Phylogeographic population diversity of Bipolaris sorokiniana ...

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Phylogeographic population diversity of Bipolarissorokiniana causing spot blotch disease in wheatand barleyPradeep Sharma ( [email protected] )

ICAR - Indian Institute of Wheat and Barley Research https://orcid.org/0000-0003-2099-1959Shefali Mishra

ICAR - Indian Institute of Wheat and Barley ResearchGarima Singroha

ICAR - Indian Institute of Wheat and Barley ResearchRajan Selvakumar

ICAR Sugarcane Breeding InstituteSanjay Kumar Singh

ICAR - Indian Institute of Wheat and Barley ResearchGyanendra Pratap Singh

ICAR - Indian Institute of Wheat and Barley Research

Research Article

Keywords: B. sorokiniana, ITS, GAPDH, TEF-α1, Demographic history, Genetic diversity, Wheat, Barley

Posted Date: May 16th, 2022

DOI: https://doi.org/10.21203/rs.3.rs-1648271/v1

License: This work is licensed under a Creative Commons Attribution 4.0 International License. ReadFull License

https://doi.org/10.21203/rs.3.rs-1648271/v1

mailto:[email protected]

https://orcid.org/0000-0003-2099-1959

https://doi.org/10.21203/rs.3.rs-1648271/v1

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AbstractBackground

The spot blotch disease caused by Bipolaris sorokiniana is economically important fungal pathogeninfecting wheat, barley and other crops. The disease is most prevalent in humid, warm wheat-growingregions and the Eastern Gangetic Plains of South Asia serving as a hotspot. very scanty and fragmentaryinformation is available on its genetic variability, demography, and divergence period. The current study isthe �rst detailed report to compare the phylogeographic patterns of B. sorokiniana isolates collected fromdifferent wheat and barley growing regions worldwide, with the goal of elucidating the demographichistory and divergence time estimates.

Results

In this study, we used multi-locus sequence (ITS, GAPDH and TEF-α1 genes) analysis to investigate thephylogeographic pattern of distribution and evolution of the 254 isolates of B. sorokiniana infecting wheatand barley . The degrees of differentiation among B. sorokiniana sequences from eighteen countries re�ectthe presence of a worldwide population that is broad and geographically undifferentiated. The analysisyielded fourty haplotypes. The ancestral haplotype was determined to be the dominant haplotype H_1followed by H_29 and H_27, in which H_1 occupies a central position in the median-joining network and isshared by multiple populations from different continents. The phylogeographic patterns of multigeneanalysis of species as well as the preponderance of a single haplotype, suggest that human-mediateddispersal may have had a signi�cant role in shaping the population of this pathogen.

Conclusions

In conclusion, the presence of one haplotype at a very high frequency and low genetic differentiation in B.sorokiniana suggests that the durability of resistance in host plants against pathogens could be improvedby exchange of elite resistance lines from different countries. For the current distribution of species,divergence time analysis suggests that haplogroups began at the Plio/Pleistocene boundary.

IntroductionSpot blotch of wheat and barley caused by Dothideomycetes fungus Bipolaris sorokiniana (Sacc.) Shoem(teleomorph Cochliobolus sativus), is one of the major production constraint in warmer areas worldwide[Dublin et al. 1991; Gupta et al. 2018]. Initial stage infection develop as tiny, dark brown scratches that lateron turn into light to dark brown oval or elongated blotches. With the progression of disease theseelongated blotches join together to form necrotic lesions leading to cell death. Yield losses are reported tovary from 15.5 to 19.6% and may be up to 100% under congenial conditions in vulnerable genotypes,reducing the seed quality [Gupta et.al. 2018; Sharma and Duveiller 2006]. Spot blotch infection results intoshrivelled, malformed, discoloured and black pointed seeds affecting grain quality and trade. The fungushas been shown to infect wild as well as cultivated Poaceae species [Hyde et al. 2019]. The wide hostrange of B. Sorokiniana results in huge genetic variability of the pathogen [Verma et al. 2020; Pandey et al.

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2008; Iftikhar et al. 2001]. Because of its seed and soil borne nature, the spot blotch pathogen is di�cult tocontrol using fungicides. At the moment, host resistance is the only realistic and practicable strategy forreducing economic losses in wheat. Understanding the genetic variability of this pathogen will aid in thedevelopment of long-lasting resistant wheat varieties.

The high genetic variability among the fungal pathogens over a wide range of environments andinvolvement of minor genes in governing resistance the foliar blight or spot blotch breeding programremains a challenge for breeders. Considering the yield losses caused by the fungus B. sorokiniana,understanding its genetic diversity by molecular characterization is critical. There is a need to investigatethe diversity of B. sorokiniana isolates found in wheat and barley producing regions. This information willhelp the breeders in formulating the breeding strategies against spot blotch of wheat and barley. Theanalysis of ITS (internal transcribed spacer) region has been widely used in studying fungal phylogeneticrelationships [Scoch et al. 2012]. Their short length, rapid evolution rate and availability of universalprimers make the internal transcribed spacer (ITS1 and ITS2) as most extensively used molecular markersin plant systematic. Polymorphic ITS sequences are result of hybridization, recombination andpseudogenization of cistrons. ITS sequence polymorphism arising due to non-functional pseudogenes andcan be distinguished by secondary structure stability, conserved motifs, substitution rates andphylogenetic positions. Recently, Kashyap et al. [2021] studied molecular diversity, halptotype distributinand gene �ow of B. sorokiniana causing spot blotch disease in wheat using ITS sequences.

