Aguiar, V. R. C. | Search bioRxiv

bioRxiv2026

Host Cellular Remodeling, Not Viral Transcriptional Inhibition, Drives Quorum Sensing Signal-Mediated Evasion of Viral Lysis in Gephyrocapsa huxleyi

Bacterial infochemicals can profoundly alter phytoplankton physiology, yet how these compounds influence viral infection outcomes remains poorly understood. The bacterial quorum sensing molecule 2-heptyl-4-quinolone (HHQ) enables the globally abundant coccolithophore, Gephyrocapsa huxleyi, to evade virus-induced mortality following infection by Emiliania huxleyi virus (EhV). HHQ reduced infectious virus production at 24 hours despite largely intact viral gene transcription, as revealed by RNA sequencing of host and viral responses during EhV-201 infection. Host transcriptomics instead revealed rapid and progressive HHQ-dependent physiological remodeling evidenced by changes in protein encoding genes associated with vesicular trafficking, and cytoskeletal organization. Transcriptional responses further implicated endolysosomal sorting, membrane remodeling, and protein modification as potential barriers to productive infection. These findings indicate that HHQ-mediated evasion of virus-induced mortality occurs downstream of viral entry and transcription, through remodeling of host processes required for virion assembly, maturation, trafficking, and release.

microbiology↗

bioRxiv2018

Expression estimation and eQTL mapping for HLA genes with a personalized pipeline

The HLA (Human Leukocyte Antigens) genes are well-documented targets of balancing selection, and variation at these loci is associated with many disease phenotypes. Variation in expression levels also influences disease susceptibility and resistance, but little information exists about the regulation and population-level patterns of expression due to the difficulty in mapping short reads to these highly polymorphic loci, and in accounting for the existence of several paralogues. We developed a computational pipeline to accurately estimate expression for HLA genes based on RNA-seq, improving both locus-level and allele-level estimates. First, reads are aligned to all known HLA sequences in order to infer HLA genotypes, then quantification of expression is carried out using a personalized index. We use simulations to show that expression estimates are not biased due to divergence from the reference genome. We applied our pipeline to GEUVADIS dataset, and compared the quantifications to those obtained with reference transcriptome, and found that a substantial portion of the variation captured by the HLA-personalized index in not captured by the standard index (23%). We describe the impact of the HLA-personalized approach on downstream analyses for seven HLA loci (HLA-A, HLA-B, HLA-C, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRB1). Although the influence of the HLA-personalized approach is modest for eQTL mapping, the p-values and the causality of the eQTLs obtained are better than when the reference transcriptome is used. Finally, we integrate information on HLA-allele level expression with the eQTL findings to show that the HLA allele is an important layer of variation to understand HLA regulation.

genomics↗