1. Introduction
1.1.
Dynamin (DNM3): Molecular and Functional Background
Dynamin 3 belongs to the super-family of the genes that code for the
dynamin GTPases which contain an amino-terminal GTPase domain,
three central domains (middle, pleckstrin homology and coiled coil) involved in
self-assembly and membrane binding, and a carboxy-terminal proline-rich domain
that links to SH3 domain-containing signaling/cytoskeletal partner proteins These factors are important for membrane
dynamics by their mechano-elastic properties. This includes the regulating of
endocytic fissions of the vesicles, linkage of the cell membrane to the actin
cytoskeleton, microtubule bundle associated for force generations. The
dynamins have been shown to mediate endocytosis of clathrin-coated pits,
vesicle budding and pseudopodia formation (Ferguson
and De Camilli 2012; Soranzo et al. 2009).
The DNM3 protein has been observed
immunochemically in human MK and murine proplatelets. DNM3 is very important for human megakaryopoiesis. Overexpression of the
DNM3 in the cord blood CD34 cells, enhance megakaryocyte colony forming unit
formation and increased expression of terminal MK transcription factors NF-E2 p
45 in vitro, while the shRNA mediated knockdown had the opposite effects. DNM3
co-localizes to the demarcation membrane systems which serves the main
reservoir during proplatelet and platelet formations. It also binds to non
muscle myosin (MYH9) which is mutated in a series of macro thrombocytopenia
patients. In the neurons DNM3
co-localize with the Wiskott Aldrich syndrome protein. Deficiency of WASP in
the hemopoietic system causes abnormally small platelets. The pan dynamin
inhibitor Dynasore inhibits MK proplatelet production in vitro which indicates
that dynamins are important for platelet size determinations (Nürnberg et al. 2012; Thon and Italiano 2012).
1.2. DNM3 and
Megakaryocytopoiesis
One extremely specialized precursor cells are
Megakaryocytes or MK which solely functions to release and produce the
platelets within circulation. These cells are often descended from the
pluripotent stem cells, and these are considering undergoing replication of DNA
without the cell division through a complicated endomitosis process (Leysi-Derilou
et al. 2012). After
endomitosis completion, the MK polyploidy begins a speedy development of
cytoplasmic stage characterized by the construction of a demarcated and
elaborated system of membrane, the cytoplasmic proteins accumulation as well as
granules required for function of platelets.
During
the developmental stages, the cytoplasm of MK undergoes a sudden restructuring
into the cytoplasmic beaded extensions proplatelets. The current research
focuses on the role of DNM3 in MK and platelets, exploring the potential role
of DNM3 in proplatelet formation and studying possible interacting partners (Reems
et al. 2008).
Currently, this member of protein motor family is
known to have a role in the development of the megakaryocytes. In
mammals, 3 separate dynamin (DNM) genes (1, 2 and 3) were found. These are
large alternatively spliced numbers of mRNAs which are formed from each gene.
It is considered that all 3 of the DNM genes and all recognized alternatively
spliced variants of mRNA from every single gene are expressed in the brain.
DNM1, found specifically in neuronal tissue, has 5 splice variants, and it is
mapped on human chromosome 9q34
DNM2 is ubiquitously expressed
and has 6 splice variants, while DNM3 is expressed largely in testes, lung,
heart and neurons and has 10 splice variants (Gieger
et al. 2011; Hisa et al. 2004; Soranzo et al. 2009).
From a genome wide
mRNA expression study in different haemopoietic cells, DNM3 was found to be
highly transcribed in megakaryocytes and to a lesser extent in platelets with
very low expression in other haemopoietic cells. In a recent study (Reems
et al. 2008),
DNM3 has been found to be expressed in human megakaryocytes, and real–time
quantitative polymerase chain reaction (qPCR) confirmed that DNM3 increased by
20.7 ± 3.4 fold during megakaryopoiesis. Electron microscopy through Immunogold
also showed that Dynamin3 is diffusely distributed right through the
megakaryocytes cytoplasm (MKs) with a punctate manifestation in pro-platelet
processes, with no obvious localization to explicit organelles. The DNM3 gene
is located on chromosome 1 in humans and extends for 467kb (Nürnberg
et al. 2012; Wang et al. 2011).
2.
SNP and SNP Genotyping
This is the measurement
of genetic variations of single nucleotide polymorphisms from individual to
individual or species to species. Single Nucleotide Polymorphisms are the
common types of genetic variations. Single Nucleotide Polymorphism is a single
base pair mutation at a specific locus consisting of 2 alleles (where the rare
allele frequency is greater than 1%). SNPs are related to be involved in the
etiology of human diseases and morphological and count of cells. Therefore they
are being analyzed as proposed markers for quantitative analysis and in association
studies in place of microsatellites (Brookes
1999; Holliday and Grigg 1993). SNPs can be
detected in a number of ways. The SNPs are thus the natural variation in the
single nucleotide positions of a genomic DNA for the population. To classify
the segment as SNP the minor allele should occur at a frequency of more than 1%
to differentiate them from point or the rare mutations. Though many of the SNPs
have the potential to be tetra-allelic but almost all are biallelic and falls
into 4 classes. There is one transition and three transversions meaning a
purine is changed to purine or pyrimidine to pyrimidine in transition while a
pyrimidine is changed to purine or vice versa in case of transversions. In human
beings, the transitional SNP of C to T and G to A and vice versa are the most
common and account for 2/3rd of all SNPs. This occurs due to the
deamination of 5-methylcytosine to thymine (Chanock
2003).
The tranversional SNPs like C to A and G to T , C to G, G to C, T to A and A to
T do all occur at similar frequencies and makes up the next 1/3 rd for all the
SNPs (Campbell
et al. 2000; Nachman et al. 1998; Taylor et al. 2001a).
SNPs are the most plausible
form of variation in the human genome and accounts for >90% of all the
sequence polymorphisms. They normally occur at a frequency of one in 1000
nucleotides hence as a rule of the thumb, there is a 0.1% chance of a base
position to be heterozygous in a particular individual (Tiu
et al. 2011). This measurement is referred to
as the nucleotide diversity index. This diversity is not constant over the
entire length of the genome. Areas of low diversity are in the X chromosomes
and areas of high diversity (around 10%) are in the human leukocyte antigen
loci. Most of the SNPs occur in the non coding regions (representing introns)
of the DNA (Kim
et al. 2008). The frequency of the SNPs in the
coding portion of the DNA is 4 fold lower than that of the non coding regions
and around half of the SNPs occurring in the coding DNA represent similar codon
changes. This attributes to the selective constraints on the base changes in
the coding sequences. As the size of the human genome is 3x109 bp
and the proportion represented by coding DNA (less than 3%) there will be an
average of 3 million base differences between two individuals about 100000 of
which occurs in the coding sequences or the exon. Considering only half of
these coding SNPs result in the variation at the amino acid level, it
represents 50,000 amino acid differences between two individuals (Collins
et al. 1998; Hirschhorn and Daly 2005; Taylor et al. 2001a).
