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MOLECULAR PROFILING OF RICE
(Oryza sativa L.) ACCESSIONS FOR
RESISTANCE TO BIOTIC STRESSES
YASHASHWINI N.
PAK-9241
DEPARTMENT OF PLANT BIOTECHNOLOGY
UNIVERSITY OF AGRICULTURAL SCIENCES
BENGALURU-65
2011
MOLECULAR PROFILING OF RICE
(Oryza sativa L.) ACCESSIONS FOR
RESISTANCE TO BIOTIC STRESSES
YASHASHWINI N.
PAK-9241
Thesis submitted to the
University of Agricultural Sciences, Bangalore
in partial fulfilment of the requirments
for the award of the Degree of
Master of Science (Agriculture)
in
PLANT BIOTECHNOLOGY
BANGALORE
JULY, 2011
Dedicated to my
Beloved Parents,
Sister and Friends
DEPARTMENT OF PLANT BIOTECHNOLOGY
UNIVERSITY OF AGRICULTURAL SCIENCES
BANGALORE 560065
CERTIFICATE
This is to certify that the thesis entitled “MOLECULAR PROFILING
OF RICE (Oryza sativa L.) ACCESSIONS FOR RESISTANCE TO
BIOTIC STRESSES” submitted in partial fulfilment of the requirments
for the degree of MASTER OF SCIENCE (AGRICULTURE) in PLANT
BIOTECHNOLOGY to the University of Agricultural Sciences, Bengaluru
is a bond fide record of research work done by Ms. YASHASHWINI. N
during the period of her study in this University under my guidance and
supervision and the thesis has not previously formed the basis for the
award of any degree, diploma, associateship, fellowship or other similar
titles.
Bangalore
July, 2011
(Dr. H. V. VIJAYAKUMARA SWAMY)
Major Advisor
Professor
APPROVED BY:
CHAIRMAN :
_________________________________
(Dr. H. V. Vijayakumara Swamy)
MEMBERS : 1. _________________________________
(Dr. H.E. Shashidhar)
2. _________________________________
(Dr. T.H. Ashok)
3. _________________________________
(Dr. N. Eranna)
4. _________________________________
(Dr. Chandrashekar Reddy)
ACKNOWLEDGEMENT
The task of acknowledging the help that was offered to me
throughout this study by my teachers and friends is bigger than the study
itself. I feel scanty of words to the magnitude of their help. I could not
have completed this work without enjoying their endless patience and
affection. Under this decorum I would like to recall all of them with utmost
gratitude.
I place on record my deep sense of gratitude with utmost sincerity
and heartfelt respect to the esteemed chairman of my Advisory Committee
Dr. H.V. Vijaykumaraswamy, Professor, Department of Plant Biotechnology,
College of Agriculture, G.K.V.K, Bengaluru, for his valuable guidance, cooperation, encouragement, help and moral support throughout the period of
my study. I admit that it has been a great fortune for me to be associated
with him during my degree programme.
With immense pleasure and deep respect I express my deep sense of
gratitude to Dr. H.E. Shashidhar, Professor, Department of Plant
Biotechnology, College of Agriculture, G.K.V.K, Bengaluru,my Advisory
Committee
member
for
his
valuable
guidance,
constant
help
and
encouragement throughout the course of my study.
I am very much grateful to my Advisory Committee members
Dr. T.H. Ashok, Professor and Head Department of Plant Biotechnology,
Dr. N. Earanna, Associate Professor, Department of Plant Biotechnology,
Dr. Chandrashekar Reddy, Professor and Head Department of Crop
physiology, for their constant supervision, valuable guidance and all the
facilities extended during the course of my research programme.
I owe a lot to my parents Chennappa, N. Gowda and Devaki, B. sister
Sushma, N. and Cousin Harish Kumar Kallegawithout whose love, affection
and blessings my research work would scarcely have been accomplished.
I express deep sense of feeling and cordial thanks to my labmates
Naveen, RakhiSoman, Askok, Suma, Raveendra, Berhanu, Vimersh, Swaroopa,
Divya and Suprabha, for their helpand suggestion without them it would been
difficult in completing the task undertaken.
I extend my special thanks to all my senoir friends YashaswiniSharma,
Wajeed, Ningaraju N. and Sourabh Joshi for their valueable suggestions.
Indeed, it is very difficult to forget the company of my beloved
friends, Shivaraj, ShwethaK.,Kuldeep, Shreedevi,Priyanka,Shwetha Y., Thuy,
Prashant, Sandeep, Sujatha, ,Savitha,YashaswiniK S., Manjunath S B.,
VinayakPiseand others whose company encouraged me a lot during my stay
here.
I owe special thanks to DBT-HRD for providing me fellowship
throughout my degree programme.
I thank teaching and non-teaching faculty of the Department of Plant
Biotechnology for their kind co-operation and encouragement during my
study and research.
I thank Raju,Tulasamma, Radhamma, Bailamma and others for their
timely help during my field work. I wish to thank all those hearts that helped
me in one or the other way during my course of study and research.
Finally I thank all those who are involved in helping me directly or
indirectly to attain this stage.
Bengaluru
July, 2011
(Yashashwini. N)
MOLECULAR PROFILING OF RICE (Oryza sativa L.)
ACCESSIONS FOR RESISTANCE TO BIOTIC STRESSES
THESIS ABSTRACT
The present study was conducted to evaluate diverse germplasm
accessions of rice for phenotypic traits under aerobic condition and also
to conduct molecular profiling for genes associated with biotic stress
resistance. Wide range of variations was observed for phenotypic traits
and also for molecular banding pattern among the 101 accessions when
evaluated under aerobic condition. Six genes/primers for which distinct
alleles were found among the genotypes were PiKh, Bph3, Bph 20(t),
Bph21(t),
xa
13,
and
OsPR#021.
Using
the
data
available
in
bioinformatics websites like Mapviewer of NCBI and Gramene site the
genes were located on chromosomes. While Xa4 and Xa21 were located
on chromosome 11, xa13 was located on chromosome 8 and xa5 on
chromosome 5 respectively. Pi2 and PiKh were placed on chromosome on
6 and 11 respectively. Bph3 and Bph18 were located on chromosomes 11
and 12. The location of the candidate genes chosen was also discerned.
OsPR#011 and OsPR#012 were located on chromosome 1, OsPR#021
and OsPR#022 on chromosome 2, OsPR#051 and OsPR#052 on
chromosome
5,
OsPR#071,
OsPR#072,
OsPR#073,
OsPR#074
on
chromosome 7, OsPR#101 on chromosome 10 and OsPR#121 on
chromosome 12.
Gene annotation was done using Gramene, TIGR,
Ensembl site and PAM site. Xa21 has genomic sequence of 3585 bases,
CDS of 2997, protein sequence of 998 amino acids.
The functional
component was production of a protein Kinase 5 precursor putative
expressed. Candidate gene primers were designed for the genes. Xa21
primer had a product size of 759bp, xa5 had 411bp, Xa4 had 487bp. All
the data were pooled in order to make a molecular profiling for biotic
stress by giving description of all primers on different chromosomes of
rice.
Signature of the Student
Signature of the Major Advisor
CONTENTS
CHAPTER
TITLE
PAGE No.
I
INTRODUCTION
1
II
REVIEW OF LITERATURE
5
III
MATERIAL AND METHODS
36
IV
EXPERIMENTAL RESULTS
51
V
DISCUSSION
69
VI
SUMMARY
78
VII
REFERENCES
81
APPENDICES
LIST OF TABLES
Table
No.
Title
Page
No.
1
List of genotypes used for screening
37-40
2
List of primers with annealing temperature used to
screen rice accessions for biotic stress
44-45
3
Correlation for grain yield and yield parameters
4
List of primers for genotypes showing resistance to
biotic stress
5
List of primers showing allele mining among genotypes
6
Details of annotation of genes for biotic stress
7
Designing Primer for candidate gene from CDS region of
the gene sequence
53
55-58
59
65-66
68
LIST OF PLATES
Plate
No.
Title
Between
Pages
1
Steps involved in finding chromosomes location using
Mapviewer
49-50
2
Steps involved in gene annotation
49-50
3
Steps involved in protein domain structure
49-50
4
Overview of genotypes in experimental plot under
aerobic condition
51-52
5
Gel picture showing polymorphic bands
54-55
6
Gel picture showing monomorphic bands
54-55
7
Allele mining of rice accession for different primers
58-59
8
Location of Chromosomes using Mapviewer
59-60
9
Pie chart diagram of all 12 chromosomes of rice
67-68
Introduction
I. INTRODUCTION
Rice is the staple food for about 2.5 billion people, covering nine
percent of the earth's arable land. Rice provides 21% of global human per
capita energy and 15% of per capita protein. Calories from rice are
particularly important in the Asian countries, especially among the poor,
accounting for 50-80% of daily caloric intake.
Asia accounts for over
90% of the world's production of rice, with China, India and Indonesia
producing the most.
Rice belongs to the genus Oryza and has two cultivated and 22
wild species. The cultivated species are Oryza sativa and Oryza
glaberrima. O. sativa is grown all over the world while O. glaberrima has
been cultivated in West Africa for the last ~3500 years. Rice is grown
under many different conditions and production systems. Submerged
cultivation is the most common method used worldwide. Rice is the only
cereal crop that can grow for long periods of time in standing water. 57
percent of rice is grown on irrigated land, 25 percent on rainfed lowland,
10 percent on the uplands, 6 percent in deep-water and 2 percent in
tidal wetlands.
Just as rice can be grown in many different environments, it has
many characteristics, making a particular variety more popular in one
region of the world than another. Rice can have a short, medium or long
grain size. It can also be waxy (sticky) or non-waxy. Some rice varieties
are considered aromatic. Rice also comes in many different colours
including brown, red, purple and black.
Plant pathogens are continually evolving to survive. Plants have
developed a set of mechanisms to face the challenges of foreign
pathogens
through
a
long
history
of
coevolution.
Among
these
mechanisms, maintaining allele (or ortholog) variation or diversity either
at gene structure level or expression level is an important way for plants
to protect themselves from pathogen attack. Plant responses to pathogen
infection are regulated by different types of genes. The disease-resistance
(r) genes mediate race-specific resistance by initiation of defence
signalling. The allelic variation of most characterized r genes and their
alleles is regulated at the gene structure level; different resistant alleles
of an r gene and its susceptible allele frequently encode different proteins
(Sun et al., 2004; Zhou et al., 2006). In a few cases, the variation of r
genes and their susceptible alleles is a regulated expression (Gu et al.,
2005; Chu et al., 2006; Romer et al., 2007). Based on our understanding
of r-gene-mediated resistance, dominant r genes function as positive
regulators, and their susceptible alleles have no function in hostpathogen interaction (Gu et al., 2005; Romer et al., 2007); in contrast,
recessive r genes appear to have no function, and their susceptible
(dominant) alleles function as negative regulators in defence responses
(Chu et al., 2006; Jiang et al., 2006). There is no report that an r gene
and its allele function as positive and negative regulators in defence
responses, respectively (Tao et al, 2009).
Rice pests can be any organisms or microbes with the potential to
reduce the yield or value of the rice crop (or of rice seeds) (Jahn et al.,
2007). Rice pests include weeds, pathogens, insects, rodents and birds. A
variety of factors can contribute to pest outbreaks, including the overuse
of pesticides and high rates of nitrogen fertilizer application. Weather
conditions also contribute to pest outbreaks. For example, rice gall
midge and army worm outbreaks tend to follow periods of high rainfall
early in the wet season, while thrips outbreaks are associated with
drought.
Plant breeding will play a key role in this coordinated effort for
increased food production. Despite optimism about continued yield
improvement from conventional breeding, new technologies such as
biotechnology will be needed to maximize the probability of success (Ortiz
1998; Ruttan 1999; Huang et al. 2002). One area of biotechnology, DNA
marker technology, derived from research in molecular genetics and
genomics, offers great promise for plant breeding. Owing to genetic
linkage, DNA markers can be used to detect the presence of allelic
variation in the genes underlying these traits. By using DNA markers to
assist in plant breeding, efficiency and precision could be greatly
increased. The use of DNA markers in plant breeding is called markerassisted selection (MAS) and is a component of the new discipline of
‘molecular breeding’.
Plant breeding has been remarkably successful in the improvement
of both qualitative and quantitative traits that affects agronomic
performance and consumer preference. However, in both, the yield level
has been reached a plateau level and further improvement has not been
achieved since last few decades.
The inadequate knowledge about
underlying biological processes continues to impede breeding progress
for quantitative inherited trait such as environment stress tolerance,
yield and even some simply inherited traits. A deeper understanding of
complex traits will facilitate further improvement of these traits
Recently, effectiveness of marker assisted selection depends on the
availability of tightly linked markers to the traits of interest. DNA marker
assisted selection accelerates the breeding process by allowing selection
for desirable traits to make place independent of environmental influence
and plant maturity. Distance between different units {Kilobytes (kb),
centimorgan (cM)} is not well understood. With the availability of rice
sequence and the very polymorphic marker detected in isogenic lines, or
in a mapping population it is possible to identify the putative candidate
genes associated with the marker sequence. Thus study of quantitative
trait related to specific character is very difficult and time taking task as
it required a well-equipped mas lab and it is time taking procedure to
find an individual QTL by using MAS technique therefore identification
and selection using bioinformatics tools such as Gramene database
mainly designed for study of grass family, could be more effective as
against the marker-assisted selection. With the advent of technique to
study variation at the ‘in silico’, the ability to generate large numbers of
polymorphic genetic markers in practically any species is enormous.
Bioinformatics and computational biology involve the use of
technique
including
applied
mathematics,
informatics,
statistics,
computer science, intelligence chemistry and biochemistry to solve
biological problems usually on the molecular level. Research in
computational biology often overlaps with systems biology. Major
research efforts in the field include sequence alignment, gene finding,
genome
assembly,
protein
structure
alignment
protein
structure
prediction, prediction of gene expression and protein–protein interaction
and the modelling of evolution.
Considering the above factors the present study was undertaken
with following objectives
1. Identifying alleles associated with resistance to bacterial leaf blight,
leaf blast and brown plant hopper using tightly linked molecular
markers and
2. Identifying chromosomal regions associated with these markers and
identifying annotated genes and designing new candidate gene
primers.
Review of Literature
II. REVIEW OF LITERATURE
Review of earlier literature pertaining to the present study in rice is
presented in this chapter under the following headings:
2.1 Habitats
2.2 Concept of biotic stress
2.3 Molecular markers
2.4 Marker-assisted selection
2.5 Concept of QTL assessment
2.6 Genome mapping
2.7 Allele mining
2.8 Bioinformatics tools
2.9 Gene annotation
2.10 Candidate gene
2.11 Molecular profiling
2.1 Habitats
Aerobic rice cultivation is a water-saving rice production system in
which potentially high yielding, fertilizer responsive adapted rice varieties
are grown in fertile aerobic soils that are non-puddled and have no
standing water. Supplementary irrigation, however, can be supplied in
the same way as to any other upland cereal crop (Wang et al., 2002;
Bouman et al., 2005).
Aerobic rice is one which grows in any rice field that is never
flooded right through the cropping season. These lands could be slightly
sloping, may be sandy and have higher degrees of percolation unlike
lowlands.
Being direct seeded, the cost of cultivation is considerably
reduced (Shashidhar, 2007, www.aerobicrice.in).
2.2 Concept of Biotic stress
Biotic stress is a stress that occurs as the result of damage done
by living organisms such as bacteria, virus, fungi, parasites, insect and
weeds.
Resistance in the host-pathogen relationship are the ability of
plants to limit the penetration, development and or reproduction of
invading pathogens. Tolerance of host plants is measured in terms of the
ability to maintain growth and yield production in spite of infection or
invasion of pathogens. Although both factors are genetically controlled,
the environment and thus nutrition of the host plant can modify to a
certain extent its expression, especially in moderately susceptible or
resistant genotypes/varieties (Graham and Webb, 1991).
The biotic and abiotic relationships that have developed determine
the existence, structure and survival of phytocoenoses in the case one or
several components of the system change. To maintain its functional and
structural integrity, a plant/organism has to be resistant towards
unfavourable factors. Each organism has a unique range of genetically
determined and phylogenetically adapted physiological resistance within
which a factor affecting it is tolerable. Concepts of stress and stress
resistance of plants are discussed on the basis of information available in
the literature. There is an intense and potential conflict between
contesting theories on the issues of how plant strategies and vegetation
processes are related to environmental stresses. In the contest of
different theories, the standard scientific practice is to look for situations
where the predictions of the theories are different and to test these
experimentally. There is strong evidence that the problem of plant stress
tolerance and avoidance is not fully understood and needs further
scientific discussions and experimental evidence on the physiological,
ecological and genetic level (Mandre, 2002).
