High throughput sequencing unravels tomato-pathogen interactions towards a sustainable plant breeding.
Hortic Res
; 8(1): 171, 2021 Aug 01.
Article
em En
| MEDLINE
| ID: mdl-34333540
ABSTRACT
Tomato (Solanum lycopersicum) is one of the most economically important vegetables throughout the world. It is one of the best studied cultivated dicotyledonous plants, often used as a model system for plant research into classical genetics, cytogenetics, molecular genetics, and molecular biology. Tomato plants are affected by different pathogens such as viruses, viroids, fungi, oomycetes, bacteria, and nematodes, that reduce yield and affect product quality. The study of tomato as a plant-pathogen system helps to accelerate the discovery and understanding of the molecular mechanisms underlying disease resistance and offers the opportunity of improving the yield and quality of their edible products. The use of functional genomics has contributed to this purpose through both traditional and recently developed techniques, that allow the identification of plant key functional genes in susceptible and resistant responses, and the understanding of the molecular basis of compatible interactions during pathogen attack. Next-generation sequencing technologies (NGS), which produce massive quantities of sequencing data, have greatly accelerated research in biological sciences and offer great opportunities to better understand the molecular networks of plant-pathogen interactions. In this review, we summarize important research that used high-throughput RNA-seq technology to obtain transcriptome changes in tomato plants in response to a wide range of pathogens such as viruses, fungi, bacteria, oomycetes, and nematodes. These findings will facilitate genetic engineering efforts to incorporate new sources of resistance in tomato for protection against pathogens and are of major importance for sustainable plant-disease management, namely the ones relying on the plant's innate immune mechanisms in view of plant breeding.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article