m
:3
u a.

.

Aw...
. "3".

i.
A.» a»...

ma

fit.“- RWW. .
33%
Quid.
V IV“ ‘1

”ya. 3

vv 5-4
ruhnluuhzib

K3. .215 .. in. ‘
,1 LE: 1.

V .I.- .
2. HA. 1.. .2.

“I. ‘Jfiufl

 

LIBRARY
Michigan State
University

This is to certify that the
thesis entitled

GENETIC DIVERSITY IN BENTGRASS (AGROST/S SPP.)
BY AFLP ANALYSIS AND STUDIES ON DISEASE
RESISTANCE TO TYPHULA INCARNA TA LASCH

Ph.D.

 

presented by

Georgina V. Vergara

has been accepted towards fulfillment
of the requirements for the

degree in Plant Breeding and Genetics -
Crop and Soil Sciences

 

<9 -- \
b.%“\ (mfg

 

Major Professoris Signature

7ft? \05

July 18, 2003

MSU is an Affinnative Action/Equat Opportunity Institution

PLACE IN RETURN Box to remove this checkout from your record.
To AVOID FINES return on or before date due.
MAY BE RECALLED with earlier due date if requested.

 

DATE DUE DATE DUE DATE DUE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6/01 c:/ClRC/DateDuo.p65-p.15

GENETIC DIVERSITY IN BENTGRASS (AGROSTIS SPP.) BY
AFLP ANALYSIS AND STUDIES ON DISEASE
RESISTANCE TO TYPHULA INCARNA TA LASCH

by

Georgina V. Vergara

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Plant Breeding and Genetics Program
Department of Crop and Soil Sciences

2003

ABSTRACT

GENETIC DIVERSITY IN BENTGRASS (A GROSTIS SPP.)
BY AFLP ANALYSIS AND STUDIES ON DISEASE RESISTANCE TO
TYPHULA INCARNA TA LASCH
By

Georgina V. Vergara

Bentgrasses (Agrostis spp.) (>220 species) are widely occurring temperate grasses
with varied ploidy levels that represent a vast resource for genetic improvement of
turfgrass cultivars. Genetic characterization would help in the selection of breeding
materials and utilization of germplasm resources. In the first part of this study, 40 plant
introductions of 14 Agrostis species from 20 countries were studied using fluorescently
labeled amplified fragment length polymorphism (AF LP) analyses. Data from 400 AF LP
markers and using Unweighted Pair Group Method with Arithmetic Mean (UPGMA)
showed genetic similarities between species ranged from 0.62 to 0.98. Principal
component analysis (PCA) distinguished seven groups. Dendrogram constructed on the
basis of genetic similarities defined groups consistent with the geographic origins and
physical and genetic attributes of the species. In the second part of this study, AFLP
analyses was performed on old and modern creeping and redtop bentgrasses, selected
MSU lines, and plant introductions. Using 355 AFLP markers and clustering analyses,
three groups were distinguished. The mean genetic similarity for creeping bentgrasses in

the first group was 0.78. Creeping bentgrasses from the US were separated as a subgroup

from the European plant introductions. Selected MSU lines were differentiated from
modern cultivars. Redtop bentgrasses were found in different groups.

Bentgrasses are susceptible to devastating winter injury caused by gray snow
mold (Typhula incarnata Lasch). In the third part of this study, 115 random amplified
polymorphic DNA (RAPD) markers on 40 isolates of gray snow mold from Michigan,
Wisconsin and Minnesota showed mean percentage polymorphism at 48%.
Dendrograms constructed showed a wide genetic distance between isolates suggesting
high variability and possibly recent colonization. The high variation within populations
could be due to outcrossing and recombination. In the last part of the study, controlled
screening procedures against T. incarnata were developed and used to search for a
resistant genotype in creeping bentgrass populations and plant introductions of Agrostis.
We selected 20 creeping bentgrass genotypes from 890 samples taken from old Northern
Michigan golf courses and identified 3 accessions of colonial bentgrasses from 40 plant

introductions with potentially useful resistance to T. incarnata.

iv

Dedicated to my family

ACKNOWLEDGMENTS

I would like to express my sincere gratitude to my major professor, Dr.
Suleiman Bughrara for all the support and encouragement he has given me to pursue
my Ph.D. in his breeding program. I am also very thankful to my committee members
Drs. Mitch McGrath, Ray Hammerschmidt and Jim Hancock for all their support,
input, understanding and helpful suggestions to my research.

I am very grateful to all my mentors in the Crop & Soil Sciences Department
at Michigan State University, including their staff and technical people. They have
made learning such a wonderful experience for me. Special mention is made to the
staff in the Crop & Soil Sciences farm where I performed my snow mold experiments.
Also thanks to Dr. G. Jung of University of Wisconsin-Madison for snow mold
isolates from Minnesota and Wisconsin. Thanks to the people at Plant Pathology
Department, Dr. Joe Vargas, Nancy and Ron who gave me so much help.

Thanks to my co-workers in the Turfgrass Genetics Laboratory, Jianping,
Dean, Han and Debbie, the staff in Sugarbeet Lab, Danielli, Suba, Susan and Scott,
my colleagues at the Crop & Soil Sciences Graduate Organization, my friends at the
MSU Filipino community who all in one time or another has provided the much
needed support and friendship. I am also specially thankful to Dr. Benildo Delos
Reyes, and my former supervisors, Drs. Glenn Gregorio and GS. Khush of IRRI.

And lastly, I am forever thankful and for having such a loving and supporting
family, to my husband Dante and my three daughters, Genevieve Gabrielle Rose,
Aura Regina and Gianina Renee who have sacrificed so much during the years I had

been away from them.

TABLE OF CONTENTS

LIST OF TABLES ............................................................................... viii
LIST OF FIGURES ................................................................................. ix
INTRODUCTION ................................................................................. 1
CHAPTER I
AF LP ANALYSIS OF GENETIC DIVERSITY
IN BENTGRASS (AGROSTIS SPP.) ................................................... 10
MATERIALS AND METHODS ........................................................ 15
RESULTS AND DISCUSSION ......................................................... 19
REFERENCES ............................................................................ 37
CHAPTER II

GENETIC DIFFERENTIATION OF TETRAPLOID
CREEPING BENTGRASS AND HEXAPLOID REDTOP
BENTGRASS GENOTYPES AND THEIR USE IN

CHAPTER III

TURFGRASS BREEDING ............................................................... 41
MATERIALS AND METHODS ....................................................... 44
RESULTS AND DISCUSSION ......................................................... 48
REFERENCES ........................................................................... 61
GENETIC VARIABILITY OF THE

GRAY SNOW MOLD (TYPHULA INCARNA TA LASCH) ........................ 64

MATERIALS AND METHODS ....................................................... 67

RESULTS AND DISCUSSION ........................................................ 72
REFERENCES ............................................................................. 89

vi

CHAPTER IV

DISEASE RESISTANCE SCREENING OF BENTGRASS

TO TYPHULA INCARNA TA LASCH .................................................. 92

MATERIALS AND METHODS ....................................................... 96

RESULTS AND DISCUSSION ........................................................ 100

REFERENCES ...................................................................... 118
APPENDICES

POTENTIAL FOR DETACHED-LEAF ASSAY
FOR GRAY SNOW MOLD SCREENING .......................................... 120

CHANGES IN CARBOHYDRATE LEVELS
IN BENTGRASS DURING COLD AND DISEASE TREATMENTS ......... 123

vii

Table 1.1

Table 1.2

Table 1.3

Table 2.1

Table 2.2

Table 2.3

Table 3.1

Table 3.2

Table 3.3

Table 3.4

Table 3.5

LIST OF TABLES

List of plant introductions (PI), species, number of
chromosomes and geographic origin of bentgrass (Agrostis spp.)
accessions ........................................................................... 18

Number of polymorphic bands obtained from different
primer combinations .............................................................. 21

Genetic similarity coefficients for 40 bentgrass (Agrostis spp.)
accessions from data of five primer combinations using
fluorescence-labeled AF LP ........................................................ 23

Cultivars, MSU experimental lines and plant introductions
(PI) lines of creeping and redtop bentgrasses (Agrostis spp.)
examined, year released or collected and their sources ...................... 46

Number of polymorphic bands obtained from different primer
combinations ...................................................................... 49

Genetic similarity coefficients for 21 genotypes of creeping
and redtop bentgrasses using fluorescence-labeled AF LP
technique ............................................................................ 50

List of sampling areas, cities and USA states for
Typhula incarnata isolates used ................................................. 69

RAPD primers and sequence, annealing temperature
and percentage polymorphism found in gray snow mold ................... 74

Analysis of variance on the radial mycelial growth of gray
snow mold isolates in vitro from different locations
in Michigan ........................................................................ 76

Genetic similarity coefficients for 40 gray snow mold
(T. incarnata) isolates from data of 115 RAPD markers using
37 primers ......................................................................... 78

Summary of AMOVA of populations of gray snow mold

based on 4 geographic locations, Michigan (MI), Upper
Michigan GIMI), Wisconsin (WI) and Minnesota (MI) .................... 87

viii

Table 3.6

Table 4.1

Table 4.2

Table 4.3.1

Table 4.3.2

Table 4.4.1

Table 4.4.2

Table 4.5

Table 4.6.1

Table 4.6.2

Table A.1

Population pairwise difference (distance method) and average
genetic diversity/loci in 4 different populations of gray
snow mold based on geographic locations ..................................... 87

List of Agrostis spp. screened for snow mold resistance
and their geographic origins ...................................................... 98

Analysis of variance of snow mold disease inoculation in
creeping bentgrass using inoculated and uninoculated (control)
as treatments in two populations from N. Michigan ........................ 102

Disease ratings and analysis of variance of candidate resistant

lines of creeping bentgrass from Population A to snow mold

(T yphula incarnata) using a completely randomized design

with 3 replicates ................................................................. 105

Recovery ratings of 28 selected creeping bentgrass lines
(Population A) from snow mold infection using CRD with
3 replicates (ranked from the best genotype) ................................ 106

Disease ratings and analysis of variance of candidate

resistant lines of creeping bentgrass from Population B to

snow mold using a completely randomized design

with 3 replicates ................................................................. 107

Recovery ratings of selected creeping bentgrass lines to
snow mold (Typhula incarnata) using CRD with 3 replicates ............ 109

Performance and analysis of variance of commercial creeping

bentgrass cultivars using CRD in controlled snow mold

screening experiments, their disease rating means and

percentage recovery ............................................................ 113

Disease ratings and analysis of variance of PI lines with

‘Penncross’ (as susceptible control) to snow mold

(Typhula incarnata) screening using CRD with 24 to 25

genotypes per accession ......................................................... 114

Recovery ratings of accesssions of Agrostis species to
gray snow mold (Typhula incarnata) using CRD with
24 to 25 genotypes per accession ............................................. 116

Disease reactions to gray snow mold using detached

leaf assay 30 days after inoculation depicted by presence (+),
absence (-) and mean percentage disease symptoms per leaf ............. 122

ix

Table A2 Changes in total non-structural carbohydrate (TNC)
levels in bentgrass following 3 days of cold treatment or
four weeks of disease inoculation .......................................... 125

Figure 1.1

Figure 1.2

Figure 1.3

Figure 1.4

Figure 2.1

Figure 2.2

Figure 2.3

Figure 3.1

Figure 3.2

Figure 3.3

LIST OF FIGURES

World regional sources of 40 accessions of Agrostis species .......... 17

UPGMA dendrogram of 40 accessions of 14 Agrostis spp.
from 20 different countries. PCA analysis distinguishes
seven groups based on Eigen values > 1.0 ................................. 26

Plot analysis of cophenetic correlation and similarity

coefficient as a measure of goodness of fit of the similarity

indices. r = 0.95951 = normalized Mantel statistic Z;

Approximate Mantel t-test: t = 11.9767; P( Z < obs. Z:

p = 1.0000). .................................................................. 27

Diagram of hybridization pathways of Agrostis species
(indicated by solid arrows) and relationships supported by
AF LP analyses (indicated by unfilled arrows) ........................... 36

Three dimensional plot of principal component analysis

(PCA) using 355 AF LP markers (observations) and bentgrass
genotypes defining three groups marked as 1, 2 and 3

from the plot options of NTSYS v2.1 (Rohlf, 2000) .................... 52

UPGMA dendrogram of creeping and redtop bentgrasses using

data from 355 AF LP markers (solid lines) and genetic

similarity of ‘Seaside’ using data from 248 AF LP markers

(dashed lines) .................................................................. 53

Plot analysis of cophenetic and similarity coefficients as a

measure of goodness of fit of the similarity indices. r = 0.94808

= normalized Mantel statistics Z; Approximate Mantel t-test:

1: 5.1548; P(Z<obs. Z: p = 1.0000) ....................................... 54

Collection sites of gray snow mold clustered in 4 populations

based on geographic locations: Minnesota (MN); Wisconsin

(WI); Lower Peninsula, Michigan (LPMI) and Upper Peninsula,
Michigan (UPMI) ............................................................ 73

Radial grth in four weeks of different gray snow mold

isolates in vitro from Michigan. P values < 0.05 as follows:

Week 1 = 0.09; Week 2 = 0.02; Week 3 = 0.02 and Week 4 =

0.09 .............................................................................. 76

RAPD profile of 40 gray snow mold isolates from Michigan (MI),

Wisconsin (WI) and Minnesota (MN) using primer OPY-02.
(L=100bp DNA ladder) ...................................................... 81

xi

Figure 3.4.1

Figure 3.4.2

Figure 3.5

Figure 4.1

Figure 4.2

Figure A.1

UPGMA dendrogram of 40 gray snow mold isolates from

different locations in Michigan (MI), Wisconsin (WI) and

Minnesota (MN), USA based on 115 RAPD markers using

Dice coefficients, NTSYS program (Rohlf, 2000) ........................ 82

Dendrogram of 40 gray snow mold isolates based on

115 RAPD markers using Nei-Li genetic distance,

neighbor joining (NJ) method and resampling options of

F reeTree (Pavlicek et al., 1999) and TreeView (Page, 2001)
programs. Numbers indicate relative bootstrap values ................. 83

Scatterplot of the first two dimensional scales for 40 gray

snow mold isolates based on principle componenet analysis

of RAPD profile. Number correspond to isolate as listed

in Table 3.1 ..................................................................... 84

A. Creeping bentgrass at the 4th week of snow mold infection,

left bottom pot in the tray is uninoculated. B. Disease after

the 6th week, left top pot in the tray is uninoculated. C. Trays

on benches at 3 days in the greenhouse D. PI lines showing
susceptible plants and accessions with good recovery. “Images

in this dissertation are presented in color. ” ............................. 101

Distribution of disease rating scores in 670 creeping
bentgrass genotypes ........................................................ 104

Changes in the total non-structural carbohydrate levels in
leaves and crown ............................................................ 126

xii

INTRODUCTION

Classification, distribution and importance of Agrostis

Bentgrass is the common name for the closely mowed, fine turfgrass widely seen
on putting greens, golf courses and parks in most temperate areas. There are an estimated
220 species of bentgrass distributed throughout the world, on all continents in a variety of
habitats. Bentgrass (Agrostis spp.) is a perennial or annual outcrossing polyploid (x=7;
2n=14, 28, 42 etc.) belonging to the Poaceae family (Watson and Dallwitz, 1992).
Distribution of the species spans shade to open habitats, low to high lands and cool to
arctic areas. Some species are helophytic (marsh plants); mesophytic (avoiding extremes
of moisture or drought) or xerophytic (susbsisting with relatively little moisture). The
importance of the genus is not limited to aesthetic applications as some species are
adapted for erosion control and revegetation programs in disturbed areas where grazing,
mining and flooding had occurred (Brown and Johnston, 1979; Cornely et al., 1983;
Winterhalder, 1990). Some species have important traits such as tolerance to drought
and heavy metals. Variation within and between Agrostis species represents a potential
for genetic improvement of the species for use as turf.

Current significant species in the USA are velvet bentgrass (A. canina L.);
colonial bentgrass (A. capillaris L. or A. tenuis Sibth); dryland or highland bentgrass (A.
castellana Boiss. & Reut.); creeping bentgrass (A. palustris Huds. or A. stolonifera L);
red top bentgrass (A. gigantea L.); and Idaho bentgrass (A. idahoensis Nash). Creeping
bentgrass is the premier turfgrass species for closely mowed golf course putting greens

(Funk, 1998). Interspecific hybridization, variation in ploidy levels and difficulty in

morphological classification has resulted in many synonymous species. Genetic
characterization of Agrostis species using molecular markers will identify duplicates in
the plant introduction collections, define extent of genetic similarity among species and
provide directions to future interspecific breeding strategies enabling exploration of

germplasm resources.

Introduction and breeding of bentgrasses in the US

Bentgrasses were introduced into the US in the early 1900’s and are believed to
have come from southern Germany, Holland, England and Belgium. Seeds identified as
“south German mixed bentgrass” (Casler, 2002) may have been a mixture of colonial
bentgrass, velvet bentgrass and creeping bentgrass, as evident in the mottled appearance
of old golf courses.

In the history of bentgrasses in the US, ‘Seaside’ was the oldest known released
cultivar in 1923. ‘Seaside’ creeping bentgrass, indigenous to coastal regions of
Washington and Oregon, was known to be an extremely variable grass that developed
into patches of individual strains with different colors, textures and densities. Most seed
supplies of cultivar ‘Seaside’ came from natural stands. In 1955, seeded variety
‘Penncross’ by Penn State University was released. It took another 30 years before
‘Providence’ became available in 1987. There are less than 20 known cultivars released
in the US between 1923 and 1995. New and improved varieties emerged in the last 15
years increasing the availability of commercial seeded varieties and out—competing
vegetative varieties that were susceptible to bacterial wilt and other diseases. Interest in

breeding improved creeping bentgrass stems from the demand for better materials with

improved agronomic quality, visual appearance and improved resistance to biotic and
abiotic factors. Turfgrass breeding involves selection of breeding lines, hybridization and
recurrent selection. Most developed cultivars of creeping bentgrass, A. palustris, in the
US such as ‘Penncross’,‘Penn A4’ and ‘L-93’ were obtained from phenotypic recurrent
selection of three or more clones descended from selection of promising lines. ‘Penn
G2’ and ‘Penn A4’, both released in 1995 were part of selections descended from six
parental clones. ‘L-93’ released in 1995 descended from fifieen parental clones. The
phylogenetic relationships of creeping bentgrasses in the US are unknown. Newer
cultivars may probably be selections coming from the common genetic pool of creeping
bentgrasses in the US. Genetic variability among creeping bentgrass cultivars was
narrow (.007 to .008) as determined using isozyrne markers and may be expanded with
the use of plant introductions (Wamke et al., 1997). Expanding genetic variability is
important because selection becomes more effective when diversity is high.

Information on the extent of genetic variability using more powerful DNA
markers among old and new creeping bentgrass cultivars, current breeding lines with
disease resistance by natural selection and potential plant introductions as parental

materials are important in turfgrass breeding programs.

Turfgrass breeding and use of molecular markers

Molecular markers have become important tools in plant breeding for
identification and selection. Isozyme markers were used to distinguish between several

creeping bentgrass cultivars (Yamamoto and Duich, 1994; Wamke et al., 1997 and 1998).

Drawbacks to isozyme use are protein instability (Jones 1983) and the limited number of
markers (Golembiewski et al., 1997) for biodiversity studies. Research using DNA
molecular markers such as restriction fragment length polymorphism (RF LP) markers
was applied to differentiate five creeping bentgrass cultivars (Caceres et al., 2000). The
potential for sequence characterized amplified regions (SCAR) markers enabled
development of species-specific probes for colonial and creeping bentgrasses, but not for
dryland bentgrass (Scheef et al., 2003). For biodiversity studies, a more powerful tool in
fingerprinting over other PCR—based techniques is by amplified fragment length
polymorphism (AFLP) analysis (Vos et al., 1995). The high number of identifiable
polymorphism was found useful for distinguishing genotypes, detecting linkages and
mapping loci in turfgrasses, such as berrnudagrass (Zhang et al., 1999) and zoysiagrass
(Ebina et al., 1999). AFLP analysis has not been reported for bentgrass. The technique
could be used to differentiate plant introductions of Agrostis species and to study

diversity among populations of bentgrass breeding materials.

Gray snow mold disease of bentgrass

Bentgrass is susceptible to a wide range of diseases (Vargas, 1994). Snow mold
(Typhula blight) causes devastating winter injury in turfgrasses. T yphula blight is caused
collectively by gray snow mold (Typhula incarnata Lasch) and speckled snow mold (T.
ishikariensis Irnai) (Hsiang et al., 1999). Typhula incarnata is important because the
fungus infects other grasses such as Poa, F estuca and Lolium and cereals. The disease
economically impacts North America, Russia, Japan and Canada and heavy fungicide

applications are currently the only means of control.

Symptoms caused by T. incarnata usually appear as circular, water-soaked or
straw-colored patches measuring 5 to 15 cm across and may coalesce (Hsiang et al.,
1999). Susceptible plants generally show water-soaked lesions and yellowish decaying
appearance; plants may be matted and appear slimy with mycelia. Mycelia, basidiospores
and or sclerotia produced become inoculum sources leading to new infection. Colonized
dead plant tissues decompose and disintegrate, and then the sclerotia fall to the thatch and
soil. The sclerotia remain as resting bodies during the summer months. Gray snow mold
was more commonly found, than speckled or pink snow mold, on golf areas where snow
cover occurred in less than 90 days (Millet, 2000). During periods of longer snowfall, the
pathogen infects and kills turfgrass in a significant larger area.

Snow molds are capable of infecting a wide range of plant hosts and bentgrasses
have a broad range of susceptibility to the fungus (National Turfgrass Evaluation
Program data, Anonymous, 2001). Typhula incarnata is ecologically versatile, adapting
to a wide geographic range (Matsumoto et al., 1995) and is reported to have several
incompatibility alleles based on mating systems (Bruehl and Machtmes, 1978). There is
no molecular information to support or describe variability in T. incarnata. Genetic
characterization using random amplified polymorphic DNA (RAPD) has been used to
characterize diversity between Typhula species (Hsiang and Wu, 2000) and study
variation within pink snow mold, Microdochium nivale (Fr.) Samuels (Mahuku et al.,
1998). RAPD analysis may be used to differentiate gray snow mold and study variation
within and among populations.

Research into snow mold disease resistance has been hampered by the absence of

controlled, reliable screening techniques, in addition to difficult identification of

pathogenic strains of the fungus. Field screening for snow mold is time consuming and
results may be unreliable due to several possible sources of variation such as presence of
multiple pathogens, unevenness of moisture or dryness in some areas, etc. Sources of
variation in scoring can be minimized using controlled conditions such as constant
temperature, concentration and age of inoculum, and the type or strain of inoculum used.
A screening procedure to identify resistant plants in replicated trials could increase the
reliability of results. Several genotypes may be screened independent of the presence of
snow in cold room chambers.

There are no known snow mold resistant cultivars of creeping bentgrass, A.
palustris or other species of turfgrass. Bentgrasses have a differential range of
susceptibilities or resistance to gray snow mold (Hsiang et al., 1999). Old golf courses
containing mixed populations of creeping bentgrasses may be possible sources for
resistant plants as natural selection pressures have occurred therein allowing the survival
of disease resistant genotypes. Another possible source for snow mold resistance is the
germplasm or plant introductions of Agrostis species. Using controlled screening
procedures, different sources can be explored to find bentgrass materials that may be used

to improve the creeping bentgrass.

OBJECTIVES

The four chapters in this dissertation aimed to contribute to turfgrass genetics and

enhance breeding efforts by:

0 Assessing the genetic diversity in 40 plant introductions of 14 Agrostis species
from 20 countries.

0 Comparing the genetic similarities of old and modern creeping bentgrasses,
the distinction of selected lines with resistance to gray snow mold, and genetic
relationships of tetraploid creeping bentgrass and hexaploid redtop bentgrass
genotypes.

o Characterizing diversity in 40 isolates of gray snow mold, T. incarnata from
populations in Michigan, Minnesota and Wisconsin, USA.

0 Developing controlled screening procedures against gray snow mold and
searching for a resistant genotype in creeping bentgrass populations and plant

introductions of A grostis.

REFERENCES

Anonymous. 2001. National Bentgrass Test -1998 . Data, Table 26. Snow mold
complex ratings of bentgrass cultivars grown on a fairway or tee. Rating of
Bentgrass Cultivars on a Green. 2001 data, Beltsville, MD.

Brown, R.W. and RS. Johnston. 1979. Revegetation of disturbed alpine rangelands. In:
Special management needs of alpine ecosystems. (Johnson, D.A. ed.) Range
science series no. 5. Denver, CO: Society for Range Management. pp 76-94.

Caceres, M.E., F. Pulpilli, E. Piano and S. Arcioni. 2000. RFLP markers are an effective
tool for the identification of creeping bentgrass (Agrostis stolonifera L.) cultivars.
Genetic Resources and Crop Evol. 47: 455-459.

Casler, M.D., Y. Range], J. Stier and G. Jung. 2003. RAPD Marker Diversity among
creeping bentgrass clones. Crop Sci. 43:688-693.

Comely, J.E., C.M. Britton, and FA. Sneva. 1983. Manipulation of flood meadow

vegetation and observations on small mammal populations. Prairie Naturalist
15:16-22.

Ebina, M., M. Kobayashi, S. Kasuga, H. Araya and H. Nakagawa. 1999. An AFLP based
genome map of Zoysia grass. PAG VII p278.

Funk, R. 1998. Opportunities for genetic improvement of underutilized plants‘for turf. 7th
Annual Rutgers Turfgrass Symposium. Rutgers University, New Jersey, USA.

Golembiewski, R.C., T.K. Danneberger, and RM. Sweeney. 1997. Potential of RAPD
markers for use in the identification of creeping bentgrass cultivars. Crop Sci.
37:212-214.

Hsiang T., N. Matsumoto and S. Millet. 1999. Biology and management of Twhula snow
molds of turfgrass. Plant Disease. 83(9):788-798.

Jones, T.W.A. 1983. Instability during storage of phosphogluco-isomerase isoenzymes
from ryegrasses (Lolium spp.). Physiol. Plant 58: 136-140.

Mahuku, G.S., T. Hsiang T. and L.Yang. 1998. Genetic diversity of Microdochium
nivale isolates from turfgrass. Mycol. Res. 102(5): 559-567.

Matsumoto, N., Abe, J. and T. Shimanuki. 1995. Variation within isolates of Typhula
incarnata from localities differing in winter climate. Mycoscience 36: 155-158.
Millet, S. 2000. The distribution, molecular characterization and management of

snowmolds in Wisconsin Golf Courses. PhD thesis. University of Wisconsin-
Madison, under DP Maxwell (adviser).

Scheef, E.A., M.D. Casler and G. Jung. 2003. Development of species-specific SCAR
markers in Bentgrass. Crop Sci. 43:345-349.

Vargas, J.M., Jr. 1994. Management of Turfgrass Diseases. CRC Press, Boca Raton FL,
USA.

Vos, R, R. Hogers, M. Bleeker, M. Reijans, T.V.D. Lee, M. Homes, A. Fritjers, J. Pot, J.
Poleman, M. Kuiper and M. Zabeau. 1995. AFLP: A new technique for DNA
fingerprinting. Nucleic Acid Res. 23(21): 4407-4414.

Wamke, S.E., D.S. Douches, BE. Branham. 1997. Relationships among creeping
bentgrass cultivars based on isozyme polymorphisms. Crop Sci. 37: 203-207.

