Increasing taxonomic and functional characterization of host-microbiome interactions by DIA-PASEF metaproteomics.

Introduction: Metaproteomics 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. Methods: We 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 discussion: We 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.

Development and validation of COEWS (COVID-19 Early Warning Score) for hospitalized COVID-19 with laboratory features: A multicontinental retrospective study.

Background: The emergence of new SARS-CoV-2 variants with significant immune-evasiveness, the relaxation of measures for reducing the number of infections, the waning of immune protection (particularly in high-risk population groups), and the low uptake of new vaccine boosters, forecast new waves of hospitalizations and admission to intensive care units. There is an urgent need for easily implementable and clinically effective Early Warning Scores (EWSs) that can predict the risk of complications within the next 24-48 hr. Although EWSs have been used in the evaluation of COVID-19 patients, there are several clinical limitations to their use. Moreover, no models have been tested on geographically distinct populations or population groups with varying levels of immune protection. Methods: We developed and validated COVID-19 Early Warning Score (COEWS), an EWS that is automatically calculated solely from laboratory parameters that are widely available and affordable. We benchmarked COEWS against the widely used NEWS2. We also evaluated the predictive performance of vaccinated and unvaccinated patients. Results: The variables of the COEWS predictive model were selected based on their predictive coefficients and on the wide availability of these laboratory variables. The final model included complete blood count, blood glucose, and oxygen saturation features. To make COEWS more actionable in real clinical situations, we transformed the predictive coefficients of the COEWS model into individual scores for each selected feature. The global score serves as an easy-to-calculate measure indicating the risk of a patient developing the combined outcome of mechanical ventilation or death within the next 48 hr.The discrimination in the external validation cohort was 0.743 (95% confidence interval [CI]: 0.703-0.784) for the COEWS score performed with coefficients and 0.700 (95% CI: 0.654-0.745) for the COEWS performed with scores. The area under the receiver operating characteristic curve (AUROC) was similar in vaccinated and unvaccinated patients. Additionally, we observed that the AUROC of the NEWS2 was 0.677 (95% CI: 0.601-0.752) in vaccinated patients and 0.648 (95% CI: 0.608-0.689) in unvaccinated patients. Conclusions: The COEWS score predicts death or MV within the next 48 hr based on routine and widely available laboratory measurements. The extensive external validation, its high performance, its ease of use, and its positive benchmark in comparison with the widely used NEWS2 position COEWS as a new reference tool for assisting clinical decisions and improving patient care in the upcoming pandemic waves. Funding: University of Vienna.

COVID-19 vaccines reduce mortality in hospitalized patients with oxygen requirements: Differences between vaccine subtypes. A multicontinental cohort study.

The aim of this study was to analyze whether the coronavirus disease 2019 (COVID-19) vaccine reduces mortality in patients with moderate or severe COVID-19 disease requiring oxygen therapy. A retrospective cohort study, with data from 148 hospitals in both Spain (111 hospitals) and Argentina (37 hospitals), was conducted. We evaluated hospitalized patients for COVID-19 older than 18 years with oxygen requirements. Vaccine protection against death was assessed through a multivariable logistic regression and propensity score matching. We also performed a subgroup analysis according to vaccine type. The adjusted model was used to determine the population attributable risk. Between January 2020 and May 2022, we evaluated 21,479 COVID-19 hospitalized patients with oxygen requirements. Of these, 338 (1.5%) patients received a single dose of the COVID-19 vaccine and 379 (1.8%) were fully vaccinated. In vaccinated patients, mortality was 20.9% (95% confidence interval [CI]: 17.9-24), compared to 19.5% (95% CI: 19-20) in unvaccinated patients, resulting in a crude odds ratio (OR) of 1.07 (95% CI: 0.89-1.29; p = 0.41). However, after considering the multiple comorbidities in the vaccinated group, the adjusted OR was 0.73 (95% CI: 0.56-0.95; p = 0.02) with a population attributable risk reduction of 4.3% (95% CI: 1-5). The higher risk reduction for mortality was with messenger RNA (mRNA) BNT162b2 (Pfizer) (OR 0.37; 95% CI: 0.23-0.59; p < 0.01), ChAdOx1 nCoV-19 (AstraZeneca) (OR 0.42; 95% CI: 0.20-0.86; p = 0.02), and mRNA-1273 (Moderna) (OR 0.68; 95% CI: 0.41-1.12; p = 0.13), and lower with Gam-COVID-Vac (Sputnik) (OR 0.93; 95% CI: 0.6-1.45; p = 0.76). COVID-19 vaccines significantly reduce the probability of death in patients suffering from a moderate or severe disease (oxygen therapy).

