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Sclerotinia Stem Rot Disease and its Management in
Brassica Sp.
Sclerotinia sclerotiorum (Lib.)
de Bary is a ubiquitous phytopathogenic fungi which has the broadest host range
of any known plant pathogen. It nearly affects more than 400 species of 275
genera which includes cruciferous vegetables like cabbage and cauliflower,
tomato, potato, sunflower, soybean, lettuce and also predominantly affects
major oil seed crops such as canola and Indian mustard (Brassica rapa and Brassica juncea). India is one of the leading producers of oil seed Brassicas accounting for 11.12 % of the world's
rapeseed-mustard production, and ranks third in the world next to China and
Canada and this pathogen is a menace during late flowering and maturity of
plants resulting up to 80% loss of yield causing a major drop in oil seed
economy
Mode of infection
The disease
infestation of this devastating necrotrophic pathogen can be called as watery
soft rot, white mold or Sclerotinia Stem rot depending on the host it infects. In winters it affects within
the plants directly from mycelia and it forms hyphal aggregates called
sclerotia in stem and soil (long term survival structures) during spring, once the
conditions are favourable the sclerotia germinate to form Apothecia to produce
Ascus and releases Ascospores in the end of spring or early summer. The mode of
infection of this fungus invades through the stomata and sub-stomatal chamber
of plants by invasion of either Ascospores or mycelium which progresses rapidly
through the leaf tissues. Pathogen infestation can be also found from the early
falling of petals which measures the
infection pressure significant with the weather conditions.
Pathogenicity
It is already well
known that Oxalic acid is known to be directly involved during infection and
host colonization and acts as a major virulence factor secreted from the pathogen.
Several associated candidate activities and their genes have also been
characterized from the Sclerotinia sclerotiorum genome. (1980 isolate, Broad institute) These include
NADPH oxidase (nox1, nox2), superoxide
dismutase (sod1), catalase (cat1), glutathione metabolism genes (gama glutamyl
transpeptidase (ggt1, and others), pH
signal regulators (pac1),
and ROS resistance regulators (yap1). The invasion and pathogenesis is mediated by a couple of cellulolytic
and pectinolytic enzymes which has a role in penetration, maceration , nutrient
acquisition , plant defense induction and symptom expressions, all enzymes are
optimally active under acidic conditions provided by oxalic acid. Some of the
major enzymes involved are pectin methyl esterase, acid proteases and aspartyl
proteinase all plays a role in degradation of cell wall proteins and
inactivation or inhibition of plant defence response proteins.
Management practices and Crop
improvement in Brassica sp:
Some traditional
management practices include ploughing the crop debris and top soil deeply to
remove the sclerotia in soil followed by crop rotation with non-host plant species
every season but severe infestations can be controlled by applying the
fungicide Carbendazim 250g/l and foliar spray application in field. There are
no complete resistant Brasica spp lines developed through either traditional breeding or molecular
breeding till date but some cultivars at different geographical location have
shown considerable amount of tolerance against the pathogen in field
conditions. The tolerant cultivars was analysed by measuring the lesion size
developed after artificial infection correlating with the stem diameter. Some
hybrids were also developed by crossing susceptible and tolerant cultivars but
none of the lines developed against Sclerotinia isolates showed complete resistance against the
pathogen.
Genetically modified approaches employs Agrobacterium tumefacience-mediated transformation, protoplast culture, somatic
hybridization and microplast techniques should be exploited for developing
transgenic plants of crops with superior resistance to Sclerotinia. Several strategies including detoxification defence,
activation and general inhibition have potential to engineer Sclerotinia resistance. It is highly necessary to understand
disease epidemic in variable environmental conditions. In recent years
scientists have developed transgenic Brassica sp harbouring various genes like chitinases, glucanases,
polygalacturonases inhibiting protein (PGIP) to produce resistance trait
against the pathogen and other genetically modified approaches like
Overexpression of WRKY transcription regulation factors, genes from MAPK Signalling
cascade and genes involved in oxalic acid production like oxalate oxidase under
a strong constitutive promoter were developed to confer resistance against Sclerotinia sclerotiorum. More emphasis will be given in identifying new novel
resistant genes and elucidating molecular mechanisms to develop complete
resistance against this devastating pathogen of major oilseed crops in near
future.
Immunoglobulins, Expression of Ig genes, Antibody Diversity and Class Switching
R.Amos samkumar
Introduction:
Blood can be separated in a centrifuge
into a fluid and a cellular fraction. The fluid fraction is the plasma and
the cellular fraction contains red blood cells, leukocytes, and platelets.
Plasma contains all of the soluble small molecules and macromolecules of blood,
including fibrin and other proteins required for the formation of blood clots.
If the blood or plasma is allowed to clot, the fluid phase that remains is
called serum. It has been known since the turn of the century that
antibodies reside in the serum. The first evidence that antibodies were
contained in particular serum protein fractions came from a classic experiment
by A. Tiselius and E. A.Kabat, in 1939. They immunized rabbits with the protein
ovalbumin (the albumin of egg whites) and then divided the immunized rabbits’
serum into two aliquots. Electrophoresis of one serum aliquot revealed four
peaks corresponding to albumin and the alpha , beta , and gamma globulins. The other serum aliquot was
reacted with ovalbumin, and the precipitate that formed was removed; the
remaining serum proteins, which did not react with the antigen, were then
electrophoresed. A comparison of the electrophoretic profiles of these two serum
aliquots revealed that there was a significant drop in the gamma globulin peak
in the aliquot that had been reacted with antigen Thus, the gamma globulin fraction was
identified as containing serum antibodies, which were called immunoglobulins,
to distinguish them from any other proteins that might be contained in the
_-globulin fraction. The early experiments of Kabat and Tiselius resolved serum
proteins intothree major nonalbumin peak alpha,beta and gamma.We now know that
although immunoglobulin G (IgG), the main class of antibody molecules, is
indeed mostly found in the _-globulin fraction, significant amounts of it and
other important classes of antibody molecules are found in the alpha and beta
fractions of serum.
Structure of
Immunoglobulins:
Antibody
molecules have a common structure of four peptide chains (Figure 4-2). This
structure consists of two identical light (L) chains, polypeptides of
about 25,000 molecular weight, and two identical heavy (H) chains, larger
polypeptides of molecular weight 50,000 or more. Like the antibody molecules
they constitute, H and L chains are also called immunoglobulins. Each light
chain is bound to a heavy chain by a disulfide bond, and by such noncovalent
interactions as salt linkages, hydrogen bonds, and hydrophobic bonds, to form a
heterodimer (H-L). Similar noncovalent interactions and disulfide bridges link
the two identical heavy and light (H-L) chain combinations to each other to
form the basic four-chain (H-L)2 antibody structure, a dimer of dimers. As we
shall see, the exact number and precise positions of these interchain disulfide
bonds differs among antibody classes and subclasses.The first 110 or so amino
acids of the amino-terminal region of a light or heavy chain varies greatly
among antibodies of different specificity. These segments of highly variable
sequence are called V regions:VL in light chains and VH in heavy. All of
the differences in specificity displayed by different antibodies can be traced
to differences in the amino acid sequences of V regions. In fact, most of the
differences among antibodies fall within areas of the V regions called complementarity-
determining regions (CDRs), and it is these CDRs, on both light and
heavy chains, that constitute the antigenbinding site of the antibody molecule.
