New Proteomic Signatures to Distinguish Between Zika and Dengue Infections.
Mol Cell Proteomics 2021;
20:100052. [PMID:
33582300 PMCID:
PMC8042398 DOI:
10.1016/j.mcpro.2021.100052]
[Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 01/16/2021] [Accepted: 01/28/2021] [Indexed: 01/22/2023] Open
Abstract
Distinguishing between Zika and dengue virus infections is critical for accurate treatment, but we still lack detailed understanding of their impact on their host. To identify new protein signatures of the two infections, we used next-generation proteomics to profile 122 serum samples from 62 Zika and dengue patients. We quantified >500 proteins and identified 13 proteins that were significantly differentially expressed (adjusted p-value < 0.05). These proteins typically function in infection and wound healing, with several also linked to pregnancy and brain function. We successfully validated expression differences with Carbonic Anhydrase 2 in both the original and an independent sample set. Three of the differentially expressed proteins, i.e., Fibrinogen Alpha, Platelet Factor 4 Variant 1, and Pro-Platelet Basic Protein, predicted Zika virus infection at a ∼70% true-positive and 6% false-positive rate. Further, we showed that intraindividual temporal changes in protein signatures can disambiguate diagnoses and serve as indicators for past infections. Taken together, we demonstrate that serum proteomics can provide new resources that serve to distinguish between different viral infections.
Analysis of human serum samples with extreme protein abundance ranges
Unique protein signatures for Zika and dengue virus infection
Temporal changes in protein signatures as indicators for past infections
Machine learning to account for confounding factors
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