A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets.

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

The systemic inflammatory landscape of COVID-19 in pregnancy: Extensive serum proteomic profiling of mother-infant dyads with in utero SARS-CoV-2.

While pregnancy increases the risk for severe COVID-19, the clinical and immunological implications of COVID-19 on maternal-fetal health remain unknown. Here, we present the clinical and immunological landscapes of 93 COVID-19 mothers and 45 of their SARS-CoV-2-exposed infants through comprehensive serum proteomics profiling for >1,400 cytokines of their peripheral and cord blood specimens. Prenatal SARS-CoV-2 infection triggers NF-κB-dependent proinflammatory immune activation. Pregnant women with severe COVID-19 show increased inflammation and unique IFN-λ antiviral signaling, with elevated levels of IFNL1 and IFNLR1. Furthermore, SARS-CoV-2 infection re-shapes maternal immunity at delivery, altering the expression of pregnancy complication-associated cytokines, inducing MMP7, MDK, and ESM1 and reducing BGN and CD209. Finally, COVID-19-exposed infants exhibit induction of T cell-associated cytokines (IL33, NFATC3, and CCL21), while some undergo IL-1ß/IL-18/CASP1 axis-driven neonatal respiratory distress despite birth at term. Our findings demonstrate COVID-19-induced immune rewiring in both mothers and neonates, warranting long-term clinical follow-up to mitigate potential health risks.

Androgen regulation of pulmonary AR, TMPRSS2 and ACE2 with implications for sex-discordant COVID-19 outcomes.

The sex discordance in COVID-19 outcomes has been widely recognized, with males generally faring worse than females and a potential link to sex steroids. A plausible mechanism is androgen-induced expression of TMPRSS2 and/or ACE2 in pulmonary tissues that may increase susceptibility or severity in males. This hypothesis is the subject of several clinical trials of anti-androgen therapies around the world. Here, we investigated the sex-associated TMPRSS2 and ACE2 expression in human and mouse lungs and interrogated the possibility of pharmacologic modification of their expression with anti-androgens. We found no evidence for increased TMPRSS2 expression in the lungs of males compared to females in humans or mice. Furthermore, in male mice, treatment with the androgen receptor antagonist enzalutamide did not decrease pulmonary TMPRSS2. On the other hand, ACE2 and AR expression was sexually dimorphic and higher in males than females. ACE2 was moderately suppressible with enzalutamide administration. Our work suggests that sex differences in COVID-19 outcomes attributable to viral entry are independent of TMPRSS2. Modest changes in ACE2 could account for some of the sex discordance.

Genetic and non-genetic factors affecting the expression of COVID-19-relevant genes in the large airway epithelium.

BACKGROUND: The large airway epithelial barrier provides one of the first lines of defense against respiratory viruses, including SARS-CoV-2 that causes COVID-19. Substantial inter-individual variability in individual disease courses is hypothesized to be partially mediated by the differential regulation of the genes that interact with the SARS-CoV-2 virus or are involved in the subsequent host response. Here, we comprehensively investigated non-genetic and genetic factors influencing COVID-19-relevant bronchial epithelial gene expression. METHODS: We analyzed RNA-sequencing data from bronchial epithelial brushings obtained from uninfected individuals. We related ACE2 gene expression to host and environmental factors in the SPIROMICS cohort of smokers with and without chronic obstructive pulmonary disease (COPD) and replicated these associations in two asthma cohorts, SARP and MAST. To identify airway biology beyond ACE2 binding that may contribute to increased susceptibility, we used gene set enrichment analyses to determine if gene expression changes indicative of a suppressed airway immune response observed early in SARS-CoV-2 infection are also observed in association with host factors. To identify host genetic variants affecting COVID-19 susceptibility in SPIROMICS, we performed expression quantitative trait (eQTL) mapping and investigated the phenotypic associations of the eQTL variants. RESULTS: We found that ACE2 expression was higher in relation to active smoking, obesity, and hypertension that are known risk factors of COVID-19 severity, while an association with interferon-related inflammation was driven by the truncated, non-binding ACE2 isoform. We discovered that expression patterns of a suppressed airway immune response to early SARS-CoV-2 infection, compared to other viruses, are similar to patterns associated with obesity, hypertension, and cardiovascular disease, which may thus contribute to a COVID-19-susceptible airway environment. eQTL mapping identified regulatory variants for genes implicated in COVID-19, some of which had pheWAS evidence for their potential role in respiratory infections. CONCLUSIONS: These data provide evidence that clinically relevant variation in the expression of COVID-19-related genes is associated with host factors, environmental exposures, and likely host genetic variation.

