Hello students; so, today we are in the last
lecture, we have discussed many technologies
different ways of looking at various type
of interaction analysis both label based and
label free platforms.
We also discussed about how to really start
analyzing the data and visualizing the data.
And finally, we have to think about how best
all this omics information which we are obtaining
how this could lead to the understanding of
the physiology.
As you can see the slide that we are generating
data set from variety of different level of
biomolecules starting from the genome, transcriptome,
proteome and then looking at this whole information
in the more systems network manner, which
eventually can help us to define the physiology
and probably helped the life scientists and
medical scientist to really provide the meaningful
information for the complex biological questions.
The complex data sets which we are obtaining
from different technologies different platforms
different omics levels.
We have to provide a much more comprehensive
view of what is the meaning of this data set.
And that is where systems biology and integrated
omics analysis have really started leading
the hope the path of this whole field and
the systems approaches are really required
to analyze and interpret these large datasets.
Which will eventually help us to find the
mechanisms of different diseases, mechanism
of different complex biological problems and
various therapies for diseases.
It may also help us to relate the different
phenotypes and what could be the relevance
to the clinical characteristics if we are
looking at more medical issues.
So, what is a system biology?.
Systems biology is systems level understanding
of the biological networks which is defined
with the network of various interacting elements
as well as the dynamic perturbations and responses
to the various cues which we obtain from those
systems.
These networks provide the insights which
cannot be analyzed just from the isolated
systems alone.
We cannot just look at the genome separately,
transcriptome separately, proteome separately
and tried to obtain the full picture.
We have to start putting the things together
in a more system wise manner which could be
analyzed using systems level tools.
So, to the common elements of system biology,
they include the networks, modelling, computation
and dynamic properties.
So, what are the approaches of system biology?
The system is an entity which maintains its
existence through mutual interaction of it
is constituent parts.
You can see at the slide that system research
consists of first identification of the parts
then characterization of these components
excluding the ones which are not the part
of a system and then identifying the interaction
of the components with each other.
Finally, we are looking at the interaction
of these components with environment which
modulates the parts either directly or indirectly
through modulation of these internal interactions.
So, what are distinct approaches in system
biology?
It can be model based or data based.
The in model based you already have the prior
data information which you want to implement
and build the model, of course it relies on
lot of computational modeling and various
type of tools for simulation, but it is very
difficult to build the detailed kinetic models
just based on this information.
Whereas, the data based systems biology approach
involves a new phenomenon to define or find
using these kind of you know data sets.
It relies essentially on various types of
omics data sets and looks at their intricate
complex relationship by looking at various
components from genome, transcriptome, proteome
and then try to define various type of pathways
and networks.
There could be different ways of looking at
the system biology approaches; one is reductionist
approach which is understanding the properties
of complex systems by studying their fundamental
parts in it is individuality or it can be
integrative approach where we want to understand
the properties of the fundamental parts and
their interactions to predict the system.
What is system biology triangle?
It involves the experiment which we do in
the wet lab to generate the data set, the
various technology platforms and computational
modeling.
All this together constitute the systems biology
triangle.
This is essentially the synergistic application
of experiment, theory, technology and modeling
to enhance our understanding of the biological
processes as the whole system rather than
looking them as a individual components.
So, I must warn you that system biology field
is very challenging because, you are looking
at not only one type of data set in isolation,
but you are trying to integrate the information.
And looking at the properties of a system
in it is whole which is not just you know,
simply adding 1, 2, 3 and making the sum of
that.
But looking at much more bigger picture and
trying to integrate the information in much
more meaningful manner.
And once you do systems analysis you might
find out that system may have it is own emerging
property on it is own which was not just possible
by looking at each pieces separately and just
by combining them together.
Understanding the dynamics of even simplest
biological networks it requires lot of computational
power using modeling, simulation and understanding
of the biological questions.
It also requires mathematical modeling and
different type of statistical approaches.
I have depicted you here the one of the omics
dataset obtained from the microarrays and
mass spectrometer and then how we were trying
to build the interaction networks of various
proteins which were involved in a given cancer.
So, by looking at this kind of information;
we want to really obtain, what are the important
nodes which might be governing the disease.
Which was otherwise not possible just by looking
at the list of the protein and what kind of
proteins are changing.
But rather now we are comprehensively analyzing
the entire dataset and looking at which could
be the major root cause of the disease and
can we identify the right nodes.
If you identify those the major area then
of course, one could think about certain therapies
which could target those nodes and then probably
those will be the root cause where one could
start making more impact for the patient treatment.
So, by next part I am going to now talk to
you about some of the major revolutions which
are happening in front of us in the field
of omics.
