Biology subjects

Hsu, W.-L.

Publications and source records attributed to Hsu, W.-L..

8 recordsLinked to original sources

bioRxiv2025

Structural and functional characterization of the antigenicity of influenza A virus hemagglutinin subtype H15

Avian H15 influenza viruses are closely related to H7 viruses, but feature a unique 9-amino acid insertion in their hemagglutinin head domain, creating an additional site for antigenic variation. Here, we characterized a panel of mouse monoclonal antibodies (mAbs) raised against the A/wedge-tailed shearwater/Western Australia/2576/1979 ancestral strain, and a human mAb isolated from an H7N9 vaccinee. We found differences in binding and neutralization profiles against the ancestral strain and drifted strains of H15 isolated after 2008. MAbs that have hemagglutination inhibition activity against the ancestral strain do not show binding to drifted strains, hinting at antigenic differences in the receptor binding site. We show that the mAbs protect in vivo and elucidate mAb-antigen interactions using negative stain and cryo-electron microscopy. The characterization of H15 antigenicity and mechanisms of antibody-mediated neutralization expands our knowledge of this rare avian influenza virus and informs our understanding of immune pressures on viral surface glycoproteins.

microbiology↗

bioRxiv2021

Macrophages break interneuromast cell quiescence by intervening the inhibition of Schwann cells in zebrafish lateral line

In the zebrafish lateral line system, interneuromast cells (INCs) between neuromasts are normally kept quiescent by underlying Schwann cells (SWCs). Upon severe injuries that cause the complete loss of an entire neuromast, INCs can occasionally differentiate into neuromasts but how they escape from the inhibition by SWCs is still unclear. Using a genetic/chemical method to specifically ablate a neuromast, we found a small portion of larvae can regenerate a new neuromast, but the regeneration was hindered by inhibiting macrophages. By in toto imaging, we further discovered heterogeneities in macrophage behavior and distribution along lateral line. We witnessed the crawling of macrophages in between injured lateral line and SWCs during regeneration and also in between the second primordium and the first mature lateral line during development. It implies that macrophages may physically separate and alleviate the inhibition from pLLn and SWCs to break the quiescence of INCs during regeneration and development in the zebrafish lateral line.

developmental biology↗

bioRxiv2019

StrainPro: a highly accurate Metagenomic strain-level profiling tool

Characterizing the taxonomic diversity of a microbial community is very important to understand the roles of microorganisms. Next generation sequencing (NGS) provides great potential for investigation of a microbial community and leads to Metagenomic studies. NGS generates DNA fragment sequences directly from microorganism samples, and it requires analysis tools to identify microbial species (or taxonomic composition) and estimate their relative abundance in the studied community. However, only a few tools could achieve strain-level identification and most tools estimate the microbial abundances simply according to the read counts. An evaluation study on metagenomic analysis tools concludes that the predicted abundance differed significantly from the true abundance. In this study, we present StrainPro, a novel metagenomic analysis tool which is highly accurate both at characterizing microorganisms at strain-level and estimating their relative abundances. A unique feature of StrainPro is it identifies representative sequence segments from reference genomes. We generate three simulated datasets using known strain sequences and another three simulated datasets using unknown strain sequences. We compare the performance of StrainPro with seven existing tools. The results show that StrainPro not only identifies metagenomes with high precision and recall, but it is also highly robust even when the metagenomes are not included in the reference database. Moreover, StrainPro estimates the relative abundance with high accuracy. We demonstrate that there is a strong positive linear relationship between observed and predicted abundances.

bioinformatics↗

bioRxiv2019

MapCaller - An integrated and efficient tool for short-read mapping and variant calling using high-throughput sequenced data

With the advance of next-generation sequencing (NGS) technologies, more and more medical and biological researches adopt NGS technologies to characterize the genetic variations between individuals. The identification of personal genome variants using NGS technology is a critical factor for the success of clinical genomics studies. It requires an accurate and consistent analysis procedure to distinguish functional or disease-associated variants from false discoveries due to sequencing errors or misalignments. In this study, we integrate the algorithms for read mapping and variant calling to develop an efficient and versatile NGS analysis tool, called MapCaller. It not only maps every short read onto a reference genome, but it also detects single nucleotide variants, indels, inversions and translocations at the same time. We evaluate the performance of MapCaller with existing variant calling pipelines using three simulated datasets and four real datasets. The result shows that MapCaller can identify variants accurately. Moreover, MapCaller runs much faster than existing methods. It is available at https://github.com/hsinnan75/MapCaller.

bioinformatics↗

bioRxiv2019

GSAlign - an efficient sequence alignment tool for intra-species genomes

Personal genomics and comparative genomics are becoming more important in clinical practice and genome research. Both fields require sequence alignment to discover sequence conservation and variation. Though many methods have been developed, some are designed for small genome comparison while some are not efficient for large genome comparison. Moreover, most existing genome comparison tools have not been evaluated the correctness of sequence alignments systematically. A wrong sequence alignment would produce false sequence variants. In this study, we present GSAlign that handles large genome sequence alignment efficiently and identifies sequence variants from the alignment result. GSAlign is an efficient sequence alignment tool for intra-species genomes. It identifies sequence variations from the sequence alignments. We estimate performance by measuring the correctness of predicted sequence variations. The experiment results demonstrated that GSAlign is not only faster than most existing state-of-the-art methods, but also identifies sequence variants with high accuracy.

