Arrieta-Ortiz Mario L, Hafemeister Christoph, Shuster Bentley, Baliga Nitin S, Bonneau Richard, Eichenberger Patrick
Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, USA.
Institute for Systems Biology, Seattle, Washington, USA.
mSystems. 2020 Jun 2;5(3):e00057-20. doi: 10.1128/mSystems.00057-20.
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 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 , 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 , , and Overall, our strategy generates novel insights into the functional context of sRNA regulation in multiple bacterial species. 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 from sequence analysis or derived from sRNA-mRNA binding experiments.
小非编码RNA（sRNA）是细菌基因表达的关键调节因子。通过互补碱基配对，sRNA影响mRNA的稳定性和翻译效率。在此，我们描述了一种网络推理方法，旨在识别sRNA介导的转录水平调控。我们使用现有的转录数据集和先验知识，通过我们的网络推理工具来推断sRNA调控子。这种方法产生全基因组范围的基因调控网络，其中包括转录因子和sRNA的贡献。我们展示了利用现有实验数据估计并将sRNA活性纳入网络推理流程的益处。我们还演示了如何挖掘这些估计的sRNA调控活性，以识别sRNA最活跃的实验条件。我们在[具体物种]中发现了45种新的经实验支持的sRNA-mRNA相互作用，优于以往基于网络的研究成果一。此外，我们的流程通过添加数据驱动的过滤步骤，对基于序列的sRNA-mRNA相互作用预测方法起到补充作用。最后，我们通过在[其他物种]中识别出24种新的、经实验支持的sRNA-mRNA相互作用，展示了我们方法的普遍适用性。总体而言，我们的策略为多种细菌物种中sRNA调控的功能背景提供了新的见解。单个细菌基因组可能有数十种小非编码RNA，其调控功能大多未被探索。尽管细菌sRNA影响广泛的生物学过程，包括抗生素抗性和致病性，但我们目前对sRNA介导的调控的理解还远远不够完整。大多数可用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靶标集得到了详细表征。为了填补这一信息空白，我们开发了一种计算策略，利用可用的转录数据以及关于已验证和推测的sRNA-mRNA相互作用的知识，来推断扩展的sRNA调控子。我们的方法有助于识别经实验支持的新相互作用，同时滤除假阳性结果。由于其数据驱动的性质，我们的方法在从序列分析预测或从sRNA-mRNA结合实验得出的候选sRNA-靶标对列表中，优先考虑生物学相关的相互作用。
