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Record W2165327969 · doi:10.1113/jphysiol.2008.163113

TRPV1 fans the flames of visceral pain

2008· letter· en· W2165327969 on OpenAlexaff
Michael Beyak

Bibliographic record

VenueThe Journal of Physiology · 2008
Typeletter
Languageen
FieldNeuroscience
TopicIon Channels and Receptors
Canadian institutionsQueen's UniversityKingston General Hospital
Fundersnot available
KeywordsTRPV1NociceptorTransient receptor potential channelCapsaicinResiniferatoxinNoxious stimulusVisceral painChemistryMechanosensitive channelsIon channelSensory systemNociceptionReceptorNeuroscienceMedicineInternal medicineBiology

Abstract

fetched live from OpenAlex

That the receptor for capsaicin is involved in pain is intuitively attractive to even the lay person. After all, who among us has not had the misfortune of inadvertently ingesting an excess dose of chili pepper, and suddenly had the pleasure of a restaurant meal transformed into flushing, diaphoresis and instant agony. Since the identification of the receptor for capsaicin, TRPV1 (Tominaga et al. 1998), and its localization to sensory neurons, there has been a virtual explosion of research into its roles in the production of pain. It has recently become recognized that induction of pain by nerve damage or inflammation involves changes in the function of primary sensory neurons (plasticity), and that these changes often involve alteration in the expression or function of a variety of types of ion channels (Beyak & Vanner, 2005). Therefore an ion channel that normally is responsible for the detection of noxious thermal stimuli is a prime target as the culprit in the development of pathological pain. Normally, the cation channel TRPV1 opens in response to noxious heat and low pH (Tominaga et al. 1998). However it is very sensitive to the presence of other modulators such as proteases (through protease activated receptors) and inflammatory mediators (e.g. bradykinin), which markedly change the temperature and pH thresholds for activation. When heterologously expressed it has not been shown to be mechanosensitive; however, experiments using transgenic mice have suggested a role for TRPV1 in visceral mechanosensitivity in the small intestine (Rong et al. 2004), colon (Jones et al. 2005) and bladder (Daly et al. 2007; Birder et al. 2002). As visceral pain is almost always elicited by a mechanical stimulus (such as contraction or distension of the gut), and that many disorders of visceral pain are characterized by visceral mechanohypersenitivity, TRPV1 is a logical target to pursue. In addition given the ‘promiscuous' sensitivities of the TRPV1 receptor, it is ideally poised to integrate potentially injurious stimuli. In this issue of The Journal of Physiology, De Schepper et al. (2008) examined the role of the TRPV1 channel in pelvic nerve afferent sensitization in a rat model of colitis, using TNBS (a model that is similar to Crohn's disease in humans). Their findings indicated that responses in pelvic afferent C-fibres to colorectal distension were enhanced after induction of colitis, and that part of this enhancement could be attenuated using a highly specific TRPV1 antagonist (N-(4-tertiarybutylphenyl)-4-(3-chlorophyridin-2-yl)tetrahydropyrazine-1(2H)carboxamide; BCTC). Furthermore the effect of the TRPV1 antagonist was limited to C-fibres (identified by conduction velocity) as opposed to Aδ fibres. Both high and low threshold C-fibres were sensitized. Immunocytochemical analysis also suggested an increase in the number of TRPV1 immunoreactive cells in colon projecting DRG neurons; however, the increase was also limited to unmyelinated (neurofilament negative) neurons. This study is important for a number of reasons. First, it adds to the growing body of literature that TRPV1 is a key player in inflammation-induced sensitization of visceral afferent neurons. Second, it has given further evidence that at least in the viscera, TRPV1 is involved in mechanosensation. Finally, it has attempted, using careful classification of fibres based on objective, established criteria (conduction velocities), to differentiate between effects on unmyelinated versus myelinated fibres. This is perhaps particularly important in this field, where there is little consensus on how to classify gastrointestinal afferents, with various laboratories across the world each using their own system (this author included!). However it raises several questions. Unlike in the somatic sensory system, where it is clear that C-type neurons represent nociceptors, in visceral afferents the situation is much less clear. In fact most colonic afferents have nociceptor like properties, whether they are classified by neuropeptide expression, neurotrophin receptors, myelination, conduction velocity, action potential morphology, etc. Further, in the present study, low threshold fibres were also sensitized, in a TRPV1 dependent fashion, thus indicating that even fibres that would not be expected to mediate painful sensation are also altered by inflammation. What is the functional consequence of increased sensitivity of low threshold afferents? Perhaps alteration in this population may result in altered extrinsic motor reflexes, resulting in disturbed motility. An alternative explanation is that the ‘phenotype’ of fibres that previously functioned as high threshold units has been altered. Nonetheless, the present paper will guide future researchers to focus their attention on the roles of C-type fibres, in particular those expressing TRPV1, in the genesis of inflammation induced afferent hypersensitivity. The lack of effect of the TRPV1 antagonist on control responses also points to the possibility of a pain therapy that targets only sensitized afferent pathways. While not all visceral afferent C-fibres are likely to be involved in the production of pain, it is important to know that this population is uniquely susceptible to the influence to inflammation, and justifies careful study of inflammation induced plasticity in this select group. Whether the TRPV1 receptor is itself mechanosensitive, or alternatively it tunes the sensitivity of mechanosensitive afferents remains to be determined.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.250
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.252
Teacher spread0.221 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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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Citations4
Published2008
Admission routes1
Has abstractyes

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