Winterhalder, K. 1990. The trigger-factor approach to the initiation of natural
regeneration of plant communities on industrially damaged lands at Sudbury,
Ontario. In: Restoration ’89; the new management challenge: Proceedings, lSt
annual meeting of the society for ecological restoration: 1989. Hughes, H. et
al.(eds.) January 16-20, Oakland, CA. Madison WI: The University of Wisconsin
Arboretum, Society for ecological restoration: pp. 215-226.

Wamke, S.E., D.S. Douches and BE. Branham. 1998. Isozyme analysis supports
allotetraploid inheritance in tetraploid creeping bentgrass (Agrostis palustris
Huds.) Crop Sci. 38:801-805.

Yamamoto, I. And J .M. Duich. 1994. Electmphoretic identification of cross-pollinated
bentgrass species and cultivars. Crop Sci. 34:792-798.

Zhang, L.H., P. Ozias-Akins, G. Kochert, S. Kresovich, R. Dean and W. Hanna. 1999.
Differentiation of bermudagrass (Cynodon spp.) genotypes by AFLP analyses.
Theor. Applied Genet. 98: 895-902.

CHAPTER I

AF LP Analysis of Genetic Diversity in Bentgrass (Agrostis spp.)

ABSTRACT

Bentgrasses (Agrostis spp.) are widely occurring temperate grasses with more
than 220 species that represent a vast resource for genetic improvement of turfgrass
cultivars. Bentgrasses are normally outcrossing species and exhibit many ploidy levels.
Difficulties in morphological characterization, which are largely subjected to
environmental influences, have resulted in many synonymous species and uncertainties in
phylogenetic relationships. To study the genetic diversity and relationships between
bentgrass species, forty accessions from the USDA’S germplasm collection representing
fourteen species of Agrostis from twenty countries were investigated using fluorescence-
labeled AF LP analysis. Four hundred AF LP markers from five chosen primer
combinations were used to differentiate between bentgrass accessions using a bulk of 25
genotypes per accession. Genetic similarities between accessions ranged from 0.62 to
0.98 showing no duplication in the collection and a high level of diversity in Agrostis.
Both principal component analysis and UPGMA dendrogram clearly distinguished seven
groups. Genetic relationships between diploids and other polyploids were revealed in the

cluster groupings.

Key words: bentgrass, Agrostis spp., genetic diversity, AFLP

10

INTRODUCTION

Bentgrass, Agrostis sp. derivation from Greek: grass, forage, is distributed
throughout the world and belongs to the Poaceae family (Watson and Dallwitz, 1992). It
is a perennial or annual outcrossing polyploid (2n=14, 28, 42 etc.) which is widely used
for putting greens, golf courses, parks and forage. Some species were used for erosion
control and revegetation programs in disturbed areas where grazing, mining and flooding
had occurred (Brown and Johnston, 1979; Comely et al., 1983; Winterhalder, 1990).
Most bentgrass species are widely adapted to temperate climates and occur in a variety of
habitats. Variation in Agrostis species denotes potential for genetic improvement of the
species for use as turf.

Much of the work in classification of Agrostis is predominantly based on
morphological and cytological features (Bjorkman, 1960). Linnaeaus grouped Agrostis
based on the presence or absence of awns, panicle shape and color, and orientation of
culms and roots. The basic chromosome number of Agrostis is x=7 and differences in
ploidy level often determine species boundaries. Diploid species (2n=2x=14) are A.
alpina Leyss., A. elegans Walt, A. canina L., A. flaccida Hack, and A. nebulosa Boiss.
& Reut. Most bentgrass species such as A. capillaris L., A. castellana Boiss. & Reut, A.
palutris Huds. and A. stolonifera L. are tetraploids with 2n=4x=28 (Brede and Sellman,
2001). Species such as A. castellana, A. gigantea R, A. alba L., A. exarata Trin, A.
clavata Trin., A. diegoensis Vasey and A. hallii Vasey are known to exist in tetraploid
and hexaploid forms (2n=6x=42). In a wide collection of A. gigantea, Jones (1955a) only

found hexaploids. Because of the outcrossing nature of bentgrass, ploidy levels need to

11

be identified. Bonos et a1. (1999) used laser flow cytometry as a rapid option to validate
the number of chromosomes of certain species of Agrostis.

Current significant species in the USA are velvet bentgrass (A. canina L.);
colonial bentgrass (A. capillaris L. or A. tenuis Sibth); dryland or highland bentgrass (A.
castellana Boiss. & Reut.); creeping bentgrass (A. palustris Huds. or A. stolonifera L.);
red top bentgrass (A. gigantea L.); and Idaho bentgrass (A. idahoensis Nash). Creeping
bentgrass is the premier turfgrass species for closely mowed golf course putting greens
(Funk, 1998).

Intra- and interspecific hybridization is possible in Agrostis, however, very few
genetic studies have explained the contribution of diploids to polyploids and their
relationship. Hybridization experiments by Davies (1953), using germplasm from the
UK, showed that hybrids of A. stolonifera x A. gigantea were among the easiest to
produce and indicated some degree of homology between the two polyploid species.
Bivalent formation during meiosis in hybrids between A. capillaris L. x A. vinealis
Schreb., established A. capillaris to be a segmental allotetraploid and fi'om hybrids of A.
stolonifera x A. canina that A. stolonifera was a strict allotetraploid (Jones, 1955a). The
use of interspecific hybrids of A. tenuis, A. stolonifera and A. gigantea and their offspring
helped decode the genome constitution in Agrostis. Jones (1955b) suggested that if A.
tennis or A. capillaris was A1A1A2A2 and A. stolonifera was A2A2A3A3, then A. gigantea
would be A1A1A2A2A3A3. The A2 genomes of the two species were not confirmed to be
absolutely identical. Creeping bentgrass (A. stolonifera) was also found to hybridize with
ticklegrass (A. scabra Willd.) and spike bentgrass (A. exarata Trin.)(Welsh et al., 1987).

A. scabra was found to hybridize with A. trinii (Probatova and Kharkevich, 1983).

12

Warnke et a1. (1998) used isozyme analysis to study allotetraploid inheritance in creeping
bentgrass and found strong genetic evidence for disomic rather than tetrasomic
inheritance.

Little is known about bentgrass genetic relationships and there is much confusion
regarding assignment to groups. Early efforts to use morphological characters in
classification resulted in many synonymous species. A. stolonifera L. was listed as being
synonymous with A. alba var. palustris Huds., A. alba var. stolonifera (L.) Sm, A.
stolonifera var. compacta Hart, A. stolonifera var. palustris (Huds.) F arw. and A.
maritima Lam. (Biota of North America Program, BONAP Poaceae Listing; Plant Gene
Resources of Canada (GRIN -CA). A. gigantea R. is synonymous with three other species
(BONAP Poaceae Listing) and may include subspecies on the basis of rhizome and shoot
development and the morphology of the spikelet (Dihoru, 1980). Species A. trinii Turcz.
was found synonymous with A. vinealis ssp. trinii (Tzvelev, 1971) and A. flacidda ssp.
trinii (Koyama, 1987) while Soreng et al.(2002) taxonomy listed A. vinealis Schreb. as
being synonymous with A. canina ssp. vinealis (Turcz.) Hult. Kurchenko (1979) listed A.
trinii and A. canina both from Russia as different species based on morpho-analytical
characters and behavior in their natural environments. Differences in phenotypic
expressions are oftentimes the result of environmental fluctuations; therefore, DNA
fingerprinting is considered a more stable and reliable technique to explore genetic
diversity and relationships.

A wide variety of DNA marker technologies have been used to differentiate
bentgrass cultivars and species such as isozyme (Yamamoto and Duich, 1994; Warnke et

al., 1997), RAPD (Golembiewski et al., 1997, Scheef et al., 2003) and RFLPs (Caceres et

13

al., 2000). These techniques are limited by the low levels of polymorphism at the intra-
and interspecific levels. A more powerful tool in fingerprinting for biodiversity studies
over other PCR based techniques is by amplified fragment length polymorphism (AF LP)
analysis (Zabeau and V08, 1993; V05 et al. 1995). AFLPS have been used successfully in
order to study genetic diversity in crops like rice (Oryza sativa L. )(Zhu et al., 1998),
cotton (Gossypium sp.)(Abdalla et al., 2001) and common bean (Phaseolus
vulgaris)(Tohme et al., 1996). The high frequency of identifiable polymorphism is useful
for distinguishing among genotypes, detecting linkages and for mapping loci in
turfgrasses. Zhang et al. (1999) used AF LPs to differentiate bermudagrass (Cynodon
spp.) genotypes and determine genetic relationships among genotypes. In addition, they
showed that the use of fluorescence-based detection of AF LPs has improved both
fragment scoring and data handling. Ebina et a1. (1999) constructed an AF LP-based
genome map of zoysiagrass (Zoysia spp.) and developed a linkage map of QTLS
associated with some major traits. AF LPs have not been used to study bentgrass.
Understanding bentgrass diversity would facilitate the efficient use of germplasm
accessions to combine the favorable agronomic and disease resistance traits to produce
superior cultivars.

The objectives of this research were to study the genetic diversity between forty
Plant Introduction (PI) accessions comprising fourteen species of Agrostis from twenty
countries and determine the genetic relationships among the species based on AF LP

profiles.

14

MATERIALS AND METHODS

Plant materials and DNA extraction

Bulked leaf samples from 25 plants each of 40 Plant Introduction (PI) accessions
(source: USDA, Regional Plant Introduction Station, Pullman, Washington, USA) of
Agrostis species from different countries were used (Table 1.1, Figure 1.1). Warnke et al.
(1997) compared different number of samples per bulk from the same accession in
bentgrass and suggested the least variation using 25 plants per bulk for analysis to
minimize against sampling errors. Tissues were ground in liquid nitrogen (Extraction
buffer: Tris-HCl, SDS, NaCl) and precipitated using chloroform, sodium acetate and
ethanol. All samples were treated with RNAse and twice reprecipitated. Extracted DNA
was stored in 1% TE buffer. DNA quality was checked by running 5 pl of the undigested
samples in 1% agarose gel containing TBE buffer and compared to EcoRl digested

samples. DNA quantification was performed using DyNA Quant 200 Fluorometer.

AFLP analysis

Approximately 150-200 ng of DNA was used to do AF LP analysis for each
accession. Digestion was conducted using two restriction enzymes, EcoRl, a six base
pair cutter and Mse-l, a four base pair cutter. The AF LP procedure used in this study was
as described by Vos et al. (1995) with modifications. Pre-amplification was done on
FTC-100 thermal cycler (MJ Research) using 72°C for 2 min, 30 cycles of 94°C for 30
sec, 60°C for 30 sec, 72°C for 1 min, followed by elongation at 72°C 5 min and 4°C hold.
PCR products from initial ligation of adapters were checked on 1.5% TBE agarose gel.

Combinations of fluorescent(*) dye labeled E and M primers each with 3 selective

15

nucleotides at the 3’ ends were used. The following E“ primers were used: E*-ACA and
E*-AGC. The following M primers were tested: M-CAT, M-CAG, M-CGG, M-CTT, M-
CCT. The 15 pl selective amplification mixture consisted of 15 pmol E*-primer, 75 pmol
M-primer , 2 mM dNTP, 1.5 mM PCR buffer, 37.5 mM MgC12, 1.0 unit Taq Polymerase
and 1 pl pre-amplified product in deionized distilled water. The PCR cycling sequence
used for selective amplification was as recommended by Vos et al. (1995). Products
from selective amplification were checked initially on 1.5% agarose and diluted four
times with 0.1 TE buffer. For separation on acrylamide gels, samples consisted of 0.5 pl
of the amplified product and 0.5 pl loading dye. The samples were denatured at 95 °C for
5 minutes prior to loading and ran on a 6% Long Ranger polyacrylamide gel with 0.7X

TBE buffer using a Licor Gel Analyzer at a constant 800 v for 6 hours at 50°C.

Chromosome analysis

Root tips of species with an unknown number of chromosomes were collected and
fixed in 3:1 ethanol (95%), acetic acid (Farmer’s fixative). The root tip was rinsed and
placed in 3N HCl for 10 to 20 minutes to soften. On a glass slide, the meristematic
region was cut and squashed with a blunt end of the needle and a drop of acetocarmine
dye was added. Slides were viewed under a phase contrast microscope to determine the
number of chromosomes from a several mitotic cells. Chromosome numbers were
determined for P1195197 and P1299461, A. Iachnantha Nees; P1230236, A. munroana
Aitch. & Hemsl.; P1283174, A. transcaspica Litv.; P1477045, A. hygrometrica Litv. and

P1362190, A. mongolica Roshev.

16

I. UC‘

I1Ilrhuwatlra\

<L

a -2\

9! Fi‘PIININ

-\

~

aoE< fizom n <N SeamED n S ”auto—.54 n wD ”05955
n <3 Sou—LE. n MP EofiuBm H mm £me SEQ can «imam n Dd ”Egan—om u OM gum—Eon .I. .E fiesta—=02 n 42 uu=ow=o2
u 02 v28— " .5 Eu: n E SEE—um u mm diam H mm £5550 .1. v5 R5860 n ma ”can—cQEBm u :0 ”eve—:5 n <O ”game;

.86on “SERVE 33338 ov me 3988 35mm“: 283 AA PEME

      
        

8 l .2 l.
ENE: usfiéoa .< 92 some: a <

      

      
        

auto—.5.
5:0m

 

E: EeEo‘ou .<
EC «.5333 .<
E: 23:58 .<
EC 8385 <
E: «5:3 .<

new 28 8880. .<
$2 995% _<
83 ESE .<
63 segue <
82 £338 .<

    

A05: «6.393.: .<

23 as.

       

aotuE<
5.32

2.9.3—

 

    

55 £82: .< 6m ...z .me eases» .<
SE E5 .< 35 £8 ”53E .<
36 Sentences .< E. .me 83338 .<
Sm <2 Seesaw < 6m ..: .5 .me £538 .<
55 £238 .< :5 8E8 .<

23.3—

 

 

 

 

17

T MW:

_ A

_II A... a.

Table 1.1.
geographic origin of bentgrass (Agrostis spp.) accessions.

List of plant introductions (PI), species, number of chromosomes and

 

 

AFLP No.“ PI No. Species Chromosome No.(2n) Geographic Source (Code)

1 194 697 A. canina 14 Netherlands (NL)

2 230 233 A. canina 14 Iran (IR)

3 237 717 A. capillaris 28 Germany (DE)

4 252 045 A. capillaris 28 Italy (IT)

5 469 217 A. capillaris 42 USA 'Highland' (U SH)
6 311 011 A. capillaris 28 Romania (R0)

7 392 338 A. capillaris 28 Former Soviet Union (RU)
8 234 685 A. capillaris 28 Denmark (DK)

9 578 528 A. capillaris 28 USA 'Exeter' (USE)

10 302 830 A. castellana 42 Spain (E81)

11 287 741 A. castellana 42 Spain (ES2)

12 240 135 A. castellana 42 Portugal (PT 1)

13 240 139 A. castellana 42 Portugal (PT2)

14 240 133 A. caste/land 42 Portugal (PT 3)

15 240 131 A. castellana 42 Portugal (PT4)

16 287 744 A. castellana 42 Spain (ES3)

17 240 142 A. castellana 42 Portugal (PTS)

18 318 928 A. castellana 42 Spain (E84)

19 240 132 A. castellana 28 Portugal (PT 6)

20 235 440 A. palustris 28 Switzerland (CH)

21 235 541 A. palustris 28 Sweden (SE)

22 204 390 A. palustrr’s 28 Turkey (TR)

23 578 530 A. palustris 28 America (US)

24 269 838 A. stolonifera 28 Germany (DE)

25 494 119 A. stolonifera 28 Netherlands (NL)

26 494 118 A. stolonifera 28 Ukraine (UA)

27 230 235 A. stolonifera 28 Iran (IR)

28 318 934 A. stolonifera 28 Spain (ES)

29 439 027 A. stolonifera 28 Former Soviet Union (RU)
30 195 917 A. Iachnantha 21" Ethiopia (ET)

3] 299 461 A. Iachnantha 21* South Africa (ZA)

32 362 190 A. mongolica 28* Mongolia (MO)

33 477 045 A. hygrometrica 28"I Uruguay (UY)

34 230 236 A. munroana 21* Iran (IR)

35 234 681 A. scabra 42 Canada (CA)

36 598 462 A. trinii 28 Russian Federation (RU)
37 440 110 A. vinealis 28 Russian Federation (RU)
38 283 174 A. transcaspica 14* Former Soviet Union (RU)
39 383 584 A. gigantea 42 Turkey (TR)

40 443 051 A. gigantea 42 America (US)

 

*Number of chromosomes were determined in this study.

"AFLP No. = designated sample number for accession in the AFLP analysis.

18

Data Analyses

Gels were visualized using Gene ImagIR 4.0 (Scanalytics, Inc.,VA, USA). Each
informative polymorphic band was scored manually as l for presence and 0 for absence.
Analyses were done using Numerical Taxonomy and Multivariate Analysis System,
NTSYS v.2.1 (Rohlf, 2000). Genetic similarities based on Jaccard’s coefficients
(Jaccard, 1908) were calculated among all possible pairs using the SIMQUAL option and
ordered in a similarity matrix. The similarity matrix was run on Sequential,
Agglomerative, Hierarchical and Nested clustering, SAHN (Sneath and Sokal, 1973)
using Unweighted Pair Group Method with Arithmetic Mean (UPGMA) as an option
(Sokal and Michener, 1958). Cophenetic correlation was calculated to measure goodness
of fit. Principal component analysis (PCA) was run using SYSTAT to identify the
number of groups based on Eigen vectors. The TREE module of NTSYS v.2.1 was used

to produce the dendrogram and cluster groupings (Rohlf, 2000).

RESULTS AND DISCUSSION

AFLP fingerprinting and polymorphism level in bentgrass accessions

Selective amplifications were made using different primer combinations from the
final products of EcoRI/Mse digestion, adapter ligation and pre-amplification. From the
initial eight selective primer combinations, five combinations were chosen for clarity,
repetitiveness in duplicated gel runs and high levels of polymorphisms. Four hundred
polymorphic markers were scored from the five chosen selective primer combinations.
The average number of polymorphic bands across the accessions ranged from 100 to 180

markers per primer combination and 20 to 145 polymorphic bands per individual lane.

19

Data in Table 1.2 showed the selected primers and number of polymorphic markers
scored. The high level of polymorphism revealed the wide genetic diversity between the
Agrostis accessions. Monte et al (1993) found that if extensive diversity exists among
taxa or genus, fewer probes or primer combinations showing high polymorphism may be
sufficient to distinguish genotypes. None of the bentgrass accessions shared an identical
DNA fingerprint. AFLP profiles between accessions indicated that the collection does
not contain duplications. The high level of polymorphism has facilitated analysis of the
genetic diversity among bentgrass genotypes.

The most robust primer combination with the highest number of polymorphism
was E-ACA/M-CAG, whereas the lowest number was observed with E-AGC/M-CGG.
Approximately 12% of the markers were specific for individual Agrostis species.
Specific AF LP markers were found for A. Iachnantha, A. vinealis, A. scabra, A.
munroana, A. stolonifera/Apalustris, A. transcaspica and A. hygrometrica, whereas no
specific markers were detected for A. canina, A. capillaris, A. castellana, A. mongolica
and A. gigantea. Data suggest the possibility of developing probes from specific markers

to effectively discriminate between some species in the future.

Diversity between accessions and species

Gaps in phylogenetic information of Agrostis have resulted from too few
hybridization studies, difficulty in obtaining old botanical records and inconsistencies in
morphological characterizations i.e. synonyms, subspecies, and re-classification of some
species. New phenotypes with different number of chromosomes and distinct

characteristics have been given new species names (Probatova and Karkevich, 1983).

20

Table 1.2. Number of polymorphic bands obtained from different primer

 

 

combinations.

Primer Pair No. Polymorphic Bands
E-ACA/M-CAT 138
E-ACA/M-CAG 142
E-AGC/M-CAT 29
E-AGC/M-CAG 69
E-ACAfM-CGG 21

 

Adapter and primer sequences were adapted from Vos et al. (1995).

E=5'-GACTGCGTACCAATTC-3', M=5'-GATGAGTCCTGAGTAA-3'.

21

line
ChIl

clu-

let:

dip

and

C01"

C06

l'P

Another possible source of uncertainty results from spontaneous natural hybrids where
lineage and ploidy levels are not known. In this study, assessment of number of
chromosomes of the fourteen species of Agrostis from the literature and conducted
chromosome analyses confirmed the wide range in ploidy levels (2n=14, 21, 42) (Table
1.1). Physical and cytological examinations of the different species showed that ploidy
level may not be indicative of plant size in bentgrass. In the germplasm collection,
diploid A. canina was much smaller compared to another diploid A. transcaspica, which
in turn had wider and longer leaves and was larger than a triploid, A. munroana.
Investigations at the molecular level may provide a clearer understanding of the diversity
and relationships in bentgrasses.

In this research, four hundred AF LP markers from five selective primer
combinations were used to compare 40 bentgrass accessions. Pairwise similarity
coefficients (sc) were computed based on shared and unique amplification products using
UPGMA (Table 1.3). Based on similarity coefficients, the closest pair would be
P1235440 (Switzerland) and P1235541 (Sweden) at 0.98. Both accessions belonged to A.
palustris (creeping bentgrass), had the same number of chromosomes and were found in
Europe. The difference between the two PI lines may be due to minimal within-species
allelic variation probably resulting from recombination events. Data in Table 1.3 also
showed that the most dissimilar pair would be P1230235, A. stolonifera from Iran and
P1477045, A. hygrometrica from Uruguay. The similarity coefficient was only 0.58
indicating large variability in the genomic constitution. Iran and Uruguay are found

geographically in distant hemispheres that separated A. stolonifera and A. hygrometrica

22

Ah hr.~< §.ia.i~nur‘-n~Ilhv‘FHAUPHVAHLh\-h‘

writ: ICCSSFILPCCU EUFCEL U)... .—C 3.3—u -.C.~.u anJIIUUUS Tenn—r. .a..:.a.:.x~a§\.v ZZSLwHCU£ CV L3. $2.0...Jmtbcu \A.~.~.:~:::V. UCUCUFV .~.MI~ 0~G3~

AA 033. E van: .oZ E 05 9 $2538.80 98 max—ans AAA: 05 5 87.808 no.“ c383: 0383 33:»;vo u .02 mAm<3

 

2: NS who who 23 23 Ba 25 N3 :5 2.2. N3 2.6 m8 a; $5 :6 85 N3 NE on
2: a... as 23 2:. 8o 22 as 2:. 23 23 Se 23 2:. as 2:. 23 NE a; 2
2: 3o 23 2:. go as as 2:. 23 c2 ”3 23 23 2:. :3 2:. N; e; 2

2: a... 8.2. 23 3o 85 23 N3 85 2:. 3o 23 23 2:. 2:. «3 e8 :

2: 2:. 23 2:. :3 2:. 2:. 23 23 2:. 2:. 23 23 23 and NE 2

2: a... 23 :3 23 2:. 23 23 2:. 23 2:. :3 :3 :3 m3 2

2: 3a 3c 23 as 23 a... 23 2:. 2:. 2:. 23 NS “3 3

2: 23 23 So as a... 23 23 2:. 3:. :3 «.8 e8 2

2: 2:. 8o 85 23 2:. 3o 2:. 3:. 23 2.5 E. 2

2: 23 2:. 3:. 32 3:. 3:. 23 23 N3 «3 :

2: as :3 :3 2:. as 33 :3 25 Ed 2

2: as 23 23 2:. 23 23 who who a

2: 85 23 23 33 :3 m2 A; w

2: 85 :3 23 23 So m; A

2: 23 23 2:. NS :5 e

2: s; 33 NS N; m

2: as Ed 2d 4

2: 2.5 m; m

2: 8a m

2: _

ewaaoaéooeom 2 2 2 2 2 3 2 2 : 2 a w a e m e m N _ :az

 

.mAm< wofinfiegoomouosm
mama: 3088588 Barn 26 .«o 8% 88m maommmoooe Adam SEEMS mmfiwfion ov pom $56308 bum—ME? £850 .A.MA 03.5.

23

AA 03...... E cam: .02 E on. 3 éeamohoo v5 Ema—as. .55.. o... 3 5.968.. .8 .38.... 0388 32.3.86 n .02 muffle...

 

E... 2.... E... E... E... E... E... E... 2.... E... E... E... 2.... 2.... E... E... E... E... E... E... E
E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E
E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E
E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E
E... E... E... E... E... E... E... E... E... E... E... 2.... E... E... E... E... E... E... E... E... E
E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E
E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E
E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E
E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E
E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... 2.... E... E... E... E... E
E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E
E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E
E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E
E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E
E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E
E... E... E... E... S... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E
E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E
E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E
2.... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E
E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E... E
Engages... E a. a. E. e. m. z m. N. .. 2 a .. E E n v m N . :...z

 

NEE... 3.33-85888c mam: 30.35388
.68.... 3m .3 8.... So... 8388.... Adam 2:8..va $895.. ow .8 356508 bus—Eam 0:280 .Aaezaautaeov .~.mA «Bah

24

... 2...... a. 63.... 62 E 2... 9 3:88.80 88 £9.35 64...... o... a. 80.....008 .o. .38.... 0.683 6033.86 u 67. .35.:

 

66.. 66.6 2.6 .56 3.6 .56 656 M66 656 666 66.6 :6 26 N56 m6 N56 3.6 26 N56 2.6 6..
66.. 666 6.6 3.6 N56 .66 V6.6 2.6 N66 M66 2.6 66.6 v.56 2.6 66.6 2.6 666 .66 66.6 6m
66.. 666 ".56 .56 666 N66 3.6 N66 .66 2.6 65.6 2.6 :6 2.6 6.6 2.6 656 2.6 an

66.. 666 666 56.6 .66 66.6 .66 N66 666 566 N66 566 666 56.6 66.6 m66 66.6 mm

66.. 56.6 .66 66.6 656 N66 N66 $6 566 566 56.6 66.6 666 66.6 >66 .66 6M

66.. .56 66.6 66.6 56.6 66.6 666 666 66.6 >66 >66 66.6 .56 666 66.6 mm

66.. 66.6 66.6 66.6 666 .66 66.6 M66 666 66.6 66.6 66.6 .66 66.6 vm

66.. .66 .66 N66 66.6 #66 666 66.6 .66 .66 .66 666 m66 mm

66.. .66 N66 56 2.6 :6 :6 65.6 2.6 3.6 666 «>6 Nm

66.. V66 N66 .66 .66 66.6 m66 66.6 66.6 66.6 666 .m

66.. N66 666 M66 566 66.6 56.6 666 66.6 .66 6m

66.. 65.6 6h6 :6 65.6 666 666 656 3.6 6N

66.. 666 «.66 .66 .66 .66 666 N66 6N

66.. 66.6 .66 N66 .66 N66 N66 N

66.. 666 M66 .66 $6 «.66 6N

66.. .86 N66 36 8.6 6N

66.. .66 66.6 3.6 .N

66.. 66.6 36 MN

66.. 666 NN

66.. .N

6.. 6... mm mm 6m mm QM mm Nm .m on 6N 6N 5N 6N 6N VN mN NN .N 3.07.

 

6...”? 60.06...-0888856 wfim: 98.38688
.08.... gm. .0 8.... 80... E86308 66% 3......me $89.86 6.. .o. 85.3.6000 582.8% 062.00 .Auczasucucuv 6.6.. 0.6....

