Machine learning uncovers the transcriptional regulatory network for the production host Streptomyces albidoflavus

Abstract

Streptomyces albidoflavus is a widely used strain for natural product discovery and production through heterologous biosynthetic gene clusters (BGCs). However, the transcriptional regulatory network (TRN) and its impact on secondary metabolism remain poorly understood. Here, we characterize the TRN using independent component analysis on 218 RNA sequencing (RNA-seq) transcriptomes across 88 unique growth conditions. We identify 78 independently modulated sets of genes (iModulons) that quantitatively describe the TRN across diverse conditions. Our analyses reveal (1) TRN adaptation to different growth conditions, (2) conserved and unique characteristics of the TRN across diverse lineages, (3) transcriptional activation of several endogenous BGCs, including surugamide, minimycin, and paulomycin, and (4) inferred functions of 40% of uncharacterized genes in the S. albidoflavus genome. These findings provide a comprehensive and quantitative understanding of the S. albidoflavus TRN, offering a knowledge base for further exploration and experimental validation.