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Resistance gene analogs are conserved and clustered in soybean. Proc Nat Acad Sci USA 93: 11746-11750

Authors:

Abstract

Sequences of cloned resistance genes from a wide range of plant taxa reveal significant similarities in sequence homology and structural motifs. This is observed among genes conferring resistance to viral, bacterial, and fungal pathogens. In this study, oligonucleotide primers designed for conserved sequences from coding regions of disease resistance genes N (tobacco), RPS2 (Arabidopsis) and L6 (flax) were used to amplify related sequences from soybean [Glycine max (L.) Merr.]. Sequencing of amplification products indicated that at least nine classes of resistance gene analogs (RGAs) were detected. Genetic mapping of members of these classes located them to eight different linkage groups. Several RGA loci mapped near known resistance genes. A bacterial artificial chromosome library of soybean DNA was screened using primers and probes specific for eight RGA classes and clones were identified containing sequences unique to seven classes. Individual bacterial artificial chromosomes contained 2-10 members of single RGA classes. Clustering and sequence similarity of members of RGA classes suggests a common process in their evolution. Our data indicate that it may be possible to use sequence homologies from conserved motifs of cloned resistance genes to identify candidate resistance loci from widely diverse plant taxa.
Proc.
Natl.
Acad.
Sci.
USA
Vol.
93,
pp.
11746-11750,
October
1996
Genetics
Resistance
gene
analogs
are
conserved
and
clustered
in
soybean
(disease/mapping/multi-gene
families/evolution)
VLADIMIR
KANAZIN*,
LAURA
FREDRICK
MAREK*,
AND
RANDY
C.
SHOEMAKER*tt
*Department
of
Agronomy,
and
tField
Crops
Research
Unit,
Agricultural
Research
Service,
U.S.
Department
of
Agriculture,
Iowa
State
University,
Ames,
IA
50011
Communicated
by
Steven
D.
Tanksley,
Cornell
University,
Ithaca,
NY,
August
19,
1996
(received
for
review
May
3,
1996)
ABSTRACT
Sequences
of
cloned
resistance
genes
from
a
wide
range
of
plant
taxa
reveal
significant
similarities
in
sequence
homology
and
structural
motifs.
This
is
observed
among
genes
conferring
resistance
to
viral,
bacterial,
and
fungal
pathogens.
In
this
study,
oligonucleotide
primers
de-
signed
for
conserved
sequences
from
coding
regions
of
disease
resistance
genes
N
(tobacco),
RPS2
(Arabidopsis)
and
L6
(flax)
were
used
to
amplify
related
sequences
from
soybean
[Glycine
max
(L.)
Merr.].
Sequencing
of
amplification
products
indi-
cated
that
at
least
nine
classes
of
resistance
gene
analogs
(RGAs)
were
detected.
Genetic
mapping
of
members
of
these
classes
located
them
to
eight
different
linkage
groups.
Several
RGA
loci
mapped
near
known
resistance
genes.
A
bacterial
artificial
chromosome
library
of
soybean
DNA
was
screened
using
primers
and
probes
specific
for
eight
RGA
classes
and
clones
were
identified
containing
sequences
unique
to
seven
classes.
Individual
bacterial
artificial
chromosomes
con-
tained
2-10
members
of
single
RGA
classes.
Clustering
and
sequence
similarity
of
members
of
RGA
classes
suggests
a
common
process
in
their
evolution.
Our
data
indicate
that
it
may
be
possible
to
use
sequence
homologies
from
conserved
motifs
of
cloned
resistance
genes
to
identify
candidate
resis-
tance
loci
from
widely
diverse
plant
taxa.
The
sequences
of
cloned
plant
disease
resistance
genes
show
that
the
majority,
whether
conferring
resistance
to
viral,
bacterial,
or
fungal
pathogens,
contain
similar
sequences
and
structural
motifs.
The
Arabidopsis
genes,
RPS2
(1,
2)
and
RPM1
(3),
conferring
resistance
to
the
bacterial
blight
patho-
gen
Pseudomonas
syringae,
the
tobacco
gene
N
(4,
5),
confer-
ring
resistance
to
tobacco
mosaic
virus,
the
rice
gene
Xa2l
(6),
conferring
resistance
to
Xanthomonas
oryzae,
the
flax
gene
L6
(7),
conferring
resistance
to
a
rust
fungus,
and
the
tomato
gene
Cf-9
(8),
conferring
resistance
to
the
fungal
pathogen
Clas-
doporium
fulvum,
all
contain
leucine-rich
repeats
that
encode
protein
motifs
often
associated
with
protein-protein
interac-
tions
or
ligand
binding
(9).
Motifs
for
a
conserved
nucleotide
binding
site
are
also
found
in
the
RPS2,
RPM],
N,
and
L6
coding
region.
It
has
been
proposed
that
the
similarities
among
resistance
genes
may
make
it
possible
to
take
advantage
of
sequence
homologies
to
identify
other
resistance
genes
(10).
Southern
hybridization
with
a
clone
from
the
Pto
locus
of
tomato
to
genomic
DNA
of
six
dicotyledonous
and
five
monocotyledonous
species
detected
homologs
in
all
species
(11)
and
suggested
that
gene
families
of
these
homologs
may
exist,
similar
to
that
observed
in
tomato.
Similarly,
conserved
motifs
from
receptor
proteins
have
been
used
to
identify
multigene
families
of
odorant
receptors
in
rat
(12)
and
con-
served
motifs
from
regulatory
proteins
have
been
used
to
identify
other
homeobox
proteins
in
Xenopus
(13).
Although
not
all
resistance
genes
have
been
demonstrated
to
reside
in
clusters,
tight
linkage
associations
of
many
resis-
tance
genes
have
been
well
established.
Genetic
linkage
of
resistance
genes
has
been
reported
in
maize
for
the
Rpl
cluster
(14-16),
in
barley
for
the
Mla
cluster
(17-20),
in
lettuce
for
a
wide
range
of
Dm
loci
(21,
22),
in
oat
for
the
Pc
cluster
(23),
and
in
flax
for
the
L
gene
cluster
(24-26).
Clustering
of
resistance
genes
suggests
that
a
common
genetic
mechanism
has
been
involved
in
their
evolution
(16).
In
this
paper
we
demonstrate
that
the
nucleotide
conserva-
tion
observed
within
disease
resistance
genes
cloned
from
widely
diverse
taxa
can
be
used
to
advantage
to
isolate
sequences
with
strikingly
similar
motifs
from
a
species
from
which
no
disease
resistance
genes
have
yet
been
cloned.
We
demonstrate
that
resistance
gene
analogs
(RGAs)
exhibit
microclustering
in
the
genome,
that
clusters
of
RGAs
contain
only
members
of
the
same
family,
and
that
mapping
of
RGA
sequences
can
place
genetic
markers
in
close
proximity
to
known
resistance
genes.
MATERIALS
AND
METHODS
Nucleic
Acid
Manipulations.
Soybean
genomic
DNA
was
prepared
as
described
in
Dellaporta
et
al.
(27).
RNA
was
prepared
according
to
Sambrook
et
al.
(28).
Electrophoresis,
blotting,
and
hybridization
was
done
using
standard
techniques
(28).
Restriction
enzyme
digestions
were
conducted
using
conditions
recommended
by
the
manufacturers.
PCR
Amplification,
Cloning,
Sequence
Analysis.
Regions
of
amino
acid
identity
in
the
N
gene
from
tobacco,
the
RPS2
gene
from
Arabidopsis,
and
the
L6
gene
from
flax
were
used
to
design
degenerate
primers.
Primer
LM638
was
designed
from
the
conserved
P-loop
sequence.
Primer
LM637
was
designed
from
a
second
region
of
amino
acid
identity
which
in
the
RPS2
protein
is
proposed
to
reside
in
a
transmembrane
region.
Primer
sequences
were
as
follows:
LM638,
5'-GGIGGIGTIG-
GIAAIACIAC-3',
and
LM637,
5'
-A(A/G)IGCTA(A/
G)IGGIA(A/G)ICC-3'.
PCR
was
performed
in
a
total
volume
of
100
,ul,
with
a
3-min
initial
denaturation
step
at
94°C
followed
by
35
cycles
as
follows:
94°C
for
1
min,
45°C
for
30
sec,
and
72°C
for
30
sec.
PCR
products
were
cloned
into
the
pGEM-T
vector
(Promega).
Clones
were
sequenced
using
the
Applied
Biosystems
model
377
PRISM
automated
sequencer,
or
manually
using
the
Sequenase
DNA
sequencing
kit
(United
States
Biochemical).
Multiple
sequences
were
obtained
for
each
RGA
class.