25

Grou 1
_ A.canina_NL p
'—I II-—— A. canina IR
A.vlnealis RU "‘ Group 2
A.scabra_'CA
A. capillaris_DE
A. capillaris_l T
A. caplllaris_ US]!
A. caplllaris_R0
fi'c""$5‘"£‘.-fi%
.cap at _
A. capillaris_ USE Gm" 3
A. castellana_ESI
A.castellana_PTI
— A. castellana_PT2
_. A.castellana PT5
A. castellana:PT3
A.castellana_ESl
A. castellana_PT6
A. castellana_ES3
A. castellana_PT4
A.castellana ESZ
A. triniLRU‘
A. glgantea__ US
L; A.palustrls_CH
A.palustrls_SE
A A.stoloni era_DE
A.stolom era NL
A.stolon era: UA
A.stolon fera ES Group 4
A.palustris_ US
A.palustrls_ TR
A.stolon3[era_IR
A.stolon fera_RU
A.mongolica M0
AJranscaspIEa RU
A. igantea T R-
a—— A. achnaniha_E T
_r 1— A.!aclmantha ZA
A.munroana 'IR _ Group 6
A.hygrometrTca_ U Y — Group 7
I

 

 

 

 

 

 

 

 

I

 

 

 

 

 

Group 5

 

 

 

 

 

I I I I I I I I I r I I I I l I I I I

0.62 0.71 0.80 0.89 0.98
Similarity coefficient

Figure 1.2. UPGMA dendrogram of 40 accessions of 14 Agrostis spp. from 20
different countries. PCA analysis distinguishes seven groups based on Eigen

values > 1.0.

Legend: CA = Canada; CH = Switzerland; DE = Germany; DK = Denmark; ES = Spain; ET =
Ethiopia; IR = Iran; IT = Italy; M0 = Mongolia; NL = Netherlands; PT = Portugal; R0 =
Romania; RU = Russia and former USSR; SE = Sweden; TR = Turkey; UA = Ukraine; US =
America; UY = Uruguay; ZA = South Africa

26

098 JO

 

089-1

 

Cophomtic coefficierls 080-

0.11-

 

.. ll 0‘ 0)..)[OIOCODO o

. WOO

oaan....j--..l..--

058 068 0.78 088 0.98
SW3] coefficient

 

 

 

Figure 1.3. Plot analysis of cophenetic correlation and similarity coefficient as a
measure of goodness of fit of the similarity indices. r = 0.95951 = normalized Mantel

statistic Z; Approximate Mantel t-test: t = 11.9767; P( Z < obs. Z: p = 1.0000).

27

and AF LP analysis suggested the two species had the least homology, genetic exchange
or introduction among the accessions studied.

A single dendogram was generated from the UPGMA cluster analysis with one
possible tie found between the closest pair (Figure 1.2). A cophenetic-value (ultrametric)
matrix was generated from the coefficients of SAHN’s cluster analysis of the distance
matrix. The cophenetic correlation was calculated (r = 0.96) as a measure of goodness of
fit and the results were plotted in a phenogram (Figure 1.3). Using SYSTAT, a rotated
PCA with the bands as observations was used to determine the number of factors or
groups based on eigenvalues greater than one. Seven groups were extracted, which
explained 72% of the total variance. The dendrogram showed a similarity coefficient of
0.73 for these seven groups.

Group 1 consisted of two accessions of A. canina (Netherlands and Iran, sc =
0.90) and A. vinealis (Russian Federation, sc = 0.76 to A. canina). Though
geographically distant in origin, the two A. canina diploids were morphologically similar
and AF LP data supported their grouping. In Figure 1, A. vinealis grouped closest to A.
canina than the other twelve species in the study and confirmed their genetic and physical
similarity. Species in Group 1 showed very fine, short erect leaves and low growth, but
A. vinealis was rhizomatous and confirmed earlier descriptions (Hubbard, 1984; Funk,
1998; Brilman, 2001). In catalogues, A. vinealis Screb. was listed as being synonymous
to eight other species names: A. canina ssp. montana Hartman, A. stricta J.F. Gmel
(BONAP Poaceae Listing), A. syreistschikowii P.A.S., A rubra L., A. ericetorum RE, A.
tenuifolia M.B., A. coarctata E. and A. pusilla D. by the Royal Botanic Garden

Edinburgh. Jones (1955a) found that A. canina ssp. montana (A. vinealis) also known as

28

brov
disti
m'Ih
whi}
char
.4. 0
had
(Hu‘

spec

shm
also
ploi.
gem

seal

acre
the

com

brown velvet bentgrass, was an autotetraploid form of A. canina and difficult to
distinguish in field specimens. Davies (1953) correlated the morphological difference
with ecological preference. Velvet bentgrass occurred in natural wet and damp soils
while brown velvet bentgrass was found in heaths and upland ground. Molecular
characterization by AFLP confirmed the relationship between A. canina and A. vinealis.
A. canina was morphologically distinguished from other species of Agrostis physically by
having a longer, more pointed ligule and shorter palea than A. capillaris or A. palustris
(Hubbard, 1984). Support that A. canina may be grouped separately from the other
species was also shown by AF LP data.

Group 2 consisted only of P1234681, A. scabra (Canada) and AF LP analysis
showed that the similarity with A. canina was only 0.74. Morphologically, A. scabra was
also a non-rhizomatous bunch type grass like A. canina but the two species differed in
ploidy levels. Dissimilarity may come from the hexaploid nature and more diverse
genetic composition of the species in Group 2. Known as ticklegrass or hairgrass, A.
scabra Willd, a delicate species was listed as being synonymous with winter bentgrass
(A. hyemalis Tuck.) but differed in flowering time (Lawrence, 1986).

Group 3 consisted of three subgroups. Subgroup A consisted of the seven
accessions of A. capillaris (colonial bentgrass) from seven countries. This indicated that
the seven allotetraploid accessions were similar and/or may have originated from
common diploid ancestors. Visual classification between colonial and highland bentgrass
was difficult. Hubbard (1984) separated them by flower color, ligule size and growth
habit. Two accessions of A. capillaris (US) cv. ‘Highland’ and cv. ‘Exeter’ were

reclassified as A. castellana (Hubbard, 1984; Shildrick, 1976; Brilrnan, 2001). Steiner

29

pr:

0L1

W.

0P

[he

of.

(NC

prc

gig
syn

3113

[fire
of ,

app.

and Lupold (1978) supported the suggestion that A. capillaris cv. ‘Highland’ should
belong to A. castellana based on the percentage distribution of the form of palea apex, the
presence and angle of basal hairs and awn types. In this study, AF LP analysis of
P1469217 ‘Highland’ and P1578528 ‘Exeter’ showed that though they contained a small
number of specific bands present only in A. castellana, they clustered more with the A.
capillaris group. There is no information whether the plant introductions were seeded in
open or isolated places during seed increase. Cross contamination at any time may add to
the taxonomic variation. Yamamoto and Duich (1994) could not distinguish ‘Highland’
and ‘Exeter’ from the other ten colonial bentgrasses using phosphoglucoisomerase,
glutamate oxalotransaminase, peroxidase and topoisomerase isozymes. Difference at the
Pox—2 locus was observed (Yamamoto and Duich, 1994) but peroxidase isozymes are
oftentimes unstable and dependent on stress conditions. Subgroup B consisted of all PI
accessions of A. castellana from Spain and Portugal with no distinction as to groupings
based on origin. The two subgroups, A and B, may share common diploid species
progenitors and may be related with A. capillaris from Germany and Italy.

Subgroup C consisted of P1598462 A. trinii (Russian Federation) and P1443051 A.
gigantea (USA). Classification of A. trinii Turcz. has been unclear and listed as being
synonymous to A. canina ssp. trinir' (Turcz.) Hulten (Soreng et al., 2003). Cluster
analysis however showed A. trinii to be dissimilar to A. canina (sc = 0.70 to 0.73).
Skolovskaya (1938) described A. trinii in Siberia and Orient as having both diploid and
tetraploid forms. The diploid form of A. trinii may have been recognized as subspecies
of A. canina (Brilrnan, 2002). P1598462 A. trinii was known to be a tetraploid and

appeared different phenotypically from A. vinealis, an autotetraploid form of A. canina.

3O

Agrostis trinii (Russian Federation) could be an allopolyploid with one chromosome set
different from A. canina and A. vinealis, thus forming separate groups. The other
member of this subgroup, A. gigantea (US) surprisingly was not grouped with the A.
gigantea (Turkey). Hexaploid A. gigantea genomic constitution of A1A1A2A2A3A3 may
have consisted of AlAl from A. canina, with the A2A2A3A3 probably from A. stolonifera
or A1A1A2A2 from A. capillaris (Jones, 1955b). AF LP data supported both possibilities.
The two PI lines of A. gigantea may possibly have different chromosome sets but share
the common A1A1 genome. Based on the clustering, P1443051 (USA) may have the
A1A1A2A2 from A. capillaris while P1383584 (Turkey) may have the A2A2A3A3 from A.
stolonifera. A. gigantea (Turkey) may have a second genomic constitution originating
from another species. In this study, A. transcaspica (Former USSR) was found to be
diploid and clustered closest with the A. gigantea (Turkey). This indicated the possibility
that A. transcaspica was closely related to this species and may be the source for the
A3A3 genome in A. gigantea (Turkey). This relationship would be interesting to examine
in future interspecific hybridization and cytogenetic studies.

Group 4 consisted mainly of the creeping bentgrass (A. palustris and A.
stolonifera), A. gigantea (Turkey), A. mongolica and A. transcaspica. There has been
considerable taxonomic confusion regarding creeping bentgrass and whether they should
also be A. stolonifera with subspecies palustris, ssp. stolonifera or ssp. gigantea. The
genetic similarity coefficient between PI accessions in this group ranged from 0.82 to
0.95. Genetic dissimilarity computed from 1- sc x 100% (Zhang et al., 1999) ranged from
5 to 18%. Turf breeders believe A. palustris (USA) have originated from and frequently

outcrossed with materials from Europe. AFLP data supported this idea and results

31

shc

fro

Tu

Eu

011

1312'

SI!

\K’

showed that USA creeping bentgrasses may share some genetic similarity with those
from Switzerland and Sweden. The most divergent in A. stolonifera would be from
Turkey, Iran and the former USSR as opposed to those originating from other parts of
Europe. No separation of groups for A. palustris and A. stolonifera was observed based
on AF LP, but they differed slightly from A. gigantea. The difference may be due to the
third chromosome set of hexaploid A. gigantea. Caceres et al.(2000) used RF LP markers
to distinguish four creeping bentgrass A. stolonifera cultivars from A. capillaris
‘Highland’. AF LP data supported that PI accessions of A. stolonifera from different parts
of the globe were in one group and differed from A. capillaris (Group 3, Subgroup A).
Species A. stolonifera also shared slight similarities with A. mongolica (sc = 0.76 to 0.87)
and A. transcaspica (sc = 0.73 to 0.80). P1362190 A. mongolica plant type was also
stoloniferous and chromosome analysis showed that the bentgrass from Eastern Europe
was also a tetraploid. UPGMA analysis grouped A. mongolica (Mongolia) closest to A.
stolonifera (Russia). Because Mongolia and Russia are geographically adjacent, these
countries may have similar environmental conditions favorable to both species. The
fourth species in Group 4, which comprised mostly of stoloniferous bentgrasses, was A.
transcaspica, P1283174 (Former USSR). Physical examination showed A. transcaspica
also to be creeping but differed in leaf characteristics. The species has wider (6 t018 mm)
dark green, thick leaves with pointed tips. Soreng et al. (2003) has listed A. transcaspica
Litv. as being synonymous to A. stolonifera ssp. transcaspica (Litv.) Tzvelev. AFLP
clustering and similarity coefficients indicated that A. transcaspica, a diploid may have

contributed to the tetraploid or hexaploid creeping bentgrass genome.

32

inc

pill

re;

“1'

3C

In;

Sp

at

Cl

l6

Group 5 consisted of A. Iachnantha N. (Ethiopia and South Africa). The African
bentgrasses were morphologically distinct from the other bentgrass species. P1195917
(Ethiopia) bentgrasses were shorter (4 to 10 inches) than P1299461 (South Africa, >10
inches). The latter also has fewer leaves, harder stalks, and produced very tall flowering
panicles (>2 feet) in the greenhouse but were highly sterile. The chromosome number
reported here differed from the listing of the Index to Plant Chromosome number (IPCN)
with gametophytic count (n=21) and sporophytic 2n=28. Chromosome analysis of
2n=21 may suggest intra- or intercrossing variation during seed increase. The two P1
accessions of A. Iachnantha has sc=0.94 based on AFLP analysis. Eleven specific AF LP
markers were found that could distinguish A. Iachnantha species from the other thirteen
species.

Group 6 consisted only of P1230236 A. munroana (Iran). Background information
about A. munroana Aitch. & Hemsl. was minimal and was earlier referred to as
Calamagrostis munroana (Aitch. & Hemsl.) Boiss. in 1884 (Soreng et al., 2003).
Chromosome analysis of plants of P1230236 showed 2n=21 which confirmed earlier
reports (Gohil and Koul, 1986; Mouinuddin et al., 1994). Physical analysis of the mature
triploid plant showed a short plant stature (2 to 6 inches) with fine (2 to 3 mm width),
flat, sofi, normal green leaves. Species A. munroana was observed to be a bunch type
bentgrass and early flowering with the florets openly branched. A. munroana plants were
morphologically distinct from triploids of A. Iachnantha. Their triploid genome
constitutions may be largely unlike and AF LP results showed sc = 0.61, thus forming

separate groups.

33

wl
dis

C01

Group 7 was the most genetically distant from the thirteen other species studied
and included A. hygrometrica (Uruguay). Seven specific AF LP markers were found for
this species. A. hygrometrica Nees. has nine synonyms in genus Agrostis or Bromidium
(Soreng et al., 2003). Plant materials from P1477045 were low growing, bunch-type
grasses with hard, lengthy flowering panicles. AF LP analysis showed that bentgrass
germplasm from Uruguay (South America) formed a separate group from other
bentgrasses of Europe, Asia or North America. Distinct ecological conditions among
continents and unique germplasm pools from which the A. hygrometrica may intercross
would differentiate the accessions.

Assessment of genetic diversity in germplasm collections from several geographic
locations using AFLP markers has been conducted for Moms germplasm (Sharma et al.,
2000) and the Triticeae tribe (Monte et al., 1993). Positive correlations were found for
cluster groupings and geographic distances. Results in this study indicated that
geographically adjacent countries like Spain and Portugal have bentgrass accessions
which also clustered together as in Group 3, Subgroup B and bentgrass accessions from
distant locations (Iran vs. Uruguay) were in separate groups with low similarity
coefficients. Possibilities of genetic introductions may have occurred with migration,
selection and breeding among the colonial, highland and creeping bentgrasses from
EurOpe and USA. Local environmental adaptation may play a significant role in Agrostis
diversity.

AF LP analysis revealed its usefulness for assessing germplasm collection for
possible duplications. It may also indicate where incorrect species determinations would

be in the GRIN system or germplasm collection. The four hundred polymorphic markers

34

from five chosen primer combinations showed the high level of diversity between the
Agrostis germplasm and distinguished the seven groups. Ploidy level differences did not
give ambiguous results in scoring as highly polymorphic, repetitive and specific bands
were found. The high cophenetic correlation showed the goodness of fit of the similarity
indices. AF LP analyses supported the hybridization paths between species (Figure 1.4).
The dendogram showed the relationships between A. canina with A. vinealis but not with
A. trinii. Cluster analysis also showed that two hexaploid A. gigantea from different
geographic sources (USA and Turkey) were not grouped together, possibly due to
different chromosome sets. A possible diploid progenitor would be A. transcaspica. The
percentage genetic dissimilarity among creeping bentgrasses indicated considerable
potential for the improvement of turf. Turfgrass breeders may develop superior cultivars
either by crosses with germplasm accessions from the same species or among varying
species. Important traits from other Agrostis species can be introduced to cultivated
bentgrasses and AFLP analysis would be a useful tool to monitor introgression and
molecular tagging. Using specific amplified products, sequence characterized amplified
primers may be developed to genetically distinguish the different bentgrass species in the
future. AF LP analysis may be used in identifying bentgrass genotypes and clusters,
constructing core collections and screening for duplicate or misclassified accessions in

germplasm collections.

35

.3588 3:95 .3 uoamomufiv mowing“ n5? .3 Btaaam 33333—2
cam $30.5 2.8 .3 vowmomvfiv 86on annoumwmo mhmkfia cougars»: .«o Sana d..— 95w:—

   
     

      

umnxv 3325:: .1
«Ex ~830on
.56:an

am": 333533 .V.
muE< 8:835

  

mmuxv uflfififiuaa. .
now—0:2 .m 8:535

     

  

mnflxv
u used

a

 

  

waflxv
aufieunefi .V

 
  

 

n<n< n<u<_<_< "Yugo
anxcnzN Quuauwmuix

 

 

 

 

3
. mm“ v
a§x~=e~$u .V

m {<33 0

”muss. “3.33mi

 
    

  

an":
cumkuuuazuhfi

 

usauuui

 

 

_<_<_<_<
«Nuxvucm
£353.

 

 

 

+

 

II

n<n<—<—< m 0

Ti

 

 

 

 

.wmflxv §§§-N~§U .wN

an: “feign

 

 

 

n<n< Euxm 5:535
Spanning—Sm 8522::

 

 

 

 

«<3 Euxm 8:5on
839282» Exes—:3

 

 

 

 

 

/ 2

_<_<
V— "XNHGN
u:..=3.V

 

 

 

 

36

Abd;

Bjor'

Ben:

Brec

Bril:

Bro

Cac

Cor

Da‘

Di}

Ebi

REFERENCES

Abdalla, A.M., O.U.K. Reddy, K.M. El-zik and A.E. Pepper. 2001. Genetic diversity and
relationships of diploid and tetraploid cottons revealed using AF LP. Theor.
Applied Genet. 102:222-229.

Bjorkman, 8.0. 1960. Studies in Agrostis and related genera. Symbolae Botanicae
Upsalienses XV 11. A-B Lundequistska Bokhandeln UPPSALA. 112p.

Bonos, S.A., K.A. Plumley and WA. Meyer. 1999. The use of laser flow cytometry for
species determination in Agrostis. Agron. Abstr. (ASA/CSSA/SSSA) 91:140.

Brede, AD. and M.J. Sellman. 2002. Three minor Agrostis species: Redtop, Highland
and Idaho Bentgrass. In Casler, M. and Duncan, R. (eds.) : Turfgrass Biology,
Genetics and Breeding. J. Wiley & Sons, Inc. N.J., USA. pp 207-224.

Brilrnan, LA. 2002. Velvet Bentgrass (Agrostis canina L.) In Casler, M. and Duncan, R.

(eds.): Turfgrass Biology, Genetics and Breeding. J. Wiley & Sons, Inc. N.J.,
USA. pp. 201-206

Brown, R.W. and RS. Johnston. 1979. Revegetation of disturbed alpine rangelands. In:
Johnson DA ed. Special management needs of alpine ecosystems. Range science
series no. 5. Denver, CO: Society for Range Management pp 76-94.

Caceres, M.B., F. Pulpilli, E. Piano and S. Arcioni. 2000. RFLP markers are an effective
tool for the identification of creeping bentgrass (Agrostis stolonifera L.) cultivars.
Genetic Resources and Crop Evol. 47(4): 455-459.

Comely, J.E., C.M. Britton, and FA. Sneva. 1983. Manipulation of flood meadow
vegetation and observations on small mammal populations. Prairie Naturalist
15:16-22.

Davies, W.E. 1953. The breeding affinities of some British species of Agrostis. Brit.
Agric. Bull 5:313-316.

Dihoru, G. 1980. Two species of Agrostis gigantea. Studii-si-Cercetari-de-Biologie,
Biologic-Vegetala 32(1): l9-26, in CAB Abstracts no. 811696587.

Ebina, M., M. Kobayashi, S. Kasuga, H. Araya and H. Nakagawa. 1999. An AFLP based
genome map of Zoysia grass. PAGVII P278.

Funk, R. 1998. Opportunities for genetic improvement of underutilized plants for turf. 7‘h
Annual Rutgers Turfgrass Symposium. Rutgers University, New Jersey.

37

(iohjL

(klenfl
Hubbax

Jaccard

Jones.

Jones.
Koyam

Kurchc

Laura
Klonte

“Quin

Plant
Probe)

Rmufi

Gohil, RN. and K.K. Koul. 1986. SOCGI plant chromosome number reports — IV. J.
Cytol. Genet. 21:155.

Golembiewski, R.C., T.K. Danneberger, and RM. Sweeney. 1997. Potential of RAPD

markers for use in the identification of creeping bentgrass cultivars. Crop Sci.
37:212-214.

Hubbard, CE. 1984. Grasses. A Guide to their Structure, Identification, Uses and
Distribution in the British Isles. Third Edition. Revised by ICE Hubbard.
Penguin Books USA Inc, New York, NY, USA.

Jaccard, P. 1908. Nouvelles recherches sur la distribution florale. Bull Soc Vaud Sci Nat
44:223-270.

Jones, K. 1955a. Species differentiation in Agrostis II. The significance of chromosome
pairing in the tetraploid hybrids of Agrostis canina subsp. montana Hartm., A.
tenuis Sibth. and A. stolonifera L. J. Genet. 54:377-393.

Jones, K. 1955b. Species differentiation in Agrostis. III. A. gigantea Roth. and its
hybrids with A. tenuis Sibth. and A. stolonifera L. J. Genet. 54:394-399.

Koyama, T. 1987. Grasses of Japan and it's Neighboring Regions. An Identification
Manual. The New York Botanical Garden, New York.

Kurchenko, E. 1979. A population-ontogenetic approach to the study of species of the
genus Agrostis. Byulleten Moskovskogo Obshchestva Ispytatelei Priorody
Biologischeskii 84(5):93-105.

Lawrence, KS. 1986. Great Plains Flora Association: Flora of the Great Plains.
University Press of Kansas. 1392 p.

Monte, J.V., C.L. McIntyre and JP. Gustafson. 1993. Analysis of phylogenetic
relationships in the Triticeae tribe using RFLPs. Theor. Appl. Genet. 86:649-655.

Mouinuddin, M., A.A. Vahiddy, and SJ. Ali. 1994. Chromosome counts in

Arundinoideae, Chloridoideae and Poideae (Poaceae) from Pakistan. Annals of
the Missouri Botanical Garden 81(4):784-791.

Plant Gene Resources of Canada. GRIN CA Taxonomy. [Online.] Available at
http://pggclaggca (accessed June 2003).

Probatova, NC. and N. Kharkevich. 1983. New taxa of Gramineae from the Khabarovsk
region. Botanicheskii-Zhurnal 68(1 0): 1408-1414.

Rohlf, F.J. 2000. NTSYS-pc Numerical Taxonomy and Multivariate Analysis System
version 2.1 Manual. Applied Biostatistics, Inc. New York, NY, USA.

38

R0)

Sch

Sha

Shii

Ska

Sne

801;

$0“

Stei

Tex

10h

Royal Botanic Garden Edinburgh. Flora Europeaea [Online.] Available at
http://www.rbge.org.uk/ (accessed June 2002).

Scheef, E.A., Casler, M. and G. Jung. 2003. Development of species-specific SCAR
markers in Bentgrass. Crop Sci. 43: 345-349.

Sharma, A., R. Sharma and H. Machii 2000. Assessment of genetic diversity in a Morus
germplasm collection using fluorescence-based AF LP markers. TAG 101:1049-
1055.

Shildrick, J .P. 1976. Highland bent: A taxonomic problem. J. Sports Turf Res. Inst.
52:142-1 50.

Skolovskaya, A.P. 193 8. A caryo-geographical study of the genus Agrostis. Cytologia
8:452-467.

Sneath, P.H.A. and RR. Sokal. 1973. Numerical Taxonomy. Freeman. San Francisco.
© 2000 by Applied Biostatistics, Inc. 573 pp.

Sokal, R. and C. Michener. 1958. A statistical method for evaluating statistical
relationships. Univ. Kan Sci Bull 38: 1409-1438.

Soreng, R.J., P.M. Davidse, F .0. Peterson, F.O. Zuloaga, E.J. Judsiewicz and TS.
Filgueiras. 2003. Catalogue of New World Grasses (Poaceae). [Online.]
Available at http://mobot.mobot.org/W3T/search/vast.html/ (accessed June 2003)

Steiner, A.M. and H. Lupold. 1978. The verification of species of Agrostis by means of
morphological characters of the florets. Landwirtschaflliche-Forschung 31(4):
359-369, in CAB Abstracts no. 790782352.

Texas A&M University. BONAP Poeaceae Listing (THE GRASS FAMILY) Biota. of

 

North America Program [Online.] Available at
http://www.csdl.tamu.edu/FLORA/bonapfams/boanmahtm. (accessed June
2002)

Tohme, J., D.O. Gonzalez, S. Beebe and MC. Duque. 1996. AFLP analysis of gene pools
of a wild bean core collection. Crop Sci. 36:1375-1384

Tzvelev, N. 1971. Novosti Sistematiki Vysshchikh Rastenii 8:58-60.

Vos, R, R. Hogers, M. Bleeker, M. Reijans, T.V.D. Lee, M. Homes, A. Fritjers, J. Pot, J.
Poleman, M. Kuiper and M. Zabeau. 1995. AFLP: A new technique for DNA
fingerprinting. Nucleic Acid Res. 23(21): 4407-4414.

39

Warnke, S.E., D.S. Douches and BE. Branham. 1997. Relationships among creeping
bentgrass cultivars based on isozyme polymorphisms. Crop Sci. 37: 203-207.

Warnke, S.E., D.S. Douches and BE. Branham. 1998. Isozyme analysis supports
allotetraploid inheritance in tetraploid creeping bentgrass (Agrostis palustris
Huds.) Crop Sci. 38: 801-805.

Watson, L. and M.J. Dallwitz. 1992. Grass Genera of the World. Wallingford Oxon, UK
CAB Intl. 1038 p.

Welsh, S.L., N.D. Atwood, S. Goodrich, and LC. Higgins eds. 1987. A Utah Flora.
Great Basin Naturalist Memoir No. 9. Provo, UT: Brigham Young University 894

p.

Winterhalder, K. 1990. The trigger-factor approach to the initiation of natural
regeneration of plant communities on industrially damaged lands at Sudbury,
Ontario. In: Hughes H et al. eds. Restoration ’89; the new management challenge:
Proceedings, 1St annual meeting of the society for ecological restoration: 1989.
January 16-20, Oakland, CA. Madison WI: The University of Wisconsin
Arboretum, Society for ecological restoration: 215-226.

Yamamoto, I. and J .M. Duich. 1994. Electrophoretic identification of cross-pollinated
bentgrass species and cultivars. Crop Sci. 34:792-798.

Zabeau, M. and P. V08. 1993. Selective restriction fragment amplification: a general
method or DNA fingerprinting. European Patent Application Number
924026297

Zhang, L.H., P. Ozias-Akins, G. Kochert, S. Kresovich, R. Dean and W. Hanna. 1999.
Differentiation of bermudagrass (Cynodon spp.) genotypes by AF LP analyses.
Theor. Applied Genet. 98:895-902.

Zhu, J., MD. Gale, S. Quarrie, M.T. Jackson, and G.J. Bryan. 1998. AFLP markers for
the study of rice biodiversity. Theor. Applied Genet. 96:602-611.

