pH level and a* value, but lower L* and b* values than NHMG. Overall, our findings enhance our understanding of the changes in muscle fiber type and meat quality during the growth in Haimen goats and provide a basis for future research on the development and transformation of muscle fibers in goats.

Editorial: Role of Non-Coding RNAs in Animals

The importance of non-coding RNAs (ncRNAs), such as microRNAs (miRNA), long non-coding RNAs (lncRNA), and circular RNAs (circRNA), in gene regulation is increasingly being appreciated in many species [...].

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.

ASAS-NANP symposium: mathematical modeling in animal nutrition: limitations and potential next steps for modeling and modelers in the animal sciences

The field of animal science, and especially animal nutrition, relies heavily on modeling to accomplish its day-to-day objectives. New data streams ("big data") and the exponential increase in computing power have allowed the appearance of "new" modeling methodologies, under the umbrella of artificial intelligence (AI). However, many of these modeling methodologies have been around for decades. According to Gartner, technological innovation follows five distinct phases: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity. The appearance of AI certainly elicited much hype within agriculture leading to overpromised plug-and-play solutions in a field heavily dependent on custom solutions. The threat of failure can become real when advertising a disruptive innovation as sustainable. This does not mean that we need to abandon AI models. What is most necessary is to demystify the field and place a lesser emphasis on the technology and more on business application. As AI becomes increasingly more powerful and applications start to diverge, new research fields are introduced, and opportunities arise to combine "old" and "new" modeling technologies into hybrids. However, sustainable application is still many years away, and companies and universities alike do well to remain at the forefront. This requires investment in hardware, software, and analytical talent. It also requires a strong connection to the outside world to test, that which does, and does not work in practice and a close view of when the field of agriculture is ready to take its next big steps. Other research fields, such as engineering and automotive, have shown that the application power of AI can be far reaching but only if a realistic view of models as whole is maintained. In this review, we share our view on the current and future limitations of modeling and potential next steps for modelers in the animal sciences. First, we discuss the inherent dependencies and limitations of modeling as a human process. Then, we highlight how models, fueled by AI, can play an enhanced sustainable role in the animal sciences ecosystem. Lastly, we provide recommendations for future animal scientists on how to support themselves, the farmers, and their field, considering the opportunities and challenges the technological innovation brings.