In Silico Detection of Sequence Variations Modifying Transcriptional Regulation

Abstract

Identification of functional genetic variation associated with increased susceptibility to complex diseases can elucidate genes and underlying biochemical mechanisms linked to disease onset and progression. For genes linked to genetic diseases, most identified causal mutations alter an encoded protein sequence. Technological advances for measuring RNA abundance suggest that a significant number of undiscovered causal mutations may alter the regulation of gene transcription. However, it remains a challenge to separate causal genetic variations from linked neutral variations. Here we present an in silico driven approach to identify possible genetic variation in regulatory sequences. The approach combines phylogenetic footprinting and transcription factor binding site prediction to identify variation in candidate cis-regulatory elements. The bioinformatics approach has been tested on a set of SNPs that are reported to have a regulatory function, as well as background SNPs. In the absence of additional information about an analyzed gene, the poor specificity of binding site prediction is prohibitive to its application. However, when additional data is available that can give guidance on which transcription factor is involved in the regulation of the gene, the in silico binding site prediction improves the selection of candidate regulatory polymorphisms for further analyses. The bioinformatics software generated for the analysis has been implemented as a Web-based application system entitled RAVEN (regulatory analysis of variation in enhancers). The RAVEN system is available at http://www.cisreg.ca for all researchers interested in the detection and characterization of regulatory sequence variation.

Transcription factor NFE2L2/NRF2 modulates chaperone-mediated autophagy through the regulation of LAMP2A

“…We then used a Python-based bioinformatics analysis to scan these binding regions for the consensus ARE as established in the JASPAR database [27]. We detected 8 putative AREs in the LAMP2 gene (Table S1) and 3 of them showed a relative score higher than 85%, a commonly used threshold for transcription factor binding-site analysis [28,29]. Moreover, the analysis also retrieved already identified AREs in the bona fide target genes of NFE2L2, HMOX1 and SQSTM1/p62 (Table 1).…”

Quantifying the effect of sequence variation on regulatory interactions

“…Here we study 20 known associations of regulatory SNPs with transcription factors, which were collected by Andersen et al [2008]. These comprise SNPs that are naturally occurring or were generated by targeted mutagenesis.…”

“…For a first test of our method we applied sTRAP to a list of known regulatory sequence variations from humans and their associated transcription factors as collected by Andersen et al [2008]. The set of SNPs is based on extensive literature search, and includes naturally occurring SNP as well as those generated in mutagenesis experiments; see Method section for more details.…”

“…For this comparison we utilized the scoring system developed in Rahmann et al [2003]. Score differences have previously been used in Andersen et al [2008] and GuhaThakurta et al [2006], but with no beneficial effect on their ability to predict functional SNPs. Although Equations (3) and (4) are based on p-values, it is important to notice that the above evaluation merely utilizes a ranking of those heuristic scores and does not provide any significance level.…”

“…Focusing on the functional role of SNPs, the computational approach has also been used to assess the overall correlation of functional SNPs with sequence features, such as predicted binding sites [GuhaThakurta et al, 2006]. In a similar spirit, Andersen et al [2008] have attempted to utilize evolutionary sequence conservation to assess the functionality of noncoding sequence variations. These authors noted that binding site predictions do not improve the detection of functional SNPs.…”