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Record W3005976641 · doi:10.1113/jp279548

TRESK is a modality‐specific brake on nociceptor excitability

2020· letter· en· W3005976641 on OpenAlexaff
Reza Sharif‐Naeini

Bibliographic record

VenueThe Journal of Physiology · 2020
Typeletter
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsNociceptorNeuroscienceDepolarizationPotassium channelNeuropathic painMembrane potentialMedicineSomatosensory systemSensory systemNociceptionChemistryBiologyInternal medicineReceptor

Abstract

fetched live from OpenAlex

Background K+ channels are constitutively open at rest and play a critical role in neural function. The activity of these channels sets the resting membrane potential near the K+ equilibrium, around −90 mV, and thus decreases excitability. The two-pore domain potassium channels (K2P) comprise a diverse family of K+ channels that contribute to neuronal resting membrane potential (Enyedi & Czirjak, 2010; Gada & Plant, 2019). With its first member identified over 20 years ago (Lesage et al. 1996), this large family comprises 15 members with a high level of expression in sensory neurons (Gada & Plant, 2019). Indeed, sensory neurons of the dorsal root and trigeminal ganglia express different members of the K2P family, of which, TRESK, TREK-1, TREK-2 and TRAAK are highly expressed and present in pain-sensing neurons (i.e. nociceptors). Over the past few years, the role of these channels in pain sensitivity has started emerging, although more studies are needed to fully understand how they adjust pain thresholds. This is especially true for the TRESK channel: in preclinical models of neuropathic pain, nerve injury is associated with a decrease in TRESK expression, whereas that of other K2P channels remains unchanged. This decrease expression is assummed to reduce the 'brake' that nociceptors face during depolarization, thus facilitating the generation of action potentials in these neurons and pain hypersensitivity (Tulleuda et al. 2011). In this issue of The Journal of Physiology, Castellanos et al. (2020) report the use of a global TRESK knockout (KO) mouse to explore the role of this channel in somatosensation. Their recordings form nociceptors isolated from TRESK KO mice confirmed previous observations that, despite displaying weaker K+ current density, these neurons have a resting membrane potential that is very close to their wild-type (WT) littermates. This observation suggests that the TRESK channel has minimal contribution to the resting membrane potential. However, the neurons of KO mice show a significantly higher membrane resistance, which is probably responsible for their lower rheobase. They also have wider action potentials (AP) and smaller hyperpolarizing after-potentials, which indicates that TRESK contributes to the repolarizing phase of the AP. When the injected depolarizing currents exceeded the AP threshold, nociceptors of TRESK KO mice fired significantly more APs, indicating that the removal of TRESK increases the excitability of nociceptors. Castellanos et al. (2020) then used a Ca imaging approach to examine the sensitivity of nociceptors to common agonists. Although the responses to capsaicin and menthol did not change, responsiveness to the TRPA1 agonist allyl isothiocyanate (AITC) was significantly reduced. This observation was also present in behavioural experiments, where KO mice displayed significantly less nociceptive behaviours after intraplantar AITC injection. In behavioural experiments, Castellanos et al. (2020) demonstrate that TRESK functions to prevent C-fibres from being activated by low-intensity mechanical stimuli and its deletion results in mechanical hypersensitivity. Indeed, TRESK KO mice had significantly lower mechanical withdrawal thresholds. Castellanos et al. (2020) then examined the temperature sensitivity of nociceptors. As opposed to heat sensitivity, where the investigators failed to see any difference between KO and WT mice, their sensitivity to cold was significantly affected. In skin–nerve preparations, the distribution of C-fibre activation thresholds by cold stimuli indicated a shift toward warmer temperatures, indicating that, during cooling, these fibres would become activated sooner than C-fibres from their WT littermates. These observations were also transposed to behavioural studies, where the sensitivity of KO mice to a 2°C (noxious cold) cold plate test is significantly higher than that of WT mice. Finally, the role of TRESK was examined in preclinical models of persistent inflammatory pain, nerve cuff-induced neuropathic pain, or chemotherapy-induced neuropathic pain. All three models showed similar levels of sensitization between TRESK KO and WT mice. Overall, the study by Castellanos et al. (2020) adds to the body of knowledge on the role of TRESK channels in pain sensitivity, indicating that regulation of channel expression is an important factor in the control of sensory neuron excitability. Interestingly, removal of the channel produces modality-specific increases in sensitivity, with a hyper-responsiveness to mechanical and cold stimuli, yet no changes to heat stimuli. This could be explained in part by the predominant expression of TRESK in non-peptidergic nociceptors (Weir et al. 2019), which have been proposed to underlie mechanical pain (Cavanaugh et al. 2009). Indeed, in the trigeminal ganglia, the channel is present in 73% of isolectin B4-positive nociceptors and 29% of calcitonin gene-related peptide-positive nociceptors (Weir et al. 2019). The study by Castellanos et al. (2020) opens the door to the possibility of developing TRESK activators that would help control the excitability of nociceptors during chronic pain conditions. No competing interests declared. Sole author. No funding was received.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.286
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2020
Admission routes1
Has abstractyes

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