Phylogeography and Population Structure Analysis Reveal Diversity by Gene Flow and Mutation in Ustilago segetum (Pers.) Roussel tritici Causing Loose Smut of Wheat

Abstract

Ustilago segetum (Pers.) Roussel tritici (UST) causes loose smut of wheat account for considerable grain yield losses globally. For effective management, knowledge of its genetic variability and population structure is a prerequisite. In this study, UST isolates sampled from four different wheat growing zones of India were analyzed using the second largest subunit of the RNA polymerase II ( RPB2 ) and a set of sixteen neutral simple sequence repeats (SSRs) markers. Among the 112 UST isolates genotyped, 98 haplotypes were identified. All the isolates were categorized into two groups ( K = 2), each consisting of isolates from different sampling sites, on the basis of unweighted paired-grouping method with arithmetic averages (UPGMA) and the Bayesian analysis of population structure. The positive and significant index of association (I A = 1.169) and standardized index of association (rBar D = 0.075) indicate population is of non-random mating type. Analysis of molecular variance showed that the highest variance component is among isolates (91%), with significantly low genetic differentiation variation among regions (8%) (F st = 0.012). Recombination (R m = 0) was not detected. The results showed that UST isolates have a clonal genetic structure with limited genetic differentiation and human arbitrated gene flow and mutations are the prime evolutionary processes determining its genetic structure. These findings will be helpful in devising management strategy especially for selection and breeding of resistant wheat cultivars.

Identification of Novel Microsatellite Markers to Assess the Population Structure and Genetic Differentiation of Ustilago hordei Causing Covered Smut of Barley

“…This clearly indicated a state of LD in both PZ and HZ populations, which might be due to the presence of epidemic isolates (i.e., isolates exhibiting strong competitive potential). These results are in line with the earlier reports of Lomholt et al (2001); Haubold and Hudson (2000), Morrison et al (2008), and Kashyap et al (2019). The current study provides the baseline information, and a large sample size coupled with ecology data of a wider geographical area in HZ and PZ is still required to validate the LD state of the U. hordei population.…”

“…GenAlEx 6.5 program (Peakall and Smouse, 2012) was used to determine polymorphic bands (%), the numbers of effective alleles (Ne), Nei's gene diversity index (H), and Shannon's information index (I). The polymorphic information content (PIC) value for each SSR marker was calculated using the formula mentioned by Kashyap et al (2019). POPGENE version 1.31 software (Yeh et al, 1999) was used to calculate the cumulative allelic diversity (Ht), mean allelic diversity within populations (Hs), the proportion of the total allelic diversity (Gst), and the gene flow (Nm) among populations.…”

“…However, in comparison to these markers, SSR emerged as a promising marker for DNA fingerprinting, genetic linkage mapping, quantitative trait locus (QTL) identification, population genetics, and evolutionary studies owing to their polymorphic attribute, excellent reproducibility, co-dominance, and omnipresence inside the genome (Ellegren, 2004;Kumar et al, 2012;Singh et al, 2014a;Rai et al, 2016). Recently, these markers have been explored for studying various phytopathogenic fungi, such as Ustilago segetum tritici (Kashyap et al, 2019), Tilletia indica (Sharma et al, 2018), Ustilago scitaminea (Zhou et al, 2008), Ustilago maydis (Jiménez-Becerril et al, 2018), Phytophthora infestans (Montarry et al, 2010), Fusarium udum (Kashyap et al, 2015), Fusarium pseudograminearum (Scott and Chakraborty, 2010), and Colletotrichum gloeosporioides (Moges et al, 2016). So far, a single report is available with a limited number of SSR markers for the analysis of genetic variation and population structure of U. hordei (Yu et al, 2016) and no comprehensive database of such markers are documented through complete genome-wide SSR mining.…”

Molecular Diversity, Haplotype Distribution and Gene Flow of Bipolaris Sorokiniana Fungus Causing Spot Blotch Disease in Different Wheat Growing Zones

“…In the current study, value of H d revealed high level of diversity in all the zones. It is worth to mention here that several haplotypes showed only a different at one site compared to their genetically closest haplotype, con rming an important role of mutation in creating haplotype diversity as also reported by earlier workers in case of different fungal crop pathogens (Kashyap et al 2019;Yang et al 2018;Brunner et al 2008). Moreover, presence of minimum number of recombination events in NWPZ, NEPZ and NHZ, re ects intragenic recombination, which may lead to variability in pathogen population in these zones.…”

Fungal Diseases of Wheat

“…Loose smut is a seedborne pathogen; on maturity, the kernel is filled with dark brown to black color teliospores. The life cycle starts when teliospores enter the ovary through feathery stigma during anthesis [8]. Under favorable conditions when a single spike is infected with two different races, then, it is possible for recombination and genetic diversity.…”