Fritz, D. | Search bioRxiv

bioRxiv2026

Evolutionarily distinct dengue virus type 2 with a unique type-specific neutralization determinant

Dengue viruses 1-4 (DENV1-4) mostly circulate in endemic cycles involving humans and Aedes mosquitoes. However, rare dengue outbreaks originate from non-human primates. These spillovers are caused by sylvatic virus genotypes that are closely related to endemic strains of the same serotype, but some are caused by strains that fall between the four canonical serotypes on phylogenetic trees. It is not clear whether these genotypic differences are reflected in corresponding antigenic differences. Here, we characterized the antigenic features of QML-22, a divergent strain most closely related to DENV2. On an antigenic map, QML-22 clustered with other globally representative DENV2 strains. QML-22 was not as well neutralized by antisera raised against endemic DENV2 and was better neutralized by antisera from DENV1, 3, and 4 infections than other strains of DENV2. QML-22 had a cluster of distinct amino acid residues in domain I of the envelope (E) protein, with only scattered point mutations elsewhere in the envelope. A potently neutralizing anti-DENV2 antibody that targets this site in domain I of the E protein, 3F9, had >1000-fold lower neutralization of QML-22 than other endemic DENV2 strains, whereas other cross-reactive and DENV2-specific antibodies neutralized QML-22 and endemic strains to similar titers. When antisera from non-human primates infected with QML-22 or DENV2 reference strain S16803 were depleted with the heterologous virions, homologous neutralization was unaffected but heterologous neutralization was reduced, suggesting homologous neutralization was due to distinct epitopes. Evolutionary analyses showed the 3F9 epitope was under relaxed selection pressure compared to other neutralizing epitopes and that QML-22 had unique amino acids in this epitope relative to the likely common ancestor of all DENV2. These findings provide insight into the antigenic diversification of DENV and the possible evolution of new serotypes.

microbiology↗

bioRxiv2023

Uncover spatially informed shared variations for single-cell spatial transcriptomics with STew

MotivationThe recent spatial transcriptomics (ST) technologies have enabled characterization of gene expression patterns and spatial information, advancing our understanding of cell lineages within diseased tissues. Several analytical approaches have been proposed for ST data, but effectively utilizing spatial information to unveil the shared variation with gene expression remains a challenge. ResultsWe introduce STew, a Spatial Transcriptomic multi-viEW representative learning method, to jointly analyze spatial information and gene expression in a scalable manner, followed by a data-driven statistical framework to measure the goodness of model fit. Through benchmarking using Human DLPFC data with true manual annotations, STew achieved superior performance in both clustering accuracy and continuity of identified spatial domains compared with other methods. STew is also robust to generate consistent results insensitive to model parameters, including sparsity constraints. We next applied STew to various ST data acquired from 10x Visium and Slide-seqV2, encompassing samples from both mouse and human brain, which revealed spatially informed cell type clusters. We further identified a pro-inflammatory fibroblast spatial niche using ST data from psoriatic skins. Hence, STew is a generalized method to identify both spatially informed clusters and disease-relevant niches in complex tissues. AvailabilitySource code and the R software tool STew are available from github.com/fanzhanglab/STew. Contactfan.3.zhang@cuanschutz.edu Supplementary informationSupplementary data are provided.

bioinformatics↗