Mauricio A. Elzo

DNA methylation and the alternative splicing of precursor messenger RNAs (pre-mRNAs) are two important genetic modification mechanisms. However, both are currently uncharacterized in the muscle metabolism of rabbits. Thus, we constructed the Tianfu black rabbit obesity model (obese rabbits fed with a 10% high-fat diet and control rabbits from 35 days to 70 days) and collected the skeletal muscle samples from the two groups for Genome methylation sequencing and RNA sequencing. DNA methylation data showed that the promoter regions of 599 genes and gene body region of 2522 genes had significantly differential methylation rates between the two groups, of which 288 genes had differential methylation rates in promoter and gene body regions. Analysis of alternative splicing showed 555 genes involved in exon skipping (ES) patterns, and 15 genes existed in differential methylation regions. Network analysis showed that 20 hub genes were associated with ubiquitinated protein degradation, muscle development pathways, and skeletal muscle energy metabolism. Our findings suggest that the two types of genetic modification have potential regulatory effects on skeletal muscle development and provide a basis for further mechanistic studies in the rabbit.

A high-fat diet (HFD) is widely recognized as a significant modifiable risk for insulin resistance, inflammation, Type 2 diabetes, atherosclerosis and other metabolic diseases. However, the biological mechanism responsible for key metabolic disorders in the PAT of rabbits subject to HFD remains unclear. Here, untargeted metabolomics (LC-MS/MS) combined with liquid chromatography (LC) and high-resolution mass spectrometry (MS) were used to evaluate PAT metabolic changes. Histological observations showed that the adipocytes cells and density of PAT were significantly increased in HFD rabbits. Our study revealed 206 differential metabolites (21 up-regulated and 185 down-regulated); 47 differential metabolites (13 up-regulated and 34 down-regulated), comprising mainly phospholipids, fatty acids, steroid hormones and amino acids, were chosen as potential biomarkers to help explain metabolic disorders caused by HFD. These metabolites were mainly associated with the biosynthesis of unsaturated fatty acids, the arachidonic acid metabolic pathway, the ovarian steroidogenesis pathway, and the platelet activation pathway. Our study revealed that a HFD caused significant lipometabolic disorders. These metabolites may inhibit oxygen respiration by increasing the adipocytes cells and density, cause mitochondrial and endoplasmic reticulum dysfunction, produce inflammation, and finally lead to insulin resistance, thus increasing the risk of Type 2 diabetes, atherosclerosis, and other metabolic syndromes.

Type 2 diabetes and metabolic syndrome caused by a high fat diet (HFD) have become public health problems worldwide. These diseases are characterized by the oxidation of skeletal muscle mitochondria and disruption of insulin resistance, but the mechanisms are not well understood. Therefore, this study aims to reveal how high-fat diet causes skeletal muscle metabolic disorders. In total, 16 weaned rabbits were randomly divided into two groups, one group was fed a standard normal diet (SND) and the other group was fed a high fat diet (HFD) for 5 weeks. At the end of the five-week experiment, skeletal muscle tissue samples were taken from each rabbit. Untargeted metabolomic analysis was performed using ultra-performance liquid chromatography combined with mass spectrometry (UHPLC-MS/MS). The results showed that high fat diet significantly altered the expression levels of phospholipids, LCACs, histidine, carnosine, and tetrahydrocorticosterone in skeletal muscle. Principal component analysis (PCA) and least squares discriminant analysis (PLS-DA) showed that, compared with the SND group, skeletal muscle metabolism in HFD group was significantly up-regulated. Among 43 skeletal muscle metabolites in the HFD group, phospholipids, LCACs, histidine, carnosine, and tetrahydrocorticosteroids were identified as biomarkers of skeletal muscle metabolic diseases, and may become potential physiological targets of related diseases in the future. Untargeted metabonomics analysis showed that high-fat diet altered the metabolism of phospholipids, carnitine, amino acids and steroids in skeletal muscle of rabbits. Notably, phospholipids, LCACs, histidine, carnopeptide, and tetrahydrocorticosteroids block the oxidative capacity of mitochondria and disrupt the oxidative capacity of glucose and the fatty acid-glucose cycle in rabbit skeletal muscle.

