Our understanding of how sex and age influence chronic pain at the molecular level is still limited with wide-reaching consequences for adolescent patients. Here, we leveraged deep proteome profiling of mouse dorsal root ganglia (DRG) from adolescent (4-week-old) and adult (12-week-old) male and female mice to investigate the establishment of neuropathic pain in the spared nerve injury (SNI)-model in parallel. We quantified over 12,000 proteins, including notable ion channels involved in pain, highlighting the sensitivity of our approach. Differential expression revealed sex- and age-dependent proteome changes upon nerve injury. In contrast to most previous studies, our comprehensive dataset enabled us to determine differentially expressed proteins (DEPs), which were shared between male and female mice of both age groups. Among these, the vast majority (94%) were also expressed and, in part, altered in human DRG of neuropathic pain patients, indicating evolutionary conservation. Proteome signatures represented numerous targets of FDA-approved drugs comprising both (i) known pain therapeutics (e.g. Pregabalin and opioids) and, importantly, (ii) compounds with high potential for future re-purposing, e.g. Ptprc-modulators and Epoetins. Protein network and multidimensional analysis uncovered distinct hubs of sex- and age-shared biological pathways impacted by neuropathic pain, such as neuronal activity and synaptic function, DNA-damage, and neuroimmune interactions. Taken together, our results capture the complexity of nerve injury-associated DRG alterations in mice at the network level, moving beyond single-candidate studies. Consequently, we provide an innovative resource of the molecular landscape of neuropathic pain, enabling novel opportunities for translational pain research and network-based drug discovery.

The peripheral nervous system (PNS) plays a critical role in pathological conditions, including chronic pain disorders, that manifest differently in men and women. To investigate this sexual dimorphism at the molecular level, we integrated quantitative proteomic profiling of human dorsal root ganglia (hDRG) and peripheral nerve tissue into the expanding omics framework of the PNS. Using data-independent acquisition (DIA) mass spectrometry, we characterized a comprehensive proteomic profile, validating tissue-specific differences between the hDRG and peripheral nerve. Through multi-omic analyses and in vitro functional assays, we identified sex-specific molecular differences, with TNFα signalling emerging as a key sexually dimorphic pathway with higher prominence in men. Genetic evidence from genome-wide association studies further supports the functional relevance of TNFα signalling in the periphery, while clinical trial data and meta-analyses indicate a sex-dependent response to TNFα inhibitors. Collectively, these findings underscore a functionally sexual dimorphism in the PNS, with direct implications for sensory and pain-related clinical translation.

A significant number of patients develop chronic pain after surgery, but prediction of those who are at risk is currently not possible. Thus, prognostic prediction models that include bio-psycho-social and physiological factors in line with the complex nature of chronic pain would be urgently required. Here, we performed a translational study in male volunteers before and after an experimental incision injury. We determined multi-modal features ranging from pain characteristics and psychological questionnaires to blood plasma proteomics. Outcome measures included pain intensity ratings and the extent of the area of hyperalgesia to mechanical stimuli surrounding the incision, as a proxy of central sensitization. A multi-step logistic regression analysis was performed to predict outcome measures based on feature combinations using data-driven cross-validation and prognostic model development. Phenotype-based stratification resulted in the identification of low and high responders for both outcome measures. Regression analysis revealed prognostic proteomic, specific psychophysical, and psychological features. A combinatorial set of distinct features enabled us to predict outcome measures with increased accuracy compared to using single features. Remarkably, in high responders, protein network analysis suggested a protein signature characteristic of low-grade inflammation. Alongside, in silico drug repurposing highlighted potential treatment options employing antidiabetic and anti-inflammatory drugs. Taken together, we present here an integrated pipeline that harnesses bio-psycho-physiological data for prognostic prediction in a translational approach. This pipeline opens new avenues for clinical application with the goal of stratifying patients and identifying potential new targets, as well as mechanistic correlates, for postsurgical pain.