DNA
sequence
analysis
was
carried
out
with
the
DNASIS
(Hitachi)
and
GCG
(University
of
Wisconsin
Genetics
Com-
puter
Group,
Madison)
sequence
analysis
packages.
Align-
ment
of
sequences
was
done
using
the
PILEUP
function
of
the
Genetics
Computer
Group.
Phylogenetic
analysis
of
amino
Abbreviations:
RGA,
resistance
gene
analog;
BAC,
bacterial
artificial
chromosome.
Data
deposition:
The
sequences
reported
in
this
paper
have
been
deposited
in
the
GenBank
data
base
(accessions
nos.
U55803-
U55812).
*To
whom
reprint
requests
should
be
addressed.
11746
The
publication
costs
of
this
article
were
defrayed
in
part
by
page
charge
payment.
This
article
must
therefore
be
hereby
marked
"advertisement"
in
accordance
with
18
U.S.C.
§1734
solely
to
indicate
this
fact.
Proc.
Natl.
Acad.
Sci.
USA
93
(1996)
11747
acid
sequences
was
performed
using
MEGA
version
1.0
(29).
The
tobacco
N
sequence
was
used
as
an
outgroup.
Genetic
Mapping
of
RGAs.
RGA
sequences
were
mapped
in
a
G.
max
x
G.
soja
population
containing
56
individuals
(30)
using
MAPMAKER
(31).
Additionally,
one
soybean
bacterial
artificial
chromosome
(BAC)
was
mapped
in
a
G.
max
x
G.
max
population
containing
196
individuals
and
in
which
the
disease
resistance
genes
Rps2
and
Rmd,
and
the
Rhizobium
responsive
gene
Rj2,
were
mapped
(32).
Data
sets
from
linkage
group
J
from
both
populations
were
combined
and
an
inte-
grated
map
was
constructed
using
the
computer
program
JOINMAP
(33).
The
Kosambi
mapping
function
was
selected
and
a
minimum
logarithim
of
odds
(lod)
score
of
3
required
for
a
two-point
linkage
to
be
included
in
analyses.
The
order
of
"anchored
loci"
defined
by
JOINMAP
output
agreed
with
the
published
order.
Therefore,
specification
of
fixed
sequences
was
not
necessary.
BAC
Library
Construction
and
Screening.
The
soybean
BAC
library
was
constructed
as
described
(34)
from
megabase-
sized
DNA
isolated
from
the
soybean
cultivar
Williams
82.
The
4-5
genome
equivalent
library
contains
-40,000
individually
picked
clones
with
an
average
insert
size
of
150
kb
and
it
is
stored
in
384-well
microtiter
dishes.
Three-dimensional
BAC
pools
for
PCR
screening
were
set
up
using
entire
plates
and
individual
rows
or
columns
from
groups
of
20
plates.
Plasmid
DNA
was
purified
from
each
pool
by
standard
alkaline
lysis
midiprep
technique
(28).
Class-specific
primers
were
designed
for
each
RGA
class
(Table
1).
PCR
amplification
was
done
as
described
above
using
an
annealing
temperature
of
55°C.
Microtiter
dishes
were
replicated
onto
nylon
membranes
(Zeta-
Probe
GT;
Bio-Rad)
and
selected
membranes
were
used
to
confirm
results
of
PCR-based
screening.
RESULTS
Cloning
and
Sequence
Analysis.
PCR
amplification
of
soy-
bean
genomic
DNA
using
degenerate
RGA
primers
resulted
in
a
product
that
appeared
to
be
a
large,
single
band
on
a
1%
agarose
gel.
However,
digestion
of
this
product
with
several
restriction
enzymes
recognizing
4-bp
sites
resulted
in
many
fragments,
whose
sum
was
much
greater
than
the
molecular
weight
of
the
original
PCR
product.
The
presence
of
a
heterogeneous
PCR
product
suggested
the
involvement
of
a
multigene
family.
These
PCR
products
were
cloned
and
-450
clones
were
analyzed.
The
clones
were
grouped
into
nine
classes
which
did
not
cross-hybridize
under
stringent
hybrid-
ization
conditions
[0.1
x
standard
saline
citrate
(SSC)/0.1%
SDS/60°C
wash].
Clones
representing
each
class
were
hybrid-
Table
1.
Sequences
of
class-specific
RGA
primer
pairs
RGA
class
ized
to
Southern
blots
of
soybean
genomic
DNA
digested
with
various
restriction
enzymes
to
identify
polymorphisms
useful
for
genetic
mapping
of
RGAs
and
to
estimate
copy
number.
Four
classes
were
used
as
probes
on
Northern
blots
containing
total
RNA
from
different
soybean
organs
(leaves,
stems,
and
roots).
Lower
levels
of
RGA
message
were
detected
in
stems
and
roots
compared
with
that
observed
from
leaves
(Fig.
1).
Differential
expression
or
accumulation
was
not
observed
in
these
tissues
as
a
result
of
wounding
or
Phytophthora
inocula-
tion
(data
not
shown).
One
to
five
clones
from
each
class
were
sequenced,
and
the
deduced
amino
acid
sequences
of
repre-
sentative
clones
from
each
class
are
shown
in
Fig.
2.
Among
sequenced
clones
only
class
4
sequences
showed
heterogeneity
(two
groups).
Alignment
of
the
amino
acid
sequences
estab-
lished
that
the
cloned
RGAs
contain
two
additional
conserved
nucleotide
binding
protein
domains
also
present
in
N,
RPS2,
and
L6.
A
search
of
GenBank
using
the
BLAST
algorithm
revealed
that
the
RGA
sequences
were most
similar
to
the
L6,
N,
and
RPS2
gene
products.
Remote
similarity,
limited
to
the
conserved
motifs
was
found
to
other
P-loop
containing
pro-
teins
(myosin
heavy
chain
homolog,
Arabidopsis;
ATPase,
Plasmodium).
Pairwise
comparisons
between
different
classes
and
be-
tween
each
class
and
the
homologous
N
and/or
L6
sequences
revealed
that
amino
acid
identities
ranged
from
30
to
66%;
similarities
ranged
from
50
to
75%.
Clone
RGA9
is
likely
to
be
a
pseudogene
because
it
contains
multiple
stop
codons
and
frame-shift
mutations.
However
it
did
show
strong
similarity
with
RGA4
(77%).
Genetic
mapping
placed
RGA9
and
RGA4
at
one
position
on
linkage
group
H.
Class
4
contained
two
subclasses,
a
and
b,
which
showed
88%
amino
acid
identity.
These
subclasses
differed
at
the
nucleotide
level
but
were
not
distinguishable
by
Southern
blot
analysis.
Classes
3
and
7
were
66%
identical
at
the
amino
acid
level;
classes
5
and
8
were
51%
identical.
CA
0
0
-o
1.5
Sequences,
5'
-*
3'
RGA1
AGTTTATAATT(C/T)(C/G)ATTGCT-
ACTACGATTCAAGACGTCCT
RGA2
AGTTTATAATT(C/T)(C/G)ATTGCT-
CACACGGTTTAAAATTCTCA
RGA3
AGTTTATAATT(C/T)(C/G)ATTGCT-
CTCTCGATTCAAAATATCAT
RGA4
TGTTACTGCTTTGTTTGGTA-
TACATCATGTGTTACCTCT
RGA5
TGCTAGAAAAGTCTATGAAG-
TCAATCATTTCTTTGCACAA
RGA6
AGCCAAAGCCATCTACAGT-
AACTACATTTCTTGCAAGT
RGA7
AGTTTATAATT(C/T)(C/G)ATTGCT-
CCGAAGCATAAGTTGCTG
RGA8
AGCGAGAGTTGTATTTAAG-
AGCCACTTTTGACAACTGC
1.5
kb
FIG.
1.
Northern
blot
analysis
of
total
soybean
RNA
from
leaves,
stems,
and
roots.
(Upper)
Ethidium
bromide-stained
RNA
gel.
(Bot-
tom)
Results
of
hybridization
of
a
Northern
blot
of
this
gel
with
an
RGA3
probe.
Hybridizations
using
RGA1,
RGA2,
and
RGA4
probes
yielded
similar
results.
Genetics:
Kanazin
et
al.
Proc.
Natl.
Acad.
Sci.
USA
93
(1996)
RGA4a
RGA4b
RGA3
RGA7
RGA1
RGA2
RGA5
RGA8
RGA6
N
L6
RGA4a
RGA4b
RGA3
RGA7
RGA1
RGA2
RGA5
RGA8
RGA6
N
L6
ol
.20
.40
.60
.80
.100
GGVGKTTLVTALFGKISP...
..QYDARCFIDDLNKKCGN
..
FGAISAQKQLLSLALHQG
......................
NMBIHNLSHGTMLIRTRL
GGVGKTTLVTALFGKISP
......
QYDARCFIDDLNKYCGD
..