Safety of influenza vaccine during COVID-19

The propensity score for each individual is the predicted probability of receiving influenza vaccination from a nonparsimonious logistic regression model using the covariates listed as clinical characteristics in Table 1. Clinical characteristics and outcome of all individuals in the cohort and in the subgroup of patients tested positive for SARS-CoV-2 All tested individuals SARS-CoV-2-positive Never vaccinated Vaccinated in 2019 p Never vaccinated Vaccinated in 2019 p Clinical characteristics (n = 9082) (n = 4138) (n = 1125) (n = 309) Age – year 49.3 [34.6, 62.9] 61.5 [46.9, 72.0] <0.001 52.7 [38.6, 64.1] 63.3 [49.2, 73.4] <0.001 Race – no (%) <0.001 <0.001 White 5985 (65.9) 3050 (73.7) 686 (61.0) 203 (65.7) Black 1695 (18.7) 833 (20.1) 246 (21.9) 91 (29.4) Other 1402 (15.4) 255 (6.2) 193 (17.2) 15 (4.9) Male sex – no (%) 4050 (44.6) 1651 (39.9) <0.001 593 (52.7) 152 (49.2) 0.30 Non-Hispanic ethnicity – no (%) 7986 (87.9) 3974 (96.0) <0.001 893 (79.4) 298 (96.4) <0.001 BMI – kg/m2 28.6 [24.4, 33.6] 29.0 [24.8, 34.9] 0.002 29.7 [26.1, 34.0] 30.0 [25.0, 35.5] 0.66 Smoking – no (%) <0.001 <0.001 Current smoker 1481 (16.3) 504 (12.2) 64 (5.7) 15 (4.9) Former smoker 1625 (17.9) 1684 (40.7) 178 (15.8) 123 (39.8) Nonsmoker 5976 (65.8) 1950 (47.1) 883 (78.5) 171 (55.3) Median annual income – USD 57,250.0 [42,500.9–74,812.2] 59,390.0 [41,635.0–79,201.0] 0.005 58,429.0 [45,161.0–76,719.0] 60,000.0 [43,097.0–81,953.0] 0.91 Exposure to COVID-19 – no (%) 4805 (52.9) 1923 (46.5) <0.001 825 (73.3) 203 (65.7) 0.01 Family history of COVID-19 – no (%) 4452 (49.0) 1849 (44.7) <0.001 795 (70.7) 205 (66.3) 0.16 Coexisting conditions – no (%) COPD 517 (5.7) 689 (16.7) <0.001 39 (3.5) 40 (12.9) <0.001 Asthma 1433 (15.8) 1195 (28.9) <0.001 121 (10.8) 66 (21.4) <0.001 Diabetes 1288 (14.2) 1493 (36.1) <0.001 177 (15.7) 111 (35.9) <0.001 Hypertension 2885 (31.8) 2677 (64.7) <0.001 387 (34.4) 205 (66.3) <0.001 Coronary artery disease 673 (7.4) 998 (24.1) <0.001 71 (6.3) 57 (18.4) <0.001 Congestive heart failure 551 (6.1) 880 (21.3) <0.001 49 (4.4) 61 (19.7) <0.001 Cancer 848 (9.3) 1149 (27.8) <0.001 72 (6.4) 71 (23.0) <0.001 Connective tissue disease 795 (8.8) 1003 (24.2) <0.001 69 (6.1) 46 (14.9) <0.001 Long-term medications – no (%) NSAIDs 1659 (18.3) 1459 (35.3) <0.001 189 (16.8) 108 (35.0) <0.001 Glucocorticoids 1066 (11.7) 1350 (32.6) <0.001 67 (6.0) 66 (21.4) <0.001 ACE inhibitors 512 (5.6) 565 (13.7) <0.001 74 (6.6) 52 (16.8) <0.001 ARB 374 (4.1) 439 (10.6) <0.001 70 (6.2) 41 (13.3) <0.001 Laboratory measurements Platelet count – (x 109/L) 239.0 [188.0, 298.0] 233.0 [176.0, 301.0] 0.005 198.0 [160.0, 250.0] 196.0 [157.0, 251.5] 0.66 Eosinophil count – (cells/μL) 70.0 [30.0, 170.0] 80.0 [30.0, 190.0] 0.001 30.0 [30.0, 30.0] 30.0 [30.0, 30.0] 0.55 Lymphocyte count – (109/μL) 1.4 [0.9, 2.1] 1.2 [0.8, 1.9] <0.001 1.1 [0.7, 1.5] 0.9 [0.6, 1.3] 0.011 Neutrophil count – (109/μL) 5.6 [3.7, 8.7] 5.9 [3.9, 8.9] 0.086 3.9 [2.9, 5.5] 4.1 [2.8, 6.5] 0.33 Hemoglobin – (g/dL) 13.2 [11.5, 14.6] 12.2 [10.0, 13.9] <0.001 13.6 [12.1, 14.9] 13.30 [11.7, 14.6] 0.07 Albumin – (g/dL) 4.00 [3.50, 4.35] 3.80 [3.40, 4.20] <0.001 3.80 [3.52, 4.10] 3.70 [3.40, 4.00] 0.10 Total bilirubin – (mg/dL) 0.4 [0.3, 0.7] 0.5 [0.3, 0.7] 0.277 0.4 [0.3, 0.6] 0.4 [0.3, 0.7] 0.07 ALT – (IU/L) 21.0 [14.0, 34.0] 19.0 [13.0, 30.00] <0.001 26.0 [17.0, 40.0] 22.0 [15.0, 37.8] 0.23 Creatinine – (mg/dL) 0.90 [0.73, 1.15] 0.99 [0.76, 1.41] <0.001 0.97 [0.80, 1.22] 1.09 [0.82, 1.42] 0.04 Outcome – no (%) Positive SARS-CoV-2 test 1125 (12.4) 309 (7.5) <0.001 Hospitalization 192 (17.1) 127 (41.1) <0.001 ICU admission 77 (6.8) 43 (13.9) <0.001 Hospital mortality 32 (2.8) 20 (6.5) 0.01 Continuous data are presented as median [IQR]. BMI stands for body mass index;USD for US dollar;COPD for chronic obstructive pulmonary disease;NSAIDS for nonsteroidal anti-inflammatory drugs;ACE for angiotensin-converting enzyme;ARB for angiotensin receptor blocker;and ICU for Intensive Care Unit. The effect of influenza vaccines, and adjuvanted vaccin s in particular, on Th17 immune responses in coronavirus immunopathology and on vaccine-induced immune enhancement [5] is unknown and needs to be closely monitored.

