参与生物修复的假单胞菌抗汞基因的硅片研究:启动子区域和调控元件的理解。

Duguma Dibbisa, Gobena Wagari
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引用次数: 0

摘要

微生物基因及其产物具有多样性,有利于重金属污染场地的生物修复。基因和基因产物的筛选在污染物的解毒中起着重要的作用。了解启动子区域及其调控元件是微生物基因研究的重要内容。据我们所知,到目前为止,还没有关于基因家族用于重金属生物修复的硅研究报告。根据目前的研究,基序分布在+1和-350 bp之间的上游tss(转录起始位点)密集分布,在-350 bp以上稀疏分布。MEME确定了转录因子结合的最佳共同候选基序,e值最低(7.20 -033),是最具统计学意义的候选基序。11个tf的EXPREG输出具有不同程度的功能,如激活,抑制,转录和双重目的进行了彻底的检查。数据显示,激活和抑制转录基因的比例分别为36.4%和54.56%。这表明大多数tf参与转录基因的抑制而不是激活。同样,EXPREG输出显示转录构象模式,如单体,二聚体,四聚体和其他因素,也进行了分析。数据表明,大多数转录构象模式为双构象模式,占96%。利用在线和离线工具进行的CpG岛分析显示,与启动子区域相比,基因体的CpG岛较少。利用机器学习方法了解mer操纵子基因簇的共同候选基序、转录因子和调控元件可以帮助我们更好地了解重金属生物修复中的基因表达模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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In Silico Study of Mercury Resistance Genes Extracted from Pseudomonas spp. Involved in Bioremediation: Understanding the Promoter Regions and Regulatory Elements

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 mer 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 (7.2e-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 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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来源期刊
Comparative and Functional Genomics
Comparative and Functional Genomics 生物-生化与分子生物学
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