By contrast, within the same antibody class, far fewer differences are seen
when one compares sequences throughout the rest of the molecule. The regions of
relatively constant sequence beyond the variable regions have been dubbed C regions,
CL on the light chain and CH on the heavy chain.Antibodies are glycoproteins;
with few exceptions, the sites of attachment for carbohydrates are restricted
to the constant region.We do not completely understand the role played by
glycosylation of antibodies, but it probably increases the solubility of the
molecules. Inappropriate glycosylation, or its absence, affects the rate at
which antibodies are cleared from the serum, and decreases the efficiency of
interaction between antibody and the complement system and between antibodies
and Fc receptors.
Types of Igs:
The
amino-terminal part of the chain, consisting of 100–110 amino acids, showed
great sequence variation among myeloma heavy chains and was therefore called
the variable (V) region. The remaining part of the protein revealed five basic
sequence patterns, corresponding to five different heavy-chain constant (C)
regions . Each of these five different heavy chains is called an isotype. The
length of the constant regions is approximately 330 amino acids for and
440 amino acids The heavy chains of a given antibody molecule determine
the class of that antibody: IgM, IgG,IgA, IgD, or IgE. Each class can have
either or light
chains. A single antibody molecule has two identical heavy chains and two
identical light chains,H2L2, or a multiple (H2L2)n of this basic
four-chain structure.
Ig genes
Rearrangement:
In 1976, S. Tonegawa and N. Hozumi
found the first direct evidence that separate genes encode the V and C regions
of immunoglobulins and that the genes are rearranged in the course of B-cell
differentiation. This work changed the field of immunology. In 1987, Tonegawa
was awarded the Nobel Prize for this work. Selecting DNA from embryonic cells
and adult myeloma cells—cells at widely different stages of development—
Tonegawa and Hozumi used various restriction endonucleases to generate DNA
fragments. The fragments were then separated by size and analyzed for their
ability to hybridize with a radiolabeled mRNA probe. Two separate restriction
fragments from the embryonic DNA hybridized with the mRNA, whereas only a
single restriction fragment of the adult myeloma DNA hybridized with the same
probe. Tonegawa and Hozumi suggested that, during differentiation of
lymphocytes from the embryonic state to the fully differentiated plasma-cell
stage (represented in their system by the myeloma cells), the V and C genes
undergo rearrangement. In the embryo, the V and C genes are separated by a
large DNA segment that contains a restriction-endonuclease site during
differentiation, the V and C genes are brought closer together and the
intervening DNA sequence is eliminated.
Light chain undergoes V-J
Rearrangements:
Expression
of both _ and _ light chains requires rearrangement of the variable-region V
and J gene segments. In humans, any of the functional V_ genes can combine with
any of the four functional J_-C_ combinations. In the mouse, things are
slightly more complicated. DNA rearrangement\ can join the V_1 gene segment
with either the J_1 or the J_3 gene segment, or the V_2 gene segment can be
joined with the J_2 gene segment. In human or mouse _ light-chain DNA, any one
of the V_ gene segments can be joined with any one of the functional J_ gene
segments.Rearranged _ and _ genes contain the following regions in order from
the 5_ to 3_ end: a short leader (L) exon, a noncoding sequence (intron), a
joined VJ gene segment, a second intron, and the constant region.
Heavy chain undergoes V-D-J Rearrangements:
Generation
of a functional immunoglobulin heavy-chain gene requires two separate
rearrangement events within the variable region. As illustrated in Figure 5-5,
a DH gene segment first joins to a JH segment; the resulting DHJH segment then
moves next to and joins a VH segment to generate a VHDHJH unit that encodes the
entire variable region. In heavy-chain DNA, variable-region rearrangement
produces a rearranged gene consisting of the following sequences, starting from
the 5_ end: a short L exon, an intron, a joined VDJ segment, another intron, and
a series of C gene segments. As with the light-chain genes, a promoter sequence
is located a short distance upstream from each heavy-chain leader sequence. Once
heavy-chain gene rearrangement is accomplished,RNA polymerase can bind to the
promoter sequence and transcribe the entire heavy-chain gene, including the
introns. Initially, both C gene segments are transcribed. Differential polyadenylation
and RNA splicing remove the introns and process the primary transcript to
generate mRNA including either the C transcript. These two mRNAs are then
translated, and the leader peptide of the resulting nascent polypeptide is
cleaved, generating finished and chains. The production of two different
heavy-chain mRNAs allows a mature,immunocompetent B cell to express both IgM
and IgD with identical antigenic specificity on its surface.
Antibody Diversity:
The factors that leads to
Antibody diversity were
• Multiple Germ line Segments
• Combinatorial V-(D)-J Joining
• Junctional Flexibility
• P-region nucleotide addition
• N-region nucleotide addition
• Somatic hypermutation
• Combinatorial association of Heavy and Light
Chains
Multiple Germ line Segments:
An inventory of functional V, D, and J gene segments in the germ-line
DNA of one human reveals 51 VH, 25 D, 6 JH, 40 V, 5 J 31 V, and 4 J gene
segments. In addition to thesefunctional segments, there are many pseudogenes.
It should be borne in mind that these numbers were largely derived from a
landmark study that sequenced the DNA of the immunoglobulin loci of a single
individual. The immunoglobulin loci of other individuals might contain slightly
different numbers
of particular types of gene segments.
Combinatorial V-(D)-J
Joining:
The
contribution of multiple germ-line gene segments to antibody diversity is
magnified by the random rearrangement of these segments in somatic cells. It is
possible to calculate how much diversity can be achieved by gene rearrangements
In humans, the ability of any of the 51 VH gene segments to combine with any of
the 27 DH segments and any of the 6 JH segments allows a considerable amount of
heavy-chain gene diversity to be generated Similarly, 40 V gene segments randomly
combining with 5 J_ segments has the potential of generating 200 possible
combinations at the locus,while 30 Vand 4 J gene segments allow up to 120
possible combinations at the human locus. It is important to realize that these
are minimal calculations of potential diversity.
Junctional Flexibilty:
The enormous
diversity generated by means of V, D, and J combinations is further augmented
by a phenomenon called junctional flexibility. As described above,
recombination involves both the joining of recombination signal sequences to
form a signal joint and the joining of coding sequences to form a coding joint.
P Region Nucleotide Addition:
As described
earlier, after the initial single-strand DNA cleavage at the junction of a
variable-region gene segment and attached signal sequence, the nucleotides at
the end of the coding sequence turn back to form a hairpin structure.This
hairpin is later cleaved by an endonuclease.This second cleavage sometimes
occurs at a position that leaves a short single strand at the end of the coding
sequence. The subsequent addition of complementary nucleotides to this strand (P-addition)
by repair enzymes generates a palindromic sequence in the coding joint, and so
these nucleotides are called P-nucleotides Variation in the position at
which the hairpin is cut thus leads to variation in thesequence of the coding
joint.
