Genome-wide patterns of promoter sharing and co-expression in bovine skeletal muscle

HBP1 promotes chicken preadipocyte proliferation via directly repressing SOCS3 transcription

Preadipocyte proliferation is an essential process in adipose development. During proliferation of preadipocytes, transcription factors play crucial roles. HMG-box protein 1 (HBP1) is an important transcription factor of cellular proliferation. However, the function and underlying mechanisms of HBP1 in the proliferation of preadipocytes remain unclear. Here, we found that the expression level of HBP1 decreased first and then increased during the proliferation of chicken preadipocytes. Knockout of HBP1 could inhibit the proliferation of preadipocytes, while overexpression of HBP1 could promote the proliferation of preadipocytes. ChIP-seq data showed that HBP1 had the unique DNA binding motif in chicken preadipocytes. By integrating ChIP-Seq and RNA-Seq, we revealed a total of 3 candidate target genes of HBP1. Furthermore, the results of ChIP-qPCR, RT-qPCR, luciferase reporter assay and EMSA showed that HBP1 could inhibit the transcription of suppressor of cytokine signaling 3 (SOCS3) by binding to its promoter. Moreover, we confirmed that SOCS3 can mediate the regulation of HBP1 on the proliferation of preadipocytes through RNAi and rescue experiments. Altogether, these data demonstrated that HBP1 directly targets SOCS3 to regulate chicken preadipocyte proliferation. Our findings expand the transcriptional regulatory network of preadipocyte proliferation, and they will be helpful in formulating a molecular breeding scheme to control excessive abdominal fat deposition and to improve meat quality in chickens.

Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data

MOTIVATION

Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples. Coordinated expression of genes may indicate that they are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Sequencing data, which are commonly normalized to remove technical variability. Here, we demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes. These false-positive associations can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular, when preprocessing large-scale heterogeneous data, quantile-based normalization methods such as smooth quantile normalization can be applied to remove technical variability while maintaining global differences in expression for samples with different biological attributes.

RESULTS

We developed SNAIL (Smooth-quantile Normalization Adaptation for the Inference of co-expression Links), a normalization method based on smooth quantile normalization specifically designed for modeling of co-expression measurements. We show that SNAIL avoids formation of false-positive associations in co-expression as well as in downstream network analyses. Using SNAIL, one can avoid arbitrary gene filtering and retain associations to genes that only express in small subgroups of samples. This highlights the method's potential future impact on network modeling and other association-based approaches in large-scale heterogeneous data.

AVAILABILITY AND IMPLEMENTATION

The implementation of the SNAIL algorithm and code to reproduce the analyses described in this work can be found in the GitHub repository https://github.com/kuijjerlab/PySNAIL.

Enhanced identification of significant regulators of gene expression

Background

Diseases like cancer will lead to changes in gene expression, and it is relevant to identify key regulatory genes that can be linked directly to these changes. This can be done by computing a Regulatory Impact Factor (RIF) score for relevant regulators. However, this computation is based on estimating correlated patterns of gene expression, often Pearson correlation, and an assumption about a set of specific regulators, normally transcription factors. This study explores alternative measures of correlation, using the Fisher and Sobolev metrics, and an extended set of regulators, including epigenetic regulators and long non-coding RNAs (lncRNAs). Data on prostate cancer have been used to explore the effect of these modifications.

Results

A tool for computation of RIF scores with alternative correlation measures and extended sets of regulators was developed and tested on gene expression data for prostate cancer. The study showed that the Fisher and Sobolev metrics lead to improved identification of well-documented regulators of gene expression in prostate cancer, and the sets of identified key regulators showed improved overlap with previously defined gene sets of relevance to cancer. The extended set of regulators lead to identification of several interesting candidates for further studies, including lncRNAs. Several key processes were identified as important, including spindle assembly and the epithelial-mesenchymal transition (EMT).

Conclusions

The study has shown that using alternative metrics of correlation can improve the performance of tools based on correlation of gene expression in genomic data. The Fisher and Sobolev metrics should be considered also in other correlation-based applications.