2.1. The Importance of SNPs
There has been a revolution in molecular genetics
with the knowledge of SNPs by providing a dense panel of genetic markers which
are distributed across the entire human genome. The markers are useful in the
study of population dynamics, evolution, to investigate the genetic basis of
complex phenotypes and in the forensic and diagnostic studies and assays (Katsanis
et al. 2001; Sachidanandam et al. 2001). The study of
SNPs for studying complex phenotypes is important in two ways.
First, wide-scale genetic association studies are
conducted to identify the disease genes and the defective proteins. The
clinical phenotypes in humans are complex because they do not follow the simple
heritance patterns but do have a significant genetic component. Typing the SNPs
from selected populations of affected .Individuals and controlled population
matched for age and race indicates that the polymorphisms are associated with
an increased risk so as to identify the disease susceptibility genes (Schork
et al. 1998). Secondly, large scale genetic
association studies have been useful to the development of effective drugs.
2.2. SNP’s in Dynamin Gene: Its association
to Platelet Morphology
The factors that are responsible for the
determination of the platelet size are revealed recently. Like the other blood
cells natural variations exists in platelet size also (marked by the mean
platelet volume) between the human population. A
genome-wide association study conducted by the HaemGen consortium has recently
identified a locus on chromosome 1q24.3 Connected with mean platelet volume
(MPV). This was found in intron 2/3 of the dynamin 3 (DNM3) genes. The highest
association of the MPV was found to be with a single nucleotide polymorphism
(SNP) rs10914144 that resides in intron 2/3 of DNM3 locus. This is a C/T
polymorphism with the minor allele (T) having a frequency of 0.17 in Caucasians
(Soranzo
et al. 2009).
Recently, DNM3 has been described in megakaryocytes (MK) supporting the
putative role of DNM3 gene in MK and platelet biology (Reems
et al. 2008).
In
the genome wide analysis study, it was reflected that 68 SNPs were
statistically associated with the variation in the human mean platelet volume
and platelet numbers. The significant challenge was to prioritize the data to
guide the detailed function. The main focus revolves around the SNPs that lie
in the non coding regions.
The SNP data was triangulated with genome wide
MK-specific transcription factor chromatin occupancy and the MK-specific gene
expression datasets (Gieger
et al. 2011; Nürnberg et al. 2012). This was based
on the concept that some SNPs act by altering the key MK cis-regulatory DNA
elements. The study focused on the MEIS1, which is a homeobox transcription factor
that is essential for the formation of platelets. As per expectation the genome
wide chromatin occupancy revealed MEIS1 occupancy at genes enriched in
megakaryocyte and platelet functions. The important sentinel SNP (likely proxy
variant SNP) identified in earlier proxy studies resides near a revealed MEIS1
occupancy site in the intronic region of the DNM3 gene.
The megakarocyte transcription factor RUNX1
also binds to this region and it was revealed that this MEIS1/RUNX1 occupancy
sites marks the alternate promoter of the DNM3 gene which is selectively used
in the MKs (Malmberg
2005; Nürnberg et al. 2012). The
transcription from this promoter sites results in skipping of the exons 1 and 2
of the canonical DNM3 gene and creates the insertion of a novel exon. Alternate
transcript is also up-regulated during the in-vitro human Mk differentiations. The
studies of reporter gene analysis and individuals with different SNP genotypes
provided evidence that the minor alleles correlated with the lower expression
of the alternate isoform and decreased MPV (Reems
et al. 2008; Wang et al. 2011).
The GWAS study hence reflected that the SNP variant
rs10914144 located within the second intron of the DNM3 gene locus was associated
with variation in the human platelet volume (Soranzo
et al. 2009). It was also found that a second
variant of SNP (rs2038479) which is found in high linkage disequilibrium with
rs10914144 marks a megakaryocyte specific alternate marker (Gieger
et al. 2011). The promoter binds MK
transcription factors MEIS1/RUNX1 and thus produces a short DNA transcript. The
minor alleles were C (A to C transversion) for rs2038479 and T (C to T
transversion) for rs10914144 and were associated with decreased expression of
alternate transcript (Reems
et al. 2008; Wang et al. 2011). Repressive
factors bind to the minor alleles of rs2038479 than the major alleles. A study
reflected that the minor allele of the non coding variant of rs342293 disrupted
a binding site of the transcription factor EVI1/MECOM leading to higher level
of transcription of the downstream gene P1K3G (Hisa
et al. 2004; Nürnberg et al. 2012).
2.3. The Importance of High
Throughput Genotyping
In the genetic association studies various
strategies of genotyping studies are implemented. In the regional strategy the
candidate region of the genome which has been identified already is screened
thoroughly for analysis of the polymorphisms that may have affected the
phenotype (Gut
2001; Syvänen 2001). The SNP genotyping technologies
have 2 components that are, (1) a method to discriminate between the alternate
alleles, and (2) a method for reporting the presence of alleles in a given set
of DNA samples (Gut
2001; Kwok 2001). There are 3 types of allele
discrimination methods:
1. Hybridization/annealing
with or without subsequent enzymatic steps
2. Primer
extension
3. Enzymatic
cleavage
For each case the technology platform used is either
homogenous or heterogeneous involving both a solid-phase and liquid-phase
microarray. Some assays do require prior primer amplification of the target
genome while others are effective in working directly on the genomic DNA (Sachidanandam
et al. 2001; Venter et al. 2001).
2.4. Principles of SNP Genotyping Methods
“The ability of hybridization with
allele-specific oligonucleotides (ASO) to detect a single base mismatch was
first shown in 1979 and then used to detect the sickle-cell mutation in the β-globin gene by Southern blot
hybridization to human genomic DNA in 1983 (Twyman 2005). Identification
of a single base change in the 6 × 109 bp of the diploid human
genome is, however, a demanding task. Not until the PCR technique was invented
did it become possible to design useful assays for genotyping SNPs in complex genomes.
Some of the early SNP-genotyping assays used for PCR products were based on ASO
hybridization in dot blot or Reverse Dot Blot formats (Syvänen
2001).
The reverse dot blot format can be viewed as the precursor of the high-density
microarray-based methods for multiplex genotyping of SNPs by ASO hybridization.