N Region Nucleotide Addition:
Variable-region
coding joints in rearranged heavy-chain genes have been shown to contain short
amino acid sequences that are not encoded by the germ-line V,D, or J gene
segments. These amino acids are encoded by nucleotides added during the D-J and
V to D-J joining process by a terminal deoxynucleotidyl transferase (TdT)
catalyzed reaction Evidence that TdT is responsible for the addition of these N-nucleotides
has come from transfection studies in fibroblasts.When fibroblasts were
transfected with the RAG-1 and RAG-2 genes,V-D-J rearrangement
occurred but no N-nucleotides were present in the coding joints.However, when
the fibroblasts were also transfected with the gene encoding TdT, then V-D-J
rearrangement was accompanied
by addition
of N-nucleotides at the coding joints. Up to 15 N-nucleotides can be added to
both the DH-JH and VH-DHJH joints. Thus, a complete heavy-chain variable region
is encoded by a VHNDHNJH unit. The additional heavychain diversity generated by
N-region nucleotide addition is quite large because N regions appear to consist
of wholly random sequences. Since this diversity occurs at V-D-J coding joints,
it is localized in CDR3 of the heavy-chain genes
Somatic Hypermutation:
All the antibody diversity described so
far stems from mechanisms that operate during formation of specific variable
regions by gene rearrangement. Additional antibody diversity is generated in
rearranged variable-region gene units by a process called somatic
hypermutation. As a result of somatic hypermutation, individual nucleotides
in VJ or VDJ units are replaced with alternatives, thus potentially altering
the specificity of the encoded immunoglobulins
Combinatorial association of heavy and
light chains:
In humans,
there is the potential to generate 8262 heavychain genes and 320 light-chain
genes as a result of variableregion gene rearrangements. Assuming that any one
of the possible heavy-chain and light-chain genes can occur randomly in the
same cell, the potential number of heavy- and light-chain combinations is
2,644,240. This number is probably higher than the amount of combinatorial
diversity actually generated in an individual, because it is not likely that
all VH and VL will pair with each other. Furthermore, the recombination process
is not completely random; not all VH,D, or VL gene segments are used at the
same frequency. Some are used often, others only occasionally, and still others
almost never.
Antibody Class Switching:
After
antigenic stimulation of a B cell, the heavy-chain DNA can undergo a further
rearrangement in which the VHDHJH unit can combine with any CH gene segment.
The exact mechanism of this process, called class switching or isotype
switching, is unclear, but it involves DNA flanking sequences (called switch
regions) located 2–3 kb upstream from each CH segment (except C). These
switch regions, though rather large (2 to 10 kb), are composed of multiple copies
of short repeats (GAGCT and TGGGG). One hypothesis is that a protein or system
of proteins that constitute the switch recombinase recognize these repeats and upon
binding carry out the DNA recombination that results in class switching.
Intercellular regulatory proteins known as cytokines act as “switch factors”
and play major roles in determining the particular immunoglobulin class that is
expressed as a consequence of switching. Interleukin 4 (IL-4), for example,
induces class switching from C to C1 or C_.In some cases, IL-4 has been
observed to induce class switching in a successive manner: first from C to C1 and then from C1 to C.Examination of
the DNA excision products produced during class switching from C to C1 showed
that a circular excision product containing C together with the 5_ end of the 1
switch region (S1) and the 3 and of the switch region (S) was generated.
Furthermore, the switch from C1 to C_ produced circular excision products
containing C1 together with portions of switch regions. Thus class switching
depends upon the interplay of three elements: switch regions, a switch
recombinase, and the cytokine signals that dictate the isotype to which the B
cell switches
Location Proteomics
R.Amos samkumar
Introduction:
Protein subcellular locations, as an
important property of proteins, are commonly learned using fluorescence
microscopy. Previous work by our group has shown that automated analysis of 2D
and 3D static images can recognize all major subcellular patterns in
fluorescence micrographs, and that automated methods can be used to distinguish
patterns that are subtly different. Since many proteins are in constant
movement within the cell, we extended our studies to time series images, which
contain both spatial and temporal information. In this paper, we present the
application of a set of temporal texture features, which do not require
predefining objects for tracking, to the classification of subcellular location
patterns. We demonstrate that these features successfully captured new
information contained in the time domain by evaluating the accuracy of automated
classification of a data set of five proteins with similar location patterns.
Location Proteomics:
Proteomics requires the discovery of
every characteristic of all proteins, including the subcellular location. A
protein’s location indicates its environment and possible function, and
therefore it is one of the key properties that need to be learned. A common way
to identify a protein’s subcellular location is to label it with fluorescent
dye, take microscope images and then make decisions by human visual inspection.
Our group has developed computer programs that can replace this last step. The
automated approach is more objective and sensitive than visual examination, and
single cell 2D and 3D images of major subcellular patterns can be classified with
accuracy over 90%. We have also grouped proteins by their similarity to build
subcellular location trees . All the machine learning and statistical tools we
have assembled are based on features that extracted from static images. We now
expand our study to time series images, which contain additional information in
the time domain. Many proteins are in constant movement. For example, many
membrane proteins are transporters that carry molecules into or out of the
cell, and cytoskeletal proteins change their patterns during the cell cycle. In
order to completely understand a protein’s behavior within the cell, analyzing
time series images will be essential
Fluorescence Microscopic images:
A number of NIH
3T3 cell lines expressing different proteins fused with green fluorescent
protein (GFP) by CDtagged have been described previously .Cells were plated on
glass-bottom culture dishes 48 h before imaging. The imaging system consists
of Laser Physics Reliant 100s 488 Argon
laser, a Yokogawa CSU10 Confocal Scanner Unit, and an Olympus IX50 microscope
with a 60x 1.4NA objective. Images were collected with a Roper Scientific/
Photometrics CoolSnap HQ Cooled CCD camera. The resulting images have 1280 x
1024 pixels in one slice and the distance between neighboring pixels was 0.11
micron. For each cell, 15 slices were taken to form a 3D stack, where the
distance of two neighboring pixels in the z direction was 0.5 micron.
Feature
Calculation for Static Images:
To perform
machine learning or statistical analysis, we represent each image using
numerical features. We have extensively described sets of such features
appropriate for analysis of subcellular patterns in static images, and have
defined a number of sets of these Subcellular Location Features (SLFs). These
include morphological features, edge features, geometric features, DNA
features, Haralick texture features and others.
We seek to determine how well the
patterns of the five GFPtagged proteins can be distinguished using either
static or temporal features. Before feeding the features into a classifier, we
used Stepwise Discriminant Analysis (SDA)
to select the features that have the best power to discriminate between
the classes. Feature selection is necessary because features that confound the
classes can reduce the classifier’s ability to learn the real differences. The
SDA algorithm has been tested against other feature selection methods and
proved to perform the best in our previous subcellular classification work.Once
a set of features was selected by SDA, it was used to train Support Vector Machine
(SVM) classifiers. An SVM is a generalized linear classifier that transforms
the features into a new feature space using kernel functions. In the new
feature space, a linear decision boundary can be drawn to separate classes. SVM
will find the maximum margin hyperplane in the feature space to insure the
minimal prediction error.
Protein Tagging methods:
Although
some systematic studies of protein location have been carried out using cell
fractionation , microscopy is the major method used for this task. With some
exceptions, the major type of microscopy used has been fluorescence microscopy.
In this technique, proteins are tagged with fluorescence probes that absorb
light of a specific wavelength range and emit light of a different (usually
higher) wavelength.The emitted light can form an image of the location pattern
of interest in a microscope.The most widely used technique to tag a protein is
immunofluorescence,in which a complex of fluorescent dye molecule and antibody
attaches to a specific protein as an antigen. Usually two antibodies are used.
The first, or primary, antibody is specific to target the protein of interest
but has no dye molecule attached. The dye is conjugated to a secondary
antibody, which has high affinity for the primary antibody.The secondary
antibody can often recognize all antibodies derived from a given species, so a
single dye-coupled antibody is reusable for a set of primary antibodies. The
availability of antibodies can limit the utility of immunofluorescence(or
immunohistochemistry, in which a chromogenic probe is used instead of a
fluorescent probe) for comprehensive tagging purposes. Another disadvantage is
that immunotechniques cannot be used to observe living cells. A powerful
alternative to immunofluorescence is tagging proteins by fusing their coding
sequence with that of green fluorescence protein(GFP) or other fluorescent
proteins. To tag a protein of interest, mole cular biology techniques are used
to combine the coding sequence of GFP with the coding sequence of the protein
(this approach can be used for cDNA or genomic DNA). The result is a sequence
that codes for a combination of the original protein and GFP. Because the
mechanism is general, it is well suited for tagging each member of a set of proteins.However,
the possibility that the GFP can alter the properties of the tagged protein
must be considered.
