XBB.1.5 mRNA COVID-19 Vaccination and Inpatient or Emergency Department Visits Among Adults Infected with SARS-CoV-2 JN.1 and XBB-Lineage Variants (preprint)

Within a multi-state viral genomic surveillance program, we conducted a case-control analysis comparing prior receipt of XBB.1.5-adapted mRNA vaccination between SARS-CoV-2-infected adults with inpatient/ED visits (proxy for severe illness) vs outpatient visits. Among 6,551 patients from September 2023-January 2024, 6.1% with inpatient/ED visits vs 12.0% with outpatient visits had received XBB.1.5 vaccination (aOR=0.41; 95% CI: 0.32-0.53). This protective association was weaker among JN.1 (aOR=0.62; 95% CI: 0.40-0.96) vs XBB-lineage (aOR=0.28; 95% CI: 0.18-0.43) variant infections (interaction, p=0.003). XBB.1.5 vaccination was also protective specifically compared to BA.4/BA.5-adapted mRNA vaccination (aOR=0.60; 95% CI: 0.45-0.79). XBB.1.5 vaccines protect against severe illness, but protection may be weaker against JN.1 vs XBB-lineage variants.

Disease diagnostics using machine learning of immune receptors (preprint)

ABSTRACT Clinical diagnoses rely on a wide variety of laboratory tests and imaging studies, interpreted alongside physical examination and documentation of symptoms and patient history. However, the tools of diagnosis make little use of the immune system’s internal record of specific disease exposures encoded by the antigen-specific receptors of memory B cells and T cells. We have combined extensive receptor sequence datasets with three different machine learning representations of the contents of immune repertoires to develop an interpretive framework, MAchine Learning for Immunological Diagnosis (Mal-ID) , that screens for multiple illnesses simultaneously. This approach can already reliably distinguish a wide range of disease states, including specific acute or chronic infections, and autoimmune or immunodeficiency disorders, and could contribute to identifying new infectious diseases as they emerge. Importantly, many features of the model of immune receptor sequences are human-interpretable. They independently recapitulate known biology of the responses to infection by SARS-CoV-2 or HIV, and reveal common features of autoreactive immune receptor repertoires, indicating that machine learning on immune repertoires can yield new immunological knowledge.

Time to reinfection and vaccine breakthrough SARS-CoV-2 infections: a retrospective cohort study (preprint)

BackgroundIt is important to understand how BNT162b2, mRNA-1273, and JNJ-78436735 COVID-19 vaccines, as well as prior infection, protect against breakthrough cases and reinfections. Real world evidence on acquired immunity from vaccines, and from SARS-CoV-2 infection, can help public health decision-makers understand disease dynamics and viral escape to inform resource allocation for curbing the spread of pandemic. MethodsThis retrospective cohort study presents demographic information, survival functions, and probability distributions for 2,627,914 patients who received recommended doses of COVID-19 vaccines, and 63,691 patients who had a prior COVID-19 infection. In addition, patients receiving different vaccines were matched by age, sex, ethnic group, state of residency, and the quarter of the year in 2021 the COVID-19 vaccine was completed, to support survival analysis on pairwise matched cohorts. FindingsEach of the three vaccines and infection-induced immunity all showed a high probability of survival against breakthrough or reinfection cases (mRNA-1273: 0.997, BNT162b2: 0.997, JNJ-78436735: 0.992, previous infection: 0.965 at 180 days). The incidence rate of reinfection among those unvaccinated and previously infected was higher than that of breakthrough among the vaccinated population (reinfection: 0.9%; breakthrough:0.4%). In addition, 280 vaccinated patients died (0.01% all-cause mortality) within 21 days of the last vaccine dose, and 5898 (3.1 %) died within 21 days of a positive COVID-19 test. ConclusionsDespite a gradual decline in vaccine-induced and infection-induced immunity, both acquired immunities were highly effective in preventing breakthrough and reinfection. In addition, for unvaccinated patients with COVID-19, those who did not die within 90 days of their initial infection (9565 deaths, 5.0% all-cause mortality rate), had a comparable asymptotic pattern of breakthrough infection as those who acquired immunity from a vaccine. Overall, the risks associated with COVID-19 infection are far greater than the marginal advantages of immunity acquired by prior infection.