Before long, the PCR technique was developed further by several groups to allow
allele-specific amplification and genotyping of SNPs. The use of ASOs as
hybridization probes or as PCR primers is the basis for the SNP genotyping
assays that are referred to as ‘homogeneous’, because they contain no
separation steps and are monitored in real time during PCR (Knight
et al. 1999). These assays are frequently used
for large-scale genotyping of SNPs today. The solid-phase assays for
enzyme-assisted genotyping, using a DNA ligase or a DNA polymerase, were also
introduced more than a decade ago. Because the enzyme-assisted methods have
proven to be more robust and to provide more specific allele distinction than
ASO hybridization (Napolitano
et al. 2004), these methods have been
multiplexed, automated and adapted to various detection strategies, and they
provide most of the current high-throughput SNP-genotyping platforms. All
methods used for genotyping SNPs in large diploid genomes depend on PCR
amplification of the genomic regions that span the SNPs before the actual
genotyping reaction. The PCR provides the required sensitivity and specificity
for distinguishing between heterozygous and homozygous SNP genotypes in large, complex
genomes. The difficulty of designing and carrying out multiplex PCR reactions
is an important factor that limits the throughput of the current SNP-genotyping
assays”. (Syvänen
2001)
2.5. Detection of SNPs
by Flow Cytometry
From the above discussion it is evident that there are many
considerations for selecting a SNP genotyping assay and the choice will depend
on the purpose of research. The main considerations are cost per data point and
the simplicity of data acquisition. For the study of genetic mapping and
diversity where large markers are required and high level of multiplexing is
needed ASPE is a cost effective on and simple than SBE. MAS, on the other hand
needs small number of markers to screen large number of samples than DH. The DH
should be considered for MAS due to low cost, less time requirement and for
simplicity (Bendall et al. 2011).
The Luminex 100 flow cytometry platform provides a robust
way of analyzing SNPs. It uses a microsphere based genotyping system which can
create multiple analyses the use of reaction specific microspheres that
fluoresce at different frequencies hence permitting multiple discrete assays in
a single tube with the same sample at the same time. Up to 120,000 genotypes
per machine in an 8 hr day could be determined through combination of 100
microspheres. High throughput analysis with multiplexing ability permits SNP
genotyping at relatively low costs. The other advantage of flow cytometry is
that it can be used for wide variety of SNP genotyping assays (Taylor et al. 2001b).
3.
Basics of Flow Cytometry
Flow Cytometry is the
methodology that analyzes and measures multiple characteristics of single
particles usually cells when they flow in a fluid stream through a beam of
light or laser. The properties measured are the particles internal complexity,
size and relative fluorescence intensity. These characteristics are determined
by optical electro coupling system which records how the cell scatters incident
laser light and emits fluorescence. A Flow Cytometry is made of 3 systems:
fluidics, optics and electronics. The fluidics system transports particles in a
stream to the laser beam for the interrogation. The optics system consists of
the laser rays needed to illuminate the sample stream and optical filters to
direct resulting light to the detectors. The electronics system converts the
detected light signals in to electric signals for processing by the computer (Fulwyler
1965; Givan 2004; Katsanis et al. 2001; Ornatsky et al. 2010).
3.1.
Components of Flow Cytometric Analysis
Particles are carried
to the laser intercept in a fluid stream and any suspended particle of 0.2 to
150 micrometers is suitable for analysis. The portion of the fluid stream where
particles are located is called the sample core (Linden
2013).
These samples scatter light because any fluorescence molecule attached to the
particle will scatter it. The scattered and fluorescence light is collected by
positioned lenses. A combination of beam filters and splitters spread this
light to the detectors which produce electrical signals proportional to the optical
signals that strikes them. The data are collected and provide information about
the various subpopulations analyzed.
The purpose of fluidics system
is to transport the particles. To do this the sample is injected into a stream
of sheath fluid within the flow chamber. It consists of a flow cell and the flow
chamber in a stream of air is called a nozzle tip. Based on the principles of
laminar flow the sample core remains separate but coxial within the sheath
fluid. The flow of the sheath fluid accelerates the particles and restricts
them to the centre of the sample core. This is called hydrodynamic focusing. The
sample pressure and the sheath fluid pressure are different. The former is
greater than the later. The sample pressure controls the sample flow rate by
changing the sample pressure relative to the sheath pressure (Linden
2013).
Increasing the sample pressure increases the flow rate by increasing width of
the sample core which allows more particles to enter the stream in a given
moment.
A high flow rate is
used for qualitative measurements like immune-phenotyping. The data are less
resolved since the cells are les in line with the wider core stream but
acquired more quickly. Lower flow rates decreases the width of the sample and
restricts the position of the particles to a smaller area. The majority of the
cells or particles pass through the center of the laser beam thus illuminating
the light in uniform manner. A lower rate is required where greater resolution
is critical like DNA analysis (Givan
2004; Katsanis et al. 2001).
Light scattering occurs
when the particles detect incident scatter light which depends on the physical
properties of the particles and complexity. Forward scatter light is
proportional to cell surface area or size and is a measure of mostly diffracted
light and is detected just of the axis of incident laser beam in the forward
direction of the photodiode. FSC provides suitable method for detecting
particles greater than a given size and is used in immune-phenotyping. Side
scattered light is proportional to the cell granularity and internal
complexity. The SSC is a measurement of mostly refracted and reflected light
which occurs in any interface within the cell where there is change of
refractive index. It is collected at 90 degrees to the laser beam by a
collection lens and redirected to the beam splitter to the detector (Michelson
2006).
A correlated measurement of FSC and SSC are used to differentiate of cell types
in heterogenous cell population and help to identify the subpopulations and is
a measure of cell inclusions and organelles.
The range over which the fluorescent compound can be activated is called
the absorption spectra. When more energy is consumed in absorption transitions
than is emitted in the fluorescent transitions then wavelengths will be emitted
and called the emission spectra (Michelson
2006).
The argon laser beam is the used laser beam because the 448nm light that emits
excites more than one flourochrome like Fluoroscein isothiocyanate (FITC). When
a fluorescent dye like FITC is conjugated to the monoclonal antibody it can
identify a cell type based on antigenic property at the surface of the cell. Peak
emission wavelength for FITC is 530nm (Givan
2004).
3.2.
Flourochromes Used in Flow Cytometry
There are many fluorescent molecules (fluorochromes) with a
potential application in flow cytometry. Some of the widely used fluorochromes
for detecting surface or intracellular epitopes are described below, including
the very latest in fluorescent probe technology – tandem dyes (Linden 2013).
1. Single Dyes
Some of these single dyes e.g. FITC have been in use for the
past 30 years but are now facing competition from alternatives like Alexa Fluor
dyes, which offer the user greater photostability and increased fluorescence (Rahman 2006).
2. Tandem Dyes
“In a tandem dye, a small fluorochrome takes a ‘piggy-back’
ride on another larger fluorochrome. When the first dye is excited and reaches
its maximal singlet state, all its energy transfers to the second dye (an
acceptor molecule), located in close proximity. This activates the second
fluorochrome, which then produces the fluorescence emission. The process is
called FRET (fluorescence resonance energy transfer). It is a clever way to
achieve higher Stokes Shifts and, therefore, increase the number of colors that
can be analyzed from a single laser wavelength.” (Orozco and Lewis 2010;
Rahman 2006)
The majority of tandem dyes have been manufactured for the
standard 488 nm laser (Figure 3:1), which is found in most flow cytometers.
Tandem dyes are very useful for multicolor fluorescence studies especially in
combination with single dyes. For example, Alexa Fluor 488, Phycoerythrin,
PerCP-Cy5.5 and PE-Cy7 can all be excited at 488 nm, but will produce green,
yellow, purple and infrared emissions respectively, which can be measured using
separate detectors (Hulspas et al. 2009; Le
Roy et al. 2009).