Phenotype- and species-specific skin proteomic signatures for incision-induced pain in humans and mice.

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.

Deep proteome profiling reveals signatures of age and sex differences in paw skin and sciatic nerve of naïve mice.

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.

Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study.

New SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to 9 days before clinical resolution (AUROC: 0·90-0·96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries.

While COVID-19 vaccines have saved millions of lives, new variants, waxing immunity, unequal rollout and relaxation of mitigation strategies mean that the pandemic will keep on sending shockwaves across healthcare systems. In this context, it is crucial to equip clinicians with tools to triage COVID-19 patients and forecast who will experience the worst forms of the disease. Prediction models based on artificial intelligence could help in this effort, but the task is not straightforward. Indeed, the pandemic is defined by ever-changing factors which artificial intelligence needs to cope with. To be useful in the clinic, a prediction model should make accurate prediction regardless of hospital location, viral variants or vaccination and immunity statuses. It should also be able to adapt its output to the level of resources available in a hospital at any given time. Finally, these tools need to seamlessly integrate into clinical workflows to not burden clinicians. In response, Klén et al. built CODOP, a freely available prediction algorithm that calculates the death risk of patients hospitalized with COVID-19 (https://gomezvarelalab.em.mpg.de/codop/). This model was designed based on biochemical data from routine blood analyses of COVID-19 patients. Crucially, the dataset included 30,000 individuals from 150 hospitals in Spain, the United States, Honduras, Bolivia and Argentina, sampled between March 2020 and February 2022 and carrying most of the main COVID-19 variants (from the original Wuhan version to Omicron). CODOP can predict the death or survival of hospitalized patients with high accuracy up to nine days before the clinical outcome occurs. These forecasting abilities are preserved independently of vaccination status or viral variant. The next step is to tailor the model to the current pandemic situation, which features increasing numbers of infected people as well as accumulating immune protection in the overall population. Further development will refine CODOP so that the algorithm can detect who will need hospitalisation in the next 24 hours, and who will need admission in intensive care in the next two days. Equipping primary care settings and hospitals with these tools will help to restore previous standards of health care during the upcoming waves of infections, particularly in countries with limited resources.

Proteome and Network Analysis Provides Novel Insights Into Developing and Established Chemotherapy-Induced Peripheral Neuropathy.

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 ß-catenin (CTNNB1) at pre-CIPN. More importantly, detailed analysis of protein networks associated with ß-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.

A proteome signature for acute incisional pain in dorsal root ganglia of mice.

ABSTRACT: After surgery, acute pain is still managed insufficiently and may lead to short-term and long-term complications including chronic postsurgical pain and an increased prescription of opioids. Thus, identifying new targets specifically implicated in postoperative pain is of utmost importance to develop effective and nonaddictive analgesics. Here, we used an integrated and multimethod workflow to reveal unprecedented insights into proteome dynamics in dorsal root ganglia (DRG) of mice after plantar incision (INC). Based on a detailed characterization of INC-associated pain-related behavior profiles, including a novel paradigm for nonevoked pain, we performed quantitative mass-spectrometry-based proteomics in DRG 1 day after INC. Our data revealed a hitherto unknown INC-regulated protein signature in DRG with changes in distinct proteins and cellular signaling pathways. In particular, we show the differential regulation of 44 protein candidates, many of which are annotated with pathways related to immune and inflammatory responses such as MAPK/extracellular signal-regulated kinases signaling. Subsequent orthogonal assays comprised multiplex Western blotting, bioinformatic protein network analysis, and immunolabeling in independent mouse cohorts to validate (1) the INC-induced regulation of immune/inflammatory pathways and (2) the high priority candidate Annexin A1. Taken together, our results propose novel potential targets in the context of incision and, therefore, represent a highly valuable resource for further mechanistic and translational studies of postoperative pain.

Proteome-based systems biology in chronic pain.

