Single-cell transcriptomics identifies prothymosin α restriction of HIV-1 in vivo.

Host restriction factors play key roles in innate antiviral defense, but it remains poorly understood which of them restricts HIV-1 in vivo. Here, we used single-cell transcriptomic analysis to identify host factors associated with HIV-1 control during acute infection by correlating host gene expression with viral RNA abundance within individual cells. Wide sequencing of cells from one participant with the highest plasma viral load revealed that intracellular viral RNA transcription correlates inversely with expression of the gene PTMA, which encodes prothymosin α. This association was genome-wide significant (Padjusted < 0.05) and was validated in 28 additional participants from Thailand and the Americas with HIV-1 CRF01_AE and subtype B infections, respectively. Overexpression of prothymosin α in vitro confirmed that this cellular factor inhibits HIV-1 transcription and infectious virus production. Our results identify prothymosin α as a host factor that restricts HIV-1 infection in vivo, which has implications for viral transmission and cure strategies.

Yanagi: Fast and interpretable segment-based alternative splicing and gene expression analysis.

BACKGROUND: Ultra-fast pseudo-alignment approaches are the tool of choice in transcript-level RNA sequencing (RNA-seq) analyses. Unfortunately, these methods couple the tasks of pseudo-alignment and transcript quantification. This coupling precludes the direct usage of pseudo-alignment to other expression analyses, including alternative splicing or differential gene expression analysis, without including a non-essential transcript quantification step. RESULTS: In this paper, we introduce a transcriptome segmentation approach to decouple these two tasks. We propose an efficient algorithm to generate maximal disjoint segments given a transcriptome reference library on which ultra-fast pseudo-alignment can be used to produce per-sample segment counts. We show how to apply these maximally unambiguous count statistics in two specific expression analyses - alternative splicing and gene differential expression - without the need of a transcript quantification step. Our experiments based on simulated and experimental data showed that the use of segment counts, like other methods that rely on local coverage statistics, provides an advantage over approaches that rely on transcript quantification in detecting and correctly estimating local splicing in the case of incomplete transcript annotations. CONCLUSIONS: The transcriptome segmentation approach implemented in Yanagi exploits the computational and space efficiency of pseudo-alignment approaches. It significantly expands their applicability and interpretability in a variety of RNA-seq analyses by providing the means to model and capture local coverage variation in these analyses.