2025/04/14 by Caolann Brady, Tom Tipton, Oliver Carnell +6 · 1 voice
Biochemistry, Genetics and Molecular Biology · Medicine · #COVID-19 Clinical Research Studies #SARS-CoV-2 and COVID-19 Research #vaccines and immunoinformatics approaches
paper · pdf · doi:10.1038/s41541-025-01103-2
openalex publication_date 2025/04/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
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.