In silico analysis of mercury resistance genes extracted from Pseudomonas spp. involved in bioremediation

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Abstract Background: Microbial gene and gene production were diverse and beneficial for heavy metal bioremediation from the contaminated sites. Screening of genes and gene products plays an important role in the detoxification of pollutants. Understanding of promoter region and its regulatory elements is a vital implication of microbial genes. To the best of our knowledge, there is no in silico analysis report so far on mer genes families used for heavy metal bioremediation. Results: The motif distribution was observed densely upstream of the TSS from +1 to -400bp and sparsely distributed above -500bp, according to the current study. MEME identified the best common candidate motifs of TFs binding with the lowest e value (7.2e 033) and is the most statistically significant candidate motif. The EXPREG output of the 11 TFs with varying degrees of function as activation, repression transcriptions, and dual purposes was thoroughly examined. Data revealed that transcriptional gene regulation in terms of activation and repression was observed at 36.4% and 54.56% respectively. This shows that the vast majority of TFs are involved in the transcription gene repression rather than activation. Likewise, EXPREG output revealed that transcriptional conformational modes, such as monomer, dimer, tetramer, and other factors, were also analyzed. The data indicated that the majority of transcriptional conformation mode was dual which accounts for 96%. CpG island analysis using online and offline tools revealed that the gene body had fewer CpG islands as compared with the promoter regions. Conclusion: Understanding the common candidate motifs, transcriptional factors binding sites, and regulatory elements of the mer operon gene cluster using a machine learning approach could help us better understand gene expression patterns in heavy metal bioremediation.

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