40

CHAPTER 11

Genetic Differentiation of Tetraploid Creeping Bentgrass and Hexaploid Redtop

Bentgrass Genotypes by AFLP and Their Use in Turfgrass Breeding

ABSTRACT

The turf industry in the last decade has seen doubling in number of new creeping
bentgrass (Agrostis stolonifera var. palustris Huds. and A. stolonifera var. stolonifera
Huds.) cultivars, many with unknown variability and lineage. Understanding the genetic
diversity of putative parental and wild stocks would be useful in breeding programs.
AF LP analysis was conducted to investigate genetic variability among old and new
cultivars of creeping bentgrasses, redtop bentgrasses (Agrostis gigantea Roth.), plant
introductions and selected genotypes with resistance to gray snow mold (Typhula
incarnata Lasch). Seven chosen primer combinations resulting in 355 polymorphic
markers were used to differentiate the bentgrasses. Three groups were extracted using
principal component analysis. Using UPGMA analysis, mean similarity coefficients of
creeping bentgrass genotypes found in the first group was 0.78. Creeping bentgrasses in
the USA were clustered as a subgroup and separated from European plant introductions,
indicating that most selection and genetic exchanges in the last fifty years have evolved
locally. Redtop bentgrasses were the most diverse and were found in different groups.
Selected lines from northern Michigan, MI 20104, MI 20215 and MI 203164 were
differentiated from the other cultivars and would be advantageous to use as sources of

disease resistant traits and for development of populations for fiJture gene mapping.

41

Predictive estimates of genetic variation and molecular identification of new cultivars and

interrelationships would be important for advanced breeding of bentgrass species.

Key words: AF LP analysis, turfgrass breeding, Agrostis stolonifera var. palustris Huds.,

A grostis stolonifera var. stolonifera Huds., Agrostis gigantea Roth.

INTRODUCTION

Creeping bentgrasses (2n=4x=28) are the premier and most widely used cool-
season turfgrasses for golf course putting greens, tees and fairways in the USA (Funk,
1998). Redtop bentgrasses (2n=6x42) are widely used in seed mixtures for rapid
vegetation cover. These two species of bentgrasses are differentiated from other species
by their profuse creeping stolons forming dense mats, vigorous shallow roots and their
attractive range of bluish-green to grayish appearance. From 1927 to 1994, less than
twenty cultivars have been released by different institutions and private seed companies
in the USA but within the past eight years, the number of cultivars that has been bred
have doubled. Creeping bentgrasses are synthetic products created from crossing of three
or more clones and recurrent selection. Improved turf quality and appearance, tolerance
to close mowing, regenerative and seed yield potential, and tolerance to diseases are
some important parameters for selection. Some of these new cultivars may be closely
related but their interrelationships are unknown. Diversity within creeping bentgrasses
appears limited as their genetic variability may be narrow (.007 to .08%) as revealed by
isozyme polymorphism (Yamamoto and Duich 1994; Warnke et al., 1997). Plant

introductions from germplasm collections could be used to widen the genetic base.

42

Morphologically, creeping bentgrasses are difficult to distinguish and taxonomic
confusion has resulted in many synonymous names for the tetraploid species. Agrostis
stolonifera L. was listed as being synonymous with A. alba var. palustris Huds.; A. alba
var. stolonifera (L.) Sm; A. stolonifera var. compacta Hart.; A. stolonifera var. palustris
(Huds.) Farw. and A. maritima Lam. (Texas A&M, BONAP Poaceae Listing 2003; Plant
Gene Resources of Canada, GRIN-CA Taxonomy, 2003). Hexaploid bentgrasses were
referred to as A. stolonifera sp. gigantea (Huds.), A. gigantea L. or A. alba sp. gigantea
Huds. Adding to the taxonomic complexity are other factors; i.e. outcrossing nature of
bentgrasses, ability to form interspecific hybrids and environmental fluctuations that may
result in differences in phenotypic expressions and inconsistencies in classification. A
more stable tool like DNA molecular marker technology may help resolve identification
and offer insights to the degree of genetic variability and relationships among cultivars
and to help define future breeding strategies.

Molecular marker studies using amplified fragment length polymorphism (AF LP)
analyses have been used to differentiate bermudagrass (Cynodon spp.) genotypes (Zhang
et al., 1999) and to construct a genomic map of zoysiagrass (Zoysia spp.) (Ebina et al.,
1999). Some bentgrass cultivars and species have been differentiated using random
amplified polymorphic DNA (RAPD) (Golembiewski et al., 1997, Scheef et al., 2001)
and restriction fragment length polymorphism (RFLP) (Caceres et al., 2000). Our recent
work in the study of genetic diversity in the Agrostis species using AFLPS has shown the
advantage of this technique for biodiversity studies, distinguishing fourteen species of
bentgrasses into seven major groups (Vergara and Bughrara, 2003). Stoloniferous

bentgrasses from several countries formed a single cluster comprised of A. stolonifera L.

43

or A. palustris Huds. , A. gigantea L. , A. mongolica Roshev. , and A. transcaspica Litv.
The percentage of genetic dissimilarity (5 to 18%) within plant introductions of creeping
bentgrasses indicated considerable potential for the improvement of turf.

Turfgrass breeders are currently interested in developing superior cultivars of A.
stolonifera var. palustris with resistance to major diseases such as dollar spot (caused by
Rutstroemia floccosum, teleomorph of Sclerotinia homeocarpa F.T. Bennett) (Powell,
1998), brown patch (Rhizoctonia solani Kuhn) and snow molds (Typhula spp. and
Microdochium nivale Fr.) (Vargas, 1994; Hsiang et al., 1999). In 2000 and 2001, the
Michigan State University (MSU) turf breeding program isolated and selected
experimental lines, MI 20104, MI 20215 and MI 203164, with resistance to gray snow
mold from replicated controlled screening trials. Our objective was to use AF LPs to
investigate genetic variability among old and new cultivars of creeping bentgrasses,
redtop bentgrasses, plant introductions and differentiate MSU experimental lines of
creeping bentgrasses. Predictive estimates of genetic variation will be useful in planning
turfgrass breeding programs and developing heterotic populations. Marker data and

identified AF LP primer combinations will be usefiil in future mapping studies.

MATERIALS AND METHODS

Plant materials and DNA extraction

Bulk leaf samples from 25 plants each of cultivars ‘Seaside’, ‘Penncross’,
‘Providence’, ‘L-93’, ‘Penn G2’, ‘Penn A4’, ‘8. Redtop’, ‘Emerald’ and redtop bentgrass
cultivar ‘S. Redtop’ were used. Widely used cultivars developed during different periods

of time were chosen. Leaf samples from identical selected clones of MI 20104, MI 20215

44

CC

II

In

bi

Sll

su

pr:

V0

COI

Wa

AF

and MI 203164 selected from Northern Michigan golf courses in 2000 were included for
comparison. In addition, Plant Introductions (PI) of eight creeping bentgrass accessions
and two redtop bentgrass accessions (source: USDA Plant Introduction Station, Pullman,
WA) from different countries were included (Table 2.1). Fresh leaf tissues were ground
in liquid nitrogen and an equal volume of extraction buffer was added. The extraction
buffer consisted of Tris-EDTA-HCI, SDS, NaCl with 0.38 g/ 100 ml buffer of sodium
bisulfite. The mixture was incubated at 65 0C for 20 min followed by the addition of 10
ml of 5M potassium acetate. After 20 min incubation with ice in a gyratory shaker, the
suspension was centrifuged at 3000 g RCF (Sorvall RT7 model) for 20 min at 5 °C. The
supernatant was collected to which 2/3 volume of cold isopropanol was added to
precipitate the DNA. All DNA samples were treated with RNAse dissolved in TE buffer
and twice reprecipitated by using 1/10 volume of 3M sodium acetate followed by two
volmnes of chilled absolute ethanol. Extracted DNA was stored in 1% TE buffer. DNA
quality was checked by running 5 ul of the undigested samples in 1% agarose gel
containing TBE buffer and compared to EcoRI digested samples. DNA quantification
was done using a DyNA Quant 200 Fluorometer (Pharmacia Biotech, CA).
AFLP analysis

Approximately 150 to 200 ng of DNA was used for AF LP analysis of each
genotype. Digestion was conducted using two restriction enzymes, EcoRI, a six base pair
cutter and Mse-I, a four base pair cutter. The AF LP procedure used in this study was as
described by Vos et a1. (1995) with modifications. Pre-amplification was done on a PTC-

100 thermal cycler (MJ Research, Inc., MA) using 72°C for 2 min, 30 cycles of 94°C for

30 sec, 60°C for 30 sec, 72°C for 1 min, followed by elongation at 72°C for 5 min and 4°C

45

Table 2.1. Cultivars, MSU experimental lines and plant introductions (PI) lines of creeping and

redtop bentgrasses (Agrostis spp.) examined, year released or collected and their sources.

 

 

Bentgrass Scientific name Year”| Source/Origin

Cultivars

Seaside A. stolonifera var. palustris 1928 Tee-2 Green Corp., US
Penncross A. stolonifera var. palustris 1955 Pure Seed Testing, Inc., US
Providence A. stolonifera var. palustris 1987 Seed Research of OR, Inc., US
L-93 A. stolonifera var. palustris 1995 Agri-Biotech Inc., US

Penn G2 A. stolonifera var. palustris 1995 Tee-2 Green Corp., US

Penn A4 A. stolonifera var. palustris 1995 Tee-2 Green Corp., US

Emerald A. stolonifera var. palustris 1973 Cebeco Intl. Seeds, Inc.,US

S. Redtop A. stolonifera var. palustris 1989 Pickseed West, Inc., US
Experimental Lines

MI 20104 A. stolonifera var. palustris 2000 Michigan State University, US
MI 20215 A. stolonifera var. palustris 2000 Michigan State University, US
M] 203164 A. stolonifera var. palustris 2000 Michigan State University, US
Plant Introductions

P1 251 945 A. stolonifera var. palustris 1958 USDA PI from Austria (AT)
P1235 440 A. stolonifera var. palustris 1956 USDA PI from Switzerland (CH)
P1 235 541 A. stolonifera var. palustris 1956 USDA PI from Sweden (SE)
P1204 390 A. stolonifera var. palustris 1953 USDA PI from Turkey (TR)

Pl 269 838 A. stolonifera var.stolonifera 1960 USDA PI from Germany (DE)
P1 494 119 A. stolonifera var. stolonifera 1984 USDA PI from Netherlands (NL)
P1 230 235 A. stolonifera vanstolom'fera 1955 USDA P1 from Iran (IR)
P1439 027 A. stolonifera var.stolonifera 1978 USDA PI from Russia (RU)

Pl 383 584 A. gigantea 1972 USDA PI from Turkey (TR)
P1443 051 A. gigante 1980 USDA PI from US

 

‘ Year of release for cultivars and year collected for experimental lines and P13.

46

hold.
gel.

nut}:
EM
M-C
prim

p01:

am

bu:

pm

10:

hold. PCR products from initial ligation of adapters were checked on 1.5% TBE agarose
gel. Combinations of fluorescent (*) dye labeled E and M primers each with 3 selective
nucleotides at the 3’ ends were used. The following E“ primers were used: E*-ACA and
E*-AGC. The following M primers were tested: M-CAT, M-CAG, M-CGG, M-CGA,
M-CTT, M-CCT. The 15 ul selective amplification mixture consisted of 15 pmol E*-
primer, 75 pmol M-primer , 2 mM dNTP, 1X PCR buffer, 37.5 mM MgC12, 1.0 unit Taq
polymerase in deionized distilled water. The PCR cycling sequence used for selective
amplification was as recommended by Vos et al. (1995). Products from selective
amplification were checked initially on 1.5% agarose and diluted four times with 0.1 TE
buffer. For separation on acrylamide gels, samples consisted of 0.5 ul of the amplified
product and 0.5 ul loading dye. The samples were denatured at 95°C for 5 min prior to
loading and run on a 6% Long Ranger polyacrylamide gel with 0.7X TBE buffer using a
LI-COR DNA Analyzer 4200 (Lincoln, NE) at a constant 800 v for 6 hours at 50°C.
Data analyses

Gels were visualized using the software Gene ImagIR 4.0 (Scanalytics, Inc.,VA).
Each informative polymorphic band was scored manually as 1 for presence and 0 for
absence. Analyses were done using Numerical Taxonomy and the Multivariate Analysis
System, NTSYS v.2.1 (Rohlf, 2000). Genetic similarities based on Jaccard’s coefficients
(Jaccard, 1908) were calculated among all possible pairs using the SIMQUAL option and
ordered in a similarity matrix. The similarity matrix was run on Sequential,
Agglomerative, Hierarchical and Nested clustering, SAHN (Sneath and Sokal, 1973)
using the Unweighted Pair Group Method with the Arithmetic Mean (UPGMA) as an

option (Sokal and Michener, 1958). Cophenetic correlation was calculated to measure

47

goodness of fit. Principal component analysis (PCA) was run using CPCA option
(NTSYS) to identify the number of groups based on eigenvectors and verified using
SYSTAT. The three dimensional PCA plot was generated from the NTSYS program
using AF LP markers as observations and bentgrass genotypes as the operational
taxonomic units (OTUs). The TREE module of NTSYS v.2.1 was used to produce the

dendrogram (Rohlf, 2000).

RESULTS and DISCUSSION

From the initial twelve primer combinations, seven combinations were chosen for
clarity and repetitiveness in duplicated gel runs (Table 2.2). A total of 380 polymorphic
markers were initially scored for the 21 genotypes from the seven combinations. Cultivar
Seaside had data from only four of the seven primer combinations. Two UPGMA
analyses were performed, first using 248 markers for 21 genotypes which included
Seaside and secondly, 355 markers for 20 genotypes, excluding ‘Seaside’. The number
of bands varied with each primer combination ranging from 100 to 150 bands and the
number of polymorphic markers ranged from 25 to 100 per individual lane. Primer
combination E-ACA/M-CAT gave the most robust polymorphism. Pairwise similarity
coefficients (sc) were computed based on shared and unique amplification products using
UPGMA (Table 2.3). The genetic similarity coefficients ranged from 0.56 to 0.93. The
most similar bentgrass genotypes were the plant introductions (PI) from Switzerland and
Sweden (sc=0.93) and the most dissimilar would be ‘Penncross’ and PI 443051

(sc=0.56).

48

Table 2.2. Number of polymorphic bands obtained from different primer combinations.

 

Primer Pair No. of Polymorphic Bands

20 genotypes 21 genotypes

 

E-ACA/M-CAT 98 94
E-ACA/M-CAG 25 22
E-ACA/M-CGG 61 60
E-ACA/M-CGA 3 1
E-AGC/M-CAT 64 62
E-AGC/M-CGG 60
E-AGC/M-CGA 26
Total markers 248 355

 

Adapter and primer sequences were adapted from Vos et al. (1995).
E=5'-GACTGCGTACCAATTCA-3', M=5'-GATGAGTCCTGAGTAAC-3'.

49

£8.38 .5: new 82m 2:3 .368? Bob Saw Ea Eco—BE n3”: mnm Bob 088 cm 2 _ mogocow madden Boa 8am...

 

 

 

dvd and dmd Ed 8d Nod 5nd Ed Gd odd and cod 3d and mod med 3d odd 8d Ed I 0283 S
8.. mod and Sd and and end and and end and mnd end mmd emd hmd mmd dmd and 3d manic mvadm
do; odd mod mud dud Ed mud Ed mud 3d add add Sd odd dud wed Sd nod cod gleam mam—m2
2: and 3d Gd mod mod mod wed mod med mod Ed Ed 8d odd 3d Gd wmd Burns 3va—

do._ mod Nod med 3d med Ned 3d Gd mod mod Gd mod 86 odd and cod 5..me dmm E:

2: 5d and wad mad dmd Ed and and dad odd and 3d Pd and mud 4212—33; 2

odd mud vwd 3d dad 3d Rd dwd Rd and mud and Ed end mud ”.51wa $2; 2

8.— dmd dwd Ed 8d 2d 3d mud end mud Ed mud mud wed 5.18m ENE 3

do; mad Ed wed 5d and and dad and dad and de Ed 313% 98$ m—

2: mad cod mad End and 3d 2.6 and add and mud :UIovv mmmE m—

2: mod end Kd End mud who Ed Vhd mud odd H< fled—3E:

dd._ cod mad mod mod mod mod mod 3d odd modem .m d—

2: odd and and End dwd 5d 3d and 3:822 a

do; dad who and and Ed dud mud 2822 w

do; wad who find and and mud wed—om :2 N.

8.. 5d 5d dud end Ed 2825 e

do; and and end Ed v< :5; m

8.— and 5d cud m0 gum V

dd; mud :.d 8-4 m

do; and 85235 N

do; 382.com _

390950

on 9 w_ E 3 m_ E 2 Q 2 o. d w A. o n v m N _ mmeEom
653508

maria vo_opm_-oo:oomouo:m wfims mommfimfion 99?: can mamaoouo mo momboaow a 8m swan—06508 bra—~86 0:280 .QN 03.;

50

Using the subroutine programs of NTSYS, a rotated PCA with the markers as
observations was used to determine the number of groups based on Eigen values greater
than one. Three groups were extracted which explained 58% of the total variance. The
first component accounted for 44% of the total variance with cultivar ‘S.Redtop’
bearing the lowest component loading (Figure 2.1). Seventeen genotypes were found
distinguished by the first component. The second component, which accounted for 8% of
the variation, separated Groups 1 and 2. The third component accounted for 5% of the
variation and distinguished P1 443051.

From the two UPGMA analyses, dendrograms were generated. The two
dendrograms did not differ in terms of genotypes within a cluster but only in the
estimates of similarity coefficients. These slight differences (0.01 to 0.02 sc) could be
due to the deletion or addition of allelic markers considered for an additional genotype.
The higher number of markers used would predict a more accurate dendrogram presented
in Figure 2.2. The dendrogram also indicated where ‘Seaside’ would cluster. The
cophenetic correlation was calculated (r = 0.95) as a measure of goodness of fit of the
similarity indices. The cophenetic values were plotted against the distances derived from
SAHN’s cluster analysis and the phenogram was plotted in Figure 2.3. Mean similarity
coefficient for all creeping bentgrasses in the first group was 0.78. Similarity coefficient
was lower between the redtop bentgrasses (sc=0.64). Group 2 consisted of A. palustris
(Iran and Russia) and Group 3 consisted only of PI 443051, A. gigantea (US). The
dendrogram showed a similarity coefficient of 0.64 for the three groups.

‘Seaside’ was found to be most similar to ‘Penncross’ with sc= 0.81 based on

UPGMA analysis using data from 248 AFLP markers (Table 2.3). The dendrogram

51

 

PI 443051

 

 

 

Dimension-3

054

I

‘4

030

006

'0 1‘)

 

-(‘-.43
0 12

 

 

Pl 230235
Pl 439027

 

 

 

 

 

 

 

 

 

Dimension-l

048 066

081

 

 

 

 

 

Penncross
Providence
L-93

MI 2021 5
MI 203164
Penn 62
Penn A4
Emerald
MI 20104

 

 

 

Pl 251945
Pl 235541
PI 235440
Pl 269838
PI 4941 19

 

Dimension-2

 

 

S. Redtop

 

 

Figure 2.1. Three dimensional plot of principal component analysis (PCA) using

355 AF LP markers (observations) and bentgrass genotypes defining three groups

marked as 1, 2 and 3 from the plot options of NTSYS v.2.1 (Rohlf, 2000).

52

 

 

_____ S easide_US

l'

 

Penncross _US
Providence _US
L-93_US

MI 20215_US

MI 203164_US
Penn G2_US
Penn A4_US
Emerald_US
MI 20104_US
__ PI 25] 945__AT
Pl 235 440_CH
P1 235 541_SE
Pl 269 838_DE
PI 494 119_NL
’ Pl 204 390_TR

—ll P1383 584_TR
S. Redtop_US

Pl 230 235_IR
_l I P] 439 027_RU

Pl 443 051_US
0.62 ' ' .070- r t '0.78' - I '0.'86' ' ' i0193

 

 

 

 

 

 

 

 

 

 

 

 

 

Similarity Coefficient

Figure 2.2. UPGMA dendrogram of creeping and redtop bentgrasses using
data from 355 AF LP markers (solid lines) and genetic similarity of ‘Seaside’

using data from 248 AF LP markers (dashed lines).

53

0.93

 

 

 

 

o
J
l o
o
o
0.85J
8°
0
0 8° 0
o (9000
WC
0.7%
Cophenetic
coefficient -
oooo‘bomoo °
‘ 0 mac 0 o
0.69
0 an 8003030
0 o
O.W..,....T....
0.54 0.64 0.74 0.83 0.93

Similarity coefficient
Figure 2.3. Plot analysis of cophenetic and similarity coefficients as a measure

of goodness of fit of the similarity indices. r = 0.94808 = normalized Mantel

statistics Z; Approximate Mantel t-test: t= 5.1548; P(Z<obs. Z: p = 1.0000).

54

using data from 355 markers showed that 14 of the 17 genotypes in Group 1, namely
‘Penncross’, ‘Providence’, ‘L-93’, ‘Penn-G2’, ‘Penn A4’, ‘Emerald’, ‘MI 20104’, ‘MI
20215’, ‘MI 203164’, PI 251945 (Austria), PI 235440 (Switzerland), PI 235541
(Sweden), PI 269838 (Denmark) and PI 494119 (Netherlands), were clustered close
together at sc=0.78. Cultivar ‘Penncross’ may share genetic similarities with modern
cultivars by a mean sc=0.75. The fourteen genotypes were slightly differentiated from P1
204390 and PI 383584 (Turkey) and ‘S. Redtop’. Cultivar ‘S. Redtop’ was the most
genetically distant in the first group. ‘MI 20104’ was most similar with ‘Emerald’ with
sc=0.88 while ‘MI 20215’ and ‘MI 203164’ were more similar to ‘Providence’ with
sc=0.79 and 0.83 respectively. ‘Providence’ and ‘L-93’ had a high sc=0.83 and shared
close identity with ‘MI 20215’ and ‘MI 203164’ as well. Dendrogram results showed the
three genotypes of A. gigantea did not group together. PI 443051, A. gigantea (US)
separated from cultivar ‘S. Redtop’ (US) and from P1 383584 (Turkey).
Differentiating old and new cultivars of creeping bentgrasses

Most developed cultivars of creeping bentgrass, A. stolonifera var. palustris, in
the USA such as ‘Penncross’, ‘Penn A4’, ‘L-93’ and others came from phenotypic
recurrent selection of three or more clones descended from selection of adapted lines
which were thought to be from earlier European origin. Warnke et al. (1997) used
isozyme polymorphisms to distinguish between several creeping bentgrasses. Eighteen
bentgrasses were divided into two groups based on cluster analyses. The first group
included 10 cultivars (‘Penncross’, ‘Emerald’, ‘Cobra’, ‘Crenshaw’, ‘Seaside’,
‘Penneagle’, ‘Putter’, ‘Trueline’, ‘Viper’ and ‘18‘h Green’). With the exception of

‘Crenshaw’, they were all strongly aligned with creeping cultivars. ‘Seaside’ is thought to

55

be the oldest within the group and may have provided some of the germplasm used in the
development of some bentgrasses in the grouping. Their genetic distance calculated from
Nei’s distance formula ranged from 0.007 to .08. The second group contained the
cultivars ‘Pennlinks’, ‘Southshore’, ‘ProCup’, ‘Lopez’, ‘Providence’, ‘SR1020’,
‘National’ and ‘Cato’. Both groups were differentiated from P1 251945, which is of
European origin by genetic distance of 0.27. They found genetic differences to be small
within cultivars and suggested that the PI line could be a source to broaden genetic
diversity of bentgrasses in the USA.

In this study, UPGMA similarity coefficients from AFLP showed that ‘Seaside’
(1928), the oldest seeded cultivar used, and ‘Penncross’ (1955) were clustered closely but
evidently showed genetic dissimilarity at 19%. Genetic dissimilarity was computed from
l-sc x 100 (Zhang et al., 1999). ‘Seaside’ creeping bentgrass, indigenous to coastal
regions of Washington and Oregon, was known to be an extremely variable grass that
develops into patches of individual strains with different colors, textures and densities.
Most seed supplies came from natural stands. Hence to date after more than seventy-five
years, ‘Seaside’ remains to be a highly heterogenous population.

In Figure 2.2, more recent cultivars, ‘Providence’ (1987) and ‘L-93’ (1995) were
also clustered together with sc=0.84. The genetic dissimilarity between ‘Providence’ and
‘L-93’ is 16% and both are differentiated from ‘Seaside’ as much as 34%. ‘Providence’
was a consistently top rated (best five) bentgrass in the National Turfgrass Evaluation
Program (NTEP 98-8) Putting Green and Fairway Test (Morris, 1997), and in trials
throughout the world. The progenitors of ‘Providence’ were five clones that Dr. Richard

Skogley of the University of Rhode Island selected as being unique and superior to

56

‘Penncross’ since 1965 (Seed Research Reports of Oregon, 2003). The newer cultivars,
‘Penn G2’ and ‘Penn A4’ (1995) were clustered together at sc=0.80 and differentiated
from its early predecessor ‘Penncross’. Pennsylvania State University developed
cultivars with the epithet ‘Penn’. ‘Providence’, ‘Penn G2’ and ‘Penn A4’ were reported
as having better resistance to dollar spot in NTEP trials (Moris, 2001a) and would be
useful as genetic stocks. Cultivars ‘Seaside’, ‘Penncross’ and ‘Emerald’ were in the
same cluster using AF LP and supported similar findings by Warnke et al.(1997). Another
similar observation to their findings from our results was that P1251945 (Austria) has a
range of sc=0.65 to 0.77 to USA cultivars. P1251945 and other PIs fi'om Switzerland,
Sweden, Denmark and Netherlands would be useful to expand genetic variability in
creeping bentgrass. In contrast to the results of Warnke et al. (1997), AFLP analysis
showed that the genetic differences among USA cultivars were higher than the previous
estimate using isozyme. Variability is important because selection becomes more
effective when diversity is high.

Creeping bentgrass from Turkey (Central Asia) was the most distant in the first
group and would be highly useful and informative for future mapping studies. Redtop
bentgrass cultivar ‘S. Redtop’ in group 1 cluster was shown to share some genetic
similarity with the tetraploid creeping bentgrasses. In contrast to creeping bentgrasses,
redtop bentgrass is not used as fine turf but would be an important source of other
important traits like drought and heavy metal tolerance (Winterhalder, 1990). Creeping
bentgrasses (USA) clustered separately as a subgroup from the P13 from Europe at sc =
0.78, an indication that most development, selection and genetic exchanges in the last

fifty years occurred locally. Support for European lineage for USA creeping bentgrasses

57

was also shown by AF LP results in Group 1 in this study. Previous findings comparing
genetic similarities of plant introductions of Agrostis species using AF LPs showed that
bentgrasses from the USA were closer to European PIs than accessions coming from
other parts of the world (V ergara and Bughrara, 2003).
Differentiating MSU experimental lines with other creeping bentgrasses

MSU experimental lines were collected from old (>50 years) Northern Michigan
golf courses which have not been overseeded for the last 10 years. Materials from these
golf courses have been through natural selection pressures for abiotic and biotic stresses
making them excellent genetic stocks for turf breeding programs. Data from AFLPs
showed that the selected experimental lines were differentiated from other creeping
bentgrasses and showed the closest genetic similarity to ‘Providence’, ‘L-93’ and
‘Emerald’. The two cultivars, ‘L-93’ and ‘Emerald’ are currently internationally known
for their dark, dense and aggressive growth habit. ‘L-93’ was rated premier out of 25
cultivars in the National Bentgrass Test - 1998 (NTEP 02-3, Morris, 2001b) on fairways
and tees and also performed better in heat stress than ‘Penncross’ (Huang and Xu, 2001).
Cultivar ‘Emerald’, is a descendant of a single synthetic clone originating from Sweden
and is widely used in blends of two or more creeping bentgrasses in the USA. ‘Emerald’
is important as it also has moderate tolerance to heat like ‘Providence’, as compared to
most cool-season bentgrasses. The high similarity coefficients (0.80 to 0.88) to top rated
modem cultivars provided predictive estimates by which MI 20104, MI 20215 and MI
203164 with resistance to snow mold disease may be used to improve performance of
newer cultivars without radically altering their genetic components. Correspondingly,

cultivars to which the sc values were less gave indication of the high genetic diversity

58

favorable for studies on combining abilities. Populations derived from crosses with large
differences in polymorphic markers may be used to map the snow mold disease
resistance trait.