FGATSAQKQLLCQALNQG.
.
...........
NMEIHNLSHGTMLVRTRL
GGVGKTTLAVAVYNSIAG
......
HFEASCFLENVRETSN.K.KGLQHLQSILLSKTVGE
.......................
KKIRLTNWREGIPIIKHRL
GGVGKTTLAAAVYNSIAD
......
HFFALCFLENVRETSK.K.HGIQHLQSNLLSETVGE
.H..
.IGVKQGISIIQHRL
GGVGKTTLALAVYNLIAL
......
HFDESCFLQNVREESN.K.HGLKHLQSIILSRLLGE
.......................
RDINLTSWQEGASMIQHRL
GGVGKTTIAREVYNLIAD
......
QFEWLCFLDNVRENSI.K.HGLVHLQKTLLSKTIGE
.......................
SSIRLGSVHEGIPIIKHRF
GGVGKTTIARKVYEAIKG
......
DFDVSCFLENIREVS
..
KTNGLVHIQK.ELSNLGVSCFLEKCKTNGLVPIVEEVFRDQLRIVDFDNLHDGRMIIANSL
GGVGKTTLARVVFKKIRN
......
RFDISCFLENVREISQ.NCDGMLSLQGKLLSHM
........................K
RMLKIQNLDEGKSIIGGIL
GGVGKTTSAKAIYSQIHR
......
RFMDKSFIESIRSVCETDSKGHVHLQEQLLSDVLNT
........................
KVRVHSIGMGTTIIEKRL
GGVGKTTIARAIFDTLLGRMDSSYQFDGACFLKDIKE
....
NRGMHSLQNALLSELLRE
.
.......................
KANYNNEEDGKHQMASRL
GGIGKTTTAKAVYNRISSC
......
FDCCCFIDNIRET.
.QEDGVVVLQKKLVSEILRI
................
DSGSVGFNNDSGGRKTIKERV
.120
.140
CHLKTLIVLDNVDQVEQLENLAL.HREYLGEGSRTIIISTNMHILRNYGVD.
.160
.180
.200
.GVYNVQLLNIWKALQLLCKFAFKSDD.IVKGYEEVTHDVLKYVNGLPLA
.KVYNVQLLKDKALQLLCKKAFKSDD.IEKGYEEVTYDVLKYVNGLPLA
KQKKVLLILDDVDEHKHLQAI.IGSPDWFGCGSRVIITTRNEHLLALHNV...ITYKVRELNERnALQLLTQKAFELEKEVDSSYNDILNRALIYASGLPLA
QQQKILLILDDVDKREQLQAL.AGRPDLFGLGSRVIITTRDKQLLACHGVE..RTYEVNELNEEHALELLSWKAFKLEK.VDPFYKDVLNRAATYASGLPLA
QRZKVLLILDDVDKRQQLKAII.VGRPDWFGPGSRVIITTRDKHILKYHEVE..RTYEVKVLNQSAALQLLKYNAFKREKN.DPSYEDVLNRVVTYASGLPLA
LLRKVLLVVDDVDDPDQLQAI.VGGTDWFGSASSVIITTRDKHLLTCHGVT..STYEVDGLNKEKALKLLSGTAFKIDK.VDPCYMRILNRVVTYASGLPLA
SNKKVLLVLDDVSELSQLENLA.GKQEWFGPGSRVIITTRDKHLLKTHGVH..LTCKARALAQNEALQLICLKAIKRDQPKKG.YLNLCKEMIECARGLPLA
FNNNVLLVLDDVNDIRQLENFSVNDQKWLGPGSRIIIITRDHEVLRSHGTV..ESYKIDLLNSGESLQLFSQKAFKRDQPLEH.ILQLSKVAVQQAGGLPLA
SGKRVLIVLDDVNEIGQLENL.CGNCEWFGQGSVIIITTRDVGLLNLFKVD..YVYXMEENDENESLELFCLHAFGEPNPRED.FNELARNVVAYCGGLPLA
RSRRIVLDDIDNKDHYLEYLAGDIDWFGNGSRIIITTRDKHLIEKNDI....
IYEVTALPDHESIQLFKQHAFGKEVPNEN.FERLSLEVVNYAKGLPLA
SRFKILVVLDDVDEKFKFEDMLGSPKDFISQ.SRFIITSRSMRVLGTLNENQCKLYEVGSMSKPRSLELFSKA.FKKNTP.PSYYETLANDVVDTTA
P
FIG.
2.
Alignment
of
the
deduced
amino
acid
sequences
of
RGAs
from
soybean.
Arrows
indicate
location
of
PCR
primers
used
to
amplify
RGA
sequences.
Underlined
regions
correspond
to
additional
conserved
domains.
Dotted
regions
indicate
gaps
in
sequence
introduced
to
maximize
alignment.
The
tobacco
N
and
the
flax
L6
amino
acid
sequences
are
included
for
comparison.
Amino
acid
sequences
were
aligned
and
neighbor-joining
analysis
resulted
in
the
production
of
a
phylogenetic
tree
with
the
nine
RGA
sequences
divided
into
several
subclades
(Fig.
3).
Mapping
of
RGA
Loci.
Genetic
mapping
of
members
of
the
different
classes
placed
them
on
8
of
the
26
linkage
groups
comprising
the
soybean
genetic
map
(Fig.
4).
The
RGA
loci
mapped
both
singly
and
in
clusters
and
were
located
on
several
of
the
linkage
groups
to
which
known
disease
resistance
genes
have
been
mapped.
A
class
6
RGA
mapped
to
linkage
group
Ni
near
Rps1
(35).
A
large
cluster
of
RGAs
representing
five
of
the
different
classes
mapped
to
linkage
group
J
and
encompassed
a
resistance
gene
cluster
including
Rmd,
Rps2,
and
Rj2
(32)
and
near
a
quantitative
trait
locus
for
soybean
cyst
nematode
resistance
(N.
Young,
personal
communication).
We
found
no
map
positions
near
other
known
resistance
genes:
Rhg4,
linkage
group
A2
(36);
Rps3
(35),
Rsv
(37),
linkage
group
F;
and
Rps4
(35),
linkage
group
G.
To
determine
more
precisely
the
map
positions
of
RGAs
relative
to
mapped
resistance
genes
on
linkage
group
J
the
0.158
RGA3
0.103
0.155
RGA7
|
0.171
RGAI
_
_
~~~~0.20
RGA2
0.086
0.262RGRGA
RGA2
0.333
L6
0.288
0.056
RGA4a
0.064
RGA4b
0.302
RGA6
0.205
N
FIG.
3.
Phylogenetic
tree
based
on
alignment
of
amino
acid
sequences
of
tobacco
N,
flax
L6,
and
nine
soybean
RGA
classes.
Numbers
above
lines
indicate
branch
length
(proportion
of
amino
acid
differences
distinguishing
classes).
composite
map
shown
in
Fig.
5
was
constructed
using
the
computer
program
JOINMAP
(33)
and
markers
common
to
both
populations.
These
data
indicate
that
at
least
two
RGAs
map
within
this
multigene
cluster.
BAC
Library
Screening.
Our
initial
screen
of
the
BAC
library
using
the
original
degenerate
primers
was
unsuccessful
because
the
primers
also
amplified
products
from
DH1OB,
the
Escherichia
coli
host
used
to
grow
and
maintain
the
library
(data
not
shown).
To
overcome
this
obstacle
we
designed
class-specific
primers
for
eight
of
the
RGA
classes
(Table
1)
and
identified
50
BACs
representing
seven
classes.
Copy
number
of
RGA
sequences
within
each
BAC
was
estimated
by
digesting
the
BACs
with
restriction
enzymes
that
did
not
have
recognition
sites
within
the
RGA
probe
sequences
and
by
hybridizing
with
each
class-specific
probe.
Fig.
6
demonstrates
results
for
six
BACs
belonging
to
classes
1,
2,
and
3.
Class
1
BACs
each
contained
3-10
copies
of
the
RGA1
sequence.
This
agreed
with
the
8-10
copies
expected
based
on
genomic
Southern
hybridization
patterns
(data
not
shown).
Genomic
Southern
blot
analyses
predicted
two
to
four
copies
of
class
2
RGAs
and
thus
far
the
class
2
BACs
isolated
appear
to
have
two
to
three
copies
of
the
class
2
RGA
sequence.
We
identified
eight
BACs
containing
class
3
RGAs.
Each
of
these
BACs
appeared
to
have
two
to
five
copies
of
RGAs.
Restriction
digests
indicated
that
some
of
the
BACs
have
common
hy-
C2
D1
H
J
L
M
Nl
P
I
I
I
I
I
I
I
I
-RGAI
RGA1
RGA6
*RGA6
RGA3
.RGA2
I~AS
RGA1
-RGA1
-RGA1
-RGA7
-RGA2
FIG.
