Welcome to the mooc interactomics course The
combination of proteomic technologies especially
the protein microarrays have potential to
be applied for wide variety of biological
applications The application of cell free
based protein microarrays have seen rampant
increase because of ease of synthesizing the
proteins by using cell free expression based
system as compared to the cell based traditional
way of protein purification and then printing
on the array surface
We discussed this aspect in our previous lecture
when we talked about use of cell free expression
system for biomarker discovery
Today we will continue our discussion on applications
of cell free expression system and other biological
goals They help us to achieve using few case
studies Finally we also touch upon the challenges
of analyzing the microarray data regardless
of which experiments you perform and what
biological questions you may want to address
However the data analysis becomes very crucial
and very challenging in microarrays experiments
The first case study for today’s detection
of potential immunogenic proteins of plasmodium
falciparum The study performed by Dooley Nichol
in 2008
Dooley Nichol used E coli based cell free
in vitro transcription and translation system
to produce 250 plasmodium falciparum generated
by the polymerase chain reaction and recombinational
cloning procedures
After synthesizing the proteins from these
two hundred fifty open reading frames authors
profiled antibodies that developed after natural
or experimental infection or after the vaccination
with the attenuated organism These are exposed
to the plasmodium falciparum either naturally
or experimentally and were screened by using
protein microarrays
In this study they identified 72 highly reactive
plasmodium falciparum antigens The proteins
express specifically in pre .. stage of plasmodium
which was CSP as well as some liver stage
specific antigens such as LSA1 They also identified
successfully several proteins by applying
cell free expression based protein micro arrays
Let us now discuss this experiment by looking
at this animation
Let us now discuss the immunological studies
in this animation The use of cell free expression
based protein microarrays for detection of
potential immunogenic proteins of plasmodium
falciparum was studied by Dooley Nichol 2008
In this study authors carried out cell free
expression of PCR amplified vectors using
.. coli in vitro transcription and translation
system They expressed 250 putative proteins
that were printed directly on to the microscopic
array slides without any need for protein
purification
These arrays were probed with serum samples
from patients who had been naturally exposed
to plasmodium falciparum and who were experimentally
exposed by means of radiation attenuated plasmodium
falciparum Authors successfully identified
72 highly immunoreactive protein antigens
as well 56 previously uncharacterized antigens
that were pseudo dominant
The study has shown some of the newly identify
targets can serve as potential vaccine targets
Let us now talk about the next case study
identification of immunogenes of Q fever causing
coxiella burnetii study performed by .. 2008
Q fever is widely spread ..disease caused
by coxiella species So identification of immunogens
of Q fever causing this disease were identified
by using protein microarrays
In this study authors used coxiella burnetii
protein microarrays to identify immunodominant
antigens
Almost 2000 open reading frame ORFs were generated
by using the cell free expression based approach
E coli in vitro transcription translation
system and then employed this protein microarray
platform for identifying the immunodominant
antigens Some of the steps involved in this
experiment will be discussed in the following
animation
Case study 4 identification of immunogenes
of Q fever caused by coxiella burnetii study
by Berito 2008 ..carried out 
in vitro transcription and translation of
nineteen hundred eighty eight Open reading
frame of C burnetii by using E coli based
cell free systems 75 percent of the open reading
frames were successfully generated as full
length proteins by using cell free expression
system and then spotted on to the nitrocellulose
arrays
These cell free expression based microarrays
were probed with sera from the patients who
had been vaccinated as well as acute Q fever
patients 50 proteins were identified that
were found to react strongly with the immune
sera From the previous lecture and this lecture
you got a glimpse of application of protein
microarrays for biomarker discovery and several
immunological studies It is time now to look
another widely used application which is protein
protein interaction by using cell free expression
based protein microarrays
In this part of lecture I will mainly focus
on nucleic acid programmable protein array
or NAPPA and how they are been applied to
study protein protein interactions
In this slide as you can see there is a small
test array which we have used to teach proteomics
course in coldstream Harvard laboratory in
New York The students made these array themselves
and as you can see the array layout there
only five handful genes were printed in duplicate
on the chip along with vector control master
mix and water
Now if you want to study the Jun and Fos protein
interaction and you can use Fos as a query
protein it will bind to Jun protein spot and
therefore two spots of Jun will light up as