SLIF:
SLIF applies
both image analysis and text interpretation to the figure and caption pairs
harvested from on-line journals, so as to extract assertions such as “Figure N
depicts a localization of type L for protein P in cell type C”. The protein
localization
pattern L is obtained by
analyzing the figure, the protein name and cell type are obtained by analysis
of the caption illustrates some of the key technical issues. The figure
encloses a prototypical figure harvested from a biomedical publication,1 and
the associated caption text. Kinase…experiments” is the associated caption from
the journal article, and that the figure contains several panels (independently
meaningful subfigures).
There is extensive interest in
automating the collection, organization and summarization of biological data.
Data in the form of figures and accompanying captions in literature present
special challenges for such efforts. Based on our previously developed search
engines to find fluorescence microscope images depicting protein subcellular
patterns, we introduced text mining and Optical Character Recognition (OCR)
techniques for caption understanding and figure-text matching, so as to build a
robust, comprehensive toolset for extracting information about protein
subcellular localization from the text and images found in online journals. Our
current system can generate assertions such as “Figure N depicts a localization
of type L for protein P in cell type C”.
Protein Subcellular Location
Databases:
An
alternative approach to creating GFP-fusions has been used in the CD-tagging
project . CD tagging creates internal GFP fusions rather than terminal
fusions. An engineered retroviral construct is created containing the GFP
coding sequence flanked by splicing acceptor and donor sites. Infection of
cells by the retrovirus results in undirected (approximately random) insertion
into the genome. If the insertion occurs in the proper reading frame in an
intronic region, a new GFP exon is created between two exons. Stable clones
that express different tagged proteins can then be isolated, and the tagged
gene identified by RT-PCR. The advantage of this genomic-tagging approach is
that endogenous regulatory sequences are preserved and thus, normal levels of
expression occur. Several mouse NIH 3T3 clones were created by this
approach and high-resolution images were
collected . The patterns in these images were automatically analyzed using the
methods described in the following sections. Yet another approach to analysis
of protein location has been taken by the Protein Atlas project, which has
focused primarily on determining protein location at the cellular level within
tissues, but also provides some information on subcellular location. The
Protein Atlas database contains images for more than 700 proteins in 48 normal
human tissues and 20 different cancers . The proteins analyzed included five
major types of protein families: receptors, kinases, phosphatases,
transcription factors, and nuclear receptors. Proteins were tagged by
immunohistochemistry using well-characterized, monospecific primary antibodies
and secondary antibodies (conjugated with horseradish peroxidase) in human tissue
microarrays. The microarrays were scanned using an automated slide-scanning
system at magnification. The resulting images were annotated by visual
inspection by pathologists.
Automated Analysis
Beginning 10 years ago, our group
carried out the initial demonstration of the feasibility of automated
classification of subcellular location patterns. For this purpose, we acquired
extensive image collections,first for Chinese hamster ovary cells and then for
HeLa cells. These collections were obtained for paraformaldehyde-fixed cells
using markers (either monoclonal antibodies or fluorescent probes) specific for
the major subcellular structures. For HeLa cells, we collected high-resolution
2D images of nine different markers (the 2D HeLa dataset). These included
images for proteins whose patterns are similar, such as antibodies against two
different Golgi proteins and antibodies against lysosomal and endosomal proteins.
In addition, markers for the actin cytoskeleton, the tubulin cytoskeleton,
mitochondria, the endoplasmic reticulum (ER),and nucleoli were used. Images of
approximately 100 cells were collected for each marker along with a parallel
DNA image. The parallel DNA images permitted the calculation of DNA-specific
features for each marker, and also were used to define a tenth subcellular
pattern in addition to those for the nine markers. Examples of these images are
shown in. Using the SLF described above, we used these images to produce the
first demonstration that all major subcellular location patterns could be
automatically recognized with reasonable accuracy. This task is one of supervised
learning or classification, in which each instance (image) is known
to belong to one of a set of predefined patterns (classes).We used images of
known classes (training data) to design a specific classifier, which can be
considered as a function (but often a very complicated function), to predict
the class when given an image. The performance of such classifiers is evaluated
using images whose class is known,but were not used for training (testing
data). A classifier with more predictive power will give higher classification
accuracy on the testing data. In our initial work, the 10 subcellular patterns
could be recognized with an overall accuracy of 84% using the SLF4 feature set
(consisting of 37 features) and a neural network classifier . We subsequently
improved this performance by adding new features and using different classifiers,
with the best performance to date (92% accuracy) having been achieved with a
majority-voting ensemble classifier and the SLF16 feature set (consisting of 47
features)
Object Based Modelling:
Models are
efficient ways to describe a system. The most practical modelshave a compact
form characterized by a small number of parameters. We can distinguish between descriptive
models, which utilize an irreversible mapping from image to features and
cannot easily be used to synthesize
new
images, and generative models, which capture the essence of a pattern in a
manner that can be used to generate new images that are, in principle,
indistinguishable from the examples it was built from. A feature matrix is an
example of a descriptive model because we cannot (usually) reconstruct patterns
from it. Building a generative model may seem daunting given the large amount
of data contained in one image. A typical 512 × 512 image with 256 gray levels
could contain 250 kilobytes. The task becomes even more challenging when
considering the variation between images of the same pattern. Fortunately, the
sufficient representation of features implies that a pattern could be modeled
in a much lower dimensional space. Because morphological features describing
the properties of objects are powerful features, we considered the possibility
that subcellular patterns could be adequately represented by considering them
to be composed of distinct combinations of objects of particular types. Our
starting point was to use some SLFs that had previously been calculated as
averages for all objects to instead describe each individual object in a
pattern. These subcellular object features (SOFs) were then used to determine
how many statistically distinguishable object types were contained in an image
collection using clustering.
References:
Text Book: Systems Biology – Ivan .V
.Moly –Humana Press
Articles:
Extracting
information from text and images for location proteomics –William .W.Cohen
Cytomics and Location Proteomics: Automated Interpretation of
Subcellular Patterns in
Fluorescence Microscope Images –Robert.F.Murphy
APPLICATION OF
TEMPORAL TEXTURE FEATURES TO AUTOMATED ANALYSIS OF PROTEIN SUBCELLULAR
LOCATIONS IN TIME SERIES FLUORESCENCE MICROSCOPE IMAGES
Yanhua Hu , Jesus Carmona1, and Robert F. MurphyGenerations of sequencing technologies
R.Amos Samkumar
Introduction:
The ability to swiftly and
accurately gain knowledge of nucleic acid composition is essential to many of
the biological sciences. As the pace of progress is high and we are moving
towards an era of synthetic genomics and personalized medicine, the demand for
highly efficient sequencing
technologies is obvious, where effortless deciphering of genetic sequences will
shed light on novel biological functions and phenotypic differences.