Figure (3:1): List of
some dyes used in flow cytometry analysis. (Givan 2004)
3.3. Fluorescence Compensation
This means that when the two fluorochromes are used for a
dual-color experiment, the true reading for fluorochrome A in FL-1 = (total
fluorescence measured in FL-1) minus (5% of fluorochrome B’s total
fluorescence). Similarly, the true reading for fluorochrome B in FL-2 =
(total fluorescence measured in FL-2) minus (17% of fluorochrome A’s total
fluorescence). Fortunately, modern Flow Cytometry analytical software
applies fluorescence compensation mathematics automatically, which simplifies
matters considerably.
3.4.
Detection and Analysis of Flow Cytometry Data
1.
Collecting the Signals
Once the light signals strike one portion of the photodiode they are
converted to the proportional number of electrons which are multiplied to
generate electrical current. The current travels to the multiplier to generate
a voltage pulse. The size of the pulse depends on number of photons detected. Once
a data file has been stored in the computer cell populations can be displayed
in several different formats. A single parameter such as FITC is displayed in
single parameter histograms where X axis represents the parameters signal value
in the channel numbers and the Y axis gives the number of events per channel
number. Signals with identical intensities accumulate in the same channel.
2.
Interpretation of Data: Gating and Quadrant Marking
A subset of the data
can be defined by a gate. Gate is a numerical or graphical boundary that is
used to define the nature of the particles for further analysis. Data analysis
from a list mode files by displaying the data in a plot and then measuring the
distribution of the events within a plot. A dot plot provides a two parameter
display data. Each dot represents one or more event. A subclass control is used
to find whether the quadrant markers will be placed. Subcellular debris and clumps can
be distinguished from the single cells by size which is estimated by the
forward scatters. Dead cells have lower forward scatter and higher side scatter
than the living cells.
A quadrant marker divides two
parameter plots into 4 sections to distinguish populations that are single positive,
negative, or double positives. The lower left quadrant is meant for events that
are negative for both parameters. The upper left quadrant parameter means
positive for Y-axis parameter, but negative for X-axis parameter. The lower
right corner quadrant means positive for X-axis parameter but negative for Y-axis
parameter while the upper right quadrant means positive for both the
parameters.
The lysed
whole blood cell analysis is the most common application of gating, and the
figure below (Figure 3:2) depicts typical graphs of SSC versus FSC when using
large cell numbers. The different physical properties of granulocytes,
monocytes and lymphocytes allow them to be distinguished from each other and
from the cellular contaminants.
Figure
(3:2): Analysis of lysed whole blood using FSC/SSC. The plotting of the density plots like above, each point or dot
represents an individual cell that has passed through the instrument. Yellow/green
hotspots indicate the large number of events resulting from discrete population
of cells. The colors makes the graph a three dimensional approach (Givan 2004).
The plot of contour
diagrams is an alternative method to demonstrate the same data. The joined
lines in above figure on right represents similar number of cells like the
density plot. Discrete populations of cells are easy to visualize in contour
maps having clear cu demarcations. The newer gating strategies utilize
fluorescence parameters along with scatter parameters (Figure 3:3). The example
of data interpretation is studied with blood cells as before.
Figure
(3:3): Lysed whole blood analysis using scatter and fluorescence. The figure on the left is a FSC/SSC plot
representing a human whole blood using smaller number of cells than in the
earlier figure. The lymphocytes, monocytes and granulocytes have been gated as
region 1(r1), region 2 (r2), and region 3 (r3) respectively. Region means an
area drawn on a plot displaying the Flow Cytometry data. On the right these same
cells are plotted as the SSC on the Y-axis versus CD-45 fluorescence on the X-axis.
CD45 is a marker expressed on all white blood cells at varying degrees but is
absent in the red blood cells and thus the R1 regions do not show the
fluorescence. Thus stating in relative terms, lymphocytes have a low SSC and
high CD45 count (R4), granulocytes have a high SSC and low CD45 count R6, while
the monocytes are somewhere between the other two. The major difference between
lymphocytes gated in R1 and those gated in R4 is the absence of red blood cells
in the later making it much purer preparation. This feature highlights the
usefulness of gating strategies that combine scatter parameter with fluorescent
parameter (Givan 2004; Ross 2002).
3.5. Interpretation of Histogram Data:
Single Parameter Histograms
These
are graphs that display a single measurement parameter (relative fluorescence
or light scatter intensity) on the X-axis and the number of events (cell count)
on the Y-axis (Figure 3:4).
Figure (3:4): A single-parameter histogram (Shapiro 2005). (Adopted from http://www.abdserotec.com/flow-cytometry-single-parameter-histograms.html)
The histogram in Figure (3:4) looks very
basic but is useful for evaluating the total number of cells in a sample that
possess the physical properties selected for or which express the marker of
interest. Cells with the desired characteristics are known as the positive
dataset.
“Ideally, Flow Cytometry will produce a single distinct peak
that can be interpreted as the positive dataset. However, in many situations,
flow analysis is performed on a mixed population of cells resulting in several
peaks on the histogram. In order to identify the positive dataset, Flow Cytometry
should be repeated in the presence of an appropriate negative isotype control”.
(Rahman 2006) (Figure 3:5)
Figure
(3:5): Flow cytometry positive dataset identification.
LEFT, using rat anti-mouse F4/80 conjugated to FITC to stain mouse peritoneal
macrophages produces two peaks. RIGHT, by running an appropriate isotype
control (rat IgG2b negative control conjugated to FITC) and overlaying its
image on the histogram (blue outline) the positive dataset is identified as the
taller red peak on the right (Shapiro 2005). (Adopted from http://www.abdserotec.com/flow-cytometry-single-parameter-histograms.html)
Analytical software packages that accompany Flow Cytometry
instruments make measuring the % of positive-staining cells in histograms easy.
For example, the F4/80 histogram is shown again below with statistics for R2
and R3 (known on this type of graph as ‘bar regions’) (Rahman 2006). (Figure 3:6)
3.6.
Statistical Analysis
In Figure (3:6), 99.83% of the negative control (blue
outline) is in R2. 28.14% of cells (red shade) ‘stain negative’ for F4/80 (R2)
compared to 71.86% in the positive dataset (R3). Additional statistics about
the peaks (median and standard deviation) is also provided automatically here
but this will vary with the software. A similar type of analysis will be
generated for two-parameter histograms (Brown and Wittwer 2000;
Givan 2004; Rahman 2006).
Figure (3:6): The flow
cytometric readings and the statistical outputs. (Brown and Wittwer 2000;
Rahman 2006)
3.7. Interpretation of Histogram Data:
Double Parameter Histograms
These are graphs that display two measurement parameters, one
on the X-axis and one on the Y- axis, and the cell count as a density (dot)
plot or contour map. The parameters could be SSC, FSC or fluorescence. Some
examples of two-parameter histograms were illustrated earlier. Another example
is the dual-color fluorescence histogram presented below (Figure 3:7).