In the past several molecular techniques like cleaved ampli�ed polymorphic sequence (CAPS) and randomampli�ed polymorphic DNA [Jaiswal et al. 2000] have been performed for ITS region analysis for pathogencharacterization [Schoch et al. 2012; De Moura Nascimento et al. 2008]. B. Sorokiniana is a variablefungus due to somatic hybridization, sexual reproduction and hetrokaryosis [Tinline 1962; Burdon and Silk1997; Lahr et al. 2011]. Eastern Gangetic Plains of South Asia is a declared hotspot of leaf blotch disease[Gupta et al. 2018; Ginkel and Rajaram 1998]

Sexual stage of compatible isolates of B. sorokiniana showed signi�cant degree of diversity from variousgeographic regions [Sultana et al. 2018]. A phylogeographic analysis offers a better understanding of theimpact of historical events on patterns of genetic diversity within and across species [Deli et al. 2019].Zhong et al. [2002] developed a rapid and reliable PCR based diagnostic marker capable of detecting thepathogen in pre-symptomatic wheat leaves as well as soil. Even markers from Pyrenophora teresf.sp.maculata has also been utilized for evaluating variations among B. sorokiniana isolates [Lu et al.2010]. EST-SSRs and ribosomal DNA polymorphism including sequence variation in the ITScharacterization of fungal isolates have been used by several research groups [Aggarwal et al. 2010; Mannet al., 2014]. Sun et al. [2015] also reported genetic variability of Chinese isolates of B. Sorokianana usingITS, β-tubulin and TEF-α1 gene markers. These primers are based on repetitive sequences of the ricegenome [Kang et al. 2021]. Bhunjun et al. [2020] also reported Bipolaris species delimitation using ITS andglyceraldehyde-3-phosphate dehydrogenase gene (GPDH). Brn1 gene sequences from B. sorokiniana werealso utilised for designing the primer pairs. Information regarding species diversity at geographical level isa prerequisite for developing disease resistant cultivars and disease management. The aim of this study

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was to understand the phylogeographic patterns of B. sorokiniana isolates infecting wheat and barleyworldwide based on multi-locus sequence typing (ITS, GPDH and TFF1‐α sequences) approach and toelucidate the demographic history and divergence time estimates. The diversity analysis will not onlyhelping in designing and improving wheat breeding programs to develop new cultivars but also developingeffective chemicals management strategies for control of leaf blight disease.

Materials And MethodsCollection of diseased samples, isolation of Bipolaris isolates and their maintenance

Fungal infected leaves of wheat and barley were collected from different locations of India(Supplementary Table S1). Small chunks of infected leaf tissues were surface sterilized and transferred onpotato dextrose agar (PDA) and were incubated at 22 ± 2oC for fungal growth. Colonies arising out of asingle spore were used to generate pure fungal cultures [Sharma et al. 2005]. Conical �asks containing 150mL of autoclaved PDA broth were inoculated with fungal mycelia from 7 days old cultures and wereincubated at 22 ± 2oC in an orbital incubator shaker (100 rpm). After a week fungal mycelia were harvestedusing double layered �lter paper and dried in between sterilized �lter layers under a laminar �ow cabinet.The dried mycelia were stored at -20°C for further molecular work. A total of 21 B. sorokiniana isolateswere thus obtained.

DNA extraction and PCR ampli�cationFor extraction of fungal genomic DNA, 40 mg of dried mycelium of each isolate of B. sorokiniana wasprocessed using CTAB method as described with minor modi�cation [Singroha et al. 2020]. UV/VISspectrophotometer (Simadzu) was used for DNA quanti�cation and the DNA was stored at 20°C for furtheruse. To amplify ITS region in the rDNA gene fragments of B. sorokiniana isolates, fungal speci�c universalprimers (ITS1) were used [White et al. 1990]. PCR ampli�cation conditions were used as mentioned earlier[Sharma et al. 2005]. The ampli�ed product was separated using agarose gel electrophoresis and viewedunder Alpha Innotech gel documentation system (Alphaimager, USA). The band of ampli�ed product wascut out from the gel and was cleared of any agarose residue using QIAquickgel extraction kit (Qiagen,Germany) as per manufacturer’s instructions. The puri�ed amplicon thus obtained was got sequencedfrom (Euro�ns Genomics Pvt Ltd, India) using the ITS1 and ITS4 PCR primers. Sequences were generatedfrom two independent sequencing reactions for each sample.

Phylogenetic analysisThe �nal dataset of 254 genes of ITS, TEF-α1 and GAPDH were used to perform multiple sequencealignment (MSA) using MEGA-X [Kumar et al. 2016] as well as to construct the phylogenetic tree using theJTT model with gamma distribution and complete deletion of removal or gaps. Finally, the tree wasvisualized using the iTOL (interactive tree of life) software.

Genetic diversity and haplotype relationship

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In the present study, 162 ITS sequences, 18 of GAPDH and 74 TEF-1α sequences of B. sorokinianaretrieved from NCBI including 21 ITS sequences obtained in this study were used for combined analysis(Table 1). The following parameters were calculated in DnaSP v. 5.101 [Librado and Rozas 2009] tointerpret sequence variation and describe populations. The number of polymorphic sites (S), averagenumber of nucleotide differences (k), nucleotide diversity(π), number of haplotypes (h), total number ofmutations (η) and the haplotype diversity (Hd).

