Pre-clinical models to define correlates of protection for SARS-CoV-2

A systems biology approach to define SARS-CoV-2 correlates of protection

Correlates of protection (CoPs) for SARS-CoV-2 have yet to be sufficiently defined. This study uses the machine learning platform, SIMON, to accurately predict the immunological parameters that reduced clinical pathology or viral load following SARS-CoV-2 challenge in a cohort of 90 non-human primates. We found that anti-SARS-CoV-2 spike antibody and neutralising antibody titres were the best predictors of clinical protection and low viral load in the lung. Since antibodies to SARS-CoV-2 spike showed the greatest association with clinical protection and reduced viral load, we next used SIMON to investigate the immunological features that predict high antibody titres. It was found that a pre-immunisation response to seasonal beta-HCoVs and a high frequency of peripheral intermediate and non-classical monocytes predicted low SARS-CoV-2 spike IgG titres. In contrast, an elevated T cell response as measured by IFNγ ELISpot predicted high IgG titres. Additional predictors of clinical protection and low SARS-CoV-2 burden included a high abundance of peripheral T cells. In contrast, increased numbers of intermediate monocytes predicted clinical pathology and high viral burden in the throat. We also conclude that an immunisation strategy that minimises pathology post-challenge did not necessarily mediate viral control. This would be an important finding to take forward into the development of future vaccines aimed at limiting the transmission of SARS-CoV-2. These results contribute to SARS-CoV-2 CoP definition and shed light on the factors influencing the success of SARS-CoV-2 vaccination.

Ensemble modeling of SARS-CoV-2 immune dynamics in immunologically naïve rhesus macaques predicts that potent, early innate immune responses drive viral elimination

Introduction

An unprecedented breadth of longitudinal viral and multi-scale immunological data has been gathered during SARS-CoV-2 infection. However, due to the high complexity, non-linearity, multi-dimensionality, mixed anatomic sampling, and possible autocorrelation of available immune data, it is challenging to identify the components of the innate and adaptive immune response that drive viral elimination. Novel mathematical models and analytical approaches are required to synthesize contemporaneously gathered cytokine, transcriptomic, flow cytometry, antibody response, and viral load data into a coherent story of viral control, and ultimately to discriminate drivers of mild versus severe infection.

Methods

We investigated a dataset describing innate, SARS-CoV-2 specific T cell, and antibody responses in the lung during early and late stages of infection in immunologically naïve rhesus macaques. We used multi-model inference and ensemble modeling approaches from ecology and weather forecasting to compare and combine various competing models.

Results and discussion

Model outputs suggest that the innate immune response plays a crucial role in controlling early infection, while SARS-CoV-2 specific CD4+ T cells correspond to later viral elimination, and anti-spike IgG antibodies do not impact viral dynamics. Among the numerous genes potentially contributing to the innate response, we identified IFI27 as most closely linked to viral load decline. A 90% knockdown of the innate response from our validated model resulted in a ~10-fold increase in peak viral load during infection. Our approach provides a novel methodological framework for future analyses of similar complex, non-linear multi-component immunologic data sets.

Single-cell-resolved interspecies comparison shows a shared inflammatory axis and a dominant neutrophil-endothelial program in severe COVID-19

A key issue for research on COVID-19 pathogenesis is the lack of biopsies from patients and of samples at the onset of infection. To overcome these hurdles, hamsters were shown to be useful models for studying this disease. Here, we further leverage the model to molecularly survey the disease progression from time-resolved single-cell RNA sequencing data collected from healthy and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected Syrian and Roborovski hamster lungs. We compare our data to human COVID-19 studies, including bronchoalveolar lavage, nasal swab, and postmortem lung tissue, and identify a shared axis of inflammation dominated by macrophages, neutrophils, and endothelial cells, which we show to be transient in Syrian and terminal in Roborovski hamsters. Our data suggest that, following SARS-CoV-2 infection, commitment to a type 1- or type 3-biased immunity determines moderate versus severe COVID-19 outcomes, respectively.

RBD-Protein/Peptide Vaccine UB-612 Elicits Mucosal and Fc-Mediated Antibody Responses against SARS-CoV-2 in Cynomolgus Macaques

Antibodies provide critical protective immunity against COVID-19, and the Fc-mediated effector functions and mucosal antibodies also contribute to the protection. To expand the characterization of humoral immunity stimulated by subunit protein-peptide COVID-19 vaccine UB-612, preclinical studies in non-human primates were undertaken to investigate mucosal secretion and the effector functionality of vaccine-induced antibodies in antibody-dependent monocyte phagocytosis (ADMP) and antibody-dependent NK cell activation (ADNKA) assays. In cynomolgus macaques, UB-612 induced potent serum-neutralizing, RBD-specific IgG binding, ACE2 binding-inhibition antibodies, and antibodies with Fc-mediated effector functions in ADMP and ADNKA assays. Additionally, immunized animals developed mucosal antibodies in bronchoalveolar lavage fluids (BAL). The level of mucosal or serum ADMP and ADNKA antibodies was found to be UB-612 dose-dependent. Our results highlight that the novel subunit UB-612 vaccine is a potent B-cell immunogen inducing polyfunctional antibody responses contributing to anti-viral immunity and vaccine efficacy.