4.
Distribution
of
RGA
markers
on
the
soybean
genetic
map.
I
11748
Genetics:
Kanazin
et
al.
Proc.
Natl.
Acad.
Sci.
USA
93
(1996)
11749
50.5
12.3
2.9
1.8
2.0
1
2
3
4
5
6
1
2
3
4
5 6
1
2
3
4
5
6
A450-1
RGA6
4.3
-
2.0-
0.5-
411
A
kb
RGA3
RGA2
RGA5
RGA1
K375
RGA
1
NA724
A233
'.RBa
\RGA*
Al199_2
L.J
RGA1
FIG.
5.
A
portion
of
linkage
group
J
showing
the
integration
of
RGA
positions
and
the
map
positions
of
Rps2,
Rmd,
and
Rj2.
The
map
was
generated
through
the
use
of
JOINMAP
(33).
Anchored
markers
used
to
create
this
composite
map
were
K375, A724,
and
A233.
Numbers
to
the
left
of
the
map
indicate
estimated
genetic
distances
between
selected
loci
in
centimorgans.
The
position
of
the
RGA1
identified
with
an
asterisk
(*)
has
been
confirmed
by
independant
mapping
(see
Materials
and
Methods).
bridizing
fragments
and
appear
to
overlap,
forming
a
contig
(see,
for
example
Fig.
6C,
lanes
4-6),
although
some
BACs
may
represent
duplicated
chromosomal
segments.
This
cluster
appears
to
contain
eight
RGA
copies.
Based
on
the
size
of
individual
BACs
the
cluster
spans
"200-300
kb,
indicating
that
the
distance
between
individual
RGA
copies
averages
20
kb.
The
class
3
RGA
mapped
to
only
one
position
on
the
soybean
map
(Fig.
4,
linkage
group
J;
and
Fig.
5).
Based
on
genomic
Southern
blot
analyses,
we
expected
five
to
eight
copies
of
class
3
RGAs;
therefore,
these
BACs
may
contain
the
entire
RGA
class
3
cluster.
DISCUSSION
Nearly
50
restriction
fragment
length
polymorphism
were
mapped
using
13
restriction
enzymes
and
9
RGA
class
se-
quences
as
probes.
Many
polymorphisms
were
likely
due
to
sequence
variations
among
class
members.
However,
given
the
microclustering
of
RGA
class
members,
and
the
small
popu-
lation
size
of
56
individuals
in
which
these
were
mapped,
it
was
not
possible
to
distinguish
very
tightly
linked
loci
from
a
single
_
..
<....
:.....
...*...:
*......
*.
:.
.-..;
n
D
._h
I.....
:C_
FIG.
6.
Southern
hybridization
of
identical
membranes
containing
six
HaeIII
digested
BACs
probed
with
RGA1
(A),
RGA2
(B),
and
RGA3
(C).
Numbered
lanes
indicate individual
BACs.
Molecular
weights
are
shown on
the
left.
Note
that
each
BAC
contains
only
one
RGA
family.
locus.
Therefore,
these
polymorphisms
resolved
to
16
positions
on
8
different
linkage
groups.
Although
very
few
resistance
genes
have
been
mapped
in
soybean,
we
mapped
RGA
sequences
close
to
several
known
genes.
One
reported
gene
cluster
in
soybean,
consisting
of
the
Rps2
locus
conferring
resistance
to
the
fungal
pathogen
Phy-
tophthora
sojae
Kaufmann
&
Gerdemann,
the
Rmd
locus
conferring
resistance
to
the
powdery
mildew
pathogen
Mi-
crosphaera
diffusa
Cooke
&
Peck,
and
the
Rj2
locus
controlling
a
nodulation
response
to
Bradyrhizobia
japonicum
(Kirchner)
Jordan,
maps
within
a
3.8-centimorgan
region
of
linkage
group
J
(32).
A
QTL
associated
with
resistance
to
soybean
cyst
nematode
resistance
is
also
placed
near
this
region
(N.
Young,
personal
communication).
Surprisingly,
a
large
group
of
RGAs
representing
five
of
the
nine
classes
mapped
in
the
region
of
this
cluster.
The
consensus
map
generated
by
JOINMAP
(33)
placed
two
RGAs
within
this
3.8-centimorgan
cluster.
This
was
confirmed
by
independant
mapping
of
an
RGA1
BAC
within
the
population
segregating
for
all
three
genes.
In
this
popu-
lation
we
mapped
the
BAC
within
the
1.8-centimorgan
region
between
Rj2
and
Rmd
(Fig.
5).
This
finding
demonstrates
that
mapping
of
RGA
sequences
can
be
beneficial
in
landing
markers
tightly
linked
to
known
resistance
genes
and
possibly
in
identifying
candidate
resistance
loci.
The
fact
that
members
of
tight
clusters
of
resistance
genes
can
confer
resistance
to
different
pathogens
is
not
surprising
considering
that
members
of
the
same
gene
family
often
maintain
only
partial
redundancy;
they
retain
a
shared
set
of
preserved
functions
but
acquire
unique
specificities
(12,
38).
For
example,
this
would
allow
tightly
linked
members
of
the
same
family
to
retain
structural
motifs
necessary
to
function
in
similar
pathways
(e.g.,
disease
resistance),
while
each
could
respond
to
unique
signals.
Analysis
of
microclusters
of
RGAs
may
have
important
implications
in
identifying
functional
genes
for
any
number
of
signal-responsive
traits.
In
tomato
the
Pto
gene
belongs
to
a
complex
locus
consisting
of
a
tightly
linked
cluster
of
five
to
seven
genes.
Pto
confers
resistance
to
P.
syringae
(11),
while
the
tightly
linked
homolog,
Fen,
confers
sensitivity
to
an
organo-
phosphate
insecticide
(39,
40).
Similar
examples
of
related
genes
that
have
acquired
unique
roles
can
be
found
among
gene
families
involved
in
the
regulation
of
floral
identity,
in
the
reception
of
specific
light
spectra,
and
in
cell
differentiation
(see
ref.
38).
The
clustering
of
signal-responsive
genes
suggests
that
common
genetic
processes-e.g.,
unequal
crossing-over
and
gene
conversion-have
acted
upon
them
during
their
evolution;
although
no
conserved
mechanisms
by
which
these
results
are
obtained
has
been
established
(16).
Genetics:
Kanazin
et
aL
Proc.
Natl.
Acad.
Sci.
USA
93
(1996)
It
is
unlikely
that
each
member
of
an
evolving
gene
family
will
remain
functional.
Without
positive
or
negative
selection
acting
to
retain
function
of
gene
copies
within
a
gene
family
the
accumulation
of
deleterious
mutation
would
quickly
result
in
the
silencing
of
redundant
genes
(38).
However,
within
an
environment
in
which
a
population
is
continually
challenged
by
a
broad
range
of
stressful
conditions
(e.g.,
pathogens),
it
could
be
possible
for
a
rich
repetoire
of
functionally
similar
genes
responsive
to
unique
signals
to
develop
(see
refs.
12
and
41).
Our
findings
demonstrate
that
conserved
sequences
from
resistance
genes
cloned
from
a
diverse
range
of
plant
taxa
can
be
used
to
identify
evolutionarily
related
genes
from
soybean.
These
related
sequences
are
distributed
throughout
the
ge-
nome,
exist
in
microclusters
of
gene
classes,
and
are
associated
with
known
resistance
genes.
The
identification
of
candidate
resistance
genes
by
restriction
fragment
length
polymorphism
mapping
using
RGA
sequences
may,
however,
have
limita-
tions.
Indeed,
it
is
likely
that
the
silenced
pseudogenes,
by
virtue
of
accumulation
of
mutation,
will
be
the
source
of
polymorphisms
between
genotypes.
If
this
is
true,
we
can
predict
that
it
will
be
these
sequences
that
are
mapped.
Therefore,
genetic
mapping
of
RGA
sequences
may
be
more
important
in
landing
markers
close
to
resistance
genes
for
subsequent
map-based
cloning,
than
for
direct
identification
of
resistance
genes.
We
are
grateful
to
Dr.
C.
Nickell
(Agricultural
Research
Service,
U.S.
Department
of
Agriculture)
for
making
available
germplasm
used
in
this
study.
We
are
also
grateful
for
the
financial
support
from
the
Iowa
Soybean
Promotion
Board.
This
paper
is
a
joint
contribution
of
Midwest
Area,
Agricultural
Research
Service,
U.S.
Department
of
Agriculture,
Field
Crops
Research
Unit
and
Journal
Paper
No.
J-16991 of
the
Iowa
Agricultural
and
Home
Economics
Experiment
Station,
Ames;
Project
3236.