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Table 1Geographical origin, host, isolates, haplotypes information and their accession number of B. sorokiniana

ITS, GAPDH and TEF 1α sequences used for genetic analysis.Isolates Accession

No.Gene Country Host Tissue

typeHaplotypes

BHU RC 9 MH209068 ITS India (NorthIndia)

wheat Seed,leaves

H1

MDSN24-D-16 MH209067 ITS India (NorthIndia)

wheat Seed,leaves

H1

K0911 MH209066 ITS India (NorthIndia)

wheat Seed,leaves

H1

D6899 MH209065 ITS India (NorthIndia)

wheat Seed,leaves

H26

UBS2 MH209063 ITS India (NorthIndia)

wheat Seed,leaves

H1

RAJ3975 MH209058 ITS India (NorthIndia)

wheat Seed,leaves

H1

BHU RC-8 MH209057 ITS India (NorthIndia)

wheat Seed,leaves

H1

W MH209056 ITS India (NorthIndia)

wheat Seed,leaves

H15

Barley 356 MH209055 ITS India (NorthIndia)

wheat Seed,leaves

H16

KO-5803 MH209054 ITS India (NorthIndia)

wheat Seed,leaves

H17

HI1461 MH209053 ITS India (NorthIndia)

wheat Seed,leaves

H18

S-13 MH209052 ITS India (NorthIndia)

wheat Seed,leaves

H1

HP-1744 MH209051 ITS India (NorthIndia)

wheat Seed,leaves

H1

B-4-2-12 MH209050 ITS India (NorthIndia)

wheat Seed,leaves

H1

B-10-A MH209049 ITS India (NorthIndia)

wheat Seed,leaves

H1

MP1261 MH209047 ITS India (NorthIndia)

wheat Seed,leaves

H1

D-6-1-6 MH209046 ITS India (NorthIndia)

wheat Seed,leaves

H1

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Isolates AccessionNo.

Gene Country Host Tissuetype

Haplotypes

HP1493 MH209045 ITS India (NorthIndia)

wheat Seed,leaves

H1


wheat Seed,leaves

H1

RAJ4252 MH209044 ITS India (NorthIndia)

wheat Seed,leaves

H19

T-1 MH209043 ITS India (NorthIndia)

wheat Seed,leaves

H20

DBW14 MH209008 ITS India (NorthIndia)

wheat Seed,leaves

H1

BHU RC10 MH209009 ITS India (NorthIndia)

wheat Seed,leaves

H21

HD2888 MH209041 ITS India (NorthIndia)

wheat Seed,leaves

H1

MDSN76-DB-16 MH209040 ITS India (NorthIndia)

wheat Seed,leaves

H1

RSP561 MH209039 ITS India (NorthIndia)

wheat Seed,leaves

H1

UBS1 MH209038 ITS India (NorthIndia)

wheat Seed,leaves

H1

94 LOK 67-19-2 MH209037 ITS India (NorthIndia)

wheat Seed,leaves

H21

HUW16 MH209036 ITS India (NorthIndia)

wheat Seed,leaves

H22


wheat Seed,leaves

H1

K1013-1 MH209034 ITS India (NorthIndia)

wheat Seed,leaves

H24

MP1266, WBI-B MH209033 ITS India (NorthIndia)

wheat Seed,leaves

H1

BR3705 MH209032 ITS India (NorthIndia)

wheat Seed,leaves

H1

S-913-14 MH209031 ITS India (NorthIndia)

wheat Seed,leaves

H1

B7 MH209030 ITS India (NorthIndia)

wheat Seed,leaves

H1


wheat Seed,leaves

H1

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Haplotypes

BHU-RC-4 MH197356 ITS India (NorthIndia)

wheat Seed,leaves

H1

CG80001 MH209028 ITS India (NorthIndia)

wheat Seed,leaves

H1

S-13-4-C25 MH209027 ITS India (NorthIndia)

wheat Seed,leaves

H1

70B-1002 MH209026 ITS India (NorthIndia)

wheat Seed,leaves

H1

DBW91 MH197355 ITS India (NorthIndia)

wheat Seed,leaves

H1


wheat Seed,leaves

H1

SeedR2 MH209023 ITS India (NorthIndia)

wheat Seed,leaves

H1

S-13-3 MH209022 ITS India (NorthIndia)

wheat Seed,leaves

H25

BBS − 1 KT864928 ITS India (NorthIndia)

Barley Seed H1

BBS-3 KT864929 ITS India (NorthIndia)

