HRM analysis provides insights on the reproduction mode and the population structure of Gnomoniopsis castaneae in Europe

Abstract

Gnomoniopsis castaneae is an emergent nut rot agent of chestnut in southern Europe. To elucidate its population genetics, three simple sequence repeat (SSR) and two hypervariable markers were developed and assessed through high‐resolution melting (HRM) analysis on 132 isolates collected from 10 sites in Italy, France and Switzerland. High allele diversity (ranging from 0.23 to 0.40 depending on site) and number of haplotypes (49) were observed. More than 70% of the molecular variance could be accounted among isolates within sites. Multilocus analysis showed absence of linkage disequilibrium, suggesting a predominant role played by sexual reproduction and random mating. Data analyses indicated the presence of at least two putative distinct subpopulations and this was confirmed by several approaches, including analysis of shared haplotypes, multivariate and Bayesian analyses. Based on data of allelic diversity, the possibility that the pathogen could have been introduced is discussed. This work assessed the genetic variability and the sexual strategies of G. castaneae in Europe, adding useful information on the epidemiology of this fungal plant pathogen.

The ecology of infection between a transmissive and a dead‐end host provides clues for the treatment of a plant disease

“…For instance, population studies demonstrated that bay leaves harbor a few dominant genotypes, which are persistent through time (Mascheretti et al 2009), precisely as expected when the host is epidemiologically relevant (Eyre et al 2013). In most infectious diseases, including SOD, infectious outbreaks are caused by highly transmissive genotypes or strains that are overly represented when compared to other genotypes (Berbegal et al 2013, Gramaje et al 2014, Sillo et al 2017). On the contrary, in substrates with nil or negligible epidemiological role, either a high turnover of genotypes occurs (e.g., in water and soil), or genetic structural variations causing a significant change in phenotype can be observed (e.g., in oaks; Eyre et al 2013Eyre et al , 2014.…”

“…, Sillo et al. ). On the contrary, in substrates with nil or negligible epidemiological role, either a high turnover of genotypes occurs (e.g., in water and soil), or genetic structural variations causing a significant change in phenotype can be observed (e.g., in oaks; Eyre et al.…”

Local epidemiology of the wood decay agent Laetiporus sulphureus in carob stands in Sicily

“…SSR-based markers are widely used and popular due to their high reproducibility and multiallelic nature, and their power for genetic characterization of populations of wood decay fungi has been demonstrated (Franzen, Vasaitis, Penttilä, & Stenlid, 2007;Gonthier et al, 2015;Maurice, Skrede, LeFloch, Barbier, & Kauserud, 2014;Travadon et al, 2012). The analysis of SSRs coupled with HRM is a robust and reproducible method (Ganopoulos, Argiriou, & Tsaftaris, 2011), as it has been successfully used in genotyping of plants (Distefano, Caruso, La Malfa, Gentile, & Wu, 2012;Xanthopoulou et al, 2014), and, more recently, of fungal pathogens (Sillo et al, 2017;Zambounis, Xanthopoulou, Karaoglanidis, Tsaftaris, & Madesis, 2016). The HRM genotyping of the ten SSR markers allowed to distinguish all isolates from one another.…”

Environmental Factors Driving the Recovery of Bay Laurels from Phytophthora ramorum Infections: An Application of Numerical Ecology to Citizen Science

“…The interest of forest pathologists in unraveling environmental factors driving plant diseases has been amplified in the last decades by the onset of relevant epidemics caused by emerging pathogens such as Phytophthora ramorum Werres, De Cock and Man in't Veld in Western North America, Heterobasidion irregulare Garbelotto and Otrosina, Hymenoscyphus fraxineus (T. Kowalski) Baral, Queloz and Hosoya, and Gnomoniopsis castaneae G. Tamietti in Europe, just to cite a few relevant examples [3][4][5][6][7][8][9][10][11][12]. The main environmental drivers underlying the success of such novel epidemics have often been identified through a numerical ecology approach, based on computational and multivariate statistical techniques suitable to deal with complex ecological datasets [10,11,[13][14][15][16][17][18][19][20][21][22].…”