Identification and Annotation of Potential Function of Regulatory Antisense Long Non-Coding RNAs Related to Feed Efficiency in Bos taurus Bulls

Liver transcriptomics-metabolomics integration reveals biological pathways associated with fetal programming in beef cattle

We investigated the long-term effects of prenatal nutrition on pre-slaughter Nelore bulls using integrative transcriptome and metabolome analyses of liver tissue. Three prenatal nutritional treatments were administered to 126 cows: NP (control, mineral supplementation only), PP (protein-energy supplementation in the third trimester), and FP (protein-energy supplementation throughout pregnancy). Liver samples from 22.5 ± 1-month-old bulls underwent RNA-Seq and targeted metabolomics. Weighted correlation network analysis (WGCNA) identified treatment-associated gene and metabolite co-expression modules, further analyzed using MetaboAnalyst 6.0 (metabolite over-representation analysis and transcriptome-metabolome integrative analysis) and Enrichr (gene over-representation analysis). We identified several significant gene and metabolite modules, as well as hub components associated with energy, protein and oxidative metabolism, regulatory mechanisms, epigenetics, and immune function. The NP transcriptome-metabolome analysis identified key pathways (aminoacyl t-RNA biosynthesis, gluconeogenesis, and PPAR signaling) and hub components (glutamic acid, SLC6A14). PP highlighted pathways (arginine and proline metabolism, TGF-beta signaling, glyoxylate and dicarboxylate metabolism) with arginine and ODC1 as hub components. This study highlights the significant impact of prenatal nutrition on the liver tissue of Nelore bulls, shedding light on critical metabolic pathways and hub components related to energy and protein metabolism, as well as immune system and epigenetics.

JustRNA: a database of plant long noncoding RNA expression profiles and functional network

Long noncoding RNAs (lncRNAs) are regulatory RNAs involved in numerous biological processes. Many plant lncRNAs have been identified, but their regulatory mechanisms remain largely unknown. A resource that enables the investigation of lncRNA activity under various conditions is required because the co-expression between lncRNAs and protein-coding genes may reveal the effects of lncRNAs. This study developed JustRNA, an expression profiling resource for plant lncRNAs. The platform currently contains 1 088 565 lncRNA annotations for 80 plant species. In addition, it includes 3692 RNA-seq samples derived from 825 conditions in six model plants. Functional network reconstruction provides insight into the regulatory roles of lncRNAs. Genomic association analysis and microRNA target prediction can be employed to depict potential interactions with nearby genes and microRNAs, respectively. Subsequent co-expression analysis can be employed to strengthen confidence in the interactions among genes. Chromatin immunoprecipitation sequencing data of transcription factors and histone modifications were integrated into the JustRNA platform to identify the transcriptional regulation of lncRNAs in several plant species. The JustRNA platform provides researchers with valuable insight into the regulatory mechanisms of plant lncRNAs. JustRNA is a free platform that can be accessed at http://JustRNA.itps.ncku.edu.tw.

Improving the annotation of the cattle genome by annotating transcription start sites in a diverse set of tissues and populations using Cap Analysis Gene Expression sequencing

Understanding the genomic control of tissue-specific gene expression and regulation can help to inform the application of genomic technologies in farm animal breeding programs. The fine mapping of promoters [transcription start sites (TSS)] and enhancers (divergent amplifying segments of the genome local to TSS) in different populations of cattle across a wide diversity of tissues provides information to locate and understand the genomic drivers of breed- and tissue-specific characteristics. To this aim, we used Cap Analysis Gene Expression (CAGE) sequencing, of 24 different tissues from 3 populations of cattle, to define TSS and their coexpressed short-range enhancers (<1 kb) in the ARS-UCD1.2_Btau5.0.1Y reference genome (1000bulls run9) and analyzed tissue and population specificity of expressed promoters. We identified 51,295 TSS and 2,328 TSS-Enhancer regions shared across the 3 populations (dairy, beef-dairy cross, and Canadian Kinsella composite cattle from 2 individuals, 1 of each sex, per population). Cross-species comparative analysis of CAGE data from 7 other species, including sheep, revealed a set of TSS and TSS-Enhancers that were specific to cattle. The CAGE data set will be combined with other transcriptomic information for the same tissues to create a new high-resolution map of transcript diversity across tissues and populations in cattle for the BovReg project. Here we provide the CAGE data set and annotation tracks for TSS and TSS-Enhancers in the cattle genome. This new annotation information will improve our understanding of the drivers of gene expression and regulation in cattle and help to inform the application of genomic technologies in breeding programs.