Barley Seed H1


Barley Seed H1


Barley Seed H1

WBS-1 KT864932 ITS India (NorthIndia)

wheat Seed H1


wheat Seed H1


wheat Seed H1


wheat Seed H1


wheat Seed H1


wheat Seed H1


wheat Seed H1

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Haplotypes


wheat Seed H1


wheat Seed H1


wheat Seed H5


wheat Seed H1


wheat Seed H1


wheat Seed H1


wheat Seed H1


wheat Seed H1


wheat Seed H1


wheat Seed H1

WLB-18-20 MK809545 ITS India(CentralIndia)

wheat NA H9


wheat NA H2

Isolate L KJ562717 ITS India(CentralIndia)

wheat NA H1

Isolate D2 KJ562716 ITS India(CentralIndia)

wheat NA H1

Isolate J KJ562715 ITS India(CentralIndia)

wheat NA H1

HD3069 KJ562714 ITS India(CentralIndia)

wheat NA H1

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Haplotypes


wheat NA H2


wheat NA H2

BS69 HM195257 ITS India(CentralIndia)

wheat NA H1

Isolate A KJ562718 ITS India(SouthIndia)

wheat NA H1

Dharwad KJ562719 ITS India(SouthIndia)

wheat NA H1

Isolate SHZ-BH2 MK640670 ITS India(SouthIndia)

wheat NA H1

BS72 HM195258 ITS India(SouthIndia)

wheat NA H1


wheat NA H1


wheat NA H1


wheat NA H1


wheat NA H1

SHZ-BS-3 MK777970 ITS India(SouthIndia)

wheat NA H1

WH.PBW.1P.04 KM066949 ITS India (WestIndia)

wheat NA H1

WLB-18-13 MK809551 ITS India (WestIndia)

wheat NA H2

WB3 MT804344 ITS India (WestIndia)

wheat NA H1

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Haplotypes


wheat NA H1


wheat NA H1


wheat NA H1


wheat NA H10


wheat NA H11

BS41 HM195251 ITS India (WestIndia)

wheat NA H1

BS42 HM195252 ITS India (WestIndia)

wheat NA H1


wheat NA H12


wheat NA H13

WLB-17-47 MN535889 ITS India (WestIndia)

wheat NA H14


wheat NA H2


wheat NA H2

BS-47 DQ367884 ITS India (WestIndia)

wheat NA H1

B131-2 LC543651 ITS Japan Barley Spike H1

BMup2 LC543650 ITS Japan Barley Spike H1

BM2 LC543649 ITS Japan Barley Spike H1

BO2 LC543648 ITS Japan Barley Spike H1

J1 MT035843.1 ITS China Wheat,Barley

NA H1

J10 MT035842.1 ITS China Wheat,Barley

NA H1

GN1 MN736413.1 ITS China Wheat,Barley

NA H1

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Haplotypes

YT1-2-2 KJ026145.1 ITS China Wheat,Barley

NA H1

WK1-3-6 KJ026144.1 ITS China Wheat,Barley

NA H1

RBC3 KJ026140.1 ITS China Wheat,Barley

NA H8

isolate 83 MN534829.1 ITS Jordan Wheat NA H1




isolate 54_F MN444784.1 ITS Morocco Barley NA H1

isolate C404-11 MN313300.1 ITS Canada Wheat NA H1

isolate C109-13 MN313291.1 ITS Canada Wheat NA H1

isolate Mm51 MK156316.1 ITS Iran Wheat NA H1



isolate 72 MG654434.1 ITS Azerbaijan Wheat root H1



strain 69 MG661715.1 ITS Azerbaijan Wheat root H1

strain_69 MK022351.1 ITS Azerbaijan Wheat root H1

strain_68 MK022350.1 ITS Azerbaijan Wheat root H1

strainAUMC14512 MT974154.1 ITS Egypt Wheat Storedgrains

H1

isolate Bs12 MW246488.1 ITS Egypt Wheat Storedgrains

H1

isolate Bs13 MW246567.1 ITS Egypt Wheat Storedgrains

H7

isolate TPQ5 KX137838.1 ITS Mexico Wheat NA H1



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Haplotypes


isolate 10 KC616341.1 ITS Syria Barley NA H3


isolate C9 KC616337.1 ITS Syria Barley NA H4


isolate CS1 JX145649.1 ITS USA Wheat NA H27

ATCC 201653 JQ070091.1 ITS USA Wheat NA H1

ATCC 201652 JQ070090.1 ITS USA Wheat NA H1

TR-Cs-8 MT271247.1 ITS Turkey Wheat,barley Soil H1

TR-Cs-10 MT271249.1 ITS Turkey Wheat,Barley

Soil H1


Soil H1


Soil H1

AB-F 67 KX622098.1 ITS Russia Seed ofcereals

NA H1


NA H1


NA H1


NA H1


NA H1

strain 98026P KF765405.1 ITS Brazil Wheat NA H1

strain 98023B KF765406.1 ITS Brazil Wheat NA H1

strain 98023P KF765407.1 ITS Brazil Wheat NA H1

strain 98026C KF765408.1 ITS Brazil Wheat NA H1

BHU RC 9 MH515071 TEF-α1 India (NorthIndia)

wheat NA H29

MDSN24-D-16 MH515070 TEF-α1 India (NorthIndia)

wheat NA H29

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Haplotypes

K0911 MH515069 TEF-α1 India (NorthIndia)

wheat NA H29

D6899 MH515068 TEF-α1 India (NorthIndia)

wheat NA H39

UBS2 MH515067 TEF-α1 India (NorthIndia)

wheat NA H29

RAJ3975 MH515066 TEF-α1 India (NorthIndia)

wheat NA H29

BHU RC-8 MH515065 TEF-α1 India (NorthIndia)

wheat NA H29

W MH515064 TEF-α1 India (NorthIndia)

wheat NA H29

Barley 356 MH515063 TEF-α1 India (NorthIndia)

wheat NA H29

KO-5803 MH515062 TEF-α1 India (NorthIndia)

wheat NA H29

HI1461 MH515061 TEF-α1 India (NorthIndia)

