WU, Y. | bioRxiv Science

bioRxiv2018

Structural insight into the mechanism of neuraminidase inhibitor-resistant mutations in human-infecting H10N8 Influenza A virus

The emergence of drug resistance in avian influenza virus (AIV) is a serious concern for public health. Neuraminidase (NA) isolated from a fatal case of avian-origin H10N8 influenza virus infection was found to carry a drug-resistant mutation, NA-Arg292Lys (291 in N8 numbering). In order to understand the full potential of H10N8 drug resistance, the virus was first passaged in the presence of the most commonly used neuraminidase inhibitors (NAIs), oseltamivir and zanamivir. As expected, the Arg292Lys substitution was detected after oseltamivir treatment, however a novel Val116Asp substitution (114 in N8 numbering) was selected by zanamivir treatment. Next generation sequencing (NGS) confirmed that the mutations arose early (after passages 1-3) and became dominant in the presence of the NAI inhibitors. Extensive crystallographic studies revealed that N8-Arg292Lys resistance results mainly from loss of interactions with the inhibitor carboxylate, while rotation of Glu276 was not impaired as observed in the N9-Arg292Lys, a group 2 NA structure. In the case of Val116Asp, the binding mode between oseltamivir and zanamivir is different. Asp151 forms stabilized hydrogen bond to guanidine group of zanamivir, which may compensate the resistance caused by Val116Asp. By contrast, the amino group of oseltamivir is too short to maintain this hydrogen bond, which result in resistant. Moreover, the oseltamivir-zanamivir hybrid inhibitor MS-257 displays higher effectiveness to Val116Asp than oseltamivir, which support this notion.\n\nAuthor SummaryAside from vaccination, NAIs are currently the only alternative for the clinical treatment and prophylaxis of influenza. Understanding the mechanisms of resistance is critical to guide in drug development. In this study, two drug-resistant NA substitutions, Val116Asp and Arg292Lys, were discovered from oseltamivir and zanamivir treatment of H10N8 virus. Crystal structural analyses revealed two distinct mechanisms of these two resistant mutations and provide the explanation for the difference in susceptibility of different NAIs. Zanamivir and laninamivir were more effective against the resistant variants than oseltamivir, and Arg292Lys results in more serious oseltamivir resistance in N9 than N8 subtype. This study is well-correlated to influenza pandemic/epidemic pre-warning, as the discovery of inhibitor resistant viruses will help for new drug preparedness.

microbiology↗

bioRxiv2017

BGDMdocker: an workflow base on Docker for analysis and visualization pan-genome and biosynthetic gene clusters of Bacterial

MotivationAt present Docker technology has received increasing level of attention throughout the bioinformatics community. However, its implementation details have not yet been mastered by most biologists and applied widely in biological researches. In order to popularizing this technology in the bioinformatics and sufficiently use plenty of public resources of bioinformatics tools (Dockerfile and image of scommunity, officially and privately) in Docker Hub Registry and other Docker sources based on Docker, we introduced full and accurate instance of a bioinformatics workflow based on Docker to analyse and visualize pan-genome and biosynthetic gene clusters of a bacteria in this article, provided the solutions for mining bioinformatics big data from various public biology databases. You could be guided step-by-step through the workflow process from docker file to build up your own images and run an container fast creating an workflow.\n\nResultsWe presented a BGDMdocker (bacterial genome data mining docker-based) workflow based on docker. The workflow consists of three integrated toolkits, Prokka v1.11, panX, and antiSMASH3.0. The dependencies were all written in Dockerfile, to build docker image and run container for analysing pan-genome of total 44 Bacillus amyloliquefaciens strains, which were retrieved from public? database. The pan-genome totally includes 172,432 gene, 2,306 Core gene cluster. The visualized pan-genomic data such as alignment, phylogenetic trees, maps mutations within that cluster to the branches of the tree, infers loss and gain of genes on the core-genome phylogeny for each gene cluster were presented. Besides, 997 known (MIBiG database) and 553 unknown (antiSMASH-predicted clusters and Pfam database) genes of biosynthesis gene clusters types and orthologous groups were mined in all strains. This workflow could also be used for other species pan-genome analysis and visualization. The display of visual data can completely duplicated as well as done in this paper. All result data and relevant tools and files can be downloaded from our website with no need to register. The pan-genome and biosynthetic gene clusters analysis and visualization can be fully reusable immediately in different computing platforms (Linux, Windows, Mac and deployed in the cloud), achieved cross platform deployment flexibility, rapid development integrated software package.\n\nAvailability and implementationBGDMdocker is available at http://42.96.173.25/bapgd/ and the source code under GPL license is available at https://github.com/cgwyx/debian_prokka_panx_antismash_biodocker.\n\nContactchenggongwyx@foxmail.com\n\nSupplementary informationSupplementary data are available at biorxiv online.

bioinformatics↗