Distribution of Transmission Potential During Nonsevere COVID-19 Illness.

BACKGROUND: Patients recovering from coronavirus disease 2019 (COVID-19) often continue to test positive for the causative virus by polymerase chain reaction (PCR) even after clinical recovery, thereby complicating return-to-work plans. The purpose of this study was to evaluate transmission potential of COVID-19 by examining viral load with respect to time. METHODS: Health care personnel (HCP) at Cleveland Clinic diagnosed with COVID-19, who recovered without needing hospitalization, were identified. Threshold cycles (Ct) for positive PCR tests were obtained and viral loads calculated. The association of viral load with days since symptom onset was examined in a multivariable regression model, which was reduced by stepwise backward selection to only keep variables significant at a level of .05. Viral loads by day since symptom onset were predicted using the model and transmission potential evaluated by examination of a viral load-time curve. RESULTS: Over 6 weeks, 230 HCP had 528 tests performed. Viral loads declined by orders of magnitude within a few days of symptom onset. The only variable significantly associated with viral load was time since onset of symptoms. Of the area under the curve (AUC) spanning symptom onset to 30 days, 96.9% lay within the first 7 days, and 99.7% within 10 days. Findings were very similar when validated using split-sample and 10-fold cross-validation. CONCLUSIONS: Among patients with nonsevere COVID-19, viral loads in upper respiratory specimens peak by 2 or 3 days from symptom onset and decrease rapidly thereafter. The vast majority of the viral load-time AUC lies within 10 days of symptom onset.

