High-resolution proteomics identifies potential new markers of Zika and dengue infections

Distinguishing between Zika and dengue virus infections is critical for treatment and anticipation of complications. However, existing biomarkers have high error rates. To identify new potential diagnostic signatures, we used next-generation proteomics to profile 122 serum samples from 62 Zika or dengue patients. We quantified >500 proteins and identified 26 proteins that were significantly differentially expressed. These proteins typically function in infection and wound healing, with several also linked to pregnancy and brain. Integrating machine learning approaches, we used 7 proteins to predict ZIKV infection correctly in 72% of the cases, outperforming other tools. The three most predictive proteins were Platelet Factor 4 Variant 1, Fibrinogen Alpha, and Gelsolin. Finally, we showed that temporal changes in protein signatures from the same patient can disambiguate some diagnoses and serve as indicators for past infections. Taken together, we demonstrate that serum proteomics can be highly valuable to diagnose even challenging samples.