Evidence map›Paper›PMID 41142362›Full record

ReviewMechanobiology in medicine2025

Synthetic mechanoreceptor engineering: From genetic encoding to DNA nanotechnology-based reprogramming.

Sihui Yang, Zhou Nie

Abstract readReview
In one paragraph

Review in Mechanobiology in medicine, 2025. 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

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

2 authors.

Sihui YangSchool of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Zhou NieState Key Laboratory of Chemo/Biosensing and Chemometrics, Hunan Provincial Key Laboratory of Biomacromolecular Chemical Biology, College of Chemistry and Chemical Engineering, College of Biology, Hunan University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Precise modulation of mechanoreceptor-mediated signal transduction is crucial for decoding cellular mechanotransduction mechanisms and programming cell fate. This review provides a comprehensive summary of recent advances in engineering synthetic mechanoreceptors, spanning from protein-centric genetic encoding to DNA nanotechnology-based non-genetic reprogramming strategies. Genetic engineering strategies employ protein structure encoding and site-directed mutagenesis to reprogram force-response functions in natural mechanoreceptors. As a complementary non-genetic approach, DNA nanotechnology leverages its programmability, modularity, and predictable mechanical properties to achieve precise control over receptor functionalities. The flourishing development of DNA mechanosensitive nanodevices has provided a promising synthetic toolkit for manipulating mechanoreceptors, enabling precise control over receptor spatial organization and signal transduction. A key innovation is the development of novel DNA-functionalized artificial mechanoreceptors (AMRs), which confer force-responsiveness to naturally non-mechanosensitive receptors without genetic modification, thereby enabling customized mechanotransduction and mechanobiological applications. Collectively, this paradigm shift highlights DNA-based non-genetic receptor engineering as a versatile and powerful toolkit, paving new avenues for mechanobiology research and pioneering force-directed therapeutic strategies in regenerative medicine.

Indexed as

DNA nanotechnologyMechanotransduction regulationNon-genetic engineeringSynthetic mechanoreceptors

Identifiers

PMID41142362
PMCPMC12552974

What OpenQuestion holds

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LicenceCC BY
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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.