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The liver plays a central role in metabolism and produces important hormones. Hepatic estrogen receptors and the release of insulin-like growth factor 1 (IGF1) are critical links between liver function and the reproductive system. However, the role of liver in pubertal development is not fully understood. To explore this question, we applied transcriptomic analyses to liver samples of pre- and post-pubertal Brahman heifers and identified differentially expressed (DE) genes and genes encoding transcription factors (TFs). Differential expression of genes suggests potential biological mechanisms and pathways linking liver function to puberty. The analyses identified 452 DE genes and 82 TF with significant contribution to differential gene expression by using a regulatory impact factor metric. Brain-derived neurotrophic factor was observed as the most down-regulated gene (P = 0.003) in post-pubertal heifers and we propose this gene influences pubertal development in Brahman heifers. Additionally, co-expression network analysis provided evidence for three TF as key regulators of liver function during pubertal development: the signal transducer and activator of transcription 6, PBX homeobox 2, and polybromo 1. Pathway enrichment analysis identified transforming growth factor-beta and Wnt signaling pathways as significant annotation terms for the list of DE genes and TF in the co-expression network. Molecular information regarding genes and pathways described in this work are important to further our understanding of puberty onset in Brahman heifers.

The identification of biological processes related to the regulation of complex traits is a difficult task. Commonly, complex traits are regulated through a multitude of genes contributing each to a small part of the total genetic variance. Additionally, some loci can simultaneously regulate several complex traits, a phenomenon defined as pleiotropy. The lack of understanding on the biological processes responsible for the regulation of these traits results in the decrease of selection efficiency and the selection of undesirable hitchhiking effects. The identification of pleiotropic key-regulator genes can assist in developing important tools for investigating biological processes underlying complex traits. A multi-breed and multi-OMICs approach was applied to study the pleiotropic effects of key-regulator genes using three independent beef cattle populations evaluated for fertility traits. A pleiotropic map for 32 traits related to growth, feed efficiency, carcass and meat quality, and reproduction was used to identify genes shared among the different populations and breeds in pleiotropic regions. Furthermore, data-mining analyses were performed using the Cattle QTL database (CattleQTLdb) to identify the QTL category annotated in the regions around the genes shared among breeds. This approach allowed the identification of a main gene network (composed of 38 genes) shared among breeds. This gene network was significantly associated with thyroid activity, among other biological processes, and displayed a high regulatory potential. In addition, it was possible to identify genes with pleiotropic effects related to crucial biological processes that regulate economically relevant traits associated with fertility, production and health, such as MYC, PPARG, GSK3B, TG and IYD genes. These genes will be further investigated to better understand the biological processes involved in the expression of complex traits and assist in the identification of functional variants associated with undesirable phenotypes, such as decreased fertility, poor feed efficiency and negative energetic balance.

The identification of biological processes related to the regulation of complex traits is a difficult task. Commonly, complex traits are regulated through a multitude of genes contributing each to a small part of the total genetic variance. Additionally, some loci can simultaneously regulate several complex traits, a phenomenon defined as pleiotropy. The lack of understanding on the biological processes responsible for the regulation of these traits results in the decrease of selection efficiency and the selection of undesirable hitchhiking effects. The identification of pleiotropic key-regulator genes can assist in developing important tools for investigating biological processes underlying complex traits. A multi-breed and multi-OMICs approach was applied to study the pleiotropic effects of key-regulator genes using three independent beef cattle populations evaluated for fertility traits. A pleiotropic map for 32 traits related to growth, feed efficiency, carcass and meat quality, and reproduction was used to identify genes shared among the different populations and breeds in pleiotropic regions. Furthermore, data-mining analyses were performed using the Cattle QTL database (CattleQTLdb) to identify the QTL category annotated in the regions around the genes shared among breeds. This approach allowed the identification of a main gene network (composed of 38 genes) shared among breeds. This gene network was significantly associated with thyroid activity, among other biological processes, and displayed a high regulatory potential. In addition, it was possible to identify genes with pleiotropic effects related to crucial biological processes that regulate economically relevant traits associated with fertility, production and health, such as MYC, PPARG, GSK3B, TG and IYD genes. These genes will be further investigated to better understand the biological processes involved in the expression of complex traits and assist in the identification of functional variants associated with undesirable phenotypes, such as decreased fertility, poor feed efficiency and negative energetic balance.