Transcriptome Profiling of the Liver in Nellore Cattle Phenotypically Divergent for RFI in Two Genetic Groups

The identification and selection of genetically superior animals for residual feed intake (RFI) could enhance productivity and minimize environmental impacts. The aim of this study was to use RNA-seq data to identify the differentially expressed genes (DEGs), known non-coding RNAs (ncRNAs), specific biomarkers and enriched biological processes associated with RFI of the liver in Nellore cattle in two genetic groups. In genetic group 1 (G1), 24 extreme RFI animals (12 low RFI (LRFI) versus 12 high RFI (HRFI)) were selected from a population of 60 Nellore bulls. The RNA-seq of the samples from their liver tissues was performed using an Illumina HiSeq 2000. In genetic group 2 (G2), 20 samples of liver tissue of Nellore bulls divergent for RFI (LRFI, n = 10 versus HRFI, n = 10) were selected from 83 animals. The raw data of the G2 were chosen from the ENA repository. A total of 1811 DEGs were found for the G1 and 2054 for the G2 (p-value ≤ 0.05). We detected 88 common genes in both genetic groups, of which 33 were involved in the immune response and in blocking oxidative stress. In addition, seven (B2M, ADSS, SNX2, TUBA4A, ARHGAP18, MECR, and ABCF3) possible gene biomarkers were identified through a receiver operating characteristic analysis (ROC) considering an AUC > 0.70. The B2M gene was overexpressed in the LRFI group. This gene regulates the lipid metabolism protein turnover and inhibits cell death. We also found non-coding RNAs in both groups. MIR25 was up-regulated and SNORD16 was down-regulated in the LRFI for G1. For G2, up-regulated RNase_MRP and SCARNA10 were found. We highlight MIR25 as being able to act by blocking cytotoxicity and oxidative stress and RMRP as a blocker of mitochondrial damage. The biological pathways associated with RFI of the liver in Nellore cattle in the two genetic groups were for energy metabolism, protein turnover, redox homeostasis and the immune response. The common transcripts, biomarkers and metabolic pathways found in the two genetic groups make this unprecedented work even more relevant, since the results are valid for different herds raised in different ways. The results reinforce the biological importance of these known processes but also reveal new insights into the complexity of the liver tissue transcriptome of Nellore cattle.

Use of a graph neural network to the weighted gene co-expression network analysis of Korean native cattle

In the general framework of the weighted gene co-expression network analysis (WGCNA), a hierarchical clustering algorithm is commonly used to module definition. However, hierarchical clustering depends strongly on the topological overlap measure. In other words, this algorithm may assign two genes with low topological overlap to different modules even though their expression patterns are similar. Here, a novel gene module clustering algorithm for WGCNA is proposed. We develop a gene module clustering network (gmcNet), which simultaneously addresses single-level expression and topological overlap measure. The proposed gmcNet includes a “co-expression pattern recognizer” (CEPR) and “module classifier”. The CEPR incorporates expression features of single genes into the topological features of co-expressed ones. Given this CEPR-embedded feature, the module classifier computes module assignment probabilities. We validated gmcNet performance using 4,976 genes from 20 native Korean cattle. We observed that the CEPR generates more robust features than single-level expression or topological overlap measure. Given the CEPR-embedded feature, gmcNet achieved the best performance in terms of modularity (0.261) and the differentially expressed signal (27.739) compared with other clustering methods tested. Furthermore, gmcNet detected some interesting biological functionalities for carcass weight, backfat thickness, intramuscular fat, and beef tenderness of Korean native cattle. Therefore, gmcNet is a useful framework for WGCNA module clustering.

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

A global population of already more than seven billion people has led to an increased demand for food and water, and especially the demand for meat. Moreover, the cost of feed used in animal production has also increased dramatically, which requires animal breeders to find alternatives to reduce feed consumption. Understanding the biology underlying feed efficiency (FE) allows for a better selection of feed-efficient animals. Non-coding RNAs (ncRNAs), especially micro RNAs (miRNAs) and long non-coding RNAs (lncRNAs), play important roles in the regulation of bio-logical processes and disease development. The functions of ncRNAs in the biology of FE have emerged as they participate in the regulation of many genes and pathways related to the major FE indicators, such as residual feed intake and feed conversion ratio. This review provides the state of the art studies related to the ncRNAs associated with FE in livestock species. The contribution of ncRNAs to FE in the liver, muscle, and adipose tissues were summarized. The research gap of the function of ncRNAs in key processes for improved FE, such as the nutrition, heat stress, and gut–brain axis, was examined. Finally, the potential uses of ncRNAs for the improvement of FE were discussed.