Gene co-expression networks contributing to the expression of compensatory growth in metabolically active tissues in cattle

Compensatory growth (CG) is an accelerated growth phenomenon which occurs in animals upon re-alimentation following a period of dietary restriction. The objective of this study was to perform gene co-expression analysis on metabolic tissues of animals undergoing CG, in order to elucidate the molecular control governing this phenomenon. Thirty Holstein Friesian bulls were fed a restricted diet for 125 days, after which they received feed ad libitum. Following 55 days of ad libitum feeding all animals were slaughtered. RNAseq and gene co-expression analyses were performed on tissue samples collected at slaughter including liver, rumen papillae and jejunum epithelium tissues. A period of CG resulted in 15 networks of co-expressed genes. One network of genes, involved in proteasome core complex, signal transduction and protein synthesis was found to be similar across liver and jejunum tissue datasets (r = 0.68, P = 0.04). Results from this study also showed that a large portion of co-expressed genes had not previously been implicated in the expression of CG, thus this study identifies novel genes involved in controlling CG across tissues, with hub genes holding potential for use as biomarkers for the selection of animals with a greater propensity to display CG.

Transcriptome profiling of individual rhesus macaque oocytes and preimplantation embryos

Early mammalian embryonic transcriptomes are dynamic throughout the process of preimplantation development. Cataloging of primate transcriptomics during early development has been accomplished in humans, but global characterization of transcripts is lacking in the rhesus macaque: a key model for human reproductive processes. We report here the systematic classification of individual macaque transcriptomes using RNA-Seq technology from the germinal vesicle stage oocyte through the blastocyst stage embryo. Major differences in gene expression were found between sequential stages, with the 4- to 8-cell stages showing the highest level of differential gene expression. Analysis of putative transcription factor binding sites also revealed a striking increase in key regulatory factors in 8-cell embryos, indicating a strong likelihood of embryonic genome activation occurring at this stage. Furthermore, clustering analyses of gene co-expression throughout this period resulted in distinct groups of transcripts significantly associated to the different embryo stages assayed. The sequence data provided here along with characterizations of major regulatory transcript groups present a comprehensive atlas of polyadenylated transcripts that serves as a useful resource for comparative studies of preimplantation development in humans and other species.

Large-scale gene co-expression network as a source of functional annotation for cattle genes

Background

Genome sequencing and subsequent gene annotation of genomes has led to the elucidation of many genes, but in vertebrates the actual number of protein coding genes are very consistent across species (~20,000). Seven years after sequencing the cattle genome, there are still genes that have limited annotation and the function of many genes are still not understood, or partly understood at best. Based on the assumption that genes with similar patterns of expression across a vast array of tissues and experimental conditions are likely to encode proteins with related functions or participate within a given pathway, we constructed a genome-wide Cattle Gene Co-expression Network (CGCN) using 72 microarray datasets that contained a total of 1470 Affymetrix Genechip Bovine Genome Arrays that were retrieved from either NCBI GEO or EBI ArrayExpress.

Results

The total of 16,607 probe sets, which represented 11,397 genes, with unique Entrez ID were consolidated into 32 co-expression modules that contained between 29 and 2569 probe sets. All of the identified modules showed strong functional enrichment for gene ontology (GO) terms and Reactome pathways. For example, modules with important biological functions such as response to virus, response to bacteria, energy metabolism, cell signaling and cell cycle have been identified. Moreover, gene co-expression networks using “guilt-by-association” principle have been used to predict the potential function of 132 genes with no functional annotation. Four unknown Hub genes were identified in modules highly enriched for GO terms related to leukocyte activation (LOC509513), RNA processing (LOC100848208), nucleic acid metabolic process (LOC100850151) and organic-acid metabolic process (MGC137211). Such highly connected genes should be investigated more closely as they likely to have key regulatory roles.

Conclusions

We have demonstrated that the CGCN and its corresponding regulons provides rich information for experimental biologists to design experiments, interpret experimental results, and develop novel hypothesis on gene function in this poorly annotated genome. The network is publicly accessible at http://www.animalgenome.org/cgi-bin/host/reecylab/d.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3176-2) contains supplementary material, which is available to authorized users.