There is debate over the functional basis of student evaluations of academics, and fresh potential for looking at the data in new ways. Student evaluation data was collated over a three year period (Semester 2 2015 to Semester 1 2018). We used a General Linear Model to estimate the variation in course scores explained by a number of coordinator and course attributes. Three significant factors collectively explain 49% of the School's variation in course scores--individual coordinator, student evaluation response rate, and mode of delivery. Next, we used hierarchical clustering to explore the inter-relationships among the eight course and teaching evaluation questions. Learning appears to be related to stimulation, whereas overall satisfaction appears to be related to quality of learning materials and course structure (i.e. aspects of course organisation). Student evaluation response rate is positively correlated to all eight course questions, but most positively to a question relating to receiving adequate feedback. This perhaps implies some reciprocity in the flow of information between student and coordinator. The overall teaching rating awarded to academics clusters most with approachability and encouragement of student input--aspects of temperament and style--and not with explanatory skill or organisational ability.

Cultivated cotton plants are the world's largest source of natural fibre, where yield and quality are key traits for this renewable and biodegradable commodity. The Gossypium hirsutum cotton genome contains ~80K protein-coding genes, making precision breeding of complex traits a challenge. This study tested approaches to improving the genomic prediction (GP) accuracy of valuable cotton fibre traits to help accelerate precision breeding. With a biology-informed basis, a novel approach was tested for improving GP for key cotton fibre traits with transcriptomics of key time points during fibre development, namely, fibre cells undergoing primary, transition, and secondary wall development. Three test approaches included weighting of SNPs in DE genes overall, in target DE gene lists informed by gene annotation, and in a novel approach of gene co-expression network (GCN) clusters created with partial correlation and information theory (PCIT) as the prior information in GP models. The GCN clusters were nucleated with known genes for fibre biomechanics, i.e., fasciclin-like arabinogalactan proteins, and cluster size effects were evaluated. The most promising improvements in GP accuracy were achieved by using GCN clusters for cotton fibre elongation by 4.6%, and strength by 4.7%, where cluster sizes of two and three neighbours proved most effective. Furthermore, the improvements in GP were due to only a small number of SNPs, in the order of 30 per trait using the GCN cluster approach. Non-trait-specific biological time points, and genes, were found to have neutral effects, or even reduced GP accuracy for certain traits. As the GCN clusters were generated based on known genes for fibre biomechanics, additional candidate genes were identified for fibre elongation and strength. These results demonstrate that GCN clusters make a specific and unique contribution in improving the GP of cotton fibre traits. The findings also indicate that there is room for incorporating biology-based GCNs into GP models of genomic selection pipelines for cotton breeding to help improve precision breeding of target traits. The PCIT-GCN cluster approach may also hold potential application in other crops and trees for enhancing breeding of complex traits. [ABSTRACT FROM AUTHOR]