Large libraries of single-chain trimer peptide-MHCs enable rapid antigen-specific CD8+ T cell discovery and analysis (preprint)

CD8 + cytotoxic T cell responses against viral infection represent a major element of the adaptive immune response. We describe the development of a peptide antigen – major histompatibility complex (pMHC) library representing the full SARS-CoV-2 viral proteome, and comprised of 634 pMHC multimers representing the A*02.01, A*24.02, and B*07.02 HLA alleles, as well as specific antigens associated with the cytomegalovirus (CMV). These libraries were used to capture non-expanded CD8 + T cells from blood samples collected from 64 infected individuals, and then analyzed using single cell RNA-seq. The discovery and characterization of antigen-specific CD8+ T cell clonotypes typically involves the labor-intensive synthesis and construction of peptide-MHC tetramers. We adapted single-chain trimer (SCT) technologies into a high throughput platform for pMHC library generation, showing that hundreds can be rapidly prepared across multiple Class I HLA alleles. We used this platform to explore the impact of peptide and SCT template mutations on protein expression yield, thermal stability, and functionality. SCT libraries were an efficient tool for identifying T cells recognizing commonly reported viral epitopes. We then constructed SCT libraries designed to capture SARS-CoV-2 specific CD8+ T cells from COVID-19 participants and healthy donors. The immunogenicity of these epitopes was validated by functional assays of T cells with cloned TCRs captured using SCT libraries. These technologies should enable the rapid analyses of peptide-based T cell responses across several contexts, including autoimmunity, cancer, or infectious disease.

Angiotensin II receptor I auto-antibodies following SARS-CoV-2 infection (preprint)

BACKGROUND: Coronavirus disease 2019 (COVID-19) is associated with endothelial activation and coagulopathy, which may be related to pre-existing or infection-induced pro-thrombotic autoantibodies such as those targeting angiotensin II type I receptor (AT1R-Ab). METHODS: We compared prevalence and levels of AT1R-Ab in COVID-19 cases with mild or severe disease to age and sex matched negative controls. RESULTS: There were no significant differences between cases and controls. However, there were trends toward a higher proportion with AT1R-Ab positivity among severe cases versus controls (32% vs. 11%, p=0.1) and higher levels in those with mild COVID-19 compared to controls (median 9.5U/mL vs. 5.9U/mL, p=0.06). CONCLUSIONS: These findings suggest that AT1R-Ab are not consistently associated with COVID-19 but do not exclude a contribution to endothelial pathology in a subset of people.

Surge Effects and Survival to Hospital Discharge in Critical Care Patients with COVID-19 During the Early Pandemic: A Cohort Study (preprint)

Background: The early months of the COVID-19 pandemic were fraught with much uncertainty and some resource constraint. We assessed the change in survival to hospital discharge over time for intensive care unit patients with COVID-19 during the first three months of the pandemic and the presence of any surge effects on patient outcomes.MethodsRetrospective cohort study using electronic medical record data for all patients with laboratory-confirmed COVID-19 admitted to intensive care units from February 25, 2020 to May 15, 2020 at one of 26 hospitals within an integrated delivery system in the Western United States. Patient demographics, comorbidities and severity of illness were measured along with medical therapies and hospital outcomes over time. Multivariable logistic regression models were constructed to assess temporal changes in survival to hospital discharge during the study period.ResultsOf 620 patients with COVID-19 admitted to the ICU (mean age 63.5 years (SD 15.7) and 69% male), 403 (65%) survived to hospital discharge and 217 (35%) died in the hospital. Survival to hospital discharge increased over time, from 60.0% in the first two weeks of the study period to 67.6% in the last two weeks. In a multivariable logistic regression analysis, the risk-adjusted odds of survival to hospital discharge increased over time (bi-weekly change, adjusted odds ratio [aOR] 1.22, 95%CI 1.04-1.40, P = 0.02). Additionally, an a priori -defined explanatory model showed that after adjusting for both hospital occupancy and percent hospital capacity by COVID-19 positive individuals and persons under investigation (PUI), the temporal trend in risk-adjusted patient survival to hospital discharge remained the same (bi-weekly change, aOR 1.18, 95% CI 1.00 to 1.38, P = 0.04). The presence of greater rates of COVID-19 positive/PUI as a percentage of hospital capacity was, however, significantly and inversely associated with survival to hospital discharge (aOR 0.95, 95% CI 0.92 to 0.98, P < 0.01). ConclusionsDuring the early COVID-19 pandemic, risk-adjusted survival to hospital discharge increased over time for critical care patients. An association was also seen between a greater COVID-19 positive/PUI percentage of hospital capacity and a lower survival rate to hospital discharge.