2.3 Molecular markers
Bacterial leaf blight (BLB)
Four genes of rice, Oryza sativa L., conditioning resistance to the
bacterial blight pathogen Xanthomonas oryzae pv. Oryzae (Xoo) were
tagged by restriction fragment length polymorphism (RFLP) and random
amplified polymorphic DNA (RAPD) markers. No recombinants were
observed between xa5 and RFLP marker loci RZ390, RG556 or RG207 on
chromosome 5. Xa-3 and Xa4 were linked to RFLP locus XNpbl81 at the
top
of
chromosome
11,
at
distances
of
2.3
cM
and
1.7
cM,
respectively.The nearest marker to Xa21, also located on chromosome
11, was the RAPD locus O07 at a distance of 5.3 cM. From this study,
the conventional map and two RFLP linkage maps of chromosome 11
were partially integrated. Using the RFLP and RAPD markers linked to
the resistance genes, rice lines that are homozygous for pairs of
resistance genes, Xa4 + xa5 and Xa4 + Xa21 were selected. Lines
carrying Xa4 + xa5 and Xa4 + Xa21 were evaluated for reaction to eight
strains of the bacterial blight pathogen, representing eight pathotypes
and three genetic lineages. As expected,the lines carrying pairs & genes
were resistant to more of the isolates than their single-gene parental
lines.Lines carrying Xa4 + xa5 were more resistant to isolates of race 4
than was either of the parental lines (quantitative complementation). No
such effects were seen for Xa4 + Xa21. Thus, combinationsof resistance
genes provide broader spectra of resistance through both ordinary gene
action expected and quantitative complementation (Yoshimura et al.,
1995).
The inheritance of resistance for bacterial blight, caused by
Xanthomonas oryzae pv. Oryzae (Xoo) was studied in Minghui 63, an elite
restorer line for a number of widely used rice hybrids in China. A new
dominant gene against a Chinese Xoo strain JL691 in both the seedling
and adult stages was identified in Minghui 63 and designated as Xa26 (t).
Using a total of 477 highly susceptible individuals from an F2 population,
the Xa26 (t) locus was mapped to a region of about 1.68 cM. This locus
co-segregated with marker R1506 and was 0.21 cM from marker RM224
on one side and 1.47 cM from marker Y6855RA on the other side, in rice
chromosome 11. A contig map composed of five non-redundant bacterial
artificial chromosome (BAC) clones and spanning approximately 500 kb
in length, was constructed. Analysis of recombination events in the Xa26
(t) region with the highly susceptible F2 individuals anchored the gene
locus to a region covered by three overlapped BAC clones. Assay of the
lines showing a double crossover in marker loci flanking Xa26 (t), in a
population of recombinant inbred lines carrying Xa26 (t), further
delineated the gene to a 20-kb fragment. The Xa26 (t) locus is tightly
linked to another bacterial blight resistance gene locus, Xa4 (Yang,
2003).
Bacterial leaf blight (BLB), is one of the major diseases which
causes poor development and lowers quality of grain. It increases the
number of underdeveloped grains, reduces weight which results in poor
maturing and a high proportion of broken rice. Therefore, it is necessary
to screen rice varieties (lines) which resist the disease. The advent of
molecular
markers
tagged
to
different
resistance
genes
enabled
convergence breeding and pyramiding of more than two different genes
into an agronomic variety.
MAS have been successfully developed
(Huang et al. 1997) for pyramiding four resistance genes into IR24
background (Tuyen and Lang, 2004).
With the pathogen Xanthomonas, breeding efforts to incorporate
single resistant gene often leads to resistance breakdown within a short
period. To overcome such breakdown of resistance and develop
germplasm with durable disease resistance, they have introgressed three
bacterial blight resistance genes, xa5, xa13, and Xa21 into a fine grain
rice variety, Samba Mahsuri, using sequence tagged site (STS) markers
linked to these genes. Since the efficiency of the STS markers linked to
recessive genes to detect homozygotes is less than 100%, they adopted
four different pyramiding schemes to minimize loss of recessive
resistance genes in advanced backcross generations. Pyramiding scheme
in which a two-gene Samba Mahsuri pyramid line containing Xa21 and
xa5 genes was crossed with the Samba Mahsuri line having xa13 gene
alone was found to be most effective in preventing the loss of an
important recessive gene xa13. They further demonstrated that there was
no yield loss due to pyramiding of multiple genes into the elite indica rice
variety (Kalmeswara et al., 2010).
Bacterial blight caused by Xoo has been and is one of the major
constraints for production by virtue of its greater adaptation and
pathotype variation since, its identification in the early seventies. The
pathogen population being very dynamic is changing at an accelerated
pace and posing a serious challenge to the agricultural sector. The
present study was undertaken to understand and to gain an insight on
the prevalent pathogen population which is a prerequisite for deployment
of the right combination of resistant genes to combat the pathogen
population. Infected leaf samples were systematically collected from a
hotspot (Maruteru) of the disease and were characterised by following
both field inoculation pathotyping and at molecular level by DNA
fingerprinting.
phenotypes
Greater
than
in
variation
virulence
was
observed
patterns.
Among
in
the
genes
molecular
and
their
combination studied, the four gene combination (Xa4+xa5+xa13+Xa21)
was found more resistant against the isolates. Such observations open
up new strategies and paradigms for aiming at focused breeding
programs for more stable and durable resistant cultivars by pyramiding
favourable genes together (Shanti et al., 2010).
A new bacterial blight recessive resistance gene Xa26 (t) was
identified from the descendant of somatic hybridization between an aus
rice cultivar (cv.) BG1222 and susceptible cv. IR24 against Chinese race
V (isolate 5226). The isolate was used to test the resistance or
susceptibility of F1 progenies and reciprocal crosses of the parents. The
results showed that F1 progenies appeared susceptibility there were 128R
(resistant):378S (susceptible) And 119R:375S plants in F2 populations
derived
from
two
crosses
of
BG1222/IR24
and
IR24/BG1222,
respectively, which both calculates into a 1R:3S ratio. 320 pairs of
stochastically selected SSR primers were used for genes initial mapping.
The screened results showed that two SSR markers, RM493 and RM446,
found on rice chromosome 1 linked to Xa26 (t). Linkage analysis showed
that these two markers were on both sides of Xa26 (t) with the genetic
distances 4.29 and 3.05 cM, respectively. The other 50 SSR markers in
this region were used for genes fine mapping. The further results
indicated that Xa26 (t) was mapped to a 1.42 cM genetic region between
RM10927 and RM10591. In order to further narrow down the genomic
region of Xa26 (t), 43 of insertion/deletion (Indel) markers (BGID1-43)
were designed according to the sequences comparison between japonica
and indica rice. Parents’ polymorphic detection and linkage assay
showed that the Indel marker BGID25 came closer to the target gene
with a 0.4 cM genetic distance. A contig map corresponding to the locus
was constructed based on the reference sequences aligned by the Xa26
(t) linked markers. Consequently, the locus of Xa26 (t) was defined to a
204 kb interval flanked by markers RM10929 and BGID25 (Chen et al.,
2011).
Blast
The fungal rice pathogen M.grisea contains repetitive DNA
sequences called MGR. We have used a DNA probe, MGR586, derived
from these sequences and crosses between rice-pathogenic and non-ricepathogenic laboratory strains of M.grisea to rapidly map genes in this
organism. The rice-pathogenic strain contained 57 EcoRI restriction
fragments that hybridize to the MGR586 probe; the other five non-rice
pathogenic parent strains contained a single MGR586 sequence. Genetic
analysis of MGR segregation detected eight linkage groups and allowed
the mapping of three pigmentation genes (Albi, Rsyl, and Bufi), the
mating type locus (Mat)), the nucleolar organizer (Rdnl), the Smol gene,
and two restriction fragment length polymorphisms linked to Smol. Our
results indicate that the MGR586 loci are randomly distributed about the
M. grisea genome and permit the construction of a well-marked linkage
map useful for future studies on genome organization and genetic
analysis in M. grisea (Romao and Hamer, 1992).
Seven sources of resistance to the two predominant races IB-1 and
IB-9 of the rice blast pathogen Pyricularia grisea were selected based on
leaf blast reaction in tests conducted under controlled greenhouse
conditions. Crosses involving resistant and susceptible parents were
made to study the inheritance of the disease reaction for different source
of resistance. The F1 and F2 progenies of all crosses, including
backcrosses to resistant and susceptible parents, were tested for reaction
to leaf blast. The data showed that resistant was controlled by one to
three genes which segregate independently in most of the donors. Nonallelic interaction among resistance genes, including dominant epistasis,
was identified (Marta and Prabhu, 1996).
M.grisea, the rice blast fungus is one of the main pathological
threats to rice crop worldwide. The genetic relatedness and the probable
mechanisms of genetic variation among the Indian isolates of rice blast
pathogen were studied. A total of 171 polymorphic markers were scored
using 33 selected random decamer primers. The isolates exhibited an
overall polymorphism of about 64%. The similarity degree value for the
isolates ranged from 0.76 to 0.92. The high polymorphism could be
explained by natural and stress induced transposition and horizontal
gene transfer. Understanding the source of pathogen variation will aid in
designing improved methods for management of the rice blast disease
(Chadha and Gopalakrishna, 2005).
To elucidate the role of silicon more clearly in biotic stress such as
pests and diseases, a silicon uptake-deficient mutant lsi1 originating
from wild-type rice (cv. Oochikara) was used. When the mutant was
grown in a seedling case, silicon did not accumulate in leaves (about 50–
80 mgg_1 dry weight), regardless of the silicon amendment. In the paddy
field, however, silicon increased three-fold (373mg /g dry weight) in
leaves with silicon amendment, compared with those (117mg /g dry
weight) with no silicon amendment. Lesion formation by M. grisea was
significantly suppressed in the leaves of the wild-type plant that had a
high accumulation of silicon, but not in the leaves of the mutant that
had a low silicon accumulation. Pest resistance was also observed in the
leaves of the wild-type plant, but not in the mutant. These results
demonstrated that silicon can protect rice plants from damage caused by
biotic stresses (Nakata et al., 2009).
Brown plant hopper
An introgression line derived from an interspecific cross between O.
sativa and O. officinalis, IR54741- 3-21-22 was found to be resistant to
an Indian biotype of brown planthopper (BPH). Genetic analysis of 95 F3
progeny rows of a cross between the resistant line IR54741-3-21-22 and
a BPH susceptible line revealed that resistance was controlled by a single
dominant gene. A comprehensive RAPD analysis using 275 decamer
primers revealed a low level of (7.1%) polymorphism between the parents.
RAPD polymorphisms were either co-dominant (6.9%), dominant for
resistant parental fragments (9.1%) or dominant for susceptible parental
fragments (11.6%). Of the 19 co-dominant markers, one primer, OPA16,
amplified a resistant parental band in the resistant bulk and a
susceptible parental band in the susceptible bulk by bulked segregant
analysis. RAPD analysis of individual F2 plants with the primer OPA16
showed
marker-phenotype
co-segregation
for
all,
with
only
one
recombinant being identified. The linkage between the RAPD marker
OPA16938 and the BPH resistance gene was 0.52 cM in coupling phase.
The 938 bp RAPD amplicon was cloned and used as a probe on 122 ClaI
digested doubled haploid (DH) plants from an IR64xAzucena mapping
population for RFLP inheritance analysis and was mapped onto rice
chromosome 11. The OPA16938 RAPD marker could be used in a cost
effective way for marker-assisted selection of BPH resistant rice
genotypes in rice breeding programs (Jena et al., 2002)
Brown plant hopper (BPH) is a destructive insect pest of rice in
Asia. Identification and the incorporation of new BPH resistance genes
into modern rice cultivars are important breeding strategies to control
the damage caused by new biotypes of BPH. In this study, a major
resistance gene, Bph18 (t), has been identified in an introgression line
(IR65482-7-216-1-2) that has inherited the gene from the wild species
O.australiensis. Genetic analysis revealed the dominant nature of the
Bph18 (t) gene and identified it as nonallelic to another gene, Bph10that
was earlier introgressed from O. australiensis. After linkage analysis
using Mapmaker followed by single-locus ANOVA on quantitatively
expressed resistance levels of the progenies from an F2 mapping
population identified with marker allele types, the Bph18(t) gene was
initially located on the sub terminal region of the long arm of
chromosome 12 flanked by the SSR marker RM463 and the STS marker
S15552. The corresponding physical region was identified in the
Nipponbare genome pseudomolecule 3 through electronic chromosome
landing (e-landing), in which 15 BAC clones covered 1.612 Mb. Eleven
DNA markers tagging the BAC clones were used to construct a highresolution genetic map of the target region. The Bph18(t) locus was
further localized within a 0.843-Mb physical interval that includes three
BAC clones between the markers R10289S and RM6869 by means of
single-locus ANOVA of resistance levels of mapping population and
marker-gene association analysis on 86 susceptible F2 progenies based
on six time point phenotyping. Using gene annotation information of
TIGR, a putative resistance gene was identified in the BAC clone
OSJNBa0028L05 and the sequence information was used to generate
STS marker 7312.T4A. The marker allele of 1,078 bp completely cosegregated with the BPH resistance phenotype. STS marker 7312.T4A
was validated using BC2F2 progenies derived from two temperate
japonica backgrounds. Some 97 resistant BC2F2 individuals out of 433
screened completely co-segregated with the resistance-specific marker
allele (1,078 bp) in either homozygous or heterozygous state. This further
confirmed a major gene-controlled resistance to the BPH biotype of
Korea. Identification of Bph18 (t) enlarges the BPH resistance gene pool
to help develop improved rice cultivars, and the PCR marker (7312.T4A)
for the Bph18 (t) gene should be readily applicable for MAS (Jena et al.,
2005).
Nilaparvata lugens (brown plant hopper, BPH), is one of the major
insect pests of rice (O. sativa L.) in the temperate rice-growing region. In
this study, ASD7 harboring a BPH resistance gene bph2 was crossed to a
susceptible cultivar C418, a japonica restorer line. BPH resistance was
evaluated using 134 F2:3 lines derived from the cross between “ASD7” and
“C418”. SSR assay and linkage analysis were carried out to detect bph2.
As a result, the resistant gene bph2 in ASD7 was successfully mapped
between RM7102 and RM463 on the long arm of chromosome 12, with
distances of 7.6 cM and 7.2 cM, respectively. Meanwhile, both
phenotypic selection and MAS were conducted in the BC1F1 and BC2F1
populations.
Selection
efficiencies
of
RM7102
and
RM463
were
determined to be 89.9% and 91.2%, respectively. It would be very
beneficial for BPH resistance improvement by using MAS of this gene (LiHong et al., 2006).
Resistance to brown plant hopper (BPH), a destructive phloem
feeding insect pest, is an important objective in rice breeding programs in
Thailand. The broad-spectrum resistance gene Bph3 is one of the major
BPH resistance genes identified so far in cultivated rice and has been
widely used in rice improvement programs. This resistance gene has
been identified and mapped on the short arm of chromosome number 6.
In this study, physical mapping of Bph3 was performed using a BC3F3
population derived from a cross between Rathu Heenati and KDML105.
Recombinant BC3F3 individuals with the Bph3 genotype were determined
by phenotypic evaluation using modified mass tiller screening at the
vegetative stage of rice plants. The recombination events surrounding the
Bph3 locus were used to identify the co-segregate markers. According to
the genome sequence of Nipponbare, the Bph3 locus was finally localized
approximately in a 190 kb interval flanked by markers RM19291 and
RM8072,
which
contain
twenty-two
putative
genes.
Additional
phenotypic experiment revealed that the resistance in Rathu Heenati was
decreased by increasing nitrogen content in rice plants through
remobilization of nitrogen. This phenomenon should be helpful for
identifying the Bph3 gene (Jairen, 2007).
The insect pests, plant hoppers cause significant yield losses.
Among the various strategies, host-plant resistance is the most practical
and economical approach to control insect pests. These hoppers are also
vectors of major viral diseases, such as grassy stunt, ragged stunt, rice
stripe virus, black streak, and tungro disease. A number of donors for
resistance have been identified and used in breeding varieties resistant to
hoppers. Several resistance genes have been identified from traditional
landraces, including wild species. As many as 21 resistance genes have
been identified for BPH. Of the 21 BPH resistance genes, 15 have been
mapped to different chromosomal locations. Some of the mapped BPH
resistance genes have become available for use in marker-assisted
selection (MAS). In addition, QTLs have also been identified for BPH,
WBPH, and GRH. Of the six hoppers, GRH is mostly found in temperate
rice-growing regions. Six resistance genes for GRH have been mapped, on
chromosomes 5, 11, 6, 3, 8, and 4. Near-isogenic lines have been
developed in a japonica background using the MAS approach. BPH
occurs in the tropics and subtropics of Asia and has remained a minor
pest of rice. One of the major challenges for plant breeders is to cope
with the frequent changes in biotypes and populations of plant hoppers,
particularly in the context of climatic change. Also focus on establishing
high-throughput screening protocols for field resistance, identifying new
genes for resistance from diverse sources, and developing varieties with
durable resistance to hoppers using MAS through pyramiding of major
genes and QTLs and to develop gene-based markers, particularly single
nucleotide polymorphism markers, to accelerate the transfer of genes
into different genetic backgrounds and for breeding varieties resistant to
hoppers (Brar et al., 2009).
