Evidence map›Paper›PMID 42680946›Full record

ReviewNature reviews. Nephrology2026

Nucleic acid sensing pathways in kidney disease development.

Magaiver Andrade-Silva, Chaelin Kang, Yena Jang, Katalin Susztak

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Nephrology, 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

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.

Magaiver Andrade-SilvaRenal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA. magaiverandrade@usp.br.ORCID http://orcid.org/0000-0001-5135-0981
Chaelin KangRenal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0002-6472-7829
Yena JangInje University College of Medicine, Busan, Republic of Korea.ORCID http://orcid.org/0000-0003-3584-6463
Katalin SusztakRenal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA. ksusztak@pennmedicine.upenn.edu.ORCID http://orcid.org/0000-0002-1005-3726

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sterile inflammation triggered by mislocalized self-nucleic acids has emerged as an important mechanism linking cellular injury to progressive kidney dysfunction. Among the key molecular pathways implicated in this response, those underlying nucleic acid sensing represent a central signalling mechanism, particularly the cytosolic DNA sensor cyclic GMP-AMP synthase (cGAS) and its downstream effector stimulator of interferon genes (STING). In both glomerular and tubular compartments, mitochondrial dysfunction, genotoxic stress and epigenetic dysregulation lead to the accumulation of cytosolic nucleic acids, including mitochondrial DNA and RNA, nuclear DNA fragments and reactivated endogenous retroelements. These signals converge on nucleic acid sensors, including STING, absent in melanoma 2 (AIM2) and endosomal Toll-like receptors (TLRs), activating proinflammatory cascades, cell death programmes and fibrotic remodelling. The latest research highlights the context-dependent engagement of these pathways across human kidney disease and in experimental models of acute kidney injury and chronic kidney disease, linking cellular damage to immune activation and fibrosis. Here, we synthesize emerging insights into the molecular programming of nucleic acid sensing in kidney disease and evaluate the therapeutic landscape, outlining opportunities and challenges for clinical translation.

Identifiers

What OpenQuestion holds

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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.