Metagenomic endeavors are providing new tools in the art of genetic
engineering, thereby enabling the design of artificial life in the service of
humanity.These future synthetic organisms may produce petrol substitutes or
provide systems for mopping up excessive carbon dioxide in the atmosphere.Perhaps
even more captivating is the possibility of resequencing larger and larger
fractions of human genomes at an ever decreasing cost, an effort that will
elucidate phenotypic variants, extending the comprehension of disease
susceptibility and pharmacogenomics, permitting personalized medicine. Although
we have not yet reached the long envisioned $1000 genome. novel approaches and
refinements of existing methods are reducing the cost per base by the day while
increasing the throughput. The establishment of a reference genome in the beginning
of this decade is now permitting cost-effective resequencing of ever larger
fractions of human genomes. The Advanced Sequencing Technology Development
Awards initiated by the National Human Genome Research Institute (NHGRI) in
2004 are beginning to show results. Advancements for the next generation
sequencing methods include not only current state-ofthe- art systems from 454
Illumina and Applied Biosystems but also single-molecule detection
approaches,capable of recognizing incorporation or hybridization events on single
molecules.
The drop in cost has led to the initiation of several sequencing
projects aiming at elucidating the variation not covered by SNP arrays. In the
Personal Genome Project,the exon regions of ten genomes are to be sequenced and
compared. Researchers at the Beijing Genomics Institute (BGI)are determined to
sequence 100 individuals of Han Chinese origin during the upcoming three years
in the Yanhuang Project and recently, an international consortium announced the
“1000 Genomes Project” where the sequence of 1000 individuals will provide “A
catalogue of human genetic variation”.The improvements in sequencing technology
and reduction in cost have allowed the first personal genomics company to begin
the sequencing of customers' genomes.To allow for a further reduction in cost
the X PRIZE Foundation in Santa Monica, CA, has introduced the Archon X PRIZE
for Genomics and will award a sum of $10 million to the first team that can design
a system capable of sequencing 100 human genomes in 10 days. Additional
requirements are an error rate of no more than one in 100,000 bases, a coverage
of at least 98% and a cost of no more than $10,000 for each sequenced genome.
Representatives from many of the different sequencing categories are
represented in the Archon X PRIZE challenge and the research world is closing
in on the $1000 genome. The race is on.
Present generation of DNA sequencing
technologies
There
are many factors to consider in DNA sequencing such as read length, bases per
second and raw accuracy. All thework in the field has led to an exponential
reduction in cost per base. Sanger sequencing has been one of the most
influential innovations in biological research since
it was first presented in 1977. A little more than 20 years later, a bioluminescence
sequencing-by-synthesis approach saw the light of day Today, Pyrosequencing has
evolved at 454 Life Sciences, generating about five hundred million bases of
raw sequence in just afew hours. This throughput, although heavily refined and improved
during the years, is something Sanger sequencing in its current form cannot
easily match. However, during the last year, Illumina and Applied Biosystems have
introduced sequencing systems offering even higher throughput than the systems
provided by 454,generating billions of bases in a single run. These novel
methods all rely on parallel, cyclic interrogation of sequences from spatially separated
clonal amplicons. Although with shorter read lengths and a slower sequence
extraction from individual features as compared to the Sanger method, the
parallelized process offers a much
higher total throughput and reduces cost significantly by generating
thousandsof bases per second. By shearing the template and parallel sequencing
of single fragments, over sampling may provide improved coverage and the
possibility of stitching together the original sequence while increasing total
accuracy. Already today these high-throughput methods are expanding our
knowledge, also in the related fields of transcriptome and proteome research.
Gene expression analysis with whole-transcriptome sequencing is possible and
furthermore, in proteome research, by sequencing DNA extracted by antibodies
targeting DNA-binding proteins (ChIP-Seq),transcription factor binding sites
and chromatin modifications can be investigated.
Terminating chains Since 1977, a total nucleic acid polymer of
approximately 1011 bases has been determined with Sanger's chain termination
sequencing method.By halting the elongation with a labeled, and thereby identifiable,
dideoxyribonucleotide triphosphate (ddNTP), the length of the fragment can be
utilized for interrogating the base identity of the terminating base.In its
current form, fluorescently labeled ddNTPs are mixed with regular,
non labeled, non terminating nucleotides in a cycle sequencing reaction
rendering elongation stops at all positions in the template. Capillary
electrophoresis can then be applied for separating sequences by length and
providing subsequent interrogation of the terminating base Initially at a high cost, refinements and
automation have improved cost effectiveness significantly. In 1985, $10 allowed reading one single base, while the same
amount of money rendered 10,000 bases 20 years later Current instruments
provided by Applied Biosystems deliver read lengths of up to 1000 bases, high raw
accuracy and allow for 384 samples to be sequenced in parallel generating 24
bases per instrument second. Projects of multiplexing and miniaturization in
order to reduce reagent volumes, lower consumable costs and increase throughput
are being pursued.Hybridization to tiling arrays The concept of allele-specific
hybridization (ASH) has been used for resequencing and genotyping purposes by
expanding a probe set, targeting a specific position in the genome, to include
interrogation of each of the four possible nucleotides.A tiling array can be fabricated
with probe sets targeting each position in the reference genome. Read length is
given by the probe length (often 25 bp) and base calling is performed by
examining the signal intensities for the different probes of each set. Accuracy
is an issue and is dependent on the ability of the assay to discriminate between
exact matches and those with a single base difference. Performance may vary
significantly due to different base compositions (different thermal annealing properties)
of different regions, resulting in problems with false positives as well as
with large inaccessible regions composed ofrepetitive sequence stretches .The
throughput is an obvious benefit, since all bases are interrogated
simultaneously and the concept has been applied to resequencing the human
chromosome 21 by Perlgen and
HIV By representing all possible sequences for a given probe length,de novo
sequencing can be performed and overlapping sequences used for sequence
assembly In a recent report, the genome of Bacteriophage λ and Escherichia coli were
resequenced by “shotgun sequencing by hybridization” with an accuracy of 99.93%
and a raw throughput of 320 Mbp/day. Parallelized Pyrosequencing.The Genome
Sequencer FLX by 454 Life Sciences and Roche depends on an
emulsion PCR followed by parallel and individual Pyrosequencing of the clonally
amplified beads in a PicoTiterPlate Emulsion PCR is a clonal amplification
performed in an oilaqueous emulsion. Unlike when digesting a genome with
restriction endonucleases, shearing will provide randomly fragmented pieces of more
or less similar length. By the addition of general adaptor sequences to the fragments, only one primer
pair is required for amplification. In the emulsion PCR, a primer-coated bead,
a DNA fragment and other necessary components for PCR (including the second
general primer) are isolated in awater micro-reactor, favoring a 1:1 bead to
fragment ratio. Once the emulsion is broken, beads not carrying any amplified
DNA are removed in an enrichment process.The amplified and enriched beads are
then distributed on the PicoTiterPlate, where a well (44 μmin diameter) allows
fixation of one bead (28 μm in diameter). However, out of the 1.6 million
wells, not all will contain a bead and not all of those that do will give a
useful sequence.Following the distribution of the DNA-carrying beads to the PicoTiterPlate
Pyrosequencing will be performed. Pyrosequencing is a sequencing-by-synthesis
method where a successful nucleotide incorporation event is detected as emitted
photons.Since the single-stranded DNA fragments on the beads have been amplified
with general tags, a general primer is annealed permitting an elongation
A DNA sequence of choice is prepared using a sequencing
reaction where regular deoxynucleotides are combined with terminating
dideoxynucleotides. Each chain terminating nucleotide is labeled with a
base-specific color. The sequencing reaction generates fragments of all lengths
and separation can be made using a gel or capillary electrophoresis where the
labeled bases reveal the sequence information at each position. Read length is approximately
700 bases. (B) Miniaturized Pyrosequencing. Highly parallel and miniaturized
Pyrosequencing reactions are achieved by first performing awater-in-oil emulsion
PCR that permits generation of hundreds of thousands of single-clone amplified beads.