Brown plant hopper (BPH) is one of the most destructive insect
pests of rice. Wild species of rice are a valuable source of resistance
genes for developing resistant cultivars. A molecular marker-based
genetic analysis of BPH resistance was conducted using an F2
population derived from a cross between an introgression line, ‘IR71033-
121-15’,
from
Oryza
minuta(Accession
number
101141)
and
a
susceptible Korean japonica variety, ‘Junambyeo’. Resistance to BPH
(biotype 1) was evaluated using 190 F3 families. Two major quantitative
trait loci (QTLs) and two significant digenic epistatic interactions between
marker intervals were identified for BPH resistance. One QTL was
mapped to 193.4-kb region located on the short arm of chromosome 4,
and the other QTL was mapped to a 194.0-kb region on the long arm of
chromosome number 12. The two QTLs additively increased the
resistance to BPH. Markers co-segregating with the two resistance QTLs
were developed at each locus. Comparing the physical map positions of
the two QTLs with previously reported BPH resistance genes, we
conclude that these major QTLs are new BPH resistance loci and have
designated them as Bph20 (t) on chromosome 4 and Bph21 (t) on
chromosome 12. This is the first report of BPH resistance genes from the
wild species O. minuta. These two new genes and markers will be useful
to rice breeding programs interested in new sources of BPH resistance
(Rahman et al., 2009)
Plant hoppers are highly destructive pests in crop production
worldwide. BPH causes the most serious damage of the rice crop globally
among all rice pests. Growing resistant varieties is the most effective and
environment-friendly strategy for protecting the crop from BPH. More
than 19 BPH resistance genes have been reported and used to various
extents in rice breeding and production. In this study, we cloned Bph14,
a gene conferring resistance to BPH at seedling and maturity stages of
the rice plant, using a map-base cloning approach. We show that Bph14
encodes a coiled-coil, nucleotide-binding and lucien-rich repeat (CC-NBLRR) protein. Sequence comparison indicates that Bph14 carries a
unique LRR domain that might function in recognition of the BPH insect
invasion and activating the defence response. Bph14 is predominantly
expressed in vascular bundles, the site of BPH feeding. Expression of
Bph14 activates the salicylic acid signalling pathway and induces callus
deposition in phloem cells and trypsin inhibitor production after plant
hopper infestation thus reducing the feeding, growth rate, and longevity
of the BPH insects. Their work provides insights into the molecular
mechanisms
of
rice
defence
against
insects
and
facilitates
the
development of resistant varieties to control this devastating insect (Du et
al., 2009).
Santhanalakshmi et al. (2009) used a population comprising of
106 F3 families along with their parents and the resistance (PTB33) and
susceptible check Taichung Native 1(TN1) were evaluated for BPH
resistance at seedling stage using the standard seed box method. The
results indicated the presence of dominant gene controlling the
resistance. Linkage analysis with SSR markers through bulked segregant
analysis method was employed for the identification of DNA markers
linked to the resistance genetic locus. Single- marker analysis through
one way analysis of variance with crop stat showed that the markers
RM3766, RM 14687, RM251 and RM7 on chromosome 3 were linked to
the resistance locus. Further it led to identification of markers RM3766
and RM14687 linked to a major QTL associated with BPH resistance
physically mapped on short arm of chromosomes 3 and it was new
source of resistance derived from the resistant parent PTB33.
Pathogen related protein
An overview of the evolution of knowledge on the nomenclature,
classification,
induction,
occurrence,
functions,
and
the
role
of
pathogenesis-related proteins (PRs) is presented. The recommendations
for naming and defining PRs are introduced and the criteria required for
the inclusion of new families into PRs are considered. The revision of the
previous view on PRs as inducible proteins has been debated for their
constitutive expression in various plant organs and in seeds. Newly-
discovered
members
of
PRs
families
are
described,
and
recent
information about functionality of PRs, substantiating their antimicrobial
action, is discussed. The biochemical and structural properties as well as
the organ, tissue, cell-localisation, the induction and regulation of PRs
are briefly outlined. Latest data about the relevance of PRs to plant
development and disease resistance are examined and the plausible
application of engineering of PRs genes for crop improvement is critically
commented. The finding that PRs, considered before as plant-specific
protein are also expressed in other organisms, suggesting these proteins
share an evolutionary origin and possess activity essential to the
functioning and survival of living organisms (Edreva, 2005).
Pathogenesis-related (PR) proteins have been used as markers of
plant defence responses, and are classified into 17 families. However,
precise information on the majority members in specific PR families is
still limited. They were interested in the individual characteristics of rice
PR1 family genes, and selected 12 putatively active genes using rice
genome databases for expressed genes. All were up regulated upon
compatible and/or incompatible rice-blast fungus interactions; three
were up regulated in the early infection period and four in the late
infection period. Upon compatible rice–bacterial blight interaction, four
genes were up regulated, six were not affected, and one was down
regulated. These results are in striking contrast to those among 22
Arabidopsis PR1 genes where only one gene was pathogen-inducible. The
responses of individual genes to salicylic acid, jasmonic acid, and
ethylene induced defence signalling pathways in rice are likely to be
different from those in dicot plants. Transcript levels in healthy leaves,
roots, and flowers varied according to each gene. Analysis of the partially
overlapping expression patterns of rice PR1 genes in healthy tissues and
in response to pathogens and other stresses would be useful to
understand their possible functions and for use as characteristic
markers for defence related studies in rice (Mitsuhara et al., 2008).
2.4 Marker Assisted Selection
Integrating
Assisted
molecular
Selection
(MAS)
marker
into
technologies
breeding
such
strategies
as
Marker
could
become
increasingly important in the coming years to achieve genetic gains with
greater speed and precision. The promise of MAS for improving polygenic
traits in a quick time-frame and in a cost-effective manner is still elusive.
There is a wider appreciation that simply demonstrating that a complex
trait can be dissected into QTLs and mapped to approximate genomic
locations using DNA markers would not serve the ultimate goal of trait
improvement. In facing the challenge of improving several lines for
quantitative traits, MAS strategies use DNA markers in one key selection
step
to
maximize
their
impact.
The
work
discusses
the
basic
requirements and the potential applications of MAS in crop plants,
recent developments in MAS strategies and genotyping techniques and
the significance of integrating MAS into conventional plant breeding
programmes (Choudhary et al., 2008).
Use of molecular markers has emerged as a powerful and efficient
approach to complement traditional plant breeding for improving crops.
An array of molecular markers are now available that include RFLP that
is based on Southern blot hybridization and, RAPD, ISSR, SSR and STS
are based on polymerase chain reaction. The AFLP and CAPS markers
are the other PCR based markers involving pre and post amplification
restriction digestion, respectively. The most recent marker system is
single nucleotide polymorphism (SNP) that utilizes the vast DNA
sequence resources available in different crop species. Each of these
markers has its own strengths and limitations. Markers are being used
in several different aspects of crop improvement including estimation of
genetic diversity, construction of high density genome maps, mapping
and tagging of genes, map-based isolation of genes and MAS. MAS were
carried out for transferring target gene(s) from one genetic background to
other using tightly linked markers (foreground selection). MAS was also
carried out to quickly recover recurrent parent genome in backcross
breeding using a large number of either random or mapped markers
having whole genome coverage (background selection). Hence, MAS
requires markers tightly linked to the genes for the target traits as well
as high-density genome maps in crops of interest. This condition is not
fulfilled in all crops and traits. MAS has been effectively employed in
pyramiding identified genes involving short breeding cycles through
background and foreground selection thereby adding resistance to
established cultivars of each crop (Prabhu et al.,2009 ).
Molecular markers like restriction fragment length polymorphism
(RFLP), single nucleotide polymorphism (SNP) and simple sequence
repeat (SSR) tightly linked to target gene have been identified in different
chromosomes to impose the genetic selection i.e. MAS. Blast fungus (P.
oryzae Cav.) can infect plants at any growth stage from seedling to
maturity and at any part e.g. leaf, node, inter-node, neck and seed.
Qualitative resistance gene may occasionally be broken down due to
numerous races of blast fungus both physiological and geographical
races. Quantitative gene resistance and gene pyramiding are the best
alternative for creating durable resistance system. At least 40 genes
conferring resistance to blast isolates with multiple alleles have been
described. Both dominant and recessive resistance alleles have been
found in many rice landraces. Morphological and isozymic markers are
limited in number. Therefore, highly polymorphic and easily detectable
SSR markers are being used in breeding for blast resistance. Bulked
segregant analysis (BSA) is the simple method for tagging resistance gene
by SSR markers. Quantitative trait loci (QTLs) have also been mapped
and most of them are linked to qualitative genes. SSR markers linked to
the gene are being used to select plants possessing the desired trait and
markers throughout the genome are being used to select plants that are
genetically similar to recurrent parent. Using SSR markers it may be
possible to select blast resistance genotypes at any stage of crop
development from any small part of crop, to conduct many round of
selection, to select without inoculums, without scoring and without
testing in hot spot or artificial inoculation (Joshi et al., 2009).
2.5 Concept of QTL assessment
Several character of plant species, among which are traits of
agronomic importance are inherited quantitatively. Yield, maturity date
and drought tolerance are example of such characters. Although the
principle of QTL analysis were first outlined and successfully applied in
the early 1920s to map a QTL for seed size in tightly linked to a gene
controlling seed pigmentation (Sax, 1923). The consuming process of
map construction was shortened considering with the introduction of
PCR based micro satellite markers, particularly suited for mapping
purposes due to their high level of polymorphism.
Quantitative trait (QT) is a term of central importance in the field of
biology and agriculture. As the term indicates, QT refers to characters
that can be measured on a quantitative scale (Arunachalam, 2001)
However, only a few successful applications of MAS for the
improvement of quantitative traits have been described to data (Ribautet
al., 2000) due mainly to weak association (in terms of genetic distance)
between markers and target QTLs and/or the high costs of MAS (Salviet
al., 2001). Certainly, a rosier picture of MAS emerges considering single
gene traits such as disease resistance (Bus et al., 2000). In few cases, the
most probable genes underlying the QTL have been identified .This is the
most useful area where markers could make an impact on plant breeding
application with precision (Hittalmani et al., 2002).
Many different software’s were developed for QTLs analysis viz;
MAPMAKER/QTL (Lincoln et al., 1992). QTL cartographer (Basten et al.,
1997) Map Manager QT (Manly et al., 1997) Q Gene QTL; multimapper
controlled by many genes. The development of molecular maps in rice
has facilitated the identification of these gene that are popularly known
as Quantative trait loci .Using Molecular markers, the exact number of
QTLs of a traits can be detected and their presence defined .Near isogenic
lines (NILs) have been widely used by pathologists as well as plant
breeders, in development of varieties as well as for mapping various
traits. QTL detection NIL for each QTL .These lines
called QTL
introgressed lines helps in developing NIL for traits(s)
2.6 Genome mapping
The genome map of an organism summarizes much of the general
information available for that species, and can serve as a reference for
the development and testing of additional genetic hypothesis. Different
mapping
population
are
as
follows,
segregating
population
(F2)
recombinant inbred lines (RILs), back crosses (BCs)populations doubled
haploid (DH) and Near Isogenic lines (NILs) (Nagarajan, 2001).
Genetic
mapping
provide
Information
needed
for
the
implementation of “Marker assisted selection” An approach growing
importance in plants and animal improvements. Genetic mapping is the
first step in the “Map Based Cloning” of genes responsible for the specific
phenotype
and
opportunities
(Sanmugasundaram, 2001).
for
engineering
novel
traits
2.7 Allele mining
Identification and access to allelic variation that affects the plant
phenotype is of the utmost importance for the utilization of genetic
resources such as in plant variety development. Considering the huge
numbers of accessions that are held collectively by genebanks, genetic
resources collections are deemed to harbour a wealth of undisclosed
allelic variants. The challenge now is to unlock this variation. Allele
mining is a research field aimed at identifying allelic variation of relevant
traits within genetic resources collections. For identified genes of known
function and basic DNA sequence, genetic resources collections may be
screened for allelic variation by e.g. the ‘tiling strategy’ using DNA chip
technology. In that approach the basic DNA sequence of a gene is spotted
on a chip in the form of large series of sequence-overlapping probes
consisting of 15-20 bases. Each base position in a fluorescently labelled
sample is then interrogated for the presence of point mutations by
monitoring hybridization signals with the spotted probes. Because the
sequence of samples is determined in comparison with the primary
composition of a gene, this method is also known as‘re-sequencing’. With
this method new point mutations, in relatively large DNA fragments, can
be detected. Once allelic variants of interest have been identified, the
approach can be optimized by focusing on target sets of polymorphisms,
for example by using SNP detection methods. Enormous sequence
information is available in public databases as a result of sequencing of
diverse crop genomes. It is important to use this genomic information for
the identification and isolation of novel and superior alleles of
agronomically important genes from crop gene pools to suitably deploy
for the development of improved cultivars. Allele mining is a promising
approach to dissect naturally occurring allelic variation at candidate
genes controlling key agronomic traits which has potential applications
in crop improvement programs. It helps in tracing the evolution of alleles,
identification of new haplotypes and development of allele-specific
markers for use in marker-assisted selection. Realizing the immense
potential of allele mining, concerted allele mining efforts are underway in
many international crop research institutes. This review examines the
concepts, approaches and applications of allele mining along with the
challenges associated while emphasizing the need for more refined
‘mining’ strategies for accelerating the process of allele discovery and its
utilization in molecular breeding (Ramkumar et al., 2010).
Allele mining exploits the deoxyribonucleic acid (DNA) sequence of
one genotype to isolate useful alleles from related genotypes. The
international project to sequence the genome of O. sativa L. cv.
Nipponbare will make allele mining possible for all genes of rice and
possibly related cereals. A rice calmodulin gene, a rice gene encoding a
late embryogenesis-associated protein, and salt-inducible rice gene to
optimize the polymerase chain reaction (PCR) for allele mining of stress
tolerance genes on identified accessions of rice and related germplasm.
Two sets of PCR primers were designed for each gene. Primers based on
the 5' and 3' untranslated region of genes were found to be sufficiently
conserved so as to be effective over the entire range of germplasm in rice
for which the concept of alleles is applicable. However, the primers based
on the adjacent amino (N) and carboxyl (C) termini amplify additional loci
(Latha et al., 2004).
2.8 Bioinformatics tools:
In
case
of
QTL
localisation
through
conventional
wet-labs
technique seems to be time taking and laborious task and having so
many limitations to overcome these all problems ‘bioinformatics’ is a
solution and widely used in data analysis.
Gramene
There are so many databases and software is available for QTL
localisation on chromosomes. ‘Gramene’ (http://www.gramene.org)is
comparative genome mapping database for grasses and a community
resource for rice (Ware et al., 2001).Gramene replace the existing
acedbdatabase ‘ricegenes’ with relational database based on oracle. It is
a collaborative project of ‘cold spring harbour laboratory’, Cornell
University and rice community. The main core base of Gramene is the
MySQL database management systems, make it a user friendly front end
system and it is a stable and well supported. Rice is used as a framework
genome to organised information for other grass species due to its
smaller genome to organised information for other grasses such as maize
(2400mb) and wheat (16000mb) having considerable larger genome. The
goals of Gramene are to (i) establish a database utilising the rice genome
as a framework for identifying and characterising genes in other grasses
(ii) provide comparative maps
between rice and other grasses based
upon orthologous sequence and a wealth of genetic and phenotypic
information available among the grasses (iii) develop a pilot study to
assign gene ontology (go) functional classification to 4000 confirmed or
predicted rice genes (iv) curate information on major mutants, strains,
phenotypes, polymorphisms and quantitative trait loci (QTLs) utilising a
structural controlled vocabulary and (v) integrated with other plant
databases to allow comparisons of conversed syntenic relationship and
mutant phenotypes (Ware et.al., 2002).
Gramene is currently a hybrid system. Legancy data including
traits,
QTLs,
strains
and
literature
citation,
are
maintained
in
ricegenesacedb databases. New data, including nucleotide sequence,
sequence annotation, physical maps and new genetic marker are
maintained in a completely redesigned system .The new system uses a
perl object model based on the bioperl (www.bioperl.org) and ensemble
code bases. The back end consists of a set of perl script running in an
apache/mod_perl environment. The best feature of ‘QTL selection’ is to
be a user friendly with mapped data. It facilitates the comparative study
of QTL and their mapped regions to investigate collinear regions found to
carry genes identified in rice genome. For convenience of searching the
traits are grouped in eight major families related to biotic and abiotic
stress, fertility, anatomy, development, vigour, quality and yield (Ware et
al., 2002).
NCBI (National Centre for Biotechnology Information)
The National Centre for Biotechnology Information (NCBI) is part of
the United States National Library of Medicine (NLM), a branch of
the National Institutes of Health. Established in 1988 as a National
Resource for Molecular Biology Information, NCBI creates public
databases,
conducts
research
in
computational
biology,
develops
software tools for analysing genome data and disseminates biomedical
information all for the better understanding of molecular processes
affecting human health and disease.
The NCBI houses genome sequencing data in GenBank and an
index of biomedical research articles in PubMed Central and PubMed, as
well as other information relevant to biotechnology. All these databases
are available online through the Entrez search engine.
