The difficult interpretation of transcriptome data: the case of the GATC regulatory network
- 作者：Riva ; Alessandra ; Delorme ; Marie-Odile ; Chevalier ; Tony ; Guilhot ; Nicolas ; Hé ; naut ; Corinne ; et. al.
- 关键词：GATC ; Statistics ; Transcriptome analysis ; Comparative genomics ; Fnr
- 刊名：Computational Biology and Chemistry
- 出版年：2004
- 期刊代码：120_14769271
- 类别：ch
- 出版时间：April, 2004
- 卷：28
- 期：2
- 页码：109-118
- 文件大小：360 K
摘要
Genomic analyses on part of Escherichia coli’s chromosome had suggested the existence of a GATC regulated network. This has recently been confirmed through a transcriptome analysis. Two hypotheses about the molecular control mechanism have been proposed—(i) the GATC network regulation is caused by the presence of GATC clusters within the coding sequences; the regulation is the direct consequence of the clusters’ hemi-methylation and therefore their elevated melting temperature, (ii) the regulation is caused by the presence of GATCs in the non-coding 500bp upstream regions of the affected genes; it is the consequence of an interaction with a regulatory protein like Fnr or CAP. An analysis of the transcriptome data has not allowed us to decide between the two hypotheses. We have therefore taken a classic genomic approach, analyzing the statistical distribution of GATC along the chromosome, using a realistic model of the chromosome as theoretical reference. We observe no particular distribution of GATC in the non-coding upstream regions; however, we confirm the presence of GATC clusters within the genes. In order to verify that the particular distribution observed in E. coli is not a statistical artefact, but has a physiological role, we have carried out the same analysis on Salmonella, making the hypothesis that the genes containing a GATC clusters should be largely the same in the two bacteria. This has been indeed observed, showing that the genes containing a GATC cluster are part of a regulation network. The present is a case study, which demonstrates that the analysis of transcriptome data does not always permit to identify the primary cause of a phenomenon observed; on the other hand, a classic genomic approach linked with a comparative study of related genomes may allow this identification.