Interfaculty Institute of Microbiology and Infection Medicine Tübingen (IMIT), University of Tübingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany.
Interfaculty Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany.
Nucleic Acids Res. 2022 Jul 5;50(W1):W682-W689. doi: 10.1093/nar/gkac371.
For decades, natural products have been used as a primary resource in drug discovery pipelines to find new antibiotics, which are mainly produced as secondary metabolites by bacteria. The biosynthesis of these compounds is encoded in co-localized genes termed biosynthetic gene clusters (BGCs). However, BGCs are often not expressed under laboratory conditions. Several genetic manipulation strategies have been developed in order to activate or overexpress silent BGCs. Significant increases in production levels of secondary metabolites were indeed achieved by modifying the expression of genes encoding regulators and transporters, as well as genes involved in resistance or precursor biosynthesis. However, the abundance of genes encoding such functions within bacterial genomes requires prioritization of the most promising ones for genetic manipulation strategies. Here, we introduce the 'Secondary Metabolite Transcriptomic Pipeline' (SeMa-Trap), a user-friendly web-server, available at https://sema-trap.ziemertlab.com. SeMa-Trap facilitates RNA-Seq based transcriptome analyses, finds co-expression patterns between certain genes and BGCs of interest, and helps optimize the design of comparative transcriptomic analyses. Finally, SeMa-Trap provides interactive result pages for each BGC, allowing the easy exploration and comparison of expression patterns. In summary, SeMa-Trap allows a straightforward prioritization of genes that could be targeted via genetic engineering approaches to (over)express BGCs of interest.
几十年来,天然产物一直是药物发现管道中寻找新抗生素的主要资源,这些抗生素主要作为细菌的次级代谢产物产生。这些化合物的生物合成由称为生物合成基因簇 (BGC) 的共定位基因编码。然而,BGC 通常在实验室条件下不表达。为了激活或过度表达沉默的 BGC,已经开发了几种遗传操作策略。通过修饰编码调节剂和转运蛋白以及参与抗性或前体生物合成的基因的表达,确实可以显著提高次级代谢物的产生水平。然而,细菌基因组中编码这些功能的基因的丰富度需要对最有前途的基因进行遗传操作策略的优先级排序。在这里,我们介绍了“次级代谢产物转录组学管道”(SeMa-Trap),这是一个用户友好的网络服务器,可在 https://sema-trap.ziemertlab.com 上获得。SeMa-Trap 促进了基于 RNA-Seq 的转录组分析,发现了某些基因与感兴趣的 BGC 之间的共表达模式,并有助于优化比较转录组分析的设计。最后,SeMa-Trap 为每个 BGC 提供了交互式结果页面,允许轻松探索和比较表达模式。总之,SeMa-Trap 允许通过遗传工程方法直接优先考虑可以靶向的基因,以(过度)表达感兴趣的 BGC。