CluGene: A Bioinformatics Framework for the Identification of Co-Localized, Co-Expressed and Co-Regulated Genes Aimed at the Investigation of Transcriptional Regulatory Networks from High-Throughput Expression Data

Pan-cancer driver copy number alterations identified by joint expression/CNA data analysis

Analysis of large gene expression datasets from biopsies of cancer patients can identify co-expression signatures representing particular biomolecular events in cancer. Some of these signatures involve genomically co-localized genes resulting from the presence of copy number alterations (CNAs), for which analysis of the expression of the underlying genes provides valuable information about their combined role as oncogenes or tumor suppressor genes. Here we focus on the discovery and interpretation of such signatures that are present in multiple cancer types due to driver amplifications and deletions in particular regions of the genome after doing a comprehensive analysis combining both gene expression and CNA data from The Cancer Genome Atlas.

Genes responsive to rapamycin and serum deprivation are clustered on chromosomes and undergo reorganization within local chromatin environments

We previously demonstrated that genome reorganization, through chromosome territory repositioning, occurs concurrently with significant changes in gene expression in normal primary human fibroblasts treated with the drug rapamycin, or stimulated into quiescence. Although these events occurred concomitantly, it is unclear how specific changes in gene expression relate to reorganization of the genome at higher resolution. We used computational analyses, genome organization assays, and microscopy, to investigate the relationship between chromosome territory positioning and gene expression. We determined that despite relocation of chromosome territories, there was no substantial bias in the proportion of genes changing expression on any one chromosome, including chromosomes 10 and 18. Computational analyses identified that clusters of serum deprivation and rapamycin-responsive genes along the linear extent of chromosomes. Chromosome conformation capture (3C) analysis demonstrated the strengthening or loss of specific long-range chromatin interactions in response to rapamycin and quiescence induction, including a cluster of genes containing Interleukin-8 and several chemokine genes on chromosome 4. We further observed that the LIF gene, which is highly induced upon rapamycin treatment, strengthened interactions with up- and down-stream intergenic regions. Our findings indicate that the repositioning of chromosome territories in response to cell stimuli, this does not reflect gene expression changes occurring within physically clustered groups of genes.

Genome-wide identification of physically clustered genes suggests chromatin-level co-regulation in male reproductive development in Arabidopsis thaliana

Co-expression of physically linked genes occurs surprisingly frequently in eukaryotes. Such chromosomal clustering may confer a selective advantage as it enables coordinated gene regulation at the chromatin level. We studied the chromosomal organization of genes involved in male reproductive development in Arabidopsis thaliana. We developed an in-silico tool to identify physical clusters of co-regulated genes from gene expression data. We identified 17 clusters (96 genes) involved in stamen development and acting downstream of the transcriptional activator MS1 (MALE STERILITY 1), which contains a PHD domain associated with chromatin re-organization. The clusters exhibited little gene homology or promoter element similarity, and largely overlapped with reported repressive histone marks. Experiments on a subset of the clusters suggested a link between expression activation and chromatin conformation: qRT-PCR and mRNA in situ hybridization showed that the clustered genes were up-regulated within 48 h after MS1 induction; out of 14 chromatin-remodeling mutants studied, expression of clustered genes was consistently down-regulated only in hta9/hta11, previously associated with metabolic cluster activation; DNA fluorescence in situ hybridization confirmed that transcriptional activation of the clustered genes was correlated with open chromatin conformation. Stamen development thus appears to involve transcriptional activation of physically clustered genes through chromatin de-condensation.

SegCorr a statistical procedure for the detection of genomic regions of correlated expression

BACKGROUND

Detecting local correlations in expression between neighboring genes along the genome has proved to be an effective strategy to identify possible causes of transcriptional deregulation in cancer. It has been successfully used to illustrate the role of mechanisms such as copy number variation (CNV) or epigenetic alterations as factors that may significantly alter expression in large chromosomal regions (gene silencing or gene activation).

RESULTS

The identification of correlated regions requires segmenting the gene expression correlation matrix into regions of homogeneously correlated genes and assessing whether the observed local correlation is significantly higher than the background chromosomal correlation. A unified statistical framework is proposed to achieve these two tasks, where optimal segmentation is efficiently performed using dynamic programming algorithm, and detection of highly correlated regions is then achieved using an exact test procedure. We also propose a simple and efficient procedure to correct the expression signal for mechanisms already known to impact expression correlation. The performance and robustness of the proposed procedure, called SegCorr, are evaluated on simulated data. The procedure is illustrated on cancer data, where the signal is corrected for correlations caused by copy number variation. It permitted the detection of regions with high correlations linked to epigenetic marks like DNA methylation.

CONCLUSIONS

SegCorr is a novel method that performs correlation matrix segmentation and applies a test procedure in order to detect highly correlated regions in gene expression.

Candidate chromosome 1 disease susceptibility genes for Sjogren's syndrome xerostomia are narrowed by novel NOD.B10 congenic mice

Sjogren's syndrome (SS) is characterized by salivary gland leukocytic infiltrates and impaired salivation (xerostomia). Cox-2 (Ptgs2) is located on chromosome 1 within the span of the Aec2 region. In an attempt to demonstrate that COX-2 drives antibody-dependent hyposalivation, NOD.B10 congenic mice bearing a Cox-2flox gene were generated. A congenic line with non-NOD alleles in Cox-2-flanking genes failed manifest xerostomia. Further backcrossing yielded disease-susceptible NOD.B10 Cox-2flox lines; fine genetic mapping determined that critical Aec2 genes lie within a 1.56 to 2.17Mb span of DNA downstream of Cox-2. Bioinformatics analysis revealed that susceptible and non-susceptible lines exhibit non-synonymous coding SNPs in 8 protein-encoding genes of this region, thereby better delineating candidate Aec2 alleles needed for SS xerostomia.