# Prediction and characterisation of the human B cell response to a heterologous two-dose Ebola vaccine

## TL;DR

This study examines how the human immune system responds to an Ebola vaccine and uses machine learning to predict antibody response strength.

## Contribution

The study introduces a machine-learning model that predicts antibody response magnitude using blood gene expression data post-Ebola vaccination.

## Key findings

- Vaccination induces robust plasma cell and lasting B cell memory responses.
- A unique B cell receptor CDRH3 sequence resembling EBOV glycoprotein-binding antibodies was identified.
- Early immune responses can predict vaccine-induced B cell immunity through systems immunology.

## Abstract

Ebola virus disease (EVD) outbreaks are increasing, posing significant threats to affected communities. Effective outbreak management depends on protecting frontline health workers, a key focus of EVD vaccination strategies. IgG specific to the viral glycoprotein serves as the correlate of protection for recent vaccine licensures. Using advanced cellular and transcriptomic analyses, we examined B cell responses to the Ad26.ZEBOV, MVA-BN-Filo EVD vaccine. Our findings reveal robust plasma cell and lasting B cell memory responses post-vaccination. Machine-learning models trained on blood gene expression predicted antibody response magnitude. Notably, we identified a unique B cell receptor CDRH3 sequence post-vaccination resembling known Orthoebolavirus zairense (EBOV) glycoprotein-binding antibodies. Single-cell analyses further detailed changes in plasma cell frequency, subclass usage, and CDRH3 properties. These results highlight the predictive power of early immune responses, captured through systems immunology, in shaping vaccine-induced B cell immunity.

Vaccination of frontline worker is an important strategy to manage Ebola outbreaks and identifying correlates of protection could lead to development of improved vaccines. Here authors predict the magnitude of the antibody response by analysing blood samples from individuals vaccinated by a heterologous two-dose regimen and using the collected cellular and transcriptomic data for training a machine learning model.