Evidence map›Paper›PMID 42068556›Full record

ArticleACS synthetic biology2026

Cross-Strain Transferability of CRISPRi Systems and Design Rules from Laboratory to Clinical

Hyerim Ban, Stephen N Rondthaler, Matthew Lebovich, Marcos A Lora, Brandon Ugbesia, Lauren B Andrews

Abstract read
In one paragraph

Article in ACS synthetic biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Hyerim BanMolecular and Cellular Biology Graduate Program, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States.
Stephen N RondthalerDepartment of Chemical and Biomolecular Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States.
Matthew LebovichDepartment of Chemical and Biomolecular Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States.
Marcos A LoraDepartment of Chemical and Biomolecular Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States.
Brandon UgbesiaDepartment of Chemical and Biomolecular Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States.
Lauren B AndrewsDepartment of Chemical and Biomolecular Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States.ORCID 0000-0003-1627-1430

Funding

Biotechnology Training Program in Applied Life SciencesT32GM135096 · NIGMS · UNIVERSITY OF MASSACHUSETTS AMHERST · PI Jeanne Ann Hardy, Ashish A. Kulkarni · 2020 to 2026
$3.9M
NIGMS NIH HHS T32 GM135096NSF National Science Foundaion NSF CBET-1943695NSF National Science Foundation NSF DMR-1904901NSF UMass ADVANCE program National Science Foundation NSF EES-1824090
6 · The paper itself

Abstract

CRISPR interference (CRISPRi) has emerged as a versatile approach for targeted gene repression in many organisms, including microbes and bacteria, due to the simple design of sequence-specific transcriptional silencing of gene expression. However, the strain-specific effects on repression efficiency and the host when translating a CRISPRi system from a laboratory strain to nonmodel strains are not well understood, yet they can present important limitations to its use. Here, we investigated the repression efficiency and toxicity of three CRISPRi systems (one dCas9 and two dCas12a variants) across four different

Indexed as

CRISPR-Cas SystemsEscherichia coliEscherichia coli ProteinsRNA, Guide, CRISPR-Cas SystemsEscherichia coli ProteinsRNA, Guide, CRISPR-Cas Systemsclinical strainCRISPR interferencedCas12dCas9multiplexed CRISPRinonmodel bacteriastrain-specific genetic design

Identifiers

PMID42068556
PMCPMC13186107

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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.