GenBank
The NCBI has had responsibility for making available the
GenBank DNA sequence database since 1992. GenBank coordinates with
individual laboratories and other sequence databases such as those of
the European Molecular Biology Laboratory (EMBL) and the DNA Data
Bank of Japan (DDBJ).
Since 1992, NCBI has grown to provide other databases in addition
to GenBank. NCBI provides Online Mendelian Inheritance in Man, the
Molecular Modelling Database (3D protein structures), dbSNP a database
of single-nucleotide polymorphisms, the Unique Human Gene Sequence
Collection, a Gene Map of the human genome, a Taxonomy Browser, and
coordinates with the National Cancer Institute to provide the Cancer
Genome Anatomy Project. The NCBI assigns a unique identifier
(Taxonomy ID number) to each species of organism.
The NCBI has software tools that are available by www browsing or
by FTP. For example, BLAST is a sequence similarity searching program.
BLAST can do sequence comparisons against the GenBank DNA
database in less than 15 seconds
2.9 Gene annotation
The study for the isolation and identification of 10828 putative fulllength cDNAs (FL-cDNA) from an indica rice cultivar, Minghui 63, with
the long-term goal to isolate all full-length cDNAs from indica genome.
Comparison with the databases showed that 780 of them are new rice
cDNAs with no match in japonica cDNA database. Totally, 9078 of the
FL-cDNAs contained predicted ORFs matching with japonica FL-cDNAs
and 6543 could find homologous proteins with complete ORFs. 53% of
the matched FL-cDNAs isolated in this study had longer 5′UTR than
japonica FL-cDNAs. In silico mapping showed that 9776 (90.28%) of the
FL-cDNAs had matched genomic sequences in the japonica genome and
10046 (92.78%) had matched genomic sequences in the indica genome.
The average nucleotide sequence identity between the two subspecies is
99.2%. A majority of FL-cDNAs(90%) could be classified with GO (gene
ontology) terms based on homology proteins. More than 60% of the new
cDNAs isolated in this study had no homology to the known proteins.
This set of FL-cDNAs should be useful for functional genomics and
proteomics studies (Kabin et al., 2004).
The O. sativa L. Indica subspecies is the most widely cultivated
rice. During the last few years, over 20,000 putative full-length cDNAs
and over 40,000 ESTs isolated from various cDNA libraries of two indica
varieties Guangluai 4 and Minghui 63 were collected. A database of the
rice indica cDNAs was therefore built to provide a comprehensive web
data source for searching and retrieving the indica cDNA clones. Rice
indica cDNA Database (RICD) is an online MySQL-PHP driven database
with a user friendly web interface. It allows investigators to query the
cDNA clones by keyword, genome position, nucleotide or protein
sequence, and putative function. It also provides a series of information,
including sequences, protein domain annotations, similarity search
results, SNPs and InDels information, and hyperlinks to gene annotation
in both The Rice Annotation Project Database (RAP-DB) and The TIGR
Rice Genome Annotation Resource, expression atlas in RiceGE and
variation report in Gramene of each cDNA. The online rice indica cDNA
database provides cDNA resource with comprehensive information to
researchers for functional analysis of indica subspecies and for
comparative genomics (Lu et al, 2008).
Rice is the first cereal genome to be completely sequenced. Since
the completion of its genome sequencing, considerable progress has been
made in multiple areas including the whole genome annotation, gene
expression profiling, mutant collection, etc. Here, the current status of
rice genome annotation and review of the methodology
assigning
biological functions to hundreds of thousands of rice genes were
summarised and also discussed the major limitations and the future
perspective in rice functional genomics. Available data analysis shows
that the rice genome encodes around 32,000 protein coding genes.
Expression analysis revealed at least 31,000 genes with expression
evidence from full length cDNA/EST collection or other transcript
profiling. In addition, various strategies to generate mutant population
including
natural,
physical,
chemical,
T-DNA,
transposon/
retrotransposon or gene silencing based mutagenesis were summarised.
Currently, more than 1 million of mutants have been generated and
27,551 of them have their flanking sequence tags. To assign biological
functions to hundreds of thousands of rice genes, global co-operations
are required, various genetic resources should be more easily accessible
and
diverse
data
from
transcriptomics,
proteomics,
epigenetics,
comparative genomics and bioinformatics should be integrated to better
understand
the
functions
of
these
genes
and
their
regulatory
mechanisms (Jiang et al., 2010).
2.10 Candidate genes
The candidate gene (CG) approach has been applied in plant
genetics in the past decade for the characterisation and cloning of
Mendelian
and
complementary
quantitative
strategy
to
trait
loci
map-based
(QTLs).
cloning
It
constitutes
and
a
insertional
mutagenesis. The goal of this paper is to present an overview of CG
analyses in plant genetics. CG analysis is based on the hypothesis that
known-function genes (the candidate genes) could correspond to loci
controlling traits of interest. CGs refers either to cloned genes presumed
to affect a given trait (‘functional CGs’) or to genes suggested by their
close proximity on linkage maps to loci controlling the trait (‘positional
CGs’). In plant genetics, the most common way to identify a CG is to look
for map co-segregation between CGs and loci affecting the trait.
Statistical association analyses between molecular polymorphisms of the
CG and variation in the trait of interest have also been carried out in a
few studies. The final validation of a CG will be provided through
physiological
analyses,
genetic
transformation
and/or
sexual
complementation. Theoretical and practical applications of validated CGs
in plant genetics and breeding are discussed (Pfliegeret al., 2001).
Candidate genes involved in both recognition (resistance gene
analogs [RGAs]) and general plant defence (putative defence response
[DR]) were used as molecular markers to test for association with
resistance in rice to blast, bacterial blight (BB), sheath blight, and brown
plant-hopper (BPH). The 118 marker loci were either polymerase chain
reaction-based
RGA
markers
or
restriction
fragment
length
polymorphism (RFLP) markers that included RGAs or putative DR genes
from rice, barley, and maize. The markers were placed on an existing
RFLP map generated from a mapping population of 116 doubled haploid
(DH) lines derived from a cross between an improved indica rice cultivar,
IR64, and a traditional japonica cultivar, Azucena. Most of the RGAs and
DR genes detected a single locus with variable copy number and mapped
on different chromosomes. Clusters of RGAs were observed, most notably
on chromosome 11 where many known blast and BB resistance genes
and QTL for blast, BB, sheath blight, and BPH were located. Major
resistance genes and QTL for blast and BB resistance located on different
chromosomes were associated with several candidate genes. Six putative
QTL for BB were located on chromosomes 2, 3, 5, 7, and 8 and nine QTL
for BPH resistance were located to chromosomes 3, 4, 6, 11, and 12. The
alleles of QTL for BPH resistance were mostly from IR64 and each
explained between 11.3 and 20.6% of the phenotypic variance. The
alleles for BB resistance were only from the Azucena parent and each
explained at least 8.4% of the variation. Several candidate RGA and DR
gene markers were associated with QTL from the pathogens and pest.
Several RGAs were mapped to BB QTL. Dihydrofolate reductase
thymidylate synthase co-localized with two BPH QTL associated with
plant response to feeding and also to blast QTL. Blast QTL also were
associated with aldose reductase, oxalate oxidase, JAMyb (a jasmonic
acid-induced Myb transcription factor), and peroxidase markers. The
frame map provides reference points to select candidate genes for
Cosegregation analysis using other mapping populations, isogenic lines,
and mutants (Ramalingam et al., 2003).
2.11 Molecular profiling
Amplified Fragment Length Polymorphism (AFLP) fingerprints were
developed for 29 Darjeeling-grown tea clones. AFLP diversity estimates
based on Jaccard’s coefficient allowed separation of the 29 clones into
three clusters. Genetic relatedness between the clones was found to be at
70% level. Random Amplified Polymorphic DNA (RAPD) analysis of DNA
of ten short-listed (on the basis of field performance for drought
tolerance) clones using 11 primers, revealed 180 PCR products of which
131 were polymorphic bands. Activity of drought-specific superoxide
dismutase (SOD) and ascorbate peroxidase (APX) isozymes was found to
be appreciably high in RR17/144, CP1, TV26 and AV2. Regression
analysis using peak areas (from scans of stained activity-gel preparation)
of Cu-Zn SOD and APX II as dependent variables and RAPD band scores
as independent variable revealed that OPAH02 primed DNA band at 1400
bp was associated with high activity of the drought tolerance-specific
isozymes. Using Fisher’s exact test (F-test), this association was found to
be at 99.9% confidence level (Rajan and Swati, 2004).
NERICA rice is interspecific inbred progeny derived from crosses
between O. sativax and O. glaberrima. In this study,
evaluation of 70
BC2 interspecific lines were developed by crossing a tropical japonica
variety (WAB 56-104) as the recurrent parent to an O. glaberrima variety
(CG 14) as the donorparent, followed by the use of anther culture to
derive doubled haploids (DH) (26 lines) or eightgenerations of inbreeding
to fix the lines (44 lines). Seven of these BC2 derived inbred lines have
beenreleased as NERICA 1 - NERICA 7. This study examined the relative
contribution of each parent and theextent of genetic differences among
these 70 sister lines using 130 well-distributed microsatellitemarkers
which cover 1725 cM of the rice genome. The average proportion of O.
sativa recurrent parent genome was 87.4% (1,508 cM), while the
observed average proportion of O. glaberrima donor genome was 6.3%
(108 cM). Non-parental alleles were detected in 83% of the lines and
contributed an average of38 cM per line (~2.2% of genomic DNA). Lines
that had undergone eight generations of inbreeding in thefield contained
significantly more non-parental alleles (av. 2.7%) compared to the DH
lines (av. 1.3%)that were developed from BC2 anthers. Using both cluster
and principal component analyses, two majorgroups were detected in
these materials. The NERICA varieties (NERICA 1 to 7) clustered in one
group while the remaining 63 lines clustered in another group,
suggesting that the second group may offer significant opportunities for
further selection and variety development (Semagn et al., 2007).
Several lowland NERICAs (New Rice for Africa) were derived from
crosses between IR64 (an O. sativa sub sp. Indica variety) and Tog5681
(an O. glaberrima variety) that possess useful traitsadapted to lowland
conditions
in
West
Africa.
The
proportion
of
parental
genomic
contribution andextent of genetic differences among these sister lines is
unknown at the molecular level.
The objectivesin this study were
therefore to determine, with 60 SSR markers that cover 1162 cM of the
rice genome,the frequency and magnitude of deviations from the
expected parental contributions among 21 BC2F10, 17 BC3F8 and 10
BC4F8 lines and determine patterns of their genetic relationships. The
estimatedaverage O. glaberrima genome coverage was 7.2% (83.5 cM) at
BC2F10, 8.5% (99.3 cM) at BC3F8 and 8.1% (93.8 cM) at BC4F8 lines. The
O. sativa parent accounted for 73.2% (851.3 cM) at BC2F10, 82.6% (959.5
cM) at BC3F8 and 78.2% (908.6 cM) at BC3F8. Non-parental alleles were
detected at all 3 backcross generations but the frequency of such alleles
at BC2 (8.8%) was twice that of BC3F8 (3.4%) and nine times that of
BC4F8 (0.9%). Both cluster and principal component analyses revealed
two major groups irrespective of the level of backcross generations and
the proportion of parental genome contribution (Ndjiondjop et al., 2008).
The study was aimed to develop the molecular marker based
safeguards for Indian rice and to protect the consignment for export from
adulteration in order to maintain credibility in the global market. Forty
four varieties of rice collected from different agro-ecological zones were 4,
set for molecular profiling in the present study. Twelve genotypes of
basmati rice including traditional varieties and evolved varieties used
here were originated in areas of Northern India close to Himalayas
designated under geographical indication for basmati rice. Other varieties
were of Central India and 1i've varieties of Silence origin. The molecular
profiling of basmati and non-basmati indica rice varieties was made by
using the Inter-SSR-PCR and SSR-PCR assays. The amplification
products were analysed using gel electrophoresis. The distance matrix
and cluster analysis was made following UPGMA method. All the varieties
were clustered in two major groups belonging to irrigated agro ecosystem and aerobic agro eco-system. Twelve basmati varieties including
traditional and evolved varieties were grouped generally in four clusters.
Twenty-two varieties of aerobic rice were clustered in to a single major
cluster. Vallabh Basmati-21 and MAUB-57 of basmati were clustered
consistently in a separate group. Based on our molecular analysis, it was
concluded that most of the basmati varieties used herein descended from
a common land race. The quality and adaptation reactions greatly
influenced the clustering of these varieties. However, the clustering
within agro ecosystem remained independent of their geographical origin
(Singh et al, 2009).
The elucidation of Bauhinia accession was done by using RAPD
markers. The experiment was done in three steps: (i) Isolation and
purification of genomic DNA from leaf tissues of Bauhinia, (ii) Generation
of molecular profiles of Bauhinia using RAPD based DNA marker system
and (iii) Evaluation of genetic relatedness of the Bauhinia samples under
study employing classificatory analysis. To achieve these objectives
Bauhinia leaf samples were collected from the AAI-DU campus and
surrounding areas of Allahabad. These leaf samples were further
processed to obtain DNA using CTAB method and SDS method. The
isolated DNA samples were separated on agarose gel by electrophoresis
and visualized under UV trans- illuminator. It was observed that the
DNAs obtained from CTAB method was of good quality compared to the
DNA isolated by the SDS method. Hence the DNA obtained from CTAB
method was used for further PCR and RAPD analysis. The RAPD primer
screening was done with 15 primers and out of which, the data of 9
primers were used for further analysis. From distance data, NJ tree was
generated. The NJ tree indicates that the dendrogram was divided into
two groups. The Bauhinia samples 8 and 9 were distant from rest of all
the Bauhinias whereas the Bauhinia samples 11 and 12 were most
similar to each other. The present study was done to provide foundation
for advanced R&D methodologies such as that once which employed in
Biodiversity and Genetic diversity analysis (Shiju, 2010).
Materials and Methods
III. MATERIALS AND METHODS
The details of the materials used, methods adopted and statistical
tools employed for analysis are presented under the respective headings.
3.1.1 Experiment 1: Identifying alleles associated with resistance to
bacterial leaf blight, leaf blast and brown plant hopper using
tightly linked molecular markers
3.1.2 Plant material
One hundred and one genotypes were selected which were
collected from different location of Karnataka (Table 1).
It includes
traditional variety, improved varieties etc.
3.1.3 Methods
3.1.3.1 Experimental site
For the genotypes selected, sowing was done during Kharif season
of 2010, with spacing of 30 x 15cm. It was raised at the experimental
fields of the Department of Biotechnology, GKVK, UAS, Bengaluru-65.
3.1.3.2 Observation recorded
The data on morphological traits were recorded from all the
genotype
selected.
The
methodology
adopted
for
recording
the
observation in field for each of character is presented below:
1. Days to flowering (DFF)
The number of days taken from sowing to panicle emergence of the
genotypes was recorded.
Table 1 : Lists of genotypes used for screening
Sl.
No.
Genotypes
Source
Variety
1
Abhilash
Mugad
2
Alursanna
3
Amruth
Shivamogga Traditional
Improved
Mugad
M 63-83 x RP 79-5 x RN-21
Anthrasali
Azucena
Bangarakaddi
Bangarakovi
Bangarusanna
Basumati
BI-33
BI-34
BI-43
Bidar local-1
Bile Dodiag
Bile kalavi
BR-2655
Buddha
Case bhatta
Champakali
Chippiga
Mugad
UAS,B”lore
Mugad
Mugad
Shivamogga
Shivamogga
UAS,B’lore
UAS,B’lore
UAS,B’lore
Mugad
Mugad
Mugad
Mandya
Mugad
Mugad
Mugad
Mugad
Buddha x IR64
Buddha x IR64
Buddha x IR64
Selection from BR2655-9-31
-
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Improved
Parentage
Traditional
Traditional
Traditional
Traditional
Traditional
Traditional
Improved
Improved
Improved
Traditional
Traditional
Traditional
Improved
Traditional
Traditional
Traditional
Traditional
CR65-5218 X Pankaj
Remarks
Susceptible to brown plant
hopper
Resistant to blast and brown
leaf spot. Escapes drought
Disease resistance
Tolerant to blast
21
22
23
24
25
Clatagogygidda
Dambersali
DoddaBangarkaddi
Doddamullare
Doddi
Shivamogga
Mugad
Mugad
Mugad
26
Dodiga D-6-2-2
27
28
29
30
31
32
33
34
35
36
Farm valya
Gandhashali
GopalDodiga
Gowrisanna
Gujarat budda
Hegge
Honasu(Red kernel)
Honnekattu
HY-258-1
Intan
Traditional
Traditional
Traditional
Traditional
Traditional
Traditional
Mugad
(landrace)
Mugad
Traditional
UAS,B’lore
Traditional
Mugad
Traditional
Shivamogga Traditional
Mugad
Traditional
Mugad
Traditional
Mugad
Traditional
Mugad
Traditional
Mugad
Traditional
Mugad
Improved
37
IR-64
Mugad
Improved
38
39
40
41
IVT(SHW)-91
J-192
Jaddubhatta
Jasmine
Mugad
Mugad
Mugad
Improved
Traditional
Traditional
Traditional
Introduction from Indonesia
IR5657-3-21 X IR2061-4655-5
-
42
Jaya
Shivamogga Improved (HYV)
T(N)1 x T-141
43
Jeerigesanna
UAS,B’lore
-
Traditional
Susceptible to brown plant
hopper
Susceptible to blast disease
Resistant to bacterial leaf blight
It is moderately susceptible to
bacterial leaf blight, and resistant
to blast
44
45
Jigguvaratiga
KagiSali
46
Kala namak
47
Kannada tumba
48
Kari kantiga
49
50
51
52
53
54
55
56
57
58
59
60
Kari mundaga
Karidaddi
Karisadi
Karna
Kempudooddi
Kerekallumutugya
KHP2
Kirwana
Kyasari
M-81
Makam
Mandyavijaya
Mara
navamiguddabhatta
MedumSali
MGD-101
61
62
63
64
MGD-103
Mugad
Mugad
Traditional
Traditional
Traditional
UAS,B’lore
aromatic
Shivamogga Traditional
Traditional
Mugad
-
Shivamogga
UAS,B’lore
Mugad
UAS,B’lore
Shivamogga
Shivamogga
UAS,B’lore
UAS,B’lore
Shivamogga
Mugad
UAS,B’lore
UAS,B’lore
Traditional
Traditional
Traditional
Improved
Traditional
Traditional
Improved
Traditional
Traditional
Traditional
Traditional
Improved
Jaya x W-1263
BG 90-2 X IR 2863-38-1
Sona X Mahsurie
Mugad
Traditional
-
Mugad
Traditional
Teqing X Binom
Mugad
Improved
Mugad
Breeder line
-
Tolerant to pests
Tolerant to blast.