The beads are then single fitted into the wells of a PicoTiterPlate where
individual Pyrosequencing reactions are taking place. A sequential addition of
the four bases is performed in a cyclic fashion and upon successful
incorporation of each base, an enzyme cascade generates light which can be
detected. Read lengths of 400 bases are now possible allowing for a total of
500 Mbp from PicoTiterPlate in each run. (C) Reverse Termination. The DNA
fragments of choice are bridge-amplified using a solid-phase PCR on a surface generating
spatially separated colonies of approximately thousand fragments each. A cyclic
sequence interrogation procedure is performed using fluorescently labeled,
reversibly terminating nucleotides. All four bases are added in each cycle and
following incorporation and stringent washing procedures, the color of each
colony is detected. The dye is then removed and the termination reversed
allowing for interrogation of the following base in each colony. At 30–35
bases, the error rate is becoming high thereby limiting the readlength. (D)
Sequencing-by-Ligation. Single-clone beads are amplified in an emulsion PCR and
immobilized in a gel. By utilizing previously introduced general tag sequences,
anchor
primers
can be annealed next to unknown sequence regions. Hybridizing and ligating degenerated
nonamers, where only one base and position in the primer is specific and
undegenerated, reveals the base at the position in question since each primer
and base is correlated to a particular color. The specificity of the ligase
permits sequencing of six or seven bases depending on the ligation direction
(5′–3′ and 3′–5′ respectively). In the illustration, the third base (a filled
circle) is interrogated at one bead and the color of ligated probe indicates an
“A” at the third position. Note that squares indicate degenerated bases). After
each cycle, the ligated products are removed and a new round of ligation is
performed by shifting the position of the specific base in the nonamers.towards
the bead. The emission of photons upon incorporation depends on a series of
enzymatic steps. Incorporation of a nucleotide by a polymerase releases a
diphosphate group (PPi), which catalyzed by ATP sulphurylase forms adenosine
triphosphate (ATP) by the use of adenosine phosphosulphate (APS). Finally, the
enzyme luciferase (together with D-luciferin and oxygen) can use the newly
formed ATP to emit light. Another enzyme, apyrase, is used for degradation of unincorporated
dNTPs aswell as to stop the reaction by degrading ATP
The presence of and competition
among all four nucleotides is claimed to reduce the chance of misincorporation.
Incomplete incorporation of nucleotides and insufficient removal of reverse terminators
or fluorophores may be the explanation for the relatively short read length of
35 bases. Although shorter read lengths than the 454 system, the throughput is
much higher and, as of February 2008, 1.5 Gbp are generated in each run, which
takes approximately 3 days.The use of paired-end libraries will generate about
3 Gbp in a single run. The raw accuracy is said to be at 98.5% and the
consensus (3×coverage) at 99.99%. The cost per base is approximately 1% of the
cost for Sanger sequencing A variant of Illumina's sequencing by synthesis
chemistry was recently reported where a hybrid of sequencing by synthesis and
Sanger method promises longer reads
True single-molecule sequencing (tSMS™) is achieved by
initially adding a poly A sequence to the 3′-end of each fragment, which allows
hybridization to complementary poly T sequences in a flow cell. After
hybridization, the poly T sequence is extended and a complementary sequence is
generated. In addition, the template is fluorescently labeled at the 3′-end and
thus, illumination of the surface reveals the location of each hybridized
template. This process allows generation of a map of the singlemolecule landscape
before the labeled template is removed. Fluorescently labeled nucleotides are
added, one in each cycle, followed by imaging. A cleavage step removes the fluorophore
and permits nucleotide incorporation in the next round. (B) Pacific
Biosciences. A zero-modewaveguide contains a single polymerase macromolecule
immobilized at the bottom (hexagon), nucleotides (circles) that are
fluorescently labeled at the triphosphates (colored triangles) and a DNA strand
which permits single-molecule real time (SMRT™) sequencing.
Conclusion:
Molecular Studies on
Endangered Plants
R.Amos Samkumar
Introduction:
Evolutionary
and conservation genetics are concerned with the ability of populations to
evolve in response to environmental change. This evolutionary response to selection
depends on the heritability of the trait(s) and the strength of selection
although genetic correlations among traits and epistasis may confound long-term
projections.Unlike molecular traits, polygenic (quantitative) traits do not
exhibitdiscrete phenotypes. Quantitative traits vary continuously,due to both
the contributions of many loci and environmental effects on those loci.
Dominance, epistasis, and pleiotropy further complicate genetic architectures,
the expression of polygenic traits, and the maintenance of genetic variation
for those traits. Despite the fact that quantitative characters are what
selection acts on, the majority of information for wild populations, and almost
all for endangered species, is for allozymes and DNA markers The degree to
which molecular markers can be used to infer the amount of genetic variation in
quantitative traits depends in part on the extent to which genetic variation is
maintained through selection versus drift, and on what form selection takes.
Genetic variation for (nearly) neutral traits is predicted to correlate well
with allozyme heterozygosity, because both are affected similarly by population
size. Conversely, the relationship may be weaker or absent for polygenic traits
under directional selection. This possible dichotomy is important. Traits
related to fitness are the ones that concern evolutionary biologists most.
Evolutionarily important processes, such as adaptation and speciation, are
driven primarily by selection.
Endangered
Species:
Various endangered
species of plants are classified in the international union for conservation of
nature (IUCN’S) Red List. The main classification are
-Threatened
-Vulnerable
-Endangered
-Extinct
Total
species classified under endangered plants category -2074
In
Vitro Conservation
of germplasm
-Explant
sterilization strategies
-Multiplication strategies
-Genetic stability of
germplasm
-Characterized by
biochememical (Isozymes) and molecular markers(DNA)
Quantitative
genetics in endangered plants Conservation:
The measurement
of genetic variation is often an important component of endangered species
management programs. Each of several tools available to measure genetic
diversity has positive and negative attrlbutes. Quantitative genetic techniques
have not received much attention in the conservation field, yet they are likely
to reveal variation that is most closely associated with components of fitness.
In addition, quantitative genetics may not be as logistically difficult for
threatened populations as was once thought. Finally, quantitative genetic
models provide a better outlook for conservation programs than single-locus
models.
A
promising approach for assessing genetic variation in endangered species and it
can be calculated by the following parameters
-Measuring quantitative genetic
traits
-Estimation of heritability and
genetic correlation
-Pedigree data
-Single locus Vs Multi Locus
technique
A Comparision of single-locus and multiple-locus techniques for measuring genetic
variation in conservation studies:
Conservation
management schemes are often designed to increase genetic variation within
declining populations, and thus the method of assessing genetic variation could
greatly influence how species are managed. Currently, some popular management
schemes include: (1) translocations of individuals among populations;(2) movement
corridors between nature reserves; and (3) captive breeding and release of
individuals into extant populations. Caution should be exercised in management
programs that entail the mixture of populations because, while such management
strategies have the potential to increase genetic variation and offset
inbreeding depression, they can also cause a decline in population fitness when
populations are adapted to different local conditions. In addition, subdivision
of populations can cause higher genetic diversity at the system. Quantitative
genetic estimates of variation may actually provide a better outlook for
endangered species management than single-locus estimates Quantitative genetic
methods are more likely to reveal a statistically significant difference between
trait means than allozyme surveys of allele frequencies. These data, combined
with estimates of trait heritability, make it easier to detect interpopulation differences
using quantitative traits. Quantitative genetic analyses also result in lower
estimates of the number of generations that it will take for a population of
genetic variation to return to levels found in large outbreeding populations.
