Cultural Characteristics, Genetic Diversity and Population Structure Analysis of an Emerging False Smut Pathogen Ustilaginoidea Virens Collected from Different Agro Climatic Zones of India

doi:10.21203/rs.3.rs-3758034/v1

Download PDF

Research Article

Cultural Characteristics, Genetic Diversity and Population Structure Analysis of an Emerging False Smut Pathogen Ustilaginoidea Virens Collected from Different Agro Climatic Zones of India

https://doi.org/10.21203/rs.3.rs-3758034/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Rice false smut (RFS) instigated by the fungus Ustilaginoidea virens, has emerged as one of the most devastating grain diseases in the mainstream of rice-planting regions worldwide. A total of fifty six distinct isolates of Ustilaginoidea virens collected from different states of India were characterized morphologically and also at molecular level. Isolates showed variation in the size of the spore, color of mycelium and shape of the hyphae. The spores were globose, irregularly round to elliptical and warty on the surface with diameters ranging from 4.12 to 6.34 µm. The maximum colony diameter was found in Telangana isolate Uv305 (66.00 mm) and minimum was in Uttar Pradesh isolate Uv214 (17.50 mm). Genetic Diversity was analyzed by using 12 SSR primers out of which 7 were found to be polymorphic The number of alleles per locus vary from 5 to10. The maximum number of polymorphism was shown by UvSSR97 followed by UVSSR79, UvSSR226 and Uv SSR276 while least polymorphism was observed in UvSSR107, and Uv163 primer. The Dendrogram generated based on polymorphic data revealed a considerable amount of diversity by grouping the isolates in five clusters. The PIC ranged from 0.079 to 0.900. The value of Shannon information index ranged from 2.99 to 4.52. The pairwise population fixation index (F_ST) value also indicated significant genetic variation among all compared geographical populations. All the isolates could be grouped into three groups according to their genetic structure confirmed through model based cluster algorithm using STRUCTURE v2.3.4. This in-depth understanding of U. virens population structure and diversity holds valuable implications for effectively managing rice false smut disease on a comprehensive scale.

U. virens

ITS

SSR

AMOVA

PcoA

Population structure

This study offers comprehensive insights into the morphology, genetic diversity, and population structure of U. virens strains sourced from various agro-climatic regions in India.
A total of 56 U. virens isolates, originating from 8 geographical populations, were categorized into three distinct genetic subgroups, revealing genetic variations both within and among geographical populations.

Rice false smut (RFS), caused by the fungus Ustilaginoidea virens, and has recently emerged as a highly destructive grain disease in rice cultivation areas around the globe. In the rice field, this disease can be detected on the panicle after grain filling stage. In the panicle, individual grains are converted into dense white mycelia which later converts into a whitish smut ball. As a result, the coloration undergoes a transformation from yellowish-orange to green, olive green, and ultimately to greenish-black smut balls. The prevalence of false smut disease in major rice-producing regions worldwide is a significant concern because it leads to substantial reductions in grain yield and lowers market value due to grain contamination (Rush et al., 2000; Singh and Pophaly, 2010). The high severity of the disease in field conditions can be attributed to the extensive cultivation of high-yield cultivars and hybrids, excessive use of chemical fertilizers, and an apparent shift in regional and global climatic patterns (Lu et al., 2009). Besides, the production of toxins in the infected grains, U. virens has the potential to cause health hazards in humans and animals (Zhang et al., 2014; Tsukui et al., 2014). In India, since 2001, the number of disease incidences are on the rise (Dodan & Singh 1996; Mandhare et al., 2008) leading to the grain yield loss ranging from 1.01 to 10.91% (Atia, 2004),while the disease prevalence of up to 85% has been recorded in different rice growing regions of India (Ladhalakshmi et al., 2012). False smut balls typically emerge approximately 20 days after the initial infection of rice panicle kernels during the rice plant's flowering stage. This infection results in one or more kernels on mature seeds of the plants being replaced by spherical, yellowish-green, velvety smut balls. When these smut balls rupture, they release powdery dark green spores (Atia, 2004). The climatic conditions that favor the occurrence of rice false smut include overcast weather, high relative humidity (> 95%), low temperatures (ranging from 25 to 30°C), water stress, and rainy days during the flowering period (Raji et al., 2016). Late sowing and the application of higher nitrogen doses also promote the disease development (Ahonsi et al., 2000). Besides, weather conditions like optimum temperature (31.36°C to 23.14°C), high relative humidity (88.85 to 73.50%), least rainfall (6.66 mm) and bright sunshine hours (6.20 hrs) were recorded more favorable for development of false smut in rice (Bhargava et al., 2018).

In India, the study of false smut has been limited due to challenges in artificially culturing and inoculating the pathogen. Traditional methods for categorizing U. virens fungal populations, involving morpho-physiological and biochemical techniques, are labor-intensive, time-consuming, and susceptible to environmental factors, making them less preferable. However, molecular markers, particularly DNA markers, offer a more precise means to analyze genetic variation at the molecular level (Weising et al., 1995). One such molecular technique, Random Amplified Polymorphic DNA (RAPD), is a PCR-based method capable of assessing genetic polymorphism in organisms without known genome sequence information. RAPD is widely employed to investigate genetic diversity across various species due to its high polymorphism, individual specificity, and stability across different environments (Du et al., 2011; Fernandez et al., 2002; Hu et al., 2012; Shang & Song 2005; Zhou et al., 2016).

Simple Sequence Repeat (SSR) markers are also extensively used in genetic studies, including fingerprinting, genetic diversity assessment, linkage mapping, quantitative trait locus (QTL) mapping, and population structure analysis because of their abundance, polymorphic nature, high repeatability, codominance, and wide distribution (Ellegren, 2004). SSRs have been recently applied in the study of various plant pathogens (Bouajila et al., 2007; Zhao et al., 2008; Zhou et al., 2016). Analyzing genetic variation within plant pathogen populations is essential for understanding the co-evolution within plant-pathogen systems. Such studies can provide insights into the pathogen's origins and patterns of dispersal influenced by environmental and human factors (Baite et al., 2014). This knowledge may open up alternative strategies for managing these pathogens (Van Belkum, 1994).

Hence, the aim of this study was to explore the morphological and molecular diversity within the U. virens population in different states in India. This exploration seeks to gain insights into the genetic makeup of the pathogen population in this region, with the ultimate goal of offering valuable data for breeding initiatives, epidemiological investigations, and enhanced disease management strategies.

Collection of diseased samples

A total of fifty-six false smut diseased samples were collected from eight different states of India (Uttar Pradesh, Uttarakhand, Haryana, Meghalaya, Odisha, Andhra Pradesh, Meghalaya, Telangana) as shown in Fig. 1.