65
66
67
68
69
70
71
Mobane
Moroberekan
Moromutant
MTU1001
Murukatabhatta
Mutalaga
NeerMulaga
Mugad
UAS,B’lore
UAS,B’lore
Mugad
Mugad
Mugad
Mugad
Traditional
Traditional
Traditional
Improved (HYV)
Traditional
Traditional
Traditional
72
Prassanna
Mugad
Improved
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
Pusasughandi
Puttabatta
Rajkamal
Rajmani
Rani
Rasi
Ratansagar
Ratnachudi
Ratnagiri-24
Sannamullare
Sannavalya
Saratiga
Sarjan
Selumanna
Siddasala
Sorata
Shivamogga
Shivamogga
Mugad
Shivamogga
Shivamogga
Mugad
Mugad
Shivamogga
Mugad
Mugad
Mugad
Mugad
Shivamogga
Shivamogga
Mugad
Mugad
Improved
Traditional
Traditional
Traditional
Traditional
Improved
Traditional
Traditional
Traditional
Traditional
Traditional
Traditional
Traditional
Traditional
Traditional
Traditional
Guinean cultivar x japonica
RP-1667-3011562 (IET 7564) IRAT-8 x N22
T(N)1 x Co.29
-
tolerant to BPH & blast
Resistant to blast disease
89
90
91
92
93
94
95
96
Sugandi
Tirluhegge
Turumuri
Waner-1
Wari Mysore sanna
Yadakmuki
Zadagi
Holesalichipiga
IR-38 (IR 70181-7097 PMI-2-UBN-3-B-13-1)
98 Pushpaka
99 KMR-3
100 Samba Mahsuri
101 BilladiMoratiga
*Traditional variety –
Shivamogga
Mugad
Mugad
Mugad
Mugad
UAS,B’lore
Mugad
Mugad
Traditional
Traditional
Traditional
Traditional
Traditional
Traditional
Traditional
Traditional
IRRI
Improved
UAS,B’lore
UAS,B’lore
UAS,B’lore
Mugad
no parentage
Traditional
Restorer line
Improved
is found
GEB-24 x T(N)1 x Mahsuri
Disease resistant.
2. Plant height at maturity stage
Plant height at maturity stage was measured in centimetres from
base to tip of leaf on main tiller.
3. Number of tillers per plant (NT)
Total number of tillers per plants at maturity were counted and
recorded.
4. Number of productive tillers per plant (NPT)
Total number of panicle bearing tillers in each plant at the time of
harvest were counted and recorded.
3.1.3.3 Statistical analysis
1. Correlation coefficient
Correlation coefficients were computed to find the association
amongst characters using the formula given by Sunderraj et al. (1972).
r xy=
{COV(x/y)}
V(x).V(y)
Where, r xy = Correlation coefficient between x and y
V(x) = variance of x
V(y) =variance of y
3.1.3.4 Genotyping of germplasm
1. Leaf sample collection
Young healthy leaves free from diseased lesions or spots were
collected from the field and placed in a plastic pouch along with the tag
identifying the sample name and number. The bags were placed in an ice
crest. The collected leaf samples were stored at -80°C until they were
processed.
2. DNA isolation of genotypes
Total DNA of 101 genotypes were extracted by following protocol of
CTAB method. The protocol used to extract the DNA is presented below.
•
Approximately 2 g of leaf tissue was ground in liquid nitrogen with the
help of a mortar and pestle.
•
CTAB extraction buffer of 10ml was added to the grounded sample
and then it was transferred to the oakridge tube .extraction buffer{1%
PVP, 2%CTAB, 1.4M Nacl, 20mM EDTA, 0.2% Mercapta ethanol,
100mM Tris HCL (pH 8)}.
•
The tubes were incubated at 650c for 30 minutes with gentle shaking
of the tubes every five minutes.
•
After it’s brought back tubes to room temperature, centrifugation was
done at 10,000 rpm for 10 minutes.
•
Supernatant was collected from the tubes and transferred to the new
tubes.
•
5ml of C : I (chloroform : Isoamyl alchohol,24:1) was added to each of
the tubes.
•
Tubes were centrifuged at 10,000 rpm for 10 minutes.
•
Upper aqueous phase was transferred into fresh tube with the help of
a micropipette.
•
Chloroform: isoamyl alcohol extraction was repeated once again to
remove protein impurities.
•
After centrifugation, upper aqueous phase of each tube was carefully
pipette in to fresh tube and the DNA was precipitated with 8ml of
isopropanol and 2ml of 1.4M Nacl.
•
Tubes were kept at -20oC for overnight and centrifuged at 10,000 rpm
for 15 minutes, supernatant was decanted and pellet was washed in
1ml of 70% ethanol and air dried till the alcohol smell was gone.
•
The DNA pellets were dissolved in 500µl of 0.1XTE and DNA sample
were stored in -20oC.
3. Quantification of DNA –Agarose gel electrophoresis
2 µl of each DNA sample was loaded on to 0.8% agarose gel and to
this 3 µl of Ethidium bromide was used for staining and then subjected
to electrophoresis at 70 volts. After electrophoresis gel was viewed under
trans-illuminator. Then gel was documented for knowing the intensity of
bands. Thus Quantification is done based on the intensity of the bands.
4. Normalization of DNA concentration for PCR
Normalisation of DNA was done to bring all DNA concentrations to
a relatively equal level (25ng/µl) by appropriate dilutions. Dilutions were
made by distilled water.
The list of primers used for research program is presented in
Table 2.
5. Polymerase chain reaction (PCR):
Amplification was carried out on Q Cycler PCR
Standardization of PCR was done as follows:
a) Initial denaturation temperature
94OC
5 min
b) Denaturation
94oC
20 sec
Table 2 : List of primers with annealing temperature used to screen rice accessions for biotic stress
Sl.
no
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Chro.
Forward and Reverse Sequence(5’-3’)
no
FP: ATCGATCGATCTTCACGAGG
Xa4
RM224
11
RP:TGCTATAAAAGGCATTCGGG
FP: GAGTCGATGTAATGTCATCAGTGC
xa5
RM122
5
RP: GAAGGAGGTATCGCTTTGTTGGAC
FP: TCCCAGAAAGCTACTACAGC
xa13
RG136
8
RP: GCAGACTCCAGTTTGACTTC
FP: AGACGCGGAAGGGTGGTTTCCCGGA
Xa21
pTA248
11
RP:AGACGCGGTAATCGAAAGATGAAA
FP: ATATACTGTAGGTCCATCCATCCA
Pi1
RM5926
11
RP:AGATAGTATAGCGAAGCAGC
FP: CTCCTTCAGCTGCTCCTC
Pi2/Z
AP5659-5
6
RP: TGATGACTTCCAAACGGTAG
FP: CCCATGCGTTTAACTATTCT
PiKh
RM206
11
RP: CGTTCCATCGATCCGTATGG
FP: CTTTGTCTATCTCAAGACAC
Bph3
RM190
6
RP: TTGCAGATGTTCTTCCTGATG
FP: TCACCGTCACCTCTTAAGTC
Bph18
RM7376
12
RP: GGTGGTTGTGTTCTGTTTGG
FP: CTGGGCTGCATACCATAGCTT
Bph20
B42
4
RP: AGGGTGTGTTCGGTAGATGG
FP: TCTCAAACCGGCTCTACCAG
Bph20(t)
B44
4
RP: TTACTGGTATGGCAGGAGCA
FP: TGGCTGTGGCAATAAT
Bph21
Si2094A
12
RP: TGCAATGCTGTGGCAATAAT
FP: TCGTCACCAAACAGCACTACA
Bph21(t) RM(B122)
12
RP: GTGACGACTCCCCAATTGTC
Candidate
FP: GTATGCTATGCTACGTGTTTATGC
OsPR#074
7
gene
RP: GCAAATACGGCTGACAGTACAG
Primers
Markers
Base
pair
Annealing
temp
Reference
140
53
Yoshimura et al., 1995
250
55
Yoshimura et al., 1995
500
55
Zhang et al., 1996
850
53
Ronald et al., 1992
155
56
Fuentes et al., 2008
330
58
Fjellstromet al., 2006
155
57
Sharma et al., 2005
143
50.6
Jairinet al.,2007
194
50
Jena et al.,2006
117
59
Rahmanet al., 2009
207
60
Rahmanet al., 2009
234
-
Rahmanet al., 2009
214
52
Rahmanet al., 2009
146
53
Mitusharaet al., 2008
15 OsPR#011
16 OsPR#071
17 OsPR#012
18 OsPR#073
19 OsPR#021
20 OsPR#022
21 OsPR#052
22 OsPR#072
23 OsPR#121
24 OsPR#101
25 OsPR#051
Candidate
gene
Candidate
gene
Candidate
gene
Candidate
gene
Candidate
gene
Candidate
gene
Candidate
gene
Candidate
gene
Candidate
gene
Candidate
gene
Candidate
gene
1
7
1
7
5
5
5
7
12
10
5
FP: ACGCCTTCACGGTCCATAC
RP: AAACAGAAAGAAACAGAGGGAGTAC
FP:CTTTAATATGTATGGAGTATGATATAAATTGTG
RP: TTATTTTCTTCTTTTATTCGAACGACAAC
FP: CGCTGTGTTTGTGTTATGTC
RP: CGTGGTTTTGTCTTTATTTCAATCC
FP: TTATATATGTATGTTCGTATGTATGTATGC
RP: TGTGTACTTATTCCATCCGACGACAC
FP: CGCAGCAACCAACCAATCTTG
RP: ACAGTTGTAGTACTCTTGTAACATCATC
FP: CCACAGAGTTTGTCAGGATTGTC
RP: CAGATTGCACACACCTGATTCC
FP: AGCTACCTGTCATTTCTTCATTTC
RP: TGCTACTCCAGAAGGAAATTAAAAG
FP: AATTAATACTGGAGTAGATGCATGTAC
RP: ACGAATAACGTACTGTATTCTGTATG
FP: ACCATCGTCGTCGTCTCATC
RP: AGCCTCTAGGGCATATCACTAAC
FP: TCGCTGCCGCTAGTACATTTC
RP: ATTAAGATCATTACATGCTTTATTGTTCAC
FP: CCTGCCTGCCTTCCTCATTC
RP: AGTGAAGATTTGGTTTCCATTGTATTG
88
150
53
Mitusharaet al., 2008
-
Mitusharaet al., 2008
53
Mitusharaet al., 2008
-
Mitusharaet al., 2008
58.9
Mitusharaet al., 2008
50.9
Mitusharaet al., 2008
54.3
Mitusharaet al., 2008
-
Mitusharaet al., 2008
59.4
Mitusharaet al., 2008
59.3
Mitusharaet al., 2008
55.6
Mitusharaet al., 2008
c) Primer annealing
55OC
30sec
d) Primer extension
72oC
30 sec
Later, steps b) to d) were repeated 35 times
e) Complete primer extension
72OC
5 min
f) Hold
4oC
Hold
6. Agarose gel electrophoresis:
The PCR products were run on 3% agarose gels to verify
amplification. The gel was electrophoresis with 1X TAE buffer at 90V for
60 min. The gel was then stained in ethidium bromide solution (1µg/ml)
for 10 min with frequent shaking and de-stained with water and
observed on a UV Tran illuminator (BIO BEE).
7. Restriction enzyme digestion of PCR product for STS:
As per the available literature, restriction enzyme digestion was
done with primer xa13 to check polymorphism. After checking the
amplification of PCR products, 15 µl volume of xa13 primer samples was
digested with 1 unit of restriction enzyme HinfI, in a 20 µl reaction
volume and samples were incubated in an incubator (Bacteriological
Incubator, Science equipment) at 37ºC for four hours then to this seven
µl of 3X dye was added .From this nine µl of the digested product was
loaded onto a 1.4% agarose gel and run for one hour. The gel was then
stained in ethidium bromide solution (1µg /ml) for 10 minute and
destained with water and observed on a UV Trans illuminator for the
presence of bands.
8. Gel documentation
The gel was run, and then documented by taking photograph
(Alpha biotech documentation unit) and the gel images were saved for
scoring and future reference.
Screening of genotypes was carried out with primers relating to
different aspect of biotic stress.
9. Scoring the bands
For markers, scorings are given, namely 1, 3, 5 and “–” which were
given to
Susceptible -3, Resistant-1, Other – 5 and Not Amplified “-”
Further, this is used for identifying tightly linked marker for resistant for
biotic stress.
10. Allele mining
It is done based on band size and those which shown different
base pairs other than the expected ones are selected. Based on this, one
can know allele specific to marker or genes. The one which is different
from the usual band is selected and it is given scoring as 5.
3.2 Experiment 2: Identifying chromosomal regions associated with
these markers and identifying annotated genes and designing
new candidate gene primers.
3.2.1 Materials for bioinformatics tools are search engines like
3.2.1.1 Gramene- Here the information provided via the database is
curated using both manual and computational methods. It is available
and web-accessible. The technological core of Gramene is the MySQL
database management system, an open source relational database
system that is stable and well supported http://www.gramene.org. From
this site, we searched for specific genes, specific markers, and accession
number.
3.2.1.2 NCBI- National centre for Biological Research. It is house of
genome sequencing data publicly available online through website.
http://www.ncbi. nih.nlm.gov/
3.2.1.3
Mapviewer:
The
Mapviewer
provides
special
browsing
capabilities for a subset of organisms in Entrez Genome. The organism
subset is shown on the Map viewer home page. Mapviewer allows viewing
and searching an organisms’ complex genome, displaying chromosome
maps and zooming with progressively greater level of details, down to the
sequence data for a chromosome it displays. Aligned chromosomes with
shared marker and gene names and for the sequence maps based on a
common
sequence
co-ordinate
system.
http://www.ncbi.nlm.nih.gov/project/mapview/map viewer
3.2.1.4 Pfam: The Pfam database is one of most important collection of
information in the world for classifying protein. The database categorises
75 percent of known protein to form a library of protein families. It also
provides
a
evolutionary
tool
which
allows
biologists
to
experimental,
classify
computational
protein
and
sequence.
http://www.pfam.sangar.ac.uk/
3.2.2 Methods for use of different bioinformatic tools
3.2.2.1 Identification of primers sequence
•
Type Gramene or gramene.org in the search window of Google.
•
Then select for markers so as to know the primers sequence and its
product size to be amplified
•
A particular gene can also be selected to know its sequence
3.2.2.2 To locate chromosome regions using Mapviewer tool of NCBI
(Plate 1)
•
Type for map viewer in Google for citing its homepage
•
From the map viewer homepage, select search monocots from the pull
down menu, enter Rice in the box labelled for and then select GO
•
On the result page, select the query that will have numerous hits
•
Click on that query this will give the result page for all the
chromosomes
•
Now type the marker and select the chromosome
•
This will give the hit of that marker
•
Click on that chromosome, this gives the result page which will show
the location of markers distributed in chromosomes
3.2.2.3 Annotation of gene by using Gramene, Plant biology site,
Pfam site (Plate 2 and 3)
•
Go for Gramene home page and select for genes
•
Type the gene name for knowing its information
•
Click on the GR number given in the Gene detailed information
•
This gives the LOC number, then click on the given LOC number
•
This gives the details of the gene expression
•
Further select the Protein sequence and Go for the Site Pfam which
exclusively gives the protein domain structure
Step 1
Go for search
Step 2
Select Query
Step 3
Select chromosomes
Step 4
Location of markers
Plate 1 : Steps in finding chromosomes location using Mapviewer
Plate 2 : Steps involved in gene annotation
Step 1
Click on
Genes
Step 2
Type gene
Step 3
Select this No.