Personalized strategies in pain management and prevention should be based on individual risk factors as early as possible, but the factors most relevant are not yet known. An innovative approach would be to integrate multi-modal risk factors, including blood proteomics, in predicting high pain responders and using them as targets for personalized treatment options. Here, we determined and mapped multi-modal factors to prognosticate a phenotype with high risk of developing pain and hyperalgesia after an experimental incision in humans. We profiled unbiased blood plasma proteome signature of 26 male volunteers, assessed psychophysical and psychological aspects before incision injury. Outcome measures were pain intensity ratings and the extent of the area of hyperalgesia to mechanical stimuli surrounding the incision as a proxy for central sensitization. Phenotype-based stratification resulted in the identification of low- and high-responders for the two different outcome measures. Logistic regression analysis revealed prognostic potential for blood plasma proteins and for psychophysical and psychological parameters. The combination of certain parameters increased the prognostic accuracy for both outcome measures, exceeding 97%. In high-responders, term-term-interaction network analysis showed a proteome signature of a low-grade inflammation reaction. Intriguingly, in silico drug repurposing indicates a high potential for specific antidiabetic and anti-inflammatory drugs already available. In conclusion, we show an integrated pipeline that provides a valuable resource for patient stratification and the identification of (i) multi-feature prognostic models, (ii) treatment targets, and (iii) mechanistic correlates that may be relevant for individualized management of pain and its long-term consequences. ### Competing Interest Statement MS received research awards and travel support by the German Pain Society (DGSS) both of which were sponsored by Astellas Pharma GmbH (Germany). MS received one-time consulting honoraria by Grunenthal GmbH (Germany). None of these funding sources influenced the content of this study, and MS declares no conflict of interest. During the past 5 yr, EPZ received financial support from Grunenthal for research activities and from Grunenthal, Novartis (Switzerland), Medtronic for advisory board activities, lecture fees, or both. None of this research support/funds was used for or influenced this manuscript, and EPZ declares no conflict of interest. The remaining authors declare that they have no conflicts of interest. ### Clinical Trial DRKS00016641 ### Funding Statement Funding: Deutsche Forschungsgemeinschaft (DFG) (SCHM 2533/6e1 and SCHM 2533/4e1 to MS, PO1319/3e1 to EPZ). Federal Ministry of Education and Research (BMBF), Germany, to EPZ (01KC1903). University of Vienna to MS. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: University Hospital Muenster of the local Ethics Committee of the Medical Faculty (registration no 2018-081-b-S) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors

IntroductionMetaproteomics is a rapidly advancing field that offers unique insights into the taxonomic composition and the functional activity of microbial communities, and their effects on host physiology. Classically, data-dependent acquisition (DDA) mass spectrometry (MS) has been applied for peptide identification and quantification in metaproteomics. However, DDA-MS exhibits well-known limitations in terms of depth, sensitivity, and reproducibility. Consequently, methodological improvements are required to better characterize the protein landscape of microbiomes and their interactions with the host.MethodsWe present an optimized proteomic workflow that utilizes the information captured by Parallel Accumulation-Serial Fragmentation (PASEF) MS for comprehensive metaproteomic studies in complex fecal samples of mice.Results and discussionWe show that implementing PASEF using a DDA acquisition scheme (DDA-PASEF) increased peptide quantification up to 5 times and reached higher accuracy and reproducibility compared to previously published classical DDA and data-independent acquisition (DIA) methods. Furthermore, we demonstrate that the combination of DIA, PASEF, and neuronal-network-based data analysis, was superior to DDA-PASEF in all mentioned parameters. Importantly, DIA-PASEF expanded the dynamic range towards low-abundant proteins and it doubled the quantification of proteins with unknown or uncharacterized functions. Compared to previous classical DDA metaproteomic studies, DIA-PASEF resulted in the quantification of up to 4 times more taxonomic units using 16 times less injected peptides and 4 times shorter chromatography gradients. Moreover, 131 additional functional pathways distributed across more and even uniquely identified taxa were profiled as revealed by a peptide-centric taxonomic-functional analysis. We tested our workflow on a validated preclinical mouse model of neuropathic pain to assess longitudinal changes in host-gut microbiome interactions associated with pain - an unexplored topic for metaproteomics. We uncovered the significant enrichment of two bacterial classes upon pain, and, in addition, the upregulation of metabolic activities previously linked to chronic pain as well as various hitherto unknown ones. Furthermore, our data revealed pain-associated dynamics of proteome complexes implicated in the crosstalk between the host immune system and the gut microbiome. In conclusion, the DIA-PASEF metaproteomic workflow presented here provides a stepping stone towards a deeper understanding of microbial ecosystems across the breadth of biomedical and biotechnological fields.