Isolation, identification and maintenance of isolates

For the isolation of pathogen the infected rice smut balls collected from different location of India (Table 1), were surface sterilized for 2 min in 1% (v/v) sodium hypo chloride solution and subsequently rinsed three times with sterile distilled water and dried with the help of sterilized blotter paper. Then, the smut balls were halved (cut in two parts from the center) using a sterilized scalpel. Further, spore balls were picked up with the help of sterilized forceps and spores were dusted from the inner surface of the ball (preferably from the transition zone of the inner most white to the peripheral orange colored region) with the help of sterilized dissecting needle onto the Petri plates containing PSA medium (200 gL^− 1 potato, 20 gL^− 1 sucrose and 20 gL^− 1 agar in 1000 mL distilled water and 100 ppm ampicillin was added to check the growth of bacteria). The spores were dusted as a homogenous spread (Bashyal et al., 2021). The plates were incubated at 27 ± 1°C in a BOD incubator (Thermotech) for 7 days. To obtain pure culture a single white fluffy colony of the fungus was picked up by using sterilized needle and transferred in PSA slant. The purity of isolates was performed by single spore isolation (Choi et al., 1999). Pathogen was identified as U. virens based on morphological and cultural characteristics as described by Ikegami (1963) and Fu et al. (2012).

Table 1

Cultural and morphological characteristics of *Ustilaginoidea virens* isolates collected from different geographical regions submitted at NCBI
S. No.	Location	Name of Isolate	Accession Number	Coordinates	Radial growth at 14 days (mm)	Color of mycelium	Shape of mycelium	Elevation	Size of conidia/ chlamydospores (µm)
1.	Haryana	Uv110	OQ645518	29.0588° N, 76.0856° E	42.50	Cream white	Circular	Flat	4.18–4.79 (± 020)
2.	Haryana	Uv111	OQ645495	29.0588° N, 76.0856° E	38.50	Cream white	Circular	Flat	5.76–6.34 (± 0.46)
3.	Odisha	Uv314	OQ645525	20.2376° N, 84.2700° E	26.00	White	Irregular	Flat	4.68–4.97 (± 0.23)
4.	Odisha	Uv404	OQ645520	20.2376° N, 84.2700° E	41.00	Yellowish green	Circular	Flat	4.93–5.65 (± 0.29)
5.	Odisha	Uv319	OQ645542	20.2376° N, 84.2700° E	23.50	Cream white	Circular	Raised	5.61–6.04 (± 0.34)
6.	Odisha	Uv311	OQ645508	20.2376° N, 84.2700° E	26.00	Cream white	Circular	Flat	5.11–5.88 (± 0.35)
7.	Odisha	Uv403	OQ645515	20.2376° N, 84.2700° E	63.30	White	Circular	Raised	5.48–5.85 (± 0.43)
8.	Odisha	Uv317	OQ645496	20.2376° N, 84.2700° E	21.00	Cream white	Circular	Flat	5.28–5.77 (± 0.32)
9.	Odisha	Uv316	OQ645538	20.2376° N, 84.2700° E	23.00	Yellowish green	Irregular	Raised	4.70–5.39 (± 0.28)
10.	Odisha	Uv302	OQ645531	20.2376° N, 84.2700° E	63.50	White	Circular	Flat	4.99–5.78 (± 0.20)
11.	Odisha	Uv307	OQ645549	20.2376° N, 84.2700° E	19.50	Yellowish green	Irregular	Raised	5.12–5.84 (± 0.30)
12.	Odisha	Uv301	OQ645529	20.2376° N, 84.2700° E	31.00	White	Circular	Flat	5.41–5.83 (± 0.32)
13.	Tripura	Uv410	OQ645519	23.5639° N, 91.6761° E	37.00	Yellowish green	Irregular	Flat	4.27–5.07 (± 0.17)
14.	Uttar Pradesh	Uv106	OQ645498	27.5706° N, 80.0982° E	39.00	White	Circular	Raised	4.51–5.32 (± 0.36)
15.	Uttar Pradesh	Uv112	OQ645499	27.5706° N, 80.0982° E	38.02	White	Irregular	Raised	4.59–5.43 (± 0.23)
16.	Uttar Pradesh	Uv120	OQ645522	27.5706° N, 80.0982° E	24.50	Cream white	Circular	Flat	4.12–4.92 (± 0.28)
17.	Uttar Pradesh	Uv105	OQ645523	27.5706° N, 80.0982° E	45.00	White	Circular	Flat	4.13–4.66 (± 0.33)
18.	Uttar Pradesh	Uv113	OQ645524	27.5706° N, 80.0982° E	41.00	White	Circular	Raised	5.01–5.54 (± 0.40)
19.	Uttar Pradesh	Uv107	OQ645504	27.5706° N, 80.0982° E	65.50	Cream white	Irregular	Flat	5.46–5.84 (± 0.32)
20.	Uttar Pradesh	Uv205	OQ645528	27.5706° N, 80.0982° E	47.50	Cream white	Irregular	Flat	5.43–5.87 (± 0.32)
21.	Uttar Pradesh	Uv313	OQ645544	27.5706° N, 80.0982° E	25.50	Yellowish green	Irregular	Flat	5.16–5.86 (± 0.25)
22.	Uttar Pradesh	Uv103	OQ645545	27.5706° N, 80.0982° E	32.00	Yellowish green	Irregular	Flat	5.27–6.02 (± 0.42)
23.	Uttar Pradesh	Uv109	OQ645510	27.5706° N, 80.0982° E	42.00	White	Circular	Raised	4.93–5.48 (± 0.44)
24.	Uttar Pradesh	Uv115	OQ645516	27.5706° N, 80.0982° E	28.00	White	Circular	Flat	5.46–6.20 (± 0.29)
25.	Uttar Pradesh	Uv215	OQ645539	27.5706° N, 80.0982° E	63.50	Cream white	Circular	Flat	5.13–5.64 (± 0.26)
26.	Uttar Pradesh	Uv216	OQ645540	27.5706° N, 80.0982° E	19.00	Cream white	Circular	Flat	5.24–5.82 (± 0.21)
27.	Uttar Pradesh	Uv213	OQ645536	27.5706° N, 80.0982° E	20.50	Cream white	Irregular	Flat	4.58–5.43 (± 0.36)
28.	Uttar Pradesh	Uv224	OQ645537	27.5706° N, 80.0982° E	28.50	Cream white	Circular	Raised	4.39–5.27 (± 0.13)
29.	Uttar Pradesh	Uv414	OQ645535	27.5706° N, 80.0982° E	23.00	Cream white	Circular	Flat	5.63–5.96 (± 0.22)
30.	Uttar Pradesh	Uv214	OQ645534	27.5706° N, 80.0982° E	17.50	Yellowish green	Circular	Flat	4.98–5.46 (± 0.25)
31.	Uttar Pradesh	Uv202	OQ645533	27.5706° N, 80.0982° E	30.50	White	Circular	Flat	4.81–5.62 (± 0.34)
32.	Uttar Pradesh	Uv309	OQ645532	27.5706° N, 80.0982° E	28.00	Cream white	Irregular	Raised	4.78–5.44 (± 0.38)
33.	Uttar Pradesh	Uv315	OQ645548	27.5706° N, 80.0982° E	25.00	Yellowish green	Circular	Raised	5.87–6.19 (± 0.47)
34.	Uttar Pradesh	Uv116	OQ645512	27.5706° N, 80.0982° E	32.00	Yellowish green	Irregular	Raised	5.16–5.81 (± 0.31)
35.	Uttar Pradesh	Uv413	OQ645513	27.5706° N, 80.0982° E	21.50	White	Circular	Flat	5.33–5.84 (± 0.48)
36.	Uttarakhand	Uv108	OQ645514	27.5706° N, 80.0982° E	45.50	White	Irregular	Raised	4.97–5.76 (± 0.14)
37.	Meghalaya	Uv406	OQ645521	30.0668° N, 79.0193° E	21.00	Yellowish green	Circular	Flat	5.24–6.12 (± 0.21)
38.	Meghalaya	Uv208	OQ645516	25.4670° N, 91.3662° E	46.00	Cream white	Irregular	Flat	5.80–6.14 (± 0.34)
39.	Meghalaya	Uv402	OQ645517	25.4670° N, 91.3662° E	51.50	White	Circular	Flat	4.21–5.07 (± 0.16)
40.	Meghalaya	Uv210	OQ645502	25.4670° N, 91.3662° E	30.50	Yellowish green	Irregular	Raised	5.07–5.76 (± 0.45)
41.	Meghalaya	Uv211	OQ645503	25.4670° N, 91.3662° E	46.00	White	Circular	Raised	4.82–5.79 (± 0.28)
42.	Meghalaya	Uv212	OQ645500	25.4670° N, 91.3662° E	32.00	Yellowish green	Irregular	Flat	5.14–6.02 (± 0.30)
43.	Meghalaya	Uv101	OQ645526	25.4670° N, 91.3662° E	43.00	White	Circular	Raised	5.57–6.11 (± 0.33)
44.	Meghalaya	Uv321	OQ645547	25.4670° N, 91.3662° E	25.50	Yellowish green	Irregular	Flat	4.26–4.88 (± 0.25)
45.	Meghalaya	Uv206	OQ645505	25.4670° N, 91.3662° E	39.50	White	Irregular	Raised	4.56–4.98 (± 0.22)
46.	Meghalaya	Uv209	OQ645501	25.4670° N, 91.3662° E	33.00	White	Circular	Flat	4.32–4.87 (± 0.25)
47.	Meghalaya	Uv104	OQ645507	25.4670° N, 91.3662° E	40.00	White	Circular	Raised	5.24–5.71 (± 0.21)
48.	Meghalaya	Uv409	OQ645516	25.4670° N, 91.3662° E	23.00	Yellowish green	Irregular	Raised	4.57–5.20 (± 0.27)
49.	Telangana	Uv408	OQ645546	18.1124° N, 79.0193° E	23.00	Yellowish green	Irregular	Raised	5.67–6.14 (± 0.22)
50.	Telangana	Uv305	OQ645506	18.1124° N, 79.0193° E	66.00	White	Circular	Raised	4.66–5.61 (± 0.14)
51.	Telangana	Uv312	OQ645543	18.1124° N, 79.0193° E	21.00	Yellowish green	Irregular	Raised	5.70–6.22 (± 0.34)
52.	Telangana	Uv412	OQ645497	18.1124° N, 79.0193° E	26.00	White	Circular	Flat	4.65–5.46 (± 0.16)
53.	Telengana	Uv411	OQ645494	18.1124° N, 79.0193° E	31.50	Cream white	Circular	Flat	5.53–6.21 (± 0.28)
54.	Telangana	Uv318	OQ645530	18.1124° N, 79.0193° E	20.50	Yellowish green	Irregular	Raised	5.30–5.97 (± 0.31)
55.	Telangana	Uv401	OQ645509	18.1124° N, 79.0193° E	36.50	White	Circular	Flat	5.59–6.11 (± 0.28)
56.	Andhra Pradesh	Uv114	OQ645511	15.9129° N, 79.7400° E	36.00	Cream white	Irregular	Flat	5.67–6.03 (± 0.34)