Select this No.
Step 5
Step 4
Plate 3 : Steps involved in protein domain structure
Step 2
Step 3
3.2.2.4 Designing of candidate gene
•
CDS(Coding DNA sequence) of candidate genes were retrieved from
TIGR
•
Primers were designed for the selected genes with set critical values
such as GC 50% and melting temperature (Tm) value
•
Out of different primers pairs made, primers with product size from
601-800bp,
201-300bp,
and
301-500bp
were
sent
designing by using bioinformatics tools like Primer 3
for
primer
Experimental Results
IV. EXPERIMENTAL RESULTS
The experimental results obtained in the study are presented
under the following sub headings:
1) Genetic parameters for yield and yield related parameters
2) Correlation studies among growth yield and yield parameters
3) Confirmation of alleles associated to biotic stress with diverse rice
accessions to tightly linked markers
4) Location of chromosomes regions associated with these markers using
bioinformatics tool
5) Annotation of genes by using Bioinformatic source and
6) Designing of new candidate gene primers
7) Profiling of all 20 primers by pie chart for 12 rice chromosomes
depicting the proportion of resistance, susceptible, allele mined band
and non-amplified bands.
4.1 Genetic parameters for yield and yield related parameters
Mean values for all the characters studied are presented in
Appendix-I. The over view of the evaluation of the accessions in the field
is shown in plate 4.
4.1.1 Days to 50 percent flowering
Among the genotypes, Doddamullare was early to reach 50 per
cent flowering and Ratnachudi was late to reach 50 percent flowering.
4.1.2 Days to maturity
Among the genotypes, Gopaldodiga and Cletagogygidda was
reached early to maturity and IR-64, Bangarasanna, Pushpaka, Rasi,
Saratiga, Sarjan, Yadakmuki, Karikantiga were late to maturity.
Overview of rice genotypes at seedling stage in field
Overview of genotypes at maturity stage
Plate 4 : Overview of rice genotypes in experimental plot under
aerobic
4.1.3 Plant Height (cm)
There was considerable variation among the genotypes for plant
height .The range observed was 58 cm to 115 cm. The tallest among the
genotypes was Azucena and the shortest was KMR-3.
4.1.3 Number of tillers per plant
Number of tillers per plant ranged from 5 to 25. The highest
number of tiller was found in Amruth and Kalanamak genotypes and
lowest was found in Yadakmuki and Kalanamak respectively.
4.1.4 Number of productive tillers per plant
Higher number of productive tillers per plant was found in Amruth
genotypes and lowest was found in Yadakmuki and Kalanamak. With the
range of 4 to 21.
4.1.5 Grain yield per plot
The grain yield per plot was found highest in Kempudoddiga with
8g and then lowest in Jaddubatta with 0.03g.
4.2 Correlation studies among growth yield and yield parameters
Characters
association
is
considered
which
includes
the
correlation between grain yield and related characters followed by
correlation among other parameters. Correlation coefficient is shown in
Table 3.
4.2.1 Correlation of grain yield with other characters
The phenotypic correlation values between traits were calculated.
The grain yield was positive and significantly correlated with plant
height, number of tillers and number of productive tillers.
Table 3 : Correlation for the grain yield and yield parameters
X1
X2
X3
X1
1
X2
.692**
1
X3
.717**
.970**
1
X4
.498**
.326**
.382**
** Correlation is significant at the 0.01 level (1-tailed).
X1-Plant height
X2-No of tillers
X3-No of productive tillers
X4-Grain yield per plant
X4
1
4.2.2 Correlation of characters other than grain yield among
themselves
Plant height was positively significant correlated with number of
tillers, number of productive tillers. Even number of tillers and number
of productive tillers are positively correlated to other characters.
4.3 Confirmation of alleles associated to biotic stress with diverse
rice accessions to tightly linked markers
The gene specific markers used are listed in table 2, Out of 25
primers, 20 primers were amplified and among these 12 primers were
gene specific markers and 8 primers were from candidate gene primers.
Among 12 primers, only one primer and among 8 candidate gene
primers, about 3 primers showed monomorphism (Plate 5 and 6). List of
genotypes showing the resistant allele for the primers are presented in
(Table 4).
4.3.1 Allele mining based on PCR product size
Among 20 primers that were amplified, 5 primers have shown
bands other than usual bands. All the 101 genotypes were studied for
each primer. The primers are PiKh, Bph3, Bph20(t) B42, Bph21 (t)
RM(B122) and one candidate gene OsPR#021(Plate 7).The genotypes that
showed variation in band size are listed in Table 5.
4.4
Location of chromosomes regions associated with these
markers using bioinformatics tool (Plate 8)
4.4.1 Mapviewer observation for BLB markers
4.4.1.1 Xa4 gene linked to marker RM224
1. Type of Map: Master map: RS98
Plate 5: Gel Pictures showing polymorphic bands
Xa4
1
2
3
4
5
6
7
8
9
10
11
12 13
14
15
16
L
140(R)
120(S)
Plate 5:1: 1: Bangaru sanna, 2: Basumati, 3: BI-33, 4: BI-34, 5:BI-43, 6:
Bidar local-1, 7:Bile dodiga, 8: Bile kalavi, 9: BR-2655, 10: Buddha, 11:
Case
bhatta,
12:Champakali,
13:Chippiga,
14:Elatogogygidda,
15:Dambersali, 16:DoddaBangarkaddi, L- 100bp ladder
Xa21
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
L
850(R)
650(S)
Plate 5:2: 1:Bangarusanna, 2:Basumati, 3:BI-33, 4:BI-34, 5:BI-43, 6:Bidar
local-1, 7:Bile Dodiga, 8:Bile Kalavi, 9:BR-2655, 10:Buddha, 11:Case bhatta,
12:Champakali,
13:Chippiga,
14:Elatogogygidda,
15:Dambersali,
16:DoddaBangarkaddi, L-100bp ladder
Pi1
L
1
2
3
4
5
6
7
8
9
10 11
12 13 14 15 16 17
18
160(s)
155(R)
Plate 5.3: 1:Gandhashali, 2:GopalDodiga, 3:Gowrisanna, 4:Gujarat Buddha,
5:Hegge, 6:Honasu(Red kernal),7: Honnekattu, 8:HY-258-1, 9:Intan, 10:IR64, 11:IVT(SHW)-91, 12:J-192, 13:Jaddubhatta, 14: Jasmine, 15: Jaya, 16:
Jeerigesanna, 17:Jigguvaratiga, 18:KagiSali, L-100bp ladder
Pi2
L
1
2
3
4
5
6
7
8
9
10
11
12
330(R)
310(S)
Plate 5.4: 1:Jigguvaratiga, 2:Kempudoddi, 3:Kerekallumutugya, 4: KHP2,
5:Kirwana, 6:Kyasari, 7:M-81, 8:Makam, 9:Mandyavijaya, 10:Mara
navamiguddabhatta, 11:MedumSali, 12:MGD-101, L-100bp ladder
PiKh
170(s)
155(R)
Plate 5.5: 1:BI-43, 2:Bidar local -1, 3:Bile Dodiga, 4:Bile Kalavi, 5:BR-2655,
6:Buddha, 7:Case bhatta, 8:Champakali, 9:Chippiga, 10:Elatogogygidda,
11:Dambersali, 12:DoddaBangarkaddi, 13:Doddamullare, L-100bp ladder
Bph18
L
1
2
3
4
5
6
7
8
9
10
11
12
13
14
210(s)
194(R)
Plate
5.6:
1:Dambersali,
2:Farm
valya,
3:Gandhasali,
4:GopalDodiga,5:Gowri sanna, 6: Gujarat Buddha, 7: Hegge, 8:Honasu(Red
kernal), 9:Honnekattu, 10:HY-258-1, 11:Intan, 12: IR-64, 13:IVT(SHW)-91,
14:J-192, 15:Jaddubhatta, L-100bp ladder
15
OsPR#021
L
1
2
3
4
5
6
7
8
9
10
11 12
13
14
15 16
17
100bp
97bp
Plate 5.7: 1:Abhilash, 2:Alursanna, 3:Amruth, 4:Anthrasali,5:Azucena,
6:Badshabhog, 7:Bangarakaddi, 8:Bangarakovi,9:Bangaru sanna, 10:
Basumati, 11: BI-33, 12: BI-34, 13:BI-43, 14: Bidar local-1, 15:Bile
dodiga, 16: Bile kalavi, 17: BR-2655, L-100bp ladder
OsPR#121
L
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
120bp
100bp
Plate
5.8:
1:
Dodiga
D-6-2-2,
2:Farm
valya,
3:Gandhasali,
4:GopalDodiga,5:Gowrisanna, 6:Gujarat Buddha, 7:Hegge, 8:Honasu(Red
kernal), 9:Honnekattu, 10:HY-258-1, 11:Intan, 12:IR-64, 13:IVT(SHW)-91,
14:J-192, 15:Jaddubhatta, 16: Jasmine, L-100bp ladder
16
Plate 6 : Gel Pictures showing monomorphic bands
Bph20 b44
L
1
2
3
4
5
6
7
8
9
10
11
12
13
14
207bp
100bp
Plate
6.1:
1:Karidaddi,
2:Karisadi,
3:Karna,
4:Kempudoddi,
5:Kerekallumutugya, 6:KHP2, 7:Kirwana, 8:Kyasari, 9:M-81, 10:Makam,
11:Mandyavijaya, 12:Mara navamiguddabhatta, 13:MedumSali, 14:MGD101, L-100bp ladder
OsPR#012
L
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
150bp
100bp
Plate
6.2:
1:Abhilash,
2:Alursanna,
3:Azucena,
4:Badshabhog,
5:Bangarakaddi, 6: Basumati, 7: BI-33, 8: BI-34, 9:BI-43, 10: Bidar local1, 11:Bile dodiga, 12: Bile kalavi, 13: BR-2655,14:Buddha, 15:Case
bhatta, 16:Champakali, 17:Chippiga, L-100bp ladder
OsPR#074
L
1
2
3
4
5
6
7
8
9
10
11 12
13
14
15
16
146bp
100bp
Plate 6.3: 1:Rajmani, 2:Rani, 3:Ratansagar, 4: Ratnachudi, 5:Ratnagiri-24,
6:Sannamullare, 7:Sannavalya, 8:Saratiga, 9:Sarjan, 10: Selumsanna,
11:Siddasala, 12:Sonamasuri, 13:Sorata, 14:Sugandi, 15:Tirluhegge, 16:
Turumuri, L-100bp ladder
OsPR#051
L
1
2
3
4
5
6
7
8
9 10
11
12
13
14
15
16
100bp
97bp
Plate 6.4: 1:Abhilash, 2:Alursanna, 3:Amruth, 4:Anthrasali,5:Azucena,
6:Badshabhog, 7:Bangarakaddi, 8: Bangarakovi,9:Bangaru sanna, 10:
Basumati, 11: BI-33, 12: BI-34, 13:BI-43, 14: Bidar local-1, 15: Bile
dodiga, 16: Bile kalavi,17:BR-2655, L-100bp ladder
17
Table 4 : List of primer for genotypes showing resistance to biotic stress
No
Names
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Ablilash
Alur sanna
Amruth
Anthrasali
Azucena
Bangara kaddi
Bangara kovi
Bangaru sanna
Basumati
BI-33
BI-34
BI-43
Bidar local-1
Bile dodiga
Bile kalavi
BR-2655
Buddha
Case bhatta
Champakali
Chippiga
Cletagogy gidda
Dambersali
Dodda bangar
kaddi
Dodda mullare
Doddi
Dodiga D-6-2-2
Farm valya
24
25
26
27
BLB BLB BLB BLB BLAST BLAST BLAST BPH BPH
BPH
BPH
BPH
h
Xa4 Xa21 xa5 xa13
Pi1
Pi2
PiK
Bph3 Bph18 Bph20B42 Bph20B44 Bph21RM
√
√
√
√
√
√
√
√
√
√
A
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
A
√
√
√
√
A
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
-
-
-
√
√
√
√
√
√
-
-
√
√
√
√
√
-
-
-
-
-
√
√
√
√
-
-
-
√
-
√
√
√
√
√
√
√
√
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
GandhaShali
Gopal doddiga
Gowri sanna
Gujarat Buddha
Hegge
Honasu(red
kernal)
Honne kattu
HY-258-1
Intan
IR-64
IVT(SHW)-91
J-192
Jaddu bhatta
Jasmine
Jaya
Jeerige sanna
Jiggu varatiga
Kagi sali
Kala namak
Kannada
tumbha
Kari kantiga
Kari mundaga
Kari daddi
Karisadi
Karna
Kempudoddi
Kerekallu
mutugya
KHP-2
√
√
√
√
√
√
-
-
√
-
√
√
√
√
√
-
-
-
-
√
-
-
-
√
-
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
-
-
-
√
√
√
√
√
√
√
√
√
-
√
√
-
A
-
√
√
√
√
√
√
√
√
√
√
-
-
-
√
√
-
√
√
-
√
√
-
√
-
√
√
√
√
√
√
√
√
-
-
√
√
√
A
√
√
√
√
A
-
-
-
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
-
-
√
-
-
√
√
-
√
√
√
√
√
√
√
√
√
-
-
√
√
√
√
√
√
√
√
√
√
√
√
√
√
-
-
√
-
-
-
√
√
√
-
-
√
-
-
-
√
√
-
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
Kirwana
Kyasari
M-81
Makam
Mandya vijaya
Mara navami
gudda bhatta
Medium Sali
MGD-101
MGD-103
Mobane
Moroberekan
Moromutant
MTU 1001
Murukata
bhatta
Mutalaga
Neer mulaga
Prassanna
Pusa sughandi
Putta batta
Raj kamal
Rajmani
Rani
Rasi
Ratan sagar
Ratna chudi
Ratnagiri-24
Sanna mullare
Sanna valya
Saratiga
Sarjan
√
√
√
√
√
√
√
-
-
√
√
√
-
√
√
√
√
√
√
√
A
A
-
√
√
√
√
√
√
√
√
-
√
√
√
√
√
-
-
-
-
-
-
-
-
√
√
√
√
√
√
√
√
√
-
-
-
√
-
√
√
-
√
√
√
-
A
A
-
-
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
-
√
-
-
-
-
√
-
-
-
√
√
A
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
-
-
-
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
A
√
√
√
-
-
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
-
A
√
√
√
√
√
√
√
√
√
-
√
√
√
√
86
87
88
89
90
91
92
93
Selun sanna
Sidda sala
Sorota
Sughandi
Tirlu hegge
Turumuri
Waner-1
Wari mysore
sanna
94 Yadakmuki
95 Zadagi
96 Holesali chipiga
97 IR-38
98 Pushpaka
99 KMR-3
100 Sambha masuri
101 Billadi morotiga
√= resistant band
A=Alleles mined
√
√
√
√
√
√
√
√
√
-
-
-
√
√
√
√
-
-
√
√
√
√
√
-
√
√
√
-
-
A
-
√
√
√
√
√
√
√
√
√
-
-
√
√
√
√
√
√
√
√
√
-
√
-
-
-
√
√
√
√
√
√
√
√
-
√
√
-
√
√
√
-
-
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
Plate 7: Allele mining of rice accession for different primers
Allele mining for PiKh
L
1 2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
(S)177bp
(R)155bp
2 bands
Plate 7.1: M-PiKh, 1—Kysari, 2--M-81, 3—Makam, 4-Madya vijaya, 5-Mara
navami, 6-Medium salli, 7-MGD-101, 8-MGD-103, 9-Mobane, 10Moroberekan, 11-Moromutant, 12-MTU1001, 13-Murukatta bhatta, 14Mutalaga, 15-Neer mulaga, 16-Prassanna, 17-Pusa sughandi,18-Putta
bhatta, 19-Rajkamal, 20-Raj mani, 21-Rani, 22-Rasi, 23-Ratan sagar.
Allele mining for Bph3
L
1 2
3
4 5 6
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
(R)143bp
(S)120bp
A.M
Double bands
Plate 7.2: L, 1-Ablilash,2-Alur sanna*,3-Amruth,4-Anthrasali,5-Azucena,6Bangara Sali,7-Bangara kovi,8-Bangara sanna,9-Basumati,10-BI-33,11-BI34,12-BI-43,13-Bidar local-1,14-Bile Dodiga,15-Bile kalavi,16-BR-2655,17Buddha,18-Case bhatta,19-Champakali, 20-Chippiga, 21-Elatigogy gidda,
22-Damersali, 23-Dodda bangarakaddi, 24-Doddi, 25-Dodiga-D-6-2-2.
