SIGffRid: A tool to search for sigma factor binding sites in bacterial genomes using comparative approach and biologically driven statistics

<p>Abstract</p> <p>Background</p> <p>Many programs have been developed to identify transcription factor binding sites. However, most of them are not able to infer two-word motifs with variable spacer lengths. This case is encountered for RNA polymerase Sigma (<it>σ</it>) Factor Binding Sites (SFBSs) usually composed of two boxes, called -35 and -10 in reference to the transcription initiation point. Our goal is to design an algorithm detecting SFBS by using combinational and statistical constraints deduced from biological observations.</p> <p>Results</p> <p>We describe a new approach to identify SFBSs by comparing two related bacterial genomes. The method, named SIGffRid (SIGma Factor binding sites Finder using R'MES to select Input Data), performs a simultaneous analysis of pairs of promoter regions of orthologous genes. SIGffRid uses a prior identification of over-represented patterns in whole genomes as selection criteria for potential -35 and -10 boxes. These patterns are then grouped using pairs of short seeds (of which one is possibly gapped), allowing a variable-length spacer between them. Next, the motifs are extended guided by statistical considerations, a feature that ensures a selection of motifs with statistically relevant properties. We applied our method to the pair of related bacterial genomes of <it>Streptomyces coelicolor </it>and <it>Streptomyces avermitilis</it>. Cross-check with the well-defined SFBSs of the SigR regulon in <it>S. coelicolor </it>is detailed, validating the algorithm. SFBSs for HrdB and BldN were also found; and the results suggested some new targets for these <it>σ </it>factors. In addition, consensus motifs for BldD and new SFBSs binding sites were defined, overlapping previously proposed consensuses. Relevant tests were carried out also on bacteria with moderate GC content (i.e. <it>Escherichia coli</it>/<it>Salmonella typhimurium </it>and <it>Bacillus subtilis</it>/<it>Bacillus licheniformis </it>pairs). Motifs of house-keeping <it>σ </it>factors were found as well as other SFBSs such as that of SigW in <it>Bacillus </it>strains.</p> <p>Conclusion</p> <p>We demonstrate that our approach combining statistical and biological criteria was successful to predict SFBSs. The method versatility autorizes the recognition of other kinds of two-box regulatory sites.</p

SIGffRid: A tool to search for sigma factor binding sites in bacterial genomes using comparative approach and biologically driven statistics

<p>Abstract</p> <p>Background</p> <p>Many programs have been developed to identify transcription factor binding sites. However, most of them are not able to infer two-word motifs with variable spacer lengths. This case is encountered for RNA polymerase Sigma (<it>σ</it>) Factor Binding Sites (SFBSs) usually composed of two boxes, called -35 and -10 in reference to the transcription initiation point. Our goal is to design an algorithm detecting SFBS by using combinational and statistical constraints deduced from biological observations.</p> <p>Results</p> <p>We describe a new approach to identify SFBSs by comparing two related bacterial genomes. The method, named SIGffRid (SIGma Factor binding sites Finder using R'MES to select Input Data), performs a simultaneous analysis of pairs of promoter regions of orthologous genes. SIGffRid uses a prior identification of over-represented patterns in whole genomes as selection criteria for potential -35 and -10 boxes. These patterns are then grouped using pairs of short seeds (of which one is possibly gapped), allowing a variable-length spacer between them. Next, the motifs are extended guided by statistical considerations, a feature that ensures a selection of motifs with statistically relevant properties. We applied our method to the pair of related bacterial genomes of <it>Streptomyces coelicolor </it>and <it>Streptomyces avermitilis</it>. Cross-check with the well-defined SFBSs of the SigR regulon in <it>S. coelicolor </it>is detailed, validating the algorithm. SFBSs for HrdB and BldN were also found; and the results suggested some new targets for these <it>σ </it>factors. In addition, consensus motifs for BldD and new SFBSs binding sites were defined, overlapping previously proposed consensuses. Relevant tests were carried out also on bacteria with moderate GC content (i.e. <it>Escherichia coli</it>/<it>Salmonella typhimurium </it>and <it>Bacillus subtilis</it>/<it>Bacillus licheniformis </it>pairs). Motifs of house-keeping <it>σ </it>factors were found as well as other SFBSs such as that of SigW in <it>Bacillus </it>strains.</p> <p>Conclusion</p> <p>We demonstrate that our approach combining statistical and biological criteria was successful to predict SFBSs. The method versatility autorizes the recognition of other kinds of two-box regulatory sites.</p

Anatomy of Escherichia coli σ(70) promoters

Information theory was used to build a promoter model that accounts for the −10, the −35 and the uncertainty of the gap between them on a common scale. Helical face assignment indicated that base −7, rather than −11, of the −10 may be flipping to initiate transcription. We found that the sequence conservation of σ(70) binding sites is 6.5 ± 0.1 bits. Some promoters lack a −35 region, but have a 6.7 ± 0.2 bit extended −10, almost the same information as the bipartite promoter. These results and similarities between the contacts in the extended −10 binding and the −35 suggest that the flexible bipartite σ factor evolved from a simpler polymerase. Binding predicted by the bipartite model is enriched around 35 bases upstream of the translational start. This distance is the smallest 5′ mRNA leader necessary for ribosome binding, suggesting that selective pressure minimizes transcript length. The promoter model was combined with models of the transcription factors Fur and Lrp to locate new promoters, to quantify promoter strengths, and to predict activation and repression. Finally, the DNA-bending proteins Fis, H-NS and IHF frequently have sites within one DNA persistence length from the −35, so bending allows distal activators to reach the polymerase

Region 2.5 of the Escherichia coli RNA polymerase sigma70 subunit is responsible for the recognition of the 'extended-10' motif at promoters.

