Electronic-cigarette, or vaping, product use-associated lung injury (EVALI) is a syndrome of acute respiratory failure characterized by monocytic and neutrophilic alveolar inflammation. Epidemiological and clinical evidence suggests a role of vitamin E acetate (VEA) in the development of EVALI, yet it remains unclear whether VEA has direct pulmonary toxicity. To test the hypotheses that aerosolized VEA causes lung injury in mice and directly injures human alveolar epithelial cells, we exposed adult mice and primary human alveolar epithelial type II (AT II) cells to an aerosol of VEA generated by a device designed for vaping oils. Outcome measures in mice included lung edema, BAL analysis, histology, and inflammatory cytokines; in vitro outcomes included cell death, cytokine release, cellular uptake of VEA, and gene-expression analysis. Comparison exposures in both models included the popular nicotine-containing JUUL aerosol. We discovered that VEA caused dose-dependent increases in lung water and BAL protein compared with control and JUUL-exposed mice in association with increased BAL neutrophils, oil-laden macrophages, multinucleated giant cells, and inflammatory cytokines. VEA aerosol was also toxic to AT II cells, causing increased cell death and the release of monocyte and neutrophil chemokines. VEA was directly absorbed by AT II cells, resulting in the differential gene expression of several inflammatory biological pathways. Given the epidemiological and clinical characteristics of the EVALI outbreak, these results suggest that VEA plays an important causal role.

The continued emergence of SARS-CoV-2 variants is one of several factors that may cause false-negative viral PCR test results. Such tests are also susceptible to false-positive results due to trace contamination from high viral titer samples. Host immune response markers provide an orthogonal indication of infection that can mitigate these concerns when combined with direct viral detection. Here, we leverage nasopharyngeal swab RNA-seq data from patients with COVID-19, other viral acute respiratory illnesses, and nonviral conditions (n = 318) to develop support vector machine classifiers that rely on a parsimonious 2-gene host signature to diagnose COVID-19. We find that optimal classifiers include an interferon-stimulated gene that is strongly induced in COVID-19 compared with nonviral conditions, such as IFI6, and a second immune-response gene that is more strongly induced in other viral infections, such as GBP5. The IFI6+GBP5 classifier achieves an area under the receiver operating characteristic curve (AUC) greater than 0.9 when evaluated on an independent RNA-seq cohort (n = 553). We further provide proof-of-concept demonstration that the classifier can be implemented in a clinically relevant RT-qPCR assay. Finally, we show that its performance is robust across common SARS-CoV-2 variants and is unaffected by cross-contamination, demonstrating its utility for improved accuracy of COVID-19 diagnostics. IMPORTANCE In this work, we study upper respiratory tract gene expression to develop and validate a 2-gene host-based COVID-19 diagnostic classifier and then demonstrate its implementation in a clinically practical qPCR assay. We find that the host classifier has utility for mitigating false-negative results, for example due to SARS-CoV-2 variants harboring mutations at primer target sites, and for mitigating false-positive viral PCR results due to laboratory cross-contamination. Both types of error carry serious consequences of either unrecognized viral transmission or unnecessary isolation and contact tracing. This work is directly relevant to the ongoing COVID-19 pandemic given the continued emergence of viral variants and the continued challenges of false-positive PCR assays. It also suggests the feasibility of pan-respiratory virus host-based diagnostics that would have value in congregate settings, such as hospitals and nursing homes, where unrecognized respiratory viral transmission is of particular concern.