Proteomic Atlas of Post-COVID Sequelae

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 harnessed the power of predictive machine learning models to explore the potential of proteomics in predicting 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 21 distinct post-COVID symptoms. Symptoms more closely linked by similar protein patterns tended to co-occur more frequently in patients. Six symptom clusters with distinct molecular pathway signatures were uncovered, with metabolic and inflammatory pathways prominently involved across several clusters. The relevance of the specific protein signatures for post-COVID symptoms could be demonstrated and explored by objective, quantifiable clinical tests, including cognitive and somatic assessments, and underlined their 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 underscores the complex and multifaceted nature of post-COVID symptoms. It emphasizes the need for systematic and more specific approaches to facilitate the development of targeted therapies and treatment strategies.