Genetic variation in quantitative characters could return to outbred levels in
103 or 104 generations. In theory, single-locus estimates, assuming neutrality,
will take on the order of 107 generations. From a single-locus perspective, population
bottlenecks or severe reductions in population size over a relatively short
period can cause large reductions in genetic variation. However, both
quantitative genetic theory and empirical work suggest that bottlenecks can
actually increase genetic variation, in the short-term by converting epistatic
variation into additive genetic variation remains questionable whether
bottlenecks are beneficial in the long-term. In addition, a smaller effective
population size (N,) is estimated as being necessary to maintain ‘normal’ levels
of genetic variation in quantitative characters than to maintain heterozygosity
of neutral alleles in a single-locus systems. Specifically, an N, of 5000 is
necessary to maintain variation in quantitative characters, and an NC several
orders of magnitude higher is necessary with single-locus models. Estimates of
genetic variation in conservation studies can vary dramatically depending on
which techniques are used. Each technique has its advantages and its limitations;
therefore, researchers should use a combination of whatever techniques are
logistically and economically feasible for the organism under study.
Quantitative
genetic analyses provide a valuable tool for estimation of ecologically
important variation in endangered species. Although quantitative genetic
analyses are inappropriate for some endangered species for reasons of logistics,
time constraints or sample-size limitations, they can be feasible for some
captively bred endangered species, natural populations for which accurate census
data already exist, or small natural populations that can be censused in their
entirety. In addition, quantitative genetic data may indicate that conservation
strategies are reasonable for species that seem hopeless when using single locus
data.
Molecular
markers in endangered plant conservation:
Various
molecular markers are used in endangered plant conservation
-RAPD
-AFLP
-RFLP
-SSR
-ISSR
Both dominantly (e.g. AFLP, RAPD, and ISSR) and
codominantly inherited markers (e.g. allozymes and microsatellites) have been
used to study population genetics and life history traits in many species.The
observed genetic variations suggest that as many populations as possible should
be considered in any planned in situ or ex situ conservation
programs for species.
• Polymerase chain reaction (PCR)-derived markers obtained with nonspecies
specific primers have become exceedingly popular since they do not request
sequence information for the target species
• The use of degenerate primers (primers developed for a particular
species and applicable to related taxa) avoids laborious, time consuming
process
RAPD
Markers for Characterization of Endangered species of medicinal plants:
RAPD markers successfully discriminates between
species
It is a Rapid and easy tool for
-Identification
-Conservation
-Sustainable use of plant species
The use of highly discriminatory methods for the
identification and characterization of genotypes is essential for plant
protection and appropriate use. We utilized the RAPD method for the genetic
fingerprinting of 11 plant species of desert origin (seven with known medicinal
value). Andrachne telephioides, Zilla spinosa, Caylusea
hexagyna, Achillea fragrantissima, Lycium shawii, Moricandia
sinaica, Rumex vesicarius, Bassia eriophora, Zygophyllum
propinquum subsp migahidii, Withania somnifera, and Sonchus
oleraceus were collected from various areas of Saudi Arabia. The five
primers used were able to amplify the DNA from all the plant species. The
amplified products of the RAPD profiles ranged from 307 to 1772 bp. A total of
164 bands were observed for 11 plant species, using five primers. The number of
well-defined and major bands for a single plant species for a single primer
ranged from 1 to 10. The highest pair-wise similarities (0.32) were observed
between A. fragrantissima and L. shawii, when five primers were
combined. The lowest similarities (0) were observed between A. telephioides and
Z. spinosa; Z. spinosa and B. eriophora; B. eriophora and
Z. propinquum.
In conclusion, the RAPD method successfully discriminates among
all the plant species, therefore providing an easy and rapid tool for
identification, conservation and sustainable use of these plant
Plant species of the desert are adapted to tolerate
multiple stresses including drought, high temperature, high solar radiation,
high wind, and salinity (Batanouny, 2001). It is noteworthy that besides their
medicinal value, endangered mammals feed on many of the herbal plants growing
in the desert. Recently, it was determined that about 35% of the species that
constitute the standing vegetation are vulnerable to elimination because they
are not represented in the seed bank of the Red Sea area (Hegazy et al., 2009).
Therefore, appropriate measures for the preservation of plant species in the
desert area are urgently needed. Proper identification is crucial for the
preservation of plants growing in extreme arid regions. Traditionally,
subjective methods based on the morphological features such as shape, color,
texture, and odor are used for the discrimination of herbal medicines. However,
these methods are difficult to apply accurately for discrimination and
authentication. The use of chromatographic techniques and marker compounds to
standardize botanical preparations is also limited because the medicines have
variable sources and chemical complexity, which is affected by growth, storage
conditions and harvest times (Joshi et al., 2004; Zhang et al., 2007)
Among the polymerase chain reaction (PCR)-based molecular
techniques, random amplified polymorphic DNA (RAPD) is convenient in
performance and does not require any information about the DNA sequence to be
amplified (Weder, 2002). Due to its procedural simplicity, the use of RAPD as
molecular markers for taxonomic and systematic analyses of plants (Bartish et
al., 2000), as well as in plant breeding and the study of genetic relationships,
has considerably increased (Ranade et al., 2001). Recently, RAPD has been used
for the estimation of genetic diversity in various endangered plant species
(Wang et al., 2005; Lu et al., 2006; Liu et al., 2007; Zheng et al., 2008). In
this study, we successfully utilized the RAPD technique for rapid
characterization of 11 plant species of the Saudi Arabian desert.
RAPD analysis for genetic
diversity in Changium smyrnioides (Apiaceae), an endangered plant - A genus endemic to eastern China and an
endangered medicinal plant Five populations were analyzed and a total of 92
amplified bands were scored from the 13 RAPD primers, and a mean of 7.1
amplified bands per primer and 69% (64 bands) percentages of polymorphic bands
(PPB) was found.Genetic diversity estimates indicated that 51.2% of
total diversity was among populations and 48.8% within populations
High Genetic differences
among remnant populations of the endangered
caesalpinia echinata - Origin
-Atlantic coastland of brazil.Genetic varaiations of five natural populations
by means of RAPD Markers Of the total Genetic varaiability
-28.5%
attributable to differences between
-29.6% population to differences within groups
-42 % to
individual differences within populations
Genetic
structure of three Endangered Plants of the Santa Rosa Plain:
Burke's goldfields (Lasthenia burkei), Sonoma sunshine (Blennosperma
bakeri), and Sebastopol meadowfoam (Limnanthes vinculans
Burke's goldfields (Lasthenia burkei), Sonoma sunshine (Blennosperma
bakeri), and Sebastopol meadowfoam (Limnanthes vinculans
To determine
the genetic variation between populations in order to inform conservation
efforts as to the distinctiveness of populations for seed banking and ex
situ conservation
To infer
possible explanations for genetic subdivisions to aid in management decisions.