The prohibition of the use of growth-promoting drug additives in feeds was implemented in China in 2020. However, rabbits can experience symptoms of intestinal disease, such as diarrhea and flatulence, when switching from standard normal diets with antibiotics to antibiotic-free diets. The molecular mechanisms related to the occurrence of these diseases as well as associated physiological and metabolic changes in the intestine are unclear. Thus, the objectives of this study were to study the pathogenesis of intestinal inflammation using untargeted metabolomics. This was done to identify differential metabolites between a group of antibiotic-free feed Hyplus rabbits (Dia) whose diet was abruptly changed from a standard normal diet with antibiotics to an antibiotic-free diet, and an antibiotic diet group Hyplus rabbits (Con) that was fed a standard normal diet with antibiotics. Morphological damage to the three intestinal tissues was determined through visual microscopic examination of intestinal Dia and Con tissue samples stained with hematoxylin and eosin (HE). A total of 1969 different metabolites were identified in the three intestinal tissues from Dia and Con rabbits. The level of 1280 metabolites was significantly higher and the level of 761 metabolites was significantly lower in the Dia than in the Con group. These differential metabolites were involved in five metabolic pathways associated with intestinal inflammation (tryptophan metabolism, pyrimidine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, lysine degradation, and bile secretion). Rabbits in the Dia group developed metabolic disorders that affected the intestinal microbiota and changed the permeability of the intestinal tract, thereby triggering intestinal inflammation, affecting feed utilization, reducing production performance, and activating the intestinal tract self-repair mechanism. Thus, the abrupt transition from a diet with antibiotics to an antibiotic-free diet affected the structure and metabolism of the intestinal tract in Hyplus rabbits. Consequently, to avoid these problems, the antibiotic content in a rabbit diet should be changed gradually or alternative antibiotics should be found.

MicroRNAs (miRNAs), small non-coding RNA with a length of about 22 nucleotides, are involved in the energy metabolism of skeletal muscle cells. However, their molecular mechanism of metabolism in rabbit skeletal muscle is still unclear. In this study, 16 rabbits, 8 in the control group (CON–G) and 8 in the experimental group (HFD–G), were chosen to construct an obese model induced by a high–fat diet fed from 35 to 70 days of age. Subsequently, 54 differentially expressed miRNAs, 248 differentially expressed mRNAs, and 108 differentially expressed proteins related to the metabolism of skeletal muscle were detected and analyzed with three sequencing techniques (small RNA sequencing, transcriptome sequencing, and tandem mass tab (TMT) protein technology). It was found that 12 miRNAs and 12 core genes (e.g., CRYL1, VDAC3 and APIP) were significantly different in skeletal muscle from rabbits in the two groups. The network analysis showed that seven miRNA-mRNA pairs were involved in metabolism. Importantly, two miRNAs (miR-92a-3p and miR-30a/c/d-5p) regulated three transcription factors (MYBL2, STAT1 and IKZF1) that may be essential for lipid metabolism. These results enhance our understanding of molecular mechanisms associated with rabbit skeletal muscle metabolism and provide a basis for future studies in the metabolic diseases of human obesity.

Meat quality related phenotypes are difficult and expensive to measure and predict but are ideal candidates for genomic selection if genetic markers that account for a worthwhile proportion of the phenotypic variation can be identified. The objectives of this study were: 1) to perform genome wide association analyses for Warner-Bratzler Shear Force (WBSF), marbling, cooking loss, tenderness, juiciness, connective tissue and flavor; 2) to determine enriched pathways present in each genome wide association analysis; and 3) to identify potential candidate genes with multiple quantitative trait loci (QTL) associated with meat quality. The WBSF, marbling and cooking loss traits were measured in longissimus dorsi muscle from 672 steers. Out of these, 495 animals were used to measure tenderness, juiciness, connective tissue and flavor by a sensory panel. All animals were genotyped for 221,077 markers and included in a genome wide association analysis. A total number of 68 genomic regions covering 52 genes were identified using the whole genome association approach; 48% of these genes encode transmembrane proteins or membrane associated molecules. Two enrichment analysis were performed: a tissue restricted gene enrichment applying a correlation analysis between raw associated single nucleotide polymorphisms (SNPs) by trait, and a functional classification analysis performed using the DAVID Bioinformatic Resources 6.8 server. The tissue restricted gene enrichment approach identified eleven pathways including “Endoplasmic reticulum membrane” that influenced multiple traits simultaneously. The DAVID functional classification analysis uncovered eleven clusters related to transmembrane or structural proteins. A gene network was constructed where the number of raw associated uncorrelated SNPs for each gene across all traits was used as a weight. A multiple SNP association analysis was performed for the top five most connected genes in the gene-trait network. The gene network identified the EVC2, ANXA10 and PKHD1 genes as potentially harboring multiple QTLs. Polymorphisms identified in structural proteins can modulate two different processes with direct effect on meat quality: in vivo myocyte cytoskeletal organization and postmortem proteolysis. The main result from the present analysis is the uncovering of several candidate genes associated with meat quality that have structural function in the skeletal muscle.