Improvement in feed conversion efficiency can improve the sustainability of beef cattle production, but genomic selection for feed efficiency affects many underlying molecular networks and physiological traits. This study describes the differences between steer progeny of two influential Angus bulls with divergent genomic predictions for residual feed intake (RFI). Eight steer progeny of each sire were phenotyped for growth and feed intake from 8 mo. of age (average BW 254 kg, with a mean difference between sire groups of 4.8 kg) until slaughter at 14-16 mo. of age (average BW 534 kg, sire group difference of 28.8 kg). Terminal samples from pituitary gland, skeletal muscle, liver, adipose, and duodenum were collected from each steer for transcriptome sequencing. Gene expression networks were derived using partial correlation and information theory (PCIT), including differentially expressed (DE) genes, tissue specific (TS) genes, transcription factors (TF), and genes associated with RFI from a genome-wide association study (GWAS). Relative to progeny of the high RFI sire, progeny of the low RFI sire had -0.56 kg/d finishing period RFI (P = 0.05), -1.08 finishing period feed conversion ratio (P = 0.01), +3.3 kg^0.75 finishing period metabolic mid-weight (MMW; P = 0.04), +28.8 kg final body weight (P = 0.01), -12.9 feed bunk visits per day (P = 0.02) with +0.60 min/visit duration (P = 0.01), and +0.0045 carcass specific gravity (weight in air/weight in air-weight in water, a predictor of carcass fat content; P = 0.03). RNA-seq identified 633 DE genes between sire groups among 17,016 expressed genes. PCIT analysis identified >115,000 significant co-expression correlations between genes and 25 TF hubs, i.e. controllers of clusters of DE, TS, and GWAS SNP genes. Pathway analysis suggests low RFI bull progeny possess heightened gut inflammation and reduced fat deposition. This multi-omics analysis shows how differences in RFI genomic breeding values can impact other traits and gene co-expression networks.

Using seven indicator traits, we investigated the genetic basis of bull fertility and predicted gene interactions from SNP associations. We used percent normal sperm as the key phenotype for the association weight matrix–partial correlation information theory (AWM‐PCIT) approach. Beyond a simple list of candidate genes, AWM‐PCIT predicts significant gene interactions and associations for the selected traits. These interactions formed a network of 537 genes: 38 genes were transcription cofactors, and 41 genes were transcription factors. The network displayed two distinct clusters, one with 294 genes and another with 243 genes. The network is enriched in fertility‐associated pathways: steroid biosynthesis, p53 signalling, and the pentose phosphate pathway. Enrichment analysis also highlighted gene ontology terms associated with 'regulation of neurotransmitter secretion' and 'chromatin formation'. Our network recapitulates some genes previously implicated in another network built with lower‐density genotypes. Sequence‐level data also highlights additional candidate genes relevant to bull fertility, such as FOXO4, FOXP3, GATA1, CYP27B1, and EBP. A trio of regulatory genes—KDM5C, LRRK2, and PME—was deemed core to the network because of their overarching connections. This trio probably influences bull fertility through their interaction with genes, both known and unknown as to their role in male fertility. Future studies may target the trio and their target genes to enrich our understanding of male fertility further. [ABSTRACT FROM AUTHOR]

Puberty is a complex physiological event by which animals mature into an adult capable of sexual reproduction. In order to enhance our understanding of the genes and regulatory pathways and networks involved in puberty, we characterized the transcriptome of five reproductive tissues (i.e. hypothalamus, pituitary gland, ovary, uterus, and endometrium) as well as tissues known to be relevant to growth and metabolism needed to achieve puberty (i.e., longissimus dorsi muscle, adipose, and liver). These tissues were collected from pre- and post-pubertal Brangus heifers (3/8 Brahman; Bos indicus x 5/8 Angus; Bos taurus) derived from a population of cattle used to identify quantitative trait loci associated with fertility traits (i.e., age of first observed corpus luteum (ACL), first service conception (FSC), and heifer pregnancy (HPG)). In order to exploit the power of complementary omics analyses, pre- and post-puberty co-expression gene networks were constructed by combining the results from genome-wide association studies (GWAS), RNA-Seq, and bovine transcription factors. Eight tissues among pre-pubertal and post-pubertal Brangus heifers revealed 1,515 differentially expressed and 943 tissue-specific genes within the 17,832 genes confirmed by RNA-Seq analysis. The hypothalamus experienced the most notable up-regulation of genes via puberty (i.e., 204 out of 275 genes). Combining the results of GWAS and RNA-Seq, we identified 25 loci containing a single nucleotide polymorphism (SNP) associated with ACL, FSC, and (or) HPG. Seventeen of these SNP were within a gene and 13 of the genes were expressed in uterus or endometrium. Multi-tissue omics analyses revealed 2,450 co-expressed genes relative to puberty. The pre-pubertal network had 372,861 connections whereas the post-pubertal network had 328,357 connections. A sub-network from this process revealed key transcriptional regulators (i.e., PITX2, FOXA1, DACH2, PROP1, SIX6, etc.). Results from these multi-tissue omics analyses improve understanding of the number of genes and their complex interactions for puberty in cattle.

