UTILIZING SEQUENCE SIMILARITY NETWORKS FOR CROSS SPECIES ELICITOR IDENTIFICATION OF STREPTOMYCES REGULATORY PROTIENS
Streptomyces bacteria produce a variety of secondary metabolites that hold clinical and agricultural value, yet their biosynthetic potential remains unrealized as many biosynthetic gene clusters are not expressed under standard laboratory conditions. Expression of these clusters is tightly regulated, often by cluster situated transcription factors. The TetR family are regulators whose activity is modulated by small molecule elicitors. Although many TetRs have been characterized, elicitors have only been identified for a small fraction of them. This lack of data presents a limitation in our ability to exploit elicitor-regulator pairs for activation of silent clusters and underscores the need for predictive and testable strategies for elicitor identification. In this work, we test the use of sequence similarity networks (SSNs) as a predictor of elicitor identity using the well characterized TetR protein, JadR2, that has a known elicitor, chloramphenicol. We utilized SSNs to identify JadR2 homologs that may also be elicited by chloramphenicol. We developed a heterologous Escherichia coli reporter system in which regulator activity was monitored using an EGFP readout of DNA binding activity. Using this system, we screened JadR2 and four homologs for responsiveness to chloramphenicol. We found that 3 homologs were elicited by chloramphenicol, all of which were formerly uncharacterized. These results demonstrate that TetR-family proteins can share elicitor responsiveness and that SSNs can be used to prioritize regulators for functional screening. This work establishes a genomics-informed and bioinformatics-guided framework for linking elicitors to their regulator, expanding the toolkit for natural product discovery by unlocking regulatory information across Streptomyces.