CD (p0.05) of radial growth at14 days = 1.64; and SE (m) ± 0.58.

Morphological variability

All the isolates of U. virens obtained from diseased samples were sub-cultured on PSA medium in Petri plates for studying morphological variation. Petri plates containing PSA medium were inoculated in the center with actively growing 5 mm mycelial discs obtained from 2 weeks old culture of each isolate separately and incubated at 27 ± 1°C. Each isolate was replicated three times. Observations of colony diameter, colony color, and pattern of growth, were recorded 14 days after inoculation. The sporulation and size of the spore were also observed under the compound microscope at 40X magnification (Nikon eclipse).

Genetic variability analysis

DNA isolation

DNA was isolated using protocol described by Murray and Thompson (1980). Culturing of fifty six isolates were done in potato sucrose broth in conical flask incubated at 27 ± 1°C with constant shaking at 200 rpm for optimum growth. Mycelium was harvested after 15 days of growth and it was grounded to fine powder using the liquid nitrogen for DNA extraction. Estimation of DNA concentration was performed at 260 nm with a Nano drop spectrophotometer (GENOVA NANO). Working solution of 25–30 ng/µL of DNA was made and stored at -20°C for future use.

(a) Molecular identification & phylogenetic analysis based on internal transcribed spacer region

The ITS region of rDNA using PCR conditions with primers ITS1 and ITS4 (White et al., 1990) was performed to confirm the identity of the isolated fungus on molecular basis as U. virens.

Amplifications were performed in a total volume of 50µl reaction containing 25 µl master mix (Thermo Scientific), 2 µl each forward and reverse primer, 2 µL DNA, 19.5 µL deionized water. The DNA amplification was done in a thermal cycler (BioRad, USA) using the following PCR cycles. The first denaturation step of 2 minutes at 94°C, followed by 35 cycles of denaturation for 30 sec at 94°C, annealing for 2 min at 55°C, extension at 72°C for 2 min and final extension at 72°C for 10 min with holding temperature at 4°C. Reaction products (8 µL) were resolved by electrophoresis in 1.2% agarose gels stained with ethidium bromide in 1X TBE buffer at 100 V for 90 min.

The amplified product was given for sequencing to Eurofins Genomics India Pvt Ltd, Bangalore. The nucleotide sequences were subjected to BLAST analysis on (http://www.ncbi.nih.gov/ index.html). The sequence data were assembled and analyzed using the programmer, BIOEDIT version 7.0.5. Assembled sequences were submitted to GenBank at (NCBI) database and accession numbers were obtained as shown in Table 1. The sequence data of ITS1 & ITS4 of all the isolates were used to construct the dendrogram using MEGA 7.0 (Kumar et al., 2016).

