The Screening of Broadly Neutralizing Antibodies Targeting the SARS-CoV-2 Spike Protein by mRNA Immunization in Mice.

Neutralizing antibodies (nAbs), the popular antiviral drugs used for the treatment of COVID-19, are effective in reducing viral load and hospitalization. Currently, most nAbs are screened from convalescent or vaccinated individuals through single B-cell sequencing which requires cutting-edge facilities. Moreover, owing to the rapid mutation of SARS-CoV-2, some approved nAbs are no longer effective. In the present study, we designed a new approach to acquiring broadly neutralizing antibodies (bnAbs) from mRNA-vaccinated mice. Using the flexibility and speed of mRNA vaccine preparation, we designed a chimeric mRNA vaccine and sequential immunization strategies to acquire bnAbs in mice within a short period. By comparing different vaccination orders, we found that the initially administered vaccine had a greater effect on the neutralizing potency of mouse sera. Ultimately, we screened a strain of bnAb that neutralized wild-type, Beta, and Delta SARS-CoV-2 pseudoviruses. We synthesized the mRNAs of the heavy and light chains of this antibody and verified its neutralizing potency. This study developed a new strategy to screen for bnAbs in mRNA-vaccinated mice and identified a more effective immunization strategy for inducing bnAbs, providing valuable insights for future antibody drug development.

Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing.

BACKGROUND: Epigenome-wide association studies (EWAS), which seek the association between epigenetic marks and an outcome or exposure, involve multiple hypothesis testing. False discovery rate (FDR) control has been widely used for multiple testing correction. However, traditional FDR control methods do not use auxiliary covariates, and they could be less powerful if the covariates could inform the likelihood of the null hypothesis. Recently, many covariate-adaptive FDR control methods have been developed, but application of these methods to EWAS data has not yet been explored. It is not clear whether these methods can significantly improve detection power, and if so, which covariates are more relevant for EWAS data. RESULTS: In this study, we evaluate the performance of five covariate-adaptive FDR control methods with EWAS-related covariates using simulated as well as real EWAS datasets. We develop an omnibus test to assess the informativeness of the covariates. We find that statistical covariates are generally more informative than biological covariates, and the covariates of methylation mean and variance are almost universally informative. In contrast, the informativeness of biological covariates depends on specific datasets. We show that the independent hypothesis weighting (IHW) and covariate adaptive multiple testing (CAMT) method are overall more powerful, especially for sparse signals, and could improve the detection power by a median of 25% and 68% on real datasets, compared to the ST procedure. We further validate the findings in various biological contexts. CONCLUSIONS: Covariate-adaptive FDR control methods with informative covariates can significantly increase the detection power for EWAS. For sparse signals, IHW and CAMT are recommended.