Biotechnology and gene mapping in lentil

Abstract

Genomic tools and genetic mapping are assisting the understanding of the lentil genome and have made possible the use of marker assisted selection for breeding purposes. Although some important traits are conferred by single genes most are determined by quantitative trait loci (QTL) and influenced by environmental factors. Genes for several traits have been genetically mapped and shown to be linked to molecular markers. These include resistance to fusarium wilt, ascochyta blight, anthracnose, and stemphylium blight. Winter hardiness and tolerance to frost have also been mapped. It is now feasible to use the linked markers in a marker assisted selection breeding program. Proteomics and metabolomics are emerging technologies that can be used to better characterize the functional mechanisms behind breeding targets.

Full text

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Biotechnology and gene mapping in lentil
by Rebecca FORD1*, Barkat MUSTAFA1, Prabhakaran SAMBASIVAM1, Michael BAUM2and P.N. RAJESH
Abstract: Genomic tools and genetic mapping are
assisting the understanding of the lentil genome
and have made possible the use of marker assisted
selection for breeding purposes. Although some
important traits are conferred by single genes
most are determined by quantitative trait loci
(QTL) and influenced by environmental factors.
Genes for several traits have been genetically
mapped and shown to be linked to molecular
markers. These include resistance to fusarium wilt,
ascochyta blight, anthracnose, and stemphylium
blight. Winter hardiness and tolerance to frost
have also been mapped. It is now feasible to use
the linked markers in a marker assisted selection
breeding program. Proteomics and metabolomics
are emerging technologies that can be used to
better characterize the functional mechanisms
behind breeding targets.
Key words: abiotic stress resistance, disease
resistance, functional genes, genetic mapping,
metabolomics, molecular markers, proteomics,
quantitative trait loci, recombinant inbred lines
Introduction
Significant advances in the availability of
genomics tools towards understanding the
function and selection of specific
components of the lentil genome have
recently been made. Several advanced
breeding programs worldwide have
implemented and are currently using
molecular assisted breeding technology.
However, this has to date been limited to the
selection of rather few traits, mostly likely
due to lack of resources for broad validation
and implementation. Nevertheless, high
throughput marker generation and
genotyping that is functionally associated,
together with novel tools such as next
generation sequencing and available genome
maps, are illuminating the complex and
intertwined nature of responses to biotic and
abiotic stimuli in the lentil genome.
________________________________________________________________________________________________________
1The University of Melbourne, Sustainable Society
Institute / Melbourne School of Land and
Environment, Melbourne, Australia
(rebeccaf@unimelb.edu.au)
2ICARDA, Aleppo, Syria
3USDA-ARS and Department of Crop and Soil
Sciences, Washington State University, Pullman,
USA
Genomics and functional gene
identification
Global gene expression profiling at the
mRNA level has been used to identify
functionally-associated genes. Characte-
rization of the RNA population under a
particular environmental and/or
developmental condition enables
understanding of the dynamic functioning of
genes as well as their mutual role in specific
regulatory networks. This approach may be
used to dissect regulatory mechanisms and
transcriptional networks involved in defence
responses to pathogen and physiological
responses to abiotic stress such as drought,
cold and salinity.
Differential gene expression methods
include cDNA-amplified fragment length
polymorphism (cDNA-AFLP) (1),
suppression subtractive hybridization (SSH)
(6), serial analysis of gene expression
(SAGE) (30), differential display (18.31),
massively parallel signature sequencing
(MPSSTM) and microarray technology (25).
Of these, microarrays have become the
method of choice for large scale systemic
analysis of differential gene expression
profiling. This method is semi-quantitative,
sensitive to low abundance transcripts that
are represented on a given array and has
been successfully used to study plant
responses to various biotic and abiotic
factors in Arabidopsis thaliana (3, 22,26),
Medicago truncatula (11,17), soybean (Glycine
max) (19,28) and chickpea (Cicer arietinum) (5,
20).
Most recently, this method was used to
elucidate the functional response to attack
from Ascochyta blight, caused by Ascochyta
lentis Vassilievsky, an important fungal
disease worldwide (8).
Differentially expressed genes were identified
among resistant (Ill7537) and susceptible
(ILL6002) genotypes, which may serve as
accurate selection tools in the future
development of varieties with increased and
sustainable resistance. For this, a cDNA
microarray was used to observe substantial
difference in functional category and timing
of gene expression among the two
genotypes, often referred to as the
Pathogen/Microbe-Associated Molecular
Pattern (P/MAMP). In particular, large
differences were observed in early up-
regulation of Resistance Gene Analogues
(RGA; Figure 1), as well as several classes of
mycotoxic producing genes such as PR4 and
PR10.In ILL7537 (resistant), RGAs were
switched on very early and quickly down
regulated before being up-regulated again.