wheat NA H29

S-13 MH515060 TEF-α1 India (NorthIndia)

wheat NA H29

HP-1744 MH515059 TEF-α1 India (NorthIndia)

wheat NA H29

B-4-2-12 MH515058 TEF-α1 India (NorthIndia)

wheat NA H29

B-10-A MH515057 TEF-α1 India (NorthIndia)

wheat NA H29

MP1261 MH515056 TEF-α1 India (NorthIndia)

wheat NA H29

D-6-1-6 MH515055 TEF-α1 India (NorthIndia)

wheat NA H29

HP1493 MH515054 TEF-α1 India (NorthIndia)

wheat NA H29


wheat NA H29

RAJ4252 MH515052 TEF-α1 India (NorthIndia)

wheat NA H29

T-1 MH515051 TEF-α1 India (NorthIndia)

wheat NA H29

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Haplotypes

DBW14 MH515017 TEF-α1 India (NorthIndia)

wheat NA H29

HD2888 MH515049 TEF-α1 India (NorthIndia)

wheat NA H29

MDSN76-DB-16 MH515048 TEF-α1 India (NorthIndia)

wheat NA H29

RSP561 MH515047 TEF-α1 India (NorthIndia)

wheat NA H29

UBS1 MH515046 TEF-α1 India (NorthIndia)

wheat NA H29

94 LOK 67-19-2 MH515045 TEF-α1 India (NorthIndia)

wheat NA H29

HUW16 MH515044 TEF-α1 India (NorthIndia)

wheat NA H29


wheat NA H29

K1013-1 MH515042 TEF-α1 India (NorthIndia)

wheat NA H29

MP1266, WBI-B MH515041 TEF-α1 India (NorthIndia)

wheat NA H29

BR3705 MH515040 TEF-α1 India (NorthIndia)

wheat NA H29

S-913-14 MH515039 TEF-α1 India (NorthIndia)

wheat NA H29

B7 MH515038 TEF-α1 India (NorthIndia)

wheat NA H29


wheat NA H29

BHU-RC-4 MH515036 TEF-α1 India (NorthIndia)

wheat NA H29

CG80001 MH515035 TEF-α1 India (NorthIndia)

wheat NA H29

S-13-4-C25 MH515034 TEF-α1 India (NorthIndia)

wheat NA H29

70B-1002 MH515033 TEF-α1 India (NorthIndia)

wheat NA H29

DBW91 MH515032 TEF-α1 India (NorthIndia)

wheat NA H29

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Haplotypes


wheat NA H29

SeedR2 MH515030 TEF-α1 India (NorthIndia)

wheat NA H29

S-13-3 MH515029 TEF-α1 India (NorthIndia)

wheat NA H29

B131-2 LC546960 TEF-α1 Japan Barley NA H31

BMup2 LC546959 TEF-α1 Japan Barley NA H31

BM2 LC546958 TEF-α1 Japan Barley NA H31

BO2 LC546957 TEF-α1 Japan Barley NA H31

WLB-18-43 MW085804 TEF-α1 India (NorthIndia)

wheat NA H33


wheat NA H34


wheat NA H33


wheat NA H33


wheat NA H33


wheat NA H35


wheat NA H33


wheat NA H33


wheat NA H36


wheat NA H30


wheat NA H37


wheat NA H30


wheat NA H33

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Haplotypes


wheat NA H30


wheat NA H30


wheat NA H38


wheat NA H33

MAFF 238877 KM093765.1 TEF-α1 Japan Barley NA H30

M1 KM243317.1 TEF-α1 China wheat NA H32

CPC 28832 MF490855.1 TEF-α1 Thailand wheat NA H29

B7 MZ327275 TEF-α1 Argentina Barley seed H40

B19 MW802821 TEF-α1 Argentina Barley seed H30

B13 MZ188975 TEF-α1 Argentina Barley seed H29

K1012 MH279672 TEF-α1 India (NorthIndia)

wheat Leaves H29

B15 KJ939504 TEF-α1 India (NorthIndia)

wheat Na H30

B11 KJ939501 TEF-α1 India (NorthIndia)

wheat Na H30

CS1 JX145648 GAPDH USA wheat Na H27

Azerbaijan 69 MH844821 GAPDH Azerbaijan wheat Na H27




strain 54 − 2 MK024323 GAPDH Turkey wheat Na H27

strain 47 MK024322 GAPDH Turkey wheat Na H27



M1 KR527151 GAPDH China wheat Na H27

MAFF 238877 KM034824 GAPDH Japan Barley Na H27

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Haplotypes

MAFF 235500 KM034823 GAPDH Japan Barley Na H27

Isolate 03 MT262924 GAPDH Kazakhstan triticale Na H27

isolate 02 MT262923 GAPDH Kazakhstan triticale Na H27

isolate 01 MT262922 GAPDH Kazakhstan triticale Na H27

B7 MZ156796 GAPDH Argentina Barley Seed H27

B_19 MW980141 GAPDH Argentina Barley Seed H28

B13 MZ327274 GAPDH Argentina Barley Seed H27

Using NETWORK ver. 4.6 [Bandelt et al. 1999], a network was constructed using the median-joiningalgorithm to describe distribution and geographical relationship between the populations of B.sorokiniana. For geographical grouping of populations AMOVA was performed in Arlequin3.5 [Exco�erand Lischer 2010].

