SARS-CoV-2 Exposure Investigations Using Genomic Sequencing Among Healthcare Workers and Patients in A Large Academic Center.

SARS-CoV-2 transmissions among healthcare personnel (HCP) and hospitalized patients are challenging to confirm. Investigation of infected persons often reveals multiple potential risk factors for viral acquisition. We combined exposure investigation with genomic analysis confirming two hospital-based clusters. Prolonged close contact with unmasked, unrecognized infectious, individuals was a common risk.

Factors associated with resistance to SARS-CoV-2 infection discovered using large-scale medical record data and machine learning.

There have been over 621 million cases of COVID-19 worldwide with over 6.5 million deaths. Despite the high secondary attack rate of COVID-19 in shared households, some exposed individuals do not contract the virus. In addition, little is known about whether the occurrence of COVID-19 resistance differs among people by health characteristics as stored in the electronic health records (EHR). In this retrospective analysis, we develop a statistical model to predict COVID-19 resistance in 8,536 individuals with prior COVID-19 exposure using demographics, diagnostic codes, outpatient medication orders, and count of Elixhauser comorbidities in EHR data from the COVID-19 Precision Medicine Platform Registry. Cluster analyses identified 5 patterns of diagnostic codes that distinguished resistant from non-resistant patients in our study population. In addition, our models showed modest performance in predicting COVID-19 resistance (best performing model AUROC = 0.61). Monte Carlo simulations conducted indicated that the AUROC results are statistically significant (p < 0.001) for the testing set. We hope to validate the features found to be associated with resistance/non-resistance through more advanced association studies.

Assessing Associations Between COVID-19 Symptomology and Adverse Outcomes After Piloting Crowdsourced Data Collection: Cross-sectional Survey Study.

BACKGROUND: Crowdsourcing is a useful way to rapidly collect information on COVID-19 symptoms. However, there are potential biases and data quality issues given the population that chooses to participate in crowdsourcing activities and the common strategies used to screen participants based on their previous experience. OBJECTIVE: The study aimed to (1) build a pipeline to enable data quality and population representation checks in a pilot setting prior to deploying a final survey to a crowdsourcing platform, (2) assess COVID-19 symptomology among survey respondents who report a previous positive COVID-19 result, and (3) assess associations of symptomology groups and underlying chronic conditions with adverse outcomes due to COVID-19. METHODS: We developed a web-based survey and hosted it on the Amazon Mechanical Turk (MTurk) crowdsourcing platform. We conducted a pilot study from August 5, 2020, to August 14, 2020, to refine the filtering criteria according to our needs before finalizing the pipeline. The final survey was posted from late August to December 31, 2020. Hierarchical cluster analyses were performed to identify COVID-19 symptomology groups, and logistic regression analyses were performed for hospitalization and mechanical ventilation outcomes. Finally, we performed a validation of study outcomes by comparing our findings to those reported in previous systematic reviews. RESULTS: The crowdsourcing pipeline facilitated piloting our survey study and revising the filtering criteria to target specific MTurk experience levels and to include a second attention check. We collected data from 1254 COVID-19-positive survey participants and identified the following 6 symptomology groups: abdominal and bladder pain (Group 1); flu-like symptoms (loss of smell/taste/appetite; Group 2); hoarseness and sputum production (Group 3); joint aches and stomach cramps (Group 4); eye or skin dryness and vomiting (Group 5); and no symptoms (Group 6). The risk factors for adverse COVID-19 outcomes differed for different symptomology groups. The only risk factor that remained significant across 4 symptomology groups was influenza vaccine in the previous year (Group 1: odds ratio [OR] 6.22, 95% CI 2.32-17.92; Group 2: OR 2.35, 95% CI 1.74-3.18; Group 3: OR 3.7, 95% CI 1.32-10.98; Group 4: OR 4.44, 95% CI 1.53-14.49). Our findings regarding the symptoms of abdominal pain, cough, fever, fatigue, shortness of breath, and vomiting as risk factors for COVID-19 adverse outcomes were concordant with the findings of other researchers. Some high-risk symptoms found in our study, including bladder pain, dry eyes or skin, and loss of appetite, were reported less frequently by other researchers and were not considered previously in relation to COVID-19 adverse outcomes. CONCLUSIONS: We demonstrated that a crowdsourced approach was effective for collecting data to assess symptomology associated with COVID-19. Such a strategy may facilitate efficient assessments in a dynamic intersection between emerging infectious diseases, and societal and environmental changes.

IgM anti-ACE2 autoantibodies in severe COVID-19 activate complement and perturb vascular endothelial function.

