Evidence map›Paper›PMID 38336860›Full record

ArticleCommunications biology2024

Snowprint: a predictive tool for genetic biosensor discovery.

Simon d'Oelsnitz, Sarah K Stofel, Joshua D Love, Andrew D Ellington

Open access · goldAbstract read
In one paragraph

Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
7.2field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Review
  8. Review
  9. Review
  10. Review
  11. Article
  12. Synthetic genomes unveil the effects of synonymous recoding.bioRxiv : the preprint server for biology · 2024
    Article
  13. Synthetic biology (Oxford, England) · 2024
    Article
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Simon d'OelsnitzDepartment of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA. simonsnitz@gmail.com.ORCID 0000-0001-7512-9157
Sarah K StofelDepartment of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.
Joshua D LoveIndependent Web Developer, Bentonville, AR, 72712, USA.
Andrew D EllingtonDepartment of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.ORCID 0000-0001-6246-5338
The University of Texas at Austin · US

Funding

Synthetic biology for controlled releaseR01EB026533 · NIBIB · UNIVERSITY OF TEXAS AT AUSTIN · PI ELLINGTON, ANDREW D · 2019 to 2022
$1.4M
NIBIB NIH HHS R01 EB026533United States Department of Commerce | National Institute of Standards and Technology (NIST) 70NANB21H100U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) R01EB026533Welch Foundation F-1654
6 · The paper itself

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 .

Indexed as

Biosensing TechniquesComputational BiologyDNAHumansLigandsPromoter Regions, GeneticDNALigands

Identifiers

PMID38336860
PMCPMC10858194
OpenAlexW4391686755

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.