Clark, A. E. | Search bioRxiv

bioRxiv2018

Development and validation of a phenotypic high-content imaging assay for assessing the antiviral activity of small-molecule inhibitors targeting the Zika virus

Zika virus (ZIKV) has been linked to the development of microcephaly in newborns, as well as Guillain-Barre syndrome. There are currently no drugs available to treat infection, and accordingly there is an unmet medical need for discovery of new therapies. High-throughput drug screening efforts focusing on indirect readouts of cell viability are prone to a higher frequency of false positives in cases where the virus is viable in the cell but the cytopathic effect is reduced or delayed. Here, we describe a fast and label-free phenotypic high-content imaging assay used to detect cells affected by the viral-induced cytopathic effect (CPE) using automated imaging and analysis. Protection from CPE correlates with a decrease in viral antigen production as observed by immunofluorescence. We trained our assay using a collection of nucleoside analogues against ZIKV; the previously reported antiviral activities of 2-C-methylribonucleosides and ribavirin against the Zika virus in Vero cells were confirmed using our developed method. Profiling of a novel library of 24 natural product derivatives using our assay revealed compound 1 as an inhibitor of ZIKV-induced cytopathic effect; activity of the compound was confirmed in human fetal neural stem cells (NSCs). The described technique can be easily leveraged as a primary screening assay for profiling large compound libraries against ZIKV, and can be expanded to other ZIKV strains and other cell lines displaying morphological changes upon ZIKV infection.

microbiology↗

bioRxiv2017

xenoGI: reconstructing the history of genomic island insertions in clades of closely related bacteria

BackgroundGenomic islands play an important role in microbial genome evolution, providing a mechanism for strains to adapt to new ecological conditions. A variety of computational methods, both genome-composition based and comparative have been developed to identify them. Some of these methods are explicitly designed to work in single strains, while others make use of multiple strains. In general, existing methods do not identify islands in the context of the phylogeny in which they evolved. Even multiple strain approaches are best suited to identifying genomic islands that are present in one strain but absent in others. They do not automatically recognize islands which are shared between some strains in the clade or determine the branch on which these islands inserted within the phylogenetic tree.\n\nResultsWe have developed a software package, xenoGI, that identifies genomic islands and maps their origin within a clade of closely related bacteria, determining which branch they inserted on. It takes as input a set of sequenced genomes and a tree specifying their phylogenetic relationships. Making heavy use of synteny information, the package builds gene families in a species-tree-aware way, and then attempts to combine into islands those families whose members are adjacent and whose most recent common ancestor is shared. The package provides a variety of text-based analysis functions, as well as the ability to export genomic islands into formats suitable for viewing in a genome browser. We demonstrate the capabilities of the package with several examples from enteric bacteria, including an examination of the evolution of the acid fitness island in the genus Escherichia. In addition we use output from simulations and a set of known genomic islands from the literature to show that xenoGI can accurately identify genomic islands and place them on a phylogenetic tree.\n\nConclusionsxenoGI is an effective tool for studying the history of genomic island insertions in a clade of microbes. It identifies genomic islands, and determines which branch they inserted on within the phylogenetic tree for the clade. Such information is valuable because it helps us understand the adaptive path that has produced living species. Given the large and growing number of sequenced microbial genomes, this sort of analysis will become increasingly useful in the future.

bioinformatics↗