Results from dendrogram analysis showed that germplasm materials or plant
introductions from Europe may also be used to widen the genetic base of modem USA
creeping bentgrasses. The estimated mean genetic dissimilarity among all creeping
bentgrass genotypes was 0.78 and suggested considerable diversity from which selection
for improved cultivars may be generated. Creeping bentgrass from other subgroups
(Central Asia) which are more differentiated by AF LPs would correspondingly further
increase genetic variability. Diverse parental combinations would create segregating
populations of various heterotic groups from which superior clones may be selected.
Genetic dissimilarity of tetraploid and hexaploid creeping bentgrasses

AF LP analysis indicated that most tetraploid creeping bentgrasses were grouped
together and separated from hexaploid stoloniferous bentgrasses (Figure 2.2). PI 204390
(A. palustris, Turkey) was the most genetically distant among the tetraploid creeping
bentgrasses in this study and may share closer genomic constitution with P1 383584 (A.
gigantea, Turkey). Having similar geographic origins, possible genetic introductions
between the progenitors of the two species may have occurred and allowed them to
evolve sympatrically. Interspecific hybridizations between tetraploid and hexaploid
bentgrasses like A. stolonifera x A. gigantea were known to occur naturally and are easy
to produce (Davies, 1953; Jones,1955). The ability of cross-speciation enhances

opportunities to transfer other important traits, e.g. tolerance to heavy metals and poor

59

soils, drought tolerance, and vigorous growth habit found in A. gigantea to creeping
bentgrass.

Hexaploid bentgrasses, A. gigantea have been found to be genetically diverse
within species and differentiated from the tetraploid A. stolonifera var. palustris using
AFLPS (Table 2.3). The three A. gigantea genotypes were found to group separately
from each other. S. Redtop (US) has only a sc = 0.59 with P1 443051, A. gigantea (US).
This indicated that their genomic constitutions (A1A1A2A2A3A3) may be dissimilar and
may have descended from different diploid and tetraploid progenitors. Much of the
extensive variation in A. gigantea could have been generated by multiple or repeated
cycles of hybridization of multiple origins. Our findings in the AFLP analysis of a
number of Agrostis species were similar and indicated that only the A1A1 genome may be
shared by the different accessions of A. gigantea (V ergara and Bughrara, 2003). Future
cytogenetic and hybridization studies may be done to explain their differences.
Applications to Turfgrass Breeding

Turf breeders may gain advantage in selection when breeding materials are
highly heterogenous and populations could be efficiently differentiated. Difficulty in
differentiating outcrossing allopolyploid turfgrass species morphologically may be
overcome with AF LP analyses. This study has shown considerable genetic dissirnilarities
between old and new cultivars, germplasm and MSU experimental lines. Dendrogram
analysis revealed that USA creeping bentgrass cultivars have locally evolved and
differentiated from European germplasm. Genetic similarity coefficients may predict
which cultivars are more similar or distant and help define strategies for breeding. The

AFLP data suggested that A. stolonifiera var. paluslris cultivars in the USA are highly

60

heterogeno‘
Experimen
improve 0
may be u
lrlerspecl

polr'morr

lmportar.

Caceres

Davies

Ebina.

Funk.

Golen

Hsian‘

Jacca;

JOHes.

heterogenous but may be firrther diversified with materials from Europe and Turkey.
Experimental materials from MSU with disease resistance to snow mold could be used to
improve cultivars. Other important traits found in plant introductions of A. gigantea,
may be used to improve ‘S. Redtop’ or transferred to creeping bentgrass cultivars by
interspecific hybridization. In the future, the identified primer combinations and
polymorphic marker data identified herein may be used to monitor introgression, map

important traits, and used for protection of indigenous materials and developed cultivars.
REFERENCES

Caceres, M.B., F. Pulpilli, E. Piano and S. Arcioni. 2000. RFLP markers are an effective
tool for the identification of creeping bentgrass (Agrostis stolonifera L.) cultivars.
Genetic Resources and Crop Evol. 47(4):455-459.

Davies, W.E. 1953. The breeding affinities of some British species of Agrostis. Brit.
Agric. Bull. 5:313-316.

Ebina, M., M. Kobayashi, S. Kasuga, H. Araya and H. Nakagawa. 1999. An AF LP based
genome map of Zoysia grass. PAGVII P278.

Funk, R. 1998. Opportunities for genetic improvement of underutilized plants for turf. 7th
Annual Rutgers Turfgrass Symposium. Rutgers University, New Jersey.

Golembiewski, R.C., T.K. Danneberger and RM. Sweeney. 1997. Potential of RAPD
markers for use in the identification of creeping bentgrass cultivars. Crop Sci.
37:212-214.

Hsiang, T., N. Matsumoto and S. Millet. 1999. Biology and management of T whula
snow molds of turfgrass. Plant Disease. 83(9):788-798.

Jaccard, P. 1908. Nouvelles recherches sur la distribution florale. Bull Soc Vaud Sci Nat
44:223-270.

Jones, K. 1955. Species differentiation in Agrostis. III. A. gigantea Roth. and its hybrids
with A. tenuis Sibth. and A. stolonifera L. J. Genet. 54:394-399.

61

Morris, K. 1997. National Bentgrass (F airway/T ee) Test — 1993. Data, Table 4. Ratings
of Bengtrass Cultivars Grown on a Fairway or Tee. NTEP 98-8, Beltsville, MD.

Morris, K. 2001a. National Bentgrass (Putting Green) Test - 1998. Data, Table 32. Dollar
Spot Ratings of Bengtrass Cultivars. NTEP 02-3, Beltsville, MD.

Morris, K. 2001b. National Bentgrass (Putting Green) Test — 1998. Data, Table 6. Mean
Turfgrass Quality Ratings of Bentgrass Cultivars. NTEP 02-3, Beltsville, MI).

Plant Gene Resources of Canada. GRIN CA Taxonomy. [Online.] Available at
http://pgrc3.agr.ca/ (accessed May 2003).

Powell, J .F. 1998. Seasonal variation and taxonomic clarification of the dollarspot
pathogen: Sclerotinia homeocarpa. Ph.D. Dissertation. Michigan State University,
MI, USA.

Rohlf, RI. 2000. NTSYS-pc Numerical Taxonomy and Multivariate Analysis System
version 2.1 Manual. Applied Biostatistics, Inc. New York, NY, USA.

Scheef, E.A., M. Casler and G. Jung. 2001. Development of SCAR markers for
identification of bentgrass species. Annual Meeting of ASA-CSSA—SSSA,
Charlotte, NC, USA.

Seed Research Reports of Oregon. [Online]. Available at
http://www.sroseed.com/Products/PDF/Providence.pdf (accessed June 2003)

Sneath, P.H.A., R.R. Sokal. 1973. Numerical Taxonomy. Freeman. San Francisco. ©
2000 by Applied Biostatistics, Inc. 573 pp.

Sokal, R.and C. Michener. 1958. A statistical method for evaluating statistical
relationships. Univ. Kan Sci Bull 38:1409-1438.

Texas A&M University. BONAP Poeaceae Listing (THE GRASS FAMILY) Biota. of
North America Program [Online.] Available at
http://www.csdl.tamu.edu/FLORA/bonapfams/bonzzpoa.htm (accessed June
2003)

Vargas, J .M., Jr. 1994. Management of Turfgrass Diseases. CRC Press, Boca Raton FL.

62

Vergara, G.V. and S. Bughrara. 2003. AF LP Analysis of genetic diversity in bentgrass
(Agrostis spp.). Crop Science (In press).

Vos, R, R. Hogers, M. Bleeker, M. Reijans, T.V.D. Lee, M. Homes, A. Fritjers, J. Pot, J.
Poleman, M. Kuiper and M. Zabeau. 1995. AP LP: A new technique for DNA
fingerprinting. Nucleic Acid Res. 23(21): 4407-4414.

Warnke, S.E., D.S. Douches and BE. Branham. 1997. Relationships among creeping
bentgrass cultivars based on isozyme polymorphisms. Crop Sci. 37: 203-207.

Winterhalder, K. 1990. The trigger-factor approach to the initiation of natural
regeneration of plant communities on industrially damaged lands at Sudbury,
Ontario. In: Hughes H et al. eds. Restoration ’89; the new management challenge:
Proceedings, 1St annual meeting of the society for ecological restoration: 1989.
January 16-20, Oakland, CA. Madison WI: The University of Wisconsin
Arboretum, Society for ecological restoration: 215-226.

Yamamoto, I. and J .M. Duich. 1994. Electrophoretic identification of cross-pollinated
bentgrass species and cultivars. Crop Sci. 34:792-798.

Zhang, L.H., P. Ozias-Akins, G. Kochert, S. Kresovich, R. Dean and W. Hanna. 1999.
Differentiation of bermudagrass (Cynodon spp.) genotypes by AF LP analyses.
Theor. Appl. Genet. 98: 895-902.

63

CHAPTER III

Genetic Variability of the Gray Snow Mold (T yplrula incarnata Lasch)

ABSTRACT

Randomly amplified polymorphic DNA (RAPD) markers were used to assess the
genetic diversity of isolates of gray snow mold, T yphula incarnata, taken from infected
turfgrasses from 40 various locations in Northern USA. Data from 115 markers using 37
RAPD primers showed 48% polymorhism. The genetic similarity coefficients 0.57 to
0.99 between isolates indicate the wide genetic diversity of the fungi. Dendrograms from
Unweighted Pair Group Method with Arithmetic Mean (UPGMA) clustering and
neighbor joining (NJ) bootstrap analyses, showed similar clades and suggest possible
recent colonization from common founder populations. Partitioning of the genetic
variance using analysis of molecular variance (AMOVA) of 4 populations based on
geographic locations: Lower Peninsula, Michigan; Upper Peninsula, Michigan,
Wisconsin and Minnesota showed that genetic variation attributable among populations
and within populations was 12.67% and 87.33% respectively. No correlation was found
between geographic distance and pairwise genetic distance of the groups. High
outcrossing and sexual recombination of T. incarnata may likely be key factors
explaining their genetic variability as shown with the low Fixation index (PST) and high

average of genetic diversity per loci within populations.

Key words: Gray snow mold, RAPD, genetic diversity, Typhula incarnata

64

INTRODUCTION

T yphula incarnata is an economically important pathogen affecting winter cereals
and perennial grasses. The fungi are known to occur in cool temporal to boreal regions of
the northern hemisphere, in countries such as Russia, Canada, North America, Europe,
Japan and other Nordic countries (Smith, 1989). Snow mold blight, collectively caused
by gray snow mold (T. incarnata) and speckled snow mold (T. ishikariensis) is ranked as
the second most damaging turfgrass disease after dollar spot of the Great Lakes Region
by the golf course superintendents (Anonymous, 1996).

The wide host specificity of the gray snow mold among turfgrasses is shown in
the pathogen’s ability to cause diseases in Agrostis, Poa, Festuca and Lolium. On golf
courses, the pathogen is ubiquitously found on the putting greens comprised of
bentgrasses (Agrostis sp.), and fairways comprised of bluegrasses (Poa sp.) and other
bentgrass mixtures. Lateral transit of the soil-bome pathogen across genera boundaries
contributes to its presence across a wide geographic range. T. incarnata has been
described as more versatile than T. ishikariensis due to its ability to adapt to less
favorable environments such as shallower and shorter duration of snow cover (Jacobs and
Bruehl, 1986; Matsumoto and Tajimi, 1985). The fungus is more common on golf
courses with less than 90 days of snow cover (Millet, 2000). The disease symptoms
usually appear as circular, water-soaked or straw-colored patches measuring 5 to 15 cm
across and may coalesce. Plants may be matted, appear slimy with mycelium and may be
covered with dust giving it a gray-white appearance, hence the name “gray snow mold”
(Jackson and F ensterrnacher, 1969). On diseased turfgrass, T. incarnata produces

numerous round, orange to brown colored bodies called sclerotia which remain as resting

65

bodies during the smnmer months and become an inoculum source when temperatures
fall. Sclerotia of T yphula snow molds are used to distinguish the different species during
field collections. Hsiang et al. (1999) provides a comprehensive review of the biology of
snow molds in turfgrasses.

The complexity of T. incarnata was revealed by the study of incompatibility
alleles in mating classes. T. incarnata is a tetrapolar species, producing viable
basidiospores and vigorous monokaryons. Bruehl and Machtrnes (1978) found four
mating classes based on clamp connections suggesting that there may be 39 alleles of
both incompatibility loci A and loci B in a field sample of 32 field dikaryons. In fungi,
locus A is responsible for the initial formation of clamps, nuclear pairing, conjugate
nuclear division and septation of clamps while locus B is responsible for nuclear
migration and fusion of clamp tip. Both loci are multi-allelic. The high fecundity of T.
incarnata and large number of incompatibility alleles suggest that this species utilizes its
sexual stage frequently. It is possible that genetic variants of the pathogen allow it to
infect a wide range of hosts. In Japan, the fungus has a wide geographic range and this is
ascribed to its ecological versatility (Matsumoto et al., 1995). Studies have not found
variation in mycelial grth rates among isolates from Japan but sclerotia from longer
and heavier snowfall regions germinated faster than those from areas of less persistent
snow cover. Despite all the information, there are no known molecular studies to support

or further describe the genetic diversity in T. incarnata.

Many approaches have been developed to look at the genetic variability of various
organisms. Randomly amplified polymorphic DNA (RAPD, Welsh and McClelland,

1990; Williams et al., 1990) has been a popular choice due to its simplicity, speed, low

66

cost and availability of primer kits. For minute living organisms like slow-growing fungi
where DNA isolation is difficult and sparse in concentration, simple polymerase chain
reaction (PCR) through RAPD would be an ideal choice. The technique has been used
previously to examine genetic diversity in several fungi (Grajal-Martin et al. 1993; Huff
et a1, 1994). RAPD has been used to characterize populations of pink snow mold,
Microdochium nivale (Mahuku et al., 1998) and speckled snow mold (Hsiang and Wu,
2000). The genetic diversity of pink snow mold isolates from turfgrass using RAPD
analyses revealed the low level of genetic differentiation among populations. For
speckled snow mold, RAPD analyses distinguished among the three main Typhula
species, the distinction of T. ishikariensis var. idahoensis from var. canadensis and

uncovered variation within isolates of the same species (Hsiang and Wu, 2000).

The objectives of this study were to examine the genetic variability of 40 T.
incarnata isolates growing on and presumed pathogenic to turfgrasses, from Michigan,
Wisconsin and Minnesota, US. Also, genetic variation within and among populations in
4 different geographic locations: the Lower and Upper Peninsula of Michigan, Wisconsin
and Minnesota, US. using RAPD analysis were of interest. Understanding the
population structure and genetic variability of gray snow mold will help in disease

resistance studies for turfgrasses.

MATERIALS AND METHODS

Pathogen
T. incarnata samples were collected from Hancock Turfgrass Research Center in

East Lansing, Michigan on April, 2001 and from golf course areas in Northern Michigan

67

'm hull
the De
llirmr'
Pathc
my

3181

DN.j

in April 2002. Isolates from a sod farm in Lansing, Michigan were obtained courtesy of
the Department of Plant Pathology at Michigan State University (MSU). Samples from
Minnesota and Wisconsin were collected by researchers from Department of Plant
Pathology, University of Wisconsin-Madison through collaboration in 2002 and DNA
samples were sent to the Turfgrass Genetics Laboratory of Crops & Soil Sciences at
MSU, courtesy of Dr. G. Jung (Table 3.1). Sclerotia from collected sites were grown,
purified and maintained in potato dextrose agar (PDA) at 5 °C. PDA broth was prepared
using 37 grams dehydrated DIFCO potato dextrose agar in 1 L. distilled water followed
by sterilization for 30 min. Mycelial growth was compared for samples obtained from

Michigan golf courses.

DNA preparation

Fungal mycelia were grown for two months in PDA prior to genomic DNA
extraction. DNA from mycelia was extracted using a modified extraction buffer to
eliminate proteins from the agar. The standard buffer consisted of Tris-EDTA-HCI at pH
8.0, SDS, NaCl with 0.38 g of sodium bisulfite per hundred ml of the buffer. Proteinase
K (14 mg/ml in IOmM Tris-HCl) was added at a volume of 400 ul per 400 u] extraction
buffer. The mixture was incubated at 65 °C for 20 min. followed by the addition of an
equal volume of 25:1 chloroformzisoamyl alcohol. Centrifugation was performed at 2800
g RCF (Sorvall RT7 model) for 20 min at 5 °C. The supernatant was collected to which
2/3 volume of cold isopropanol was added to precipitate the DNA. All DNA samples
were treated with RNAse dissolved in TE buffer and twice reprecipitated by using 1/10

volume of 3M sodium acetate followed by two volumes of chilled absolute ethanol.

68

Table 3.1. List of sampling areas, cities and USA states for T yphula incarnata
isolates used.

 

 

No. CODE SOURCE CITYfi STATE:
1 Ml-LN 1 Michigan State University East Lansing MI
2 MI-LN 2 Michigan State University East Lansing MI
3 Ml-LN 3 Michigan State University East Lansing MI
4 M1-LN-8 Sod Farm Lansing MI
5 MI-HS 4 Birchwood Golf Course (GC) Harbor Springs MI
6 MI-HS 4-1 Birchwood GC Harbor Springs M1
7 MI-HS 5 Birchwood GC Harbor Springs MI
8 MI-PT 4-7 Walloon Petoskey MI
9 MI-PT 4-8 Walloon Petoskey MI
10 MI-PT 5 Walloon Petoskey MI
11 Wl-NE 75.16.1 Perry's Landing Marion WI
12 Wl-NE 68. 17.1 Nicolet Laona W1
13 WI-NW 54.7.3 Park Falls Park Falls WI
14 Wl-NW 70.14.3 Spooner Spooner W1
15 WI-SE 62.8.1 Meadow Springs Jefferson W1
16 WI-SE 65.3.5 Moor Downs Waukesha WI
17 Wl-SE 96.152 The Squires Port Washington WI
18 Wl-SW 61.3.3 Prairie du Chien Prairie du Chien W1
19 Wl-SW 7618.5 Towne Edgerton W1
20 WI-SW 84.181 The Valley Muskego WI
21 MI-LSE 2-5 L'Anse GC L'Anse, UP M1
22 MI-LSE 9-5 L'Anse GC L'Anse, UP MI
23 MI-Long 1-2 Lakewood Blackshire Oscoda M1
24 MI-Long 15-2 Lakewood Blackshire Oscoda MI
25 M1-PL-6-3 Portage Lake Houghton, UP MI
26 MI-Tree 5-1 Treetops Gaylord MI
27 MI-KML 2-2 Keeweenaw MTM Lodge Copper Harbor, UP MI
28 Ml-Glad 4-5 Gladstone GC Gladstone, UP MI
29 Ml-IR 7-2 Indian River GC Indian River MI
30 Ml-PL 2-4 Portage Lake Houghton, UP M1
31 MN-CW 11-1 Crosswoods GC Cross Lake MN
32 MN-CW 18-4 Crosswoods GC Cross Lake MN
33 MN-OH 3-1 Oak Harbor GC Baudette MN
34 MN-CW 9-2 Crosswoods GC Cross Lake MN
35 MN-LP 18-1 Long Prairie GC Long Prairie MN
36 MN-IM 9-3 Ironman GC Detroit Lakes MN
37 MN-IM 4-1 Ironman GC Detroit Lakes MN
38 MN-NCL 3-3R Northland GC North Mankato MN
39 MN SN 2 Superior National Lutsen MN
40 MN WF 2-1 Whitefish Pequot Lakes MN

 

IUP = Upper Pensinsula

2 M1 = Michigan; W1 = Wisconsin ; MN = Minnesota , USA.

69

Extracted DNA was collected by microcentrifugation at 8,000 g RCF (Marathon 16
model, Fisher Scientific) for 30 sec. and the supernatant was discarded. DNA was re-
dissolved at room temperature and stored in 1% TE buffer. DNA quality was checked by
running 5 ul of the undigested samples in 1% agarose gel containing TBE buffer. All
DNA sample concentrations were quantified using DyNA Quant 200 Fluorometer
(Pharmacia Biotech, CA) and concentrations were adjusted to equal volumes of

approximately 8 ng/ul prior to RAPD analysis.

RAPD Analyses

Decamer random primers from Operon kits (Operon Technologies, Inc. Alameda,
CA, USA) and primer sequences used by Hsiang & Wu (2000) for RAPD analysis of
other snow molds were tested for PCR amplification. Synthesized primers were made by
MWG Biotech, NC. Thirty-seven primers were chosen for strong signals and
reproducibility. The primers, sequences and annealing temperature are listed in Table
3.2. The DNA amplification mixture consisted of 2.5 ul of DNA (~20 ng), 2 ul of lmM
dNTP mix, 2 ul of 25 mM MgC12, 2 ul of 10 ng/ul Primer, 1 unit Taq Polymerase in 0.2
111 volume and distilled deionized H20 for a complete volume of 20 ul. PCR amplification
was performed in a thermal cycler, PTC-IOO (MJ Research, Inc., MA) with one cycle of
94 °C for 2 min, followed by 35 cycles of 94 °C for 30 sec, 40 0C for 30 sec and 72 0C for
1 min and a final cycle of 72 0C for 15 min. Only 3 RAPD primers were amplified at 36
°C temperature and the other 34 RAPD primers gave good amplification products at 40

°C. Fragments were separated in 1.5% TBE agarose gel and stained with ethidium

7O

bromide. PCR reactions and gel analyses were conducted at least two times for each

primer for replication and verification of results.

Analyses of genetic similarity

Bands were scored for presence as l or absence as 0. Missing or ambiguous
bands (approximately < 2.0%) were designated as 999. Analyses were done using
Numerical Taxonomy and the Multivariate Analysis System, NTSYS v.2.1 (Rohlf, 2000).
Genetic similarities or similarity coefficients (sc) based on Dice’s estimate (Dice, 1945)
were calculated among all possible pairs using the SIMQUAL option and ordered in a
similarity matrix. Landry and Lapointe (1996) compared several coefficients for use with
RAPD markers and suggested the use of Jaccard and Dice coefficients for 12 markers and
more. The similarity matrix was run on Sequential, Agglomerative, Hierarchical and
Nested clustering (SAHN) (Sneath and Sokal, 1973) using the Unweighted Pair Group
Method with the Arithmetic Mean (UPGMA) as an option (Sokal and Michener, 1958).
Principal component analysis (PCA) was run after doing correlation between similarity
coefficients using T. incarnata isolates as operational taxonomic units (OTUs). The two
dimensional plot was generated from Eigen vectors and Eigen values calculated from
EIGEN option. The TREE module of NTSYS v.2.1 was used to produce the dendrogram
(Rohlf, 2000).

Data from RAPD markers were subjected to bootstrap analysis using another
software, FreeTree (Pavlicek et al., 1999). The distance similarity matrix was computed
using the Nei-Li option, followed by neighbor joining (NJ) clustering method with

resampling analysis using 100 repetitions. On the reference tree, the bootstrapping values

71

were copied into the bracketed form of the tree. The form of the tree was copied and
pasted into the TreeView program (Page, 2001) to draw the dendrogram. The NJ

dendrogram contained the bootstrap information.

Analyses of Molecular Variance (AMOVA)

The isolates were assigned to 4 populations based on proximal geographic
locations as Lower Peninsula, Michigan (LPMI), Upper Peninsula, Michigan (UPMI),
Wisconsin (WI) and Minnesota (MN) (Figure 3.1, Table 3.2). To compare the
populations’ genetic diversity, AMOVA (Arlequin 2.0) program enabled partitioning of
the RAPD variation between and within groups using variance and covariance
components. Fixation index (FST), pairwise difference between populations and average

diversity per loci were determined using the software package.

RESULTS AND DISCUSSION

Gray snow mold mycelial growth in vitro

Sclerotia of the fungi from East Lansing (HC), Lansing (SF), Harbor Spring (HS),
Petoskey (PT) were grown in PDA and were measured weekly for mycelial growth. HC
and SF represented Mid-Michigan while HS and PT were from Northern Michigan.
Mean mycelial growth was calculated by taking the growth differences between weekly
measurements for all isolates and taking the grand average. The mean grth rate was
established at 0.32 cm. per week. Gray snow mold has been described as a weak
saprophyte, slow growing with varying cultural habits in vitro (Hsiang et al., 1999).

Analysis of variance on the radial growth of the different isolates using 3 replicates per

72

 

O. . 0
° UPMI
O O C
O .0
MN - ,
O
0
WI
0
O
O O
, ° LPMI

 

 

 

 

Nfifiofia Jam mtedsrléiu.

 

 

Figure 3.1. Collection sites of gray snow mold clustered in 4 populations based on
geographic locations: Minnesota (MN); Wisconsin (WI); Lower Peninsula, Michigan

(LPMI) and Upper Peninsula, Michigan (UPMI).

73

Table 3.2. RAPD primers and sequence, annealing temperature and percentage
polymorphism found in gray snow mold. (OP = Operon primer kits; P =
Biotechnology Laboratory, British Columbia primer).

