Evidence map›Paper›PMID 36375746›Full record

ArticleMetabolic engineering2023

Biosensor-assisted titratable CRISPRi high-throughput (BATCH) screening for over-production phenotypes.

Jian Wang, Chenyi Li, Tian Jiang, Yajun Yan

Open access · greenAbstract read
In one paragraph

Article in Metabolic engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
3.9field-weighted citation impact, top 5% 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

18 citing papers in PubMed, 48 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Advancing lignocellulosic conversion though biosensor-enabled metabolic engineering.Green chemistry : an international journal and green chemistry resource : GC · 2025
    Review
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
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  13. Application of functional genomics for domestication of novel non-model microbes.Journal of industrial microbiology & biotechnology · 2024
    Review
  14. Review
  15. Article
  16. Review
  17. Article
  18. Transcription factor-based biosensors for screening and dynamic regulation.Frontiers in bioengineering and biotechnology · 2023
    Review
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.

Jian WangSchool of Chemical, Materials and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA, 30602, USA.
Chenyi LiSchool of Chemical, Materials and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA, 30602, USA.
Tian JiangSchool of Chemical, Materials and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA, 30602, USA.
Yajun YanSchool of Chemical, Materials and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA, 30602, USA. Electronic address: yajunyan@uga.edu.
University of Georgia · US

Funding

Engineering Dynamic Control of Natural Product Biosynthesis in BacteriaR35GM128620 · NIGMS · UNIVERSITY OF GEORGIA · PI Yajun Yan · 2018 to 2026
$3.0M
NIGMS NIH HHS R35 GM128620
6 · The paper itself

Abstract

With rapid advances in the development of metabolic pathways and synthetic biology toolkits, a persisting challenge in microbial bioproduction is how to optimally rewire metabolic fluxes and accelerate the concomitant high-throughput phenotype screening. Here we developed a biosensor-assisted titratable CRISPRi high-throughput (BATCH) screening approach that combines a titratable mismatch CRISPR interference and a biosensor mediated screening for high-production phenotypes in Escherichia coli. We first developed a programmable mismatch CRISPRi that could afford multiple levels of interference efficacy with a one-pot sgRNA pool (a total of 16 variants for each target gene) harboring two consecutive random mismatches in the seed region of sgRNA spacers. The mismatch CRISPRi was demonstrated to enable almost a full range of gene knockdown when targeting different positions on genes. As a proof-of-principle demonstration of the BATCH screening system, we designed doubly mismatched sgRNA pools targeting 20 relevant genes in E. coli and optimized a PadR-based p-coumaric acid biosensor with broad dynamic range for the eGFP fluorescence guided high-production screening. Using sgRNA variants for the combinatorial knockdown of pfkA and ptsI, the p-coumaric acid titer was increased by 40.6% to o 1308.6 mg/l from glycerol in shake flasks. To further demonstrate the general applicability of the BATCH screening system, we recruited a HpdR-based butyrate biosensor that facilitated the screening of E. coli strains achieving 19.0% and 25.2% increase of butyrate titer in shake flasks with sgRNA variants targeting sucA and ldhA, respectively. This work reported the establishment of a plug-and-play approach that enables multilevel modulation of metabolic fluxes and high-throughput screening of high-production phenotypes.

Indexed as

Escherichia coliHigh-Throughput Screening AssaysCoumaric AcidsCRISPR-Cas SystemsMetabolic EngineeringPhenotypeCoumaric Acidsp-coumaric acidBiosensorButyrateMismatch CRISPRip-Coumaric acidTitratable repression

Identifiers

PMID36375746
PMCPMC9845192
OpenAlexW4308782088

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Registered trials

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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.