(b) Genetic diversity based on SSR primers:

Twelve SSR primers (Bag et al., 2021; Zhou et al., 2004&2008) were taken to analyse the genetic diversity among the different isolates (Table 2). PCR amplifications were performed in a total volume of 50 µL reaction containing 25 µL master mix (Thermo), 2 µL each forward and reverse primer, 2 µL DNA, 19.5 µL deionized water. The DNA amplification was done in a thermal cycler (BioRad, USA) using the following PCR cycles. The first denaturation step of 2 minutes at 94°C, followed by 35 cycles of denaturation for 30 sec at 94°C, annealing for 2 min at different temperature (Table 2), extension at 72°C for 2 min and final extension at 72°C for 10 min with holding temperature at 4°C. Reaction products (8 µl) were resolved by electrophoresis in 1.2% agarose gels stained with ethidium bromide in 1X TBE buffer at 100 V for 90 min. Polymorphic DNA bands were scored as binary data, with the presence and absence of bands scored as 1 and 0, respectively. The polymorphism data were entered into the software package Numerical Taxonomy Multivariate Analysis System (NTSYS-pc, version 2.02, Exeter Biological Software, Setauket, NY) to establish a dendrogram using the unweighted pair-group method with arithmetic means (UPGMA) algorithm in the SAHN program.

Table 2

SSR Primers used for genetic diversity analysis in *Ustilaginoidea virens* taken for the study
Primer name	Sequence	Monomorphic/ Polymorphic	Tm (C)	No. of alleles
UvSSR97	AGGTGATGAAGGCGAGACAT	Polymorphic	56	10
	AAGGCAGCAACAAGACCAAG
UvSSR164	AATGAGGAGGAGTTGGCTGG	Polymorphic	56	7
	TGATGATGGTGTGATGGAGC
UvSSR79	ATCCATCACCATCACTGGC	Polymorphic	55	8
	CAGACCATCGCTTCTTACAACA
UvSSR163	CAAGGTGACTGACTGACGGA	Polymorphic	57	5
	TACCGAGGACGGAGCATAGA
UvSSR12	GGCTGTGGTCAACTGTCTCA	Monomorphic	57	-
	ACAACGAGGAAGCGAGGAA
UvSSR309	CTTCCGTCCGTCTTGGTCTA	Monomorphic	55	-
	TTGGTCTCTGGTGTGTTGC
UvSSR210	TCTTGTCTGGTCTGGTTGGA	Monomorphic	55	-
	CGCTGCTGTTCGTTGTGTT
UvSSR121	GAATGGAAGACTGTGACGGC	Monomorphic	57	-
	CTTACGCACCTCCGACTTGT
UvSSR199	TTGCCTCTGTCGTTCACCA	Monomorphic	57	-
	ACTGTGCTGTGCTGTGTGCT
UvSSR226	CCGACTTACTGAAGGTGACG	Polymorphic	55	8
	GAACTGGCTGCTGATGATTG
UvSSR276	GCATTCTGATTCTCTCTTGAC	Polymorphic	54	8
	GGACAACGCACAACACTAACA
UvSSR107	CCATCAGTCATCAGCCATCA	Polymorphic	54	5
	CGCCAAGAACAAGGAAGAAC

Population Genetic Analysis

The fixation index (F_ST) is a means of measuring population differentiation and genetic distance. The pairwise FSTs can be used as short-term genetic distances between populations, with the application of a slight transformation to linearize the distance with population divergence time. Here, F_ST values of pairwise population comparisons were calculated using Arlequin 3.1 (Peakall et al., 2012). Gene ALEx v 6.5.02 (Evanno et al., 2005) was used to study Principal coordinate analysis (PCoA) to detect the genetic differentiation among the populations. The population structure model was constructed using STRUCTURE 2.3.3 (Pritchard et al., 2000) software and 10 replications were run for each K. Each run was implemented with a burn period of 50,000 steps with 50,000 Monte Carlo Markow chain replicates. Each genotype was run for the range of genetic clusters from value of k = 1 to 10 by taking correlated allele frequency into account. The plateau of delta k was obtained by plotting LNPD values derived for each k. The final population was determined using structure Harvester Program (Peakall et al., 2012). The components of variance between isolates taken from different states were estimated using the analysis of molecular variance (AMOVA) using Gene ALEx v 6.5.02.

Cultural and morphological variability

All the isolates of U. virens produced well defined colonies on potato sucrose agar (PSA) medium. The culture on medium showed white, cream white, yellowish green with raised or flat colony morphology (Fig. 2). The shape of mycelial growth was circular or irregular, slight undulations, fluffy, compact and leathery. Chlamydospores were formed at the center or margin of the colony, which appeared orange or yellowish and later greenish in color (Table 1). Rate of mycelial growth was inconsistent as their growth patterns diverse from very slow, slow, moderate, to fast, and the color changed depending on maturity time. Color of Uv407, Uv411, Uv413, Uv414, Uv217, Uv113 changed from white to creamish after 14 days while the color of 10 isolates (Uv410, Uv404, Uv408, Uv212, Uv304, Uv411, Uv103, Uv412, Uv409, Uv319) remained yellowish green (Table 3).

The isolates showed much variation in colony growth diameter. The maximum radial growth measured after 14 days of inoculation was in Telangana isolate Uv305 (66.00mm) and minimum was observed in Uv214 (17.50 mm) from Uttar Pradesh. Significant variation in isolagtes was observed for size of conidia,(Table 1). The spore size also varied in different isolates. The conidia were spherical, hyaline and warty. The average size of conidia of different isolates of U. virens ranged from 4.12–6.34 µm (Table 1). The largest conidia were found in Haryana isolate Uv111 (5.76–6.34 µm) and the smallest in Uttar Pradesh isolate Uv120 (4.12–4.92µm). Nineteen Isolates (Uv106; Uv108; Uv112; Uv105; Uv402; Uv211; Uv314; Uv101; Uv206; Uv209; Uv305; Uv104; Uv403; Uv412; Uv109; Uv115; Uv302; Uv401; Uv301) formed white colonies whereas 17 isolates (Uv410; Uv404; Uv406; Uv210; Uv212; Uv321; Uv408; Uv103; Uv313; Uv312; Uv409; Uv116; Uv316; Uv214; Uv307; Uv315; Uv318) produced yellowish green colonies and 16 isolates produced cream white colonies as shown in Table 3. The shape of most of the colony was circular (23) and irregular (36). The texture of the colonies varied from flat (23) and raised (33) (Fig. 3)

Genetic variability analysis:

ITS based Identification and Phylogenetic analysis

BLAST analysis of the sequence obtained using ITS 1 and ITS4 primers confirmed that all the isolates taken for the study belonged to U. virens. Sequences were assembled and deposited in NCBI database. The accession numbers of all the isolates are provided in Table 1. Phylogenetic tree based on the ITS region among different isolates of Ustilaginoidea virens generated by Mega X v.7.0 shows two clusters (Fig. 4); Cluster 1 consists of maximum number of isolates from different states like Uttar Pradesh, Telangana, Odisha, Meghalaya, Haryana and Tripura. Further this cluster is subdivided into two subgroups. Subgroup I consist of all the isolates of Meghalaya except isolate Uv321 which formed a grouping with subgroup 2 isolates along with other isolates from different states. Subgroup I is the most divergent group as it contains isolates from all states except Andhra Pradesh and Uttarakhand.. In the main Cluster 2 only four isolates were grouped together, of which three from Uttar Pradesh (Uv 215, Uv216 andUv224) and one from Odisha (Uv404). Rhizoctonia solani (HG934415.1) was taken as an outgroup for the construction of phylogenetic tree.

