通过整合倒置条件下的动态数据揭示铜绿假单胞菌的主要o2响应基因及其网络。

IF 1.4
Feng Q He, Wei Wang, Ping Zheng, Padhmanand Sudhakar, Jibin Sun, An-Ping Zeng
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引用次数: 9

摘要

确定铜绿假单胞菌PAO1 (Pseudomonas aeruginosa PAO1, PA)适应氧可用性改变的基因网络,对于更好地了解PA的应激反应和致病性至关重要。我们对连续培养系统中PA从高氧张力到低氧张力(HLOT)和从低氧张力到高氧张力(LHOT)的反向转变进行了高时间分辨率(HTR)转录组分析。从这些对两种瞬时条件均有响应的基因中,我们鉴定出85个必需氧可用性响应基因(EORGs),包括预期的编码精氨酸发酵酶的基因(arcDABC)。结合结合基序搜索、文献和HTR数据,构建了PA EORGs的调控网络。值得注意的是,我们的研究结果表明,只有众所周知的氧响应转录因子控制的子网络与文献知识之间的一致性非常高,例如,厌氧调节因子(ANR)和群体感应调节因子RhIR分别控制了87.5%和83.3%的子网络。这些结果不仅揭示了PA及其转录调控网络的严格EORGs,而且还强调了在给定条件下,仅对明显受影响的调控子实现高准确性的推断调控网络可能是可行的,而不是在基因组尺度上对所有表达的调控子都可行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Essential O2-responsive genes of Pseudomonas aeruginosa and their network revealed by integrating dynamic data from inverted conditions.

Identification of the gene network through which Pseudomonas aeruginosa PAO1 (PA) adapts to altered oxygen-availability environments is essential for a better understanding of stress responses and pathogenicity of PA. We performed high-time-resolution (HTR) transcriptome analyses of PA in a continuous cultivation system during the transition from high oxygen tension to low oxygen tension (HLOT) and the reversed transition from low to high oxygen tension (LHOT). From those genes responsive to both transient conditions, we identified 85 essential oxygen-availability responsive genes (EORGs), including the expected ones (arcDABC) encoding enzymes for arginine fermentation. We then constructed the regulatory network for the EORGs of PA by integrating information from binding motif searching, literature and HTR data. Notably, our results show that only the sub-networks controlled by the well-known oxygen-responsive transcription factors show a very high consistency between the inferred network and literature knowledge, e.g. 87.5% and 83.3% of the obtained sub-network controlled by the anaerobic regulator (ANR) and a quorum sensing regulator RhIR, respectively. These results not only reveal stringent EORGs of PA and their transcription regulatory network, but also highlight that achieving a high accuracy of the inferred regulatory network might be feasible only for the apparently affected regulators under the given conditions but not for all the expressed regulators on a genome scale.

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