(*=Allele mine)
Allele mining for Bph 20tB42
L
1 2
3 4 5
6
7
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
117bp
100bp
2 band
Plate73: L,1-Ablilash,2-Alur sanna,3-Amruth,4-Anthrasali,5-Azucena,6Bangara Sali,7-Bangara kovi,8-Bangara sanna,9-Basumati,10-BI-33,11-BI34,12-BI-43,13-Bidar local-1,14-Bile Dodiga,15-Bile kalavi,16-BR-2655,17Buddha,18-Case bhatta,19-Champakali,20-Chippiga21-Elatigogy gidda,22Damersali,23-Dodda bangarakaddi,24-Doddi,25-Dodiga-D-6-2-2.
Allele mining of OsPR#021
L
1 2 3
4 5 6 7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
100bp
95bp
Double band
Plate7.4: L,1-Karna, 2-Kempudaddi, 3-Kerekallu mutugya, 4-KHP2, 5kirwana,
6-Kyasari,
7-M-81,
8-Makam,9-Madya
vijaya,
10-Mara
navamiBuddha,11-Medium
Sali,12-MGD-101,13-MGD-103,14-Mobane,15Moroberekan,16-Moromutant,17-MTU1001,18-Murukata
battha,
19Mutalaga, 20-Neer Mulaga, 21-Prassana,22-Pusa sughandi,23-Putta
bhatta,24-Rajkamal,25-Rajmani
Allele mining for xa13 primer
L
1 2 3 4 5
6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
1100bp
1000bp
Plate 7.5:L, 1:Kyasari, 2:M-81, 3:Makam, 4:Mandya vijaya, 5:Mara
navamiguttalya,
6:MediumSali,
7:MGD101,
8:MGD103,9:Mobane,
10:Moroberekan, 11:MTU1001, 12:Murukata batta, 13:Mutalaga, 14:Neer
Mulaga, 15:Prassanna, 16:Pusa sughandi, 17:Putta bhatta, 18:Rajkamal,
19:Rajmani, 20:Rani, 21:Rasi, 22:Ratan sagar, 23:Ratna chudi,
24:Ratnagiri-2, 25:Sanna valya
Allele mining for Bph21(t)
L 1 2 3 4
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
100bp
Double bands
Plate
7.6:
L,1-Kari
mundaga,2-Karidaddi,3-Karisadi,4-Karna,5Kempudoddi, 6-Kerekallu mutaliga,7-KHP2, 8-Kirwana, 9-Kysari, 10-M81,11-Makam,12-Madya vijaya,13-Mara navami,14-Medium salli,15-MGD101,16-MGD-103,17-Mobane,
18-moroberekan,
19-Moromutant,
20MTU1001,21-Murukatta
bhatta,
22-Mutalaga,23-Neer
mulaga,24Prassanna,25-Pusa sughandi.
Table 5 : List of primers showing allele mined among genotypes
Primers
Genotypes
HY-258-1,
PiKh
Jigguvaratiga,
Ratnagiri-2
Alursanna,
Kirwana,
Bph3
Mandyavijaya,
MGD-103,
Mobane
Bph20(t)B42
Bph21(t)RMB122
Bangarakovi,
Bangarusanna
Murukathabattha
Neermulaga
Makam,
OsPR#021
MTU-1001,
Neermulaga
Kannada tumbha,
xa13
Pusasughandi,
Yadakmuki
2. Symbol: RM224-11RS98
3. cM distance: 17.9
4. Type of marker-SSR
5. Present on chromosome-11
4.4.1.2 xa5 gene linked to marker RM122
1. Type of Map: Master map: RDOO
2. Symbol: RM224-5RDOO
3. cM distance: 0
4. Type of marker-SSR
5. Present on chromosome-5
4.4.1.3 Xa21 gene linked to pTA 248
1. Type of Map: Master map: RC94
2. Symbol: RM224-11RC94
3. cM distance: 99.6
4. Type of marker-RFLP
5. Present on chromosome-11
4.4.1.4 xa13 gene linked to RG136
1. Type of Map: Master map: RW99
2. Symbol: RM224-8RW99
3. cM distance: 108.6
4. Type of marker-RFLP
5. Present on chromosome-8
4.4.2 Mapviewer observation for Blast markers
4.4.2.1 Pi2 linked to AP5659.5
1. Type of Map: Master map: HordeumvulgareUnigene cluster
2. Linked to many Unigene
4.4.2.2 PiKh linked to RM206
1. Type of Map: Master map:RS98
2. Symbol: RM206-11RS98
3. cM distance: 78.4
4. Type of marker-SSR
5. Present on chromosome-11
4.4.3 Mapviewer observation for BPH markers
4.4.3.1 Bph3 linked to RM190
1. Type of Map: Master map: RD01
2. Symbol: RM190-11RD01
3. cM distance: 7.4
4. Type of marker-SSR
5. Present on chromosome-11
4.4.3.2 Bph18 linked to RM7376
1. Type of Map: Comparative map
2. Symbol: RM190-11RD01
3. Base pair position: 23,44353
4. Type of marker-SSR
5. Present on chromosome-11
4.4.4 Mapviewer observation for OsPR genes
4.4.4.1 OsPR#011
1. Region displayed: 16,261,359bp-16,262,183bp
2. Located near: Os.39902
Os.64972
3. Present on Chromosome-1
4.4.4.2 OsPR#012
1. Region displayed: 16,285,610-16,286,360bp
2. Located near:
3. Present on Chromosome-1
4.4.4.3 OsPR#021
1. Region displayed: 33,402,150-33,403,070bp
2. Located near: Os.88341
3. Present on Chromosome 2
4.4.4.4 OsPR#022
1. Region displayed: 33,419,080-33,420,610bp
2. Located near: Not Specified
3. Present on Chromosome 2
4.4.4.5 OsPR#051
1. Region displayed: 29,541,500-29,542,510bp
2. Located near: Os.12748
3. Present on Chromosome 5
4.4.4.6 OsPR#052
1. Region displayed: 29,551,650-29,554,990bp
2. Located near: Not Specified
3. Present on Chromosome 5
4.4.4.7 OsPR#071
1. Region displayed: 1,317,330-1,318,100bp
2. Located near: Os.5077
Os.50993
Os.9014
3. Present on Chromosome 7
4.4.4.8 OsPR#071
1. Region displayed: 1,430,980-1,318,100bp
2. Located near: Not Specified
3. Present on Chromosome 7
4.4.4.9 OsPR#073
1. Region displayed: 1,435,590-1,436,450bp
2. Located near: Not Specified
3. Present on Chromosome 7
4.4.4.10 OsPR#074
1. Region displayed: 1,510,340-1,511,260bp
2. Located near: Not Specified
3. Present on Chromosome 7
4.4.4.11 OsPR#101
1. Region displayed: 6,135,270-6,135,960bp
2. Located near: Os.46275
3. Present on Chromosome 10
4.4.4.12 OsPR#121
1. Region displayed: 27,112,370-27,114,050bp
2. Located near: Os.61739
Os.18620
Os.80929
3. Present on Chromosome 12
4.5 Annotation of genes by using Bioinformatic source
Annotation of gene has been done by using sites like TIGR, Pfam,
Plant PAN Site and the details of the annotation is given in table 6.
4.6 Designing of new candidate gene primers
Designing of candidate gene primer is done by using the CDS of
the annotated gene and also with the help of Primer3 input tool software.
Certain parameters such as Base pair size, GC content and Tm value
were taken into consideration. The candidate genes designed in the study
are presented in Table 7.
Plate 8 : Location of Chromosome using Mapviewer
OsPR#011
Chromosome No 1
OsPR#012
Chromosome No 1
OsPR#021
Chromosome No 2
OsPR#022
Chromosome No 2
OsPR#051
Chromosome No 5
OsPR#052
Chromosome No 5
OsPR#071
Chromosome No 7
OsPR#072
Chromosome No 7
OsPR#073
Chromosome No 7
OsPR#074
Chromosome No 7
OsPR#101
Chromosome No 10
OsPR#121
Chromosome No 12
Table 6.1 : Details of annotation of genes for biotic stress
Gene
symbol
Chromosome
no
Accession
no
Loc no
Genomic
length
CDS
length
Protein
length
Putative function
Xa21
11
GR:0061029 Os11g35500
3585
2997
998
Receptor-like protein
Kinase 5 precursor
putative expressed
xa13
8
GR:0061021 Os07g10580
694
471
156
PROLM26-Prolamin
precursor,expressed
xa5
5
GR:0061015 Os03g04520
8964
2895
964
RNA recognition motif
containing protein
putative, expressed
Xa4
11
GR:0061014 Os07g10580
694
471
156
PROLM26-Prolamin
precursor,expressed
Pi1
11
GR:0060630 Os07g10580
694
471
156
PROLM26-Prolamin
precursor,expressed
Pi2/Z
6
GR:0060628 Os07g10580
694
471
156
PROLM26-Prolamin
precursor,expressed
Bph3
4
GR:0060091 Os07g10580
694
471
156
PROLM26-Prolamin
precursor,expressed
BPH18
11
GR:0061429 Os07g10580
694
471
156
PROLM26-Prolamin
precursor,expressed
Table 6.2 : Details of annotated candidate gene for biotic stress
Gene
symbol
Chromoso
me no
Accession
no
LOC_ no
OsPR#011
1
AK107926
Os01g28450
OsPR#012
1
AK121108
Os01g28500
OsPR#021
2
AK107467
Os02g54540
OsPR#022
2
AK105575
Os02g54560
OsPR#051
5
AK071326
Os05g51660
OsPR#052
5
AK100748
Os05g51680
OsPR#71
7
AK060057
Os07g03279
OsPR#72
7
AK062949
Os07g03580
OsPR#73
7
AK063248
Os07g03590
OsPR#74
7
AU163470
OsPR#101
10
OsPR#121
12
Location
Strand
Genomic
length
Forward
825
Reverse
755
Forward
922
Forward
1,528
Reverse
1,008
Reverse
3,342
Forward
765
Forward
721
14355911436446
Forward
855
Os07g03710
15103421511256
Forward
915
AU070895
Os10g11500
61352666135958
Forward
693
AK100940
Os12g43700
2711404827112369
Reverse
1,680
1626135916262183
1628636316285609
3340214933403070
3341908033420607
2954250829541501
2955499029551649
13173381318097
14309831431703
Function
Pathogen-related protein PRB3
precursor, putative expressed
Pathogen-related protein PRMS
precursor, putative expressed
Pathogen-related protein PR-1
precursor, putative expressed
Pathogen-related protein PR-1
precursor, putative expressed
SCP-like extra cellular protein,
expressed
SCP-like extra cellular protein,
expressed
SCP-like extra cellular protein,
expressed
SCP-like extra cellular protein,
expressed
Basic form of pathogenesis-related
protein 1 precursor, putative,
expressed
Pathogenesis-related protein
PRB1-3 precursor, putative
expressed
Pathogenesis-related protein
PRB1-2 precursor, putative
expressed
SCP-like extra cellular protein,
expressed
Table 7 : Designing primer for candidate gene from CDS region of
the gene sequence
Xa21
Oligo
Start
length
Tm
GC%
Any
3’
Seq
Left
primer
1104
20
60.01
50.00
2.00
1.00
ATCCCTTCCCTCTTC
CTTCA
Right
primer
1862
20
60.01
50.00
6.00
3.00
GCAAGTGTCGAAAC
GAGACA
SEQUENCE SIZE: 2937
INCLUDED REGION SIZE: 2937
PRODUCT SIZE: 759, PAIR ANY COMPL: 3.00, PAIR 3' COMPL: 2.00
Xa4
Oligo
Start length
Tm
GC%
Any
3’
Seq
Left
primer
40
20
59.97 50.00
3
2
CAATCGCATCTCCTGCTACA
Right
primer
248
20
59.90 50.00
2
2
ACAATCGCCTGAACGCTACT
SEQUENCE SIZE: 411
INCLUDED REGION SIZE: 411
PRODUCT SIZE: 209, PAIR ANY COMPL: 2.00, PAIR 3' COMPL: 1.00
xa5
Oligo
Start length
Tm
GC% Any 3’
Seq
Left
primer
421
20
60.05
50
4
0
GTGAAGGTCCCATCGAAAGA
Right
primer
907
20
59.99
50
6
0
TGCTTGTCGACCTTCTCCTT
SEQUENCE SIZE: 2895
INCLUDED REGION SIZE: 2895
PRODUCT SIZE: 487, PAIR ANY COMPL: 7.00, PAIR 3' COMPL: 3.00
4.7 Profiling of all 20 primers by pie chart for 12 rice chromosomes
depicting the proportion of resistance, susceptible, allele mined
band and non-amplified bands
Pie chart for 12 chromosomes is depicted in Plate 9 which shows
the percentage of resistant band, susceptible band, allele mined band
and bands which are not amplified. Here all the primers are arranged
according to the chromosome number where most of the primers recede
in Chromosome 11.
Plate 9 : Pie chart diagram of all 12 chromosomes of rice
OsPR#012
upper band
lower band
Chr no.1
N.A
Others
Chr no 2
Chr.no 3
Bph20B42
Chr.no 4
Resistant
band
susecpitbl
e
N.A
Chr. No. 5
Chr. no. 6
Chr no 7
Chr. No. 8
Chr. No.9
Chr.no.10
Pikh
Resistant
band
susecpitbl
e
N.A
Chr. No.11
Pikh
Chr no.12
Resistant
band
susecpitbl
e
N.A
D i s c us s i o n
V. DISCUSSION
Aerobic rice is a water-conserving rice production system in which
potentially high yielding, fertilizer responsive adapted rice varieties are
grown in by direct seeding, in unpuddled fields with no need for standing
water any time during the cropping season. Supplementary irrigation,
however, can be supplied in the same way as to any other upland cereal
crop (Wang et al., 2002; Bouman et al., 2005; Shashidhar, 2007). In
temperate areas of northern China about 140 000 ha are already
cultivated with aerobic rice using “Han Dao” varieties that are especially
adapted to the aerobic soil conditions. The yields that can be obtained
here range from 4.5 to 6.5 t ha-1 (Wang et al., 2002). For the tropics, the
aerobic rice system is still under development. But also, high yields can
be obtained; in transplanted aerobic rice, e.g. up to 6t ha-1 (Bouman et
al., 2005). In India, BI-33 has been released for aerobic cultivation
(Verulkar et al., 2010).
Irrigated rice has very low water-use efficiency as it consumes
3000–5000 litres of water to produce 1 kg of rice. The traditional rice
production
system
not
only
leads
to
wastage
but
also
causes
environmental degradation and reduces fertilizer use efficiency. Along
with high water requirement, the traditional system of transplanted rice
production in puddled soil on long run leads to destruction of soil
aggregates and reduction in macro pore volumes, and to a large increase
in micro pore space which subsequently reduce the yields of post rice
crops, e.g. wheat (Shashidhar, 2007; www.aerobicrice.in)
One of the major future challenges for agriculture is to produce
more food with less water. Rice is mainly grown in the submerged
conditions but there is a need to develop strategy for growing rice under
aerobic conditions to decrease water use in rice production. Globally 80
percent of the agricultural land area is fed and drought is known to
cause substantial reduction in the economic yield of crop plants
(Widawsky and O’Toole, 1990).
Application of MAS in breeding for resistance to bacterial leaf
blight (BLB) is the most advanced in rice breeding program, both in
terms of science and in producing commercial products. A number of
new varieties have been released with different Xa combinations in the
past decade.The pathway to success dated back to over 20 years when a
concerted effort was made to produce a comprehensive series of nearisogenic lines for all bacterial blight resistance genes (Xa). Most of the
discovered Xa genes are bred into the indica recurrent parents IR24, a
variety with wide adaptability and high agronomic qualities (Casiana et
al., 2009).
Similar to the success with Xa genes, several blast resistance
genes (Pi) have been reported in literature. However, the use of MAS in
Blast resistance breeding has lagged far behind BLB. There are at least
two reasons for this gap; firstly, breeding for blast resistance cannot
follow the simple pyramiding of major R genes as in bacterial blight.
Secondly,Pi genes have been bred into the genetic backgrounds of Co39
(indica types)
and
LTH
(japonica types)
because
of
their
wide-
susceptibility to blast (Fukuta et al., 2004, Kobayashi et al., 2007,
Fukuta et al., 2009 and Casiana et al., 2009).
Application of MAS for selection of insect resistance is most
advanced because of the strong interaction phenotypes among
gall
midge. For tropical Asia, the key insect problem amenable to a MAS
breeding approach is BPH because the number of genes identified for
BPH is high (Rahman et al., 2009). Major genes conferring BPH
resistance in several cultivated rice and wild species have been mapped
with markers that will facilitate MAS for BPH resistance (Jena et al.,
2006 and Casiana et al., 2009).
The results of the present study are discussed under the following
subheadings
1. Evaluation of rice accessions for grain yield and yield related
parameters
2. Confirmation of biotic stress related markers with diverse genetic
material and identification of alleles associated with them
3. Location of chromosome region by bioinformatics tools and
4. Annotation of gene and designing of candidate gene primers using
bioinformatics tools.
5.1 Evaluation of rice accession for grain yield & yield related
attributes
The success of breeding programmes in any crop species depends
mainly on the understanding of certain genetic parameters and
interrelationships of various characters with grain yield. It is important
to isolate genotypes which can compromise between grain yield and
biotic stress. Such genotype performs better under stress conditions.