BackgroundSome clinical features of severe COVID-19 represent blood vessel damage induced by activation of host immune responses initiated by the coronavirus SARS-CoV-2. We hypothesized autoantibodies against angiotensin-converting enzyme 2 (ACE2), the SARS-CoV-2 receptor expressed on vascular endothelium, are generated during COVID-19 and are of mechanistic importance.MethodsIn an opportunity sample of 118 COVID-19 inpatients, autoantibodies recognizing ACE2 were detected by ELISA. Binding properties of anti-ACE2 IgM were analyzed via biolayer interferometry. Effects of anti-ACE2 IgM on complement activation and endothelial function were demonstrated in a tissue-engineered pulmonary microvessel model.ResultsAnti-ACE2 IgM (not IgG) autoantibodies were associated with severe COVID-19 and found in 18/66 (27.2%) patients with severe disease compared with 2/52 (3.8%) of patients with moderate disease (OR 9.38, 95% CI 2.38-42.0; P = 0.0009). Anti-ACE2 IgM autoantibodies were rare (2/50) in non-COVID-19 ventilated patients with acute respiratory distress syndrome. Unexpectedly, ACE2-reactive IgM autoantibodies in COVID-19 did not undergo class-switching to IgG and had apparent KD values of 5.6-21.7 nM, indicating they are T cell independent. Anti-ACE2 IgMs activated complement and initiated complement-binding and functional changes in endothelial cells in microvessels, suggesting they contribute to the angiocentric pathology of COVID-19.ConclusionWe identify anti-ACE2 IgM as a mechanism-based biomarker strongly associated with severe clinical outcomes in SARS-CoV-2 infection, which has therapeutic implications.FUNDINGBill & Melinda Gates Foundation, Gates Philanthropy Partners, Donald B. and Dorothy L. Stabler Foundation, and Jerome L. Greene Foundation; NIH R01 AR073208, R01 AR069569, Institutional Research and Academic Career Development Award (5K12GM123914-03), National Heart, Lung, and Blood Institute R21HL145216, and Division of Intramural Research, National Institute of Allergy and Infectious Diseases; National Science Foundation Graduate Research Fellowship (DGE1746891).

An Update on Severe Acute Respiratory Syndrome Coronavirus 2 Diversity in the US National Capital Region: Evolution of Novel and Variants of Concern.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants concerning for enhanced transmission, evasion of immune responses, or associated with severe disease have motivated the global increase in genomic surveillance. In the current study, large-scale whole-genome sequencing was performed between November 2020 and the end of March 2021 to provide a phylodynamic analysis of circulating variants over time. In addition, we compared the viral genomic features of March 2020 and March 2021. METHODS: A total of 1600 complete SARS-CoV-2 genomes were analyzed. Genomic analysis was associated with laboratory diagnostic volumes and positivity rates, in addition to an analysis of the association of selected variants of concern/variants of interest with disease severity and outcomes. Our real-time surveillance features a cohort of specimens from patients who tested positive for SARS-CoV-2 after completion of vaccination. RESULTS: Our data showed genomic diversity over time that was not limited to the spike sequence. A significant increase in the B.1.1.7 lineage (alpha variant) in March 2021 as well as a transient circulation of regional variants that carried both the concerning S: E484K and S: P681H substitutions were noted. Lineage B.1.243 was significantly associated with intensive care unit admission and mortality. Genomes recovered from fully vaccinated individuals represented the predominant lineages circulating at specimen collection time, and people with those infections recovered with no hospitalizations. CONCLUSIONS: Our results emphasize the importance of genomic surveillance coupled with laboratory, clinical, and metadata analysis for a better understanding of the dynamics of viral spread and evolution.

Durable SARS-CoV-2 B cell immunity after mild or severe disease.

Multiple studies have shown loss of severe acute respiratory syndrome coronavirus 2-specific (SARS-CoV-2-specific) antibodies over time after infection, raising concern that humoral immunity against the virus is not durable. If immunity wanes quickly, millions of people may be at risk for reinfection after recovery from coronavirus disease 2019 (COVID-19). However, memory B cells (MBCs) could provide durable humoral immunity even if serum neutralizing antibody titers decline. We performed multidimensional flow cytometric analysis of S protein receptor binding domain-specific (S-RBD-specific) MBCs in cohorts of ambulatory patients with COVID-19 with mild disease (n = 7), and hospitalized patients with moderate to severe disease (n = 7), at a median of 54 days (range, 39-104 days) after symptom onset. We detected S-RBD-specific class-switched MBCs in 13 of 14 participants, failing only in the individual with the lowest plasma levels of anti-S-RBD IgG and neutralizing antibodies. Resting MBCs (rMBCs) made up the largest proportion of S-RBD-specific MBCs in both cohorts. FCRL5, a marker of functional memory on rMBCs, was more dramatically upregulated on S-RBD-specific rMBCs after mild infection than after severe infection. These data indicate that most SARS-CoV-2-infected individuals develop S-RBD-specific, class-switched rMBCs that resemble germinal center-derived B cells induced by effective vaccination against other pathogens, providing evidence for durable B cell-mediated immunity against SARS-CoV-2 after mild or severe disease.

