ABSTRACT
Knowledge of the factors contributing to the development of protective immunity after vaccination with COVID-19 mRNA vaccines is fragmentary. Thus we employed high-temporal-resolution transcriptome profiling and in-depth characterization of antibody production approaches to investigate responses to COVID-19 mRNA vaccination. There were marked differences in the timing and amplitude of the responses to the priming and booster doses. Notably, two distinct interferon signatures were identified, that differed based on their temporal patterns of induction. The first signature (S1), which was preferentially induced by type I interferon, peaked at day 2 post-prime and at day 1 post-boost, and in both instances was associated with subsequent development of the antibody response. In contrast, the second interferon signature (S2) peaked at day 1 both post-prime and post-boost but was found to be potently induced only post-boost, where it coincided with a robust inflammation peak. Notably, we also observed post-prime-like (S1++,S20/+) and post-boost-like (S1++,S2++) patterns of interferon response among COVID-19 patients. A post-boost-like signature was observed in most severely ill patients at admission to the intensive care unit and was associated with a shorter hospital stay. Interestingly, severely ill patients who stayed hospitalized the longest showed a peculiar pattern of interferon induction (S1-/0,S2+), that we did not observe following the administration of mRNA vaccines. In summary, high temporal resolution profiling revealed an elaborate array of immune responses elicited by priming and booster doses of COVID-19 mRNA vaccines. Furthermore, it contributed to the identification of distinct interferon-response phenotypes underpinning vaccine immunogenicity and the course of COVID-19 disease.
Subject(s)
COVID-19 , Inflammation , Severe Acute Respiratory SyndromeABSTRACT
Background. Robust biomarkers that predict disease outcomes amongst COVID19 patients are necessary for both patient triage and resource prioritisation. Numerous candidate biomarkers have been proposed for COVID19. However, at present, there is no consensus on the best diagnostic approach to predict outcomes in infected patients. Moreover, it is not clear whether such tools would apply to other potentially pandemic pathogens and therefore of use as stockpile for future pandemic preparedness. Methods. We conducted a multi cohort observational study to investigate the biology and the prognostic role of interferon alpha inducible protein 27 (IFI27) in COVID19 patients. Findings. We show that IFI27 is expressed in the respiratory tract of COVID19 patients and elevated IFI27 expression is associated with the presence of a high viral load. We further demonstrate that systemic host response, as measured by blood IFI27 expression, is associated with COVID19 severity. For clinical outcome prediction (e.g. respiratory failure), IFI27 expression displays a high positive (0.83) and negative (0.95) predictive value, outperforming all other known predictors of COVID19 severity. Furthermore, IFI27 is upregulated in the blood of infected patients in response to other respiratory viruses. For example, in the pandemic H1N1/09 swine influenza virus infection, IFI27 like genes were highly upregulated in the blood samples of severely infected patients. Interpretation. These data suggest that prognostic biomarkers targeting the family of IFI27 genes could potentially supplement conventional diagnostic tools in future virus pandemics, independent of whether such pandemics are caused by a coronavirus, an influenza virus or another as yet to be discovered respiratory virus.
Subject(s)
Infections , Hematologic Diseases , Tumor Virus Infections , COVID-19 , Respiratory InsufficiencyABSTRACT
BackgroundWhile major progress has been made to establish diagnostic tools for the identification of SARS-CoV-2 infection, determining the severity of COVID-19 remains an unmet medical need. There is a limited availability of hospital resources in this or any pandemic, and appropriately gauging severity would allow for some patients to safely recover in home quarantine, while ensuring that sicker patients get needed care. MethodsWe here developed a blood-based generalizable host-gene-expression-based classifier for the severity of viral infections and validated it in multiple viral infection settings including COVID-19. We used training data (N=705) from 21 retrospective transcriptomic clinical studies of influenza and other viral illnesses looking at a preselected panel of host immune mRNAs. ResultsWe selected 6 host mRNAs and trained a logistic regression classifier with a training cross-validation AUROC of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1,417 samples across 21 independent retrospective validation cohorts the locked 6-mRNA classifier had an AUROC of 0.91 for discriminating patients with severe vs. non-severe infection. Next, in an independent cohort of prospectively enrolled patients with confirmed COVID-19 (N=97) in Athens, Greece, the 6-mRNA locked classifier had an AUROC of 0.89 for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed an isothermal qRT-LAMP (loop-mediated isothermal gene expression) assay for the 6-mRNA panel to facilitate implementation as a rapid assay. ConclusionsWith further study, the classifier could assist in the risk assessment of patients with confirmed SARS-CoV-2 infection and COVID-19 to determine severity and level of care, thereby improving patient management and healthcare burden.