B8532 - PATOLOGIA VEGETALE AVANZATA

Learning outcomes

At the end of the course, students will have acquired knowledge of how plants perceive and decode signals from biotic and abiotic disease agents, as well as an understanding of the main virulence factors involved in infectious diseases. Students will be able to critically evaluate the application of disease control and prevention measures in order to develop sustainable plant protection strategies. Through the analysis of selected case studies covering different types of plant diseases, students will enhance their professional knowledge and develop creativity, critical thinking, and problem-solving skills.

Course contents

Module 2: Computational Biology Applied to Plant Pathology (3 ECTS) Prerequisites

Basic knowledge of Plant Pathology, molecular biology, genetics, and the main methodologies used for the identification and characterization of plant pathogens is required.

The module introduces students to the principles and applications of computational biology in the study of plant diseases and in the characterization of plant pathogenic microorganisms. The development of high-throughput sequencing technologies and advanced approaches for biological data analysis has profoundly transformed plant pathology, enabling an integrated investigation of pathogen genetic diversity, the molecular mechanisms underlying plant–pathogen interactions, the evolutionary processes driving pathogen emergence and adaptation, and the structure of microbial communities associated with plants.

Through theoretical lectures, analysis of real datasets, practical exercises, and discussion of case studies, students will acquire knowledge of the main computational strategies used for the analysis of genomic, transcriptomic, and metagenomic data. The course will provide students with skills applicable to advanced diagnostics, epidemiological monitoring, pathogen characterization, and the development of innovative strategies for the sustainable management of plant diseases.

The course covers the following topics:

1. Introduction to computational biology applied to plant pathology

The role of large-scale biological data (big data) in the study of plant diseases. Principles of bioinformatics, management and analysis of biological data, major genomic databases, and computational tools applied to the study of plant pathogens. (6 hours)

2. Plant pathogen genomics and comparative analyses

Genome assembly and annotation, comparative genomics, identification of genes associated with pathogenicity and virulence, analysis of pathogen genetic variability, population genomics, and genomic epidemiology. Application of approaches based on whole-genome sequencing (WGS) for the study of pathogen evolution, adaptation, and dissemination. (6 hours)

3. Transcriptomics and gene expression analysis in plant–pathogen interactions

Principles of RNA sequencing (RNA-seq), differential gene expression analysis, identification of candidate genes involved in pathogenicity, virulence, and host adaptation. Application of transcriptomic approaches to investigate plant responses and the molecular mechanisms associated with infection processes. (6 hours)

4. Metabarcoding and metagenomics for the study of plant-associated microbiomes

Principles and applications of amplicon-based sequencing, metabarcoding, and shotgun metagenomics for the characterization of microbial communities associated with healthy and diseased plants. Analysis of microbial diversity, identification of emerging pathogens, and investigation of interactions among pathogens, microbiota, and hosts. (6 hours)

5. Practical exercises, case studies, and applications

Analysis of genomic, transcriptomic, and metagenomic datasets through dedicated bioinformatics workflows. Interpretation of computational analyses applied to real cases of pathogen diagnosis, epidemiology, and molecular characterization of plant pathogens. (6 hours)

Teaching methods

The module will be structured through lectures, practical exercises, and activities involving the analysis of real biological datasets. The lectures will focus on presenting the theoretical principles of computational biology applied to plant pathology, with particular emphasis on genomic, transcriptomic, and metagenomic approaches used to study plant pathogens and plant–microorganism interactions.

Practical exercises will be dedicated to the analysis and interpretation of datasets generated through high-throughput sequencing technologies (whole genome sequencing, RNA-seq, metabarcoding, and metagenomics), using biological databases, bioinformatics tools, and dedicated computational workflows.

Case studies from the scientific literature and real-world applications in plant pathology will also be discussed, with the aim of developing students’ ability to integrate molecular, genomic, and epidemiological information for the characterization and monitoring of plant pathogens.

Teaching activities may also include seminars delivered by experts in the field and guided discussions aimed at critically evaluating innovative approaches applied to plant disease diagnosis, surveillance, and sustainable disease management.

Assessment methods

The final assessment consists of a written and/or oral examination aimed at evaluating students’ acquired knowledge of the principles of computational biology applied to plant pathology and their ability to interpret genomic, transcriptomic, and metagenomic data in the context of plant disease research.

Teaching tools

Teaching activities will be supported by the use of multimedia materials, electronic presentations, scientific articles, and updated bibliographic resources related to the topics covered in the course. Public biological databases, genomic analysis platforms, and bioinformatics tools will also be used for the processing and interpretation of molecular data.

During practical exercises, computational environments and dedicated workflows will be employed for the analysis of genomic, transcriptomic, and metagenomic data, with particular emphasis on tools for sequence analysis, genome annotation, comparative genomics, gene expression analysis, and microbial community characterization.

Students will be provided with real datasets, teaching materials, analysis protocols, and operational guidelines to facilitate the learning of computational approaches applied to plant pathology. Activities may include the use of online platforms and open-source resources for bioinformatics analyses and access to scientific databases.

Office hours

See the website of Riccardo Baroncelli

See the website of Elena Baraldi