The aim of this work was to investigate the association between polymorphisms located at the HSP90AA1 ovine gene promoter and gene expression rate under different environmental conditions, using a mixed model approach. Blood samples from 120 unrelated rams of the Manchega sheep breed were collected at three time points differing in environmental conditions. Rams were selected on the basis of their genotype for the transversion G/C located 660 base pairs upstream the gene transcription initiation site. Animals were also genotyped for another set of 6 SNPs located at the gene promoter. Two SNPs, G/C−660 and A/G−444, were associated with gene overexpression resulting from heat stress. The composed genotype CC−660-AG−444 was the genotype having the highest expression rates with fold changes ranging from 2.2 to 3.0. The genotype AG−522 showed the highest expression levels under control conditions with a fold change of 1.4. Under these conditions, the composed genotype CC−601-TT−524-AG−522-TT−468 is expected to be correlated with higher basal expression of the gene according to genotype frequencies and linkage disequilibrium values. Some putative transcription factors were predicted for binding sites where the SNPs considered are located. Since the expression rate of the gene under alternative environmental conditions seems to depend on the composed genotype of several SNPs located at its promoter, a cooperative regulation of the transcription of the HSP90AA1 gene could be hypothesized. Nevertheless epigenetic regulation mechanisms cannot be discarded.

While physiological differences across skeletal muscles have been described, the differential gene expression underlying them and the discovery of how they interact to perform specific biological processes are largely to be elucidated. The purpose of the present study was, firstly, to profile by cDNA microarrays the differential gene expression between two skeletal muscle types, Psoas major (PM) and Flexor digitorum (FD), in beef cattle and then to interpret the results in the context of a bovine gene coexpression network, detecting possible changes in connectivity across the skeletal muscle system. Eighty four genes were differentially expressed (DE) between muscles. Approximately 54% encoded metabolic enzymes and structural-contractile proteins. DE genes were involved in similar processes and functions, but the proportion of genes in each category varied within each muscle. A correlation matrix was obtained for 61 out of the 84 DE genes from a gene coexpression network. Different groups of coexpression were observed, the largest one having 28 metabolic and contractile genes, up-regulated in PM, and mainly encoding fast-glycolytic fibre structural components and glycolytic enzymes. In FD, genes related to cell support seemed to constitute its identity feature and did not positively correlate to the rest of DE genes in FD. Moreover, changes in connectivity for some DE genes were observed in the different gene ontologies. Our results confirm the existence of a muscle dependent transcription and coexpression pattern and suggest the necessity of integrating different muscle types to perform comprehensive networks for the transcriptional landscape of bovine skeletal muscle.Electronic supplementary materialThe online version of this article (doi:10.1007/s10142-010-0175-2) contains supplementary material, which is available to authorized users.

Background: The fibre type attributes and the relationships among their properties play an important role in the differences in muscle capabilities and features. Comprehensive characterisation of the skeletal muscles should study the degree of association between them and their involvement in muscle functionality. The purposes of the present study were to characterise the fibre type composition of a trunk (Psoas major, PM) and a limb (Flexor digitorum, membri thoraci, FD) muscle in the bovine species and to study the degree of coordination among contractile, metabolic and histological properties of fibre types. Immunohistochemical, histochemical and histological techniques were used.

We propose a data-driven reverse engineering approach to isolate the components of a gene interaction and regulatory network. We apply this method to the construction of a network for bovine skeletal muscle. Key nodes in the network include muscle-specific genes and transcription factors. muscle-specific genes are identified from data mining the USA National Cancer Institute, Cancer Genome Anatomy Project database, while transcription factors are predicted by accurate function annotation. A total of 5 microarray studies spanning 78 hybridisations and 23 different experimental conditions provided raw expression data. A recently-reported analytical method based on multivariate mixed-model equations is used to compute gene co-expression measures across 624 genes. The resulting network included 102 genes (of which 40 were muscle-specific genes and 7 were transcription factors) that clustered in 7 distinct modules with clear biological interpretation.