Context. Wagyu Feeder Check is a genomic-based tool designed to provide genomic estimated breeding values (GEBV) for five feedlot growth and carcase traits. At present, Wagyu Feeder Check is based on a reference population of 8316 genotyped and phenotyped Australian fullblood (FB; N = 2120) Wagyu and Wagyu-crossed (XB; N = 6196) cattle, principally Wagyu × Angus F1 animals. Aims. We provide technical details behind the development of the Wagyu Feeder Check and validate the ability of its GEBV to predict differences in performance of Wagyu cattle in daily weight gain at feedlot, carcase weight, carcase eye muscle area, carcase marbling score and carcase rump fat at the P8 site. Methods. Data supplied from eight commercial supply chains across Australia was used to generate GEBV using mixed-model equations that incorporated a genomic relationship matrix build with 82 504 autosomal markers. The bias, dispersion, and accuracy of the GEBV were evaluated using a four-way cross-validation scheme where, in each turn, the phenotypes from a random 1549 (or 25%) XB cattle were set as missing. Key results. The genomic estimate of the Wagyu content in the FB and XB population averaged 99.12% and 59.55%, respectively, and with most of the non-Wagyu content associated with Angus. The estimates of heritability (± s.e.) were 0.497 ± 0.016, 0.474 ± 0.004, 0.347 ± 0.014, 0.429 ± 0.003 and 0.422 ± 0.003 for daily weight gain at feedlot, carcase weight, eye muscle area, marbling and rump fat, respectively. Averaged across the four XB validation populations, the accuracy of GEBV was 0.624, 0.634, 0.385, 0.620, and 0.526 for the same set of traits. Conclusions. Genomic predictions generated by Wagyu Feeder Check can predict differences in feedlot and carcase performance of Australian Wagyu cattle. Given the large content of Angus in the XB population, further research is required to determine the predictive ability of GEBV in Wagyu × Bos indicus and Wagyu × dairy animals. Implications. Commercial feedlot operators finishing animals with a strong Wagyu breed component will benefit from using Wagyu Feeder Check for decision making. [ABSTRACT FROM AUTHOR]

This study aimed to identify the target genes of IGFBP3（insulin growth factor binding protein）protein and to investigate its target genes effects on the proliferation and differentiation of Hu sheep skeletal muscle cells. IGFBP3 was an RNA-binding protein that regulates mRNA stability. Previous studies have reported that IGFBP3 promotes the proliferation of Hu sheep skeletal muscle cells and inhibits differentiation, but the downstream genes that bind to it have not been reported yet. We predicted the target genes of IGFBP3 through RNAct and sequencing data, and verified by qPCR and RIP（RNA Immunoprecipitation）experiments, and demonstrated GNAI2（G protein subunit alpha i2）as one of the target gene of IGFBP3. After interference with siRNA, we carried out qPCR, CCK8, EdU, and immunofluorescence experiments, and found that GNAI2 can promote the proliferation and inhibit differentiation of Hu sheep skeletal muscle cells. This study revealed the effects of GNAI2 and provided one of the regulatory mechanisms of IGFBP3 protein underlying sheep muscle development. [ABSTRACT FROM AUTHOR]