Chair of Applied and Molecular Microbiology, Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany.
Department of Biochemistry, Center for Plant Biology, Purdue University, West Lafayette, Indiana, USA.
Microbiol Spectr. 2021 Oct 31;9(2):e0089821. doi: 10.1128/Spectrum.00898-21. Epub 2021 Sep 15.
Fungal secondary metabolites are widely used as therapeutics and are vital components of drug discovery programs. A major challenge hindering discovery of novel secondary metabolites is that the underlying pathways involved in their biosynthesis are transcriptionally silent under typical laboratory growth conditions, making it difficult to identify the transcriptional networks that they are embedded in. Furthermore, while the genes participating in secondary metabolic pathways are typically found in contiguous clusters on the genome, known as biosynthetic gene clusters (BGCs), this is not always the case, especially for global and pathway-specific regulators of pathways' activities. To address these challenges, we used 283 genome-wide gene expression data sets of the ascomycete cell factory Aspergillus niger generated during growth under 155 different conditions to construct two gene coexpression networks based on Spearman's correlation coefficients (SCCs) and on mutual rank-transformed Pearson's correlation coefficients (MR-PCCs). By mining these networks, we predicted six transcription factors, named MjkA to MjkF, to regulate secondary metabolism in A. niger. Overexpression of each transcription factor using the Tet-On cassette modulated the production of multiple secondary metabolites. We found that the SCC and MR-PCC approaches complemented each other, enabling the delineation of putative global (SCC) and pathway-specific (MR-PCC) transcription factors. These results highlight the potential of coexpression network approaches to identify and activate fungal secondary metabolic pathways and their products. More broadly, we argue that drug discovery programs in fungi should move beyond the BGC paradigm and focus on understanding the global regulatory networks in which secondary metabolic pathways are embedded. There is an urgent need for novel bioactive molecules in both agriculture and medicine. The genomes of fungi are thought to contain vast numbers of metabolic pathways involved in the biosynthesis of secondary metabolites with diverse bioactivities. Because these metabolites are biosynthesized only under specific conditions, the vast majority of the fungal pharmacopeia awaits discovery. To discover the genetic networks that regulate the activity of secondary metabolites, we examined the genome-wide profiles of gene activity of the cell factory Aspergillus niger across hundreds of conditions. By constructing global networks that link genes with similar activities across conditions, we identified six putative global and pathway-specific regulators of secondary metabolite biosynthesis. Our study shows that elucidating the behavior of the genetic networks of fungi under diverse conditions harbors enormous promise for understanding fungal secondary metabolism, which ultimately may lead to novel drug candidates.
真菌次生代谢产物被广泛用作治疗药物,是药物发现计划的重要组成部分。阻碍新型次生代谢产物发现的一个主要挑战是,其生物合成途径在典型的实验室生长条件下转录沉默,使得难以确定它们所嵌入的转录网络。此外,尽管参与次生代谢途径的基因通常在基因组上的连续簇中找到,称为生物合成基因簇(BGCs),但情况并非总是如此,尤其是对于途径活性的全局和途径特异性调节剂。为了解决这些挑战,我们使用了在 155 种不同条件下生长的子囊菌细胞工厂黑曲霉的 283 个全基因组基因表达数据集,基于 Spearman 相关系数(SCCs)和互秩转换 Pearson 相关系数(MR-PCCs)构建了两个基因共表达网络。通过挖掘这些网络,我们预测了六个转录因子,命名为 MjkA 至 MjkF,来调节黑曲霉中的次生代谢。使用 Tet-On 盒过表达每个转录因子都调节了多种次生代谢产物的产生。我们发现 SCC 和 MR-PCC 方法相互补充,使潜在的全局(SCC)和途径特异性(MR-PCC)转录因子的划分成为可能。这些结果突出了共表达网络方法识别和激活真菌次生代谢途径及其产物的潜力。更广泛地说,我们认为真菌中的药物发现计划应该超越 BGC 范式,专注于理解次生代谢途径所嵌入的全局调控网络。在农业和医学领域都迫切需要新型生物活性分子。真菌的基因组被认为包含大量参与生物合成具有多种生物活性的次生代谢产物的代谢途径。由于这些代谢产物仅在特定条件下合成,因此绝大多数真菌药物学仍有待发现。为了发现调节次生代谢物活性的遗传网络,我们检查了细胞工厂黑曲霉在数百种条件下的全基因组基因活性谱。通过构建连接条件下具有相似活性的基因的全局网络,我们鉴定了六个潜在的全局和途径特异性次生代谢物生物合成调节剂。我们的研究表明,阐明真菌在不同条件下遗传网络的行为对于理解真菌次生代谢具有巨大的潜力,这最终可能导致新的药物候选物。