Beyond the Biosynthetic Gene Cluster Paradigm: Genome-Wide Coexpression Networks Connect Clustered and Unclustered Transcription Factors to Secondary Metabolic Pathways.

Min Jin Kwon,Antonis Rokas,Charlotte Steiniger,Timothy C Cairns,Jennifer H Wisecaver,Abigail L Lind,Carmen Regner,Carsten Pohl,Vera Meyer,Gustavo H Goldman

doi:10.1128/spectrum.00898-21

Abstract

ABSTRACTFungal 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.IMPORTANCE 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.

Highlights

Fungal secondary metabolites are widely used as therapeutics and are vital components of drug discovery programs
Using the s correlation coefficients (SCCs) approach, we previously estimated the global transcriptional activities of A. niger biosynthetic gene clusters (BGCs) among the 283 microarray experiments by assessing the gene expression of the predicted core enzymes [33]. These data highlighted that BGC expression varies considerably, with genes encoding core enzymes transcriptionally deployed frequently, rarely (.5 experiments), or not at all [33]. We reasoned that this microarray meta-analysis was a promising resource for further interrogation of BGCs using a mutual rank-transformed Pearson’s correlation coefficient (MRPCC) approach [34, 36]
Transforming PCCs into mutual ranks has been shown to improve the recovery of known pathways as discrete subgraphs in global coexpression networks [35]

Summary

Introduction

Fungal secondary metabolites are widely used as therapeutics and are vital components of drug discovery programs. 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). The most well-known clinical applications of these molecules include their use as antibiotics, cholesterol-lowering agents, and immunosuppressants (e.g., penicillin, statins, and cyclosporins, respectively) [2] They play an important role in drug discovery programs, with recently marketed therapeutics consisting of either fungal SMs or their semisynthetic derivatives [3]. This strategy cannot be used for the approximately 40% of fungal BGCs that lack a resident TF [17]

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Results

Conclusion