Metabogenomic analysis to functionally annotate the regulatory role of long non-coding RNAs in the liver of cows with different nutrient partitioning phenotype

Long non-coding RNAs (lncRNAs) hold gene regulatory potential, but require substantial further functional annotation in livestock. Applying two metabogenomic approaches by combining transcriptomic and metabolomic analyses, we aimed to identify lncRNAs with potential regulatory function for divergent nutrient partitioning of lactating crossbred cows and to establish metabogenomic interaction networks comprising metabolites, genes and lncRNAs. Through correlation analysis of lncRNA expression with transcriptomic and metabolomic data, we unraveled lncRNAs that have a putative regulatory role in energy and lipid metabolism, the urea and tricarboxylic acid cycles, and gluconeogenesis. Especially FGF21, which correlated with a plentitude of differentially expressed genes, differentially abundant metabolites, as well as lncRNAs, suggested itself as a key metabolic regulator. Notably, lncRNAs in close physical proximity to coding-genes as well as lncRNAs with natural antisense transcripts appear to perform a fine-tuning function in gene expression involved in metabolic pathways associated with different nutrient partitioning phenotypes.

Comparative Analysis of the Circular Transcriptome in Muscle, Liver, and Testis in Three Livestock Species

Circular RNAs have been observed in a large number of species and tissues and are now recognized as a clear component of the transcriptome. Our study takes advantage of functional datasets produced within the FAANG consortium to investigate the pervasiveness of circular RNA transcription in farm animals. We describe here the circular transcriptional landscape in pig, sheep and bovine testicular, muscular and liver tissues using total 66 RNA-seq datasets. After an exhaustive detection of circular RNAs, we propose an annotation of exonic, intronic and sub-exonic circRNAs and comparative analyses of circRNA content to evaluate the variability between individuals, tissues and species. Despite technical bias due to the various origins of the datasets, we were able to characterize some features (i) (ruminant) liver contains more exonic circRNAs than muscle (ii) in testis, the number of exonic circRNAs seems associated with the sexual maturity of the animal. (iii) a particular class of circRNAs, sub-exonic circRNAs, are produced by a large variety of multi-exonic genes (protein-coding genes, long non-coding RNAs and pseudogenes) and mono-exonic genes (protein-coding genes from mitochondrial genome and small non-coding genes). Moreover, for multi-exonic genes there seems to be a relationship between the sub-exonic circRNAs transcription level and the linear transcription level. Finally, sub-exonic circRNAs produced by mono-exonic genes (mitochondrial protein-coding genes, ribozyme, and sno) exhibit a particular behavior. Caution has to be taken regarding the interpretation of the unannotated circRNA proportion in a given tissue/species: clusters of circRNAs without annotation were characterized in genomic regions with annotation and/or assembly problems of the respective animal genomes. This study highlights the importance of improving genome annotation to better consider candidate circRNAs and to better understand the circular transcriptome. Furthermore, it emphasizes the need for considering the relative “weight” of circRNAs/parent genes for comparative analyses of several circular transcriptomes. Although there are points of agreement in the circular transcriptome of the same tissue in two species, it will be not possible to do without the characterization of it in both species.

Exploring the Regulatory Potential of Long Non-Coding RNA in Feed Efficiency of Indicine Cattle

Long non-coding RNA (lncRNA) can regulate several aspects of gene expression, being associated with complex phenotypes in humans and livestock species. In taurine beef cattle, recent evidence points to the involvement of lncRNA in feed efficiency (FE), a proxy for increased productivity and sustainability. Here, we hypothesized specific regulatory roles of lncRNA in FE of indicine cattle. Using RNA-Seq data from the liver, muscle, hypothalamus, pituitary gland and adrenal gland from Nellore bulls with divergent FE, we submitted new transcripts to a series of filters to confidently predict lncRNA. Then, we identified lncRNA that were differentially expressed (DE) and/or key regulators of FE. Finally, we explored lncRNA genomic location and interactions with miRNA and mRNA to infer potential function. We were able to identify 126 relevant lncRNA for FE in Bos indicus, some with high homology to previously identified lncRNA in Bos taurus and some possible specific regulators of FE in indicine cattle. Moreover, lncRNA identified here were linked to previously described mechanisms related to FE in hypothalamus-pituitary-adrenal axis and are expected to help elucidate this complex phenotype. This study contributes to expanding the catalogue of lncRNA, particularly in indicine cattle, and identifies candidates for further studies in animal selection and management.