Conversely, the same genes were up-
regulated 24 hours later in ILL6002
(susceptible) and at much higher levels. Thus
the question arises as to whether these genes
act as „surveillence molecules‟ or
recognition/receptors to quickly initiate
subsequent defence signalling cascades in the
resistant genotype and it‟s a case of a little
too much, too late in the susceptible
genotype? Perhaps the failure to quickly
recognise the invading pathogen prior to
colonisation leads to the high susceptibility
response.
Similarly, in the early stage of invasion,
several other classes of defence responses are
seen to be initiated much faster in the
resistant ILL7537 genotype. In fact, the
classic symptoms associated with an
hypersensitive response (HR), such as
browning of tissue and necrosis around the
point of invasion, is not seen at all in
ILL6002, and less frequently in ILL5588 (cv.
Northfield; moderately resistant), when
compared to ILL7537. Early evidence of this
differential response is seen by tracking
expression of superoxide dismutase, a
enzyme used in the “mopping up” process
of reactive oxygen species (ROS) following
an oxidative burst, whereby the gene is
expressed much sooner and at higher levels
in ILL7537 than in ILL6002 (Figure 2).
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A major current limitation of microarray
technology for lentil is the lack of pre-
requisite lentil-specific functional genome
data (cDNA/EST sequences) to place as
probes upon the arrays. However, several
research teams (AgriFood, Canada and
VicDPI, Australia) are preparing large lentil
EST data sets, as well as developing single
nucleotide polymorphism (SNP) markers
that may be used for genotype-phenotype
association and validation study. Once these
tools are available, high-throughput
functional genomic assessment using arrays
will be next leap in lentil biotechnology
towards faster, smarter and more sustainable
trait selection. However, prior to the
accurate use in selection programs of
molecular markers, that have been
functionally validated, their genomic
positioning is required.
Mapping the lentil genome
Although some agronomically important
traits are governed by single genes, most are
governed by quantitative trait loci (QTL),
influenced by both genetic and
environmental factors. Since the expression
of a QTL is likely to vary among populations
and environments, their genomic location
and effect must be determined for a specific
genetic background and environment (2).
The previous “orphan” status of the lentil
genome has meant that most existing
framework genome maps contain many non-
functional RAPD, AFLP, ISSR and SSR-type
markers, which are effective for saturating
the entire genome but are not directly related
to desirable traits or QTL. However, the
newly developed gene/locus specific EST
and SNP markers are reproducible and
represent definite genomic regions. Their
placement on existing maps will draw
together the functional and physical
association for ultimate accurate trait
selection.
Mapping the lentil genome
Although some agronomically important
traits are governed by single genes, most are
governed by quantitative trait loci (QTL),
influenced by both genetic and
environmental factors. Since the expression
of a QTL is likely to vary among populations
and environments, their genomic location
and effect must be determined for a specific
genetic background and environment (2).
The previous “orphan” status of the lentil
genome has meant that most existing
framework genome maps contain many non-
functional RAPD, AFLP, ISSR and SSR-type
markers, which are effective for saturating
the entire genome but are not directly related
to desirable traits or QTL. However, the
newly developed gene/locus specific EST
and SNP markers are reproducible and
represent definite genomic regions. Their
placement on existing maps will draw
together the functional and physical
association for ultimate accurate trait
selection.
The current status of marker-
assisted breeding
Using non-functional markers (7), there
were first mapped five QTL for height of the
first ramification, three for plant height, five
for flowering, seven for pod dehiscence, one
for shoot number and one for seed diameter.
Subsequently, QTL have been identified
conditioning winter survival and injury,
however, only one of five QTL was
expressed in all environments assessed. QTL
conditioning resistance to ascochyta blight
(23), stemphylium blight (24), rust and white
mould have also been mapped. Also, the
major QTL underpinning physical seed
quality traits such as size, shape and colour
have been mapped (Inder et al., Melbourne
University, unpublished). However, ideally,
the “candidate” gene(s) actually controlling a
trait of interest would be used for marker-
assisted selection (MAS). Hence, genomic
regions where the trait is mapped should be
characterized at high resolution (since
recombination rates may vary at different
genomic regions) and be validated across
genetic backgrounds, in order to determine
their utility in MAS and to potentially
uncover the functional gene(s) themselves.
