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.

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.

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.