 

 

RAPD Sequence Anneal (°C) No. of Bands No. of Bands Percentage

5’ to 3’ Temperature Visible Polymorphic Polymorphism
P143 TCG CAG AAC G 36 2 1 50.0
P162 CTA GAT GTG C 36 5 2 40.0
P177 TCA GGC AGT C 36 7 4 57.1
P715 CCA CCA CCC A 40 11 6 54.5
P731 CCC ACA CCA C 40 6 3 50.0
P732 CAC CCA CCA C 40 5 2 40.0
P701 CCC ACA ACC C 40 9 4 44.4
OPA-O8 GTG ACG TAG G 40 7 5 71.4
OPA-20 GTT GCG ATC C 40 9 5 55.6
OPC-04 CCG CAT CTA C 40 6 3 50.0
OPC-OS GAT GAC CGC C 40 7 3 42.9
OPC-06 GAA CGG ACT C 40 6 3 50.0
OPC-07 GTC CCG ACG A 40 6 2 33.3
OPC-08 TGG ACC GGT G 40 12 8 66.7
OPC-lO TGT CTG GGT G 40 7 4 57.1
OPC-13 AAG CCT CGT C 40 2 1 50.0
OPC-15 GAC GGA TCA G 40 6 2 33.3
OPC-16 CAC ACT CCA G 40 3 2 66.7
OPC-19 GTT GCC AGC C 40 8 5 62.5
OPC-20 ACT TCG CCA C 40 6 2 33.3
OPY-02 CAT CGC CGC A 40 14 7 50.0
OPY-03 ACA GCC TGC T 40 2 1 50.0
OPY-05 GGC TGC GAC A 40 10 4 40.0
OPY-06 AAG GCT CAC C 40 6 4 66.7
OPY-07 AGA GCC GTC A 40 5 3 60.0
OPY-lO CAA ACG TGG G 40 5 2 40.0
OPY-l3 GGG TCT CGG T 40 7 4 57.1
OPY-14 GGT CGA TCT G 40 6 5 83.3
OPY-15 AGT CGC CCT T 40 6 3 50.0
OPY-16 GGG CCA ATG T 40 7 3 42.9
OPY-l7 GAC GTG GTG A 40 5 3 60.0
OPY-18 GTG GAG TCA G 40 5 1 20.0
OPY-19 TGA GGG TCC C 40 5 2 40.0
OPY-20 AGC CGT GGA A 40 9 2 22.2
OPX-Ol CTG GGC ACG A 40 8 3 37.5
OPX-06 ACG CCA GAG G 40 7 3 42.9
OPX-l3 ACG GGA GCA A 40 10 3 30.0
Total 247 120
Mean 6.7 3.2 48.7

 

74

clone for each week are summarized in Table 3.3 and growth progress was graphed in
Figure 3.2. Significant differences in mycelial growth between isolates were observed in
the 2nd and 3rd week. No significant variation in radial growth was found in 1St and the 4th
week although the isolates from Petoskey (Northern Michigan) appeared to have
germinated and elongated faster in the 1St week and contributed to a significant difference
in the F-tests for the 2MI and 3rd week. Isolates from Mid-Michigan were found to
achieve the growth size of those from Northern Michigan in four weeks. Northern
Michigan has longer and more severe snow patterns than Mid-Michigan and may have
provided selection pressures leading to variation in germination and grth patterns of
these psychrophilic organisms. However, no significant differences in sclerotial and
mycelial growth were detected at the end of this four week study. The results support
previous findings comparing growth of isolates in Japan (Matsumoto et al., 1995).
Information on mycelial growth rate will be useful for the selection of vigorous
inoculum, timing for DNA extraction and for planning inoculum preparation in firture
disease resistance screening experiments. A growth period of two months was later
established as good for a full plate of mycelial growth for DNA extraction and for

transfer to cornmeal agar for future inoculation work.

RAPD analyses and genetic similarities

Thirty-seven RAPD primers or 60% of the tested primers yielded at least one
scorable fragment. The 37 primers used, their sequences, annealing temperature and
number of amplified bands are presented in Table 3.2. The number of bands ranged from

2 to 14 with a mean of 6.7 bands/primer. The percentage mean polymorphism was

75

Table 3.3. Analysis of variance on the radial mycelial growth of gray snow mold

isolates in vitro from different locations in Michigan.

 

Mean mycelial grth (cm.) in weeks

 

 

Source No. of
Samples 1 2 3 4
East Lansing (HC) 3 0.27 i 0.2 0.60 + 0.1 1.70 i 0.2 2.20 i 0.2
Lansing (SF) 3 0.37 i 0.3 0.67 i 0.3 1.40 i 0.2 2.47 i 0.2
Harbor Spring (HS) 3 0.23 i 0.2 0.50 _‘l_' 0.2 1.17 i 0.2 2.50 i- 0.3
Petoskey (PT) 3 0.43 i 0.2 0.97 i 0.4 2.27 i 0.3 3.10 i 0.5
F-Values 3.03 5.09* 6.51 * 3.13

 

* Significantly different P<0.05.

 

 

 

growth in cm

 

 

Figure 3.2 Radial growth in four weeks of different gray snow mold isolates in vitro
from Michigan. P values < 0.05 as follows: Week 1 = 0.09; Week 2 = 0.02; Week 3 =
0.02 and Week 4 = 0.09.

Legend: Hancock Research Center, East Lansing (HC); Sod F arm,Lansing (SF); Harbor Spring (HS);
and Petoskey (PT)

76

48.7%. The high level of discriminatory bands confirms RAPD as an ideal choice for
genetic variability testing. A sample RAPD profile using OPY-02, the primer yielding the
highest number of observed bands is shown in Figure 3.3.

Of the 120 polymorphic bands observed, 115 were chosen based on reliability.
Genetic similarities were obtained using Dice estimates. Similarity coefficients ranged
from 0.58 to 0.95 between different isolates from different locations (Table 3.4). Isolates
no. 1 and 2 (MI), (numbered according to Table 3.1) are clones proving Koch’s postulate,
since Mi-2 was the pathogenic fungus recovered from plants infected with Mi-l,
continuously re-cultured every three months within a year. Genetic similarity was 100%,
suggesting no recombination within the infected plants. Mi-3, an isolate from the same
sampling site has 95% genetic similarity to Mi-l and Mi-2, indicating variability arising
probably from allelic diversity and recombination. The next closest pair was Mi-S and
Mi-6 (sc=0.93) and Mi-8, 9, 10 (sc > 84%). Several isolates have sc=0.58 from pairwise
comparisons, as in isolates from Detroit Lakes, MN versus those taken fi'om East
Lansing, MI (MN-36 vs. MI-l, Table 3.4); from Laona, WI versus Gladstone, MI (WI-12
vs. Ml-28); from Spooner, WI versus North Mankato, MN (WI-14 vs. MN-36).
Apparently, there is no correlation between geographical distance and the similarity
coefficient for gray snow mold. UPGMA dendrogram produced and cluster analysis also
showed no specific grouping, indicating that the isolates were highly heterogenous
(Figure 3.4 and 3.5). Principal component analysis on the correlation of similarity
coefficients between isolates indicate 2 groups based on eigen values greater than 1. The
first component accounted for 71% of the variance and the second component accounted

for only 2%, which did not really define specific groups. Scatter plot of the 40 gray snow

77

 

mm 2%... 5 68m: 8&9; he 36:56 2688 683386 u 62......

 

 

664 666 666 $6 666 2.6 $6 666 6n6 666 666 3.6 666 666 2.6 666 N66 :6 666 666 cm
66; 2.6 2.6 666 3.6 666 :6 666 2.6 «66 666 :6 666 86 $6 666 666 86 $6 6—
66._ 666 36 3.6 36 $6 666 2.6 $6 :6 2.6 2.6 :6 666 3.6 666 :6 666 M:

66.. 2.6 2.6 $6 $6 $6 :6 86 «66 666 2.6 $6 $6 2.6 666 666 666 2

66.. 2.6 36 Q6 «66 Q6 ~66 2.6 :6 666 36 $6 666 666 ~66 2.6 o—

66._ 606 666 ~66 2.6 3.6 666 2.6 2.6 666 86 3.6 666 3.6 666 m—

66._ «66 666 666 ~66 666 $6 $6 2.6 666 $6 $6 $6 36 E

66; 666 :6 666 666 666 :6 «66 $6 :6 ~66 N66 36 2

66; :6 $6 36 $6 36 $6 $6 666 $6 ~66 66.6 N—

66._ 2.6 3.6 :6 N66 36 ”66 2.6 26 ~66 666 _—

66._ 666 m66 56 P6 P6 Q6 «66 S6 666 6—

66; M66 36 :6 :6 3.6 6w6 £6 36 6

66._ _66 666 3.6 2.6 36 $6 666 w

66.. 36 666 36 $6 666 2.6 N.

66; 66.6 666 2.6 666 666 o

66; N66 666 2.6 3.6 m

66.. 86 36 ~66 v

66._ 066 666 m

66; 666 N

66; _

cm 6_ f 2 2 2 I 2 S 2 6_ 6 a N. c n v m N _ :62
.3255 um 63m: €8.38 Q3

3 _ mo 8% 80¢ moan—0mm 3385.63 KV 208 265 69% 66 .86 33:53 35 mamma— mfiomommooo 35:86 36:00 Adam 93.;

78

_.m 03.2. 5 62m: 820$ 8.“ .5685: 2683 6033306 n .02....

 

 

2.6 3.6 N66 36 62.6 3.6 666 666 606 2.6 N66 :6 666 2.6 606 066 ”06 666 2.6 N66 9.
62.6 2.6 606 62.6 666 62.6 066 606 36 62.6 62.6 2.6 92.6 2.6 62.6 2.06 2.06 no.6 62.6 2.6 66
2.06 62.6 906 36 2.06 62.6 $6 606 6m6 «06 W66 62.6 «2.6 02.6 2.06 36 36 :6 v.66 62.6 mm
2.6 606 606 006 006 36 N06 666 N06 066 2.6 $6 066 62.6 2.6 2.6 3.6 2.6 2.6 2.6 2.6
:6 36 36 36 36 606 $6 096 86 36 02.6 2.6 :6 N66 36 36 2.06 86 666 $6 on
$6 2.6 $6 606 066 666 $6 36 $6 2.6 02.6 02.6 2.6 2.6 $6 $6 ”06 62.6 606 606 mm
$6 86 696 ”06 86 066 006 No.6 36 62.6 02.6 2.6 02.6 36 606 666 006 606 86 86 em
$6 $6 606 606 $6 2.6 $6 86 36 2.66 26 02.6 62.6 2.6 606 666 v.06 606 606 36 mm
2.6 2.6 2.6 62.6 606 62.6 36 696 2.66 N66 2.06 N66 606 62.6 N66 2.6 62.6 N66 666 666 mm
006 3.6 62.6 606 606 62.6 86 36 666 666 62.6 $6 3.6 62.6 N66 62.6 ”06 3.6 2.6 62.6 _m
606 606 62.6 36 62.6 who 86 N66 666 62.6 62.6 36 N66 62.6 $6 666 606 36 $6 36 66
62.6 62.6 666 62.6 62.6 2.6 36 2.66 36 62.6 3.6 2.6 :6 :6 606 606 606 2.6 $6 666 6N
606 N66 56 36 606 606 6w6 «m6 666 $6 666 666 066 2.6 606 606 36 62.6 666 «66 mm
86 ”06 2.6 36 $6 006 606 N66 ”06 2.6 02.6 2.6 02.6 2.6 2.2.6 2.2.6 62.6 2.6 62.6 62.6 mm
2.6 666 N66 ”06 $6 3.6 2.06 606 62.6 606 $6 2.6 N66 2.6 :6 606 606 606 $6 36 cm
2.06 606 606 2.06 36 2.6 6m6 36 36 2.66 62.6 $6 666 ”66 666 666 666 3.6 62.6 666 mm
2.06 36 :6 2.66 666 $6 36 62.6 ”06 666 ”66 62.6 2.6 3.6 3.6 606 2.66 ”66 ”06 no.6 vm
62.6 62.6 62.6 N66 666 62.6 N06 066 No.6 2.6 N66 36 N66 26 2.66 36 ”66 2.6 V2.6 3.6 mm
62.6 3.6 2.2.6 N66 62.6 62.6 $6 36 2.66 62.6 2.6 $6 86 62.6 ”06 666 62.6 36 2.6 2.2.6 mm
2.06 36 no.6 6m6 666 36 36 R6 6m6 v06 :6 v2.6 N66 36 $6 2.66 36 2.6 606 62.6 _N
cm 6- 3 C m: 2 3 m— N. Z S 6 w 2. o w v m N _ :62

 

.mbfitm 2.6 wfimz $832: as 2 6 6o 83 82.6

wows—0mm 332.895 .5 208 >55 3% 66 .86 88:58 85 main $58—$08 55:8? 36:00 .Auetaaucucuv 6.1m 03:.

79

6m 2an 5 63m: 820$ .56 .5686: 2683 683386 .I. 62......

 

 

66; N66 N66 56 N66 666 M66 m66 666 66.6 66.6 _66 666 66.6 66.6 666 666 66.6 66.6 666 66
66._ 66.6 66.6 66.6 66.6 66.6 666 666 66.6 #66 N66 666 66.6 m6.6 N66 66.6 66.6 _66 Q6 66
66._ 66.6 666 M66 66.6 66.6 666 m66 66.6 66.6 66.6 666 666 66.6 66.6 66.6 _66 666 mm

66._ m66 66.6 66.6 666 m66 66.6 66.6 66.6 666 666 N66 66.6 666 666 66.6 _66 6m

66._ 66.6 66.6 3.6 _66 66.6 66.6 666 ~66 66.6 666 666 66.6 66.6 66.6 $6 66

66; M66 666 666 66.6 66 66.6 V66 m66 66.6 66.6 666 66.6 666 66.6 mm

66.2 666 666 66.6 66.6 66.6 66.6 66.6 66.6 66.6 66.6 66.6 66.6 66.6 cm

66; 66.6 _66 66.6 66.6 666 66.6 666 V6.6 3.6 666 66.6 ~66 mm

66._ 666 66.6 66.6 86 .66 66.6 _66 66.6 66.6 m66 66.6 mm

66; _66 _66 66.6 66.6 66.6 66 36 666 N66 ..666 _m

66.6 66.6 N66 666 666 66.6 66.6 V6.6 666 666 66

66; 66.6 666 66.6 66.6 66.6 66.6 66.6 666 66.

66.6 66.6 66.6 666 66.6 66.6 666 666 mm

66.2 M66 66.6 66.6 66.6 66.6 N66 6.6.

66._ 666 666 66.6 N66 36 em

66._ ~66 66.6 66.6 666 mm

66.6 _66 66.6 666 66

66.. V66 V66 66

66._ 666 mm

66._ _N

66 6m mm 66 66 mm 3” mm mm Tm 66 6N mm 6N 6N mm 6N mm mm .6 :62

 

2586.26 66 653 $8.38 DE 2 6 6o 83 :86

86286 6332326 .66 22: >65 68w 66 86 33868 35 mam: $566608 63.868? 26:00 .Auotasfiuucuv 6.9.6 «Bah.

80

MI WI

L 12 3 4 5 6 7 8 91011121314151617181920

600bp

 

L 212223 2425 262728 29303132 33 3435 363738 3940

 

 

MI MN
Figure 3.3. RAPD profile of 40 gray snow mold isolates from Michigan (MI),
Wisconsin (WI) and Minnesota (MN) using primer OPY-02.(L=100bp DNA ladder).

Lane numbers refer to isolated listed in Table 3.1.

81

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

C»
on

 

 

 

 

 

 

 

 

 

 

 

L
3.5.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

r I r I l I I I U T l l T T I T I T I

l
0.52 0.63 0.75 0.87 0.99
Similarity Coefficient

Figure 3.4.1. UPGMA dendrogram of 40 gray snow mold isolates from different
locations in Michigan (MI), Wisconsin (WI) and Minnesota (MN), USA based on 115
RAPD markers generated using Dice coefficients, NTSYS program (Rohlf, 2000).

82

11 Nit-37

 

 

 

 

 

 

 

 

l' Mi-23
I. M-26
Wi-11
W1-17
W1-19
Wi-16
Wi-18
IVE-24
Wl-13
Wl-12
WI-14
Wl-15
Mi-7

IVE-8

3 5 lMi-10

M-9

MM
22: SIM-5
IVE-6

M-3
L. . -
fl M_1
Mi-z
7 -
0.] 5,— M 21
I— Mi-22

 

 

 

 

 

100

 

 

L4.

 

 

 

 

 

 

 

Figure 3.4.2. Dendrogram of 40 gray snow mold isolates based on 115 RAPD
markers using Nei-Li genetic distance, neighbor joining (NJ) method and resampling
options of FreeTree (Pavlicek et al., 1999) and TreeView (Page, 2001) programs.
Numbers beside node indicate relative bootstrap frequencies.

83

Dimension 2

 

 

 

.. in" ““9““ _........ ‘" " .
o... {,1 6..
«ffls ‘ ‘8 .5wa
.1” " ‘ "
‘7 6 A?
,."' ‘ 1 \a.
0.22- Ml," 3,3
A 4 i
0.09 28 H é
* . 13 ‘ 24 A22 \
b 1'4 A27 '3‘ ’3'
I." ~._ ............................. 6 «3.8 ...... £16 . 15 " 3"
#3“: ....-:"{.'.:.-' 037 34 ' “-63.1 11"! if.
.0134 ff.“ ' ....... O 17 0 ‘23 7"“ .. 9v” 1""
036 "'""-,,__ .12 .19 . . a“ .“J
1 .- . . 3 3 fl .3»;- ..
f' .G “Mm. -- -..-........-....... 2......«uw- g _,.-f
MN - “MA 26 3:, .
039 40 A29
~ A39. w"
'0‘39 r T T w-vcEE-«w...r.. ........ w __.‘. ''''''' ”M" I
0.66 0.72 0.77 .

Dimension 1

 

0.88

Figure 3.5. Scatterplot of the first two dimensional scales for 40 gray snow

mold isolates based on principle component analysis of RAPD profile. Number

correspond to isolate as listed in Table 3.1.

Legend: Michigan ‘ , Upper MichiganA
Wisconsin
Minnesota °

84

mold isolates using the first 2 dimensional scales showed that distribution between
isolates coming from different geographical sources overlapped (Figure 3.5). The
intertwined cluster corresponded to the first major group defined by PCA, with Mi-28
and Mi-21, representing a minor group, presumably outliers defined by the second

component.

The distance data was used to generate a single NJ tree afier carrying out 100
replicates on the bootstrap test. The UPGMA tree for the 115 RAPD markers was shown
Figure 3.4.1 and the NJ tree was given in Figure 3.4.2. While the two dendrograms
differed topographically or in general appearance, the small clades were broadly similar
for some of the isolates. As an example, in the UPGMA tree, Mi-l, Mi-2, Mi-3 belonged
to one clade and would similarly appear as a subclade for the NJ tree. Also in the
UPGMA tree, Mi-7, Mi-8, Mi-9 and Mi-lO would be a subclade; Mi-S, Mi-6 and Mi-7
would be another subclade and the two subclades would belong to the same single clade
for the NJ tree. Isolate pairs of Mi-33 and Mi-37; Mn-38 and Mn-40 or Wi-l6 and Wi-18
would appear as pairs also in both trees. Isolates with low bootstrap frequencies of 1 and
2 (Mi-28 or Mn-36, respectively) from the NJ tree were found in different clades in the
UPGMA tree. The bootstrapping method revealed that there could possibly be three
major clades and was found to have a good resolution to distinguish the isolates than by
the UPGMA method. The first major clade in the NJ tree composed of 18 fungal isolates
from the three states, Michigan, Minnesota and Wisconsin. The second major clade
included 8 isolates from Wisconsin and Mi-24 (Michigan) while the third major clade

consisted of 12 isolates from Michigan and Wi-15 (Wisconsin).

85

From the two dendrograms, the long branches in the UPGMA tree indicated the
wide genetic diversity of the T. incarnata isolates and the relatively long branches in the
NJ tree also gave indication of the wide genetic distance between clades. The low
bootstrap values for the shorter nodes in the NJ tree suggest relatively recent clonal
differentiation probably arising from the same founder group. Similar results were found
for trichomonad parasites using the RAPD method and using repeated bootstrap analysis

(Hampl et al., 2001).

The results generally showed that gray snow molds were highly variable. Small
groups or clades could be discerned using a resampling approach. Heterogeneity can
arise from several factors by way of sexual recombination as T. incarnata is highly
outbred (Bruehl and Machtmes, 1978), selection pressures from the environment or from
the fungi’s complex tetrapolar sexual mating systems. With different mating classes, the
fungus may have different structures for male and female hyphae or heterothallic in
nature which may increase out crossing. Future studies should be made to define genetic

variation in association with mating groups.

AMOVA analyses

An analysis of molecular variance of the genetic distances between populations
defined by geographic clustering indicate that 12.67% of the genetic variation was
attributable among populations and 87.33% within populations (Table 3.5). The presence
of high variation within populations in contrast to lower levels of genetic differentiation

among geographical locations confirms the hypothesis that migration results in low levels

86

Table 3.5 Summary of AMOVA of populations of gray snow mold based on 4
geographic locations, Michigan (MI), Upper Michigan (UMI), Wisconsin (WI) and
Minnesota (MI).

 

 

 

Source of Degrees of Sum of Variance Percentage
Variation Freedom squares components of variation
Among

populations 3 64.60 1 .29 12.67
Within

populations 36 321.3 8.93 87.33
Total 39 386.00 10 22

Fixation Index FST: 0.1267

 

Table 3.6. Population pairwise difference (distance method) and average genetic
diversity/loci in 4 different populations of gray snow mold according to geographic
locations.

 

 

Population MI MN UMI WI Average genetic
diversity/loci

MI 0.00 0.19 i 0.11

MN 0.17 0.00 0.26 i 0.14

UMI 0.03 0.06 0.00 0.34 i 0.20

WI 0.13 0.19 0.10 0.00 0.30 i 0.16

 

87

of differentiation between geographical locations and that sexual recombination and
dissemination result in higher levels of genetic diversity (Mahuku et al., 1998).

Fixation index (FST) measures the reduction in heterozygosity in an individual
due to non-random mating within its subpopulation due to genetic drift (Wright’s F-
statistics). The index value of 0 indicates panmixis (random mating) and the value of 1
indicates extreme isolation. The FST index for T. incarnata population, based on
geographic groups based on permutation in the AMOVA was estimated at 0.13. To
compare the F ST index, most large vertebrates with higher number of nuclear genes and
capacity for recombination and even capable of migration would have a mean value of
FST <0.2. The relatively low F ST index for T. incarnata suggests that heterozygosity
may be highly maintained more through random mating.

Population pairwise difference or genetic distance between the 4 populations were
measured and presented in Table 3.6. The results indicated close values for genetic
distance between populations, MN and WI (0.19) compared to MN vs. LPMI (0.17).
Geographic distance hence does not significantly differentiate the populations of snow
mold between the three states. The population genetic distance between LPMI and UPMI
was only 0.03, suggesting no significant difference in genetic diversity between the two
populations. The average genetic diversity per loci was estimated for each population
(Table 3.6) and isolates from Upper Peninsula, Michigan (UPMI) had the highest
diversity (0.34) followed by WI (0.30), MN (0.26) and LPMI (0.19).

In summary, RAPD markers were used to assess genetic diversity in T. incarnata
and demonstrated the vast differences in isolates genetically. In other fungi like speckled

snow mold, RAPD was able to isolate clonal groups of T. ishikariensis var. idahoensis

88

and var. canadensis. Our results from the dendrograms, 2 dimensional scale plot,
AMOVA analyses however all indicate that isolates of T. incarnata may not be
sufficiently diverged and isolated to identify clonal groups. The high amount of genetic
variability found within geographic groups probably was attributable to high rates of
sexual recombination and random mating. Geographic distance was unlikely a major
contributing factor population differentiation due to human interference by way of
transport, contaminated equipment, similar selection pressures from fungicide
applications, same source of infected sod (Smith et al., 1989) and similarity of hosts or
cultivars of turfgrasses at different locations. The results obtained from gray snow mold
are similar to those for pink snow mold, where genetic diversity among individuals
within populations was high (Mahuku et al., 1998). High variability have also been
found for other sexually producing fungi in local populations (Huff et al., 1994; Morjane
et al. 1994). Immigration of propagules from outside areas and presence of a sexual state
would increase genetic diversity (Morjane et al., 1994). RAPD markers would be highly
useful as a simple tool in future investigations to characterize variation in T. incarnata
according to mating groups, host specificity, virulence and study how mitigating forces of

selection, recombination and migration may affect the population genetics of this species.

REFERENCES

Anonymous, 1996. Golf course superintendents of America 1996. Report. Lawrence,
KS, p 77.

Bruehl, G.W. and R. Machtmes. 1978. Incompatibility alleles of Typhula incarnata.
Phytopathology. 68: 131 1-1313.

89

Dice, LR. 1945. Measures of the amount of ecologic association between species.
Ecology, 26: 297-302.

Grajal-Martin, M.J., C.J. Simon and F.J. Muehlbauer . 1993. Use of random amplified
polymorphic DNA (RAPD) to characterize race 2 of F usarium oxysporum f. sp.
pisi. Phytopath. 83: 612-614.

Hampl, V., A. Pavlicek, A. and J. F legr. 2001. Construction and bootstrap analysis of
DNA fingerprinting-based phylogenetic trees with the freeware program
FreeTree: application to trichomonad parasites. Int. J. Syst. Evol. Microbiol.
51:731-735.

Hsiang, T., T. Matsumoto and S. Millet. 1999. Biology and Management of T yphula
snow molds of turfgrass. Plant Disease. 83(9): 788-798.

Hsiang, T. and C. Wu. 2000. Genetic relationships of pathogenic T yphula species
assessed by RAPD, ITS-RFLP and ITS sequencing. Mycol Res. 104(1): 6-22.

Huff, D.R., T.E. Bunting and K.A. Plumley. 1994. Use of random amplified polymorphic
DNA (RAPD) for the detection of genetic variation in Magnaporthe poae.
Phytopath 84:1312-1316.

Jacobs, D.L. and G.W. Bruehl. 1986. Saprophytic ability of Typhula incarnata, Typhula
idahoensis and Typhula ishikariensis. Phytopathology 76(7): 695-698.

Jackson, N. and Fenstermacher, J .M. 1969. Typhula blight: its cause, epidemiology and
control. J. Sports Turf Res. Inst. 45: 67-73.

Landry, RA. and F .J . Lapointe. 1996. RAPD problems in phylogenetics. Zoologica
Scripta, 25:283-290.

Page, R.D.M. 2001. TreeView (Win 32) v.1.6.6. [Online] at
http://taxonomy.zoology_.gla.ac.uk/rod/rod.html (accessed July 2003).

Pavlicek, A., S. Hrda and J. F legr. 1999. FreeTree — freeware program for the
construction of phylogenetic trees on the basis of distance data and for
bootstrp/jacknife analysis of the trees robustness. Application in the RAPD
analysis of genus Frenkelia. Folia Biologica (Praha) 45: 97-99.

Mahuku, G.S, Hsiang T. and Yang, L. 1998. Genetic diversity of Microdochium nivale
isolates from turfgrass. Mycol. Res. 102(5): 559-567.

Matsumoto, N. and A. Tajimi. 1985. Field survival of sclerotia of T yphula incarnata and
T. ishikariensis biotype A. Can. J. Bot. 63: 1126-1128.

Matsumoto, N., J. Abe, T. Shimanuki. 1995. Variation within isolates of Typhula
incarnata from localities differing in winter climate. Mycoscience 36: 155-158.

90

Millet, S. 2000. The distribution, molecular characterization and management of
snowmolds in Wisconsin Golf Courses. PhD thesis. University of Wisconsin-
Madison, under DP Maxwell adviser.

Morjane, H., J. Geistlinger, M. Harrabi, K. Weising and G. Kahl. 1994. Oligonucleotide
fingerprinting detects genetic diversity among Ascohyta rabeiei isolates from a
single chickpea field in Tunisia. Current Genetics 26: 191-197.

Rohlf, F.J. 2000. NTSYS-pc Numerical Taxonomy and Multivariate Analysis System
version 2.1 Manual. Applied Biostatistics, Inc. NY, USA.

Smith, J.D., N. Jackson and A.R. Woolhouse. 1989. Fungal diseases of amenity turf
grasses. E. & F.N. Spon, NY.

Sneath, P.H.A. and RR. Sokal. 1973. Numerical Taxonomy. Freeman. San Francisco. ©
2000 by Applied Biostatistics, Inc. 573 pp.

