Frontiers in ImmunologySystems BiologyPeer-reviewed journal article
Metabolic dyshomeostasis induced by SARS-CoV-2 structural proteins reveals immunological insights into viral olfactory interactions
Mercedes Lachén‐MontesiD, Naroa MendizuriiD, Karina AusíniD, Miriam EchaideiD, Ester BlancoiD, Luisa ChocarroiD, María de ToroiD, David EscorsiD, Joaquín Fernández‐IrigoyeniD, Grazyna T. KochaniD, Enrique SantamaríaiD
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Abstract
One of the most common symptoms in COVID-19 is a sudden loss of smell. SARS-CoV-2 has been detected in the olfactory bulb (OB) from animal models and sporadically in COVID-19 patients. To decipher the specific role over the SARS-CoV-2 proteome at olfactory level, we characterized the in-depth molecular imbalance induced by the expression of GFP-tagged SARS-CoV-2 structural proteins (M, N, E, S) on mouse OB cells. Transcriptomic and proteomic trajectories uncovered a widespread metabolic remodeling commonly converging in extracellular matrix organization, lipid metabolism and signaling by receptor tyrosine kinases. The molecular singularities and specific interactome expression modules were also characterized for each viral structural factor. The intracellular molecular imbalance induced by each SARS-CoV-2 structural protein was accompanied by differential activation dynamics in survival and immunological routes in parallel with a differentiated secretion profile of chemokines in OB cells. Machine learning through a proteotranscriptomic data integration uncovered TGF-beta signaling as a confluent activation node by the SARS-CoV-2 structural proteome. Taken together, these data provide important avenues for understanding the multifunctional immunomodulatory properties of SARS-CoV-2 M, N, S and E proteins beyond their intrinsic role in virion formation, deciphering mechanistic clues to the olfactory inflammation observed in COVID-19 patients.
- organism: mouse
- assay: proteomics
- assay: bulk RNA-seq
- research focus: cell-cell communication
- research focus: differential expression
- approach: machine learning (classical)
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