The difficult interpretation of transcriptome data: the case of the GATC regulatory network.
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 500 bp 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.
Riva
Alessandra
A
CNRS, Laboratoire Génome et Informatique, Tour Evry 2, 523 Place des Terrasses, 91034 Evry cedex, France. gucki@genopole.cnrs.fr
Delorme
Marie-Odile
MO
Chevalier
Tony
T
Guilhot
Nicolas
N
Hénaut
Corinne
C
Hénaut
Alain
A
D019295:Computational Biology; D004926:Escherichia coli / Q000235:genetics; D023281:Genomics; D005838:Genotype; D008780:Methyltransferases / Q000235:genetics; D018895:Microsatellite Repeats / Q000235:genetics / Q000502:physiology*; D012475:Salmonella / Q000235:genetics; D013223:Statistics as Topic; D013696:Temperature; D014158:Transcription, Genetic
D008780:Methyltransferases; C022556:mRNA (2'-O-methyladenosine-N6-)-methyltransferase