Sokal, R. and C. Michener. 1958. A statistical method for evaluating statistical
relationships. Univ. Kan. Sci. Bull. 38:1409-1438

Staub, J ., J. Bacher and K. Poetter. 1996. Sources of potential errors in the application of
random amplified polymorphic DNA in cucumber. Hort Sci. 31: 252-266.

Van de Zande, L. and R. Bijlsma. 1995. Limitations of the RAPD technique in
phylogeny reconstruction in Drosophila. J. Evol. Biol. 8: 645-656.

Vargas, J .M., Jr. 1994. Management of Turfgrass Diseases. CRC Press, Boca Raton FL.

Welsh, J. and M. McClelland. 1990. Fingerprinting genomes using PCR with abritrary
primers. Nucleic Acids Research 8: 7213-7218.

Williams, J .G.K, A.R. Kubelik, K.J. Livak, J .A. Frafalski and S.V. Tingey. 1990. DNA
polymorphisms amplified by arbitrary primers are useful as genetic markers.
Nucleic Acids Res. 18: 6631-6535.

91

CHAPTER IV

Disease Resistance Screening of Bentgrass to T yphula incarnata Lasch

ABSTRACT

Bentgrass (Agrostis spp.) is susceptible to a wide range of diseases. One
economically devastating disease is gray snow mold blight, caused by T yphula incarnata
Lasch. There are no known resistant cultivars of creeping bentgrass (A. palustris) or any
other turfgrass species to T. incarnata. Research into snow mold disease resistance is
hampered by the absence of rapid screening techniques, in addition to poor identification
of pathogenic strains of the fungus. Using a controlled screening procedure described
herein, we have selected 20 creeping bentgrass genotypes from 890 samples from old
Northern Michigan golf courses and identified 3 accessions of colonial bentgrasses (A.
capillaris) with strong resistance to the gray snow mold. Six commercial creeping
bentgrass cultivars ‘L-93’, ‘Penn A4’, ‘Penn G2’, ‘Penncross’, ‘Providence’ and
‘Emerald’ were all found susceptible. The resistant genotypes identified will be useful to

the development of creeping bentgrass cultivars.

Key words: Snow mold resistance, controlled screening, creeping bentgrass, colonial

bentgrass

92

INTRODUCTION

Snow mold diseases are important causes of winter injury in grasses and cereals
in North America, Canada, Russia, Japan, and the Nordic countries. Typhula snow molds
are known by different names such as gray snow mold (T. incarnata), snow scald,
speckled snow mold (T. ishikariensis) and Typhula blight (collectively caused by T.
incarnata and T. ishikariensis) (Vargas, 1994). Gray snow mold (T. incarnata) causes
serious damage in turfgrass and is common on golf courses in climates with less than 90
days of snow cover (Millet, 2000). The pathogen is very slow growing and symptoms
caused by the different species are difficult to distinguish (Hsiang et al, 1999).
Symptoms usually appear as circular, water-soaked or straw-colored patches measuring 5
to 15 cm across. Plants may be matted and appear slimy with mycelium. Usually only the
leaves appear diseased and dead and the crown may survive to produce new leaves in the
spring. Mycelium, basidiospores or sclerotia that are produced may be sources of
inoculum that lead to new infection. Colonized dead plant tissues decompose and
disintegrate, and then the sclerotia fall to the thatch and soil where they oversurnmer, and
remain as resting bodies during the summer months. Susceptible plants generally show
the water-soaked lesions and a yellowish decaying appearance. The thin crust of
mycelium is white and may be covered with dust giving it a gray-white appearance

(hence the name “gray snow mold”) (Jackson and Fensterrnacher, 1969).

Gray snow mold is a cold-loving or ‘psychrophilic’ organism. Snow molds have
the ability to modify their intracellular conditions favorable to survival (Snider et al.,
2000). The lack of high ice nucleation activity combined with the presence of antifreeze

activity in all fungal fractions indicates that snow molds can moderate their environment

93

to inhibit or modify intra- and extracellular ice formation, which helps explain their
ability to grow at subzero temperatures under snow cover. Pathogenicity of the fungi is
not dependent on ice nucleation activity to cause freeze wounding of host plants.
Another factor contributing to snow mold survival may be the pathogen’s capacity to
utilize a broad range of substrates, from live tissues to dead organisms. Jacobs and
Bruehl (1986) theorized that T. incarnata is unable to establish itself sufficiently in its
host to cause disease problems, while Matsumoto and Tajimi (1985) described the fungus
more as an ‘opportunistic parasite’ attacking senescent or moribund plant tissues beneath

the snow, where the low temperature reduces the activity of other antagonistic microflora.

There is little information regarding the pathogenicity and variability of the gray
snow mold. Snow mold resistance evaluation among bentgrass cultivars is currently
done in the field with the National Turfgrass Evaluation Procedure (NTEP) trials using
naturally infected plots. In the 1995 NTEP trial, twenty seven A. palustris varieties from
twenty four sites were observed to show a wide range of resistance to the Typhula
species, but no variety was found to be strongly resistant. Results from NTEP scored in
2000 at the Michigan State University Hancock Research Center showed snow mold
disease infection ranged from 20% to 90% of the plot area. Although NTEP provides
information on the performance of bentgrass cultivars, compounding biotic or abiotic
factors, e. g. the presence of multiple pathogens, competition, and the moisture or dryness
in some areas, limit the applicability of the findings and do not give an accurate
description of a cultivar’s resistance. Field studies are also dependent on the duration of

snow cover and melt.

94

 

Under controlled conditions, Millet (2000) inoculated the creeping bentgrass (A.
stolonifera sp. palustris) cultivar ‘Penncross’ with isolates of the three snow molds (T.
incarnata, T. ishikariensis and T. phacorrhiza) from Wisconsin area using the Fast Turf
screening system. Creeping bentgrass was grown in 35-mm film cans and incubated in
cold chambers for three weeks at temperatures of 41 °F and 50 °F. The disease was rated
at 21, 28 and 35 days. The results indicated that gray and speckled snow molds were not
significantly different from each other in their ability to cause disease. T. phacorrhiza
was not as strongly aggressive and the author postulated its role as a decomposer, termed
‘senectophatic disorder’, which colonized dying grass as the other snow molds were
becoming less active. The study by Millet (2000) is conceptually important because it
highlighted developing a controlled screening procedure against snow mold. Resistance
in the host plants however was not precisely determined because all three frmgal species
were used as inoculants concurrently, disregarding the probability of competition
between Typhula species. Under field conditions, a thin line separates the circular

damaged spots created by different species and these disease patches never overlapped.

Studies measuring resistance in turfgrass and other crops to gray snow mold or
snow molds are generally few in number. In grasses, Lolium perenne is considered the
most susceptible to gray snow mold, followed by Festuca arundinaceae, A. palustris,
Poa annua, P. pratensis and F. rubra. (Hsiang, 1999). Within each species, there can be a
broad range of susceptibility among cultivars. Wu and Hsiang (1998) found a strong
positive correlation between susceptibility of 12 turfgrass species to T. incarnata and
their susceptibility to T. ishikariensis. There are no known resistant cultivars or species

of bentgrass and the severity of damage warrants research to find a resistant genotype

95

under controlled conditions. Despite the low resistance found in creeping bentgrasses,
selection of varieties for durable resistance can be done because heritability estimates for
snow mold resistance in grasses are high (Gaudet et al., 1999).

Bentgrass, Agrostis sp. a derivation from Greek: grass, forage, has about 220
species distributed throughout the world (Watson and Dallwitz, 1992). It is a perennial or
annual outcrossing polyploid (x=7, 2n=14, 21, 28, 42, etc.). The wide genetic variability
implies potential for finding a resistant plant among the bentgrass species. Other possible
sources for snow mold resistance may be in naturally selected clones of creeping
bentgrass. To identify such sources, creeping bentgrass (A. stolonifiera sp. palustris)
samples were collected from old Northern Michigan golf courses that have not been
sprayed with fungicide or overseeded for the last 10 years. Natural selection and
recombination events could have played a significant role in creating resistant creeping
bentgrass. Identifying resistant clones through controlled screening experiments would
contribute to turfgrass improvement programs. Controlled screening would also enable
rapid testing of several materials independent of the presence of snow and with high
repeatability.

The objectives of this study were to develop a controlled physiological screening
system for resistance to T. incarnata and to search for plants resistant to the pathogen
from several creeping bentgrass cultivars, naturally selected clones obtained from

Michigan and plant introduction lines of Agrostis spp.

MATERIAL AND METHODS

Plant materials

The materials were divided into three groups:

96

a. Creeping bentgrass collected in April 2000 from golf courses in Northern
Michigan - Plugs were collected from areas bordering snow mold circular
patches. A total of 890 genotypes were used as follows: 220 samples
(Population A) for developing the procedure and another 670 samples
(Population B) for additional screening.

b. Commercial creeping bentgrasses, 8 to 10 plants each of cultivars, ‘L-93’,
‘Penn A4’, ‘Penn G2’, ‘Penncross’, ‘Providence’ and ‘Emerald’ were used.

c. Forty accessions of Agrostis sp. plant introductions, representing 14 species
obtained from USDA Washington Pullman Station were used. Seeds of 25
plants per accession were germinated on filter paper and transferred into pots
(1 seedling /pot) (Table 4.1).

The plants were grown in No. 2 pots (4x4x4 inches) containing peat soil
mixture and supplemented with Peter’s fertilizer. Plant height was maintained by
clipping at 1.0 inch. Several ‘Penncross’ plants were also grown in pots and used as a

susceptible plant control.

Pathogen

Sclerotia of gray snow mold (T. incarnata) were collected fiom Hancock
Turfgrass Research Center in April 2001 and grown in potato dextrose agar (PDA, 39g/L
with streptomycin and penicillin antibiotics) at 5 °C for 2 months. Each isolate was then
transferred to sterilized cornmeal mixture (1 part cornmeal to 2 parts silica sand with 5%
PDA broth, autoclaved for 40 minutes) for multiplication. Growth and incubation in
cornmeal was made at 5 0C for another two months. A phase contrast microscope was

used to verify the presence of T. incarnata in the cornmeal mixture by checking for the

97

Table 4.1. List of Agrostis spp. screened for snow mold resistance and their geographic

 

 

origins.
Species No. of accessions Geographic origins
A. canina 2 Netherlands, Iran
A. capillaris 7 Europe
A. castellana 10 USA, Spain, Portugal
A. palustris 5 USA, Turkey, Europe
A. stolonifera 6 USA, Europe, Russia
A. mongolica 1 Mongolia
A. Iachnantha 2 Africa
A. munroana 1 Iran
A. hygrometrica 1 Uruguay
A. scabra 1 Canada
A. trinii 1 Russia
A. vinealis 1 Russia
A. transcaspica 1 Russia
A. gigantea 2 USA, Turkey

 

presence of mycelia and visually checking for the presence of orange to brown sclerotia.
The fastest growing and virulent isolate identified from pre-screening was chosen and

used for the controlled screening procedures.

Controlled screening and rating procedure
Initial screening was done in controlled plant grth chambers using day

temperature of 7 °C and night temperature of 4 °C for 4 weeks. However the disease did

98

not progress well. After the optimum temperature of 5 °C was determined, all succeeding
tests were performed in the cold room available at the Crop & Soil Sciences farm at
Michigan State University. In the first screening, 220 creeping bentgrasses (Population
A) from the N. Michigan collection were randomly put in trays at 10 plants per tray. The
plants were brought into the cold room (5 °C) for 3 days for acclimation prior to disease
inoculation. Equal amounts of inoculum (l g) were put into the center of each pot and
covered with moistened cheesecloth. To compare treatments of inoculated versus
uninoculated plants, only 9 plants/tray were inoculated and 1 pot was left untreated. The
trays were filled with water and put inside plastic bags to maintain high humidity and
optimize disease severity. Visual inspection and scoring for resistant and susceptible
plants was done at 4 and 6 weeks using Horsfall-Barratt system with a scale of 0 = no
disease to 10 = completely dead plant. Susceptible plants were classified by the growth
of the infection area as characterized by increased area of sofi, watery, yellow or brown
lesions and widespread development of mycelia. Two people rated and the means of two
ratings were used for analysis. Plants were returned to the greenhouse (GH) after 6
weeks and scores were taken again after 3 days. The previously yellowing leaves would
appear brown and dried at this time. Recovery was scored afier another 10 days (1, 3, 5,
7, 9, 10 scale, with 10 as 100% recovered). Statistical analysis using Proc GLM option of
SAS was done to test significance between uninfected and infected treatments. The
plants were ranked based on the disease scores and recovery.

Candidate resistant plants were divided into three clones and screened for a
second time using 'Penncross’ as the susceptible check, and all plants in the tray were

inoculated. Screening for the resistant lines from Population B (670 creeping bentgrass),

99

the 6 commercial cultivars and 40 plant introductions followed the procedure described
above using completely randomized design (CRD). Statistical Analysis Software (SAS
version 8.0) was used for data analyses. Arcsin transformation on percentage means for
each score was calculated and F-Test was generated using the PROC GLM with LSD

options of SAS.

RESULTS AND DISCUSSION

Screening creeping bentgrass populations

The growth conditions in the cold room of 5 °C temperature and high humidity
were found favorable for T. incarnata. At 4 weeks, fungal mycelia were visible on
creeping bentgrass and leaves appeared water-soaked and slightly yellowed (Figure
4.1.A). At 6 weeks, lesions were turning brown on whole leaves of some of the
inoculated plants with a wider spread of infected area (Figure 4.1.B). Some plants
appeared to be entirely damaged and this was more apparent when plants were brought to
the greenhouse and scored after 3 days (Figure 4.1.C). The results of the controlled
screening system using two populations of creeping bentgrass from old Michigan golf
courses: population A (220 plants) and population B (670 plants), are shown in Table 4.2.
In both populations, significant differences between the two treatments, inoculated and
uninoculated (as control) were observed at the 4th week, 6th week and afier 3 days in the
greenhouse. Control plants were mostly rated as 0 (no disease) but a few plants were
rated as l or 2 (for slightly yellowed). This is largely in contrast to the inoculated plants

where the mean disease scores in six weeks ranged from 5.51 (SO-60% infected) to 7.53

100

= :38 5 38:36.5 Ea neESKmEV 2:: S “smug: 5382 poem £9» 286808 98 $53 2:33on
make"? 8:: E .9 33:8on 05 5 92% m «a 3:83 :0 93$. .0 doe—«385:: mm mg 05 5 «on 98 “mm: J83 so 2: note
0385 .m 6083855. mm mg 05 5 8m Eaton c2 .aouoomfi 208 308 we x83 av 05 “a 389:3 9:320 .< .3. «Earn

101

 

Table 4.2. Analysis of variance of snow mold disease inoculation in creeping

bentgrass using inoculated and uninoculated (control) as treatments in two populations

from N. Michigan.

 

 

 

Population Treatments N Snow mold Ratingl Recovery

and F-values3 Means2
4th week 6th week GH 3d

A. 220 plants Control 23 0.95 i 1.5 1.24 i 2.0 1.87 i 2.7 8.2 i 2.8
Inoculated 197 5.51 i 1.2 6.46 1 1.2 7.53 i 1.1 3.2 i 2.1
F -value 228.15" 241.97“l 266.07“ 190.04"

B. 670 plants Control 63 0.84 i 1.0 1.40 i 1.6 2.40 :t 1.9 8.07 i 2.2
Inoculated 607 5.67 i 1.1 6.83 i 1.1 8.02 i 1.0 2.73 i 2.2
F-value 898.78” 993.72" 787.1" 480.28“

 

1 Disease severity was rated on as Horsfall-Baratt scale of 0 (no infection) to 10 (completely dead).
Scores are the means of N (number of samples).

2 Recovery rated on a scale of 0 (no recovery) to 10 (complete recovery).

3 Ratings were converted into percentages as in l=10% and 10=100%. Arcsin transformation on done

on the percentage values and run using Proc GLM option of the SAS system.

"' ‘Treatment means were found significant different at P<0.05.

102

(70-80%, highly infected). There were significant differences between the two
treatments, uninoculated and inoculated, suggesting the inoculation treatment was
effective to cause disease. The frequency pattern of snow mold disease scores in
population B was graphed and generally follows a bell distribution curve (Figure 4.2).
The continuous distribution pattern is suggestive of a horizontal type of resistance
controlled by a few minor genes. In contrast, if resistance trait was a vertical resistance
type controlled by a major gene, frequency data distribution will be in 2 discrete columns.

Recovery was measured after another 10 days in the greenhouse. Uninoculated
creeping bentgrasses recovered significantly better than the inoculated plants. Recovery
for infected plants was generally low at 27% to 32%.

From population A, 28 plants were chosen as candidate resistant plants after
ranking. Another 59 plants were chosen as candidate resistant plants from population B.
The candidate resistant plants were divided into three clones and subjected to a second
screening. A second replicated screening was necessary to determine accuracy of the
first rating for the resistant genotypes as disease escapes could be factor for erroneous
rating. Replication and testing against a highly susceptible check, ‘Penncross’ ensured
better selection for the resistant genotype.

The results of replicated screening of selected genotypes from population A and B
are shown in Table 4.3 and Table 4.4. Analysis of variance (F-test) and t-tests (LSD)
showed significant differences in the 6th week, GH 3 days rating and the means of the
three ratings (Table 4.3.1). Ratings were not found significant across the 28 creeping
bentgrass genotypes at the 4th week (P<0.10) and but were highly significant at the 6‘h

week and GH at P<.05. Nine genotypes (Nos. 15, 4, 21, 6, 8, 26, 22,24 and 28)

103

 

 

300.0
250.0 ,
200.0
150.0
100.0

Frequency

50.0
0.0

 

1 2 3 4 5 6 7 8 9

Snow mold Disease Score

 

 

Figure 4.2. Distribution of disease rating scores in 670 creeping bentgrass genotypes.

104

Table 4.3.1. Disease ratings and analysis of variance of candidate resistant lines of
creeping bentgrass from Population A to snow mold (T yphula incarnata) using a
completely randomized design with 3 replicates.

 

Snow mold Rating" 2

 

 

Candidate

Resistant line 3 4th Week 6th Week GH 3 days Means
15 3.5 4.5 5.5 4.8 1'
4 4.5 5.3 7.8 5.9 T
21 6.0 5.5 7.2 6.2 1'
6 5.7 6.3 7.0 6.3 1'
8 4.5 7.7 6.7 6.3 T
26 4.7 6.3 8.0 6.3 T
22 5.0 6.8 7.7 6.5 1’
24 5.7 6.0 7.8 6.5 ‘l'
28 5.2 7.0 7.3 6.5 T
2 5.8 6.3 7.7 6.6
9 5.8 6.7 7.3 6.6
20 6.0 5.8 8.0 6.6
27 5.8 6.8 7.2 6.6
17 6.2 6.7 7.3 6.7
19 5.8 7.2 7.3 6.8
25 5.7 5.8 7.3 6.8
3 6.0 7.5 7.3 6.9
11 6.3 7.1 7.3 6.9
12 5.8 6.8 8.2 6.9
23 6.5 6.7 7.5 6.9
10 5.3 7.5 8.2 7.0
7 6.2 7.5 7.5 7.1
13 6.8 7.2 7.5 7.1
16 6.5 7.3 7.3 7.1

l 6.7 7.5 7.5 7.2
5 6.5 7.2 8.0 7.2
18 6.5 7.8 7.3 7.2
14 7.3 7.8 7.5 7.5
Penncross 7.0 7.8 8.3 7.7
F-test (P<.0001)4 1.62 1.69" 2.07" 1.84"
LSD (P=0.05) 1.8 1.8 1.0 1.1

 

' Disease severity rated on a Horsfall-Barratt scale of 0 (no infection) to 10 (completely dead).

2 Each score was taken independently by 2 raters and the mean was calculated for 3 replicates. ‘4'" and ‘6“"
week ratings were taken in the cold room at 5°C. ‘GH 3 days’ ratings were taken 3 days in the greenhouse
after pots were taken out of the cold room.

3 A total of 28 candidate resistant lines were selected from 220 individual creeping bentgrass genotypes and
divided into three replicates.’ Penncross’ was used as the susceptible plant control.

‘ Ratings were converted into percentages as in l=lO% and 10=100%. Arcsin transformation was done on
the percentage values and run using Proc GLM option of the SAS system for calculation of F values.

"' Scores were found significantly different at P<0.10.
" Scores were found significantly different at P<0.05.

1 Disease rating means were found to be significantly different from ‘Penncross’.

105

Table 4.3.2. Recovery ratings of 28 selected creeping bentgrass lines (Population A) from
snow mold infection using CRD with 3 replicates (ranked from the best genotype).

 

Recovery Rating" 2

 

 

Candidate Resistant Line Means
2 8.7 T
8 8.0 T
15 8.0 T
22 7.7 T
24 7.7 T
9 7.0 T
17 7.0 T
25 7.0 T
26 7.0 T
7 6.7 T
23 6.7 T
6 6.3 T
18 6.3 T
21 6.3 T
3 6.0 T
5 6.0 T
27 6.0 T
28 6.0 T
14 5.3 T
19 5.3 T
20 5.3 T
10 5.0
13 4.7
11 4.0
16 4.0
Penncross 4.0
l 3.7
4 3.3
12 2.0

F test (1><0.0001)3 2.33"

LSD(P=0.05) 2.90

 

' Recovery rated on a scale of 0 (no recovery) to 10 (complete recovery).
2 Each score was taken independently by 2 raters and the mean for 3 replicates.

3 Ratings were converted into percentages as in 1=10% and 10=100%. Arcsin transformation on done on
the percentage values and run using the Proc GLM option of the SAS system.

"”" Scores were found significantly different at P<0.05.

T Recovery ratings were found to be significantly better than ‘Penncross’.

106

Table 4.4.1. Disease ratings and analysis of variance of candidate resistant lines of
creeping bentgrass from Population B to snow mold using a completely randomized
design with 3 replicates.

 

Snow mold Rating" 2

 

 

Candidate

Resistant line 3 4th Week 6th Week GH 3 days Means
336 3.0 4.3 5.0 4.1 T
226 3.0 3.5 6.7 4.4 T
223 3.2 5.0 5.2 4.4 T
560 3.2 4.8 5.3 4.4 T
367 3.2 3.5 6.7 4.4 T
191 3.3 3.8 6.3 4.5 T
585 3.3 3.3 7.0 4.6 T
79 3.5 4.8 5.8 4.7 T
249 3.5 4.3 6.3 4.7 T
645 3.2 4.0 7.0 4.7 T
247a 4.0 4.5 6.0 4.8 T
247b 3.7 5.2 6.0 4.9 T
484 4.3 4.5 6.0 4.9 T
261 3.3 4.7 7.2 5.1 T
587 4.0 4.2 7.2 5.1 T
595 3.5 4.7 7.2 5.1 T
148 4.5 4.8 6.2 5.2
152 3.8 5.2 6.7 5.2
242 4.5 5.2 6.0 5.2
110 4.7 5.2 6.0 5.3
194 4.3 5.3 6.2 5.3
121 4.3 5.2 6.5 5.3
122 3.7 4.7 7.7 5.3
404 4.5 5.3 6.3 5.4
156 4.5 5.2 6.7 5.4
368 4.5 4.2 7.7 5.4
640 4.5 4.2 7.7 5.4
117 4.0 5.0 7.7 5.6
175 4.7 5.0 7.0 5.6
239 4.3 5.8 6.5 5.6
648 4.2 4.7 7.8 5.6
544 4.3 5.3 7.2 5.6
639 4.7 4.3 8.2 5.7
313 4.2 4.8 8.2 5.7
656 4.3 4.7 8.2 5.7
406 5.0 4.8 7.5 5.8
392 5.0 5.5 7.0 5.8
227 5.2 5.7 6.8 5.9
647 4.8 5.8 7.0 5.9
193 4.7 5.8 7.5 6.0
370 4.5 5.8 7.7 6.0
435 4.5 5.3 8.2 6.0
527 4.5 5.7 7.8 6.0
39 4.5 6.0 7.7 6.1
636 5.0 6.2 7.0 6.1
49 5.2 5.5 7.5 6.1
289 5.3 5.8 7.0 6.1
635 5.2 5.3 7.7 6.1

6 5.2 5.3 7.8 6.1
528 5.5 4.7 8.3 6.2

107

Penncross 3.5 6.8 8.8 6.2

146 4.8 6.3 7.7 6.3
604 5.8 5.5 7.7 6.3
644 5.7 5.5 8.2 6.4
145 5.5 5.7 8.3 6.5
570 5.7 6.0 8.2 6.6
291 5.2 6.7 8.2 6.7
649 5.5 6.7 8.0 6.7
522 5.8 6.7 7.8 6.8
F-test (P<.0001)4 2.28" 2.37” 2.92" 3.20"
LSD (P=0.05) 1.4 1.4 1.4 1.0

 

' Disease severity rated on a Horsfall-Barratt scale of 0 (no infection) to 10 (completely dead).

2 Each score was taken independently by 2 raters and the mean for 3 replicates. ‘4'!" and ‘6"" week ratings
were taken in the cold room at 5°C. ‘GH 3 days’ was ratings taken 3 days in the greenhouse after pots were
taken out of the cold room.

3 A total of 59 breeding lines were selected from 670 individual creeping bentgrass genotypes and divided
into three replicates. ‘Penncross’ was used as the susceptible plant control.

‘ Ratings were converted into percentages as in 1=10% and 10=100%. Arcsin transformation was done on
the percentage values and run using Proc GLM option of the SAS system.

” Scores were found significantly different at P<0.05.

T Disease rating means were found to be significantly less than ‘Penncross’.

108

Table 4.4.2. Recovery ratings of selected creeping bentgrass lines to snow mold
(Typhula incarnata) using CRD with 3 replicates.

 

Recovery Rating" 2

 

 

Breeding Line Means
194 7.9 T
336 7.4 T
79 7.3 T
152 7.2 '1'
6 7.1 '1’
242 7.1 1'
554 6.9 T
175 6.7 T
404 6.7 T
247b 6.6 T
640 6.5 '1'
484 6.3 T
570 6.3 T
223 6.2 T
560 6.2 T
585 6.2 T
122 6.1 T
522 6.1 T
247a 6.0 T
587 6.0 T
645 6.0 T
110 5.9 T
249 5.8
227 5.8
239 5.8
39 5.7
370 5.7
649 5.7
636 5.5
639 5.5
604 5.4
49 5.3
648 5.3
289 5.3
121 5.2
145 5.2
226 5.2
656 5.2
117 5.0
291 5.0
644 5.0
595 4.9
191 4.8
313 4.8
146 4.8
261 4.7
367 4.4
647 4.3
368 4.2
406 4.2

109

193 4.1

148 4.0
528 3.8
635 3.7
435 3.5
527 3.0
Penncross 2.9
F test (P<0.0001)3 2.77M
LSD(P=0.05) 2.08

 

‘ Recovery rated on a scale of 0 (no recovery) to 10 (complete recovery).
2 Each score was taken independently by 2 raters and the mean for 3 replicates.

3 Ratings were converted into percentages as in 1=10% and 10=100%. Arcsin transformation on done
on the percentage values and run using the Proc GLM option of the SAS system.

T Recovery means were found to be significantly better than ‘Penncross’.

" Scores were found to be significantly different at P<0.05.

110

performed significantly better than susceptible check ‘Penncross’ from the replicated
trials and candidate resistant line No. 15 was found to be the most resistant genotype.
Recovery from disease was also found to be significantly different among genotypes
(Table 4.3.2). Genotype No. 15 was ranked third in recovery. Twenty-one lines
recovered better than ‘Penncross’. Genotype No. 4 which had the second best resistant
score or low disease rating had poor recovery indicating that resistance and recovery are
independent traits. Eight genotypes could be considered as potential breeding materials
with good snow mold resistance and significant recovery from cold and disease
treatments.