Genetic diversity analysis using SSR markers

Genetic diversity in the pathogen was evaluated using 12 SSR primers out of which 7 primers showed polymorphism (Fig. S1). Genetic relationships of the U. virens of the isolates were studied by evaluating the SSR profile obtained. The Amplified fragments ranged in the size from 100 to 1200 bp. The number of alleles per locus vary from 5 to 10 (Table 3). The maximum number of polymorphism was shown by UvSSR97 followed by UVSSR79, UvSSR226 and Uv SSR276 while least polymorphism was observed in UvSSR107, and Uv163 primer.

Table 3

Grouping of isolates of *Ustilaginoidea virens* based on colony color
S. No.	Color of mycelium	Name of isolates
1.	White	Uv106, Uv108, Uv112, Uv105, Uv402, Uv113, Uv211, Uv314, Uv101, Uv206, Uv209, Uv305,Uv104, Uv403, Uv412, UV109, UV 115, Uv202, UV302, UV401, UV420
2.	Cream white	Uv110, Uv208, Uv120, Uv107, Uv205, Uv311, Uv111, Uv411, Uv114, Uv319, Uv317, Uv215, Uv216, Uv213, Uv224, Uv414, Uv309
3.	Yellowish green	Uv410, Uv404, Uv406, Uv210, Uv212, Uv321, Uv408, Uv103, Uv313, Uv312, Uv409, Uv116, Uv316, Uv214, Uv307, Uv315, Uv318

Each of the polymorphic fragments of U. virens isolates obtained were built in binary pairwise matrix with presence or absence of determined bands scored as 1 0r 0 respectively. Binary Matrices were analyzed by NTSYSPc (version 2.02, Exeter Biological Software, Setauket, NY). The Jaccard similarity coefficients were used to generate a cluster Dendrogram using SAHN clustering programme selecting NTSYS (Fig. 5). The similarity coefficient ranged from 0.79 to 1.00. The Dendrogram constructed through the NTSYS analysis group the isolates into five major Clusters; Cluster 1 comprises a substantial group of 21 isolates, with 12 of them originating from Uttar Pradesh (Uv215, Uv120, Uv116, Uv107, Uv313, Uv315, Uv309, Uv213, Uv216, Uv214, Uv103, and Uv115). Additionally, 4 isolates come from Odisha (Uv404, Uv302, Uv307 and Uv316), 2 from Telangana (Uv 408 & Uv411), one from each Tripura (Uv410), Meghalaya (Uv409) and Uttarakhand (Uv108). In Cluster 2, there are 18 isolates forming a group, including 7 from Uttar Pradesh (Uv109, Uv205, Uv112, Uv105, Uv224, Uv413, and Uv414), 4 from Meghalaya (Uv210, Uv211, Uv406, Uv212), 4 from Odisha (Uv311, Uv317, Uv319, Uv314), 2 from Telangana (Uv401 and Uv312), and 1 from Haryana (Uv110). Cluster 3 consists of only six isolates, with 2 from Meghalaya (Uv104 and Uv206), and one each from Telangana (Uv305), Odisha (Uv403), Andhra Pradesh (Uv114), and Haryana (Uv111). Similarly, cluster 4 includes 7 isolates, originating from Uttar Pradesh (Uv106, Uv113), Meghalaya (Uv208, Uv209, Uv402), Telangana (Uv318), and Odisha (Uv301). The remaining four isolates form cluster 5, with representatives from Uttar Pradesh (Uv202), Meghalaya (Uv321 and Uv101), and Telangana (Uv412).

Population genetic structure

The population structure analysis using a model-based tool grouped 56 U. virens isolates into three major groups. According to the principle that close location indicates a close relationship and distant location indicates a distant relationship, a PCoA of all isolates was conducted. The PCoA also divided all of the isolates into three major groups (Fig. 6a). The first four principal coordinates accounted for 86.62% (16.89%, 30.86%, and 38.87% by first, second, and third principal coordinate, respectively) of the total variation. Both the cluster analysis and PCoA indicated diversity among the 56 U. virens isolates collected from different states of India. The PCoA showed that the U. virens were clustered according to the origin. Genetic differences among isolates with in populations and between different populations were confirmed through model based cluster algorithm using STRUCTURE v2.3.4. According to the principle of maximum likelihood value, the k value 3 was selected as the number of populations, therefore all the isolates could be grouped into three groups according to their genetic structure (Fig. 6b and c).

The AMOVA was conducted for studying the total variations among the populations within the group and among the isolates within the populations. Higher variance (88%) was detected within populations/individuals and less variance (12%) was observed among populations (Fig. 7)

Rice false smut stands as a significant menace in regions where rice cultivation thrives globally, severely impacting both rice yield and its quality. Previous research have primarily focused on aspects such as the infection processes (Hu et al., 2014; Tang et al., 2013), mycotoxins (Sun et al., 2017; Meng et al., 2019), and pathogenic factors (Fu et al., 2022; Zhang et al., 2022) within the primary causative agent of rice false smut, U. virens. However, there remains a noteworthy gap in knowledge concerning the organized exploration of the pathogen's biological characteristics and genetic structure. Such insights hold immense potential for illuminating effective strategies for disease management and resistance breeding. In light of this, the current study aims to study into the morphology, genetic diversity, and population structure of this organism.

Exploring the morphology and growth characteristics of U. virens holds significant importance in enhancing our understanding of the persistent presence of rice false smut and in devising effective control strategies against this disease (Sharanabasav et al., 2021). A comprehensive grasp of genetic diversity and the exploration of population structure among plant pathogens serve several critical purposes, including delineating evolutionary relationships, identifying potential sources for marker-assisted selection, and optimizing disease management through the judicious use of suitable fungicides (Ciampi et al., 2011; Marulanda et al., 2014, McDonald and Linde, 2002).