This has been made more of a possibility
with next generation sequencing of genomic
fragments, such as BACs, associated with the
QTL region of interest.
Figure 1. The differential timing of expression of RGA sequences among seedlings inoculated
and un-inoculated with
Ascochyta lentis
(left) ILL7537 and (right) ILL6002 genotypes
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Figure 2. Evidence of (left) a differential early HR between ILL7537 and ILL6002 to
Ascochyta lentis inoculation and (right) HR symptoms seen in ILL7537 including browning,
necrosis, cell wall thickening (CWT) and cytoplasmic aggregation (CA).
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
6h 24h 48h 72h 96h
IlLL7537 (R)
ILL6002 (S)
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
6h 24h 48h 72h 96h
IlLL7537 (R)
ILL6002 (S)
Differential expression ratio
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Meanwhile, there are several markers
available for different traits that have the
potential for use in MAS and gene
pyramiding (Table 2). These include
SCARW19 and SCARB18 linked to and
flanking the AbR1A. lentis resistance loci
(27). These enabled successful pyramiding of
the AbR1 and ral2A. lentis resistance loci
together with the LCt2Colletotrichum
truncatum (anthracnose) resistance loci (23).
Most recently the sequence related amplified
polymorphism (SRAP) marker, ME4XR16c,
has been validated for utility in selecting
resistance to stemphylium disease (24).
The future of lentil
biotechnology
Without doubt, reports using
biotechnology approaches such as
proteomics and metabolomics will soon
begin to emerge for lentil, in order to
discover and better characterize the
functional mechanisms behind the breeding
targets. This will include a thorough
investigation of pathogen effector and host
recognition factors involved in disease
defence. In particular, the whole genome
sequence of the Ascochyta lentis genome has
recently become available and is currently
being annotated (Ford and Lichtensvieg,
unpublished). This will be searched for
possible effector-related sequences in
comparative studies for respective gene
expression and protein/metabolite molecules
to determine lentil host recognition factors.
Also, it is envisaged that next generation
sequencing technologies will uncover
families of host transcription factors (i.e. Myb
genes) and downstream genes that are key in
the specific biochemical pathways for many
stress tolerance and quality traits. With the
advancement in functional genomics,
expression QTL (eQTL) can be identified
for the traits of interest by coupling global
genome expression profiling and suitable
genetic materials. Since eQTL affect the
expression of the genes for the trait of
interest, the markers linked to this eQTL will
have enormous reliability in MAS compared
to the markers identified by traditional QTL
analysis. Ultimately, and with sufficient
funding, precise formulation of superior and
high yielding genotypes will emerge through
the combination of lentil „omics‟ approaches
that will be delivered to a multitude of
environments and market preferences. ■
Table 1 Published genetic linkage maps for lentil; mapping populations and types of markers
mapped
Population mapped
Marker types mapped
Citation
Interspecific F2
RFLP, isozymes, morphological
Havey and Muehlbauer, 1989
Inter-subspecific RIL
RFLP, RAPD, AFLP
Eujayl et al., 1998
Intraspecific F2
RAPD, ISSR,
Rubeena et al., 23
Intraspecific RIL
RAPD, ISSR, AFLP
Kahraman et al., 24
Inter-subspecific F2
RAPD, ISSR, AFLP, SSR
Durán et al., 24
Inter-subspecific RIL
AFLP, SSR
Hamwieh et al., 25
Intraspecific RIL
SSR, ITAP
Phan et al., 27
Intraspecific RIL
SSR, RAPD, SRAP
Saha et al., 2010
Table 2 Molecular markers closely associated with desirable lentil breeding traits for use in
marker-assisted selection
Trait mapped
Associated molecular markers
Citation
Fusarium wilt resistance (Fw)
OPK15
Eujayl et al., 1998
Ascochyta blight resistance (AbR1)
RV01, RB18, SCARW19
Ford et al., 1999
Ascochyta blight resistance (ral2)
UBC227, OPD-10
Chowdury et al., 2001
Ascochyta blight resistance (mapped as a QTL)
C-TTA/M-AC (QTL1 and QTL2), M20 (QTL3)
Rubeena et al., 2003
Anthracnose resistance (Lct2)
OPE06, UBC704
Tullu et al., 2003
Frost tolerance (Frt)
OPS-16
Eujayl et al., 1999
Winter hardiness
UBC808-12
Kahraman et al., 2004
Fusarium wilt resistance (Fw)
SSR59-2B, p17m30710
Hamwieh et al., 2005
Stemphylium resistance
SRAP ME5XR10 and ME4XR16c
Saha et al., 2010
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