BACKGROUND:Acute pain after surgery is common and often leads to chronic post-surgical pain, but neither treatment nor prevention is currently sufficient. We hypothesised that specific protein networks (protein-protein interactions) are relevant for pain after surgery in humans and mice.METHODS:Standardised surgical incisions were performed in male human volunteers and male mice. Quantitative and qualitative sensory phenotyping were combined with unbiased quantitative mass spectrometry-based proteomics and protein network theory. The primary outcomes were skin protein signature changes in humans and phenotype-specific protein-protein interaction analysis 24 h after incision. Secondary outcomes were interspecies comparison of protein regulation as well as protein-protein interactions after incision and validation of selected proteins in human skin by immunofluorescence.RESULTS:Skin biopsies in 21 human volunteers revealed 119/1569 regulated proteins 24 h after incision. Protein-protein interaction analysis delineated remarkable differences between subjects with small (low responders, n=12) and large incision-related hyperalgesic areas (high responders, n=7), a phenotype most predictive of developing chronic post-surgical pain. Whereas low responders predominantly showed an anti-inflammatory protein signature, high responders exhibited signatures associated with a distinct proteolytic environment and persistent inflammation. Compared to humans, skin biopsies in mice habored even more regulated proteins (435/1871) 24 h after incision with limited overlap between species as assessed by proteome dynamics and PPI. Immunohistochemistry confirmed the expression of high priority candidates in human skin biopsies.CONCLUSIONS:Proteome profiling of human skin after incision revealed protein-protein interactions correlated with pain and hyperalgesia, which may be of potential significance for preventing chronic post-surgical pain. Importantly, protein-protein interactions were differentially modulated in mice compared to humans opening new avenues for successful translational research.

Metaproteomics is gaining momentum in microbiome research due to the multi-dimensional information it provides. However, current approaches have reached their detection limits. We present a highly-sensitive metaproteomic workflow using the extra information captured by Parallel Accumulation-Serial Fragmentation (PASEF) technology. The comparison of different acquisition modes and data analysis software packages showed that DIA-PASEF and DIA-NN doubled protein identifications of mouse gut microbiota and, importantly, also of the host proteome. DIA-PASEF significantly improved peptide detection reproducibility and quantification accuracy, which resulted in more than twofold identified taxa, reaching depths comparable to metagenomic studies. Consequently, DIA-PASEF exhibited improved coverage of functional networks revealing 131 additional pathways compared to DDA-PASEF. We applied our optimized workflow to a pre-clinical mouse model of chronic pain, in which we deciphered novel host-microbiome interactions. In summary, we present here a metaproteomic approach that paves the way for increasing the functional characterization of microbiome ecosystems and is applicable to diverse fields of biological research.

Chemotherapy-induced peripheral neuropathy (CIPN) is a debilitating side-effect of cancer therapies. So far, the development of CIPN cannot be prevented, neither can established CIPN be reverted, often leading to the cessation of necessary chemotherapy. Thus, there is an urgent need to explore the mechanistic basis of CIPN to facilitate its treatment. Here we used an integrated approach of quantitative proteome profiling and network analysis in a clinically relevant rat model of paclitaxel-induced peripheral neuropathy. We analysed lumbar rat DRG at two critical time points: (1) day 7, right after cessation of paclitaxel treatment, but prior to neuropathy development (pre-CIPN); (2) 4 weeks after paclitaxel initiation, when neuropathy has developed (peak-CIPN). In this way we identified a differential protein signature, which shows how changes in the proteome correlate with the development and maintenance of CIPN, respectively. Extensive biological pathway and network analysis reveals that, at pre-CIPN, regulated proteins are prominently implicated in mitochondrial (dys)function, immune signalling, neuronal damage/regeneration, and neuronal transcription. Orthogonal validation in an independent rat cohort confirmed the increase of β-catenin (CTNNB1) at pre-CIPN. More importantly, detailed analysis of protein networks associated with β-catenin highlights translationally relevant and potentially druggable targets. Overall, this study demonstrates the enormous value of combining animal behaviour with proteome and network analysis to provide unprecedented insights into the molecular basis of CIPN. In line with emerging approaches of network medicine our results highlight new avenues for developing improved therapeutic options aimed at preventing and treating CIPN.

