Endres, S. | bioRxiv Science

bioRxiv2023

Prior flavivirus immunity skews the yellow fever vaccine response to expand cross-reactive antibodies with increased risk of antibody dependent enhancement of Zika and dengue virus infection

Human pathogenic flaviviruses pose a significant health concern and vaccination is the most effective instrument to control their circulation. How pre-existing immunity to antigenically related viruses modulates immunization outcome remains poorly understood. In this study, we evaluated the effect of vaccination against tick-borne encephalitis virus (TBEV) on the epitope immunodominance and immunogenicity of the yellow fever 17D vaccine (YF17D) in a cohort of 250 human vaccinees. Following YF17D vaccination, all study participants seroconverted and generated protective neutralizing antibody titers. At day 28, TBEV pre-immunity did not affect the polyclonal neutralizing response which largely depended on the IgM fraction. We found that sera from TBEV-immunized individuals enhanced YF17D vaccine virus infection via antibody-dependent enhancement (ADE). Upon vaccination, individuals with TBEV pre-immunity had higher concentrations of cross-reactive IgG antibodies with limited neutralizing capacity against YF17D whereas vaccinees without prior flavivirus exposure showed a non-cross-reacting response. Using a set of recombinant YF17D envelope protein mutants displaying different epitopes, we identified quaternary epitopes as the primary target of neutralizing antibodies. Sequential immunizations redirected the IgG response towards the pan-flavivirus fusion loop epitope (FLE) with the potential to mediate enhancement of dengue and Zika virus infections whereas TBEV naive individuals elicited an IgG response directed towards neutralizing epitopes without an enhancing effect. We propose that the YF17D vaccine effectively conceals the FLE and primes a neutralizing IgG response in individuals with no prior flavivirus exposure. In contrast, the response in TBEV-experienced recipients favors weakly-neutralizing, cross-reactive epitopes potentially increasing the risk of severe dengue and Zika disease due to ADE.

immunology↗

bioRxiv2021

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge.

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. Most predictions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

neuroscience↗