At some bacterial promoters, a 5'-TG-3' sequence element, located one base upstream of the -10 hexamer element, provides an essential motif necessary for transcription initiation. We have identified a mutant of the Escherichia coli RNA polymerase sigma70 subunit that has an altered preference for base sequences in this 'extended -10' region. We show that this mutant sigma70 subunit substantially increases transcription from promoters bearing 5'-TC-3' or 5'-TT-3' instead of a 5'-TG-3' motif, located one base upstream of the -10 hexamer. The mutant results from a single base pair substitution in the rpoD gene that causes a Glu to Gly change at position 458 of sigma70. This substitution identifies a functional region in sigma70 that is immediately adjacent to the well-characterized region 2.4 (positions 434-453, previously shown to contact the -10 hexamer). From these results, we conclude that this region (which we name region 2.5) is involved in contacting the 5'-TG-3' motif found at some bacterial promoters: thus, extended -10 regions are recognized by an extended region 2 of the RNA polymerase sigma70 subunit

Associations of <i>Toll-Like Receptor</i> and <i>β-Defensin</i> Polymorphisms with Measures of Periodontal Disease (PD) in HIV+ North American Adults: An Exploratory Study

<div><p>Polymorphisms in toll-like receptor (<i>TLR</i>) and β-defensin (<i>DEFB</i>) genes have been recognized as potential genetic factors that can influence susceptibility to and severity of periodontal diseases (PD). However, data regarding associations between these polymorphisms and PD are still scarce in North American populations, and are not available in HIV+ North American populations. In this exploratory study, we analyzed samples from HIV+ adults (n = 115), who received primary HIV care at 3 local outpatient HIV clinics and were monitored for PD status. We genotyped a total of 41 single nucleotide polymorphisms (SNPs) in 8 <i>TLR</i> genes and copy number variation (CNV) in <i>DEFB4</i>/<i>103A</i>. We performed regression analyses for levels of 3 periodontopathogens in subgingival dental plaques (<i>Porphyromonas gingivalis</i> [<i>Pg</i>], <i>Treponema denticola</i> [<i>Td</i>], and <i>Tannerella forsythia</i> [<i>Tf</i>]) and 3 clinical measures of PD (periodontal probing depth [PPD], gingival recession [REC], and bleeding on probing [BOP]). In all subjects combined, 2 SNPs in <i>TLR1</i> were significantly associated with <i>Td</i>, and one SNP in <i>TLR2</i> was significantly associated with BOP. One of the 2 SNPs in <i>TLR1</i> was significantly associated with <i>Td</i> in Caucasians. In addition, another SNP in <i>TLR1</i> and a SNP in <i>TLR6</i> were also significantly associated with <i>Td</i> and <i>Pg</i>, respectively, in Caucasians. All 3 periodontopathogen levels were significantly associated with PPD and BOP, but none was associated with REC. Instrumental variable analysis showed that 8 SNPs in 6 <i>TLR</i> genes were significantly associated with the 3 periodontopathogen levels. However, associations between the 3 periodontopathogen levels and PPD or BOP were not driven by associations with these identified SNPs. No association was found between <i>DEFB4</i>/<i>103A</i> CNV and any periodontopathogen level or clinical measure in all samples, Caucasians, or African Americans. Our exploratory study suggests a role of <i>TLR</i> polymorphisms, particularly <i>TLR1</i> and <i>TLR6</i> polymorphisms, in PD in HIV+ North Americans.</p></div

Diagrammatic Representation of Instrumental Variable Analysis.

<p>Instrumental variable models use associations C and A to estimate the relationship between an exposure/risk factor and an outcome (B). Note that the instrument is not supposed to have a direct effect on the outcome, hence this line (C) is dashed. Abbreviations: <i>Pg</i>, <i>Porphyromonas gingivalis</i>; <i>Td</i>, <i>Treponema denticola</i>; <i>Tf</i>, <i>Tannerella forsythia</i>; PPD, periodontal probing depth; BOP, bleeding on probing.</p

Association Between Periodontopathogen Levels and <i>TLR</i> SNPs^*.

<p>Association Between Periodontopathogen Levels and <i>TLR</i> SNPs^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0164075#t002fn001" target="_blank">*</a>}.</p

Association Between Clinical Measures of PD and <i>TLR</i> SNPs^*.

<p>Association Between Clinical Measures of PD and <i>TLR</i> SNPs^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0164075#t003fn001" target="_blank">*</a>}.</p