Nucleic Acids ResearchSynthetic BiologyPeer-reviewed journal article
Mapping the phenotypic landscape of a transcriptional repressor using Deep Mutational Scanning and Growth-based Quantitative Sequencing
Zachary JanseniD, Xuan Le, Qiyao WeiiD, Devon L. KulhanekiD, Nina Alperovich, Olga B Vasilyeva, Andrew R. GilmouriD, David RossiD, Ross T ThyeriD
Abstract
CymR is a TetR-family transcriptional repressor that recognizes a well-defined operator sequence in the promoter PcymRC. The native ligand cumate and several structurally related aromatic acids bind at an allosteric site and induce a conformational change in CymR, resulting in release from the DNA operator and de-repression of the promoter. The amino acid residues that contribute to these core functions have not been mapped, nor has the protein been subjected to extensive mutagenesis to modify its function. Here, for the first time, we integrate Deep Mutational Scanning (DMS) with Growth-based Quantitative Sequencing (GROQ-Seq) to evaluate a comprehensive phenotypic landscape of CymR variants, including single amino acid insertions and deletions. We measure this library across a concentration gradient of small molecule inducers to construct an induction curve for all library members. From this analysis, we identify amino acids throughout the protein that are essential for repressor function and discover several mutations that improve the sensitivity of CymR to the ligand perillic acid. In addition, rarely investigated insertion mutants are revealed to be a key driver of novel phenotypes, including several regions of CymR where insertions result in an inverted phenotype and the isolation of variants exhibiting an unusual band-stop phenotype. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/693801v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@121b16org.highwire.dtl.DTLVardef@b0478aorg.highwire.dtl.DTLVardef@128f2ecorg.highwire.dtl.DTLVardef@164a426_HPS_FORMAT_FIGEXP M_FIG C_FIG
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- 1
- FWCI
- 3.96
- Field percentile
- top 11%
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