Rice false smut disease was more or less random in nature until 2000, with a history of traces to little strength of disease, and very a small amount of places with modest to strict intensity in the rice-growing states of Assam and Bihar in eastern India. The disease has since strengthened, turning into a main disease in different parts of Assam, Bihar, Odisha, and West Bengal between 2001 and 2014 (Laha et al., 2016). Understanding the morphological description of the pathogens will provide a theoretical basis to form optimal disease control strategies. Diverse geographical regions in diverse rice cultivars have formed numerous colony and chlamydospore characteristics in culture media. The colony color in most of the strains of U. virens changes liable on maturity time, being initially white and changing to yellow. The rate of mycelial growth is mutable, as the growth patterns range from very slow to slow, moderate, and fast.

In the present study morphological characterstics for the 56 isolates varied with respect to their growth rate, period to transformation from mycelia to chlamydospore-producing stage, color, and texture of colony. Distinct mycelial growth and initiation of sporulation was observed among the isolates. Color of Uv407, Uv411, Uv413, Uv414, Uv217, Uv113 changed from white to creamish after 14 days and color of Uv410, Uv404, Uv408, Uv212, Uv304, Uv411, Uv103, Uv412, Uv409, Uv319 does not changed after 14 days and remained yellowish green. The maximum radial growth of mycelium was found in UV305 (66.00mm) and minimum was in Uv214 (17.50 mm). The largest conidia were found in Haryana isolate Uv111 (5.76–6.34µm) and the smallest in Uttar Pradesh isolate Uv120 (4.12–4.92µm).These results agreed with Ladhalakshmi et al. (2012) and Baite and Sharma (2015).

In recent years, the advancement of molecular techniques has greatly propelled research into the genetic diversity of plant pathogenic fungi. Numerous molecular markers have been identified and proven useful in investigating the population genetic structure and genetic similarity of these fungi. These markers encompass methods such as Random Amplified Polymorphic DNA (RAPD), Repetitive Extragenic Palindromic PCR (Rep-PCR), Amplified Fragment Length Polymorphism (AFLP), Simple Sequence Repeats (SSRs), and Single-Nucleotide Polymorphisms (SNPs). Among these, SSR markers are widely employed for assessing the genetic makeup of pathogens due to their higher numbers, polymorphic nature, excellent repeatability, codominance, and widespread occurrence.

In this study, we utilized SSR markers to assess genetic diversity among various isolates of false smut pathogens primarily collected from eight different states of India. Our aim was to determine if any population structure was correlated with geographical origin. Furthermore, these results offer valuable insights into the patterns of occurrence and effective control of rice false smut disease. The number of alleles detected per marker ranged from five to ten. The Shannon information index exhibited values ranged from 2.99 to 4.52. The pairwise population fixation index (F_ST) value also indicated significant genetic variation among all compared geographical populations. SSR markers have likewise been used in genetic analysis of the population structure of V. virens (teleomorph of false smut pathogen) by Jia et al. (2015) in China. Moreover, studies on the biological characteristics and genetic structure are valuable for improving the strategies for disease management and resistance breeding; yet, insufficient is known regarding these topics. Hauser and Wang (2005) reported the ITS1 and ITS4 sequence may vary significantly at the sequence level but high level of conservation at the structural level. A Morphological and growth characteristics study of U. virens was important for understanding the occurrence of Rice false smut disease and their dominance in India.

In earlier research, the U. virens strains were categorized into two genetic clusters using the STRUCTURE tool (Sun et al., 2013; Jia et al., 2015; Bag et al., 2021). However, in our current study, we have classified 56 U. virens isolates from eight different geographical populations into three distinct genetic subgroups. Isolates belonging to North Eastern plain zone and Northern India exhibit a close genetic relationship. The clustering pattern can be attributed to the morphological variation observed among isolates irrespective of their geographical collection sites. These findings were also supported by the AMOVA and PCoA. The AMOVA revealed the presence of 88% variation within populations and only 12% variation among populations. This study will help us to understand the genetic structure of the pathogen population in the region with a view to provide useful information for breeding programs, epidemiological studies and improved disease management practices. Further, it is necessary to collect more isolates covering more states and combine the pathogenicity analysis to determine the relationship between genetic variation and pathotypes.

In conclusion, this study offers comprehensive insights into the morphology, genetic diversity, and population structure of U. virens strains sourced from various agro-climatic regions in India. The morphological characteristics of U. virens strains collected from North, North Eastern, and Southern regions exhibited notable diversity, highlighting clear distinctions among the fungi. A total of 56 U. virens isolates, originating from 8 geographical populations, were categorized into three distinct genetic subgroups, revealing genetic variations both within and among geographical populations. This in-depth understanding of U. virens' population structure and diversity holds valuable implications for effectively managing rice false smut disease on a comprehensive scale.

Acknowledgements: The authors are thankful to the Head, Division of Plant Pathology for providing facilities. This work is funded by Science and Engineering Research Board, Government of India (SERB project CRG/2021/004204)

Authors contribution: Conceptualization of the study: BMB& GKS; Guided data analysis and interpretation: BMB; Performed the experiments and data analysis: GKY, CC, SS, MAS; Drafted the manuscript and edited critically: BMB, SS, GKY, MAS, AKG, SKS, CC. All authors edited and approved the manuscript for submission.

Ethics Approval: Not applicable

Competing Interests: The authors declare no competing interests.

Data Availability Statement: The sequence data for phylogenetic study has been submitted in the NCBI database for all the isolates taken for the study and the accession number for the same has been provided in the manuscript (table1). Binary data for the polymorphism analysis using SSR markers are available from the corresponding author upon reasonable request.