Genomic diversity of SARS-CoV-2 during early introduction into the Baltimore-Washington metropolitan area.

The early COVID-19 pandemic was characterized by rapid global spread. In Maryland and Washington, DC, United States, more than 2500 cases were reported within 3 weeks of the first COVID-19 detection in March 2020. We aimed to use genomic sequencing to understand the initial spread of SARS-CoV-2 - the virus that causes COVID-19 - in the region. We analyzed 620 samples collected from the Johns Hopkins Health System during March 11-31, 2020, comprising 28.6% of the total cases in Maryland and Washington, DC. From these samples, we generated 114 complete viral genomes. Analysis of these genomes alongside a subsampling of over 1000 previously published sequences showed that the diversity in this region rivaled global SARS-CoV-2 genetic diversity at that time and that the sequences belong to all of the major globally circulating lineages, suggesting multiple introductions into the region. We also analyzed these regional SARS-CoV-2 genomes alongside detailed clinical metadata and found that clinically severe cases had viral genomes belonging to all major viral lineages. We conclude that efforts to control local spread of the virus were likely confounded by the number of introductions into the region early in the epidemic and the interconnectedness of the region as a whole.

Healthy donor T cell responses to common cold coronaviruses and SARS-CoV-2.

BACKGROUNDT cell responses to the common cold coronaviruses have not been well characterized. Preexisting T cell immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been reported, and a recent study suggested that this immunity was due to cross-recognition of the novel coronavirus by T cells specific for the common cold coronaviruses.METHODSWe used the enzyme-linked immunospot (ELISPOT) assay to characterize the T cell responses against peptide pools derived from the spike protein of 3 common cold coronaviruses (HCoV-229E, HCoV-NL63, and HCoV-OC43) and SARS-CoV-2 in 21 healthy donors (HDs) who were seronegative for SARS-CoV-2 and had no known exposure to the virus. An in vitro expansion culture assay was also used to analyze memory T cell responses.RESULTSWe found responses to the spike protein of the 3 common cold coronaviruses in many of the donors. We then focused on HCoV-NL63 and detected broad T cell responses to the spike protein and identified 22 targeted peptides. Interestingly, only 1 study participant had a significant response to SARS-CoV-2 spike or nucleocapsid protein in the ELISPOT assay. In vitro expansion studies suggested that T cells specific for the HCoV-NL63 spike protein in this individual could also recognize SARS-CoV-2 spike protein peptide pools.CONCLUSIONHDs have circulating T cells specific for the spike proteins of HCoV-NL63, HCoV-229E, and HCoV-OC43. T cell responses to SARS-CoV-2 spike and nucleocapsid proteins were present in only 1 participant and were potentially the result of cross-recognition by T cells specific for the common cold coronaviruses. Further studies are needed to determine whether this cross-recognition influences coronavirus disease 2019 (COVID-19) outcomes.

Durable SARS-CoV-2 B cell immunity after mild or severe disease.

Multiple studies have shown loss of SARS-CoV-2 specific antibodies over time after infection, raising concern that humoral immunity against the virus is not durable. If immunity wanes quickly, millions of people may be at risk for reinfection after recovery from COVID-19. However, memory B cells (MBC) could provide durable humoral immunity even if serum neutralizing antibody titers decline. We performed multi-dimensional flow cytometric analysis of S protein receptor binding domain (S-RBD)-specific MBC in cohorts of ambulatory COVID-19 patients with mild disease, and hospitalized patients with moderate to severe disease, at a median of 54 (39-104) days after onset of symptoms. We detected S-RBD-specific class-switched MBC in 13 out of 14 participants, including 4 of the 5 participants with lowest plasma levels of anti-S-RBD IgG and neutralizing antibodies. Resting MBC (rMBC) made up the largest proportion of S-RBD-specific class-switched MBC in both cohorts. FCRL5, a marker of functional memory when expressed on rMBC, was dramatically upregulated on S-RBD-specific rMBC. These data indicate that most SARS-CoV-2-infected individuals develop S-RBD-specific, class-switched MBC that phenotypically resemble germinal center-derived B cells induced by effective vaccination against other pathogens, providing evidence for durable B cell-mediated immunity against SARS-CoV-2 after recovery from mild or severe COVID-19 disease.