Lymphocytes were stained with anti-CD3 in the FITC channel (X-axis) and anti-HLA-DR
in the PE channel (Y-axis). CD3 and HLA-DR are markers for T cells and B cells,
respectively.
Figure
(3:7): Two-parameter (dual-color fluorescence) histogram. The R2
encompasses the PE-labeled B cells – note their positive shift along the PE
axis. The R5 contains the FITC-labeled T cells (positively shifted along the
FITC axis). The top right quadrant contains a few ‘activated T cells’ (about 4%
in this sample) that possess some HLA-DR expression also. As these stain with
both antibody markers they are grouped in their own region (R3). The R4
contains cells negative for both FITC and PE (no shift) (Ross 2002). (Adopted from http://www.abdserotec.com/flow-cytometry-two-parameter-histograms.html)
Currently, Flow Cytometry can be performed on samples labeled
with up to 17 fluorescence markers simultaneously. Therefore a single
experiment can yield a large set of data for analysis using various
two-parameter histograms.
3.8. Flow Cytometric Determination of
Intracellular Pathogens
Staining intracellular antigens like cytokines can be
difficult because antibody based probes cannot pass sufficiently through the
plasma membrane into the interior of the cell. To improve the situation, cells
should first be fixed in suspension and then permeabilized before adding the
fluorochrome. This allows probes to access intracellular structures while
leaving the morphological scatter characteristics of the cells intact. Many
commercial kits are available today that provide the reagents to carry out
these crucial steps e.g. Leukoperm (Figure 3:8).
Figure
(3:8): Flow Cytometry showing leucoperm used in conjunction with an antibody
that recognizes MOMA-2. The MOMA-2 is an intracellular antigen
in mouse macrophages and monocytes. After fixation and permeabilization (b), the
positive dataset begins (Ross
2002; Shapiro 2005). (Adopted from http://www.abdserotec.com/flow-cytometry-intracellular-antigens.html)
3.9.
Flow Cytometry and Immunophenotyping
All normal cells express a variety of cell surface markers, dependent on
the specific cell type and degree of maturation. However, abnormal growth may
interfere with the natural expression of markers resulting in overexpression of
some and under-representation of others. Flow cytometry can be used to
immunophenotype cells and thereby distinguish between healthy and diseased
cells (Figure 3:9). It is unsurprising that today immunophenotyping is one of
the major clinical applications of flow cytometry, and is used to aid the
diagnosis of myelomas, lymphomas and leukaemias (Szczepański
et al. 2006). It can also be used to monitor
the effectiveness of clinical treatments. The differences between the blood
profiles of a healthy individual and one suffering from leukemia, for instance,
are very dramatic. This can be seen from the FSC versus SSC plots in Figures (3:9;
3:11). In the healthy person the cell types are clearly defined, whereas blood
from a leukemia patient is abnormal and does not follow the classic profile.
Figure
(3:9): Immunophenotyping of lymphocytes of normal and leukemia patients by flow
cytometry (Brown and Wittwer
2000; Shapiro 2005). (Adopted from http://www.abdserotec.com/flow-cytometry-immunophenotyping.html)
Testing the patient’s lymphocytes for specific cell surface
markers also reveals more about the condition (Figure 3:10).
Figure
(3:10): Diagnosing leukemia by flow cytometry. Being CD3-negative,
CD20-positive and HLA-DR-positive, a clinician could diagnose with certainty
that this patient is suffering from a B cell lineage leukemia or lymphoma. The
precise classification of disease may be determined using further antibodies (Brown and Wittwer 2000;
Shapiro 2005).
(Adopted from http://www.abdserotec.com/flow-cytometry-immunophenotyping.html)
Figure (3:11): Flow cytometric analysis of
platelet activation and hence morphology. Note the use of mouse antibody
markers to tag platelet receptor proteins. (Adapted from www.bdbiosciences.com)
3.10. Flow Cytometric Analysis of Cloned
Cell Lines
While analyzing a cloned cell line to determine whether
it is positive for a particular molecule, percentage calculations are not ideal
as it would be either 100% negative or positive. If you were analyzing a cloned
cell line to determine if it were positive for a particular molecule, you would
most likely not use percentages. Since a cloned cell line consists of a single
population, in most cases it would either be 100% negative or 100% positive.
It’s also possible that it would express low amounts of the molecule in question.
In that case, it would still be positive but dim.
For these cases where we want to
compare fluorescence intensities and measure the degree of positivity, we would
compare the geometric means or medians of the subclass control data versus the
sample data. If the sample data statistic is greater than that of the subclass
control data by some limit set by the user, it would be considered positive.
The greater the difference between the two, the more molecules are expressed per
cell and the more positive, or brighter the population (Givan
2004; Katsanis et al. 2001).
Besides being used to measure
positivity, geometric means or medians can be used to estimate the quantity of
molecules (ligands) expressed per cell. Special software programs like
Quanticalc use the median in conjunction with the data from a standard curve to
calculate the number of antibodies that are bound per cell. This information is
used to calculate the number of ligands per cell (Julius
et al. 1972; Ornatsky et al. 2010; Qian et al. 2010).
Materials and Methods
The
basic principle was to study the expression of Dynamin-3 protein in genotypes
with TT and CC single nucleotide polymorphisms amongst platelets. To study the
concentration of Dynamin 3 protein first of all the platelets were
permeabilized, platelet count was also taken to find the platelet numbers in
individuals with different genotypes (TT or CC). Two types of antibodies were
used as markers for the DNM3 antigenic determinant DNM3-abcam and DNM3-GTX
antibody and were suitably tagged to anti-GTX and anti-Abcam FITC antibodies. Thus
tagging to this primary antibody, the FTIC secondary antibody was tagged to
analyze the signals emitted as a function of cell count and DNM3 protein
through flow cytometry. The usage of different markers against the same epitope
helps to prove the robustness of data crosschecked by two methodologies. The
markers on the platelet surface were tagged by rabbit immunoglobulin to detect
fibrinogen attached platelet surface proteins as markers of platelet structure.
The individual steps are described below:
1.
Machine Standardization
Before the machine is
turned on we checked whether waste was full or not and if so it was blenched
with 2 tablets in 200ml water. The sheath fluid used was Isoton II. The machine
was started as usual and EXPO32 was used as start up function, and waited for
one hour and then the quality control test was done. To do the QC 15 drops of
Fluorospheres in the tubes for QC was put in position 1 and wax mixed through
the hands. Protocol for QC check done as usual and the QC results were noted. The
QC for the Sysmex machine (hematology analyzer) was taken from fridge and mixed
first for 5min with buffer and the power was set on, the tube rinsed and the QC
sample (after drying) was added.
2.
Preparation of Buffer Solutions
The flask was first
washed with PBS. A 500ml buffer was prepared (25ml EDTA and BSA) with 475ml
PBS. The buffer was filtered using a suitable filter.
3.
