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Integrated multiomics analysis to infer COVID-19 biological insights
Mahmoud Sameh1, Hossam M Khalaf2, Ali Mostafa Anwar1
1Basic Research Department, Proteomics and Metabolomics Research Program, Children's Cancer Hospital 57357 (CCHE-57357), Cairo, Egypt.
Insights
This study reveals a novel multi-omics approach for understanding severe COVID-19. Integrating knowledge and statistical networks offers a comprehensive view of the disease, aiding in early detection and therapy development.
Area of Science:
- * Molecular biology and systems biology
- * Infectious disease research
- * Biomarker discovery
Background:
- * Limited understanding of the severe COVID-19 pathogen landscape persists three years post-pandemic.
- * Urgent need for early detection and effective therapies for severe COVID-19 patients.
- * Viral infection impacts extend beyond pulmonary damage, affecting host cellular machinery.
Purpose of the Study:
- * To profile the plasma proteome and metabolome in severe COVID-19 patients.
- * To compare conventional single-omics with multi-omics integrative methods for disease profiling.
- * To identify a comprehensive method for understanding the molecular landscape of severe COVID-19.
Main Methods:
- * High-resolution mass spectrometry used to analyze plasma proteome and metabolome.
- * Analysis included conventional single-omics and multi-omics integrative approaches.
- * Development and application of a knowledge-statistical network for data integration.
Main Results:
- * The integrated knowledge-statistical network approach outperformed other methods in pathway detection.
- * This integrated method identified a greater number of features within biological pathways.
- * Demonstrated a comprehensive molecular landscape of severe COVID-19.
Conclusions:
- * The knowledge-statistical network provides a superior method for analyzing complex biological systems.
- * This approach enhances understanding of molecular mechanisms in diseases like COVID-19.
- * Offers potential for multi-dimensional therapeutic strategies targeting multiple pathogenic factors.
Abstract:
Three years after the pandemic, we still have an imprecise comprehension of the pathogen landscape and we are left with an urgent need for early detection methods and effective therapy for severe COVID-19 patients. The implications of infection go beyond pulmonary damage since the virus hijacks the host's cellular machinery and consumes its resources. Here, we profiled the plasma proteome and metabolome of a cohort of 57 control and severe COVID-19 cases using high-resolution mass spectrometry. We analyzed their proteome and metabolome profiles with multiple depths and methodologies as conventional single omics analysis and other multi-omics integrative methods to obtain the most comprehensive method that portrays an in-depth molecular landscape of the disease. Our findings revealed that integrating the knowledge-based and statistical-based techniques (knowledge-statistical network) outperformed other methods not only on the pathway detection level but even on the number of features detected within pathways. The versatile usage of this approach could provide us with a better understanding of the molecular mechanisms behind any biological system and provide multi-dimensional therapeutic solutions by simultaneously targeting more than one pathogenic factor.
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