Chronic pain represents a major medical challenge in the 21st century. Enormous efforts have been invested towards deciphering the complexity of chronic pain from different angles (molecular, physiological, psychosocial, and behavioral) in both preclinical and clinical settings. While progress has been made, our understanding of the underlying mechanisms of chronic pain remains insufficient. Consequently, chronic pain treatment is often inadequate. It lacks efficacy in most patients and is associated with detrimental side effects - a situation which calls for urgent changes in pain research and management. In this review we propose that protein-centric systems biology can significantly contribute to pain research. This approach may introduce the long-awaited paradigm shift in pain research from single targets to multidimensional cellular networks. We critically discuss how recent advances in reproducible and comprehensive proteome profiling can be exploited by pain researchers in the following ways: to gain mechanistic insights into chronic pain and its diverse forms, to facilitate clinical trials and the search for new drug targets, and to objectively assess chronic pain and its stages in individual patients by defining so-called protein disease signatures (PDS). We feel that the integration of proteomics into the toolbox of pain researchers and physicians alike will open new avenues towards a better understanding and management of chronic pain. SIGNIFICANCE STATEMENT: The immense challenges associated with chronic pain call for urgent changes in pain research and management. Here, we highlight the enormous potential of a proteome-based systems biology approach for advancing our understanding of chronic pain from a mechanistic, translational and clinical angle.

Vti1b promotes TRPV1 sensitization during inflammatory pain.

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.

Region-Resolved Quantitative Proteome Profiling Reveals Molecular Dynamics Associated With Chronic Pain in the PNS and Spinal Cord.

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.

Myotubularin related protein-2 and its phospholipid substrate PIP2 control Piezo2-mediated mechanotransduction in peripheral sensory neurons.

Piezo2 ion channels are critical determinants of the sense of light touch in vertebrates. Yet, their regulation is only incompletely understood. We recently identified myotubularin related protein-2 (Mtmr2), a phosphoinositide (PI) phosphatase, in the native Piezo2 interactome of murine dorsal root ganglia (DRG). Here, we demonstrate that Mtmr2 attenuates Piezo2-mediated rapidly adapting mechanically activated (RA-MA) currents. Interestingly, heterologous Piezo1 and other known MA current subtypes in DRG appeared largely unaffected by Mtmr2. Experiments with catalytically inactive Mtmr2, pharmacological blockers of PI(3,5)P2 synthesis, and osmotic stress suggest that Mtmr2-dependent Piezo2 inhibition involves depletion of PI(3,5)P2. Further, we identified a PI(3,5)P2 binding region in Piezo2, but not Piezo1, that confers sensitivity to Mtmr2 as indicated by functional analysis of a domain-swapped Piezo2 mutant. Altogether, our results propose local PI(3,5)P2 modulation via Mtmr2 in the vicinity of Piezo2 as a novel mechanism to dynamically control Piezo2-dependent mechanotransduction in peripheral sensory neurons.

Optimization of Experimental Parameters in Data-Independent Mass Spectrometry Significantly Increases Depth and Reproducibility of Results.

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.

WITHDRAWN: Heralds of parallel MS: Data-independent acquisition surpassing sequential identification of data dependent acquisition in proteomics.

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.

New targeted approaches for the quantification of data-independent acquisition mass spectrometry.

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.

Nocistatin sensitizes TRPA1 channels in peripheral sensory neurons.

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.

Exploring novel paths towards protein signatures of chronic pain.

Pain is a major symptom of many medical conditions and the worldwide number one reason for people to seek medical assistance. It affects the quality of life of patients and poses a heavy financial burden on society with high costs of treatment and lost productivity. Furthermore, the treatment of chronic pain presents a big challenge as pain therapeutics often lack efficacy and exhibit minimal safety profiles. The latter can be largely attributed to the fact that current therapies target molecules with key physiological functions throughout the body. In light of these difficulties, the identification of proteins specifically involved in chronic pain states is of paramount importance for designing selective interventions. Several profiling efforts have been employed with the aim to dissect the molecular underpinnings of chronic pain, both on the level of the transcriptome and proteome. However, generated results are often inconsistent and non-overlapping, which is largely due to inherent technical constraints. A potential solution may be offered by emerging strategies capable of performing standardized and reproducible proteome analysis, such as data-independent acquisition-mass spectrometry (DIA-MS). We have recently demonstrated the applicability of DIA-MS to interrogate chronic pain-related proteome alterations in mice. Based on our results, we aim to provide an overview on DIA-MS and its potential to contribute to the comprehensive characterization of molecular signatures underlying pain pathologies.

Native Piezo2 Interactomics Identifies Pericentrin as a Novel Regulator of Piezo2 in Somatosensory Neurons.

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.

Standardized Profiling of The Membrane-Enriched Proteome of Mouse Dorsal Root Ganglia (DRG) Provides Novel Insights Into Chronic Pain.

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.