The LexA protein is a transcriptional repressor of the bacterial SOS DNA repair system, which comprises a set of DNA repair and cellular survival genes that are induced in response to DNA damage. Its varied DNA binding motifs have been characterized and reported in the Escherichia coli, Bacillus subtilis, rhizobia family members, marine magnetotactic bacterium, Salmonella typhimurium and recently in Mycobacterium tuberculosis and this motifs information has been used in our theoretical analysis to detect its novel regulated genes in radio-resistant Deinococcus radiodurans genome. This bacterium showed presence of SOS-box like consensus sequence in the upstream sequences of 3166 genes with >60% motif score similarity percentage (MSSP) on both strands. Attempts to identify LexA-binding sites and the composition of the putative SOS regulon in D. radiodurans have been unsuccessful so far. To resolve the problem we performed theoretical analysis with modifications on reported data set of genes related to DNA repair (61 genes), stress response (145 genes) and some unusual predicted operons (21 clusters). Expression of some of the predicted SOS-box regulated operon members then was examined through the previously reported microarray data which confirm the expression of only single predicted operon i.e. DRB0143 (AAA superfamily NTPase related to 5-methylcytosine specific restriction enzyme subunit McrB) and DRB0144 (homolog of the McrC subunit of the McrBC restriction modification system). The methodology involved weight matrix construction through CONSENSUS algorithm using information of conserved upstream sequences of eight known genes including dinB, tagC, lexA, recA, uvrB, yneA of B. subtilis while lexA and recA of D. radiodurans through phylogenetic footprinting method and later detection of similar conserved SOS-box like LexA binding motifs through both RSAT & PoSSuMsearch programs. The resultant DNA consensus sequence had highly conserved 14 bp SOS-box like binding site.
© 2008 The Author(s). Published by Journal of Integrative Bioinformatics.
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