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Msystems ; : e0067122, 2022.
Article in English | MEDLINE | ID: covidwho-2161805


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.

American Journal of Respiratory and Critical Care Medicine ; 205(1), 2022.
Article in English | EMBASE | ID: covidwho-1927857


Background: Latent class analyses in patients with acute respiratory distress syndrome (ARDS) have identified “hyper-inflammatory” and “hypo-inflammatory” phenotypes with divergent clinical outcomes and treatment responses. ARDS phenotypes are defined using plasma biomarkers and clinical variables. It is currently unknown if these phenotypes have distinct pulmonary biology and if pre-clinical models of disease replicate the biology of either phenotype. Methods: 45 subjects with ARDS (Berlin Definition) and 5 mechanically ventilated controls were selected from cohorts of mechanically ventilated patients at UCSF and ZSFG. Patients with COVID-19 were excluded from this analysis. A 3-variable classifier model (plasma IL-8, protein C, and bicarbonate;Sinha 2020) was used to assign ARDS phenotypes. Tracheal aspirate (TA) RNA was analyzed using established bulk and single-cell sequencing pipelines (Langelier 2018, Sarma 2021). Differentially expressed (DE) genes were analyzed using Ingenuity Pathway Analysis (IPA). Microbial community composition was analyzed with vegan. Fgsea was used to test for enrichment of gene sets from experimental ARDS models in genes that were differentially expressed between each phenotype and mechanically ventilated controls. Results: Bulk RNA sequencing (RNAseq) was available from 29 subjects with hypoinflammatory ARDS and 10 subjects with hyperinflammatory ARDS. 2,777 genes were differentially expressed between ARDS phenotypes. IPA identified several candidate upstream regulators of gene expression in hyperinflammatory ARDS including IL6, TNF, IL17C, and interferons (Figure 1A). 2,953 genes were differentially expressed between hyperinflammatory ARDS and 5 ventilated controls;in contrast, only 243 genes were differentially expressed between hypoinflammatory ARDS and controls, suggesting gene expression in the hypoinflammatory phenotype was more heterogeneous. Gene sets from experimental models of acute lung injury were enriched in hyperinflammatory ARDS but not in hypoinflammatory ARDS (Figure 1B). Single cell RNA sequencing (scRNAseq) was available from 6 additional subjects with ARDS, of whom 3 had hyperinflammatory ARDS. 14,843 cells passed quality control filters. Hyperinflammatory ARDS subjects had a markedly higher burden of neutrophils (Figure 1C), including a cluster of stressed neutrophils expressing heat shock protein RNA that was not present in hypoinflammatory ARDS. Expression of a Th1 signature was higher in T cells from hyperinflammatory ARDS. Differential expression analysis in macrophages identified increased expression of genes associated with mortality in a previous study of ARDS patients (Morell 2019). Conclusions: The respiratory tract biology of ARDS phenotypes is distinct. Hyperinflammatory ARDS is characterized by neutrophilic inflammation with distinct immune cell polarization. Transcriptomic profiling identifies candidate preclinical disease models that replicate gene expression observed in hyperinflammatory ARDS.

American Journal of Respiratory and Critical Care Medicine ; 203(9), 2021.
Article in English | EMBASE | ID: covidwho-1277339


Background: The coronavirus disease 2019 (COVID-19) pandemic has led to a rapid increase in the incidence of acute respiratory distress syndrome (ARDS). The distinct features of pulmonary biology in COVID-19 ARDS compared to other causes of ARDS, including other lower respiratory tract infections (LRTIs), are not well understood. Methods: Tracheal aspirates (TA) and plasma were collected within five days of intubation from mechanically ventilated adults admitted to one of two academic medical centers. ARDS and LRTI diagnoses and were verified by study physicians. Subjects were excluded if they received immunosuppression. TA from subjects with COVID-ARDS was compared to gene expression in TA from subjects with other causes of ARDS (OtherARDS) or mechanically ventilated control subjects without evidence of pulmonary pathology (NoARDS). Plasma concentrations of IL-6, IL-8, and protein C also were compared between these groups. Upstream regulator and pathway analysis was performed on significantly differentially expressed genes with Ingenuity Pathway Analysis (IPA). Subgroup analyses were performed to compare gene expression in COVID to ARDS associated with other viral LRTIs and bacterial LRTIs. The association of interferon-stimulated gene expression with SARS-CoV2 viral load was compared to the same association in nasopharyngeal swabs in a cohort of subjects with mild SARS-CoV2. Results: TA sequencing was available from 15 subjects with COVID, 32 subjects with other causes of ARDS (OtherARDS), and 5 mechanically ventilated subjects without evidence of pulmonary pathology (NoARDS). 696 genes were differentially expressed between COVID and OtherARDS (Figure 1A). IL-6, IL-8, B-cell receptor, and hypoxia inducible factor-1a signaling were attenuated in COVID compared to OtherARDS. Peroxisome proliferator-activated receptor (PPAR) and PTEN signaling were higher in COVID compared to OtherARDS (Figure 1B). Plasma levels of IL-6, IL-8, and protein C were not significantly different between COVID and OtherARDS. In subgroup analyses, IL-8 signaling was higher in COVID compared to viral LRTI, but lower than bacterial LRTI. Type I/III interferon was higher in COVID compared to bacterial ARDS, but lower compared to viral ARDS (Figure 1C). Compared to nasopharyngeal swabs from subjects with mild COVID-19, expression of several interferon stimulated genes was less strongly correlated with SARS-CoV2 viral load in TA (Figure 1D). IPA identified several candidate medications to treat COVID-19, including dexamethasone, G-CSF, and etanercept. Conclusions: TA sequencing identifies unique features of the host response in COVID-19. These differentially expressed pathways may represent potential therapeutic targets. An impaired interferon response in the lung may increase susceptibility to severe SARS-COV2.