Structurally and functionally distinct early antibody responses predict COVID-19 disease trajectory and mRNA vaccine response

IgG1 glycosylation highlights premature aging in Down syndrome

Down syndrome (DS) is characterized by lowered immune competence and premature aging. We previously showed decreased antibody response following SARS-CoV-2 vaccination in adults with DS. IgG1 Fc glycosylation patterns are known to affect the effector function of IgG and are associated with aging. Here, we compare total and anti-spike (S) IgG1 glycosylation patterns following SARS-CoV-2 vaccination in DS and healthy controls (HC). Total and anti-Spike IgG1 Fc N-glycan glycoprofiles were measured in non-exposed adults with DS and controls before and after SARS-CoV-2 vaccination by liquid chromatography-mass spectrometry (LC-MS) of Fc glycopeptides. We recruited N = 44 patients and N = 40 controls. We confirmed IgG glycosylation patterns associated with aging in HC and showed premature aging in DS. In DS, we found decreased galactosylation (50.2% vs. 59.0%) and sialylation (6.7% vs. 8.5%) as well as increased fucosylation (97.0% vs. 94.6%) of total IgG. Both cohorts showed similar bisecting GlcNAc of total and anti-S IgG1 with age. In contrast, anti-S IgG1 of DS and HC showed highly comparable glycosylation profiles 28 days post vaccination. The IgG1 glycoprofile in DS exhibits strong premature aging. The combination of an early decrease in IgG1 Fc galactosylation and sialylation and increase in fucosylation is predicted to reduce complement activity and decrease FcγRIII binding and subsequent activation, respectively. The altered glycosylation patterns, combined with decreased antibody concentrations, help us understand the susceptibility to severe infections in DS. The effect of premature aging highlights the need for individuals with DS to receive tailored vaccines and/or vaccination schedules.

Early immune markers of clinical, virological, and immunological outcomes in patients with COVID-19: a multi-omics study

Background

The great majority of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infections are mild and uncomplicated, but some individuals with initially mild COVID-19 progressively develop more severe symptoms. Furthermore, there is substantial heterogeneity in SARS-CoV-2-specific memory immune responses following infection. There remains a critical need to identify host immune biomarkers predictive of clinical and immunological outcomes in SARS-CoV-2-infected patients.

Methods

Leveraging longitudinal samples and data from a clinical trial (N=108) in SARS-CoV-2-infected outpatients, we used host proteomics and transcriptomics to characterize the trajectory of the immune response in COVID-19 patients. We characterized the association between early immune markers and subsequent disease progression, control of viral shedding, and SARS-CoV-2-specific T cell and antibody responses measured up to 7 months after enrollment. We further compared associations between early immune markers and subsequent T cell and antibody responses following natural infection with those following mRNA vaccination. We developed machine-learning models to predict patient outcomes and validated the predictive model using data from 54 individuals enrolled in an independent clinical trial.

Results

We identify early immune signatures, including plasma RIG-I levels, early IFN signaling, and related cytokines (CXCL10, MCP1, MCP-2, and MCP-3) associated with subsequent disease progression, control of viral shedding, and the SARS-CoV-2-specific T cell and antibody response measured up to 7 months after enrollment. We found that several biomarkers for immunological outcomes are shared between individuals receiving BNT162b2 (Pfizer-BioNTech) vaccine and COVID-19 patients. Finally, we demonstrate that machine-learning models using 2-7 plasma protein markers measured early within the course of infection are able to accurately predict disease progression, T cell memory, and the antibody response post-infection in a second, independent dataset.

Conclusions

Early immune signatures following infection can accurately predict clinical and immunological outcomes in outpatients with COVID-19 using validated machine-learning models.

Funding

Support for the study was provided from National Institute of Health/National Institute of Allergy and Infectious Diseases (NIH/NIAID) (U01 AI150741-01S1 and T32-AI052073), the Stanford's Innovative Medicines Accelerator, National Institutes of Health/National Institute on Drug Abuse (NIH/NIDA) DP1DA046089, and anonymous donors to Stanford University. Peginterferon lambda provided by Eiger BioPharmaceuticals.

Antibodies elicited by SARS-CoV-2 infection or mRNA vaccines have reduced neutralizing activity against Beta and Omicron pseudoviruses

Multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants that have mutations associated with increased transmission and antibody escape have arisen over the course of the current pandemic. Although the current vaccines have largely been effective against past variants, the number of mutations found on the Omicron (B.1.1.529) spike protein appear to diminish the protection conferred by preexisting immunity. Using vesicular stomatitis virus (VSV) pseudoparticles expressing the spike protein of several SARS-CoV-2 variants, we evaluated the magnitude and breadth of the neutralizing antibody response over time in individuals after infection and in mRNA-vaccinated individuals. We observed that boosting increases the magnitude of the antibody response to wild-type (D614), Beta, Delta, and Omicron variants; however, the Omicron variant was the most resistant to neutralization. We further observed that vaccinated healthy adults had robust and broad antibody responses, whereas responses may have been reduced in vaccinated pregnant women, underscoring the importance of learning how to maximize mRNA vaccine responses in pregnant populations. Findings from this study show substantial heterogeneity in the magnitude and breadth of responses after infection and mRNA vaccination and may support the addition of more conserved viral antigens to existing SARS-CoV-2 vaccines.

Distinct systemic and mucosal immune responses during acute SARS-CoV-2 infection

Coordinated local mucosal and systemic immune responses following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection either protect against coronavirus disease 2019 (COVID-19) pathologies or fail, leading to severe clinical outcomes. To understand this process, we performed an integrated analysis of SARS-CoV-2 spike-specific antibodies, cytokines, viral load and bacterial communities in paired nasopharyngeal swabs and plasma samples from a cohort of clinically distinct patients with COVID-19 during acute infection. Plasma viral load was associated with systemic inflammatory cytokines that were elevated in severe COVID-19, and also with spike-specific neutralizing antibodies. By contrast, nasopharyngeal viral load correlated with SARS-CoV-2 humoral responses but inversely with interferon responses, the latter associating with protective microbial communities. Potential pathogenic microorganisms, often implicated in secondary respiratory infections, were associated with mucosal inflammation and elevated in severe COVID-19. Our results demonstrate distinct tissue compartmentalization of SARS-CoV-2 immune responses and highlight a role for the nasopharyngeal microbiome in regulating local and systemic immunity that determines COVID-19 clinical outcomes.