The Immune Signatures data resource, a compendium of systems vaccinology datasets

Vaccines are among the most cost-effective public health interventions for preventing infection-induced morbidity and mortality, yet much remains to be learned regarding the mechanisms by which vaccines protect. Systems immunology combines traditional immunology with modern 'omic profiling techniques and computational modeling to promote rapid and transformative advances in vaccinology and vaccine discovery. The NIH/NIAID Human Immunology Project Consortium (HIPC) has leveraged systems immunology approaches to identify molecular signatures associated with the immunogenicity of many vaccines. However, comparative analyses have been limited by the distributed nature of some data, potential batch effects across studies, and the absence of multiple relevant studies from non-HIPC groups in ImmPort. To support comparative analyses across different vaccines, we have created the Immune Signatures Data Resource, a compendium of standardized systems vaccinology datasets. This data resource is available through ImmuneSpace, along with code to reproduce the processing and batch normalization starting from the underlying study data in ImmPort and the Gene Expression Omnibus (GEO). The current release comprises 1405 participants from 53 cohorts profiling the response to 24 different vaccines. This novel systems vaccinology data release represents a valuable resource for comparative and meta-analyses that will accelerate our understanding of mechanisms underlying vaccine responses.

Phenotypes of disease severity in a cohort of hospitalized COVID-19 patients: Results from the IMPACC study

BACKGROUND

Better understanding of the association between characteristics of patients hospitalized with coronavirus disease 2019 (COVID-19) and outcome is needed to further improve upon patient management.

METHODS

Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) is a prospective, observational study of 1164 patients from 20 hospitals across the United States. Disease severity was assessed using a 7-point ordinal scale based on degree of respiratory illness. Patients were prospectively surveyed for 1 year after discharge for post-acute sequalae of COVID-19 (PASC) through quarterly surveys. Demographics, comorbidities, radiographic findings, clinical laboratory values, SARS-CoV-2 PCR and serology were captured over a 28-day period. Multivariable logistic regression was performed.

FINDINGS

The median age was 59 years (interquartile range [IQR] 20); 711 (61%) were men; overall mortality was 14%, and 228 (20%) required invasive mechanical ventilation. Unsupervised clustering of ordinal score over time revealed distinct disease course trajectories. Risk factors associated with prolonged hospitalization or death by day 28 included age ≥ 65 years (odds ratio [OR], 2.01; 95% CI 1.28-3.17), Hispanic ethnicity (OR, 1.71; 95% CI 1.13-2.57), elevated baseline creatinine (OR 2.80; 95% CI 1.63- 4.80) or troponin (OR 1.89; 95% 1.03-3.47), baseline lymphopenia (OR 2.19; 95% CI 1.61-2.97), presence of infiltrate by chest imaging (OR 3.16; 95% CI 1.96-5.10), and high SARS-CoV2 viral load (OR 1.53; 95% CI 1.17-2.00). Fatal cases had the lowest ratio of SARS-CoV-2 antibody to viral load levels compared to other trajectories over time (p=0.001). 589 survivors (51%) completed at least one survey at follow-up with 305 (52%) having at least one symptom consistent with PASC, most commonly dyspnea (56% among symptomatic patients). Female sex was the only associated risk factor for PASC.

INTERPRETATION

Integration of PCR cycle threshold, and antibody values with demographics, comorbidities, and laboratory/radiographic findings identified risk factors for 28-day outcome severity, though only female sex was associated with PASC. Longitudinal clinical phenotyping offers important insights, and provides a framework for immunophenotyping for acute and long COVID-19.

FUNDING

NIH.