SUMMARY Flaviviruses cause systemic or neurotropic-encephalitic pathology in humans. The flavivirus nonstructural protein 1 (NS1) is a secreted glycoprotein involved in viral replication, immune evasion, and vascular leakage during dengue virus infection. However, the contribution of secreted NS1 from related flaviviruses to viral pathogenesis remains unknown. Here, we demonstrate that NS1 from dengue, Zika, West Nile, Japanese encephalitis, and yellow fever viruses selectively binds to and alters permeability of human endothelial cells from lung, dermis, umbilical vein, brain, and liver in vitro and causes tissue-specific vascular leakage in mice, reflecting the pathophysiology of each flavivirus. Mechanistically, each flavivirus NS1 leads to differential disruption of endothelial glycocalyx components, resulting in endothelial hyperpermeability. Our findings reveal the capacity of a secreted viral protein to modulate endothelial barrier function in a tissue-specific manner both in vitro and in vivo, potentially influencing virus dissemination and pathogenesis and providing targets for antiviral therapies and vaccine development.

SARS-CoV-2 infection is characterized by peak viral load in the upper airway prior to or at the time of symptom onset, an unusual feature that has enabled widespread transmission of the virus and precipitated a global pandemic. How SARS-CoV-2 is able to achieve high titer in the absence of symptoms remains unclear. Here, we examine the upper airway host transcriptional response in patients with COVID-19 (n = 93), other viral (n = 41) or non-viral (n = 100) acute respiratory illnesses (ARIs). Compared with other viral ARIs, COVID-19 is characterized by a pronounced interferon response but attenuated activation of other innate immune pathways, including toll-like receptor, interleukin and chemokine signaling. The IL-1 and NLRP3 inflammasome pathways are markedly less responsive to SARS-CoV-2, commensurate with a signature of diminished neutrophil and macrophage recruitment. This pattern resembles previously described distinctions between symptomatic and asymptomatic viral infections and may partly explain the propensity for pre-symptomatic transmission in COVID-19. We further use machine learning to build 27-, 10- and 3-gene classifiers that differentiate COVID-19 from other ARIs with AUROCs of 0.981, 0.954 and 0.885, respectively. Classifier performance is stable across a wide range of viral load, suggesting utility in mitigating false positive or false negative results of direct SARS-CoV-2 tests.

Dengue virus (DENV) is the most prevalent, medically important mosquito-borne virus. Disease ranges from uncomplicated dengue to life-threatening disease, characterized by endothelial dysfunction and vascular leakage. Previously, we demonstrated that DENV nonstructural protein 1 (NS1) induces endothelial hyperpermeability in a systemic mouse model and human pulmonary endothelial cells, where NS1 disrupts the endothelial glycocalyx-like layer. NS1 also triggers release of inflammatory cytokines from PBMCs via TLR4. Here, we examined the relative contributions of inflammatory mediators and endothelial cell-intrinsic pathways. In vivo, we demonstrated that DENV NS1 but not the closely-related West Nile virus NS1 triggers localized vascular leak in the dorsal dermis of wild-type C57BL/6 mice. In vitro, we showed that human dermal endothelial cells exposed to DENV NS1 do not produce inflammatory cytokines (TNF-α, IL-6, IL-8) and that blocking these cytokines does not affect DENV NS1-induced endothelial hyperpermeability. Further, we demonstrated that DENV NS1 induces vascular leak in TLR4- or TNF-α receptor-deficient mice at similar levels to wild-type animals. Finally, we blocked DENV NS1-induced vascular leak in vivo using inhibitors targeting molecules involved in glycocalyx disruption. Taken together, these data indicate that DENV NS1-induced endothelial cell-intrinsic vascular leak is independent of inflammatory cytokines but dependent on endothelial glycocalyx components.

Mosquitoes are major infectious disease-carrying vectors. Assessment of current and future risks associated with the mosquito population requires knowledge of the full repertoire of pathogens they carry, including novel viruses, as well as their blood meal sources. Unbiased metatranscriptomic sequencing of individual mosquitoes offers a straightforward, rapid, and quantitative means to acquire this information. Here, we profile 148 diverse wild-caught mosquitoes collected in California and detect sequences from eukaryotes, prokaryotes, 24 known and 46 novel viral species. Importantly, sequencing individuals greatly enhanced the value of the biological information obtained. It allowed us to (a) speciate host mosquito, (b) compute the prevalence of each microbe and recognize a high frequency of viral co-infections, (c) associate animal pathogens with specific blood meal sources, and (d) apply simple co-occurrence methods to recover previously undetected components of highly prevalent segmented viruses. In the context of emerging diseases, where knowledge about vectors, pathogens, and reservoirs is lacking, the approaches described here can provide actionable information for public health surveillance and intervention decisions.

We studied the host transcriptional response to SARS-CoV-2 by performing metagenomic sequencing of upper airway samples in 238 patients with COVID-19, other viral or non-viral acute respiratory illnesses (ARIs). Compared to other viral ARIs, COVID-19 was characterized by a diminished innate immune response, with reduced expression of genes involved in toll-like receptor and interleukin signaling, chemokine binding, neutrophil degranulation and interactions with lymphoid cells. Patients with COVID-19 also exhibited significantly reduced proportions of neutrophils and macrophages, and increased proportions of goblet, dendritic and B-cells, compared to other viral ARIs. Using machine learning, we built 26-, 10- and 3-gene classifiers that differentiated COVID-19 from other acute respiratory illnesses with AUCs of 0.980, 0.950 and 0.871, respectively. Classifier performance was stable at low viral loads, suggesting utility in settings where direct detection of viral nucleic acid may be unsuccessful. Taken together, our results illuminate unique aspects of the host transcriptional response to SARS-CoV-2 in comparison to other respiratory viruses and demonstrate the feasibility of COVID-19 diagnostics based on patient gene expression.