Integrated network analysis reveals distinct regulatory roles of transcription factors and microRNAs

Analysis of transcription regulatory networks has revealed many principal features that govern gene expression regulation. MicroRNAs (miRNAs) have emerged as another major class of gene regulators that influence gene expression post-transcriptionally, but there remains a need to assess quantitatively their global roles in gene regulation. Here, we have constructed an integrated gene regulatory network comprised of transcription factors (TFs), miRNAs, and their target genes and analyzed the effect of regulation on target mRNA expression, target protein expression, protein-protein interaction, and disease association. We found that while target genes regulated by the same TFs tend to be co-expressed, co-regulation by miRNAs does not lead to co-expression assessed at either mRNA or protein levels. Analysis of interacting protein pairs in the regulatory network revealed that compared to genes co-regulated by miRNAs, a higher fraction of genes co-regulated by TFs encode proteins in the same complex. Although these results suggest that genes co-regulated by TFs are more functionally related than those co-regulated by miRNAs, genes that share either TF or miRNA regulators are more likely to cause the same disease. Further analysis on the interplay between TFs and miRNAs suggests that TFs tend to regulate intramodule/pathway clusters, while miRNAs tend to regulate intermodule/pathway clusters. These results demonstrate that although TFs and miRNAs both regulate gene expression, they occupy distinct niches in the overall regulatory network within the cell.

Multi-breed and multi-trait co-association analysis of meat tenderness and other meat quality traits in three French beef cattle breeds

Background

Studies to identify markers associated with beef tenderness have focused on Warner–Bratzler shear force (WBSF) but the interplay between the genes associated with WBSF has not been explored. We used the association weight matrix (AWM), a systems biology approach, to identify a set of interacting genes that are co-associated with tenderness and other meat quality traits, and shared across the Charolaise, Limousine and Blonde d’Aquitaine beef cattle breeds.

Results

Genome-wide association studies were performed using ~500K single nucleotide polymorphisms (SNPs) and 17 phenotypes measured on more than 1000 animals for each breed. First, this multi-trait approach was applied separately for each breed across 17 phenotypes and second, between- and across-breed comparisons at the AWM and functional levels were performed. Genetic heterogeneity was observed, and most of the variants that were associated with WBSF segregated within rather than across breeds. We identified 206 common candidate genes associated with WBSF across the three breeds. SNPs in these common genes explained between 28 and 30 % of the phenotypic variance for WBSF. A reduced number of common SNPs mapping to the 206 common genes were identified, suggesting that different mutations may target the same genes in a breed-specific manner. Therefore, it is likely that, depending on allele frequencies and linkage disequilibrium patterns, a SNP that is identified for one breed may not be informative for another unrelated breed. Well-known candidate genes affecting beef tenderness were identified. In addition, some of the 206 common genes are located within previously reported quantitative trait loci for WBSF in several cattle breeds. Moreover, the multi-breed co-association analysis detected new candidate genes, regulators and metabolic pathways that are likely involved in the determination of meat tenderness and other meat quality traits in beef cattle.

Conclusions

Our results suggest that systems biology approaches that explore associations of correlated traits increase statistical power to identify candidate genes beyond the one-dimensional approach. Further studies on the 206 common genes, their pathways, regulators and interactions will expand our knowledge on the molecular basis of meat tenderness and could lead to the discovery of functional mutations useful for genomic selection in a multi-breed beef cattle context.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-016-0216-y) contains supplementary material, which is available to authorized users.

Robust coordination of cardiac functions from gene co-expression reveals a versatile combinatorial transcriptional control

The necessary overall coordination of cardiac cellular functions is little known at the mRNA level. Focusing on energy production and cardiac contraction, we analyzed microarray data from heart tissue obtained in groups of mice and rats in normal conditions and with a left ventricular dysfunction. In each group and for each function, we identified genes positively or negatively correlated with numerous genes of the function, which were called coordinated or inversely coordinated with the function. The genes coordinated with energy production or cardiac contraction showed the coupling of these functions in all groups. Among coordinated or inversely coordinated genes common to the two functions, we proposed a fair number of transcriptional regulators as potential determinants of the energy production and cardiac contraction coupling. Although this coupling was constant across the groups and unveiled a stable gene core, the combinations of transcriptional regulators were very different between the groups, including one half that has never been linked to heart function. These results highlighted the stable coordination of energy production or cardiac contraction at the mRNA level, and the combinatorial and versatile nature of potential transcriptional regulation. In addition, this work unveiled new transcriptional regulators potentially involved in normal or altered cardiac functional coupling.