1.
Bent,
A.,
Kunkel,
B.,
Dahlbeck,
D.,
Brown,
K.,
Schmidt,
R.,
Giraudat,
J.,
Leung,
J.
&
Staskawicz,
B.
(1994)
Science
265,
1856-1860.
2.
Mindrinos,
M.,
Katagiri,
F.,
Yu,
G.-L.
&
Ausubel,
F.
(1994)
Cell
78,
1089-1099.
3.
Grant,
M.,
Godiard,
L.,
Straube,
E.,
Ashfield,
T.,
Lewald,
J.,
Sattler,
A.,
Innes,
R.
&
Dangl,
J.
(1995)
Science
269,
843-846.
4.
Dinesh-Kumar,
S.,
Whitham,
S.,
Choi,
D.,
Hehl,
R.,
Corr,
C.
&
Baker,
B.
(1995)
Proc.
Nati.
Acad.
Sci.
USA
92,
4175-4180.
5.
Whitham,
S.,
Dinesh-Kumar,
S.
P.,
Choi,
D.,
Hehl,
R.,
Corr,
C.
&
Baker,
B.
(1994)
Cell
78,
1101-1115.
6.
Song,
W.-Y.,
Wang
G.-L.,
Chen,
L.-L.,
Kim,
H.-S.,
Pi,
L.-Y.,
Holsten,
T.,
Gardner,
J.,
Wang,
B.,
Zhai,
W.-X.,
Zhu,
L.-H.,
Fauquet,
C.
&
Ronald,
P.
(1995)
Science
270,
1804-1806.
7.
Lawrence,
G.,
Finnegan,
E.
J.,
Ayliffe,
M.
&
Ellis,
J.
(1995)
Plant
Cell
7,
1195-1206.
8.
Jones,
D.
A.,
Thomas,
C.
M.,
Hammond-Kosack,
K.
E.,
Balint-
Kurti,
P.
J.
&
Jones,
J.
D.
G.
(1994)
Science
266,
789-793.
9.
Kobe,
B.
&
Deisenhofer,
J.
(1994)
Trends
Biochem.
Sci.
19,
415-421.
10.
Staskawicz,
B.,
Ausubel,
F.,
Baker,
B.,
Ellis,
J.
&
Jones,
J.
(1995)
Science
268,
661-667.
11.
Martin,
G.,
Brommonschenkel,
S.,
Chunwongse,
J.,
Frary,
A.,
Ganal,
M.,
Spivey,
R.,
Wu,
T.,
Earle,
E.
&
Tanksley,
S.
(1993)
Science
262,
1432-1436.
12.
Buck,
L.
&
Axel,
R.
(1991)
Cell
65,
175-187.
13.
King,
M.
W.
&
Moore,
M.
J.
(1994)
Nucleic
Acids
Res.
22,
3990-3996.
14.
Hulbert,
S.
H.
&
Bennetzen,
J.
L.
(1991)
Moi.
Gen.
Genet.
226,
377-382.
15.
Saxena,
K.
M.
S.
&
Hooker,
A.
L.
(1968)
Genetics
61,
1300-1305.
16.
Sudapak,
M.
A.,
Bennetzen,
J.
L.
&
Hulbert,
S.
H.
(1993)
Ge-
netics
132,
119-125.
17.
Mahadevappa,
M.,
DeScenzo,
R.
&
Wise,
R.
(1994)
Genome
37,
460-468.
18.
DeScenzo,
R.
A.,
Wise,
R.
P.
&
Mahadevappa,
M.
(1994)
Mol.
Plant-Microbe
Interact.
7,
657-666.
19.
Jorgensen,
J.
H.
(1992)
Euphytica
63,
141-152.
20.
Gorg,
R.,
Hollricher,
K.
&
Schulze-Lefert,
P.
(1993)
Plant
J.
3,
857-866.
21.
Bonnier,
F.
J.
M.,
Reinink,
K.
&
Groenwald,
R.
(1994)
Phyto-
pathology
84,
462-468.
22.
Maisonneuve,
B.,
Bellec,
Y.,
Anderson,
P.
&
Michelmore,
R.
W.
(1994)
Theor.
Appl.
Genet.
89,
96-104.
23.
Rayapati,
P.
J.,
Lee,
M.,
Gregory,
J.
W.
&
Wise,
R.
P.
(1994)
Theor.
Appl.
Genet.
89,
831-837.
24.
Shepherd,
K.
&
Mayo,
G.
(1972)
Science
175,
375-380.
25.
Ellis,
J.
G.,
Lawrence,
G.
J.,
Finnegan,
E.
J.
&
Anderson,
P.
A.
(1995)
Proc.
Natl.
Acad.
Sci.
USA
92,
4185-4188.
26.
Islam,
M.
R.
&
Sheperd, K.
W.
(1991)
Hereditas
114,
125-129.
27.
Dellaporta,
S.
L.,
Wood,
J.
&
Hicks,
J.
B.
(1983)
Plant
Mol.
Biol.
Rep.
1,
19-21.
28.
Sambrook,
J.,
Fritsch,
E.
&
Maniatis,
T.
(1989)
Molecular
Clon-
ing:
A
Laboratory
Manual
(Cold
Spring
Harbor
Lab.
Press,
Plainview,
NY),
2nd
Ed.
29.
Kumar,
S.,
Tamura,
K.
&
Nei,
M.
(1993)
MEGA:
Molecular
Evolutionary
Genetics
Analysis
(Pennsylvania
State
Univ.,
Uni-
versity
Park),
Version
1.0.
30.
Keim,
P.,
Diers,
B.,
Olson,
T.
&
Shoemaker,
R.
C.
(1990)
Genetics
126,
735-742.
31.
Lander,
E.,
Green,
P.,
Abrahamson,
J.,
Barlow,
A.,
Daly,
M.,
Lincoln,
S.
&
Newburg,
L.
(1987)
Genomics
1,
174-181.
32.
Polzin,
K.
M.,
Lohnes,
D.
G.,
Nickell,
C.
D.
&
Shoemaker,
R.
C.
(1994)
J.
Hered.
85,
300-303.
33.
Stam,
P.
(1993)
Plant
J.
3,
739-744.
34.
Marek,
L.
F.
&
Shoemaker,
R.
C.
(1996)
Soybean
Genet.
Newsl.
23,
in
press.
35.
Diers,
B.
W.,
Mansur,
L.,
Imsande,
J.
&
Shoemaker,
R.
C.
(1992)
Crop
Sci.
32,
377-383.
36.
Weisemann,
J.
M.,
Matthews,
B.
E.
&
Devine,
T.
E.
(1992)
Theor.
Appl.
Genet.
85,
136-138.
37.
Yu,
Y.
G.,
Saghai-Maroof,
M.
A.,
Buss,
G.
R.,
Maughan,
P.
J.
&
Tolin,
S.
A.
(1994)
Phytopathology
84,
60-64.
38.
Pickett,
F.
B.
&
Meeks-Wagner,
D.
R.
(1995)
Plant
Cell
7,
1347-1356.
39.
Martin,
G.,
Frary,
A.,
Wu,
T.,
Brommonschenkel,
S.,
Chun-
wongse,
J.,
Earle,
E.
&
Tanksley,
S.
(1994)
Plant
Cell
6,
1543-
1552.
40.
Loh,
Y.-T.
&
Martin,
G.
B.
(1995)
Proc.
Natl.
Acad.
Sci.
USA
92,
4181-4184.
41.
Dangl,
J.
L.
(1995)
Cell
80,
363-366.
11750
Genetics:
Kanazin
et
aL
... In the pursuit of deeper insights into R-proteins, researchers embarked on the cloning of resistance gene analogs (RGAs) using the degenerate primer technique [22][23][24][25]. This technique was applicable for the R-proteins with prior known conserved motifs (i.e. ...
... This technique was applicable for the R-proteins with prior known conserved motifs (i.e. P-loop to GLPL motifs) along with their degeneracy sequence information and it has been successfully applied in various plant species, such as rice, wheat, maize, barley, and vegetable crops [22][23][26][27]. While the degenerate primer technique has successfully facilitated the identi cation of RGAs, it also possesses several drawbacks such as reduced speci city, increased primer complexity, potential for non-target ampli cation due to the ambiguous nature of degenerate primers, limited coverage as they target conserved regions among related sequences, and the time-consuming process of primer design. ...
... In light of the novel primer design strategy employed in this experiment, which focuses on the speci city of protein domains in qPCR primers, a comparison was conducted with traditional degenerate primers utilized in previous studies as follows. The use of degenerate oligonucleotide primers technique [25,27,40] has signi cantly advanced the cloning of RGAs across a wide range of plant species [18,26] including potato [23], lettuce [41], maize [40], Vitis species [30], sugar beet [26], cotton [37], Avena species [23,36], pearl millet [28], rice [39], soybean [22,38], tobacco [29], cocoa [31], coffee trees [32], western white pine [32], and pepper [34]. This technique relies on the presence of degeneracy in the nucleotide sequence and requires prior knowledge of the degenerate positions within the target sequence for primer design. ...