Significant differences among disease rating means of 59 candidate resistant
plants were found from Population B at the 4th week, 6th week and GH 3 days rating
stages (Table 4.4.1). Sixteen genotypes were found with significantly better disease
resistance than ‘Penncross’. Genotype No. 336 was found to be the most resistant and
with the second best recovery (Table 4.4.2). From these 16 best resistant genotypes, only
12 plants were selected as breeding lines because 4 genotypes did not recover well. The
line with the second least disease rating, No. 226, did not perform better than ‘Penncross’
supporting previous findings that resistance and recovery are independent traits. A total
of 20 resistant genotypes were found from 890 clones taken from Northern Michigan old

golf courses.
Screening commercial creeping bentgrass cultivars

Six of the most popular commercial creeping bentgrass cultivars ‘L—93’, ‘Penn

A4’, ‘Penn G2’, ‘Penncross’, ‘Providence’ and ‘Emerald’, were subjected to the

111

developed snow mold screening procedure using complete randomized design with 8 to
10 replicates per cultivar. Significant differences were found among them for disease
rating means and percentage recovery (Table 4.5). ‘L-93’, ‘Penn A4’, ‘Penn G2’,
‘Penncross’ and ‘Providence’ were grouped together by t-tests (LSD) and were not
significantly different in mean disease ratings. Cultivar ‘Emerald’ was grouped
separately and hence appeared as more susceptible than the first group. No commercial
cultivar was found resistant.

The six commercial cultivars significantly varied in recovery performance with
‘L-93’ having the best rating. The results support the ratings from NTEP that ‘L-93’ is
the superior creeping bentgrass cultivar. ‘Penncross’ showed 39% mean recovery and this
is close to the previous recovery ratings of 4.0 (40% recovery) in the trials using
Populations A and B in the first two tests. ‘Penn G2’, ‘Penn A4’ and ‘Providence’ had

low recovery ratings and did not differ as a group based on t-tests.

Screening of PI Lines

Forty plant introductions were subjected to the snow mold screening procedure,
with 24 to 25 representative genotypes per accession. Disease rating means and analysis
of variance showed that ten accessions significantly performed better than ‘Penncross’
(Table 4.6.1). The ten accessions were species of A. canina, A. capillaris, A. vinealis
and A. mongolica. The best performing colonial bentgrasses (A. capillaris) came from
Europe. T. incarnata is widely distributed in Europe, Russia and parts of Northern
Hemisphere and bentgrasses originating from these areas may have potential resistance to

the pathogen. None of the creeping bentgrass species (A. stolonifera or A. palustris) was

112

 

Table 4.5 . Performance and analysis of variance of commercial creeping bentgrass
cultivars using CRD in controlled snow mold screening experiments, their disease
rating means1 and percentage recovery 2.

 

 

Genotype No. of Disease Percentage
Samples Rating Means" Recovery3

L-93 10 5.9 _+_ 0.54‘I 61.00 % : 10.22‘I

Penn A4 8 5.6 i. 0.48' 19.37 % : 13.74c

Penn G2 10 5.4 i 0.41‘ 27.00 % i. 13.78c

Penncross 10 5.8 i 0.76' 39.00 % :. 7.74b

Providence 10 5.5 i 0.20' 16.50 % i. 7.09c

Emerald 10 6.5 i 0.71" 29.50 % : 3.29"c

F-test 5.28" 31.54"

LSD 0.9 16.08

 

lDisease severity rated on Horsfall-Baratt scale of 0 (no infection) to 10 (completely dead). Disease
rating means is the average of mean ratings at the 4th week, 6th week and at 3 days in the greenhouse
taken independently by 2 raters and mean for given number of samples.

2 Recovery was taken at 17 days in the greenhouse and means of the percentage recovery of the

number of samples.

3 Means followed by the same letter are not significantly different.

" Scores were found significantly different at P<0.05

113

 

Table 4.6.1. Disease ratings and analysis of variance of resistance to gray snow mold
of different bentgrass species with ‘Penncross’ (as susceptible control) using CRD
with 24 to 25 genotypes per accession.

 

Snow mold Ratingl’ 2

 

Species (MSU No.) N 4th Week 6th Week GH 3 days Means

 

A. capillaris MSU-4 24 4.1 6.4 6.6 5.7 T
A. capillaris MSU-6 25 4.5 6.3 6.4 5.7 T
A. canina MSU-2 25 4.4 6.3 6.9 5.9T
A. canina MSU-l 25 4.0 5.7 8.8 6.2 T
A. capillaris MSU-3 25 4.2 6.6 7.7 6.2 T
A. capillaris MSU-8 25 5.9 7.2 5.4 6.2 T
A. vinealis MSU-37 25 4.5 6.4 7.8 6.2 T
A. capillaris MSU-9 25 5.8 7.3 5.7 6.3 T
A. mongolica MSU-32 25 4.3 6.7 7.9 6.3 T
A. capillaris MSU-5 25 5.9 7.2 6.0 6.4 T
A. gigantea MSU-40 25 4.9 6.6 8.1 6.5
A. capillaris MSU-7 25 5.5 7.1 7.1 6.6
A. Iachnantha MSU-30 25 4.6 6.5 8.6 6.6
A. hygrometrica MSU-33 25 4.3 6.7 8.7 6.6
A. scabra MSU-35 25 4.5 6.0 9.2 6.6
A. trinii MSU-36 25 5.5 6.9 7.5 6.6
A. stolonifera MSU-29 25 4.7 6.9 8.4 6.7
A. castellana MSU-16 25 4.9 7.0 8.2 6.7
A. castellana MSU-18 25 5.0 6.7 8.5 6.7
A. castellana MSU-l3 25 5.2 6.9 8.2 6.8
A. castellana MSU-15 24 5.5 7.2 7.9 6.9
A. castellana MSU-17 25 5.5 7.4 7.9 6.9
A. palustris ‘Penncross’ 25 5.5 7.4 7.9 6.9
A. castellana MSU-l4 25 5.3 7.3 8.2 6.9
A. castellana MSU-12 25 5.6 7.2 8.1 7.0
A. castellana MSU-11 25 5.0 7.1 8.8 7.0
A. stolonifera MSU-27 25 5.2 6.9 8.9 7.0
A. transcaspica MSU-38 25 5.6 7.3 8.2 7.0
A. palustris MSU-20 25 5.3 7.2 8.6 7.0
A. gigantea MSU—39 25 5.5 7.2 8.4 7.0
A. stolonifera MSU-25 25 5.0 7.5 8.8 7.1
A. stolonifera MSU-26 25 5.9 7.4 8.0 7.1
A. palustris MSU-21 25 5.8 7.3 8.4 7.2
A. palustris MSU-23 25 5.6 7.3 8.6 7.2
A. munroana MSU-34 25 5.4 6.9 9.3 7.2
A. castellana MSU-10 25 5.6 7.1 9.0 7.2
A. stolonifera MSU-24 25 5.8 7.7 8.3 7.3
A. castellana MSU-19 25 5.6 7.2 9.0 7.3
A. palustris MSU-22 25 5.9 7.7 8.3 7.3
A. stolonifera MSU-28 25 5.8 7.3 9.2 7.4
A. Iachnantha MSU-31 25 5.4 7.7 9.3 7.5
F test (P<0.0001)3 6.5" 5.8" 11.2" 8.38‘”I
LSD(P=0.05) 0.6 0.5 0.7 0.4

 

' Disease severity rated on a Horsfall-Barratt scale of 0 (no infection) to 10 (completely dead).

2 Each score was taken independently by 2 raters and the mean for 25 replicates. ‘4‘“’ and ‘6"" week

114

3 Ratings were taken in the cold room at 40°F. ‘GH 3 days’ were ratings taken 3 days in the greenhouse
after pots were taken out of the cold room.

4 Ratings were converted into percentages as in 1=10% and 10=100%. Arcsin transformation on done
on the percentage values and run using the Proc GLM option of the SAS system.

T Disease rating means found to be significantly different from ‘Penncross’.

“ Scores were found significantly different at P<0.05.

115

Table 4.6.2. Recovery ratings of accesssions of Agrostis species to gray snow mold
(T yphula incarnata) using CRD with 24 to 25 genotypes per accession.

 

Recovery Ratingl’ 2

 

 

MSU No. Species Means
MSU-8 A. capillaris 6.1 T
MSU-9 A. capillaris 6.0 T
MSU-5 A. capillaris 5.3 T
MSU-7 A. capillaris 4.1
MSU-26 A. stolonifera 3.9
MSU-6 A. capillaris 3.7
Penncross A. palustris 3.5
MSU-2 A. canina 3.4
MSU-4 A. capillaris 2.9
MSU-l4 A. castellana 2.9
MSU-36 A. trinii 2.9
MSU-12 A. castellana 2.8
MSU-13 A. castellana 2.8
MSU-l7 A. castellana 2.8
MSU-32 A. mongolica 2.8
MSU-15 A. castellana 2.6
MSU-l6 A. castellana 2.6
MSU-22 A. palustris 2.5
MSU-20 A. palustris 2.4
MSU-3 A. capillaris 2.1
MSU-21 A. palustris 2.1
MSU-30 A. Iachnantha 2.1
MSU-l8 A. castellana 2.0
MSU-37 A. vinealis 2.0
MSU-40 A. gigantea 1.9
MSU-23 A. palustris 1.7
MSU-38 A. transcaspica 1.7
MSU-24 A. stolonifera 1.6
MSU-39 A. gigantea 1.6
MSU-l9 A. castellana 1.5
MSU-25 A. stolonifera 1.5
MSU-29 A. stolonifera 1.5
MSU-1 A. canina 1.4
MSU-11 A. castellana 1.2
MSU-27 A. stolonifera l.l
MSU-33 A. hygrometrica 0.9
MSU-10 A. castellana 0.8
MSU-28 A. stolonifera 0.3
MSU-34 A. munroana 0.3
MSU-35 A. scabra 0.3
MSU-31 A. Iachnantha 0.2
F test (1><0.0001)3 14.915M
LSD(P=0.05) 1.0

 

1 Recovery rated on a scale of 0 (no recovery) to 10 (complete recovery).

2 Each score was taken independently by 2 raters and the mean for 25 replicates.

3 Ratings were converted into percentages as in 1=10% and 10=100%. Arcsin transfonnation on done
on the percentage values and run using the Proc GLM option of the SAS system.

T Recovery means were found to be significantly different from ‘Pencross’.

" Scores were found significantly different at 0. =0.05.

116

found to be significantly different from the susceptible check ‘Penncross’ (A. palustris).
Colonial bentgrasses and A. mongolica have the same ploidy level (2n=4x, tetraploid) as
creeping bentgrasses and may potentially be donors of snow mold resistance. However
A. mongolica had very poor recovery. Only three accessions showed significant recovery
after snow mold infection (Table 4.6.2). These three accessions were all colonial
bentgrasses.

In summary, a suitable physiological screening technique was developed against
gray snow mold (T. incarnata) that would enable rapid, reproducible and controlled
determination of resistance. We selected 20 resistant creeping bentgrass genotypes from
890 samples from old Northern Michigan golfcourses. Original populations of
bentgrasses in temperate North America were derived from highly heterogenous
populations of South German bentgrass mixtures (Casler et al., 2003). Decades of natural
selection must have eliminated many unadapted plants in favor of plants with pest
resistance and stress tolerances needed to survive management and edaphic factors that
define their local environment (Casler et al., 1996 and 2003). Natural variation existing
in old golf courses has been useful as a foundation for several creeping bentgrass
programs (Engelke et al., 1995; Hurley et al., 1994).

Current top commercial creeping bentgrasses, ‘L-93’, ‘Penn A4’, ‘Penn G2’,
‘Penncross’, ‘Providence’ and ‘Emerald’ were all found susceptible to gray snow mold.
Among the PI accessions or 14 Agrostis species, some accessions of colonial bentgrasses
(A. capillaris) appears to have the best resistance to snow mold. The 2001 National
Bentgrass test sponsored by the USDA and National Turfgrass Federation findings

showed that from the snow mold complex ratings of 26 bentgrass cultivars grown on a

117

fairway or tee, only one cultivar of colonial bentgrass, SR 7100 was found to be resistant
to snow mold. Future experiments need to compare the colonial bentgrass selections and
re-check SR7100 resistance to gray snow mold, and or screen for resistance against other

isolates of gray, speckled or pink snow molds.

REFERENCES

Anonymous. 1996. 1993 National Bentgrass (Green) Test - 1995. Data, Table 21.
Percent Typhula ratings of Bentgrass Cultivars on a Green. NTEP 97-6, Beltsville,
MD.

Anonymous. 2001. National Bentgrass Test -1998 . Data, Table 26. Snow mold
complex ratings of bentgrass cultivars grown on a fairway or tee. Rating of
Bentgrass Cultivars on a Green. 2001 data, Beltsville, MD.

Casler, M.D., J.F. Pedersen, G.C. Eizenga and SD. Startton. 1996. Germplasm and
cultivar development. In LE Moser et al (eds.) Cool-season forage grasses.
American Society of Agronomy, Madison, WI. Pp. 413-469.

Casler, M.D., Y. Range], J. Stier and G. Jung. 2003. RAPD Marker Diversity among
creeping bentgrass clones. Crop Sci. 43:688-693.

Engelke, M.C., V.G. Lehman, W.R. Kneebone, P.F. Colbaugh, J.A. Reinert and WE.
Knoop. 1995. Registration of ‘Crenshaw’ creeping bentgrass. Crop Sci. 35:590.

Gaudet, DA. and A. Larouche A. 1999. Towards an understanding of snow mould
resistance. In Intl Workshop on Plant-Microbe Interactions at Low Temperatures
under Snow and Intl Comm. On Global Climate and Plant Environmental Stress,
Akureyri, Iceland.

Hurley, R.H., V.G. Lehman, J.A. Murphy and CR. Funk. 1994. Registration of
‘Southshore’ creeping bentgrass. Crop Sci. 34:1124-1125.

Hsiang T., N. Matsumoto and S. Millet. 1999. Biology and management of Typhula
snow molds of turfgrass. Plant Disease. 83(9):788-798.

Jackson, N and Fensterrnacher J .M. 1969. Typhula blight: its cause, epidemiology and
control. J. Sports Turf Res. Inst. 45: 67-73.

118

Jacobs, D.L. and G.W. Bruehl. 1986. Saprophytic ability of T yphula incarnata, T yphula
idahoensis, and Typhula ishikariensis. Phytopathology 76(7): 695-698.

Matsumoto, N. and A. Tajimi. 1985. Field survival of sclerotia of Typhula incarnata and
T. ishikariensis biotype A. Can. J. Bot. 63: 1126-1128.

Millet, S. 2000. The distribution, molecular characterization and management of snow

molds in Wisconsin Golf Courses. Ph.D. thesis. University of Wisconsin-
Madison, under DP Maxwell adviser.

Snider, C.S., T. Hsiang, G. Zhao and M. Griffith. 2000. Role of ice nucleation and anti-
freeze activities in pathogenesis and growth of snow molds. Phytopathology
90(4):354-361.

Vargas, J .M., Jr. 1994. Management of turfgrass diseases. CRC Press, Boca Raton, F 1.

Watson, L. and M.J. Dallwitz. 1992. Grass Genera of the World. Wallingford Oxon, UK
CAB Intl. 1038p.

Wu, C. and T. Hsiang. 1998. Pathogenicity and formulation of T. phacorrhiza, a
biocontrol agent of gray snow mold. Plant Disease. 82: 1003-1006.

119

 

APPENDIX A

Potential for Detached-Leaf Assay for Gray Snow Mold Screening

Detached-leaf assay is a rapid, economical way to do pathogenicity tests and the
procedure has been used to study many host-pathogen interaction systems in dicots
(Dufresne, 2000); large leaf blade monocots such as rice and corn (Pitkin et al., 1999),
but not on small leaf blade grasses. Unavailability of disease screening methods for gray
snow mold, temperature sensitivity and slow-growing characteristics of the fungus (Table
3.2, Chapter III) led to initial skepticism whether detached-leaf assay could be use as an
alternative to whole plant physiological screening against T. incarnata. To assess the
feasibility of the procedure, six leaves each of susceptible bentgrass cultivars,
‘Penncross’ and ‘L-93’ and resistant bentgrass, ‘MSU 20215’ and ‘MSU-8’ were utilized.
Three treatments were performed: a) no inoculation, b) inoculated at the leaf center
(pathogen invasion through stomates) and c) inoculated at the base (pathogen invasion
through wounded site). The leaves were swabbed with 10% sodium hypochlorite, rinsed
3x in sterilized dionized distilled water and placed on moistened sterile filter paper in
petri dishes. On the petri dish cover, a single layer of sterilized cheesecloth was attached
to hold moisture and for diffused lighting. Agar plugs (2 cm.diam.) with gray snow mold
mycelia were applied at the center or base of the leaves. The plates were sealed with
parafilm. The experiment was conducted in the cold room at 5 °C.

After 4 weeks, visual inspection showed that leaves of the resistant lines MSU

20215 and MSU-8 did not show any signs of yellow discoloration or infection despite

120

 

low lighting, susceptible plants, ‘L-93’ and ‘Penncross’ inoculated at the center or at the
site of wounding showed 40% to 90% symptoms of yellow discoloration in patches or
whole leaf, and the leaves from resistant plants showed ‘localized necrosis’ at the site
where the inoculum was applied (Table A.1). Since none of the control plants showed
the symptoms of yellow patches, the experiments indicated that discoloration was
associated with disease infection and not due to wounding or cold treatment. This
finding is important as an additional support to the validity for a rating system to be used
in the developed system for controlled screening against gray snow mold in bentgrasses.
The advantage of the detached leaf assay would be an ability to initially screen large
populations of genotypes using smaller resources for inoculum, less space for cold room
and easier handling, sufficing the problem or need for large greenhouse spaces to
maintain populations. Selected candidate resistant plants may then be further subjected
to screening with replicates on whole plant basis to measure plant recovery as well.

The leaves from resistant plants manifested ‘localized necrosis’ or ‘browning’
indicative of oxidation of phenols, hypersensitive response (HR) or cell death (Goodman
and Novacky, 1994; Hammerschmidt and Nicholson, 1999) at the site of inoculation that
may probably restrict further invasion of the pathogen. More investigation through
cytological analysis is needed to show that mycelial growth was inhibited in resistant
plants, as HR may only be a single part of the defense strategy. Efficiency of the
detached leaf assay should be compared with the controlled whole plant screening
method on a wider scale. Future experiments may also contrast the differences if any, in

basic leaf resistance and whole plant resistance.

121

Table A. 1. Disease reactions to gray snow mold using detached leaf assay 30 days after
inoculation depicted by presence (+), absence (-) and mean percentage disease symptoms
per leaf .

 

 

Treatments & Disease Reactions

Plant Material
Yellowing Lesion(s) Localized
(Mean % Ileaf) expansion necrosis

 

A. Uninoculated - - -

B. Center inoculated

Susceptible ‘Penncross’ + (70%) + -
Susceptible ‘L-93’ + (60%) + -
Resistant ‘MSU20015’ +( 5%) - +
Resistant ‘PI-8’ - - +

C. Wound inoculated

Susceptible ‘Penncross’ + (80%) + -
Susceptible ‘L-93’ + (70%) + -
Resistant ‘MSU200 l 5 ’ - - +
Resistant ‘PI-8’ - - +

 

122

APPENDIX B

Changes in Carbohydrate Levels in Bentgrass

During Cold and Disease Treatments

The nature of gray snow mold resistance is still unknown. In general, genetics of
resistance to gray snow mold in winter wheat appears to be probably polygenic and non-
specific (Gaudet and Larouche, 1999). In winter barley, resistance may possibly be
controlled by a single major gene (Cavelier, 1989). Previous studies implying the
polygenic nature of disease resistance to snow mold in winter wheat (T riticum aestivum
L) report the involvement of A) pathogenesis related (PR) proteins such as chitinase, B-
1,3-glucanase, peroxidase, gamma-thionin, and a lipid transfer protein (Gaudet et al.,
2000); B) changes in osmotic potential during cold hardening (Gaudet et al., 2001) and
C) changes in the form and quantity of carbohydrates that may directly affect fungal
development within the plant (Gaudet et al., 1999). Resistant winter wheat cultivars
possessed higher levels and more highly polymerized fructans than susceptible cultivars.
Nakajima and Abe (1994) suggested that depletion of carbohydrate reserves, as affected
by duration of snow cover is a major determining factor in snow mold resistance.
Yoshida et a1 (1998) similarly found that snow mold resistant wheat cultivars tended to
metabolize carbohydrates more slowly, suggesting that the enzymatic metabolism of
carbohydrates to cryoprotective sugars differed between the two contrasting types of

resistance during winter stress.

123

Disease resistance screening for gray snow mold in bentgrasses from my
preceding work showed a wide range of resistance, that was probably under polygenic
control, and the mechanism for leaf and crown resistance may also be different and

independent as suggested by the differences in recovery reactions.

As a preliminary study to evaluate the changes in total non-structural
carbohydrate (TNC) levels in snow mold resistant and susceptible creeping bentgrasses
and to see whether TNC differences existed between the leaf and crown, susceptible
cultivars ‘Penncross’ and ‘L-93’ and resistant line ‘MSU 20215’ were used for the study.
Leaf samples for carbohydrate analysis were collected 5 cm. above the base of the plant
and crown samples were cut 2 cm. above the soil base. The leaf and crown tissues were
rinsed in deionized distilled water 3x and stored in —80°C before freeze-drying. Three
different treatments were conducted as follows: A) no treatment or control B) 3 days cold
treatment and C) gray snow mold infection for 4 weeks. Plant tissues were prepared for
analysis following the procedures of Smith (1981). Freeze-drying was done at —80°C for
90 minutes for the crowns and 60 minutes for the leaves. Samples were ground by hand
until powder-like and placed in 50 ml flasks and re-dried at 70°C for another 30 minutes
before sealing bottles. Samples were sent to University of Missouri for TNC analysis
and analyzed following Smith (1981) procedure developed at the University of

Wisconsin-Madison.

The results generally indicated an increase in TNC levels after 3 days of cold
treatment in the leaves for both resistant and susceptible plants (Table A2, Figure Al).
The percentage increase in TNC in leaves after cold treatment ranged from 41.12% (L-

93), 64.7% (Penncross), and 121% in the resistant plant (MSU20215). In the crowns, the

124

Table A.2. Changes in total non-structural carbohydrate (TNC) levels in bentgrass
following 3 days of cold treatment or four weeks of disease inoculation.

 

 

 

Tissue & TNC levels
Plant Material
Control Cold % Difference Disease % Difference
Leaves:
Penncross 12.1 19.93 64.7 % 3.94 -80.2 %
L-93 9.59 13.54 41.1 % 6.72 -50.3 %
MSU20015 (R) 3.71 8.21 121.3 % 4.87 -40.7 %
Crown:
Penncross 19.76 14.06 - 28.8 % 4.74 -66.3 %
L-93 16.81 8.18 -51.3 % 6.5 -20.5 %
MSU20015 11.57 12.48 7.8 % 5.39 -56.8 %

 

125

 

 

 

 

 

 

 

 

 

LEAVES

 

 

 

 

 

 

 

 

 

 

 

CROWN

Figure A. 1. Changes in the total non-structural carbohydrate levels
in leaves and crown.

126

TNC levels decreased by 28.8% (Penncross) and 51.13% (L-93) in the two susceptible
lines, while TNC increased by 7.8% in the resistant plant (MSU20215). The data are
preliminary but indicate that there may be an association of increased TNC with cold
acclimation and higher TNC in resistant creeping bentgrass. After 4 weeks of gray snow
mold infection, TNC levels in the leaves dropped for both resistant and susceptible
plants. The percentage decrease in TNC were 80.2% (Penncross), 50.3% (L-93) and
lowest at 40.7% (MSU20215). The percentage decreases of TNC levels in the crowns
were 66.3% (Penncross), 20.5% (L-93) and 56.8% (MSU20215). More studies should be
conducted to explain the TNC differences in the crown at this point and whether TNC

levels at the crown could be associated with snow mold resistance.

REFERENCES TO APPENDICES

Dufresne, M., S. Perfect, J. Pellier, A. Bailey and T. Langin. 2000. A GAL-4 like protein
is involved in the switch between biotrophic and necrotrophic phases of the
infection process of Colletotrichum lindemuthianum on common bean. Plant Cell.
12:1579-1589.

Cavelier, M. 1989. Evaluation and interpretation of competition phenomena between
strains of Typhula incarnata Lasch. J. Phytopathology. 127: 55-68.

Goodman, RN. and A.J. Novacky. 1994. The hypersensitive reaction in plants to
pathogens. APS Press, St. Paul, MN. Pp24.

Gaudet, DA. and A. Larouche A. 1999. Towards an understanding of snow mould
resistance. In International Workshop on Plant-Microbe Interactions at Low
Temperatures under Snow and International Conference On Global Climate and
Plant Environmental Stress, Akureyri, Iceland.

Gaudet, DA, A. Larouche and M. Yoshida. 1999. Low temperature wheat-fungal
interactions: A carbohydrate connection. Physiologia Plantarum. 106(4): 437-444.

127

Gaudet, DA, A. Larouche, M. Frick, J. Davoren, B. Puchalski, and A. Ergon. 2000.
Expression of plant defense related (PR protein) transcripts during hardening and
dehardening of winter wheat. Physiol. & Mol. Plant Path. 57(1): 15-24.

Gaudet, DA, A. Larouche and B. Puchalski. 2001. Effect of plant age on water content

in crowns of fall rye and winter wheat cultivars differing in snow mold resistance.
Canad. J. Plant Science. 81(3): 541-550.

Hammerschmidt, R. and R.L. Nicholson. 1999. A survey of plant defense responses to
pathogens. In Inducible Plant Defenses Against Pathogens and Herbivores:
Biochemistry, Ecology & Agriculture. A.A. Agarwal, S. Tuzun and E. Bent (eds).
APSS Press, St. Paul, MN (USA). Pp55-71.

Nakajima, T. and J. Abe J. 1994. Development of resistance to Microdochium nivale in
winter wheat during autumn and decline of the resistance under snow. Can. J. Bot.
72: 1211-1215.

Pitkin, J.W., A. Nikolskaya, A. Ahn and J .D. Walton. 2000. Reduced virulence caused
by meiotic instability of the TOX2 chromosome of the maize pathogen
CocIiobolus carbonum. Molecular Plant-Microbe Interactions. 13(1):80-87.

Smith, D. 1981. Removing and analyzing total nonstructural carbohydrates from plant
tissue. Agricultural Bulletin Publication from University of Wisconsin-Madison.

Yoshida, M., J. Abe, M. Moriyama, and T. Kuwabara. 1997. Carbohydrate levels among

winter wheat cultivars in freezing tolerance and snow mold resistance during
autumn and winter. Physiologia Plantarum.]03: 8-16.

128

 

MHMH’

1

1 111
3

1 1811!”? F:

.AN RIMS 1.111012}!de 1;.
. 1 >1 ‘

. ‘1‘ ‘11;1 1‘ ‘ [pr 1“

1 1 1 .1 T

‘11 T 1‘ T ml 1 1 1.3 ‘1.

1 . 1 1 . 1 ‘ 1

1293 02504 7790