you can see in the slide So all the six blocks
they are duplicate of Jun proteins which are
interacting with Fos protein and so Jun Fos
protein interactions can be established by
using the protein microarray system
The previous array which we talked was spot
testarray but if you really want to perform
the protein wide screen to test protein protein
interaction so you have to use high density
protein arrays Now in this slide I am showing
you high density protein arrays to test the
same protein protein interaction of Jun and
Fos protein pairs
In this study students used Fos as query protein
and then identified Jun printed four times
on chip as a target Jun Fos was used as model
system to demonstrate how protein protein
interactions can be studied by using cell
free expression based NAPPA microarray system
Let us look at some more case studies where
protein microarrays have been used for study
the protein interactions
The next case study is performed by Ramachandra
Nitol 2004 to identify novel protein protein
interactions using NAPPA microarrays
Authors reported generation of self assembling
microarrays which was one of the novel technology
reported in science in 2004 in this study
Ramachandran Nitol used a pair wise interaction
among 29 human DNA replication initiating
proteins which recapitulated the regulation
of CDT1 binding to the selected replication
proteins and map its geminin binding domain
by using NAPPA approach Let me describe some
of the steps involved in this experiment by
showing you this animation
Protein interaction studies Case study five
Identification of novel protein protein interactions
using nucleic acid programmable protein microarrays
study by Ramachandran Nitol 2004 Ramachandran
Nitol tested the use of NAPPA microarrays
by immobilizing twenty nine sequence verified
human genes involved in the replication initiation
on the array surface 
and then expressing them in duplicate with
rapid ratico slide lysate
The expressed proteins bound to the anti GST
antibodies which are the captured antibodies
present on the array surface
Authors made use of each of this expressed
protein to probe another duplicate of array
of the same twenty nine proteins thereby generating
a 29 by 29 protein interaction matrix 110
interactions were detected between proteins
of the replication initiation complex of which
63 were previously undetected
Let us now discuss the next case study high
density NAPPA array approach for studying
well characterized gene pairs a study by Ramachandran
Nitol 2008
The previous study which we discussed was
more proof of concept where handful proteins
were taken to study the protein protein interactions
whereas this time authors used high density
arrays with thousands of protein features
will printed
In this study authors used high density NAPPA
approach to study the binary interactions
between several well characterized interacting
protein pairs such as Jun and Fos p53 and
MDM2 Now selective binding to these interactions
were identify by using specific antibodies
In protein interaction studies it becomes
very tedious to test our protein interactions
in whole directions For example if one testing
the Jun and Fos interaction it should work
in either way Jun as a query or Fos as query
a protein
If Fos is printed on the array Jun should
be able to bind if used as query protein or
similarly if is Jun printed on the array then
Fos protein can be used as query molecule
to test the protein interactions Many times
these interaction becomes unidirectional It
becomes very tedious to show that interaction
is working in either of these directions but
in this study author showed that protein interaction
of Jun Fos protein pair can be shown in both
the directions
In addition to showing that protein expression
and protein interaction works it is also interesting
to perform the co expression where author
showed that even the query protein need not
to purify and one could expressed that DNA
along with the in vitro transcription translation
mix printed on the protein chip surface
So if you have protein microarrays features
are printed on the chip and then you have
generated the contents by using cell free
expression based system Now you want to study
the interaction and for that you have to purify
the protein and used it as interactor
By using co expression now you can use protein
specific antibody to identify the interaction
or you can use tag specific antibody to detect
interactions However in this study authors
used co expression It means the query protein
along with the arrays protein were expressed
by using cell free expression system so that
there was no need to purify the (crude) query
protein as well
Let us talk about co expression So if you
have protein microarrays features are printed
on the chip and then you have generated the
contents by using cell free expression system
and now you want to study the interaction
and for that you need to purify a protein
and use it as interactors You can use protein
specific antibody to identify the interaction
or you can use tag specific anti body for
detecting the interactions but in this study
authors used co expression
Let us talk about co expression experiment
It means the query protein along with the
arrayed protein were expressed by using cell
free expression system so that there was no
need to purify the query protein also When
the protein interaction has to be performed
you can take the cDNA of Fos protein for example
mix it in rapid reticosyte lysate along with
