Mai, T. T. | bioRxiv Science

bioRxiv2021

Efficacy of heat-killed and formalin-killed vaccines against Tilapia tilapinevirus in juvenile Nile tilapia (Oreochromis niloticus)

Tilapia tilapinevirus (also known as tilapia lake virus, TiLV) is considered to be a new threat to the global tilapia industry. The objective of this study was to develop simple cell culture-based heat-killed (HKV) and formalin-killed (FKV) vaccines for the prevention of disease caused by TiLV. The fish were immunized with 100 {micro}L of either HKV or FKV by intraperitoneal injection with each vaccine containing 1.8 x 106 TCID50 inactivated virus. A booster vaccination was carried out at 21-day post vaccination (dpv) using the same protocol. The fish were then challenged with a lethal dose of TiLV at 28 dpv. The expression of five immune genes (IgM, IgD, IgT, CD4 and CD8) in the head kidney and spleen of experimental fish was assessed at 14 and 21 dpv and again after the booster vaccination at 28 dpv. TiLV-specific IgM responses were measured by ELISA at the same time points. The results showed that both vaccines conferred significant protection, with relative percentage survival (RPS) of 71.3% and 79.6% for HKV and FKV, respectively. Significant up-regulation of IgM and IgT was observed in the head kidney of fish vaccinated with HKV at 21 dpv, while IgM, IgD and CD4 expression increased in the head kidney of fish receiving FKV at the same time point. After booster vaccination, IgT and CD8 transcripts were significantly increased in the spleen of fish vaccinated with the HKV, but not with FKV. Both vaccines induced a specific IgM response in both serum and mucus. In summary, this study showed that both HKV and FKV are promising injectable vaccines for the prevention of disease caused by TiLV in Nile tilapia.

immunology↗

bioRxiv2019

Improved inference and prediction of bacterial genotype-phenotype associations using pangenome-spanning regressions

Discovery of influential genetic variants and prediction of phenotypes such as antibiotic resistance are becoming routine tasks in bacterial genomics. Genome-wide association study (GWAS) methods can be applied to study bacterial populations, with a particular emphasis on alignment-free approaches, which are necessitated by the more plastic nature of bacterial genomes. Here we advance bacterial GWAS by introducing a computationally scalable joint modeling framework, where genetic variants covering the entire pangenome are compactly represented by unitigs, and the model fitting is achieved using elastic net penalization. In contrast to current leading GWAS approaches, which test each genotype-phenotype association separately for each variant, our joint modelling approach is shown to lead to increased statistical power while maintaining control of the false positive rate. Our inference procedure also delivers an estimate of the narrow-sense heritability, which is gaining considerable interest in studies of bacteria. Using an extensive set of state-of-the-art bacterial population genomic datasets we demonstrate that our approach performs accurate phenotype prediction, comparable to popular machine learning methods, while retaining both interpretability and computational efficiency. We expect that these advances will pave the way for the next generation of high-powered association and prediction studies for an increasing number of bacterial species.

microbiology↗