TY  - JOUR
AB  - Background: Transcriptional regulation of gene activity is essential for any living organism. Transcription factors therefore recognize specific binding sites within the DNA to regulate the expression of particular target genes. The genome-scale reconstruction of the emerging regulatory networks is important for biotechnology and human medicine but cost-intensive, time-consuming, and impossible to perform for any species separately. By using bioinformatics methods one can partially transfer networks from well-studied model organisms to closely related species. However, the prediction quality is limited by the low level of evolutionary conservation of the transcription factor binding sites, even within organisms of the same genus. Results: Here we present an integrated bioinformatics workflow that assures the reliability of transferred gene regulatory networks. Our approach combines three methods that can be applied on a large-scale: re-assessment of annotated binding sites, subsequent binding site prediction, and homology detection. A gene regulatory interaction is considered to be conserved if (1) the transcription factor, (2) the adjusted binding site, and (3) the target gene are conserved. The power of the approach is demonstrated by transferring gene regulations from the model organism Corynebacterium glutamicum to the human pathogens C. diphtheriae, C. jeikeium, and the biotechnologically relevant C. efficiens. For these three organisms we identified reliable transcriptional regulations for similar to 40% of the common transcription factors, compared to similar to 5% for which knowledge was available before. Conclusion: Our results suggest that trustworthy genome-scale transfer of gene regulatory networks between organisms is feasible in general but still limited by the level of evolutionary conservation.
DA  - 2009
DO  - 10.1186/1752-0509-3-8
LA  - eng
IS  - 1
M2  - 8
PY  - 2009
SN  - 1752-0509
SP  - 8-
T2  - BMC Systems Biology
TI  - Reliable transfer of transcriptional gene regulatory networks between taxonomically related organisms
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-17832401
Y2  - 2024-11-22T01:56:20
ER  -