A Network Medicine Approach to Investigation and Population-based Validation of Disease Manifestations and Drug Repurposing for COVID-19.

The global Coronavirus Disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to unprecedented social and economic consequences. The risk of morbidity and mortality due to COVID-19 increases dramatically in the presence of co-existing medical conditions while the underlying mechanisms remain unclear. Furthermore, there are no proven effective therapies for COVID-19. This study aims to identify SARS-CoV-2 pathogenesis, diseases manifestations, and COVID-19 therapies using network medicine methodologies along with clinical and multi-omics observations. We incorporate SARS-CoV-2 virus-host protein-protein interactions, transcriptomics, and proteomics into the human interactome. Network proximity measure revealed underlying pathogenesis for broad COVID-19-associated manifestations. Multi-modal analyses of single-cell RNA-sequencing data showed that co-expression of ACE2 and TMPRSS2 was elevated in absorptive enterocytes from the inflamed ileal tissues of Crohn's disease patients compared to uninflamed tissues, revealing shared pathobiology by COVID-19 and inflammatory bowel disease. Integrative analyses of metabolomics and transcriptomics (bulk and single-cell) data from asthma patients indicated that COVID-19 shared intermediate inflammatory endophenotypes with asthma (including IRAK3 and ADRB2). To prioritize potential treatment, we combined network-based prediction and propensity score (PS) matching observational study of 18,118 patients from a COVID-19 registry. We identified that melatonin (odds ratio (OR) = 0.36, 95% confidence interval (CI) 0.22-0.59) was associated with 64% reduced likelihood of a positive laboratory test result for SARS-CoV-2. Using PS-matching user active comparator design, melatonin was associated with 54% reduced likelihood of SARS-CoV-2 positive test result compared to angiotensin II receptor blockers or angiotensin-converting enzyme inhibitors (OR = 0.46, 95% CI 0.24-0.86).

A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19.

The global coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to unprecedented social and economic consequences. The risk of morbidity and mortality due to COVID-19 increases dramatically in the presence of coexisting medical conditions, while the underlying mechanisms remain unclear. Furthermore, there are no approved therapies for COVID-19. This study aims to identify SARS-CoV-2 pathogenesis, disease manifestations, and COVID-19 therapies using network medicine methodologies along with clinical and multi-omics observations. We incorporate SARS-CoV-2 virus-host protein-protein interactions, transcriptomics, and proteomics into the human interactome. Network proximity measurement revealed underlying pathogenesis for broad COVID-19-associated disease manifestations. Analyses of single-cell RNA sequencing data show that co-expression of ACE2 and TMPRSS2 is elevated in absorptive enterocytes from the inflamed ileal tissues of Crohn disease patients compared to uninflamed tissues, revealing shared pathobiology between COVID-19 and inflammatory bowel disease. Integrative analyses of metabolomics and transcriptomics (bulk and single-cell) data from asthma patients indicate that COVID-19 shares an intermediate inflammatory molecular profile with asthma (including IRAK3 and ADRB2). To prioritize potential treatments, we combined network-based prediction and a propensity score (PS) matching observational study of 26,779 individuals from a COVID-19 registry. We identified that melatonin usage (odds ratio [OR] = 0.72, 95% CI 0.56-0.91) is significantly associated with a 28% reduced likelihood of a positive laboratory test result for SARS-CoV-2 confirmed by reverse transcription-polymerase chain reaction assay. Using a PS matching user active comparator design, we determined that melatonin usage was associated with a reduced likelihood of SARS-CoV-2 positive test result compared to use of angiotensin II receptor blockers (OR = 0.70, 95% CI 0.54-0.92) or angiotensin-converting enzyme inhibitors (OR = 0.69, 95% CI 0.52-0.90). Importantly, melatonin usage (OR = 0.48, 95% CI 0.31-0.75) is associated with a 52% reduced likelihood of a positive laboratory test result for SARS-CoV-2 in African Americans after adjusting for age, sex, race, smoking history, and various disease comorbidities using PS matching. In summary, this study presents an integrative network medicine platform for predicting disease manifestations associated with COVID-19 and identifying melatonin for potential prevention and treatment of COVID-19.

