A new tandem repeat-based genotyping scheme for the global surveillance of Xanthomonas citri pv. mangiferaeindicae, an understudied bacterial pathogen of major importance to mango and cashew production

Abstract

Bacterial canker, caused by Xanthomonas citri pv. mangiferaeindicae (Xcm), is a disease that has a devastating impact on mango and cashew industries in many regions. Yet, despite its agricultural importance for these Anacardiaceae species, Xcm has been neglected. Little is known about its epidemiology, evolution and molecular interactions with host plants. The most relevant studies reporting its genetic structure were primarily based on amplified fragment length polymorphism (AFLP) data. This technique provides reliable assessments of the genetic relatedness among bacteria, but is limited in terms of interlaboratory comparisons. Alternative genotyping techniques are required to decipher the global epidemiology and geographic expansion of Xcm. Herein, we screened the genome of the Xcm strain CFBP1716 for tandem repeats. We developed and evaluated the performance of an optimized Multi Locus Variable number of tandem repeat Analysis (MLVA), targeting 16 tandem repeat loci primarily with large repeat units, i.e., minisatellites (MLVA-16). To achieve this, we genotyped a comprehensive collection of 152 Xcm strains, representative of the pathogen's worldwide genetic diversity, together with some reference strains of X. citri pv. anacardii, another genetically-related pathogen of Anacardiaceae. MLVA-16 allowed us to distinguish the two pathovars. Although MLVA-16 was slightly less discriminative than AFLP, the two derived datasets were strongly correlated, suggesting that MLVA-16 provides a good phylogenetic signal. Five clusters with some geographic coherence were delineated, based on discriminant analysis of principal components. The two major clusters grouped strains from multiple geographic origins. In contrast, all strains that have emerged on mango or cashew in West Africa grouped in one cluster, which did not contain any strains of different origin. MLVA-16 represents an opportunity to improve our understanding of the structure of Xcm populations, by sharing genotyping data. The MLVA-16 data generated in this study was deposited in a dedicated online database