Ahonsi, M. O., Adeoti, A. A., Erinle, I. D., Alegbejo, T. A., Singh, B. N., & Sy. A. A. (2000). Effect of variety and sowing date on false smut incidence in upland rice in Edo state, Nigeria. International Rice Research Notes, 25, 14. https://doi.org/10.5281/zenodo.7001956
Atia, M. M. M. (2004). Rice false smut Ustilaginoidea virens in Egypt. Journal of Plant Disease and Protection, 111, 71–82. http://dx.doi.org/10.1007/BF03356134
Bag, M. K., Ray, A., Masurkar, P., Devanna, B. N., Parameswaran, C., Baite, M., Rath, P. C., Nayak. (2021). A Genetic diversity and population structure analysis of isolates of the rice false smut pathogen Ustilaginoidea virens in India. Plant Pathology, 70, 1085–1097. https://doi.org/10.1111/ppa.13352
Baite, M. S., Sharma, R. K., Devi, T. P., Sharma, P., & Kamil, D. (2014). Morphological and molecular characterization of Ustilaginoidea virens isolates causing false smut of rice in India. Indian Phytopathology, 67, 222–227. http://dx.doi.org/10.58537/jorangrau.2023.51.2.02
Baite, M. S., & Sharma, R. K. (2015). Isolation technique and culture conditions of false smut pathogen Ustilaginoidea virens of rice. Indian Phytopathology, 68(1), 50–55.
Bashyal, B. M., Parmar, P., Zaidi, N. W., Sunani, S. K., Prakash, G., & Aggarwal, R. (2021). Improved methodology for the isolation of false smut pathogen Ustilaginoidea virens of rice. Indian Phytopathology, 74(1), 249–252. http://dx.doi.org/10.1007/s42360-020-00282-3
Bhargava, P., Kumar, A., & Kumar, S. (2018). Epidemiological studies of false smut disease of rice (Ustilaginoidea virens) in Bihar. Journal of Pharmacognosy and Phytochemistry, 7(1), 1537–1540.
Bouajila, A., Abang, M. M., Haouas, S., Udupa, S., Rezgui, S., & Baum, M. (2007). Genetic diversity of Rhynchosporium secalis in Tunisia as revealed by path type, AFLP, and microsatellite analyses. Mycopathologia, 163, 281–294. https://doi.org/10.1007/s11046-007-9012-0
Choi, Y. W., Hyde, K. D., & Ho, W. (1999). Single spore isolation of fungi. Fungal Diversity, 3, 29–38.
Ciampi, M. B., Baldauf, C., Vigna, B. B. Z., Souza, A. P., Sposito, M. B., & Amorim, L. (2011). Isolation and characterization of microsatellite loci in Colletotrichum acutatum, the causal agent of post-bloom fruit drop on citrus. Conservation Genetics Resources, 3, 651–654. https://doi.org/10.1007/s12686-011-9425-4
Devi, T. K., & Singh, N. I. (2007). Aerobiology and epidemiology of false smut disease of rice by Ustilaginoidea virens (Syn. Clariceps oryzae-satirae) in Thoubal district Manipur. Journal of Mycopathology Research, 45(1), 107–108.
Dodan, D. S., & Singh, R. (1996). False smut of rice: present status. Agricultural Research, 17(4), 227–240.
Du, Y. D., Xing, M., Yang, Z. Y., Liu, Y. F., & Chen, X. (2011). Genetic diversity caused by environmental stress in natural populations of Niupidujuan as revealed by RAPD technique. Plant Ecology, 27, 641–645.
Ellegren, H. (2004). Microsatellites: simple sequences with complex evolution. Nat Rev Genet, 5, 435–445. https://doi.org/10.1038/nrg1348
Evanno, G., Regnaut, S., & Goudet, J. (2005). Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology, 14, 2611–2620. DOI: 10.1111/j.1365-294X.2005.02553.x
Excoffier, L., Laval, G., & Schneider, S. (2005). Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evolutionary Bioinformatics Online, 1, 47–50.
Fernandez, M. E., Figueiras, A. M., & Benito, C. (2002). The use of ISSR and RAPD markers for detecting DNA polymorphism, genotype identification and genetic diversity among barley cultivars with known origin. Theoretical and Applied Genetics, 104, 845–851. https://doi.org/10.1007/s00122-001-0848-2
Fu, R., Ding, L., Zhu, J., Li, P., & Zheng, A. (2012). Morphological structure of propagules and electrophoretic karyotype analysis of false smut Villosiclava virens in rice. Journal of Microbiology, 50(2), 263–269. DOI: 10.1007/s12275-012-1456-3
Fu, R. T., Chen, C., Wang, J., Liu, Y., Zhao, L. Y., & Lu, D. H. (2022). Transcription Profiling of Rice Panicle in response to Crude Toxin Extract of Ustilaginoidea virens. Frontiers in Microbiology, 13, 701489. https://doi.org/10.3389/fmicb.2022.701489
Hu, M. L., Luo, L. X., Wang, S., Liu, Y. F., & Li, J. Q. (2014). Infection processes of Ustilaginoidea virens during artificial inoculation of rice panicles. European Journal of Plant Pathology, 139, 67–77. https://doi.org/10.1007/s10658-013-0364-7
Hu, Z. H., Zhao, L. H., Kong, B. H., Chen, H. R., Fan, J. H., Liu, F., et al. (2012). Comparison and analysis of molecular methods ISSR and RAPD used for assessment of genetic diversity in Alternaria alternata Southwest China. Journal of Agricultural Science, 3, 917–921.
Ikegami, H. (1963). Occurrence and development of sclerotia of the rice false smut fungus. Research Bulletin of the Faculty of Agriculture, 18, 47–53.
Jia, Q., Gu, Q., Zheng, L., Hsiang, T., Luo, C., & Huang, J. (2015). Genetic analysis of the population structure of the rice false smut fungus, Villosiclava virens, in China using microsatellite markers mined from a genome assembly. Plant Pathology, 64, 1440–1449. https://doi.org/10.1111/ppa.12373
Kumar, S., Stecher, G., & Tamura, K. (2016). MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets, Molecular Biology and Evolution, 33(7), 1870–1874. https://doi.org/10.1093/molbev/msw054
Ladhalakshmi, D., Laha, G. S., Singh, R., Karthikeyan, A., Mangrauthia, S. K., Sundaram, R. M., Thukkaiyannan, P., & Viraktamath, B. C. (2012). Isolation and characterization of Ustilaginoidea virens and survey of false smut disease of rice in India. Phytoparasitica, 40(2), 171–176. https://doi.org/10.1007/s12600-011-0214-0
Li, S. C., Li, W. B., Huang, B., Cao, X. M., Zhou, X. Y., Ye, S. M., et al. (2013). Natural variation in PTB1 regulates rice seed setting rate by controlling pollen tube growth. Nature Communications, 4, 2793. https://doi.org/10.1038/ncomms3793
Lu, D. H., Yang, X. Q., Mao, J. H., Ye, H. L., Wang, P., Chen, Y. P., He, Z. Q., & Chen, F. (2009). Characterizing the pathogenicity diversity of Ustilaginoidea virens in hybrid rice in China. Journal of Plant Pathology, 91(2), 443–451. https://doi.org/10.4454/JPP.V91I2.976
Mandhare, V. K., Gawade, S. B., Game, B. C., & Padule, D. N. (2008). Prevalence and incidence of bunt and false smut in paddy (Oryza sativa L.) Seeds in Maharashtra. Agricultural Science Digest, 28(4), 292–294.
