Transcriptome analysis of controlled and therapy-resistant childhood asthma reveals distinct gene expression profiles.

BACKGROUND: Children with problematic severe asthma have poor disease control despite high doses of inhaled corticosteroids and additional therapy, leading to personal suffering, early deterioration of lung function, and significant consumption of health care resources. If no exacerbating factors, such as smoking or allergies, are found after extensive investigation, these children are given a diagnosis of therapy-resistant (or therapy-refractory) asthma (SA). OBJECTIVE: We sought to deepen our understanding of childhood SA by analyzing gene expression and modeling the underlying regulatory transcription factor networks in peripheral blood leukocytes. METHODS: Gene expression was analyzed by using Cap Analysis of Gene Expression in children with SA (n = 13), children with controlled persistent asthma (n = 15), and age-matched healthy control subjects (n = 9). Cap Analysis of Gene Expression sequencing detects the transcription start sites of known and novel mRNAs and noncoding RNAs. RESULTS: Sample groups could be separated by hierarchical clustering on 1305 differentially expressed transcription start sites, including 816 known genes and several novel transcripts. Ten of 13 tested novel transcripts were validated by means of RT-PCR and Sanger sequencing. Expression of RAR-related orphan receptor A (RORA), which has been linked to asthma in genome-wide association studies, was significantly upregulated in patients with SA. Gene network modeling revealed decreased glucocorticoid receptor signaling and increased activity of the mitogen-activated protein kinase and Jun kinase cascades in patients with SA. CONCLUSION: Circulating leukocytes from children with controlled asthma and those with SA have distinct gene expression profiles, demonstrating the possible development of specific molecular biomarkers and supporting the need for novel therapeutic approaches.

oPOSSUM-3: advanced analysis of regulatory motif over-representation across genes or ChIP-Seq datasets.

oPOSSUM-3 is a web-accessible software system for identification of over-represented transcription factor binding sites (TFBS) and TFBS families in either DNA sequences of co-expressed genes or sequences generated from high-throughput methods, such as ChIP-Seq. Validation of the system with known sets of co-regulated genes and published ChIP-Seq data demonstrates the capacity for oPOSSUM-3 to identify mediating transcription factors (TF) for co-regulated genes or co-recovered sequences. oPOSSUM-3 is available at http://opossum.cisreg.ca.

Validation of skeletal muscle cis-regulatory module predictions reveals nucleotide composition bias in functional enhancers.

We performed a genome-wide scan for muscle-specific cis-regulatory modules (CRMs) using three computational prediction programs. Based on the predictions, 339 candidate CRMs were tested in cell culture with NIH3T3 fibroblasts and C2C12 myoblasts for capacity to direct selective reporter gene expression to differentiated C2C12 myotubes. A subset of 19 CRMs validated as functional in the assay. The rate of predictive success reveals striking limitations of computational regulatory sequence analysis methods for CRM discovery. Motif-based methods performed no better than predictions based only on sequence conservation. Analysis of the properties of the functional sequences relative to inactive sequences identifies nucleotide sequence composition can be an important characteristic to incorporate in future methods for improved predictive specificity. Muscle-related TFBSs predicted within the functional sequences display greater sequence conservation than non-TFBS flanking regions. Comparison with recent MyoD and histone modification ChIP-Seq data supports the validity of the functional regions.

JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles.

JASPAR (http://jaspar.genereg.net) is the leading open-access database of matrix profiles describing the DNA-binding patterns of transcription factors (TFs) and other proteins interacting with DNA in a sequence-specific manner. Its fourth major release is the largest expansion of the core database to date: the database now holds 457 non-redundant, curated profiles. The new entries include the first batch of profiles derived from ChIP-seq and ChIP-chip whole-genome binding experiments, and 177 yeast TF binding profiles. The introduction of a yeast division brings the convenience of JASPAR to an active research community. As binding models are refined by newer data, the JASPAR database now uses versioning of matrices: in this release, 12% of the older models were updated to improved versions. Classification of TF families has been improved by adopting a new DNA-binding domain nomenclature. A curated catalog of mammalian TFs is provided, extending the use of the JASPAR profiles to additional TFs belonging to the same structural family. The changes in the database set the system ready for more rapid acquisition of new high-throughput data sources. Additionally, three new special collections provide matrix profile data produced by recent alternative high-throughput approaches.

oPOSSUM: integrated tools for analysis of regulatory motif over-representation.

The identification of over-represented transcription factor binding sites from sets of co-expressed genes provides insights into the mechanisms of regulation for diverse biological contexts. oPOSSUM, an internet-based system for such studies of regulation, has been improved and expanded in this new release. New features include a worm-specific version for investigating binding sites conserved between Caenorhabditis elegans and C. briggsae, as well as a yeast-specific version for the analysis of co-expressed sets of Saccharomyces cerevisiae genes. The human and mouse applications feature improvements in ortholog mapping, sequence alignments and the delineation of multiple alternative promoters. oPOSSUM2, introduced for the analysis of over-represented combinations of motifs in human and mouse genes, has been integrated with the original oPOSSUM system. Analysis using user-defined background gene sets is now supported. The transcription factor binding site models have been updated to include new profiles from the JASPAR database. oPOSSUM is available at http://www.cisreg.ca/oPOSSUM/

Inference of transcriptional regulation relationships from gene expression data.

MOTIVATION: In order to find gene regulatory networks from microarray data, it is important to first find direct regulatory relationships between pairs of genes. RESULTS: We propose a new method for finding potential regulatory relationships between pairs of genes from microarray time series data and apply it to expression data for cell-cycle related genes in yeast. We compare our algorithm, dubbed the event method, with the earlier correlation method and the edge detection method by Filkov et al. When tested on known transcriptional regulation genes, all three methods are able to find similar numbers of true positives. The results indicate that our algorithm is able to identify true positive pairs that are different from those found by the two other methods. We also compare the correlation and the event methods using synthetic data and find that typically, the event method obtains better results. AVALIABILITY: software is available upon request.