Multi-transcriptomics reveals RLMF axis-mediated signaling molecules associated with bovine feed efficiency

Abstract

The regulatory axis plays a vital role in interpreting the information exchange and interactions among mammal organs. In this study on feed efficiency, it was hypothesized that a rumen-liver-muscle-fat (RLMF) regulatory axis exists and scrutinized the flow of energy along the RLMF axis employing consensus network analysis from a spatial transcriptomic standpoint. Based on enrichment analysis and protein-protein interaction analysis of the consensus network and tissue-specific genes, it was discovered that carbohydrate metabolism, energy metabolism, immune and inflammatory responses were likely to be the biological processes that contribute most to feed efficiency variation on the RLMF regulatory axis. In addition, clusters of genes related to the electron respiratory chain, including ND (2,3,4,4L,5,6), NDUF (A13, A7, S6, B3, B6), COX (1,3), CYTB, UQCR11, ATP (6,8), clusters of genes related to fatty acid metabolism including APO (A1, A2, A4, B, C3), ALB, FG (A, G), as well as clusters of the ribosomal-related gene including RPL (8,18A,18,15,13, P1), the RPS (23,27A,3A,4X), and the PSM (A1-A7, B6, C1, C3, D2-D4, D8 D9, E1) could be the primary effector genes responsible for feed efficiency variation. The findings demonstrate that high feed efficiency cattle, through the synergistic action of the regulatory axis RLMF, may improve the efficiency of biological processes (carbohydrate metabolism, protein ubiquitination, and energy metabolism). Meanwhile, high feed efficiency cattle might enhance the ability to respond to immunity and inflammation, allowing nutrients to be efficiently distributed across these organs associated with digestion and absorption, energy-producing, and energy-storing organs. Elucidating the distribution of nutrients on the RLMF regulatory axis could facilitate an understanding of feed efficiency variation and achieve the study on its molecular regulation.

Mining key circRNA-associated-ceRNA networks for milk fat metabolism in cows with varying milk fat percentages

“…The function “blockwiseConsensusModules” was employ used to calculate the consensus topology overlap and produce consensus modules. Based on the WGCNA analysis parameters of Yang et al [ 72 ], we set the following: power = soft threshold power β (when r = 0.80); modules containing 20 genes as a minimum number (minModuleSize = 20); the module detection sensitivity of 2 ( deepSplit = 2); module merged cut height of 0.25 (mergeCutHeight = 0.25, i.e., merged into one module if the correlation coefficient of eigengenes within the module was greater than 0.75). To avoid rearrangement of eigengene within modules according to intramodular connectivity (KME), we set the following parameters: minKMEtoStay = 0, maxBlockSize = 10,000, and the remaining parameters followed the default values of the function.…”

“…A PPI network analysis was performed following the methodology previously described by Yang et al [ 72 ]. Protein network interactions were obtained using the Strings website ( https://string-db.org/ , v11.0), where a minimum interaction score of 0.90 deemed sufficient to obtain high-confidence protein network interactions.…”

Characterization of feed efficiency-related key signatures molecular in different cattle breeds

“…In different studies, differences in experimental conditions (e.g. breed, age, feeding practices) often also lead to the differences in gene expression [ 13 , 19 ], making the search for RFI-related molecular markers a hot research topic. Therefore, more attention should be paid to searching for RFI-related signatures.…”

“…It is involved in many biological processes in the body and performs many essential functions such as detoxification and energy supply [ 20 ]. In studies on RFI in the liver of beef cattle, it was found that low RFI in the liver mainly elevated the efficiency of substance metabolism and energy metabolism, and reduced the energy required for biological processes such as immunity and inflammation [ 19 ]. At the same time, even at the same level of RFI, gene expression varies dramatically between studies [ 13 ].…”

Yeast culture repairs rumen epithelial injury by regulating microbial communities and metabolites in sheep

“…Gastrointestinal tract harbors a diverse range of microorganisms, which interact in symbiotic and competitive relationships, forming an abundant and complex microbial community (Yang et al, 2022). The rumen is an essential digestive organ for ruminants and is also the principal site for microbial fermentation of volatile fatty acids (Yang et al, 2023). At present, to pursue higher fattening efficiency, high-energy grain feed is often used in the feeding management of meat ruminants.…”