21
populations genotyping 577 individuals
were surveyed
42
inter-simple-sequence-repeats (ISSR) and random amplified polymorphic DNA
(RAPD) nuclear DNA markers to measure genetic characteristics
Species wide,
geographically separate populations of L. burkei,
S.sunshine,S.meadowfoam were geneticall
y characterized using Analysis of
Molecular Variance (AMOVA)
A Population genetic study
of the endangered plant species Limonium duforii using AFLP Markers - Endemic to east mediterranean Coastof
spain Genetic varaiation and population
structure in this species was studied using Amplified fragment length polymorphism markers
• 152 individuals
• 3 different primers
• 251 bands
• 51 were polymorphic
• 65 Phenotypes Characterized
• High Reproducibility and nucleotide divergence – A
much better DNA Fingerprinting technique
DNA barcoding of endangered Indian Paphiopedilum species:
The
indiscriminate collections of Paphiopedilum species from the wild for their
exotic ornamental flowers have rendered these plants endangered. Although the
trade of these endangered species from the wild is strictly forbidden, it
continues unabated in one or other forms that elude the current identification
methods. DNA barcoding that offers identification of a
species
even if only a small fragment of the organism at any stage of development is
available could be of great utility in scrutinizing the illegal trade of both
endangered plant and animal species. Therefore, this study was undertaken to
develop DNA barcodes of Indian species of Paphiopedilum along with their three
natural hybrids using loci from both the chloroplast and nuclear genomes. The
five loci tested for their potential as effective barcodes were RNA
polymerase-b subunit (rpoB), RNA polymerase-b’ subunit (rpoC1), Rubisco large
subunit (rbcL) andmaturase K (matK) from the chloroplast genome and nuclear
ribosomal internal transcribed spacer (nrITS) from the nuclear genome. The
intra- and inter-specific divergence values and species discrimination rates
were calculated by Kimura 2 parameter (K2P) method using MEGA 4.0. The matK
with 0.9% average inter-specific divergence value yielded 100% species resolution,
thus could distinguish all the eight species of Paphiopedilum unequivocally.
The species identification capability of these sequences was further confirmed as
each of the matK sequences was found to be unique for the species when a BLAST
analysis of these sequences was carried out on NCBI. nrITS, although had 4.4%
average inter-specific divergence value, afforded only 50% species resolution. DNA
barcodes of the three hybrids also reflected their parentage.
The five loci tested for their potential as effective barcodes
were RNA polymerase-b subunit (rpoB), RNA polymerase-b’ subunit (rpoC1),
Rubisco large subunit (rbcL) and maturase K (matK) from the chloroplast genome
and nuclear ribosomal internal transcribed spacer (nrITS) from the nuclear
genome
DNA barcoding that offers identification of a species even if only
a small fragment of the organism at any stage of development is available could
be of great utility in scrutinizing the illegal trade of both endangered plant
and animal species
Molecular diversity of arbuscular mycorrhizal fungi in
Prunus africana, an endangered medicinal tree species in dry Afromontane forests of Ethiopia
The molecular diversity of arbuscular mycorrhizal (AM) fungi
colonizing roots of Prunus africana and of AM fungal spores obtained
The internal transcribed spacer (ITS) rDNA region from colonized
roots and single spores of three AM fungal spore types was amplified, cloned
and sequenced using AM fungal specific primers. Phylogenetic analysis using the
5.8S rDNA data set revealed that Twenty of the AM fungal types identified are
new to Ethiopia and to science
Why Molecular Studies on Endangered plants?
To Understand the genetic structure and composition of rare and
endangered species.These plants have low genetic diversity due to the survival
rate is decreasing in the wilds.It can be conserved by in situ and in
vitro methods (DNA Banking-To search for novel genes)
Recent advancements like
•
Phytochemical Markers – Identification of structure and
elucidation of molecules-
•
Analysed through MS and NMR
•
To distinguish between genus and taxonomic differentiationNew
DNA markers like SAMPL, SSCP ,SNPs, CAPS
and SCAR
•
Cryopreservation techniques
•
Micrografting and Micropropagation
• New DNA markers like SAMPL,
SSCP ,SNPs, CAPS and SCAR are also used in the conservation of endangered plants
The
high level of geographic and climatic diversity of Europe provides various
habitats withover than 12,500 vascular plants (NATURA 2000 Newsletter Areas of
particularly high plantdiversity include the mountainous areas around the
Mediterranean and Black Sea with the floras of Spain, Greece, Italy, Bulgaria
and Romania enclosing the highest number of both endemic and endangered plant
species. According to the latest release of World Conservation Union (http://cms.iucn.org/about/work/programmes/species/red_list)
one in four mammals, one in eight birds,one third of all amphibians and 70% of
the world’s assessed plants on the 2007 Red List are in jeopardy. Plants are
endangered by a combination of factors: over-collecting, unsuitable agriculture
and forestry practices, urbanization, pollution, habitat destruction,
fragmentation and degradation, spread of invasive alien species (non-indigenous
species that heavily colonize a particular habitat) and climate change (PITMAN
& al)Global concern about the loss of valuable genetic resources has
stimulated many new programs for the conservation of plant genetic resources.
Within past decade several conservation strategies were developed mainly in the
terms of in situ and ex situ conservation. Wild life conservation
is based mainly on in situ conservation (conserving species on theirs
natural habitats). Ex situ conservation involves preservation and
maintenance of samples of living organisms outside their natural habitat, in
the form of whole plants, seed, pollen, vegetative propagules, tissue or cell
cultures. Ex situ techniques are generally used to complement in situ
methods but in some cases are the only possible techniques to conserve
certain species.Among ex situ conservation methods the most common are
cultivation in botanic gardens, seed storage, and in vitro cultivation.
The world’s 2204 botanic gardens cultivate more than one third of the world’s
flowering plants (BGCI Report )Although cultivation in botanic gardens is an
efficient way to conserve endangered species ex situ, it is limited in time and
space and it has to overcome acclimatisation and accommodation problems. Among
the various ex situ conservation methods, seed storage seems to be one
of the most convenient for long-term conservation. This involves desiccation
and storage at low temperatures. However, there are a large number of
threatened species, which produce immature, sterile or recalcitrant seeds that quickly
lose viability and do not survive desiccation; hence conventional seed storage
strategies are not suitable. Advances in biotechnology, especially in vitro culture
techniques and molecular biology, provide some important tools for conservation
and management of plant genetic resources. Several in vitro techniques
have been developed, mostly for vegetatively propagated and recalcitrant seed
producing species, with recent establishment of extensive germplasm collections.
& al)
.
Conclusion:
The loss of plant genetic resources has made necessary the development
of new ex situ conservation methods. Advances in biotechnology
provide new methods for plant germplasm conservation and evaluation. Biotechnological tools
like in vitro culture, cryopreservation, and molecular markers offer a valuable alternative to
plant diversity studies, management of genetic resources and ultimately conservation. This review
summarizes the recent advances in plant conservation biotechnology with special
emphasis on the preservation efforts of the Romanian flora. Strategies, as well
as the plant species used for establishment and maintenance of germplasm collections
are presented.
References:
•
Biotechnology
for Endangered Plant Conservation: A Critical Overview ANCA PAUNESCU
•
Quantitative
genetics: a promising approach for the assessment of genetic variation in
endangered species –Andrew Storfer
•
DNA
fingerprinting of Eucalyptus graniticola: a critically endangered relict
species or a rare hybrid? -MAURIZIO ROSSETTO,
FRANKLIN LUCAROTTI, STEPHEN D. HOPPER & KINGSLEY ,W. DIXON
•
RAPD
analysis for genetic diversity in Changium smyrnioides (Apiaceae), an
endangered plant
Chengxin Fu*, Yingxiong Qiu, and Hanghui
Kong
•
DNAbarcoding
of endangered Indian Paphiopedilum species
IFFAT PARVEEN,*
HEMANT K. SINGH,* SAURABH RAGHUVANSHI,† UDAI C. PRADHAN
and SHASHI B.
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