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. 2024 Feb 9;7(1):163.
doi: 10.1038/s42003-024-05849-8.

Snowprint: a predictive tool for genetic biosensor discovery

Simon d'Oelsnitz  1   2 , Sarah K Stofel  3 , Joshua D Love  4 , Andrew D Ellington  3
Affiliations

Snowprint: a predictive tool for genetic biosensor discovery

Simon d'Oelsnitz et al. Commun Biol. .

Abstract

Bioengineers increasingly rely on ligand-inducible transcription regulators for chemical-responsive control of gene expression, yet the number of regulators available is limited. Novel regulators can be mined from genomes, but an inadequate understanding of their DNA specificity complicates genetic design. Here we present Snowprint, a simple yet powerful bioinformatic tool for predicting regulator:operator interactions. Benchmarking results demonstrate that Snowprint predictions are significantly similar for >45% of experimentally validated regulator:operator pairs from organisms across nine phyla and for regulators that span five distinct structural families. We then use Snowprint to design promoters for 33 previously uncharacterized regulators sourced from diverse phylogenies, of which 28 are shown to influence gene expression and 24 produce a >20-fold dynamic range. A panel of the newly repurposed regulators are then screened for response to biomanufacturing-relevant compounds, yielding new sensors for a polyketide (olivetolic acid), terpene (geraniol), steroid (ursodiol), and alkaloid (tetrahydropapaverine) with induction ratios up to 10.7-fold. Snowprint represents a unique, protein-agnostic tool that greatly facilitates the discovery of ligand-inducible transcriptional regulators for bioengineering applications. A web-accessible version of Snowprint is available at https://snowprint.groov.bio .

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. The Snowprint workflow.
A RefSeq or GenBank accession ID is used to fetch the protein sequence of the regulator and the DNA sequence of the local genetic context (1). The inter-operon region, predicted to contain the regulator’s corresponding operator, is then scanned for inverted repeat sequences (2). BLAST is then used to collect regulator homologs (3), which are used to collect homologous inter-operon regions (4) that are also scanned for inverted repeats similar to that found for the original regulator (5). The homologous inverted repeat sequences are then used to create a consensus sequence, representing the predicted operator, and associated metrics (6), which are displayed in a browser (7). The Snowprint logo was designed using a vector graphics editor.
Fig. 2
Fig. 2. Benchmarking Snowprint.
a Benchmarking workflow. Experimentally validated operator regulator pairs are collected from the literature, and regulators for each pair are passed through the Snowprint workflow. Predicted operators are then compared to validated operators. The regulator is colored purple and the operator is colored blue (b) Similarity scores for predicted operators among several structural regulator families. The E-value of 0.01 was used as a threshold to indicate significance (Supplementary Table 1). The “Other” group contains regulators from the MerR, ArsR, PadR, TrpR, and ROK structural families. c Phylogenetic diversity of the benchmarking dataset. Phylogeny.fr and iTOL were used to generate the phylogenetic tree graphic. Separate phyla are color coded and labeled. d Representative examples of predicted operator motifs generated by Snowprint. The Snowprint logo was designed using a vector graphics editor.
Fig. 3
Fig. 3. Domestication of mined TetR regulators using Snowprint.
a Schematic of genetic circuits used to assess operator predictions. The “Repressed” circuit expresses the mined regulator and GFP, both under the control of a promoter containing the operator predicted for that regulator. The “Unrepressed” circuit differs from the former in that it expresses a control regulator, CamR, in place of the mined regulator. b Operator motifs generated by Snowprint for all mined regulators. Sequence motif logos were generated using LogoJS (c) Fluorescence of E. coli cells expressing either the repressed or unrepressed circuits for each regulator. Data represents the mean of three biological replicates. Equivalent data displaying individual data points and standard deviation is found in Supplementary Fig. 5.
Fig. 4
Fig. 4. Discovery of novel genetic sensors for biomanufacturing-relevant ligands.
a Twenty four newly domesticated regulators were separately induced with 100 uM of six different ligands dissolved in DMSO in E. coli. Data represents the induction ratio in fluorescence over E. coli cells bearing the identical plasmids induced with DMSO only, performed in biological triplicate. Equivalent data displaying individual data points and standard deviation can be found in Supplementary Fig. 7. b–e Dose response measurements for the BAK71752.1, SMC09139, SEE04737, and SMC09139 regulators with tetrahydropapaverine (THP), geraniol, olivetolic acid, and ursodiol, respectively. The ligand concentration was chosen based on the compound’s solubility limit in 1% DMSO. Assays were performed in biological triplicate and individual data points are shown.
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