Biology subjects

Wu, N. C.

Publications and source records attributed to Wu, N. C..

3 recordsLinked to original sources

bioRxiv2018

Bacterial Glycosyltransferase-mediated Cell-surface Chemoenzymatic Glycan Editing: Methods and Applications

AbstractChemoenzymatic glycan editing that modifies glycan structures directly on the cell surface has emerged as a complementary tool to metabolic oligosaccharide engineering. In this article, we report the discovery that three bacterial enzymes--Pasteurella multocida 2-3-sialyltransferase M144D mutant (Pm2,3ST-M144D), Photobacterium damsel 2-6-sialyltransferase (Pd2,6ST) and Helicobacter mustelae 1-2-fucosyltransferase (Hm1,2FT)--can serve as highly efficient tools for cell-surface glycan editing. Among these three enzymes, the two sialyltransferases were also found to be tolerant to large substituents introduced to the C-5 position of the cytidine monophosphate N-acetylneuraminic acid donor, including biotin and fluorescent dyes. Combining these enzymes with our previously discovered Helicobacter pylori 1-3-FT, we developed a live cell-based assay to probe host-cell glycan-mediated influenza A virus (IAV) infection including both wild-type and mutant strains of human H1N1 and H3N2 influenza subtypes. At high SiaNAc2-6-Gal levels, the ability of a viral strain to induce the host cell death is positively correlated with the SiaNAc2-6-Gal binding affinity of its haemagglutinin. Surprisingly, the creation of sLeX on the host cell surface via in situ 1-3-Fuc editing also exacerbated the killing induced by several wild-type IAV strains as well as a mutant known as HK68-MTA. Structural alignment of HAs from the wild-type HK68 and HK68-MTA revealed the formation of a putative hydrogen bond between Trp222 of HA-HK68-MTA and the C-4 hydroxyl group of the 1-3-linked fucose of sLeX. This interaction is likely to be responsible for the better binding affinity of HA-HK68-MTA to sLeX and accordingly the enhanced host-cell killing compared with the wild-type HK68.

biochemistry↗

bioRxiv2018

Deep mutational scanning of hemagglutinin helps predict evolutionary fates of human H3N2 influenza variants

Human influenza virus rapidly accumulates mutations in its major surface protein hemagglutinin (HA). The evolutionary success of influenza virus lineages depends on how these mutations affect HAs functionality and antigenicity. Here we experimentally measure the effects on viral growth in cell culture of all single amino-acid mutations to the HA from a recent human H3N2 influenza virus strain. We show that mutations that are measured to be more favorable for viral growth are enriched in evolutionarily successful H3N2 viral lineages relative to mutations that are measured to be less favorable for viral growth. Therefore, despite the well-known caveats about cell-culture measurements of viral fitness, such measurements can still be informative for understanding evolution in nature. We also compare our measurements for H3 HA to similar data previously generated for a distantly related H1 HA, and find substantial differences in which amino acids are preferred at many sites. For instance, the H3 HA has less disparity in mutational tolerance between the head and stalk domains than the H1 HA. Overall, our work suggests that experimental measurements of mutational effects can be leveraged to help understand the evolutionary fates of viral lineages in nature -- but only when the measurements are made on a viral strain similar to the ones being studied in nature.\n\nSignificance StatementA key goal in the study of influenza virus evolution is to forecast which viral strains will persist and which ones will die out. Here we experimentally measure the effects of all amino-acid mutations to the hemagglutinin protein from a human H3N2 influenza strain on viral growth in cell culture. We show that these measurements have utility for distinguishing among viral strains that do and do not succeed in nature. Overall, our work suggests that new high-throughput experimental approaches may be useful for understanding virus evolution in nature.

evolutionary biology↗

bioRxiv2018

MiCoP: Microbial Community Profiling method for detecting viral and fungal organisms in metagenomic samples

BackgroundHigh throughput sequencing has spurred the development of metagenomics, which involves the direct analysis of microbial communities in various environments such as soil, ocean water, and the human body. Many existing methods based on marker genes or k-mers have limited sensitivity or are too computationally demanding for many users. Additionally, most work in metagenomics has focused on bacteria and archaea, neglecting to study other key microbes such as viruses and eukaryotes.\n\nResultsHere we present a method, MiCoP (Microbiome Community Profiling), that uses fast-mapping of reads to build a comprehensive reference database of full genomes from viruses and eukaryotes to achieve maximum read usage and enable the analysis of the virome and eukaryome in each sample. We demonstrate that mapping of metagenomic reads is feasible for the smaller viral and eukaryotic reference databases. We show that our method is accurate on simulated and mock community data and identifies many more viral and fungal species than previously-reported results on real data from the Human Microbiome Project.\n\nConclusionsMiCoP is a mapping-based method that proves more effective than existing methods at abundance profiling of viruses and eukaryotes in metagenomic samples. MiCoP can be used to detect the full diversity of these communities. The code, data, and documentation is publicly available on GitHub at: https://github.com/smangul1/MiCoP

bioinformatics↗