Detection of chromosomal regions showing differential gene expression in human skeletal muscle and in alveolar rhabdomyosarcoma

Differential gene expression analysis in ageing muscle and drug discovery perspectives

Identifying therapeutic target genes represents the key step in functional genomics-based therapies. Within this context, the disease heterogeneity, the exogenous factors and the complexity of genomic structure and function represent important challenges. The functional genomics aims to overcome such obstacles via identifying the gene functions and therefore highlight disease-causing genes as therapeutic targets. Genomic technologies promise to reshape the research on ageing muscle, exercise response and drug discovery. Herein, we describe the functional genomics strategies, mainly differential gene expression methods microarray, serial analysis of gene expression (SAGE), massively parallel signature sequence (MPSS), RNA sequencing (RNA seq), representational difference analysis (RDA), and suppression subtractive hybridization (SSH). Furthermore, we review these illustrative approaches that have been used to discover new therapeutic targets for some complex diseases along with the application of these tools to study the modulation of the skeletal muscle transcriptome.

Altered chromosomal positioning, compaction, and gene expression with a lamin A/C gene mutation

BACKGROUND

Lamins A and C, encoded by the LMNA gene, are filamentous proteins that form the core scaffold of the nuclear lamina. Dominant LMNA gene mutations cause multiple human diseases including cardiac and skeletal myopathies. The nuclear lamina is thought to regulate gene expression by its direct interaction with chromatin. LMNA gene mutations may mediate disease by disrupting normal gene expression.

METHODS/FINDINGS

To investigate the hypothesis that mutant lamin A/C changes the lamina's ability to interact with chromatin, we studied gene misexpression resulting from the cardiomyopathic LMNA E161K mutation and correlated this with changes in chromosome positioning. We identified clusters of misexpressed genes and examined the nuclear positioning of two such genomic clusters, each harboring genes relevant to striated muscle disease including LMO7 and MBNL2. Both gene clusters were found to be more centrally positioned in LMNA-mutant nuclei. Additionally, these loci were less compacted. In LMNA mutant heart and fibroblasts, we found that chromosome 13 had a disproportionately high fraction of misexpressed genes. Using three-dimensional fluorescence in situ hybridization we found that the entire territory of chromosome 13 was displaced towards the center of the nucleus in LMNA mutant fibroblasts. Additional cardiomyopathic LMNA gene mutations were also shown to have abnormal positioning of chromosome 13, although in the opposite direction.

CONCLUSIONS

These data support a model in which LMNA mutations perturb the intranuclear positioning and compaction of chromosomal domains and provide a mechanism by which gene expression may be altered.

FGFR1 over-expression in primary rhabdomyosarcoma tumors is associated with hypomethylation of a 5' CpG island and abnormal expression of the AKT1, NOG, and BMP4 genes

Rhabdomyosarcoma (RMS), the most common pediatric soft tissue sarcoma likely results from abnormal proliferation and differentiation during skeletal myogenesis. Multiple genetic alterations are associated with the three RMS histopathological subtypes, embryonal, alveolar, and pleomorphic adult variant. Recently, we reported the novel amplification of the FGFR1 gene in a RMS tumor. The involvement of FGFR1 in RMS was now further studied in primary tumors and RMS cell lines by mutation screening, quantitative RNA expression, and methylation analyses. No mutation was found by DHPLC and sequencing of the entire FGFR1 coding sequence and exon-intron boundaries. However, FGFR1 over-expression was detected in all primary RMS tumors and cell lines tested. A hypomethylation of a CpG island upstream to FGFR1 exon 1 was identified in the primary RMS tumors, using sodium bisulfite modification method, suggesting a molecular mechanism to FGFR1 over-expression. Expression analysis of additional genes, AKT1, NOG and its antagonist BMP4, which interact downstream to FGFR1, demonstrated expression differences between primary RMS tumors and normal skeletal muscles. Our data suggest an important role for FGFR1 and FGFR1-downstream genes in RMS tumorigenesis and a possible association with the deregulation of proliferation and differentiation of skeletal myoblasts in RMS.

Hidden Markov model for defining genomic changes in lung cancer using gene expression data

The study of gene expression patterns in relationship to chromosomal position, the "transcriptome map," has become an area of active research and has revealed unexpected chromosomal regions within which gene expression levels are highly correlated. In cancer research, these regional changes in gene expression that may result from alterations at the chromosome level such as gene amplification or loss. To facilitate the search for such regions utilizing gene expression data, we have developed a hidden Markov model (HMM). Maximum penalized likelihood is used to estimate the parameters in the model. This method is applied to a lung cancer microarray experiment, including 86 human lung adenocarcinomas. Several regions identified through the HMM are consistent with known recurrent regions of amplification or deletion in this cancer. We further demonstrate the association of these abnormal expression regions with measures of disease status, such as tumor stage, differentiation, and survival. These findings suggest that genes in these regions may play a major role in the process of carcinogenesis of the lung. Our proposed method provides a valuable tool to accurately pinpoint regions of abnormal expression for further investigation.

ChroCoLoc: an application for calculating the probability of co-localization of microarray gene expression

With the production of whole genome microarray chips the ability arises to investigate whether the regulation of particular groups of genes may be influenced by their chromosomal localization. Chromosome Co-Localization probability calculator (ChroCoLoc) is a publicly available web-based tool for the analysis of co-localization of co-expressed genes identified by microarray experiments.

AVAILABILITY

http://www.ebi.ac.uk/expressionprofiler/