Biology subjects

Amatya, S.

Publications and source records attributed to Amatya, S..

3 recordsLinked to original sources

bioRxiv2026

Identification of a pan-orthoebolavirus-reactive antibody from an rVSV-EBOV vaccinated individual

Orthoebolaviruses such as Ebola virus (EBOV), Sudan virus (SUDV) and Bundibugyo virus (BDBV) can cause severe disease with high case-fatality rates. While licensed EBOV vaccines and therapeutic antibodies protect against EBOV infection, no single monoclonal antibody currently provides broad protection across multiple orthoebolaviruses. Here, we analyzed the humoral immune response of an rVSV-EBOV vaccinee to identify pan-orthoebolavirus-neutralizing antibodies. Using BDBV- and SUDV-glycoproteins for single B cell-sorting, we identified B10, which neutralized authentic EBOV and SUDV, with potent activity against SUDV compared with established cross-reactive antibodies. Structural analysis mapped antibody B10 binding to the pan-orthoebolavirus conserved GP2-stalk/HR2 region, associated with asymmetric trimer destabilization and spike opening. In vivo, B10 showed significant prophylactic efficacy in an EBOV mouse model and partial protection with antiviral activity in a SUDV mouse model. Together, these findings demonstrate that rVSV-EBOV vaccination induced the development of a broadly orthoebolavirus-neutralizing antibody that holds exeptional therapeutic potential.

immunology↗

bioRxiv2019

Single-cell RNA-seq of differentiating iPS cells reveals dynamic genetic effects on gene expression

Recent developments in stem cell biology have enabled the study of cell fate decisions in early human development that are impossible to study in vivo. However, understanding how development varies across individuals and, in particular, the influence of common genetic variants during this process has not been characterised. Here, we exploit human iPS cell lines from 125 donors, a pooled experimental design, and single-cell RNA-sequencing to study population variation of endoderm differentiation. We identify molecular markers that are predictive of differentiation efficiency, and utilise heterogeneity in the genetic background across individuals to map hundreds of expression quantitative trait loci that influence expression dynamically during differentiation and across cellular contexts.

genomics↗

bioRxiv2018

Combined single cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity

BackgroundAlternative splicing is a key regulatory mechanism in eukaryotic cells and increases the effective number of functionally distinct gene products. Using bulk RNA sequencing, splicing variation has been studied across human tissues and in genetically diverse populations. This has identified disease-relevant splicing events, as well as associations between splicing and genomic variations, including sequence composition and conservation. However, variability in splicing between single cells from the same tissue or cell type and its determinants remain poorly understood.\n\nResultsWe applied parallel DNA methylation and transcriptome sequencing to differentiating human induced pluripotent stem cells to characterize splicing variation (exon skipping) and its determinants. Our results shows that variation in single-cell splicing can be accurately predicted based on local sequence composition and genomic features. We observe moderate but consistent contributions from local DNA methylation profiles to splicing variation across cells. A combined model that is built based on sequence as well as DNA methylation information accurately predicts different splicing modes of individual cassette exons (AUC=0.85). These categories include the conventional inclusion and exclusion patterns, but also more subtle modes of cell-to-cell variation in splicing. Finally, we identified and characterized associations between DNA methylation and splicing changes during cell differentiation.\n\nConclusionsOur study yields new insights into alternative splicing at the single-cell level and reveals a previously underappreciated link between DNA methylation variation and splicing.

bioinformatics↗