Evidence map›Paper›PMID 40839605›Full record

ArticlePain2025

Ribosome profiling reveals that post-transcriptional control of Nalf1 by heterogeneous nuclear ribonucleoprotein L is required for paclitaxel-induced neuropathic pain.

June Bryan de la Peña, Guadalupe García, Zachary T Campbell

Abstract read
In one paragraph

Article in Pain, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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.

June Bryan de la PeñaDepartment of Anesthesiology, University of New Mexico, Albuquerque, NM, United States.
Guadalupe GarcíaDepartment of Anesthesiology, University of Wisconsin-Madison, Madison, WI, United States.
Zachary T CampbellDepartment of Anesthesiology, University of Wisconsin-Madison, Madison, WI, United States.ORCID 0000-0002-3768-6996

Funding

Profiling Translation in Nociceptor PlasticityR01NS114018 · NINDS · UNIVERSITY OF WISCONSIN-MADISON · PI Zachary Campbell · 2020 to 2026
$3.2M
NIH/NINDS R01NS114018NINDS NIH HHS R01 NS114018
6 · The paper itself

Abstract

abstractSensory neurons are integral to the genesis and maintenance of neuropathic pain. The molecular mechanisms that mediate long-lived changes in their excitability are unclear. Here, we leverage functional genomics approaches to survey changes in RNA abundance and translation in dorsal root ganglion neurons from a mouse model of paclitaxel-induced neuropathic pain. We focus specifically on females as paclitaxel is a first-line therapy for breast cancer. The sequencing data indicate that substantially more changes occur at the level of translation (n = 404) than transcription and decay (n = 109). We discovered that a core subunit of the sodium leak channel (NALCN) channel, auxiliary factor 1 (NALF1), is preferentially translated in response to paclitaxel. This effect is mediated by the RNA-binding protein heterogeneous nuclear ribonucleoprotein L (HNRNP L). Heterogeneous nuclear ribonucleoprotein L binds a 14 base CA-rich element (CARE) in the Nalf1 3' untranslated region (3'UTR). Genetic elimination of either HNRNP L, the Nalf1 CARE motif, or the pore-forming subunit of the nonselective NALCN diminishes pain amplification in vivo. Collectively, these results illustrate that an element situated in a 3'UTR is required for neuropathic pain in female mice.

Indexed as

Antineoplastic Agents, PhytogenicNeuralgiaPaclitaxelRibosomesAnimalsDisease Models, AnimalFemaleGanglia, SpinalMiceMice, Inbred C57BLRibosome ProfilingAntineoplastic Agents, PhytogenicPaclitaxel3′UTRCAREHNRNP LNALF1PainTranslational control

Identifiers

PMID40839605
PMCPMC12353069

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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.