Preparation of Fixed and Permeabilized Platelets
10ml of blood sample
was collected and kept stored with citrate at room temperature to prevent
clotting. Then this blood sample was spun at 1200rpm for 17 min and the plasma
fraction was separated from it. Then Apyrase 0.25U/ml was added which means
6.25μl of apyrase is required. ThenPGE1 (1microlitre/ml was added) and
centrifugation was done at 2000rpm for 10 mins. The supernatant was removed and
1 ml of THG buffer (THG = 20mlTH from the stock + 200μl of Glucose 0.5 molar
solution). The platelet wash was carried out with this buffer and at second
wash PGE1 was used. Platelets were counted off.
4.
Method of Platelet Permeabilization
For permeabilization of
platelets, 40μl of platelets were added in all tubes (NPEM and PEM). A 40μl of
Fix and Perm agent A was added to the PEM tubes only. Incubation was carried
out for 15mins. Platelets were washed with 2ml wash buffer and centrifuged for
6min at 3200rpm. A 40μl of Fix and Perm agent B was added to the PEM tubes
only. Earlier steps were repeated as above and then antibodies were added to
all tubes.
5.
Methods of Antibody labeling of Markers
1. Method of Adding Antibodies to
Platelets for labeling External Membrane Proteins (Fibrinogen and P-Selectin)
Tubes were prepared by
adding 40μl of antibody and 40μl of platelets in plastic tubes and were
centrifuged. The tubes were incubated for 30 minutes at room temperature. After
30mins, 2ml buffer was added to each tube by a special pipette. Centrifugation
was carried out at 3000rpm for 6mins and the supernatant was removed and
shaked. Washing was repeated and 2 ml buffer to each tube was added. A 40μl of
platelets were then taken in the 16 tubes, and 40μl of primary antibody was
added to each tube, and 10μl secondary PE antibody was added. Negative control
was marked as antibody that does not bind to platelets.
2. Method of Antibody Preparations for
labeling the DNM3 Protein
The DNM3 GTX and Abcam
antibodies (both DNM3-PDK) have a concentration of 1mg/ml and to get the
required concentration of 50mcgm/ml a dilution ratio of 1:20 was used. For the
12 tubes, 18μl of the antibody and 342μl of the buffer were used. For the
primary rabbit antibody (11.1 mg/ml) at 50mcg/ml (11000/50), 500μl was needed
that is 2.5μl of antibody and 497.5μl of buffer. For the anti-rabbit FITC 20μl
of 1:25 dilution was used, therefore 60μl FITC + 1440μl buffer were required.
6.
Analysis of Data
1.
Flow
Cytometry Readings
The results were generated
and analyzed for the various parameters, All-X median, all % gated, BX- median,
B % gated both the sample genotypes. All X median signifies the ungated counts
in totality, and BX represents the specific gate through which the particular
data points are taken and B5 represents the amount of data points expressed as
a totality with respect to the total of other gates or as a percentage of All-X
data points. The readings were taken in SSC and FSC channels and also the
double marker channels. Results were statistically interpreted on the basis of
p values between the mean of the differences of the groups of values generated by
SPSS software.
2.
Statistical
Interpretation
Tests of statistical
significance that is the one tailed and two tailed t tests were carried out to
interpret the findings. This means that the two tail tests were carried to find
out whether there is significant difference in the values estimated of the
variables in the two genotypes. The p value was calculated and a value less
than 0.05 was considered statistically significant that means there is
significant differences in the two genotypes regarding the value of a
particular variable and has happened to
due to the effects of the condition and not by chance and the null hypothesis
is rejected. If the values were >0.05 we infer that there is no significant
difference between the two values of a variable in both the groups and the null
hypothesis is retained. The one tail tests done for DNM3 abcam and GTX was to
find whether the levels of DNM3 in one genotype is significantly higher or
lower than the other and also whether GTX bound to more regions of DNM3 than the
other genotype for interpreting results.
7.
Methodology Summary
First of all, the total
platelet count of both the genotypes was found out by automated cell counter.
Then the flow cytometric data in both the aspects of FSC and SSC was used to determine
the number and the volume of the platelets and thus the total external
morphology in relation to the single nucleotide polymorphism of the DNM3 gene.
The speculation is that if due to SNP, the DNM3 transcript could be short or
long then it will lead to different protein conformations of the DNM3 proteins
and itself the platelet surface proteins. To find out the DNM3 expressions the
permeabilized platelets were used. To detect the DNM3 protein in human
platelets, western blotting was done to analyze the DNM3 protein transcript, after
analysis of western blot data the flow cytometry analysis was done to detect
the extent of expression of DNM3 gene in the two groups of genotypes. Platelets
were permeabilized because staining intracellular proteins like DNM3 can be
difficult because antibody-based probes cannot pass sufficiently through the
plasma membrane into the interior of the cell. To improve the situation, cells
should first be fixed in suspension and then permeabilized before adding the
fluorochrome. This allowed probes (DNM3 abcams and GTX antibodies) to access
intracellular structures while leaving the morphological scatter
characteristics of the cells intact. Primary and secondary antibodies tagged with
FTIC were used for screening purposes in the flow cytometry. The primary rabbit
immunoglobulin and anti-rabbit immunoglobulin (secondary antibodies) were used
to label the surface proteins on the platelet membranes.
Results
Results are
based on 10 samples with TT and CC DNM3 genotype from Cambridge Bioresource,
five samples each
|
Platelet Count in TT Genotype
Samples
|
Platelet Count in CC Genotype
Samples
|
|
323
|
493
|
|
348
|
359
|
|
461
|
536
|
|
755
|
797
|
|
708
|
694
|
|
Average = 519
|
Average = 575.8
|
Figure
(3:12): The platelet count (in multiples of 103) in TT and CC genotype
of human platelet samples.
|
TT All X Median
|
CC All X Median
|
|
0.268
|
0.286
|
|
0.288
|
0.283
|
|
0.271
|
0.28
|
|
0.28
|
0.291
|
|
0.278
|
0.268
|
|
Average = 0.279
|
Average = 0.2795
|
Figure
(3:13): The flow cytometric values (PERM platelets All X Median) in TT and CC
genotype of human platelet samples.
|
TT All X-Median RGIgG
|
CC All X-median RBIgG
|
|
3.625
|
3.895
|
|
4.27
|
4.52
|
|
4.035
|
4.46
|
|
3.95
|
3.945
|
|
4.185
|
4.18
|
|
Average = 4.013
|
Average = 4.2
|
Figure
(3:14): The flow cytometric values (RbIgG All X Median) in TT and CC genotype
of human platelet samples.
|
TT BX Median DNM3
Abcam
|
CC BX Median DNM3
Abcam
|
|
25
|
25.05
|
|
35
|
28.85
|
|
21.6
|
26.85
|
|
18.75
|
19.57
|
|
24.15
|
16.35
|
|
Average = 24.9
|
Average = 23.34
|
Figure
(3:15): The flow cytometric values (DNM3 Abcam Bx-medium) in TT and CC genotype
of human platelet samples.
|
TT BX Median DNM3 GTX
|
CC BX Median DNM3 GTX
|
|
26.8
|
23.6
|
|
24.1
|
26.1
|
|
18.4
|
21.8
|
|
16.8
|
19.9
|
|
18
|
13.8
|
|
Average = 20.82
|
Average = 21.04
|
Figure
(3:16): The flow cytometric values (DNM3 GTX Bx-median) in TT and CC genotype
of human platelet samples.