other in vitro transcription translation machinery
As you can see the slide the mix the whole
cell lysate on chip surface and then after
incubation when proteins are expressed at
the same time query cDNA will also express
the proteins and then if it finds the binding
partner it is going to bind to those features
which can be detected by using protein specific
or tag specific antibody
By performing this type of experiment authors
allowed co expression it means involvement
from both query and target proteins expressed
in the same environment and allowed very natural
protein interactions to happen There is good
likelyhood that they are going to identify
the right interactors So let me show you the
steps involved in this study by showing you
the following animation
High density NAPPA approach for studying well
characterized genes pits study by Ramachandran
Nitol 2008 In this study authors made use
of high density nucleic acid programmable
protein arrays to study protein protein interactions
647 unique genes were printed on to the array
surface and expressed by adding the cell free
expression based system After addition of
cell free expression based system proteins
containing GST tag were synthesized and bound
on to the captured antibody
cDNA of query protein was also added to the
same mixture such that the query was co expressed
but remain unbound due to the lack of the
tag capturing agent These protein microarrays
were then probed with anti bodies specific
to the query proteins Authors detected various
protein interactions using well known query
proteins such as Jun Fos and MDM2
We have talked about various applications
by employing cell free expression based protein
microarrays and discussed biomarkers screening
immunological studies and protein protein
interactions over the last two lectures Now
regardless of what applications you want to
perform on these arrays you are going to generate
large amount of data So the volume of data
generated from microarray experiments are
prodigious
It becomes important to develop the appropriate
informatics system so that one can analyze
the data uniformly and make some very good
output from this data analysis
So the image analysis when you are talking
about high density approaches data analysis
the image analysis becomes very challenging
For example you can see an image here for
the protein microarrays and I have shown a
spot The expression of this particular immunogenic
protein is so high that it is spilled over
to the neighbouring proteins
Now one need to correct this type of error
and remove the spots which are in the periphery
of these proteins Scaling up is good approach
because one wants to perform high throughput
experiment so that thousands of features can
be studied simultaneously However while scaling
up especially when you are using cell free
expression based approaches one need to be
cautious that what need to be the optimum
intensity for the arrays because if there
is of spillover of expressed protein on the
neighbouring protein spot that is going to
affect values of the neighbouring spots as
well
The slide shows how protein is diffused to
the neighbouring spots and the neighbouring
spot need to be corrected for data analysis
Similarly one need to perform the background
correction normalization and use various parameters
to perform good microarray data analysis Let
us now discuss with the Dr Sudesh Srivastavaa
the micro data analysis What are the challenges
involved and in the subsequent lecture we
will talk about in much more detail the different
steps and detail of data analysis for microarray
based system
Sanjeeva Srivastava: uhh What are the major
challenges of the microarray data analysis
Also I would like to get your comments that
what should be the good statical design when
biologists are starting some experiment for
the microarray because most of the time there
are chemical samples they would like to get
some very useful biological information from
this
Dr Sudesh Srivastava: Statistical is very
important aspects of this biological experiments
and I think the statistician get involved
from the beginning of the experiments and
the the important of statistician as you mentioned
about the sample not only the sample size
but also to understand the experiment and
control the variation in any biological experiment
and so it is a very good idea to have a statistician
on the beginning of the experiment and where
they can contribute not only on the data analysis
point but also to conducting and performing
a optimal design experiment way so you can
have as precise and as useful information
out of the data of the experiment you are
trying to achieve
Sanjeeva Srivastava: practice very important
uhh but there are many ways of analyzing microarray
data and what are the different approaches
which are available and which one would you
consider as good approach
Dr Sudesh Srivastava: Yes There are I see
a statistical is the methods are a tools and
its depends on your objectives So when whenever
you are trying to perform a experiment I think
one has to be very clear with the objectives
and which when you talk to the statistician
they will let you know what methods are appropriate
to your experiment to your hypothesis and
in that case uhh statistician will let you
know how you should go and perform the experiment
and not only experiment but also to control
the biological or technical error which are
involved in when you are performing experiment
