Chandelier
Senior Member (Voting Rights)
Proteomic Profiling of Post-COVID -19 Sequelae
• Symptom-specific proteomic models outperformed a generalized Post-COVID classification approach.
• Six symptom clusters with unique molecular pathway signatures were identified.
• Proteomic signatures correlated with objective cognitive, sensory, and psychiatric assessments.
• Comorbidities and polygenic risk scores shaped symptom-specific protein profiles.
This study employed machine learning-based classification to explore the potential of proteomics in identifying associations with individual Post-COVID symptoms and their collective manifestation as PCS.
The analysis utilized a panel of approximately 2900 proteins measured in 495 COVID-19 patients. The study identified 235 unique proteins associated with 15 of the 21 evaluated Post-COVID symptoms. Symptoms more closely linked by similar protein profiles tended to co-occur more frequently in patients.
Six symptom clusters with characteristic molecular pathway associations were identified, with metabolic and inflammatory pathways prominently involved across several clusters. The relevance of the specific protein profiles for Post-COVID symptoms further explored by objective, quantifiable clinical tests, including cognitive and somatic assessments, providing supportive evidence for their biological relevance.
Data from various modalities, including pre-existing conditions, disease risk factors and genetic susceptibility, revealed relevant relations that may contribute to PCS heterogeneity.
This work illustrates the complex and multifaceted nature of Post-COVID symptoms. It emphasizes the need for systematic and more specific approaches to facilitate the identification of potential pathways for future therapeutic investigation.
Web | DOI | Molecular & Cellular Proteomics | Open Access
Golriz Khatami, Sepehr; Baumkötter, Rieke; Köck, Thomas; Alam, Ayesha Syed Mamoor; Chalabi, Julian; Rapp, Steffen; Marini, Federico; Schepers, Markus; Bopp, Tobias; Bittner, Stefan; Schuster, Alexander K.; Beutel, Manfred E.; Münzel, Thomas; Lackner, Karl J.; Cate, Vincent ten; Wild, Philipp S.
Highlights
• Machine learning identified 235 proteins linked to 15 of the 21 evaluated symptoms Post-COVID symptoms.• Symptom-specific proteomic models outperformed a generalized Post-COVID classification approach.
• Six symptom clusters with unique molecular pathway signatures were identified.
• Proteomic signatures correlated with objective cognitive, sensory, and psychiatric assessments.
• Comorbidities and polygenic risk scores shaped symptom-specific protein profiles.
Abstract
The emergence of Post-COVID sequelae (PCS) represents a global challenge. However, understanding of biological mechanisms and the definition of quantifiable risk factors remains limited.This study employed machine learning-based classification to explore the potential of proteomics in identifying associations with individual Post-COVID symptoms and their collective manifestation as PCS.
The analysis utilized a panel of approximately 2900 proteins measured in 495 COVID-19 patients. The study identified 235 unique proteins associated with 15 of the 21 evaluated Post-COVID symptoms. Symptoms more closely linked by similar protein profiles tended to co-occur more frequently in patients.
Six symptom clusters with characteristic molecular pathway associations were identified, with metabolic and inflammatory pathways prominently involved across several clusters. The relevance of the specific protein profiles for Post-COVID symptoms further explored by objective, quantifiable clinical tests, including cognitive and somatic assessments, providing supportive evidence for their biological relevance.
Data from various modalities, including pre-existing conditions, disease risk factors and genetic susceptibility, revealed relevant relations that may contribute to PCS heterogeneity.
This work illustrates the complex and multifaceted nature of Post-COVID symptoms. It emphasizes the need for systematic and more specific approaches to facilitate the identification of potential pathways for future therapeutic investigation.
Graphical abstract
Web | DOI | Molecular & Cellular Proteomics | Open Access