Discussion
From the review of literature regarding the
DNM3 and it is expressed functional protein , it is evident that the functional
transcript of DNM3 is required for the normal process of megakaryopoiesis and a
transcript chain short of the predicted length leads to decreased mean platelet
volume and number. Single nucleotide polymorphisms in the intronic regions of
the variants are an established cause of the DNM3 expression in the human
population. This is due to the SNP at these leads to difficulty in binding to
the transcription factors which regulates the expression of the DNM3 mRNA. The
SNP are attributed to the various variants and the minor alleles are the T
alleles resulting from C to T transversions and the C alleles resulting from
the A to C transversions. These are designated as minor alleles as their
frequency occurs in more than 1%. It was postulated that these SNP caused
decreased MPV and platelet numbers. A genome-wide
association study conducted identified a locus on chromosome 1q24.3 which was
connected with mean platelet volume (MPV).This was found in intron 2/3 of the
dynamin 3 DNM3 genes. The highest association of the MPV was found to be with a
single nucleotide polymorphism (SNP) rs10914144 that resides in intron 2/3 of
DNM3 locus. This is a C/T polymorphism with the minor allele (T) having a frequency
of 0.17 in Caucasians reflected that the SNP variant
rs10914144 located within the second intron of the DNM3 gene locus was
associated with variation in the human platelet volume.
It was also found that a second variant of SNP
(rs2038479) which is found in high linkage disequilibrium with rs10914144 marks
a megakaryocyte specific alternate marker. The promoter binds MK transcription
factors MEIS1/RUNX1 and thus produces a short DNA transcript. The minor alleles
were C (A to C transversion) for rs2038479 and T (C to T transversion) for
rs10914144 and were associated with decreased expression of alternate
transcript. Repressive factors bind to the minor alleles of rs2038479 than the
major alleles. It was also revealed from a study that the minor allele of the
non coding variant of rs342293 disrupted a binding site of the transcription
factor EVI1/MECOM leading to higher level of transcription of the downstream
gene P1K3G. Thus it is seen that neither a single SNP nor a single
transcription factor is sufficient to explain the expression of a structural
(or functional) gene. This provided the basis of our study to analyze and
speculate the various protein-protein and protein- DNA instructions and what
are the probable factors causing alterations or no alterations in the DNM3 gene
expression in our subjects.
First of all, the total platelet count of both
the genotypes was found out by automated cell counter. Then the flow cytometric
data in both the aspects of FSC and SSC were used to determine the number and
the volume of the platelets and thus the total external morphology in relation
to the single nucleotide polymorphism of the DNM3 gene. The speculation is that
if due to SNP, the DNM3 transcript could be short or long then it will lead to
different protein conformations of the DNM3 proteins and itself the platelet
surface proteins.
To find out the DNM3 expressions we used the
permeabilized platelets and to detect the DNM3 protein. Western blotting was done
to analyze the DNM3 protein transcript. After analysis of western blot data the
flow cytometry analysis was done to detect the extent of expression of DNM3
gene in the two groups of genotypes. Platelets were permeabilized because
staining intracellular proteins like DNM3 can be difficult because
antibody-based probes cannot pass sufficiently through the plasma membrane into
the interior of the cell. To improve the situation, cells should first be fixed
in suspension and then permeabilized before adding the fluorochrome. This allowed
probes (DNM3 abcams and the GTX antibodies, both DNM3-PDK) to access
intracellular structures while leaving the morphological scatter
characteristics of the cells intact. Primary and secondary antibodies tagged
with FTIC were used for screening purposes in the flow cyotmetry. The primary
rabbit immunoglobulin and anti- rabbit immunoglobulin (secondary antibodies)
were used to label the surface proteins on the platelet membranes.
The flow cytometric data analysis was
used for both ungated data (All X Median was used to find the total population
of the cells or proteins) and gated data (for specific cells or proteins the BX-Median
data was analyzed through the process of gating). This means we used gates to
indicate their expression in the specific subsets (if at all would have been
isolated). The median data can be used to estimate the quantity of molecules
(ligands) expressed per cell. Special
software programs like Quanticalc use the median in conjunction with the data
from a standard curve to calculate the number of antibodies that are bound per
cell. This information is used to calculate the number of ligands per cell.
This approach was used because for detecting small molecules like proteins or
ligands within or on the surface of the cell the tests for positivity will be
limited to a specific population always and all the other gates will appear
countless.
In Figure (3:12), which represented
the platelet count in TT and CC genotype of the samples.( where p>0.05)
indicated that although there was no difference statistically in the platelet
counts of TT and CC genotype individuals, but marginally the platelet count was
more in the CC genotype. This finding of small marginal increase in platelet counts
can be correlated with the literature findings that the CC genotype which is
the major genotype is responsible for platelet count.
In Figure (3:13) which represented
the flow cytometric values (PERM platelets All X Median) in TT and CC genotype
of the samples.(p>0.05). This means that equal numbers of PERM platelets
were used for the comparison of DNM3 expression in these cells for data
reproducibility and this is indicated by the SSC versus FSC plot where the
total platelet count was indicated. The p- selectin PE (Phycoerythrin dye)
channel and fibrinogen FTIC channel gated and ungated figures were used for
platelets morphological features to mark fibrinogen and p-selectin functional
concentration of both the genotypes. P-selectin mediates rolling of platelets
and leukocytes on activated endothelial cells. After platelet activation,
P-selectin is translocated from intracellular granules to the external membrane
(Merten
and Thiagarajan 2000), whereas fibrinogen aggregates
platelets by bridging glycoprotein (GP) IIb/IIIa between adjacent platelets (Floyd
and Ferro 2012). Thus, P selectin is an active
platelet marker and indicates the platelet morphology while fibrininogen marks
platelet receptors.
The platelet bound fibrinogen was detected
by the rabbit immunoglobulin as indicated in Figure (3:14), the results
indicated that in the CC genotype more fibrinogen is bound to the platelet
receptor proteins (p<0.05) and this was in the All X median data but when
cross checked the data with specific gates such as the BX- median data, it was
found that there was no statistical significant difference between the two data
in the CC and TT genotypes indicating the expression of proteins in both the
genotypes platelet surface was almost similar. But the gate wanted to use was
for the fibrinogen molecule and hence taking the All-X median data would be a
misnomer, but still physiologically accepting the data the fibrinogen markers
were less in the TT genotype.
In Figure (3:15), the DNM3-abcam labeling of DNM3 in
both the TT and the CC genotypes indicated that in both the genotypes DNM3 are
equally expressed and exhibited similarity in concentration (p>0.05). Once
again, the BX gated data was considered as it indicated a specific ligand based
detection procedure and only the permeabilized platelets DNM3 had to be labeled
and screened. By this analysis one wanted to detect the overall expression and
concentration in both the genotypes and found it same.
