Evidence map›Paper›PMID 38468105›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2024

In Silico Design, In Vitro Construction, and In Vivo Application of Synthetic Small Regulatory RNAs in Bacteria.

Michel Brück, Bork A Berghoff, Daniel Schindler

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Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Article
  6. Synthetic biology (Oxford, England) · 2024
    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

3 authors.

Michel BrückMax Planck Institute for Terrestrial Microbiology, Marburg, Germany.
Bork A BerghoffInstitute for Microbiology and Molecular Biology, Justus-Liebig University Giessen, Giessen, Germany.
Daniel SchindlerMax Planck Institute for Terrestrial Microbiology, Marburg, Germany. Daniel.Schindler@mpi-marburg.mpg.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Small regulatory RNAs (sRNAs) are short non-coding RNAs in bacteria capable of post-transcriptional regulation. sRNAs have recently gained attention as tools in basic and applied sciences, for example, to fine-tune genetic circuits or biotechnological processes. Even though sRNAs often have a rather simple and modular structure, the design of functional synthetic sRNAs is not necessarily trivial. This protocol outlines how to use computational predictions and synthetic biology approaches to design, construct, and validate synthetic sRNA functionality for their application in bacteria. The computational tool, SEEDling, matches the optimal seed region with the user-selected sRNA scaffold for repression of target mRNAs. The synthetic sRNAs are assembled using Golden Gate cloning and their functionality is subsequently validated. The protocol uses the acrA mRNA as an exemplary proof-of-concept target in Escherichia coli. Since AcrA is part of a multidrug efflux pump, acrA repression can be revealed by assessing oxacillin susceptibility in a phenotypic screen. However, in case target repression does not result in a screenable phenotype, an alternative validation of synthetic sRNA functionality based on a fluorescence reporter is described.

Indexed as

RNA, Small UntranslatedBacteriaEscherichia coliGene Expression Regulation, BacterialRNA, BacterialRNA, MessengerRNA, BacterialRNA, MessengerRNA, Small UntranslatedFunctional sRNA profilingGolden Gate cloningPost-transcriptional regulationSeed regionSynthetic biologySynthetic sRNA predictionSynthetic sRNAs

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.