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Blood proteomics and multimodal risk profiling of human volunteers after incision injury: a translational study for advancing personalized pain management after surgery

ABSTRACT: 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 an experimental incision injury. We determined multi-modal factors ranging from pain characteristics, psychological questionnaires to blood proteomics. Outcome measures after incision were 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 parameters. A combinatorial set of distinct parameters 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 for 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 tostratify patients and identify potential new targets as well as mechanistic correlates for postsurgical pain.

INSTRUMENT(S): Orbitrap Exploris 480

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Plasma

SUBMITTER: Manuela Schmidt

LAB HEAD: Prof. Manuela

PROVIDER: PXD033592 | Pride | 2025-01-17

REPOSITORIES: pride

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Project description:Acute postsurgerical pain and it's management represent a major clinical challenge. In particular, severe and prolonged pain impairs immediate recovery and might lead to long-term consequences like chronic pain, opioid abuse, and reduced quality of life. Rodent incision models greatly contributed to the understanding of cutaneous wound repair, but the underlying mechanisms' knowledge remains incomplete, yet translational data are urgently needed to develop novel treatment options and preventive measures. We combined extensive sensory phenotyping with unbiased quantitative proteomics in human and mouse skin after an incision to assess interspecies protein signatures to overcome this gap. Additionally, stratification of human volunteers based on the hyperalgesic area after surgery in correlation with the corresponding proteomic fingerprint revealed novel insides into human-specific mechanisms.Unbiased skin proteome analysis of both species unveiled comparable protein signatures after incision, notably for inflammatory activity and actin polymerization. Upon incision, we could discern 50 commonly regulated proteins, amounting to 61% of all regulated human and 10% regulated mouse proteins. Notably, the direction of regulation upon incision, i.e. up-or downregulated, was conserved in humans and mice to a great extent. Integrative analysis of pain-related phenotyping with quantitative mass spectrometry identified hitherto unknown skin protein signatures, providing a tool for stratification on the protein level. Protein-protein interaction (PPI)-networks differed between volunteers with small incision-related hyperalgesic areas (termed"low responder") versus those with large areas ("high responder"). In particular, PPIs of volunteers exhibiting a large hyperalgesic area were characterized by a predominant dysregulated proteolytic environment associated with persistent inflammation reponse. In contrast, PPI- networks of low responders point to anti-inflammatory processes. Here, we present a framework for specific-specific alterations in human and mice upon incision, highlighting similarities and differences on a protein network level. Moreover, the detection of developing hyperalgesia and pain after surgery in volunteers and it's correlation with corresponding molecular fingerprints indicates the need for tailored treatments to prevent chronification processes in humans after surgery.

Project description:Background: Results obtained from our previous study using a label-free quantitation (LF) proteomic approach in which dynamic range compression was used to profile lower abundance proteins, demonstrated few proteins that were differentially abundant within the synovial fluid (SF) when comparing Autologous Chondrocyte Implantation (ACI) responders and non-responders at baseline (1). This study builds upon our previous findings by assessing higher abundance proteins within these SFs; providing a more global proteome analysis from which we can understand more fully the biology underlying ACI success or failure. Methods: Isobaric tagging for relative and absolute quantitation (iTRAQ) proteomics was used to assess SFs from ACI responders (mean Lysholm improvement of 33; n=14) and non-responders (mean Lysholm decrease of 14; n=13) at the two stages of surgery (cartilage harvest and chondrocyte implantation). Differentially abundant proteins were investigated using pathway analyses and the iTRAQ proteomic dataset was combined with our published proteomic dataset of dynamically compressed SFs, from which an interactome network model of systemic protein interactions was generated. Results: iTRAQ proteomics has confirmed our previous finding that there is a marked proteome shift in response to cartilage harvest (70 and 54 proteins demonstrating ≥2.0 fold change between Stages I and II in responders and non-responders, respectively) and has highlighted 28 proteins that were differentially abundant between responders and non-responders to ACI, that were not found in the LF study; 16 of which were altered at baseline. Two protein abundance changes (Complement C1S subcomponent and Matrix metalloproteinase 3 (MMP3), have been biochemically validated. Combination of the iTRAQ and LF proteomic datasets has generated in-depth SF proteome information that has been used to generate interactome networks representing ACI success or failure, from which functional pathways that are dysregulated in ACI non-responders have been identified. Conclusions: Several candidate biomarkers for baseline prediction of ACI outcome have been identified. A holistic overview of the SF proteome in responders and non-responders to ACI has been profiled providing a better understanding of the biological pathways underlying clinical outcome, particularly the differential response to cartilage harvest in non-responders.

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Blood proteomics and multimodal risk profiling of human volunteers after incision injury: a translational study for advancing personalized pain management after surgery

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