In genome-wide prediction, independence of marker allele substitution effects is typically assumed; however, since early stages of this technology it has been known that nature points to correlated effects. In statistics, graphical models have been identified as a useful and powerful tool for covariance estimation in high dimensional problems and it is an area that has recently experienced a great expansion. In particular, Gaussian concentration graph models (GCGM) have been widely studied. These are models in which the distribution of a set of random variables, the marker effects in this case, is assumed to be Markov with respect to an undirected graph G. In this paper, Bayesian (Bayes G and Bayes G-D) and frequentist (GML-BLUP) methods adapting the theory of GCGM to genome-wide prediction were developed. Different approaches to define the graph G based on domain-specific knowledge were proposed, and two propositions and a corollary establishing conditions to find decomposable graphs were proven. These methods were implemented in small simulated and real datasets. In our simulations, scenarios where correlations among allelic substitution effects were expected to arise due to various causes were considered, and graphs were defined on the basis of physical marker positions. Results showed improvements in correlation between phenotypes and predicted breeding values and accuracies of predicted breeding values when accounting for partially correlated allele substitution effects. Extensions to the multiallelic loci case were described and some possible refinements incorporating more flexible priors in the Bayesian setting were discussed. Our models are promising because they allow incorporation of biological information in the prediction process, and because they are more flexible and general than other models accounting for correlated marker effects that have been proposed previously.

Several statistical models used in genome-wide prediction assume independence of marker allele substitution effects, but it is known that these effects might be correlated. In statistics, graphical models have been identified as a useful tool for covariance estimation in high dimensional problems and it is an area that has recently experienced a great expansion. In Gaussian covariance graph models (GCovGM), the joint distribution of a set of random variables is assumed to be Gaussian and the pattern of zeros of the covariance matrix is encoded in terms of an undirected graph G. In this study, methods adapting the theory of GCovGM to genome-wide prediction were developed (Bayes GCov, Bayes GCov-KR and Bayes GCov-H). In simulated and real datasets, improvements in correlation between phenotypes and predicted breeding values and accuracies of predicted breeding values were found. Our models account for correlation of marker effects and permit to accommodate general structures as opposed to models proposed in previous studies which consider spatial correlation only. In addition, they allow incorporation of biological information in the prediction process through its use when constructing graph G, and their extension to the multiallelic loci case is straightforward.

The objectives of this study were to determine the fraction of additive genetic variance explained by the SNP from the Illumina Bovine3K chip; to compare the ranking of animals evaluated with genomic-polygenic, genomic, and polygenic models; and to assess trends in predicted values from these 3 models for residual feed intake (RFI), daily feed intake (DFI), feed conversion ratio (FCR), and postweaning BW gain (PWG) in a multibreed Angus-Brahman cattle population under subtropical conditions. Data consisted of phenotypes and genotypes from 620 bulls, steers, and heifers ranging from 100% Angus to 100% Brahman. Phenotypes were collected in a GrowSafe automated feeding facility (GrowSafe Systems, Ltd., Airdrie, Alberta, Canada) from 2006 to 2010. Variance components were estimated using single-trait genomic-polygenic mixed models with option VCE (Markov chain Monte Carlo) of the program GS3. Fixed effects were contemporary group (year-pen), age of dam, sex of calf, age of calf, Brahman fraction of calf, and heterozygosity of calf. Random effects were additive SNP, animal polygenic, and residual effects. Genomic predictions were computed using a model without polygenic effects and polygenic predictions with a model that excluded additive SNP effects. Heritabilities were 0.20 for RFI, 0.31 for DFI, 0.21 for FCR, and 0.36 for PWG. The fraction of the additive genetic variance explained by SNP in the Illumina 3K chip was 15% for RFI, 11% for DFI, 25% for FCR, and 15% for PWG. These fractions will likely differ in other multibreed populations. Rank correlations between genomic-polygenic and polygenic predictions were high (0.95 to 0.99; P < 0.0001), whereas those between genomic-polygenic and genomic predictions were low (0.65 to 0.74; P < 0.0001). Genomic-polygenic, genomic, and polygenic predictions for all traits tended to decrease as Brahman fraction increased, indicating that calves with greater Brahman fraction were more efficient but grew more slowly than calves with greater Angus fraction. Predicted SNP values were small for all traits, and those above and below 0.2 SNP SD were in multiple chromosomes, supporting the contention that quantitative traits are determined by large numbers of alleles with small effects located throughout the genome. Copyright © 2012. American Society of Animal Science .