Aufiero, Gaetano (2025) Development of a graphical user interface tool for dual and bulk RNA-seq analysis and its application to Phelipanche ramosa-tomato parasite-host interaction. [Tesi di dottorato]

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Abstract

Over the past few decades, bioinformatics has gone from analysing just a few protein sequences with command-line tools to becoming a broad, interdisciplinary field capable of processing massive genomic and transcriptomic datasets (Chapter 1). The transcriptome comprises the complete set of RNA molecules, both protein-coding and non-coding, produced by a genome in a cell or tissue, as shaped by genetic and environmental influences. Transcriptomic analysis systematically profiles this RNA population under defined conditions to capture the dynamic relationship between genotype and phenotype. When the goal is to measure the average gene-expression profile of tissues or cell populations, bulk RNA-sequencing (bulk RNA-seq) analysis is typically employed. In host-parasite systems where physical separation of the organisms is impractical, dual RNA-sequencing (dual RNA-seq), a specialized bulk RNA-seq approach, enables simultaneous profiling of transcripts from both organisms. The approach analyses mixed host-parasite tissue samples and uses in silico methods to distinguish sequencing reads by their origin (Chapter 2). Plant parasitism provides a valuable opportunity to apply dual RNA-seq, and the interaction between Phelipanche ramosa and tomato (Solanum lycopersicum) serves as an excellent model system. Indeed, dual RNA-seq can be used to monitor parasitic development and host responses in resistant and susceptible tomato lines to identify genes and metabolic pathways associated with resistance. The third chapter highlights the usefulness of dual RNA-seq and explores the key molecular players involved in the host-parasite interaction (Chapter 3). Dual RNA-seq strategy has been applied successfully to analyse the interactions involving phylogenetically distant organisms (e.g., plant and fungal systems), where greater sequence divergence reduces ambiguous alignments. Indeed, a central technical challenge of dual RNA-seq is reliable in silico read assignment: high sequence similarity between interacting organisms increases cross-mapping (where RNA reads from one organism incorrectly align to the genome of the other). Consequently, host plant-parasitic plant systems demand methodological refinement and a rigorous feasibility assessment to ensure accurate read discrimination (Chapter 4). Bulk and dual RNA-seq analyses typically require programming expertise. To democratize data analysis, an open-access graphical user interface (GUI) called inDAGO was developed. inDAGO implements the two validated dual-RNA-seq approaches: sequential approach (mapping RNA reads onto each plant's genome separately), and the combined approach (mapping RNA reads onto a merged genome of both organisms). Moreover, inDAGO provides an extension for bulk RNA-seq analysis. The software was tested and validated using a simulated dataset (Chapter 5). In the context of identifying tomato genes associated with resistance to Phelipanche ramosa, one strategy could be comparing resistant introgression lines (ILs) such as IL6.3 with a susceptible cultivar (i.e., S. lycopersicum cv. M82). These ILs are derived from the wild tomato species Solanum pennellii, which acts as a donor of chromosomal regions introgressed into the background of the S. lycopersicum cv. M82. Since the introgressed regions originate from different species, their gene expression profiles could not be directly compared to those of the cv. M82 without bias. Chapter 6 examines these cross-species expression issues and demonstrates that discriminating IL expression reads between parental genomes could reduce bias (Chapter 6). Finally, this thesis applies the combined dual RNA-seq approach using inDAGO to classify reads from IL expression data by parental origin, enabling the identification of the IL6.3 introgressed region into the background of the cv. M82. Successively genes within the introgressed region were characterized. The region harbours a heterogeneous set of defence-related genes, which may explain the observed difference in resistance between the resistant and susceptible lines (Chapter 7). This thesis led to three main outcomes: (i) dual RNA-seq was shown to be reliable even when applied to taxonomically close organisms; (ii) a GUI was developed to make the analysis accessible to researchers without programming expertise; and (iii) the tolerance mechanisms of S. pennellii were preliminarily investigated. Together, these results establish a foundation for future transcriptomic studies of S. pennellii introgression lines interacting with Phelipanche species, using the inDAGO software to perform a dual RNA-seq analysis. Moreover, inDAGO can be applied to study other interacting organisms as well as facilitate the characterization of hybrid genomes (Chapter 8).

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