Individualizing Risk Prediction for Positive Coronavirus Disease 2019 Testing: Results From 11,672 Patients.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is sweeping the globe. Despite multiple case-series, actionable knowledge to tailor decision-making proactively is missing. RESEARCH QUESTION: Can a statistical model accurately predict infection with COVID-19? STUDY DESIGN AND METHODS: We developed a prospective registry of all patients tested for COVID-19 in Cleveland Clinic to create individualized risk prediction models. We focus here on the likelihood of a positive nasal or oropharyngeal COVID-19 test. A least absolute shrinkage and selection operator logistic regression algorithm was constructed that removed variables that were not contributing to the model's cross-validated concordance index. After external validation in a temporally and geographically distinct cohort, the statistical prediction model was illustrated as a nomogram and deployed in an online risk calculator. RESULTS: In the development cohort, 11,672 patients fulfilled study criteria, including 818 patients (7.0%) who tested positive for COVID-19; in the validation cohort, 2295 patients fulfilled criteria, including 290 patients who tested positive for COVID-19. Male, African American, older patients, and those with known COVID-19 exposure were at higher risk of being positive for COVID-19. Risk was reduced in those who had pneumococcal polysaccharide or influenza vaccine or who were on melatonin, paroxetine, or carvedilol. Our model had favorable discrimination (c-statistic = 0.863 in the development cohort and 0.840 in the validation cohort) and calibration. We present sensitivity, specificity, negative predictive value, and positive predictive value at different prediction cutoff points. The calculator is freely available at https://riskcalc.org/COVID19. INTERPRETATION: Prediction of a COVID-19 positive test is possible and could help direct health-care resources. We demonstrate relevance of age, race, sex, and socioeconomic characteristics in COVID-19 susceptibility and suggest a potential modifying role of certain common vaccinations and drugs that have been identified in drug-repurposing studies.

Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19.

BACKGROUND: Coronavirus Disease 2019 is a pandemic that is straining healthcare resources, mainly hospital beds. Multiple risk factors of disease progression requiring hospitalization have been identified, but medical decision-making remains complex. OBJECTIVE: To characterize a large cohort of patients hospitalized with COVID-19, their outcomes, develop and validate a statistical model that allows individualized prediction of future hospitalization risk for a patient newly diagnosed with COVID-19. DESIGN: Retrospective cohort study of patients with COVID-19 applying a least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to retain the most predictive features for hospitalization risk, followed by validation in a temporally distinct patient cohort. The final model was displayed as a nomogram and programmed into an online risk calculator. SETTING: One healthcare system in Ohio and Florida. PARTICIPANTS: All patients infected with SARS-CoV-2 between March 8, 2020 and June 5, 2020. Those tested before May 1 were included in the development cohort, while those tested May 1 and later comprised the validation cohort. MEASUREMENTS: Demographic, clinical, social influencers of health, exposure risk, medical co-morbidities, vaccination history, presenting symptoms, medications, and laboratory values were collected on all patients, and considered in our model development. RESULTS: 4,536 patients tested positive for SARS-CoV-2 during the study period. Of those, 958 (21.1%) required hospitalization. By day 3 of hospitalization, 24% of patients were transferred to the intensive care unit, and around half of the remaining patients were discharged home. Ten patients died. Hospitalization risk was increased with older age, black race, male sex, former smoking history, diabetes, hypertension, chronic lung disease, poor socioeconomic status, shortness of breath, diarrhea, and certain medications (NSAIDs, immunosuppressive treatment). Hospitalization risk was reduced with prior flu vaccination. Model discrimination was excellent with an area under the curve of 0.900 (95% confidence interval of 0.886-0.914) in the development cohort, and 0.813 (0.786, 0.839) in the validation cohort. The scaled Brier score was 42.6% (95% CI 37.8%, 47.4%) in the development cohort and 25.6% (19.9%, 31.3%) in the validation cohort. Calibration was very good. The online risk calculator is freely available and found at https://riskcalc.org/COVID19Hospitalization/. LIMITATION: Retrospective cohort design. CONCLUSION: Our study crystallizes published risk factors of COVID-19 progression, but also provides new data on the role of social influencers of health, race, and influenza vaccination. In a context of a pandemic and limited healthcare resources, individualized outcome prediction through this nomogram or online risk calculator can facilitate complex medical decision-making.