ABSTRACTNociceptors play an essential role in both acute pain and chronic pain conditions. and have recently been classified into distinct subsets using single-cell transcriptional profiling. In this study, we examined protein levels in dorsal root ganglia using DIA Mass-spectrometry technologies with NaV1.8Cre+/-; ROSA26-flox-stop-flox-DTA (Diphtheria toxin fragment A) mutant mice (NaV1.8Cre-DTA), in which NaV1.8-expressing neurons (mainly nociceptors) in dorsal root ganglia (DRG) were ablated. The results show that 353 transcripts and 78 proteins, including nociceptor-specific sodium channels NaV1.8 (Scn10a) and NaV1.9 (Scn11a), were specifically expressed in nociceptors of DRG. A comparative analysis revealed that about 40% of nociceptor-specific proteins are shared within the nociceptor-specific transcript dataset. Scatter plots show that the proteome and transcriptome datasets in nociceptors have a moderate correlation (r = 0.4825), indicating the existence of post-transcriptional and post-translational gene regulation in nociceptors. This combined profiling study provides a unique resource for sensory studies, especially for pain research.

Pain after surgery is common, and its management remains a clinical challenge. Severe acute and prolonged post-surgical pain impairs immediate recovery and leads to long-term consequences like chronic pain, opioid dependency, and reduced quality of life. Althought rodent pain incision models exist, translation to patients is still hampered. To bridge this gap, we combined sensory phenotyping with quantitative proteomics and protein networks in humans and mice after skin incision representing an established model for surgical pain. Initially, we revealed, for the first time, similarities and differences of protein-protein interaction (PPI) networks across both species. Next, we comprehensively phenotyped humans for pain-related symptoms and observed phenotypes with incision-induced proteome changes. Remarkably, post-incision PPI-networks differed between volunteers with small incision-related hyperalgesic areas (“Low responder”) versus those with large areas (“High responder”). The latter exhibited a pronounced proteolytic environment associated with persistent inflammation, while an anti-inflammatory protein signature was observed in Low responders. Taken together, we provide unprecedented insights into peripheral processes relevant for developing hyperalgesia and pain after incision. This knowledge will immensely facilitate bidirectional translational pain studies and guide future research on the pathophysiology of pain after surgery and the discovery of novel targets for its treatment and prevention.

The age and sex of studied animals profoundly impact experimental outcomes in biomedical research. However, most preclinical studies in mice use a wide-spanning age range from 4 to 20 weeks and do not assess male and female mice in parallel. This raises concerns regarding reproducibility and neglects potentially relevant age and sex differences, which are largely unknown at the molecular level in naïve mice. Here, we employed an optimized quantitative proteomics workflow in order to deeply profile mouse paw skin and sciatic nerves (SCN) – two tissues implicated in nociception and pain as well as diseases linked to inflammation, injury, and demyelination. Remarkably, we uncovered significant differences when comparing male and female mice at adolescent (4 weeks) and adult (14 weeks) age. Our analysis deciphered protein subsets and networks that were correlated with the age and/or sex of mice. Notably, among these were proteins/biological pathways with known (patho)physiological relevance, e.g., homeostasis and epidermal signaling in skin, and, in SCN, multiple myelin proteins and regulators of neuronal development. Extensive comparisons with available databases revealed that various proteins associated with distinct skin diseases and pain exhibited significant abundance changes in dependence on age and/or sex. Taken together, our study uncovers hitherto unknown sex and age differences at the level of proteins and protein networks. Overall, we provide a unique proteome resource that facilitates mechanistic insights into somatosensory and skin biology, and integrates age and sex as biological variables – a prerequisite for successful preclinical studies in mouse disease models.