Marulanda, M. L., Lopez, A. M., Isaza, L., & Lopez, P. (2014). Microsatellite isolation and characterization for Colletotrichum spp., causal agent of anthracnose in Andean blackberry. Genetics and Molecular Research, 13, 7673–7685. 10.4238/2014.September.26.5
Mathew, S. B., Sharma, R. K., Devi, T. P., Sharma, P., & Kamil, D. (2014). Morphological and molecular characterization of Ustilaginoidea virens isolates causing false smut of rice in India. Indian Phytopathology, 67(3), 222–227.
McDonald, B. A., & Linde, C. (2002). Pathogen population genetics, evolutionary potential, and durable resistance. Annual Review of Phytopathology, 40, 349–379. 10.1146/annurev.phyto.40.120501.101443.
Meng, J., Gu, G., Dang, P., Zhang, X., Wang, W., Dai, J., Liu, Y., Lai, D., & Zhou, L. (2019). Sorbicillinoids from the fungus Ustilaginoidea virens and their phytotoxic, cytotoxic, and antimicrobial activities. Frontiers in Chemistry, 7, 435. https://doi.org/10.3389/fchem.2019.00435
Murray, H. G., & Thompson, W. F. (1980). Rapid isolation of high molecular weight DNA. Nucleic Acids Research, 8, 4321–4325. https://doi.org/10.1093%2Fnar%2F8.19.4321
Peakall, R., & Smouse, P. E. (2012). GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics, 28, 2537–2539. http://dx.doi.org/10.1093/bioinformatics/bts460
Pritchard, J. K., Stephens, M., & Donnelly, P. (2000). Inference of population structure using multilocus genotype data. Genetics, 155, 945–959. doi: 10.1093/genetics/155.2.945.
Raji, P., Sumiya, K. V., Dhanya, S., Remya, K., & Narayanankutty, M. C. (2016). Screening of rice varieties and in vitro evaluation of botanicals against false smut pathogen, Ustilaginoidea virens. International Journal of Agricultural Science Research, 6(2), 79–86.
Rani, R., & Sharma, V. K. (2018). Diversity analysis of northern Indian isolates of Ustilaginoidea virens. Indian Phytopathology, 71(1), 33-42. http://dx.doi.org/10.1007/s42360-018-0004-4
Rush, M. C., Shahjahan, A. K. M., Jones, J. P. (2000). Outbreak of false smut of rice in Louisiana. Plant Disease, 84(1), 100. 10.1094/PDIS.2000.84.1.100D
Shang, H. M., & Song, W. B. (2005). Separation and relationship of ten marine scuticociliates (Protozoa, Ciliophora) using RAPD fingerprinting method. Acta Oceanologica Sinica, 23, 78–85.
Sharanabasav, H., Pramesh, D., Prasannakumar, M. K., Chidanandappa, E., Yadav, M. K., Ngangkham, U., Parivallal, B., Raghavendra, B. T., Manjunatha, C., Sharma, S. K., et al (2021). Morpho-molecular and mating-type locus diversity of Ustilaginoidea virens: An incitant of false smut of rice from Southern parts of India. Journal of Applied Microbiology, 131, 2372–2386. https://doi.org/10.1111/jam.15087
Song, J. H., Wei, W., Lv, B., Lin, Y., Yin, W. X., Peng, Y. L., et al (2016). Rice false smut fungus hijacks the rice nutrients supply by blocking and mimicking the fertilization of rice ovary. Environmental Microbiology, 18, 3840–3849. 10.1111/1462-2920.13343
Sun, X. Y., Kang, S., Zhang, Y. J., Tan, X. Q., Yu, Y. F., He, H. Y., Zhang, X. Y., Liu, Y. F., Wang, S., & Sun, W. X. (2013). Genetic Diversity and Population Structure of Rice Pathogen Ustilaginoidea virens in China. PLoS ONE, 8(9), e76879. https://doi.org/10.1371/journal.pone.0076879
Sun, W., Wang, A., Xu, D., Wang, W., Meng, J., Dai, J., Liu, Y., Lai, D., & Zhou, L. (2017). New Ustilaginoidins from rice false smut balls caused by Villosiclava virens and their phytotoxic and cytotoxic activities. Journal of Agricultural and Food Chemistry, 65, 5151–5160. doi: 10.1021/acs.jafc.7b01791.
Tang, Y. X., Jin, J., Hu, D. W., Yong, M. L., Xu, Y., & He, L. P. (2013). Elucidation of the infection process of Ustilaginoidea virens (teleomorph: Villosiclava virens) in rice spikelets. Plant Pathology, 62(1), 1–8. https://doi.org/10.1111/j.1365-3059.2012.02629.x
Tsukui, T., Nagano, N., Umemura, M., Kumagai, T., Terai, G., Machida, M., & Asai, K. (2014). Ustiloxins, fungal cyclic peptides, are ribosomally synthesized in Ustilaginoidea virens. Bioinformatics, 31(7), 981–985. https://doi.org/10.1093/bioinformatics/btu753
Van Belkum, A. (1994). DNA fingerprinting of medically important microorganisms by use of PCR. Clinical Microbiology Reviews, 7(2), 174–184. doi/10.1128/cmr.7.2.174
White, T. J., Burns, T., Lee, S., & Taylor, J. (1990). Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetic. In M. Innis, D. Gelfand, J. Sinsky, & T. White (Eds.), PCR Protocols: A Guide to Methods and Applications (pp. 315–322). Academic Press.
Zhang, Y., Zhang, K., Fang, A., Han, Y., Yang, J., Xue, M., Bao, J., Hu, D., Zhou, B., Sun, X., & Li, S. (2014). Specific adaptation of Ustilaginoidea virens occupying host florets revealed by comparative and functional genomics. Nature Communications, 5, s3849. https://doi.org/10.1038/ncomms4849
Zhang, N., Yang, J., Fang, A., Wang, J., Li, D., Li, Y., Wang, S., Cui, F., Yu, J., Liu, Y., et al (2020). The essential effector SCRE1 in Ustilaginoidea virens suppresses rice immunity via a small peptide region. Molecular Plant Pathology, 21, 445–459. doi/10.1111/mpp.12894
Zhou, Y., Chao, G. M., Liu, J. J., Zhu, M. Q., Wang, Y., & Feng, B. L. (2016). Genetic diversity of Ustilago hordei in Tibetan areas as revealed by RAPD and SSR. Journal of Integrative Agriculture, 15(11), 2299–2308. DOI:10.1016/S2095-3119(16)61413-2
Zhou, Y. L., Pan, Y. J., Xie, X. W., Zhu, L. H., Xu, J. L., Wang, S., et al (2008). Genetic diversity of rice false smut fungus, Ustilaginoidea virens and its pronounced differentiation of populations in north China. Journal of Phytopathology, 156(9), 559–564. doi/10.1111/j.1439-0434.2008.01387.x

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Cultural Characteristics, Genetic Diversity and Population Structure Analysis of an Emerging False Smut Pathogen Ustilaginoidea Virens Collected from Different Agro Climatic Zones of India

Status:

Version 1

Abstract

Figures

Key message

Introduction

Methods

Collection of diseased samples

Isolation, identification and maintenance of isolates

Morphological variability

Genetic variability analysis

DNA isolation

(a) Molecular identification & phylogenetic analysis based on internal transcribed spacer region

(b) Genetic diversity based on SSR primers:

Population Genetic Analysis

Results

Cultural and morphological variability

Genetic variability analysis:

ITS based Identification and Phylogenetic analysis

Genetic diversity analysis using SSR markers

Population genetic structure

DISCUSSION

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1