Stringhini, S. | Search bioRxiv

bioRxiv2024

Immune escape of Omicron lineages BA.1, BA.2, BA.5.1, BQ.1, XBB.1.5, EG.5.1 and JN.1.1 after vaccination, infection and hybrid immunity

In the 5th year after the emergence of SARS-CoV-2, Omicron lineages continue to evolve and cause infections. Here, we used eight authentic SARS-CoV-2 isolates to assess their capacity to escape immunity of different exposure histories and their replicative capacity in polarized human airway epithelial cells (HAE) derived from the nasal and bronchial epithelium. Using live-virus neutralization assays of 108 human sera or plasma of different immunological backgrounds, progressive immune escape was observed from B.1 (ancestral virus) to EG.5.1, but no significant difference between EG.5.1 and JN.1.1. Vaccinated individuals without natural infection and individuals with a single infection, but no vaccination showed markedly reduced or completely lost neutralization against the latest variants, while in those with hybrid immunity almost all sera showed some neutralization capacity. Furthermore, although absolute titers differed between groups, the pattern of immune escape between the variants remains comparable with strongest loss of neutralization observed for the latest variants. In vitro studies with HAE at 33{degrees}C and 37{degrees}C showed some, but minor differences in virus replication and innate immune responses upon infection. Notably, infection with XBB.1.5, EG.5.1 and JN.1.1 showed slightly increased viral growth in nasal HAE at 33{degrees}C. Altogether, these data underscore increasing immune escape across heterogeneous immunological backgrounds with gradually increasing antibody escape of evolving Omicron lineages until variant EG.5.1, but not any further for the latest dominant lineage JN.1.1. They also suggest that viral dynamics within Omicron lineages are driven by a combination of immune evasion and increase in viral replication.

microbiology↗

bioRxiv2022

Omics-informed CNV calls reduce false positive rate and improve power for CNV-trait associations

Copy number variations (CNV) are believed to play an important role in a wide range of complex traits but discovering such associations remains challenging. Whilst whole genome sequencing (WGS) is the gold standard approach for CNV detection, there are several orders of magnitude more samples with available genotyping microarray data. Such array data can be exploited for CNV detection using dedicated software (e.g., PennCNV), however these calls suffer from elevated false positive and negative rates. In this study, we developed a CNV quality score that weights PennCNV calls (pCNV) based on their likelihood of being true positive. First, we established a measure of pCNV reliability by leveraging evidence from multiple omics data (WGS, transcriptomics and methylomics) obtained from the same samples. Next, we built a predictor of omics-confirmed pCNVs, termed omics-informed quality score (OQS), using only PennCNV software output parameters. Promisingly, OQS assigned to pCNVs detected in close family members was up to 35% higher than the OQS of pCNVs not carried by other relatives (P < 3.0-10-90), outperforming other scores. Finally, in an association study of four anthropometric traits in 89,516 Estonian Biobank samples, the use of OQS led to a relative increase in the trait variance explained by CNVs of up to 34% compared to raw pCNVs or previous quality scores. Overall, we put forward a flexible framework to improve any CNV detection method leveraging multi-omics evidence, applied it to improve PennCNV calls and demonstrated its utility by improving the statistical power for downstream association analyses.

bioinformatics↗