Evidence map›Paper›PMID 32139676›Full record

ArticleNature communications2020

A genome-wide approach for identification and characterisation of metabolite-inducible systems.

Erik K R Hanko, Ana C Paiva, Magdalena Jonczyk, Matthew Abbott, Nigel P Minton, Naglis Malys

Abstract read
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed
–field-weighted citation impact
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

44 citing papers in PubMed.

  1. Optimized cumate toolkit for tunable protein expression duringApplied and environmental microbiology · 2026
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  5. Programming Next-Generation Synthetic Biosensors by Genetic Circuit Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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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

6 authors.

Erik K R HankoBBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Life Sciences, The University of Nottingham, Nottingham, NG7 2RD, UK.ORCID http://orcid.org/0000-0002-2850-7773
Ana C PaivaCentre for Biomolecular Sciences, School of Life Sciences, The University of Nottingham, Nottingham, NG7 2RD, UK.ORCID http://orcid.org/0000-0001-8040-3683
Magdalena JonczykBBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Life Sciences, The University of Nottingham, Nottingham, NG7 2RD, UK.ORCID http://orcid.org/0000-0003-2117-1143
Matthew AbbottBBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Life Sciences, The University of Nottingham, Nottingham, NG7 2RD, UK.ORCID http://orcid.org/0000-0002-2192-3512
Nigel P MintonBBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Life Sciences, The University of Nottingham, Nottingham, NG7 2RD, UK.ORCID http://orcid.org/0000-0002-9277-1261
Naglis MalysBBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Life Sciences, The University of Nottingham, Nottingham, NG7 2RD, UK. n.malys@gmail.com.ORCID http://orcid.org/0000-0002-5010-310X

Funding

Biotechnology and Biological Sciences Research Council BB/L013940/1
6 · The paper itself

Abstract

Inducible gene expression systems are vital tools for the advancement of synthetic biology. Their application as genetically encoded biosensors has the potential to contribute to diagnostics and to revolutionise the field of microbial cell factory development. Currently, the number of compounds of biological interest by far exceeds the number of available biosensors. Here, we address this limitation by developing a generic genome-wide approach to identify transcription factor-based inducible gene expression systems. We construct and validate 15 functional biosensors, provide a characterisation workflow to facilitate forward engineering efforts, exemplify their broad-host-range applicability, and demonstrate their utility in enzyme screening. Previously uncharacterised interactions between sensors and compounds of biological relevance are identified by employing the largest reported library of metabolite-responsive biosensors in an automated high-throughput screen. With the rapidly growing genomic data these innovative capabilities offer a platform to vastly increase the number of biologically detectable molecules.

Indexed as

GenomeBacteriabeta-AlanineBiosensing TechniquesEnzymesGene Expression Regulation, BacterialHost SpecificityLigandsMetabolic EngineeringMetabolomeReproducibility of Resultsbeta-AlanineEnzymesLigands

Identifiers

PMID32139676
PMCPMC7057948

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.