In Figure (3:16), the results indicated that after
specific tagging antibodies like DNM3-GTX to the Homeobox specific regions of
proteins which might not be expressed if the protein is mutated or SNP resulted
change which might finally influence the expression of these proteins. The
binding of DNM3-GTX to the DNM3 was again similar in both the genotypes
indicating there was no difference in the structural properties of the specific
targeted region of the protein domain (DNM3) and hence one can conclude that
there no change in the structure of DNM3 was noted in both the genotypes.
From the above data it seems quite
plausible that SNPs may be involved in protein size and expression but in this study
that was not reflected in comparison to both the genotypes as far as the
production of a functional DNM3 is involved. The literature findings were not
confirmed in our study because the proteins (dynamins) which are being
responsible for platelet size and volume were equally expressed in both the
genotypes which deviated from the findings in earlier studies (Reems
et al. 2008). This was equally supported from
this intra-platelet data which correlated to the findings that the DNM3
expression and features were same as was found with the flow cytometric
findings with both the DNM3-abcam and the DNM3-GTX antibodies in both the
genotypes TT and CC.
Earlier studies revealed the fact that some SNPs
acted by altering the key MK cis regulatory DNA elements. It was found MEIS1,
which is a homeobox transcription factor is essential for the formation of
platelets. As per expectation the genome wide chromatin occupancy revealed
MEIS1 occupancy at genes enriched in megakaryocyte and platelet functions. SNPs
were identified in earlier proxy studies resided near a revealed MEIS1
occupancy site in the intronic region of the DNM3 gene (Nürnberg
et al. 2012). Evidence suggested that megakaryocyte
transcription factor RUNX1 also binds to this region and it was revealed that
this MEIS1/RUNX1 occupancy sites marked the alternate promoter of the DNM3 gene
which is selectively used in the MKs. The transcription from this promoter
sites resulted in skipping of the exons 1 and 2 of the canonical DNM3 gene and
created the insertion of a novel exon which resulted in shorter transcripts of
the DNM3 gene. So there can be a probability that although there was a SNP and
this might have inhibited MEIS1 binding to the DNM3 DNA so to RUNX1 but there
can be other proteins which might get bind to that SNP portion to complement
the actions of MEIS1 on DNM3 gene expression.
This could have acted through the theory of DNA
Logic Gate (Bonnet
et al. 2013). This means if binding of a
transcription factor at one portion due to one SNP is altered but another
transcription factor may bind to that SNP and cause the DNA polymerase to
express the DNM3 mRNA which might have occurred in this study. This puts the
way of logic which means if either transcription factor can cause expression of
the DNM3 mRNA then it can be considered a “OR” gate logic. The logic is either
transcription factor binding can cause the final functional gene to be
expressed( like in Boolean Algebra 1 0r 0 =1, here 1 means one transcription
factor and 0 means the another but the resultant 1 means the function, in this
case expression of the DNM3 gene will be intact). If it would been an AND gate
then we could have concluded that the SNP makes a favorable platform for
binding to other transcription factor at other SNP may compensate for the
altered association with MEISI binding factors due to our proposed SNP. The AND
gate logic will put that both 1+1=1 that is the presence of both the
transcription factors are necessary for production of the DNM3 gene expression.
While the NAND gate would have suggested that if both proteins and
transcription factors bind to DNM3 DNA then there would be no DNM3 expression.
Perhaps the most possible explanation can be the OR gate which means definitely
alternate transcription factors which can bind efficiently at the SNP should be
present. The other way different SNP might have been there that favoured the
regulation of DNM3 gene expression by accepting transcription factors which
might be even MEIS1 to bind on them and maintain DNM3 gene expressions.
The rabbit
antibody which was used as a marker for the fibrinogen bound receptor proteins
on the platelet membranes indicated that in the CC genotype more fibrinogen is
bound to the platelet receptor proteins (p<0.05) and this was in the All X
median data but when the data was cross checked with specific gates such as the
BX- median data it was found there was
no statistical significant difference between the two data in the CC and TT
genotypes indicating the expression of proteins in both the genotypes platelet
surface was almost same (p>0.05). Since the gate used was for the fibrinogen
molecule and hence taking the All-X median data would be a misnomer, but still
physiologically accepting the data the fibrinogen markers were less in the TT
genotype. This can be explained again by the Logic gate that the platelet
receptors for protein receptors like fibrinogen is up-regulated by dynamin and
may be other protein/s acting as a OR gate fashion so even if the dynamin
expressions were same in both genotypes the AND gate logic with the absence of
other proteins that interact with Dynamin to cause the receptor up-regulations
was missing and that effect was not under the purview of this study.
The only thing in this study that matched literature
results were that, the platelet counts were high in the CC genotype than in the
TT genotype but on the other hand these values were not statistically
significant again. This might be due to the fact that, the sample size
considered in this study was low and there might have been individual
differences and may be the homeobox dependent marker could not target the
proper SNP site to reveal the structural difference in the DNM3 protein. The
other probability is that apart from Dynamin 3 other proteins were deficit to
influence Megakaryocytopoiesis in the TT genotype.
Conclusion
The
findings in this study though do not confirm to the literature in terms of
statistical differences but there might be clinical correlates to it. The
findings in this study may give an insight to the probable compensating
mechanisms via repression modulation through alternate transcription factors,
or may be a resultant effect of Boolean function related to the principle of
DNA logic gates on the expression of the DNM3 gene. This means though the
genotypes are different and an altered function of the DNM3 gene is speculated,
but in vivo the situation is completely different. For the expression of DNM3 gene,
binding of transcription factors in the intron region is crucial, but there
might be two regions in the intronic portion and so two transcription factors
may be involved in expression of the DNM3 gene. If binding of a transcription
factor at one portion is altered also another transcription factor with no SNP
or rather another SNP can provide a site for binding of another transcription
factor and the resultant effect will produce the full functional Dynamin GTPase
protein as were the findings in this study.
This means that the altered binding of the
transcription factors may be adjusted by “OR” logic gates. Elucidating these
molecular mechanisms in further studies is potential to identify the newer
components and regulatory pathways involved in thrombopoiesis regulated by the
MK specific DNM3 isoform. Even it was seen from literature survey, shorter MK
specific DNM3 isoform influences the activity of protein to keep it in normal
functioning mode and produces normalized platelets. The answer could lie in the
GTPase domain which resides in the amino terminal portion of the molecule and
might be impacted by alternate transcription sites which might exist to
compensate the SNP mediated thrombocytopenia. So further studies related to the
identification of various transcription factors and the intronic regions to
which they find could throw more light to study the protein-protein and
protein-DNA interactions for producing the DNM3 protein. There can be other
proteins also which could influence platelet morphology and their interaction
should also be an active area of interest in regulating the difference of platelet
morphology in TT and CC genotypes. This might throw more light onto the
investigation of role of dynamins in platelet production and membrane
remodeling