Preprint
Full-text available
Background: In the quest to identify new resistance genes analogous to those found in other plant species, a novel primer designing strategy is introduced for the first time. Unlike traditional methods that rely on prior information about degeneracy positions, this new approach involves designing primers based on specific domain positions within the candidate resistance gene and eliminates the need for prior knowledge of degeneracy. By using this new approach, it becomes possible to uncover resistance genes and understand their functional interactions with pathogens. Additionally, this approach sheds light on the redundancy and diversity of resistance genes. Notably, this primer designing strategy exhibits remarkable sensitivity, allowing the detection of elusive low-abundance target sequences that were previously challenging to identify using degeneracy-based designs. Results: The qPCR primers, designed using the novel approach of protein domain-specific regions, underwent standardization and validation in endpoint PCR. Subsequent melt curve analysis in qPCR revealed that out of the ten primers tested, six NB-ARC family protein domain-specific qPCR primers (NB-ARC_2, NB-ARC_3, NB-ARC_4, NB-ARC_8, NB-ARC_12, and NB-ARC_17) exhibited a single peak melt curve, indicating precise amplification of the conserved NB-ARC domain of the R-protein. This confirms their specificity and reliability for target detection, enabling the identification of new resistance gene analogues. Conclusion: Our innovative protein domain-specific qPCR primer design approach allows for precise and accurate PCR amplification, overcoming the limitations of traditional degenerate primers. It enables targeted amplification of specific domain regions within resistance proteins, uncovering both conserved domains and novel resistance genes or gene analogs. The use of these primers also captures the redundancy of resistance genes, offering improved accuracy and reliability in target gene identification. This breakthrough represents a significant advancement in molecular biology research and opens new possibilities for identifying resistance gene analogs. To the best of our knowledge, this is the first report of identifying resistance gene analogs using “protein domain-specific” region based qPCR primer design approach.
... Sequence analysis of LaRGAP63 showed 85.40 and 84.29 per cent homology with mosaic virus resistance protein and disease-resistant protein RPP-4, respectively. A number of RGAs were found to be linked with virus, bacteria and nematodes resistance in many crop species (Kanazin et al., 1996;Leister et al., 1996;Yu et al., 1996;Speulman et al., 1998;Spielmeyer et al., 2000). Saha et al. (2013) also identified one molecular marker (sgRGC 18) linked with ToLCNDV resistance in sponge gourd. ...
Article
Full-text available
Yellow mosaic disease caused by Tomato leaf curl New Delhi virus (ToLCNDV) causes 100 percent losses in ridge gourd under epidemic conditions, particularly in the tropics and sub-tropics of India. Plant breeding approaches led by the marker-assisted selection have gained increased momentum in virus resistance breeding to hasten the development of resistant varieties. In the present study, an effort has been made to identify molecular markers linked to yellow mosaic disease resistance loci in an F2 population derived from a cross between susceptible ‘Arka Prasan’ and resistant ‘IIHR-Sel-1’ of ridge gourd. All the molecular markers were amplified in parents, and one polymorphic marker clearly distinguished the contrasting parents. The primers LaRGAP 63 produced a polymorphic DNA fragment that co-segregated with yellow mosaic disease reaction phenotypically in the F2 population. The identified marker will be helpful to the breeders for introgression of resistance loci into the elite background.
... The characteristic motifs of the NB domain have been extensively employed to distinguish the different R protein classes [34] and to define resistance gene homologs in model and crop species [35][36][37]. The NB domain is involved in the controls of protein functioning [38], the binding to the nucleotide ATP enables an active conformation while the binding to ADP determines an inactive conformation [39]. ...
Article
Full-text available
The nucleotide-binding and leucine-rich repeat (NB-LRR) genes, also known as resistance (R)-genes, play an important role in the activation of immune responses. In recent years, large-scale studies have been performed to highlight the diversification of plant NB-LRR repertories. It is well known that, to provide new functionalities, NB-LRR sequences are subject to duplication, domain fusions and acquisition and other kinds of mutations. Although some mechanisms that govern NB-LRR protein domain adaptations have been uncovered, to retrace the plant-lineage-specific evolution routes of R protein structure, a multi-genome comparative analysis was performed. This study allowed us to define groups of genes sharing homology relationships across different species. It is worth noting that the most populated groups contained well-characterized R proteins. The arsenal profile of such groups was investigated in five botanical families, including important crop species, to underline specific adaptation signatures. In addition, the dissection of 70 NB domains of well-characterized R-genes revealed the NB core motifs from which the three main R protein classes have been diversified. The structural remodeling of domain segments shaped the specific NB-LRR repertoires observed in each plant species. This analysis provided new evolutionary and functional insights on NB protein domain shuffling. Taken together, such findings improved our understanding of the molecular adaptive selection mechanisms occurring at plant R loci.
... Among the various classes of R-genes, the nucleotide-binding site-leucine-rich repeat (NBS-LRR) class genes are widely characterized (Cannon et al. 2002). The NBS domain possesses several conserved motifs that provide an opportunity to design oligonucleotides and utilize polymerase chain reaction (PCR)based approaches to search for homologous or similar sequences called RGAs in other related genera or species (Kanazin et al. 1996). Based on this principle, conserved regions of NBS-LRR resistance genes of oil palm and date palm were used to design primers to discover RGAs in the coconut genome (Rajesh et al. 2015). ...
Chapter
Abiotic stressors associated with the climate change phenomenon are a serious threat to crop cultivation, especially in perennials like coconut (Cocos nucifera L.). In this challenging scenario, genomics-based crop improvement strategies are warranted to expedite the development of varieties resistant/tolerant to abiotic stresses and to enhance the rate of genetic gains. The genomic design of abiotic stress tolerance in coconut has not progressed as desired due to multiple challenges, including its perennial nature, breeding behavior, high heterozygosity, and long gestation period. Nonetheless, developments in the field of next-generation breeding approaches involving low-cost sequencing technologies, genotyping-by-sequencing, big-data analysis tools, and other RNA or protein-based genome-wide analyses have offered enormous prospects to design climate-smart coconut capable of withstanding most of the abiotic stresses. This chapter provides a perspective outlook on abiotic stress in coconut, salient accomplishments in adopting various genomic tools to develop stress-tolerant genotypes and a way forward.
... LRR domain interacts with pathogens directly or indirectly. These conserved R gene domains have been used to design the primer to identify and screen resistant gene analogs (RGAs) within or related crops (Kanazin et al. 1996). ...
Chapter
Full-text available
Barley is regarded as the globe’s fourth major cereal crop. A variety of airborne, seedborne, and soilborne infective agents attack barley, causing a variety of barley diseases and substantial losses in agricultural output. Brown and yellow rusts, smut, net blotches, spot blotches, barley yellow dwarf, and molya disease are among the most serious diseases. In general, employing integrated disease management approaches is the best way to handle barley diseases. Growing resistant or tolerant varieties with the fewest foliar fungicides is the most effective approach for barley disease treatments. However, managing soilborne pathogens in barley plants is problematic due to a deficiency in distinguishing symptoms for diagnosis and the absence of fungicides or nematicides that are effective for these pathogens. Recently, nanotechnology has driven the advancement of creative concepts and agricultural productivity with a broad scope for managing plant infections and pests. The antimicrobial properties of metallic and metal oxide nanoparticulates such as silver, selenium, titanium dioxide, zinc oxide, and iron oxide have been extensively researched. In this chapter, we go over barley disease and the role of nanomaterials in reducing the incidence of disease and diagnosis, as well as barley seed germination, physiology, and nutritional quality of barley grain.KeywordsLeaf rust diseaseNet Blotch diseasePowdery mildewBarley yellow dwarfBarley smutSpot blotchFungicidesNanoparticulate
Article
Full-text available
NBARC domain-containing R-genes play a significant role against the pathogen across plant species. Being a conserved domain, R-genes have become the most studied area for pathogen resistance breeding. Pisum sativum is one of the most important pulses and vegetable crops globally. Available genomic and transcriptomic data have been utilized in this study to identify and analyse the NBARC domain-containing R-genes in the Pisum sativum genome. A set of 119 NBARC domain-containing candidate resistance genes have been identified, and an evolutionary relationship has been established, which grouped the R-genes into eight distinct clades. The resistance genes are positioned in twenty-five clusters, and chr05 had the highest number of R-genes. The identified genes had an average of 3 exons and had mostly nuclear localization signals indicative of transcription regulation. Based on conserved domain, they were categorized into six groups of which CNL type was found predominant. Gene expression analysis of the candidate R-genes of Pisum sativum revealed very diverse expression patterns across tissues. Our findings may add comprehensive knowledge and resource to identify disease-resistant genes and linked markers and help in resistance breeding in garden pea.
