Simultaneous identification of differential gene expression and connectivity in inflammation, adipogenesis and cancer

Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process

MOTIVATION

Coexpression networks have recently emerged as a novel holistic approach to microarray data analysis and interpretation. Choosing an appropriate cutoff threshold, above which a gene-gene interaction is considered as relevant, is a critical task in most network-centric applications, especially when two or more networks are being compared.

RESULTS

We demonstrate that the performance of traditional approaches, which are based on a pre-defined cutoff or significance level, can vary drastically depending on the type of data and application. Therefore, we introduce a systematic procedure for estimating a cutoff threshold of coexpression networks directly from their topological properties. Both synthetic and real datasets show clear benefits of our data-driven approach under various practical circumstances. In particular, the procedure provides a robust estimate of individual degree distributions, even from multiple microarray studies performed with different array platforms or experimental designs, which can be used to discriminate the corresponding phenotypes. Application to human T helper cell differentiation process provides useful insights into the components and interactions controlling this process, many of which would have remained unidentified on the basis of expression change alone. Moreover, several human-mouse orthologs showed conserved topological changes in both systems, suggesting their potential importance in the differentiation process.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Gene expression profiling of Hereford Shorthorn cattle following challenge with Boophilus microplus tick larvae

The ability of cattle to resist tick infestations is partly genetically determined. In order to better define the nature of Bos taurus resistance to the cattle tick Boophilus microplus, skin gene expression was studied using a cattle skin derived cDNA microarray. Expression profiles were determined in skin biopsies sampled from three highly tick resistant animals (HR) and two animals with lower tick resistance (LR) at time 0, immediately before challenge, and again 24 h after challenge. The analysis of the resulting expression data addressed two biological questions: first, for any animal exposed to ticks, which genes are differentially expressed in the 24 h following challenge; and second, which genes are differentially expressed between animals of high and low resistance at 24 h after challenge? In total, 214 genes were found to be differentially expressed in response to larval challenge across all the animals. Seventy-two genes were upregulated and 76 were downregulated at 24 h after challenge. Genes with significantly altered gene expression levels following tick infestation were predominantly keratin genes or mitochondrial genes, as well as odorant binding protein (OBP) and Bos taurus major allergen BDA20. In addition, we identified 66 genes with differential expression between HR and LR animals at 24 h. Of these, genes representing the extracellular matrix and immunoglobulin gene expression pathways were overrepresented. Three differentially expressed genes, OBP, Bos taurus major allergen BDA20 and dendritic cell protein HFL-B5 were further analysed by quantitative reverse transcription PCR (qRT-PCR). The qRT-PCR assay results closely mirrored the expression profiles found in the microarray experiment.

A gene coexpression network for bovine skeletal muscle inferred from microarray data

We present the application of large-scale multivariate mixed-model equations to the joint analysis of nine gene expression experiments in beef cattle muscle and fat tissues with a total of 147 hybridizations, and we explore 47 experimental conditions or treatments. Using a correlation-based method, we constructed a gene network for 822 genes. Modules of muscle structural proteins and enzymes, extracellular matrix, fat metabolism, and protein synthesis were clearly evident. Detailed analysis of the network identified groupings of proteins on the basis of physical association. For example, expression of three components of the z-disk, MYOZ1, TCAP, and PDLIM3, was significantly correlated. In contrast, expression of these z-disk proteins was not highly correlated with the expression of a cluster of thick (myosins) and thin (actin and tropomyosins) filament proteins or of titin, the third major filament system. However, expression of titin was itself not significantly correlated with the cluster of thick and thin filament proteins and enzymes. Correlation in expression of many fast-twitch muscle structural proteins and enzymes was observed, but slow-twitch-specific proteins were not correlated with the fast-twitch proteins or with each other. In addition, a number of significant associations between genes and transcription factors were also identified. Our results not only recapitulate the known biology of muscle but have also started to reveal some of the underlying associations between and within the structural components of skeletal muscle.