PLoS ONEMicrobiologyPeer-reviewed journal article
Starve a cold or feed a fever? Identifying cellular metabolic changes following infection and exposure to SARS-CoV-2.
Emma Kate LovedayiD, Hope D. WelhaveniD, Ayten Ebru ErdoganiD, Kyle S. Hain, Luke F. DomanicoiD, Connie B. ChangiD, Ronald K. JuneiD, Matthew P. TayloriD
Abstract
Viral infections induce major shifts in cellular metabolism elicited by active viral replication and antiviral responses. For the virus, harnessing cellular metabolism and evading changes that limit replication are essential for productive viral replication. In contrast, the cellular response to infection disrupts metabolic pathways to prevent viral replication and promote an antiviral state in the host cell and neighboring bystander cells. This competition between the virus and cell results in measurable shifts in cellular metabolism that differ depending on the virus, cell type, and extracellular environment. The resulting metabolic shifts can be observed and analyzed using global metabolic profiling techniques to identify pathways that are critical for either viral replication or cellular defense. SARS-CoV-2 is a respiratory virus that can exhibit broad tissue tropism and diverse, yet inconsistent, symptomatology. While the factors that determine the presentation and severity of SARS-CoV-2 infection remain unclear, metabolic syndromes are associated with more severe manifestations of SARS-CoV-2 disease. Despite these observations a critical knowledge gap remains between cellular metabolic responses and SARS-CoV-2 infection. Using a well-established untargeted metabolomics analysis workflow, we compared SARS-CoV-2 infection of human lung carcinoma cells. We identified significant changes in metabolic pathways that correlate with either productive or non-productive viral infection. This information is critical for characterizing the factors that contribute to SARS-CoV-2 replication that could be targeted for therapeutic interventions to limit viral disease.
- organism: human
- assay: metabolomics
- research focus: differential expression
- approach: statistical model
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- 3th percentile
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