Characteristics and Factors Associated with COVID-19 Infection, Hospitalization, and Mortality Across Race and Ethnicity (preprint)

Background: Data on the characteristics of COVID-19 patients disaggregated by race/ethnicity remain limited. We evaluated the sociodemographic and clinical characteristics of patients across the major racial/ethnic groups and assessed their associations with COVID-19 outcomes.Methods: This retrospective cohort study analyzed patients who were tested for SARS-CoV-2 in a large, integrated health system spanning California, Oregon, and Washington between March 1 and August 30, 2020. Sociodemographic and clinical characteristics were obtained from electronic health records. Odds of SARS-CoV-2 infection, COVID-19 hospitalization, and in-hospital death were assessed with multivariate logistic regression.Findings: 289,294 patients with known race/ethnicity were tested for SARS-CoV-2 by PCR, of whom 27.5% were non-White minorities. 15,605 persons tested positive, with minorities representing 58.0%. Disparities were widest among Hispanics, who represented 40.5% of infections but 12.8% of those tested. Hispanics were generally younger than white patients and had a higher rate of diabetes, but fewer other comorbidities. Of the 3,197 patients hospitalized, 58.9% were non-White. 459 patients died, of whom 49.8% were minorities. Racial/ethnic distributions of outcomes across the health system tracked with state-level statistics. Increased odds of testing positive and hospitalization were associated with all minority races/ethnicities except American Indian/Alaska Native. Hispanic patients had the highest odds of testing SARS-CoV-2 positive (OR [95% CI]: 3.68 [3.52-3.84]) and Native Hawaiian/Pacific Islander patients had the highest odds of COVID-19 hospitalization (2.13 [1.48 - 3.06]). Hispanic patients also exhibited increased morbidity, including need for mechanical ventilation. In multivariate modeling, Hispanic race/ethnicity was associated with increased odds of hospital mortality (1.75 [1.15-2.67]) among patients over age 70, but hospital mortality was not increased for any race/ethnicity sub-population in the multivariate model.Interpretation: Major healthcare disparities were evident, especially among Hispanics who tested positive at a higher rate, and required excess hospitalization despite younger age and need for mechanical ventilation compared to their expected demographic proportions. As characteristics of patients vary between race/ethnicity, targeted, culturally-responsive interventions are needed to address the increased risk of poor outcomes among minority populations with COVID-19.Funding: This study was supported by Biomedical Advanced Research and Development Authority under Contract HHSO10201600031C administered by Merck and Co. (JDG, JRH). J.H. and R.R. were funded by National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number OT2 TR003443.Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: The protocol for this study was reviewed and approved by the PSJH Institutional Review Board (IRB #: STUDY2020000203).

Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories (preprint)

Host immune responses play central roles in controlling SARS-CoV2 infection, yet remain incompletely characterized and understood. Here, we present a comprehensive immune response map spanning 454 proteins and 847 metabolites in plasma integrated with single-cell multi-omic assays of 221,748 PBMCs in which whole transcriptome, 192 surface proteins, and T and B cell receptor sequence were analyzed within the context of clinical measures from 50 COVID19 patient samples. Our study reveals novel cellular subpopulations, such as proliferative exhausted CD8+ and CD4+ T cells, and cytotoxic CD4+ T cells, that may be features of severe COVID-19 infection. We condensed over 1 million immune features into a single immune response axis that independently aligns with many clinical features and is also strongly associated with disease severity. Our study represents an important resource towards understanding the heterogeneous immune responses of COVID-19 patients and may provide key information for informing therapeutic development.