So but the methods as I said there are these
new methods are involving each and every day
in this field and is because the biological
problems are very complex It is not like you
can answer by telling one method So uhh but
of course there are some standard methods
been used in other fields and people are trying
to use the soon like same methods in biological
point of view and I would like to say the
all the methodology developed in the statistics
been based on small sample size and now in
the recent years
So I would say and nowaadays we are dealing
with huge data size particularly in biological
field So in that case we have to come up with
new methods or new methodology and statistic
which can handle the more appropriately and
more objective oriented statistic statistical
method
Sanjeeva Srivastava: So I am coming that its
not possible to really list out one best method
Dr Sudesh Srivastava: Yes
Sanjeeva Srivastava: But at least can you
provide few possible solutions
Dr Sudesh Srivastava: Absolutely I will truly
agree with you on that and there is no unique
method and is not only in biological point
of but is in general as well because statistics
should be statistical is always dependent
more dependent all the experiment dependent
techniques so it only develops type of data
or the experiment objectives you are trying
to achieve
Sanjeeva Srivastava: uhh Sudesh in the microarray
field biologist apply that to identify differential
expression of genes and what type of issues
do you see like in terms of analyzing this
data sets from the microarrays and what are
different ways of analyzing the data and any
comments on that
Dr Sudesh Srivastava: I would like to say
the statistical method used in identifying
differential expression gene analysis starts
from experimented design till the end of the
analysis for the till the final conclusion
of the analysis
So there are a number of methods available
and particularly at design stage because design
stage there is different kind of a design
like you must have heard about the loop design
reference designs and also the factorial design
it is depending on your situation You try
to perform your experiment and all this experimental
design are basically depend on the statistical
tool you wanted use for your data analysis
and like when you are using reference design
or you using loop design Sso basically you
trying to compare the two treatment of the
by the control or sorry yes or the tumor
So in that case you used .. like when you
are comparing just two conditions and in case
if you dealing more more than two condition
then you go little bit analysis of various
kinds of analysis and then you uhh use booststrap
method and you use sams method and also there
is another method called wrest method So these
are the all methods appropriate in your your
situation when you are dealing with identifying
differential expressions
Sanjeeva Srivastava: So there are a lot of
options available but it always becomes challenging
to apply which one is a real good method and
that is why we need
Dr Sudesh Srivastava : Ohh ya absolutely absolutely
Sanjeeva Srivastava: and that is why we need
some good statistical .. gene for
Dr Sudesh Srivastava : I am I am glad to hear
that word because ahh most of the times what
happen uhh they scientist or basic scientist
research they go to the statistician when
they are done with their experiment and they
go to detect the data to statistician and
they ask to you could you please analyze the
data and so in that case I would like to say
there is a very famous saying from.. ahh when
you to the statistician with your data so
on the statistician can do postpartum of your
data and can tell you how the data dies
And so with that remark I would like to say
if you are trying to conduct any research
process or trying to perform any research
oriented biological question so I would say
go to the statistician from the beginning
and they can help you to at least get the
optimal way of experiment itself so that that
you can do the best analysis or best mathematics
tool to appropriate your situation So I would
say yes statistician should be or must be
involve from the beginning of the experiment
Sanjeeva Srivastava: So I must say that this
is the take home from this interview that
its not at the end but actually its on the
beginning when we need to involve a statistician
for the large set of analysis if we want to
perform some different high throughput experiments
and especially genomics and proteomics mix
is very important because we invest lot of
technology lot of samples and if our central
design is not pretty well then later on the
thing will fail
So with that thought I will conclude this
interview and I would like to thank Dr Sudesh
for being with us and sharing some experience
on microarrays data analysis and challenges
Thank you very much
Dr Sudesh Srivastava: Thank you
Sanjeeva Srivastava: Thus in today’s lecture
we looked at different types of application
of cell free expression system through various
case studies We also discussed the role of
stringent statistics for robust data analysis
with a leading expert Dr Sudesh Shrivastav
Once data is obtained from such high throughput
experiments one gets a holistic view of protein
regulation and cell functioning That brings
in .. biology to test biological hypothesis
developed from such studies and also understand
functional biology at another level We will
look into these aspects in the forthcoming
lecture Thank you