Sensitization of the transient receptor potential ion channel vanilloid 1 (TRPV1) is critically involved in inflammatory pain. To date, manifold signaling cascades have been shown to converge onto TRPV1 and enhance its sensitization. However, many of them also play a role for nociceptive pain, which limits their utility as targets for therapeutic intervention. Here, we show that the vesicle transport through interaction with t-SNAREs homolog 1B (Vti1b) protein promotes TRPV1 sensitization upon inflammation in cell culture but leaves normal functioning of TRPV1 intact. Importantly, the effect of Vti1b can be recapitulated in vivo: Virus-mediated knockdown of Vti1b in sensory neurons attenuated thermal hypersensitivity during inflammatory pain without affecting mechanical hypersensitivity or capsaicin-induced nociceptive pain. Interestingly, TRPV1 and Vti1b are localized in close vicinity as indicated by proximity ligation assays and are likely to bind to each other, either directly or indirectly, as suggested by coimmunoprecipitations. Moreover, using a mass spectrometry-based quantitative interactomics approach, we show that Vti1b is less abundant in TRPV1 protein complexes during inflammatory conditions compared with controls. Alongside, we identify numerous novel and pain state-dependent binding partners of native TRPV1 in dorsal root ganglia. These data represent a unique resource on the dynamics of the TRPV1 interactome and facilitate mechanistic insights into TRPV1 regulation. We propose that inflammation-related differences in the TRPV1 interactome identified here could be exploited to specifically target inflammatory pain in the future.

To obtain a thorough understanding of chronic pain, large-scale molecular mapping of the pain axis at the protein level is necessary, but has not yet been achieved. We applied quantitative proteome profiling to build a comprehensive protein compendium of three regions of the pain neuraxis in mice: the sciatic nerve (SN), the dorsal root ganglia (DRG), and the spinal cord (SC). Furthermore, extensive bioinformatics analysis enabled us to reveal unique protein subsets which are specifically enriched in the peripheral nervous system (PNS) and SC. The immense value of these datasets for the scientific community is highlighted by validation experiments, where we monitored protein network dynamics during neuropathic pain. Here, we resolved profound region-specific differences and distinct changes of PNS-enriched proteins under pathological conditions. Overall, we provide a unique and validated systems biology proteome resource (summarized in our online database painproteome.em.mpg.de), which facilitates mechanistic insights into somatosensory biology and chronic pain—a prerequisite for the identification of novel therapeutic targets.

The use of data‐independent acquisition (DIA) approaches for the reproducible and precise quantification of complex protein samples has increased in the last years. The protein information arising from DIA analysis is stored in digital protein maps (DIA maps) that can be interrogated in a targeted way by using ad hoc or publically available peptide spectral libraries generated on the same sample species as for the generation of the DIA maps. The restricted availability of certain difficult‐to‐obtain human tissues (i.e., brain) together with the caveats of using spectral libraries generated under variable experimental conditions limits the potential of DIA. Therefore, DIA workflows would benefit from high‐quality and extended spectral libraries that could be generated without the need of using valuable samples for library production. We describe here two new targeted approaches, using either classical data‐dependent acquisition repositories (not specifically built for DIA) or ad hoc mouse spectral libraries, which enable the profiling of human brain DIA data set. The comparison of our results to both the most extended publically available human spectral library and to a state‐of‐the‐art untargeted method supports the use of these new strategies to improve future DIA profiling efforts.

Comprehensive, reproducible and precise analysis of large sample cohorts is one of the key objectives of quantitative proteomics. Here, we present an implementation of data-independent acquisition using its parallel acquisition nature that surpasses the limitation of serial MS2 acquisition of data-dependent acquisition on a quadrupole ultra-high field Orbitrap mass spectrometer. In deep single shot data-independent acquisition, we identified and quantified 6,383 proteins in human cell lines using 2-or-more peptides/protein and over 7100 proteins when including the 717 proteins that were identified on the basis of a single peptide sequence. 7739 proteins were identified in mouse tissues using 2-or-more peptides/protein and 8121 when including the 382 proteins that were identified based on a single peptide sequence. Missing values for proteins were within 0.3 to 2.1% and median coefficients of variation of 4.7 to 6.2% among technical triplicates. In very complex mixtures, we could quantify 10,780 proteins and 12,192 proteins when including the 1412 proteins that were identified based on a single peptide sequence. Using this optimized DIA, we investigated large-protein networks before and after the critical period for whisker experience-induced synaptic strength in the murine somatosensory cortex 1-barrel field. This work shows that parallel mass spectrometry enables proteome profiling for discovery with high coverage, reproducibility, precision and scalability.