Demographic history and Divergence timeTo determine if B. sorokiniana populations underwent any population expansion in the recent past, weconstructed mismatch distributions and compared them with the predicted distributions from populationexpansion models [Roger and Harpending 1992]. DnaSP v. 5.101 [Librado and Rozas 2009] was used todetermine the calculation of class I neutrality tests: Tajima's D [Tajima et al. 1989], and Fu's Fs [Fu 1997]population size change test R2 for detecting departure from the mutation/drift equilibrium.

Signi�cance of test was built on 1000 coalescent simulations. A zero value of Tajima's D indicatesneutrality while a signi�cant value is representative of heterogenous mutation rate and populationexpansion. Higher negative value of Fu’Fs indicate population diversity, and lower R2 values are predictedunder this population growth expansion.

The R2 test performs well even with a limited sample size. The distribution of genetic variations betweenpairs of haplotypes (Mismatch distribution) was calculated using the program DnaSP v.5.101 and thepremise of panmictic populations. In general, multimodal distributions are compatible with demographicstability or numerous expansion events; whereas unimodal distributions commonly indicate that thepopulation has recently expanded in both population and geographic terms [Roger 1995]. The Mismatchdistribution was calculated using all populations combined, as well as by geographic distribution andhaplotype groups.

BEAST 1.8.3 [Drummond et al. 2012] was used to estimate the time since the most recent commonancestor using a Bayesian method. Analyses were carried out using ITS sequences from B. sorokinanapopulations, as well as from two additional outgroups (Alternaria arboresescen and Pyrenophoragraminea) using the GTR nucleotide substitution model, which was found as the best model for the data

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in MEGA7 [Kumar et al. 2016]. We utilised an uncorrelated relaxed clock with a lognormal relaxeddistribution. The previous coalescent tree was of a constant size, which is best suited for trees describingconnections among individuals in the same population/species. The analysis was carried out usingdefault parameters for the Markov Chain Monte Carlo (MCMC), setting 10 million generations and 10% ofthe �rst runs discarded. Tree Annotator v1.8.0 was used to merg the resultant trees, and FigTree v1.4.0 wasused to display consensus tree with the divergence periods [Rambaut and Drummind 2010].

Results

Analysis of genetic diversity and phylogenetic relationshipsPCR amplicons of ~ 550 bp obtained from all the 21 isolates of B. sorokiniana were sequencedbidirectionaly. Sequences thus obtained had been deposited in GenBank (Supplementary Table S1).Average nucleotide composition of the ITS sequences of B. sorokiniana represented 20.12% A, 20.13% C,20% and 50.5% G + C content. For the analysis, we downloaded 141 ITS region sequences available in theNCBI database.

We took advantage of the 18 GAPDH and 74 TEF-1α gene sequences of B. sorokiniana available in theNCBI database for construction of phylogenetic analysis. The concatenated sequence ITS-GAPDH- TEF-1αof 254 B sorokiniana isolates with 1268 base pairs nucleotide was aligned. The phylogenetic tree wascalculated to �nd evolutionary relationships among the B sorokinianaresulted into three groups(Fig. 1).Most of the isolates were grouped in Clade I which contains ITS sequences from differentgeographic regions and 5 TEF-1α sequences from China and Japan (Fig. 1). However, clade II containedGAPDH and TEF-1α sequences of B. sorokiniana while clade III has only TEF-1α sequences. Thephylogenetic tree, on the other hand, was highly supported and corresponded to the haplotype network.

A total of 40 diverse haplotypes (Hd = 0.71 ± 0.02 ) were identi�ed in 254 sequences of B. Sorokiniana(Table 1). However, nucleotide diversity (π) was 0.33 ± 0.019 across all populations and the averagedifferences in the number of nucleotides (K) among the sequences was 75.5 (Table 2) and the averagenumber of segregating sites were 225. Therefore, each population is a representative of overall diversity ofthe species.

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Table 2Analysis of genetic diversity and neutrality test of B. sorokiniana representing different countries