Chapter
At different stages of host–pathogen interactions, natural conditions and modern agricultural settings foster the evolution of new and virulent pathogenic races by selection pressure on R genes in crucifer genotypes. Polyploidy in Albugo candida contributes to race diversification and evolution by selection pressure imposed from cultivated Brassica species. Molecular studies have revealed high degree of genetic diversity within Albugo pathogenic to Brassica species with wide host range and six new specialized species along with virulent races. ITS and COX2, molecular approaches of phylogenetic analysis and morphological variations of oospores have divided A. candida into three groups (1, 2, 3) infecting different cruciferous with different races. Several races/pathotypes of A. candida have been identified at global level infecting different crucifers. The virulence of A. candida races varies from very narrow to wide infecting different Brassica species and genotypes. Phylogenetic analysis and nucleotide sequences have distinguished races of A. candida infecting different crucifers in different countries of the world. Albugo candida isolates from different crucifers have been characterized based on virulence genes specificity to their hosts in Australia. A single dominant gene AvrAC1 controls avirulence in race 2 of A. candida infecting B. rapa cv. Torch. The genetic variation among geographically distinct isolates of A. brassicae has been assessed using RAPD-PCR markers. The genetic structure of A. brassicicola population indicates the occurrence of sexual recombination’s with a cryptic sexual stage. Mutation of Amr1 gene in A. brassicicola causes increased virulence. Three pathotypes of A. brassicae viz., RM1, RM2, and V3 have been identified using eight commonly cultivated Brassica species. The isolates of A. brassicae collected from different geographical areas are different in conidial morphology, cultural characteristics, and symptom production on different hosts. A brassicae isolates from different locations show high degree of genetic similarity. There is differential protein expression by the isolates of A. brassicae. The isolates of A. brassicae show variability in biochemical contents, sensitivity to different fungicides and plant extracts. The variation in relative levels of virulence in the form of disease incidence, severity, and defoliation within and between Alternaria spp. causing leaf spot disease on rapeseed-mustard has been recorded. Several pathotypes of Alternaria species have been identified with different designations using differential determinant attributes. The function of effectors/genes, ChELP1, and ChELP2 homologs of Lysm proteins has been characterized in C. higginsianum. In India, powdery mildew pathogen produces both asexual and sexual stages on B. juncea, so there is every possibility for Erysiphe cruciferarum to express pathogenic variability. The isolates of Hyaloperonospora parasitica collected from different countries, locations, and host species have been characterized in the form of pathotypes showing host specificity and differential virulence. The isolates of H. parasitica from different host species and genotypes have been molecularly identified as pathotypes with their virulence potential using DNA (RAPD) finger printing. The isolates of H. parasitica collected and characterized on different crucifers as race and pathotypes cannot be compared in the absence of use of standardized host differentials (isogenic lines) and uniform designation/nomenclature system at international level. Hybridization of Hyaloperonospora isolates has also been observed. The evolution of virulent races in Leptosphaeria has been observed in the areas where crops with major gene resistance coupled with genetic uniformity have been sown. The avirulence genes of L. maculans races have been identified from Europe. Leptosphaeria maculans field populations have high level of gene diversity and genotypic diversity in France. An avirulent mutated AvrLm gene of L. maculans has been identified to detect new R genes for breeding blackleg resistant cultivars of Brassica. The genome-wide DNA variants and SNP haplotypes of L. maculans pathotypes have been identified. Pathotypes of Plasmodiophora brassicae has been identified from various countries using different differential host sets and designating pathotypes in their own way without following any sound uniform system of nomenclature. The genomes of P. brassicae pathotypes Pb2, Pb5, and Pb8 have been sequenced to gain insight into genome variations and its correlation with host specificity. Proteomics of P. brassicae has revealed potential effectors/genes of pathotypes. The isolates of P. brassicae from different countries have been phylogenetically analyzed for similarities and differences of pathotypes. Polymorphism in P. brassicae in case of Korean isolates collected from field has been recorded. The Cr811 gene in P. brassicae pathotypes P5 plays an important role as molecular markers for identification of race P5 and other new virulences. The different geographical isolates of P. brassicae have been identified by rDNA sequence polymorphism. Pathogenic and genetic variations in P. brassicae pathotypes infecting a weed C. flexuosa and crucifers have been analyzed using RAPDs in Japan. The virulence of P. brassicae changes during interaction with CR genes in the host cultivars. The changes in P. brassicae pathotypes structure under field conditions have been analyzed through whole genome DNA similarity sequences. The application of RAPD seq has revealed distinct P. brassicae populations under Canadian conditions. ITS sequencing and phylogenetic analysis of P. capsellae isolates from Brassica hosts clearly differentiated them in to virulence factors/races. High level of genetic diversity and recombination’s in Sclerotinia populations in tropical countries has been identified with the groups of isolates as MCGs and MLHs infecting crucifers. The aggressiveness of Sclerotinia isolates on Brassica has been measured using AUDPC as one of the parameters. The sequence variation of ITS region of S. sclerotiorum isolates revealed higher heterogenecity and genetic variability showing presence of clonal and sexual progenies of the pathogen in B. juncea growing areas of India. The genetic diversity is higher in Sclerotinia populations from Canola and Sunflower in the NCUS. Distribution of microsatellite haplotypes regions is often on multiple crops. The genetic diversity and populations structure of S. sclerotiorum has been analyzed with DNA (RAPD) sequences from different countries of the world. The virulence factors of Sclerotinia has been identified on the basis of pathogenicity as virulent pathotypes (aggressiveness), genomic factors (dsRNA) controlled Hypo virulence, MCGs, cloned variables, haplotypes, genetic diversity of isolates, and population biology studies with markers such as MCGs, DNA finger printing or micro satellites. The TuMV isolates collected from different hosts could not be characterized on Brassica species for virulence. The molecular characterization of isolates from different species of Verticillium indicated that isolates infecting crucifers are long spored (amphihaploids or allodiploids) because of Verticillium interspecific hybridization events. Black rot of crucifers’ bacterium has been characterized into pathovars of X. campestris on different species of crucifers. The X. campestris isolates from oilseed rape are more genetically diverse with specialization to their nonhost than their Brassica hosts. The virulent effectors/genes of pathogens have different evolutionary mechanisms to counteract with the defense mechanisms of the host. The identification, classification, and utilization of crucifer’s pathogen pathotypes from different countries is most challenging task since scientists have not followed standardized uniform system and procedures in selecting host differentials, designation and nomenclature of pathotypes, pure isolates (single spore), and scoring of infection phenotypes. The expression of high genetic diversity by crucifers’ pathogens under natural as well as modern agriculture settings is another challenge of variation in pathogenic populations.KeywordsPathogenomicsPathogenic variabilityAlbugo PathogenomicsPolyploidy to Albugo race diversificationPhylogenetic relationshipVirulence spectrum of virulence genesInheritance of avirulenceAlternaria pathogenomicsGenetic variationMutation of Amr1 geneEvolution of virulenceIdentification of pathological variationsColletotrichum pathogenomicsErysiphe pathogenomicsIdentification of pathotypesHyaloperonospora pathogenomicsIdentification of virulence effectors/genesHeterothallism and homothallismLeptosphaeria pathogenomicsEvolution of virulenceAvirulence allelesGenetic variabilityCharacterization of avirulent mutated AvrLm geneIdentification of genome-wide DNA variantsSNP haplotypePlasmodiophora pathogenomicsDifferentials for identification of pathotypesGenome comparison of pathotypesProteomics of pathotypesPhylogenetic analysis of pathotypesMolecular marker for identificationIdentification of geographical isolatesPathogenic and genetic variationWhole genome DNA of pathotypePseudocercosporella pathogenomicsIdentification of virulence factors/racesSclerotinia pathogenomicsGenetic diversityStructureRandom amplified polymorphic DNA markersTurnip mosaic virus pathogenomicsPathological and biological characteristicsVerticillium pathogenomicsXanthomonas pathogenomicsIdentification of pathotypesEvolution of virulent effectorsChallenges of pathotype identificationGenetic variation
Chapter