Opioids, agonists of µ-opioid receptors (µORs), are the strongest pain killers clinically available. Their action includes a strong central component, which also causes important adverse effects. However, µORs are also found on the peripheral endings of nociceptors and their activation there produces meaningful analgesia. The cellular mechanisms downstream of peripheral µORs are not well understood. Here, we show in neurons of murine dorsal root ganglia that pro-nociceptive TRPM3 channels, present in the peripheral parts of nociceptors, are strongly inhibited by µOR activation, much more than other TRP channels in the same compartment, like TRPV1 and TRPA1. Inhibition of TRPM3 channels occurs via a short signaling cascade involving Gβγ proteins, which form a complex with TRPM3. Accordingly, activation of peripheral µORs in vivo strongly attenuates TRPM3-dependent pain. Our data establish TRPM3 inhibition as important consequence of peripheral µOR activation indicating that pharmacologically antagonizing TRPM3 may be a useful analgesic strategy.

This article has been withdrawn by the authors. This article did not comply with the editorial guidelines of MCP. Specifically, single peptide based protein identifications of 9-19% were included in the analysis and discussed in the results and conclusions. We wish to withdraw this article and resubmit a clarified, corrected manuscript for review.

Chronic pain is a complex disease with limited treatment options. Several profiling efforts have been employed with the aim to dissect its molecular underpinnings. However, generated results are often inconsistent and nonoverlapping, which is largely because of inherent technical constraints. Emerging data-independent acquisition (DIA)-mass spectrometry (MS) has the potential to provide unbiased, reproducible and quantitative proteome maps - a prerequisite for standardization among experiments. Here, we designed a DIA-based proteomics workflow to profile changes in the abundance of dorsal root ganglia (DRG) proteins in two mouse models of chronic pain, inflammatory and neuropathic. We generated a DRG-specific spectral library containing 3067 DRG proteins, which enables their standardized quantification by means of DIA-MS in any laboratory. Using this resource, we profiled 2526 DRG proteins in each biological replicate of both chronic pain models and respective controls with unprecedented reproducibility. We detected numerous differentially regulated proteins, the majority of which exhibited pain model-specificity. Our approach recapitulates known biology and discovers dozens of proteins that have not been characterized in the somatosensory system before. Functional validation experiments and analysis of mouse pain behaviors demonstrate that indeed meaningful protein alterations were discovered. These results illustrate how the application of DIA-MS can open new avenues to achieve the long-awaited standardization in the molecular dissection of pathologies of the somatosensory system. Therefore, our findings provide a valuable framework to qualitatively extend our understanding of chronic pain and somatosensation.

The ability of somatosensory neurons to perceive mechanical stimuli relies on specialized mechanotransducing proteins and their molecular environment. Only recently has the identity of a major transducer of mechanical forces in vertebrates been revealed by the discovery of Piezo2. Further work has established its pivotal role for innocuous touch in mice. Therefore, Piezo2 offers a unique platform for the molecular investigation of somatosensory mechanosensation. We performed a mass spectrometry-based interactomics screen on native Piezo2 in somatosensory neurons of mouse dorsal root ganglia (DRG). Stringent and quantitative data analysis yielded the identity of 36 novel binding partners of Piezo2. The biological significance of this data set is reflected by functional experiments demonstrating a role for Pericentrin in modulating Piezo2 activity and membrane expression in somatosensory neurons. Collectively, our findings provide a framework for understanding Piezo2 physiology and serve as a rich resource for the molecular dissection of mouse somatosensation.

The ability of sensory neurons to detect potentially harmful stimuli relies on specialized molecular signal detectors such as transient receptor potential (TRP) A1 ion channels. TRPA1 is critically implicated in vertebrate nociception and different pain states. Furthermore, TRPA1 channels are subject to extensive modulation and regulation - processes which consequently affect nociceptive signaling. Here we show that the neuropeptide Nocistatin sensitizes TRPA1-dependent calcium influx upon application of the TRPA1 agonist mustard oil (MO) in cultured sensory neurons of dorsal root ganglia (DRG). Interestingly, TRPV1-mediated cellular calcium responses are unaffected by Nocistatin. Furthermore, Nocistatin-induced TRPA1-sensitization is likely independent of the Nocistatin binding partner 4-Nitrophenylphosphatase domain and non-neuronal SNAP25-like protein homolog 1 (NIPSNAP1) as assessed by siRNA-mediated knockdown in DRG cultures. In conclusion, we uncovered the sensitization of TRPA1 by Nocistatin, which may represent a novel mechanism how Nocistatin can modulate pain.