Arbuscular mycorrhizal (AM) fungi are obligate biotrophs whose molecular biology has long been difficult to investigate due to limited genomic resources and the occurrence, in experimental samples, of a high quantity of host plant material. High-throughput short-read RNA sequencing has become a key approach for characterizing AM fungal transcriptomes, but requires tailored bioinformatic strategies to overcome challenges such as mixed-species datasets, low fungal RNA abundance, and the absence of high-quality reference AM fungal genomes. This chapter presents a robust pipeline for AM fungal transcriptome analysis based on short-read RNA data, combining de novo assembly, functional annotation, and differential expression analysis. The workflow is applicable both to host-free systems, where fungal reads can be directly assembled, and to symbiotic conditions, where preprocessing steps are necessary to remove plant-derived reads and enrich for fungal transcripts. Methods to check read quality, remove contaminants, including the host, assemble and annotate transcriptome using homology- and domain-based tools are described. Finally, procedures for robust statistical analysis of differential gene expression are outlined, enabling the identification of molecular pathways involved in fungal development, nutrient exchange, and symbiotic function. Together, these methods provide a comprehensive framework for generating reliable and meaningful insights into the transcriptomes of AM fungi.
Transcriptome Analysis of Arbuscular Mycorrhizal Fungi Using Short-Read Sequencing
Stefano Ghignone
2026
Abstract
Arbuscular mycorrhizal (AM) fungi are obligate biotrophs whose molecular biology has long been difficult to investigate due to limited genomic resources and the occurrence, in experimental samples, of a high quantity of host plant material. High-throughput short-read RNA sequencing has become a key approach for characterizing AM fungal transcriptomes, but requires tailored bioinformatic strategies to overcome challenges such as mixed-species datasets, low fungal RNA abundance, and the absence of high-quality reference AM fungal genomes. This chapter presents a robust pipeline for AM fungal transcriptome analysis based on short-read RNA data, combining de novo assembly, functional annotation, and differential expression analysis. The workflow is applicable both to host-free systems, where fungal reads can be directly assembled, and to symbiotic conditions, where preprocessing steps are necessary to remove plant-derived reads and enrich for fungal transcripts. Methods to check read quality, remove contaminants, including the host, assemble and annotate transcriptome using homology- and domain-based tools are described. Finally, procedures for robust statistical analysis of differential gene expression are outlined, enabling the identification of molecular pathways involved in fungal development, nutrient exchange, and symbiotic function. Together, these methods provide a comprehensive framework for generating reliable and meaningful insights into the transcriptomes of AM fungi.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