The 101 diverse rice accessions were evaluated under aerobic
conditions for the following characters viz.,days to 50% flowering, days to
maturity, plant height, number of tillers, number of productive tillers per
plant and total yield per plot.
Among the genotypes evaluated Dodda Mullare was early to reach
50 per cent flowering and Ratnachudi was late to reach 50 per cent
flowering. Breeders always look for genotype, which are early to mature.
Further days to maturity were also less in genotype Gopaldodiga
and Eletagogygidda. Early flowering and early maturity genotypes are
likely to escape stress condition if occurs.
Plant height was highest in Azucena (115 cm) and the lowest was
in KMR3 (58 cm). Genotypes with dwarf characters are preferred over
taller ones, since the dwarf type are high yielders as compared to taller
genotypes. More number of tillers per plants was observed in Amruth
and Selumsanna (25) and the lowest in Yadakmuki and Kalanamak (5).
However, genotype Amruth (21) had more number of productive tillers
per plant compared to the rest of the genotypes. It is always ideal to have
good number of productive tillers even though the total numbers of tillers
are less. Total yield per plant was highest in Kempudoddi (8.02g). Similar
line of work has been carried earlier (Prasad et al., 1996).
5.2 Confirmation of biotic stress related markers with diverse
genetic material and identification of alleles associated with
them
Molecular markers are rapidly being adopted for crop improvement
by researchers globally as an effective and appropriate tool for basic and
applied
studies
addressing
biological
components
in
agricultural
production systems (Jones et al., 1997; Mohan, 1997; Prioul et al.,
1997). Use of markers in applied breeding programmes can range from
facilitating appropriate choice of parents for crosses to mapping/ tagging
of gene blocks associated with economically important traits.The
usefulness of DNA markers for germplasm characterization and of
genomic regions that are involved in the expression of traits of interest
for single gene transfer has been well demonstrated.
However, when several genomic regions must be manipulated,
marker assisted selection has turned out to be less useful. Currently,
MAS of single alleles is perhaps the most powerful approach that uses
DNA markers effectively.
Among 25 markers used, only 20 markers produced reproducible
amplified
products
using
the
protocols
specified
in
the
papers
published.Out of which 12 were from gene specific markers and eight
were candidate gene markers. Among 12 gene-specific markers, four
were for BLB, three were for blast and five for BPH. All manifested the
polymorphic bands. This primer product was treated with restriction
enzymes so as to differentiate resistant and susceptible band but all
genotypes,
after
treatment,
showed
monomorphic
bands
of
the
susceptible type.
For bacterial leaf blight studies, we used four resistant gene
markers they are Xa21, Xa4, xa13, xa5.Similar studies was done for BLB
resistance, where in Nine F1plants from each of the crosses between
Mahsuri/IRBB60
(donor
for
four
BLB
resistant
genes
like
Xa21,Xa4,xa13,xa5),PRR78/IRBB60,IR58025B/IRBB60,Pusa6B/IRBB60
were tested for their heterozygosity for the R gene linked markers and
were backcrossed using the female parent (Shanti et al., 2010).
The PCR marker linked to xa-13 allowed efficient screening of 166
local accessions and 25 parents of hybrid rice.Where marker RG136
produced a resistant banding pattern with the size of 1500bp and
susceptible band of 1000bp (Pha and Lang, 2004).In our study we have
got susceptible band for xa13 gene with 1000bp and a few other alleles
which is more than 1000bp in some genotypes.
For blast studies we used gene specific markers like Pi1, Pi2, and
PiKh. Marker assisted backcross breeding has been successfully utilised
in transferring blast resistance genes Pi-Kh and Piz5 (Prabhu, et al.,
2009).The diversity of alleles in blast resistance genotypes was measured
for four SSR markers and one STS marker. These methods are accurate
enough to apply in practice to select varieties that have blast resistance
genes for breeding rice (Lang et al., 2008).The markers used in the
present study showed a polymorphic band and PiKh marker showed a
variable banding pattern along with the known banding pattern similar
to the earlier results (Prabhu et al., 2009.
Brown plant hopper (BPH) is a destructive insect pest of rice in
Asia. Identification and the incorporation of new BPH resistance genes
into modern rice cultivars are important breeding strategies to control
the damage caused by new biotypes of BPH. For Brown plant hopper,
gene specific markers used were Bph3, Bph18t, Bph20t, Bph20tB44 and
Bph21 (t).To find molecular markers tightly linked to the Bph3 locus.
Jairen, et al., (2007) used SSR markers surrounding the target regions
that had been identified in previous studies. Applying this approach,
they were able to detect markers associated with the major resistance
gene. Based on the SSR analysis and linkage analysis, in the present
study, assigned the major resistance gene Bph3 to the short arm of rice
chromosome 6. Among the primers studied, all primers showed
polymorphic bands, in addition some primers have shown allelic
variation.Bph3 has shown unique band and it’s seen in Alursanna
.Similar line of work was carried out by (Latha et al., 2004).
Twelve candidate genes (CGs) were used for this study. Among
these only eight CGs (OsPR#012, OsPR#051, OsPR#074, OsPR#021,
OsPR#022, OsPR#052, OsPR#101, and OsPR #121) were amplified.
Among this CGs, three CGs were showing monomorphic bands. Here
based on upper band and lower band polymorphic study is done.
Allele mining exploits the deoxyribonucleic acid (DNA) sequence of
one genotype to isolate useful alleles from related genotypes. The
international
project
to
sequence
the
genome
of
O.
sativa
L.
cv.Nipponbare will make allele mining possible for all genes of rice and
possibly related cereals.
Studies on the biotic stress using markers, 4 primers i.e. Pikh,
Bph20(t) B42, Bph21(t) and OsPR#021 have shown double bands in
some genotypes and 2 primers i.e. xa13 with genotypes, Kannada
tumbha, Pusasughandi and Yadakmuki and Bph3 with genotypes
Alursanna have shown allelic variation by change in base pair size Latha
et al., (2004) have carried out
similar type of work on
biotic stress
using 64 germplasm accessions using the six primer and the results
were in conformity with the present study.
5.3 Location of chromosome region by bioinformatics tools
Map Viewer was used to view assembled genomes (either draft or
complete) and is a valuable tool for the identification and localization of
genes and other biological features. Multiple map displays are aligned
based on shared marker and gene names when available, and sequence
map displays are based on a common sequence coordinate system.
Sequence data for chromosome regions of interest can be downloaded,
biological annotations can be viewed in graphical format and/or
downloaded in tabular format and gene models can be manipulated in
the associated ModelMaker tool (http://www.edavar.com/sicence.htm).
Further, the location of the marker/primer on the specific
chromosome region, using bioinformatic tools (Mapviewer of NCBI and
the Gramene) was made.
The genes Xa4 and Xa21 were located on
chromosome 11, xa13 on chromosome 8 and xa5 on chromosome 5. Pi2
and PiKh were located on chromosomeon 6 and 11 respectively. Bph3 and
BPh18 were located on chromosomes 11 and 12 respectively. All
candidate gene chromosomes are known with OsPR#011 and OsPR#012
on chromosome 1, OsPR#021 and OsPR#022 on chromosome 2,
OsPR#051 and OsPR#052 on chromosome 5, OsPR#071, OsPR#072,
OsPR#073, OsPR#074 on chromosome 7, OsPR#101 on chromosome 10
and OsPR#121 on chromosome 121. Similar packages were used earlier
to locate gene specific markers and candidate gene markers (Lincoln et
al., 1992).
5.4
Annotation of gene and designing of candidate gene from
bioinformatics tools.
5.4.1 Gene Annotation
Rice is the first cereal genome to be completely sequenced. Since
the completion of its genome sequencing, considerable progress has been
made in multiple areas including the whole genome annotation, gene
expression profiling, mutant collection, etc. With the help of sites like
gramene, TIGR, Pfam annotation of genes could be done. From the
gramene site, identification of accession number was done and with help
of this LOC_ Number was found from Ensembl and TIGR. In rice genome
project there are many options, when selected on gene expression icon.
LOC_ Number was pasted there and then clicking on the LOC_ Number
full expression of locus specific genome sequence, CDS sequence and
protein sequence was obtained. Even Gene’s putative function is also
given (Jiang et al., 2010).
In case of candidate gene, PAM site was used for annotation where
in based on LOC_Number given in literature was selected and pasted in
this site which gives the LOC_Number genome sequence and also UTR
region sequence.Along with that type of strand, its position is also
displayed.
5.4.2 Designing of candidate gene
Bioinformatics tool ( Primer3) has a very powerful PCR primer
design program having
considerable control including size of product
desired, primer size , Tm range and presence/absence of a 3’-GC clamp
(http://molbio.tools.Ca/PCR.htm). If CDS of gene is pasted as query into
the dialogue box , then the base pair is set inorder to get a proper PCR
product size. When the primer is designed, Tm value and GC percentage
is checked and then the reliable forward and reverse sequence was
selected for that specific gene (Pflieger et al., 2001) .
Then pooling of all gene specific markers and candidate gene is
done to see the entire molecular profile for biotic stress.
Further in continuation of the present study, the alleles mined can
be sequenced for use in breeding using MAS. Future Studies on
annotated genes as the informative marker loci to enable fine-mapping of
the locus using CG approach and designing of more candidate gene
primers may be carried for improved molecular profiling for rice
accessions for biotic stress.
Summary
VI. SUMMARY
The present study involved molecular profiling of diverse rice
germplasm accessions for loci know to be associated with biotic stresses.
The experiments to identifying alleles known to be associated with
bacterial leaf blight, leaf blast and brown plant hopper using tightly
linked molecular markers were carried out. The chromosomal regions
associated with these markers and the genes annotated at these regions
were also identified. The data was used to design new candidate gene
primers.
Wide range of variation was observed among 101 diverse
accessions evaluated under aerobic conditions. There was a significant
difference among genotypes for all characters like, days to 50% flowering,
days to maturity, plant height, number of tillers, number of productive
tillers per plant. Among the genotypes evaluated.
Most notable
genotypes for a few key agronomic traits were Gopaldodiga (very early
maturity),
Azucena
(tallest
genotype)
Amruth
and
Selumsanna
(maximum number of tillers) and Amruth (maximum number of
productive tillers per plant) were notable.
With regard to the molecular profiling, six primers manifested
unique allelic differences in the accessions HY-258-1, Jigguvaratiga,
Ratnagiri-2 (for PiKh primer), Alursanna, Kirwana, Mandyavijaya, MGD103, Mobane (for Bph3), Bangarakovi, Bangarusanna (for Bph 20(t)),
Murukathabattha,
Pusasughandi,
Neermulaga
Yadakmuki
Neermulaga (for OsPR#021).
(for
(for
Bph21(t)),
xa13)
and
Kannada
Makam,
tumbha,
MTU-1001,
All alleles mined were showing double
bands but only the genotype Alursanna was showing a unique band.
Further, identification the location of the marker/primer on the
specific chromosome region; using bioinformatic tools (Mapviewer of
NCBI and the Gramene) was made.
The genes Xa4 and Xa21 were
located on chromosome 11, xa13 on chromosome 8 and xa5 on
chromosome 5. Pi2 and PiKh were located on chromosomeon 6 and 11
respectively. Bph3 and Bph18 were located on chromosomes 11 and 12
respectively. All candidate gene chromosomes are known with OsPR#011
and OsPR#012 on chromosome 1, OsPR#021 and OsPR#022 on
chromosome 2, OsPR#051 and OsPR#052 on chromosome 5, OsPR#071,
OsPR#072, OsPR#073, OsPR#074 on chromosome 7, OsPR#101 on
chromosome 10 and OsPR#121 on chromosome 121.
Gene annotation for these genes was done with Gramene, TIGR,
and Ensembl. In these sites, details were obtained regarding the locus
number, genome sequence (GS), CDS, protein sequence (PS) and their
putative function. Where Xa21 has GS of 3585, CDS of 2997, PS of 998
with function having receptor like protein Kinase 5 precursor putative
expressed, xa5
has GS of 8964, CDS of 2895, PS of 964 with function
having RNA recognition motif containing protein precursor putative
expressed and for xa13, Xa4, Pi1, Pi2, Bph3, Bph18, it has GS of 694,
CDS of 471, PS of 156 with function having PROLM26-Prolamin
precursor expressed. For CGs, gene annotation was done from PAM site
where its location, strand, genomic length (GL) and function were known.
OsPR#011, OsPR#021, OsPR#022, OsPR#071, OsPR#072, OsPR#073,
OsPR#074 OsPR#101 were having forward strand and their GL were 825,
2,378, 1,528, 765, 721, 855, 915 and 693. OsPR#012, OsPR#051,
OsPR#52, OsPR#121 were having reverse strand and their GL were 755,
1,008, 3,342 and 1,680 respectively.
Candidate gene was found using Primer3 site. For genes like Xa21
with product size of 759bp, xa5 with product size of 411bp, Xa4 with
product size of 487bp were designed.
All these study were pooled in order to make a molecular profiling
for biotic stress by giving description of all primers on different
chromosomes of rice. This study will help to know the fine-mapping of
locus using candidate genes approach and designing of more CGs which
is useful in breeding through MAS.
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Appendix
APPENDIX
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
Name
Abhilash
Alur sanna
Amruth
Anthrasali
Azucena
Bangara kaddi
Bangara kovi
Bangaru sanna
Basumati
BI-33
BI-34
BI-43
Bidar local-1
Bile Dodiag
Bile kalavi
BR-2655
Buddha
Case bhatta
Champakali
Chippiga
Clatagogy gidda
Dambersali
Devmallige
Dodda mullare
Doddi
Dodiga D-6-2-2
Farm valya
Gandhashali
Gopal Dodiga
Gowri sanna
Gujarat budda
Hegge
Honasu(Red kernel)
Honne kattu
HY-258-1
Intan
IR-64
IVT(SHW)-91
X1
0
64
68
97
115
89
80
89
69
87
0
84
86
86
100
0
90
0
95
93
94
94
0
100
68
88
93
101
94
92
0
0
85
110
97
0
73
77
X2
0
18
26
12
8
10
11
9
21
21
0
16
13
7
7
0
10
0
10
9
11
11
0
8
8
10
18
7
9
19
0
0
9
12
13
0
11
15
X3
0
14
21
10
8
8
8
7
17
19
0
12
11
7
8
0
8
0
8
8
9
9
0
7
8
9
15
7
9
14
0
0
6
9
11
0
9
13
x4
0
22
382
312
67
17
53
45
53
358
0
193
50
26
42
0
294
0
247
162
39
233
0
230
0
245
44
285
256
0
0
0
103
113
155
0
334
65
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
102
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
J-192
Jaddu bhatta
Jasmine-85
Jaya
Jeerige sanna
Jiggu varatiga
Kagi Sali
Kala namak
Kannada tumba
Kari kantiga
Kari mundaga
Karidaddi
Kari Isadi
Karna
Kempudooddi
Kerekallu mutugya
KHP_2
Kirwana
Kyasari
M-81
Makam
Mandya vijaya
Mara navami gudda bhatta
Medium Sali
MGD-101
MGD-103
Mobane
Moroberekan
Moromutant
MTU1001
Murukata bhatta
Mutalaga
Neer Mulaga
Prassanna
Pusa sughandi
Putta batta
Rajkamal
Rajmani
Rani
Rasi
Ratan sagar
103
77
86
0
0
89
69
75
98
89
95
84
94
75
112
93
78
77
92
0
0
0
90
104
97
68
77
0
0
67
100
79
0
77
81
82
91
109
76
69
64
13
13
10
0
0
11
11
5
11
11
16
16
9
15
12
13
14
9
10
0
0
0
13
9
12
11
13
0
0
25
11
12
0
8
10
14
10
13
21
15
12
9
9
8
0
0
9
8
4
9
8
12
12
8
15
10
8
11
6
11
0
0
0
10
10
10
9
9
0
0
20
8
10
0
6
9
11
9
11
11
12
11
80
3
180
8
0
45
7
0
365
230
45
80
76
148
612
32
143
90
83
0
0
0
187
321
140
255
47
0
0
153
220
80
0
262
170
90
100
268
30
60
70
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
Ratna chudi
Ratnagiri-24
Sanna mullare
Sanna valya
Saratiga
Sarjan
Selum anna
Sidda sala
Sona masuri
Sorata
Sugandi
Tirlu hegge
Turumuri
Waner-1
Wari Mysore sanna
Yadakmuki
Zadagi
Holesali chipiga
IR-38
Pushpaka
KMR-3
Samba Mahsuri
Billadi Moratiga
86
91
0
79
80
71
100
0
0
114
88
92
88
0
90
73
100
95
76
60
58
0
97
14
13
0
10
8
20
26
0
0
13
12
12
10
0
11
5
11
11
15
16
23
0
9
16
11
0
7
6
18
18
0
0
12
10
9
9
0
9
4
8
8
13
13
16
0
8
20
26
0
100
20
67
36
0
0
345
119
17
155
257
253
49
268
77
180
140
60
0
174