Trends in Molecular Diagnosis and Diversity Studies for Phytosanitary Regulated Xanthomonas.
Vittoria CataraJaime CuberoJoël F PothierEran BosisClaude BragardEdyta ĐermićMaria C HolevaMarie-Agnès JacquesFrancoise PetterOlivier PruvostIsabelle RobèneDavid John StudholmeFernando TavaresJoana G VicenteRalf KoebnikJoana CostaPublished in: Microorganisms (2021)
Bacteria in the genus Xanthomonas infect a wide range of crops and wild plants, with most species responsible for plant diseases that have a global economic and environmental impact on the seed, plant, and food trade. Infections by Xanthomonas spp. cause a wide variety of non-specific symptoms, making their identification difficult. The coexistence of phylogenetically close strains, but drastically different in their phenotype, poses an added challenge to diagnosis. Data on future climate change scenarios predict an increase in the severity of epidemics and a geographical expansion of pathogens, increasing pressure on plant health services. In this context, the effectiveness of integrated disease management strategies strongly depends on the availability of rapid, sensitive, and specific diagnostic methods. The accumulation of genomic information in recent years has facilitated the identification of new DNA markers, a cornerstone for the development of more sensitive and specific methods. Nevertheless, the challenges that the taxonomic complexity of this genus represents in terms of diagnosis together with the fact that within the same bacterial species, groups of strains may interact with distinct host species demonstrate that there is still a long way to go. In this review, we describe and discuss the current molecular-based methods for the diagnosis and detection of regulated Xanthomonas, taxonomic and diversity studies in Xanthomonas and genomic approaches for molecular diagnosis.
Keyphrases
- climate change
- escherichia coli
- randomized controlled trial
- systematic review
- healthcare
- single molecule
- transcription factor
- risk assessment
- loop mediated isothermal amplification
- genetic diversity
- gene expression
- machine learning
- sensitive detection
- deep learning
- multidrug resistant
- electronic health record
- big data
- sleep quality
- circulating tumor
- life cycle
- case control