Dibbisa Duguma, Wagari Gobena
School of Biological Sciences and Biotechnology, Haramaya University, Dire Dawa, Ethiopia.
Department of Animal Science, Oda Bultum University, Chiro, Ethiopia.
Int J Genomics. 2022 Aug 12;2022:6185615. doi: 10.1155/2022/6185615. eCollection 2022.
Microbial genes and their product were diverse and beneficial for heavy metal bioremediation from the contaminated sites. Screening of genes and gene products plays a significant role in the detoxification of pollutants. Understanding of the promoter region and its regulatory elements is a vital implication of microbial genes. To the best of our knowledge, there is no in silico study reported so far on gene families used for heavy metal bioremediation. The motif distribution was observed densely upstream of the TSSs (transcription start sites) between +1 and -350 bp and sparsely distributed above -350 bp, according to the current study. MEME identified the best common candidate motifs of TFs (transcription factors) binding with the lowest e value (7.2-033) and is the most statistically significant candidate motif. The EXPREG output of the 11 TFs with varying degrees of function such as activation, repression, transcription, 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 most TFs are involved in transcription gene repression rather than activation. Likewise, EXPREG output revealed that transcriptional conformational modes, such as monomers, dimers, tetramers, and other factors, were also analyzed. The data indicated that most of the 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 compared to the promoter regions. Understanding the common candidate motifs, transcriptional factors, and regulatory elements of the operon gene cluster using a machine learning approach could help us better understand gene expression patterns in heavy metal bioremediation.
微生物基因及其产物具有多样性,对受污染场地的重金属生物修复有益。基因和基因产物的筛选在污染物解毒中起着重要作用。了解启动子区域及其调控元件是微生物基因的重要意义。据我们所知,目前尚未有关于用于重金属生物修复的基因家族的计算机模拟研究报道。根据当前研究,在转录起始位点(TSS)上游+1至-350 bp之间观察到基序分布密集,而在-350 bp以上分布稀疏。MEME识别出与最低e值(7.2-033)结合的转录因子(TF)的最佳常见候选基序,并且是最具统计学意义的候选基序。对11种具有不同功能程度(如激活、抑制、转录和双重作用)的TF的EXPREG输出进行了全面检查。数据显示,在激活和抑制方面的转录基因调控分别为36.4%和54.56%。这表明大多数TF参与转录基因抑制而非激活。同样,EXPREG输出显示,还分析了转录构象模式,如单体、二聚体、四聚体和其他因素。数据表明,大多数转录构象模式是双重的,占96%。使用在线和离线工具进行的CpG岛分析表明,与启动子区域相比,基因体中的CpG岛较少。使用机器学习方法了解操纵子基因簇的常见候选基序、转录因子和调控元件,有助于我们更好地理解重金属生物修复中的基因表达模式。