BackgroundReference genes with stable expression are required to normalize expression differences of target genes in qPCR experiments. Several procedures and companion software have been proposed to find the most stable genes. Model based procedures are attractive because they provide a solid statistical framework. NormFinder, a widely used software, uses a model based method. The pairwise comparison procedure implemented in GeNorm is a simpler procedure but one of the most extensively used. In the present work a statistical approach based in Maximum Likelihood estimation under mixed models was tested and compared with NormFinder and geNorm softwares. Sixteen candidate genes were tested in whole blood samples from control and heat stressed sheep.ResultsA model including gene and treatment as fixed effects, sample (animal), gene by treatment, gene by sample and treatment by sample interactions as random effects with heteroskedastic residual variance in gene by treatment levels was selected using goodness of fit and predictive ability criteria among a variety of models. Mean Square Error obtained under the selected model was used as indicator of gene expression stability. Genes top and bottom ranked by the three approaches were similar; however, notable differences for the best pair of genes selected for each method and the remaining genes of the rankings were shown. Differences among the expression values of normalized targets for each statistical approach were also found.ConclusionsOptimal statistical properties of Maximum Likelihood estimation joined to mixed model flexibility allow for more accurate estimation of expression stability of genes under many different situations. Accurate selection of reference genes has a direct impact over the normalized expression values of a given target gene. This may be critical when the aim of the study is to compare expression rate differences among samples under different environmental conditions, tissues, cell types or genotypes. To select reference genes not only statistical but also functional and biological criteria should be considered. Under the method here proposed SDHA/MDH1 have arisen as the best set of reference genes to be used in qPCR assays to study heat shock in ovine blood samples.

Variations on the transcriptome from one skeletal muscle type to another still remain unknown. The reliable identification of stable gene coexpression networks is essential to unravel gene functions and define biological processes. The differential expression of two distinct muscles, M. flexor digitorum (FD) and M. psoas major (PM), was studied using microarrays in cattle to illustrate muscle-specific transcription patterns and to quantify changes in connectivity regarding the expected gene coexpression pattern. A total of 206 genes were differentially expressed (DE), 94 upregulated in PM and 112 in FD. The distribution of DE genes in pathways and biological functions was explored in the context of system biology. Global interactomes for genes of interest were predicted. Fast/slow twitch genes, genes coding for extracellular matrix, ribosomal and heat shock proteins, and fatty acid uptake centred the specific gene expression patterns per muscle. Genes involved in repairing mechanisms, such as ribosomal and heat shock proteins, suggested a differential ability of muscles to react to similar stressing factors, acting preferentially in slow twitch muscles. Muscle attributes do not seem to be completely explained by the muscle fibre composition. Changes in connectivity accounted for 24% of significant correlations between DE genes. Genes changing their connectivity mostly seem to contribute to the main differential attributes that characterize each specific muscle type. These results underscore the unique flexibility of skeletal muscle where a substantial set of genes are able to change their behavior depending on the circumstances.

Analysis of data from complementary DNA microarray experiments is an area of intense research. Options include models at the gene level or at the global level, the latter combining information from all of the profiled genes. In general, a joint analysis is expected to be more powerful than gene-specific analyses. Global analysis of microarray data requires fitting a model that jointly performs data normalization and analyses. The objective of this study was to assess the optimality of alternative models for data normalization and analysis in an experiment to identify differentially expressed genes between 2 muscles in Avileña Negra Ibérica calves. Three major groups of models were explored according to several aspects including spatial arrangement of spots, other technical sources of variation such as dye effects, assumptions related to effects included in the model, and gene-specific effects. In addition, 3 sources of heterogeneity of residual variance were investigated. All models were compared by Bayes factors and cross-validation predictive densities. The model that included array-block, dye, muscle, and array-dye as systematic effects and all gene-related components as random effects was the best model for normalization and analysis of these data under heterogeneity of residual variances. Furthermore, level of intensity seemed to be the major source of heteroscedasticity for all models investigated. Such models rendered the best goodness of fit without compromising the predictive ability. The best model also provided the best performance to detect genes differentially expressed with the lowest false discovery rate. The large differences found for the model comparison criteria across models indicate the importance of defining the factors that more accurately account for experiment-wide variability to ensure correct inference on differential expression of genes. Our results also illustrate the importance of the experimental setup to account for possible sources of bias in the detection of differentially expressed genes.