Population N Hd π S K Fu'Fs Tajima's D R2

India 161 0.74 ± 0.028 0.34 ± 0.01 273 96.5 44.4 0.278 0.175

Japan 11 0.76 ± 0.08 0.45 ± 0.06 208 101.78 22.85 -0.05 0.167

Syria 4 1 ± 0.177 0.009 ± 0.002

1 0.66 -0.361 0.389 0.182

USA 4 1 ± 0.17 0.28 ± 0.152 128 64 2.988 -0.88*** 0.43

Argentina 6 1 ± 0.096 0.5 ± 0.107 202 114.5 2.78 -0.26 0.282

Jorden 4 0.5 ± 0.265 0.01 ± 0.006 2 1 4.419 -0.84 0.433

Egypt 3 1 ± 0.272 0.0896 ± 0.03

2 1.33 - - 0.362

Turkey 8 0.57 ± 0.09 0.33 ± 0.05 291 166 27.43 2.64*** 0.285

China 9 0.833 ± 0.127

0.240 ± 0.110

238 69.63 7.4 -1.796* 0.215

Azerbaijan 10 0.64 ± 0.10 0.30 ± 0.0 306 163 27.17 2.43** 0.264

Mexico 4 0.5 ± 0.265 0.0019 ± 0. 2 1 1.099 -0.709 0.433

Russia 5 1 ± 0.126 0.045 ± 0.012

51 27.2 0.874 -0.97 0.218

Kazakhstan 6 0.6 ± 0.12 0.34 ± 0.07 282 169 20.9 2.4 0.3

Morroco 4 0.8 ± 0.22 0.006 ± 0.002

6 3.16 0.811 -0.314 0.365

Total 239 0.7 ± 0.027 0.33 ± 0.01 225 75.5 60.02 -0.105 0.3

Note: N: Number of sequences; Hd = haplotype diversity; π = nucleotide diversity; S = variable sites ; K = average number of nucleotide difference; Statistical signi�cance: * P < 0.05; ** P < 0.01; *** P < 0.001; Anegative Tajima’s D signi�es an excess of low frequency polymorphisms relative to expectationindicating population size expansion or positive selection; A negative FuFs value provides strongevidence for past population expansion, and rule out the possibility of background selection, andevolutionary forces that produce a pattern similar to population expansion.

Haplotype relationship and population structureWe constructed a median joining tree utilising 254 sequences based on ITS, GAPDH, and TEF-α 1 genes todetermine the population structure and evolutionary ancestry of B sorokiniana (Fig. 2). The system in the Bsorokiniana lineages, the tree indicates a star-shaped divergence. The bulk of the haplotypes wereconnected to H_1, the most common haplotype, which comprises 127 genes from various regions,followed by H_29, the second most common haplotype, which has 45 genes. Other haplotypes with lowergene counts include H_27, which has 17 genes, and H_2, H_30, and H_33, which each contain 8 genes and

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were linked by a single or maximum three mutation phases. Except for a few Asian countries, haplotypeH_1 was detected in the network's core and was shared by all B sorokiniana populations around the globe.H_27, on the other hand, represents a population that is geographically varied. Further analysis wasfocused on the haplogroups and was de�ned by geography based on the network's topology.

Analysis of molecular variance (AMOVA) represented a low value of FST (0.05) when considered as a singlegroup comprising all the populations. This analysis revealed 5.8% inter-species variation and 94.15% intra-species variation (Table 3). This study proposes a weak genetic structural variation but underpins theaction of gene �ow. The F-statistic value was found to be very low (0.18) when the populations wereanalysed based on geographical grouping and the percentage of variation was 7.44% among groups,27.77% among population within groups and 91.28% within population within groups (Table 3).

Table 3AMOVA of ITS, GAPDH and TEF-1α sequences for geographic and haplogroups of

B. sorokiniana

F-statistic

Groups variation % st sc ct

Among populations 5.84681 0.05847 - -

within populations 94.15319

st sc ct

Among groups 7.44 0.182 0.233 -0.066

Among populations within groups 27.77

within population within groups 91.28

Population demography events and divergence timeBy examining the frequency distribution of pairwise differences across sequences, Roger [1995] proposeddemographic expansions in the past. A recent demographic growth is indicated by a unimodal distributionderived from the mismatch distribution of total samples. On a single geographical continent, themultimodal pattern was identi�ed. Haplogroups from Morocco and Egypt from African continent (Fig. 3)showed a multimodal pattern, but groups from other continents e.g. America and Europe showed aunimodal pattern, re�ecting a recent population expansion or range extension with a high degree ofmovement within neighbourhoods [Exco�er and Lischer 2010].

The demographic history of earlier population increase was deciphered using Tajima's D and Fu's Fs tests.The �ndings of Tajima's D test and Fu's Fs test are shown in Table 3. For numerous groups, Tajima's Dvalues were signi�cant, including China, Turkey, Azerbaijan, Argentina, and Kazakhstan. However, exceptfor Syria, Fu's Fs were positive, and p-values for numerous groups were statistically signi�cant, showingthat there was an excess of low frequency polymorphisms relative to expectation, indicating that the nullhypothesis of population neutrality could be rejected (Table 2). The low R2 score, on the other hand,

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showed recent growth. B. sorokiniana and its outgroups (A. alternata and P. herbarum), respectively,diverged 22.2 and 15.6 million years ago (mya). Divergence within B. sorokiniana, on the other hand,began 11.8 mya, in the middle of the Miocene epoch. B. sorokiniana initially appeared 10.9 mya on threecontinents: Asia, Europe, and America. Our study focused on estimating the timing of divergence of Asiansamples (speci�cally Indian samples), and we determined that Indian lineages split 10.7 mya. We alsofound that about 6.4 mya, the African and American lineages split off (Fig. 4).

DiscussionSpot blotch has emerged as one of the most important pathogen particularly with regards to wheat andbarley production throughout the world inclduingn south east Asia [Gupta et al. 2018]. Host resistance iseconomic and vaibale approche for managing the pathogen. However, these measures have not yieldedsatisfactory results thus far. Spot blotch variation is extremely useful for identifying resistant germplasm,deploying cultivars with different resistance genes, and analyzing the emergance of new pathotypes. Agenetically diverse pathogen population screening is required for an effective resistance breedingprogramme. As a result, understaning the natural pathogene genetic variation is critical for developingeffective management strageties. Moreover, genetic variability among naturally occurring populations isan outcome of evolution and demographic processes and provides an important tool to determine theevolutional potential of a species [Zhang et al 2017]. Here, we examined the intra-speci�c geneticvariability of B. sorokiniana (using ITS, GAPDH and TEF1α region of B. sorokiniana) to acknowledge theevolution and phylogeography of the species.

