Title

Session 7: Decoding Infectious Disease Omics Data: COVID-19 Case Study

Presenter Information/ Coauthors Information

Ali Rahnavard, George Washington University

Presentation Type

Invited

Track

Other

Abstract

Infectious diseases are a great challenge to the world due to the high rate of transmissibility and dynamics of viral populations over time and space. The enduring success of treatment strategies (e.g., vaccine, drug) requires characterizing viral evolution during the initial and subsequent treatments and discrete host/viral genomic variants associated with different clinical outcomes and viral phenotypes (e.g., drug resistance and vaccine escape mutants). We developed and applied novel computational approaches to integrate diverse data types (viral genomics, host genomics, clinical data, treatment data, health outcome data), identify patterns of associations among these data types, and thereby identify targets of biomarkers associated with treatments and disease outcomes and how these change over time and with interactions of different viral and host populations. In an investigation of COVID-19 as an exemplar system, we found SARS-COV-2 mutations occur at a very interesting rate; for example, nonstructural protein 3 (nsp3) variation across our data population co-occurred with Spike protein, a target for most COVID-19 vaccines. We also investigated human body responses to the infection using metabolomics and proteomics profiling. We discovered important pathways that explained organ dysfunctions, such as lung inflammation. We identified biomarkers such as citrulline and Hyaluronan-binding protein 2 indicative of multi-system tissue dysfunction and can be used for COVID-19 diagnosis. The methodology developed is generally applicable across infectious disease outbreaks and systems in humans, agriculture, and nature as more and more omics data become available in such systems.

Start Date

2-8-2022 11:00 AM

End Date

2-8-2022 12:00 PM

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Feb 8th, 11:00 AM Feb 8th, 12:00 PM

Session 7: Decoding Infectious Disease Omics Data: COVID-19 Case Study

Dakota Room 250 A/C

Infectious diseases are a great challenge to the world due to the high rate of transmissibility and dynamics of viral populations over time and space. The enduring success of treatment strategies (e.g., vaccine, drug) requires characterizing viral evolution during the initial and subsequent treatments and discrete host/viral genomic variants associated with different clinical outcomes and viral phenotypes (e.g., drug resistance and vaccine escape mutants). We developed and applied novel computational approaches to integrate diverse data types (viral genomics, host genomics, clinical data, treatment data, health outcome data), identify patterns of associations among these data types, and thereby identify targets of biomarkers associated with treatments and disease outcomes and how these change over time and with interactions of different viral and host populations. In an investigation of COVID-19 as an exemplar system, we found SARS-COV-2 mutations occur at a very interesting rate; for example, nonstructural protein 3 (nsp3) variation across our data population co-occurred with Spike protein, a target for most COVID-19 vaccines. We also investigated human body responses to the infection using metabolomics and proteomics profiling. We discovered important pathways that explained organ dysfunctions, such as lung inflammation. We identified biomarkers such as citrulline and Hyaluronan-binding protein 2 indicative of multi-system tissue dysfunction and can be used for COVID-19 diagnosis. The methodology developed is generally applicable across infectious disease outbreaks and systems in humans, agriculture, and nature as more and more omics data become available in such systems.