Multi-tissue omics analyses reveal molecular regulatory networks for puberty in composite beef cattle

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.

Computational identification of potential transcriptional regulators of TGF-ß1 in human atherosclerotic arteries

TGF-ß is protective in atherosclerosis but deleterious in metastatic cancers. Our aim was to determine whether TGF-ß transcriptional regulation is tissue-specific in early atherosclerosis. The computational methods included 5 steps: (i) from microarray data of human atherosclerotic carotid tissue, to identify the 10 best co-expressed genes with TGFB1 (TGFB1 gene cluster), (ii) to choose the 11 proximal promoters, (iii) to predict the TFBS shared by the promoters, (iv) to identify the common TFs co-expressed with the TGFB1 gene cluster, and (v) to compare the common TFs in the early lesions to those identified in advanced atherosclerotic lesions and in various cancers. Our results show that EGR1, SP1 and KLF6 could be responsible for TGFB1 basal expression, KLF6 appearing specific to atherosclerotic lesions. Among the TFs co-expressed with the gene cluster, transcriptional activators (SLC2A4RG, MAZ) and repressors (ZBTB7A, PATZ1, ZNF263) could be involved in the fine-tuning of TGFB1 expression in atherosclerosis.

Annexin A1: novel roles in skeletal muscle biology

Annexin A1 (ANXA1, lipocortin-1) is the first characterized member of the annexin superfamily of proteins, so called since their main property is to bind (i.e., to annex) to cellular membranes in a Ca(2+) -dependent manner. ANXA1 has been involved in a broad range of molecular and cellular processes, including anti-inflammatory signalling, kinase activities in signal transduction, maintenance of cytoskeleton and extracellular matrix integrity, tissue growth, apoptosis, and differentiation. New insights show that endogenous ANXA1 positively modulates myoblast cell differentiation by promoting migration of satellite cells and, consequently, skeletal muscle differentiation. This suggests that ANXA1 may contribute to the regeneration of skeletal muscle tissue and may have therapeutic implications with respect to the development of ANXA1 mimetics.

Genetic architecture of gene expression in ovine skeletal muscle

Background

In livestock populations the genetic contribution to muscling is intensively monitored in the progeny of industry sires and used as a tool in selective breeding programs. The genes and pathways conferring this genetic merit are largely undefined. Genetic variation within a population has potential, amongst other mechanisms, to alter gene expression via cis- or trans-acting mechanisms in a manner that impacts the functional activities of specific pathways that contribute to muscling traits. By integrating sire-based genetic merit information for a muscling trait with progeny-based gene expression data we directly tested the hypothesis that there is genetic structure in the gene expression program in ovine skeletal muscle.

Results

The genetic performance of six sires for a well defined muscling trait, longissimus lumborum muscle depth, was measured using extensive progeny testing and expressed as an Estimated Breeding Value by comparison with contemporary sires. Microarray gene expression data were obtained for longissimus lumborum samples taken from forty progeny of the six sires (4-8 progeny/sire). Initial unsupervised hierarchical clustering analysis revealed strong genetic architecture to the gene expression data, which also discriminated the sire-based Estimated Breeding Value for the trait. An integrated systems biology approach was then used to identify the major functional pathways contributing to the genetics of enhanced muscling by using both Estimated Breeding Value weighted gene co-expression network analysis and a differential gene co-expression network analysis. The modules of genes revealed by these analyses were enriched for a number of functional terms summarised as muscle sarcomere organisation and development, protein catabolism (proteosome), RNA processing, mitochondrial function and transcriptional regulation.

Conclusions

This study has revealed strong genetic structure in the gene expression program within ovine longissimus lumborum muscle. The balance between muscle protein synthesis, at the levels of both transcription and translation control, and protein catabolism mediated by regulated proteolysis is likely to be the primary determinant of the genetic merit for the muscling trait in this sheep population. There is also evidence that high genetic merit for muscling is associated with a fibre type shift toward fast glycolytic fibres. This study provides insight into mechanisms, presumably subject to strong artificial selection, that underpin enhanced muscling in sheep populations.