New insights into genetic susceptibility of COVID-19: an ACE2 and TMPRSS2 polymorphism analysis.

BACKGROUND: Coronavirus Disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has now been confirmed worldwide. Yet, COVID-19 is strangely and tragically selective. Morbidity and mortality due to COVID19 rise dramatically with age and co-existing health conditions, including cancer and cardiovascular diseases. Human genetic factors may contribute to the extremely high transmissibility of SARS-CoV-2 and to the relentlessly progressive disease observed in a small but significant proportion of infected individuals, but these factors are largely unknown. MAIN BODY: In this study, we investigated genetic susceptibility to COVID-19 by examining DNA polymorphisms in ACE2 and TMPRSS2 (two key host factors of SARS-CoV-2) from ~ 81,000 human genomes. We found unique genetic susceptibility across different populations in ACE2 and TMPRSS2. Specifically, ACE2 polymorphisms were found to be associated with cardiovascular and pulmonary conditions by altering the angiotensinogen-ACE2 interactions, such as p.Arg514Gly in the African/African-American population. Unique but prevalent polymorphisms (including p.Val160Met (rs12329760), an expression quantitative trait locus (eQTL)) in TMPRSS2, offer potential explanations for differential genetic susceptibility to COVID-19 as well as for risk factors, including those with cancer and the high-risk group of male patients. We further discussed that polymorphisms in ACE2 or TMPRSS2 could guide effective treatments (i.e., hydroxychloroquine and camostat) for COVID-19. CONCLUSION: This study suggested that ACE2 or TMPRSS2 DNA polymorphisms were likely associated with genetic susceptibility of COVID-19, which calls for a human genetics initiative for fighting the COVID-19 pandemic.

COVID-19-related Genes in Sputum Cells in Asthma. Relationship to Demographic Features and Corticosteroids.

Rationale: Coronavirus disease (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). ACE2 (angiotensin-converting enzyme 2), and TMPRSS2 (transmembrane protease serine 2) mediate viral infection of host cells. We reasoned that differences in ACE2 or TMPRSS2 gene expression in sputum cells among patients with asthma may identify subgroups at risk for COVID-19 morbidity.Objectives: To determine the relationship between demographic features and sputum ACE2 and TMPRSS2 gene expression in asthma.Methods: We analyzed gene expression for ACE2 and TMPRSS2, and for ICAM-1 (intercellular adhesion molecule 1) (rhinovirus receptor as a comparator) in sputum cells from 330 participants in SARP-3 (Severe Asthma Research Program-3) and 79 healthy control subjects.Measurements and Main Results: Gene expression of ACE2 was lower than TMPRSS2, and expression levels of both genes were similar in asthma and health. Among patients with asthma, male sex, African American race, and history of diabetes mellitus were associated with higher expression of ACE2 and TMPRSS2. Use of inhaled corticosteroids (ICS) was associated with lower expression of ACE2 and TMPRSS2, but treatment with triamcinolone acetonide did not decrease expression of either gene. These findings differed from those for ICAM-1, where gene expression was increased in asthma and less consistent differences were observed related to sex, race, and use of ICS.Conclusions: Higher expression of ACE2 and TMPRSS2 in males, African Americans, and patients with diabetes mellitus provides rationale for monitoring these asthma subgroups for poor COVID-19 outcomes. The lower expression of ACE2 and TMPRSS2 with ICS use warrants prospective study of ICS use as a predictor of decreased susceptibility to SARS-CoV-2 infection and decreased COVID-19 morbidity.