Mandelli, C. | bioRxiv Science

bioRxiv2026

Grapevine Red Blotch Virus Induces Haplotype-specific Genetic and Epigenetic Responses

Haplotype-resolved genomes provide a powerful framework for uncovering allele-specific regulatory mechanisms obscured in collapsed diploid references. Despite growing evidence of cultivar- and clone-dependent variation in disease severity caused by Grapevine Red Blotch Virus (GRBV), the contribution of haplotype-specific transcriptional and epigenetic regulation to host-virus interactions remains poorly understood. Here, we integrate a haplotype-resolved grapevine genome with time-resolved transcriptomic and whole-genome bisulfite sequencing to dissect allele-specific regulatory responses to GRBV infection. Allele-aware RNA-seq analysis revealed extensive haplotype-dependent transcriptional remodeling, with both shared and divergent temporal expression programs across infection stages. Soft clustering and weighted gene co-expression network analysis (WGCNA) identified haplotype-specific co-expression modules and regulatory hubs, uncovering asymmetric network organization and distinct antiviral strategies between parental haplotypes. Notably, chloroplast-associated defense networks emerged as conserved but highly infection-sensitive modules, exhibiting haplotype-dependent recovery or sustained disruption during infection. Methylome profiling demonstrated that GRBV infection induces pronounced haplotype-specific epigenetic reprogramming, with differential DNA methylation concentrated in promoter-proximal and transposable element-associated regions. Together, our results demonstrate that haplotype-resolved, multi-omic analyses reveal regulatory complexity and divergent antiviral strategies that are hidden by collapsed-genome approaches. This work provides new mechanistic insight into grapevine-virus interactions and lays a foundation for leveraging allelic variation to improve disease resilience in clonally propagated crops.

plant biology↗

bioRxiv2025

Machine Learning Reveals Intrinsic Determinants of siRNA Efficacy

Small interfering RNAs (siRNAs) are widely used in therapeutics and agriculture for sequence-specific gene silencing. However, siRNA efficacy remains difficult to predict due to complex dependencies on sequence, structure, and thermodynamic properties. Existing computational tools largely rely on heuristic rules or pre-scored features, limiting generalizability and biological interpretability. Here, we present a machine learning model to predict siRNA efficacy directly from intrinsic antisense sequence features. Using a dataset of 2,428 experimentally validated siRNAs, we developed a comprehensive feature set that encompasses sequence composition, regulatory motifs, thermodynamic parameters, and structural complexity. We trained and evaluated multiple models for both regression and classification tasks. Support Vector Regression (SVR) achieved the best regression performance overall, with a predictive accuracy of R = 0.719 and R2 = 0.516, while logistic regression achieved the best classification results with ROC = 0.886 and F1 = 0.809 using a combination of composition, motif, and thermodynamic features. Among all features, position-specific nucleotides were the strongest predictors of efficacy, with a uracil at the 5'antisense end (P1_U) and an adenine at the 3'end (P19_A) showing the highest influence, consistent with known mechanisms of strand selection and RISC loading. Our approach improves both predictive power and biological interpretability compared to existing methods, eliminating reliance on external scoring functions. The resulting framework supports rational siRNA design for therapeutic applications, functional genomics, and non-transgenic crop protection strategies.

bioinformatics↗