bioinformatics↗

bioRxiv2018

Adenosine Deaminase Acting on RNA 1 Associates with Orf Virus OV20.0 and Enhances Viral Replication

Orf virus (ORFV) infects sheep and goats and is also an important zoonotic pathogen. The viral protein OV20.0 has been shown to suppress innate immunity by targeting the double-stranded RNA (dsRNA)-activated protein kinase (PKR) by multiple mechanisms. These mechanisms include a direct interaction with PKR and binding with two PKR activators, dsRNA and the cellular PKR activator (PACT), which ultimately leads to the inhibition of PKR activation. In the present study, we identified a novel association between OV20.0 and adenosine deaminase acting on RNA 1 (ADAR1). OV20.0 bound directly to the dsRNA binding domains (RBDs) of ADAR1 in the absence of dsRNA. Additionally, OV20.0 preferentially interacted with RBD1 of ADAR1, which was essential for its dsRNA binding ability and for the homodimerization that is critical for intact adenosine (A)-to-inosine (I) editing activity. Finally, the association with OV20.0 suppressed the A-to-I editing ability of ADAR1, while ADAR1 played a proviral role during ORFV infection by inhibiting PKR phosphorylation. These observations revealed a new strategy used by OV20.0 to evade antiviral responses via PKR.\n\nImportanceViruses evolve specific strategies to counteract host innate immunity. ORFV, an important zoonotic pathogen, encodes OV20.0 to suppress PKR activation via multiple mechanisms, including interactions with PKR and two PKR activators. In this study, we demonstrated that OV20.0 interacts with ADAR1, a cellular enzyme responsible for converting adenosine (A) to inosine (I) in RNA. The RNA binding domains, but not the catalytic domain, of ADAR1 are required for this interaction. The OV20.0-ADAR1 association affects the functions of both proteins; OV20.0 suppressed the A-to-I editing of ADAR1, while ADAR1 elevated OV20.0 expression. The proviral role of ADAR1 is likely due to the inhibition of PKR phosphorylation. As RNA editing by ADAR1 contributes to the stability of the genetic code and the structure of RNA, these observations suggest that in addition to serving as a PKR inhibitor, OV20.0 might modulate ADAR1-dependent gene expression to combat antiviral responses or achieve efficient viral infection.

microbiology↗

bioRxiv2018

UniLoc: A universal protein localization site predictor for eukaryotes and prokaryotes

There is a growing gap between protein subcellular localization (PSL) data and protein sequence data, raising the need for computation methods to rapidly determine subcellular localizations for uncharacterized proteins. Currently, the most efficient computation method involves finding sequence-similar proteins (hereafter referred to as similar proteins) in the annotated database and transferring their annotations to the target protein. When a sequence-similarity search fails to find similar proteins, many PSL predictors adopt machine learning methods for the prediction of localization sites. We proposed a universal protein localization site predictor - UniLoc - to take advantage of implicit similarity among proteins through sequence analysis alone. The notion of related protein words is introduced to explore the localization site assignment of uncharacterized proteins. UniLoc is found to identify useful template proteins and produce reliable predictions when similar proteins were not available.

bioinformatics↗

bioRxiv2016

The accuracy and bias of single-step genomic prediction for populationsunder selection

In single-step analyses, missing genotypes are explicitly or implicitly imputed, and this requires centering the observed genotypes, ideally using the mean of the unselected founders. If genotypes are only available on selected individuals, centering on the unselected founder mean is impossible. Here, computer simulation is used to study an alternative analysis that does not require centering genotypes but fits the mean {micro}g of unselected individuals as a fixed effect. To improve numerical properties of the analysis, centering the entire matrix of observed and imputed genotypes, using their sample means can be done in addition to fitting {micro}g. Starting with observed diplotypes from 721 cattle, a 5 generation population was simulated with sire selection to produce 40,000 individuals with phenotypes of which the 1,000 sires had genotypes. The next generation of 8,000 genotyped individuals was used for validation. Evaluations were undertaken: with (J) or without (N) {micro}g when marker covariates were not centered; and with (JC) or without (C) {micro}g when all marker covariates were centered. A pedigree based evaluation was less accurate than genomic analyses. Centering did not influence accuracy of genomic prediction, but fitting {micro}g did. Accuracies were improved when the panel comprised only QTL, models JC and J had accuracies of 99.2%; and models C and N had accuracies of 85.6%. When only markers were in the panel, the 4 models had accuracies of 63.9%. In panels that included causal variants, fitting {micro}g in the model improved accuracy, but had little impact when the panel contained only markers.

genomics↗