Crucifer’s crops are very important crop commodity with very significant contribution in the world’s need of human and animals yielding edible and industrial oil along with vegetables and forage crops of economic, trade, and food security. These groups of crops are challenged by numerous pathogens threatening their production at global level. Some of them have been exploited to reveal genomics of host–pathogen interaction to comprehend the molecular mechanisms of infection and pathogenesis for better management of diseases they cause and influence yield and quality losses. The genome of six Brassica crops has been sequenced after the sequencing of Arabidopsis thaliana genome. The genome size of A. thaliana is smallest with 125 Mb and B. napus genome is largest with 925 Mb. To get deeper insight into the molecular and biological functions of host–pathogen interaction, genome of major pathogens of crucifers has been sequenced and analyzed. Among pathogens, P. brassicae genome is compact and smallest with 24.2–25.5 Mb and G. orontii genome causing powdery mildew is largest with 160 Mb in size. The genome analysis of pathogens has facilitated phylogenetic, host specificity, pathogenicity factors/genes, and molecular events during crucifer’s host–pathogen interactions. The perception and understanding of molecular and genetical mechanisms of host–pathogen recognition and events of pathogenesis are regulated by genomics modulation of interacting host and pathogen. Pathogenomics have revealed host specificity, change of host range, and evolution of pathogenic variability. The R genes regulate functions, and molecular mechanisms of host–pathogen interactions. The timing of the induction of genes in R, and S varieties is crucial for mounting effective host defense to the pathogens. Transcriptomic analysis has revealed genes strongly associated with pathogenesis. The genes involved in cell response signaling, cell wall degradation, protein degradation, enzymes production, host transcriptional, and hormonal regulation are differentially expressed. The virulence mechanisms are transition period from biotrophy to necrotrophy to facilitate the acquisition of host nutrients by the pathogen. Differential expression of up and down regulated genes, and functional groups has been identified during host–pathogen interaction. The whole genome analysis of host and pathogen offers potential unbiased insight into the molecular mechanisms of host-pathosystem in crucifers.For rapid, accuracy, less laborious, and less expensive way of detection and identification of crucifer’s pathogens, pathotypes, and effectors genes, molecular protocols have been used at field, and controlled laboratory conditions. The distinct lineages that had diverged from each other have also been detected. Molecular markers (RAPD, ESTs) have been used for detection and identification of genetical, and pathological variation and clades of pathogens. The molecular approaches have been allowed detection, identification, and quantification of pathogens host cell entry, area covered by pathogens in host tissues, seed, and soil. The RT-PCR protocols have been developed to detect, and discriminate AGs of R. solani from field, soil, and viruses along with their strains from Brassica species. High degree of genetic diversity has been recorded in major pathogens of crucifers in the form of pathotypes/races/strains differing in virulence, and host range on Brassica species and genotypes. The evolution of new virulence’s of pathogens is more common in areas where crops with major gene resistance coupled with genetic uniformity have been sown. The genomes of P. brassicae pathotypes Pb2, Pb5, and Pb8 have been sequenced to gain insight into genome variations and its correlations with host specificity. Phylogenetics of pathotypes has been assessed. The changes in pathotypes structure under field conditions have been analyzed through whole genome DNA similarity sequences. The infection of pathogens in Brassica species activates host metabolism to regulate carbohydrates, respiration, lipid profile, enzymes, toxins, H2O2, OH, phenols, hormones, nucleic acid, proteins, electrolytes, GSL, ROS, and other metabolites which affects crucifer’s physiology, biochemistry, and molecular events leading to pathogenesis. Several genes are differentially expressed during host–pathogen interaction for virulence at different stages of host infection and disease development. The genomics of crucifers host-pathosystem has been studies with simple, reproducible, and standardized methodology to elaborate genome sequencing of host as well as pathogen, events of host and pathogen interaction, signaling pathways, expression of different genes, analysis of transcriptome, biochemical changes, pathogenic variability, molecular markers, transition period from biotrophy to necrotrophy, detection, and identification of pathogens, pathotypes, and genes during the process of infection and pathogenesis of crucifer cops.KeywordsCrucifer’sPathogensGenomicsGenome sequencingEvents of host–pathogen interactionOmics technologiesSignaling pathwaysQRT-PCRMolecular functionsChoice of reference genesPathogens genome assemblyHost–pathogen recognitionPathogenomicsDifferential proteins/genes expressionHost-transcript regulationTransition from biotrophy to necrotrophyHormonal regulationGene expressionSpliceosome genesEvolutionary genomicsMolecular detection and identification of pathogensPathotypes and genesMolecular markersGenomic prediction toolsCorrect diagnosisSelection pressureRace virulenceHost differentialsProteomicsAggressivenessHypovirulenceMetabolomicsToxinsHost-biochemistryAltered metabolismProtocolsMethodologyTechniques
Article
Poncirus polyandra, a plant species with extremely small populations in China, has become extinct in the wild. This study aimed to identify functional genes that improve tolerance to abiotic and biotic stresses. Here, we present a high-quality chromosome-scale reference genome of P. polyandra. The reference genome is 315.78 Mb in size, with an N50 scaffold size of 32.07 Mb, and contains nine chromosomes with 20,815 protein-coding genes, covering 97.82% of the estimated gene space. We identified 17 rapidly evolving nucleotide-binding-site (NBS) genes, three C-repeat-binding factors (CBF) genes, 19 citrus greening disease (Huanglongbing, HLB) tolerance genes, 11 citrus tristeza virus (CTV) genes, and one citrus nematode resistance gene. A divergence time of 1.96 million years ago was estimated between P. polyandra and P. trifoliata. This is the first genome-scale assembly and annotation of P. polyandra, which will be useful for genetic, genomic, and molecular research and provide guidance for the development of conservation strategies.
Chapter
In plant-pathogen interactions, signal activation and transduction confer resistance in plants against various pathogens. Communication between host and pathogen is the prime step for a pathogen to cause infection. The molecular basis of pathogen response in plants depends on the pathogen types. Hypersensitive reactions usually result from Avr-R interactions that restrict pathogens’ development through cell death. These avr genes can be recognized directly and indirectly by the resistance (R) gene. The NBS-LRR family is an important resistance gene (R gene) family in plants, which is divided into subclasses. Resistant gene analogues (RGAs) are candidates for R genes that have a significant role in defense response against disease-causing pathogens and are classified into two classes. The first class is based on the immediate recognition of a pathogen called resistance genes (R genes), while the second class is based on the defense response generated by recognition events. Hence, this chapter attempts to delineate a comprehensive overview of resistance genes, their classes, identification, and characterization in plants.KeywordsDisease resistanceR genesResistance genes analogsPlant-pathogen interaction
Article
Full-text available
An F2 oat population was produced by crossing the diploid (n=7) species Avena strigosa (CI 3815) with A. wiestii (CI 1994), resistant and susceptible, respectively, to 40 isolates of Puccinia coronata, the causal agent of crown rust. Eighty-eight F2 individuals were used to construct an RFLP linkage map representing the A genome of cultivated hexaploid oat. Two hundred and eight RFLP loci have been placed into 10 linkage groups. This map covers 2416 cM, with an average of 12 cM between RFLP loci. Eighty-eight F3 lines, derived from F2 individuals used to construct the map, were screened for resistance to 9 isolates of P. coronata. One locus, Pca, was found to confer a dominant resistance phenotype to isolates 203, 258, 263, 264B, 290, 298, 325A, and 345. Pca also conferred resistance to isolate 276; however, an unlinked second gene may also be involved.
Article
Two resistances to downy mildew derived from Lactuca serriola were characterized genetically and mapped using molecular markers. Classical genetic analysis suggested monogenic inheritance; however, the presence of multiple, tightly-linked genes in each case could not be eliminated. Therefore, they were designated resistance factors R17 and R18. Analysis with molecular markers known to be linked to clusters of resistance genes quickly revealed linkage of R18 to the major cluster of resistance genes and provided six linked markers, three RAPD (Random Amplified Polymorphic DNA) markers and three codominant SCAR (Sequence Characterized Amplified Region) markers. The mapping of R17 required the screening of arbitrary RAPD markers using bulked segregant analysis; this provided five linked markers, three of which segregated in the basic mapping population. This demonstrated loose linkage to a second cluster of resistance genes and provided additional linked markers. Two RAPD markers linked to R17 were converted into SCARs. The identification of reliable PCR-based markers flanking each gene will aid in selection and in combining these resistance genes with others.
Article
Restriction fragment length polymorphisms (RFLPs) and microsatellites or simple sequence repeats (SSRs) were used as genetic markers to identify the chromosomal location of Rsv, a gene conferring resistance to soybean mosaic virus (SMV). An F 2 population was constructed from a cross between soybean line PI 96983 as the resistant parent and cultivar Lee 68 as the susceptible parent. Twenty-five RFLP and three SSR loci, polymorphic between the parental lines, were analyzed in 107 F 2 individuals. Genotypes of Rsv were determined by inoculating F 2.3 progeny RACT with the G1 strain of SMV. Data also were collected for an additional soybean gene (w1/W1), which controls anthocyanin pigmentation in hypocotyls and flowers [...]