Beyond the biosynthetic gene cluster paradigm: Genome-wide co-expression networks connect clustered and unclustered transcription factors to secondary metabolic pathways

Abstract

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 in 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 datasets of the ascomycete cell factory Aspergillus niger generated during growth under 155 different conditions to construct two gene co-expression networks based on Spearman’s correlation coefficients (SCC) and on mutual rank-transformed Pearson’s correlation coefficients (MR-PCC). By mining these networks, we predicted six transcription factors named MjkA – MjkF to concomitantly regulate secondary metabolism in A. niger. Over-expression of each transcription factor using the Tet-on cassette modulated production of multiple secondary metabolites. We found that the SCC and MR-PCC approaches complemented each other, enabling the delineation of global (SCC) and pathway-specific (MR-PCC) transcription factors, respectively. These results highlight the great potential of co-expression network approaches to identify and activate fungal secondary metabolic pathways and their products. More broadly, we argue that novel 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.