Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks.
Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks.
复制标题
细菌小RNA调控网络的推断及其与转录因子驱动的调控网络的整合
DOI：
10.1128/msystems.00057-20
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发表时间：
2020-06-02
期刊：
影响因子：
6.4
通讯作者：
Eichenberger P
中科院分区：
文献类型：
--
作者：
Arrieta-Ortiz ML;Hafemeister C;Shuster B;Baliga NS;Bonneau R;Eichenberger P
小分子非编码RNA（Small noncoding RNAs，sRNAs）是细菌基因表达的关键调控因子。通过互补碱基配对，sRNA影响mRNA的稳定性和翻译效率。在这里，我们描述了一个网络推理方法，旨在确定转录水平的siRNA介导的调节。我们使用现有的转录数据集和先验知识来推断sRNA调节子使用我们的网络推理工具，Inferelator。这种方法产生了包括转录因子和sRNA的贡献的全基因组基因调控网络。我们展示了使用现有实验数据估计sRNA活动并将其纳入网络推理管道的好处。我们还展示了如何这些估计的sRNA调节活动可以挖掘，以确定实验条件下，sRNA是最活跃的。我们发现了45种新的实验支持的大肠杆菌中的sRNA-mRNA相互作用，优于以前的基于网络的努力。此外，我们的管道通过添加数据驱动的过滤步骤来补充基于序列的sRNA-mRNA相互作用预测方法。最后，我们通过鉴定24种新的实验支持的铜绿假单胞菌、金黄色葡萄球菌和枯草芽孢杆菌中的sRNA-mRNA相互作用来展示我们的方法的普遍适用性。总的来说，我们的策略对多种细菌物种中sRNA调控的功能背景产生了新的见解。重要性单个细菌基因组可能有几十个小的非编码RNA，它们的调控功能基本上还没有被探索过。虽然细菌sRNAs影响广泛的生物过程，包括抗生素耐药性和致病性，但我们目前对sRNAs介导的调控的理解还远未完成。大多数可用的信息仅限于少数经过充分研究的细菌物种;即使在这些物种中，也只有部分sRNA靶标被详细描述。为了缩小这一信息差距，我们开发了一种计算策略，利用现有的转录数据和知识的验证和推定的sRNA-mRNA相互作用，推断扩展的sRNA调节子。我们的方法有利于识别实验支持的新的相互作用，同时过滤掉假阳性结果。由于其数据驱动的性质，我们的方法优先考虑从序列分析或从sRNA-mRNA结合实验中通过计算机预测的候选sRNA-target对列表之间的生物相关相互作用。
Small noncoding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect mRNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional data sets and prior knowledge to infer sRNA regulons using our network inference tool, the Inferelator. This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines using available experimental data. We also demonstrate how these estimated sRNA regulatory activities can be mined to identify the experimental conditions where sRNAs are most active. We uncover 45 novel experimentally supported sRNA-mRNA interactions in Escherichia coli, outperforming previous network-based efforts. Additionally, our pipeline complements sequence-based sRNA-mRNA interaction prediction methods by adding a data-driven filtering step. Finally, we show the general applicability of our approach by identifying 24 novel, experimentally supported, sRNA-mRNA interactions in Pseudomonas aeruginosa, Staphylococcus aureus, and Bacillus subtilis. Overall, our strategy generates novel insights into the functional context of sRNA regulation in multiple bacterial species. IMPORTANCE Individual bacterial genomes can have dozens of small noncoding RNAs with largely unexplored regulatory functions. Although bacterial sRNAs influence a wide range of biological processes, including antibiotic resistance and pathogenicity, our current understanding of sRNA-mediated regulation is far from complete. Most of the available information is restricted to a few well-studied bacterial species; and even in those species, only partial sets of sRNA targets have been characterized in detail. To close this information gap, we developed a computational strategy that takes advantage of available transcriptional data and knowledge about validated and putative sRNA-mRNA interactions for inferring expanded sRNA regulons. Our approach facilitates the identification of experimentally supported novel interactions while filtering out false-positive results. Due to its data-driven nature, our method prioritizes biologically relevant interactions among lists of candidate sRNA-target pairs predicted in silico from sequence analysis or derived from sRNA-mRNA binding experiments.