Khan Essa Ahsan, Rückert-Reed Christian, Dahiya Gurvinder Singh, Tietze Lisa, Fages-Lartaud Maxime, Busche Tobias, Kalinowski Jörn, Shingler Victoria, Lale Rahmi
Department of Biotechnology and Food Science, Faculty of Natural Sciences, Norwegian University of Science and Technology, Trondheim 7491, Norway.
Bielefeld University, Center for Biotechnology (CeBiTec), Technology Platform Genomics, Bielefeld 33615, Germany.
Nucleic Acids Res. 2025 Apr 10;53(7). doi: 10.1093/nar/gkaf306.
The variable sigma (σ) subunit of the bacterial RNA polymerase holoenzyme determines promoter specificity and facilitates open complex formation during transcription initiation. Understanding σ-factor binding sequences is therefore crucial for deciphering bacterial gene regulation. Here, we present a data-driven high-throughput approach that utilizes an extensive library of 1.54 million DNA templates providing artificial promoters and 5' untranslated region sequences for σ-factor DNA-binding motif discovery. This method combines the generation of extensive DNA libraries, in vitro transcription, RNA aptamer, and deep DNA and RNA sequencing. It allows direct assessment of promoter activity, identification of transcription start sites, and quantification of promoter strength based on mRNA production levels. We applied this approach to map σ54 DNA-binding sequences in Pseudomonas putida. Deep sequencing of the enriched RNA pool revealed 64 966 distinct σ54 binding motifs, significantly expanding the known repertoire. This data-driven approach surpasses traditional methods by directly evaluating promoter function and avoiding selection bias based solely on binding affinity. This comprehensive dataset enhances our understanding of σ-factor binding sequences and their regulatory roles, opening avenues for new research in biology and biotechnology.
细菌RNA聚合酶全酶的可变σ（σ）亚基决定启动子特异性，并在转录起始过程中促进开放复合物的形成。因此，了解σ因子结合序列对于破译细菌基因调控至关重要。在这里，我们提出了一种数据驱动的高通量方法，该方法利用一个包含154万个DNA模板的广泛文库，这些模板提供人工启动子和5'非翻译区序列，用于发现σ因子DNA结合基序。该方法结合了广泛DNA文库的生成、体外转录、RNA适体以及深度DNA和RNA测序。它允许直接评估启动子活性、鉴定转录起始位点，并根据mRNA产生水平对启动子强度进行定量。我们应用这种方法来绘制恶臭假单胞菌中σ54的DNA结合序列。对富集的RNA池进行深度测序，揭示了64966个不同的σ54结合基序，显著扩展了已知的序列库。这种数据驱动的方法通过直接评估启动子功能并避免仅基于结合亲和力的选择偏差，超越了传统方法。这个全面的数据集增强了我们对σ因子结合序列及其调控作用的理解，为生物学和生物技术的新研究开辟了道路。