According to phylogenetic study, B. sorokiniana was a single species with no distinct groups, since all ofthe sequences taken from different countries were clustered together in a single group and outgroups wereput in separate group. Haplotype diversity is in�uenced by mutation, marker discovery, recombination, anddemography [Stumpf 2004]. In present study, 40 haplotypes have been observed in a group of 254 isolatesbased on mutli-locus gene sequence with predominant haplotype H_1 comprising of 127 individuals (50%of total isolates) with distinct geographies and host speci�cities. These results were indicative of highgene �ow occurring among the geographically distinct populations of the pathogen. The haplotypenetwork has a dispersed topology due to its high singleton ratio. This kind of phylogenetic inference isindicative of a recent population expansion that evolved from a small number of founders following agenetic bottleneck [Zhang et al. 2017]. The results of median-joining network analysis of B. sorokinianaisolates revealed that few haplotype were population speci�c and unique while others were evolutionarilydistinctive (H_1) and exhibited signi�cant levels of gene �ow among populations. The Asian haplotypesH_2, H_4, H_6, H_7, H_12, H_15, H_16, H_17, H_18, H_19, H_20, H_21, H_22, H_23, H_25, H_26 and H_39appeared to have evolved from the predominant haplotype H_1 (Fig. 1). The haplotypes H_1, H_3, H_4,H_27, H_28, H_29, H_30, H_31, and H_40 were from barley isolates of India, Morocco, Syria, Turkey, Japan,China and Syria origin respectively (Table 1). We found that Indian isolates were diversed and spread in thenetwork in 16 haplotypes i.c. H_1, H_2, H_5, H_9, H_10, H_11, H_12, H_13, H_14, H_29, H_30, H_33, H_34,H_35, H_36, and H_37. A star-like structure of the median joining network discovered in this study alsooffers evidence to support the presumption of divergence from neutrality for constant or uniform

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population size (Fu's Fs and Tajima's D) owing to spot blotch fungal population expansion. Introduction,domestication, and continuing cultivation of B. sorokiniana host plants may therefore be a feasibleexplanation for population increase in various wheat growing zones. This form of fungal populationmobility has been seen in a range of fungal diseases [Dietzel et al. 2019; Zaffarano et al. 2008; Katoch etal. 2016].

The indices were non-signi�cantly positive in Indian group, showing divergent demographic historiesamong analysed regions. Tajimas’D is negative for most of the groups but statistically positive and Fu’s Fswere negative for only Syria but statistically signi�cant, indicating that the B. sorokiniana populations didnot match a simple model of neutrality and rejected the null hypothesis of constant population size. These�ndings postulated that the divergence from neutrality for constant population size (Tajima’s D and Fu’sFs) was caused by the recent population diversi�cation. The introduction of B. sorokiniana might be thecause of the species recent population expansion, as Bipolaris now exhibits pathogenic specialization on avariety of wild and cultivated host plants. This type of range expansion has also been reported in manyfungal and insect species [Katoch et al. 2016; Prabhakar et al. 2012; Wang et al. 2016]. The dating analysisshows that the species B. sorokiniana originated from Middle Miocene era (~ 19 million years ago). TheMiocene era had the longest and most humid environments in the geological history. According toPalaeogeographic analysis, the western part of central Asia transitioned from a humid climate to a semi-arid climate [Lauer et al 2017; Zhang and Zhang 2012]. This climatic change is thought to haveaccelerated speciation due to habitat alternations caused by progressive acidi�cation and the quaternaryglacial-interglacial cycles. As a result, the climatic change results in contraction and speciation. Divergencetimes estimated for the primary genetic breaks were placed within the Plio/Pleistocene periods (~ 2 mya)(Fig. 4), a period in which the average temperature of the earth began to decrease rapidly and for whichextensive records document changes in forest distribution associated with climatic cycles. The �rst breakamong the groups occurred in Asian lineages of B. Sorokiniana (the oldest ones). From this study, we alsocame to know that three sequences from Asia (KM093765, KJ939504 and KJ939501) clustered with P.herbarum outgroup in phylogenetic tree which indicate that these sequence might have evolved from P.Herbarum since 15.6 million years ago.

DeclarationsAcknowledgements

The project work was supported by the grants from Indian Council of Agricultural Research, New Delhi toPS (ICAR- LBSYA F. No. 64-1-17).

Ethics declarations

Con�ict of interest

Authors declare no con�ict of interest for this study.

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Ethical approvalNot applicable for this study.

Consent to participateNot applicable for this study.

Consent to publishNot applicable for this study.

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Figures

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Figure 1

Phylogenetic analysis of ITS, GAPDH and TEF-α1 nucleotide sequences of B. sorokiniana.

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Figure 2

Median joining network of rDNA haplotype of B. sorokiniana populations. Size of the circle is related withfrequency of haplotypes and colours indicate the proportion of individual sampled in different populationswithin the study area

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Figure 3

Mismatch distribution relevant to geographic and haplotypic groups of B. sorokiniana. Observed frequencyof pairwise differences (solid line) are compared against expected frequencies under expandingpopulation size (dotted line) whereas �gures in clockwise represents shows mismatch distribution acrossthe continents, Africa, America, Asia and Europe, respectively.

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Figure 4

Chronogram with divergence times. Numbers annotated on each node represent the divergence timesbetween clades, and the standard deviations (95% of high posterior density) are represented by the bluebars.

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