Biology subjects

Mai, T. T.

Publications and source records attributed to Mai, T. T..

4 recordsLinked to original sources

bioRxiv2023

Reliable genetic correlation estimation via multiple sample splitting and smoothing

In this paper, we aim to investigate the problem of estimating the genetic correlation between two traits. Instead of making assumptions about the distribution of effect sizes of the genetic factors, we propose the use of a high-dimensional linear model to relate a trait to genetic factors. To estimate the genetic correlation, we develop a generic strategy that combines the use of sparse penalization methods and multiple sample splitting approaches. The final estimate is determined by taking the median of the calculations, resulting in a smoothed and reliable estimate. Through simulations, we demonstrate that our proposed approach is reliable and accurate in comparison to naive plug-in methods. To further illustrate the advantages of our method, we apply it to a real-world example of a bacterial GWAS dataset, specifically to estimate the genetic correlation between antibiotic resistant traits in Streptococus pneumoniae. This application not only validates the effectiveness of our method but also highlights its potential in real-world applications.

bioinformatics↗

bioRxiv2022

Usefulness of the pancreas as a prime target for histopathological diagnosis of Tilapia parvovirus (TiPV) infection in Nile tilapia, Oreochromis niloticus

Tilapia parvovirus (TiPV) is an emerging virus reportedly associated with disease and mortality in farmed tilapia. Although previous descriptions of histopathological changes are available, the lesions reported in these are not pathognomonic. Here, we report Cowdry type A inclusion bodies (CAIB) in the pancreas as a diagnostic histopathological feature found in adult Nile tilapia naturally infected with TiPV. This type of inclusion body has been well-known as a histopathological landmark for the diagnosis of other parvoviral infections in shrimp and terrestrial species. Interestingly, this lesion could be exclusively observed in pancreatic acinar cells, both in the hepatopancreas and pancreatic tissue along the intestine. In situ hybridization (ISH) using a TiPV-specific probe revealed the intranuclear presence of TiPV DNA in multiple tissues, including the liver, pancreas, kidney, spleen, gills, and the membrane of oocytes in the ovary. These findings suggest that although TiPV can replicate in several tissue types, CAIB manifest exclusively in pancreatic tissues. In addition to TiPV, most diseased fish were co-infected with Streptococcus agalactiae, and presented with multifocal granulomas secondary to this bacterial infection. Partial genome amplification of TiPV was successful and revealed high nucleotide identity (> 99%) to previously reported isolates. In summary, this study highlights the usefulness of pancreatic tissue as a prime target for histopathological diagnosis of TiPV in diseased Nile tilapia. This pattern may be critical when determining the presence of TiPV infection in new geographic areas, where ancillary testing may not be available. TiPV pathogenesis in this landmark organ warrants further investigation.

pathology↗

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↗