I am going to give you some very brief examples
and also going to give the perspective directly
from the experts, the scientists who are working
on this field and who have actually laid the
foundation for the areas.
So, first in the field of interactomics; the
big project has come forward which is the
human protein atlas.
The scientist Mathias William and his big
team, they are all working towards generating
antibodies for each protein and looking at
that how the proteins are localized, how proteins
interact and they are providing a lot of interesting
biological insight looking at the proteins
by different type of experiments which are
based on the antibodies.
So, let me have a perspective of Dr. Mathias
Wilhelm and Dr. Emma to talk to you about
what are the goals of these big project of
human protein atlas and what are the major
accomplishments so far.
Dr. Emma : So, we have during the last 10
years as part of the human protein atlas project.
We have been making at cell atlas a sub cellular
map of the human proteome using an antibody
based approach.
So, we generate high resolution present images
and we have been generating hundreds of thousands
of such images and then we have been classifying
the patterns and sorting it into different
categories.
So, we could finally, provide the first map
of the first sub cellular map of the human
proteome and see which proteins are in the
mitochondria which proteins are on the plasma
membrane and so on.
And the most interesting findings we made
here is that as much as half of all proteins
localized to several places in the cell and
we also see a lot of single cell variations.
So, the multi localizing proteins as we call
them it is very interesting from a biological
perspective.
We do not know the biological consequences
of this it might be so, that these proteins
have contact specific functions or even moon
light in different parts of the cell, but
we cannot really tell we only we can only
observe make the observation that they are
in multiple places.
Then, someone else would have to do or would
have to do in depth biological studies look
at splicing isoforms or post translational
modifications to see if it is how this multi
localization is achieved and also what are
the biological consequences.
My opinion is that antibodies should be viewed
upon small chemical reagents, they have on
and off target binding.
So, you have to validate your results in any
assay where you use them.
So, we fail more than half of the antibodies
that we use.
So, we put a lot of effort into validation
of our data and we have validation scores
that kind of denotes the reliability of the
results.
So, that is a big part of our job.
Dr. Sanjeeva Srivastava: Next I will like
to talk to you about the field of proteogenomics.
While lot of things we talked about different
proteomic technologies.
But you have also seen that how NGS is contributing,
next generation sequencing and genomic field
is contributing, immensely towards our understanding
of the biology.
So, how to integrate this information?
Can we start integrating the most crucial
molecules say genome and proteome together
and that has actually led to the field of
proteogenomics which is essentially going
to look at you know a lot of information which
was not possible, otherwise by looking at
the molecules alone.
Probably with this cartoon you will appreciate
that how the proteo-genomic analysis of cancer
and other complex diseases could elucidate
the functional consequence of somatic mutations,
narrow the candidate nomination for the driver
genes which may have the large deletions or
amplified regions and probably identify the
therapeutic targets.
The National Cancer Institute and National
Institute of Health in the USA; they have
really made huge contribution in this area
of proteogenomic investigations.
There are series of interesting paper which
I have shown on the screen.
They have showed the utility of integrating
these technologies and how one could actually
understand the very complex cancers in much
more efficient manner.
Especially the studies on the collateral cancer,
breast cancer and ovarian cancer have really
given the path that how to integrate this
information and get much more meaningful insight
for the same complex disease which was otherwise
not possible.
So, in the field of proteogenomics, which
actually we offer another course on this is
a much complex subject and requires much more
discussion, but to give you a flavor it involves
from the same sample and it is more used in
the case of cancer and other complex diseases.
from the same patient sample can we look at
DNA and look at their whole genome sequence
and whole exome sequence, then look at the
RNA analysis RNA sequencing in transcriptome
analysis and protein and analyzed using LCMS,
MSMS and then start aligning the data I start
analyzing the data big data to obtain the
proteogenomic information which has given
those paper which I just showed you in the
previous slide.
There is a many beautiful studies which have
shown the impact of these studies and they
have also shown that you know how if you build
the layer wise information as you can see
in one of this nature study shown in the breast
cancer, that if you are looking at different
subtypes like Basal like, HER2, Luminal A,
Luminal B; these cancer type if you are only
looking at one layer of information only at
the gene level or even only the protein level
that may not help you to obtain the entire
picture.
But even some time information of the phosphorylation
level or PTM level is much more powerful than
just looking at all of this other molecules.
So, you have to you know get an idea that
how this entire information can be put together
and can give us much more meaningful insight.
So, based on this idea that how to utilize
these omics field omics technology the ex
US vice president Joe Biden.
He started the project cancer moonshot.
Idea was can we accelerate our pace of doing
cancer research and whatever we are able to
achieve in next 20 years can we achieve in
5 years.
So, in this manner, scientist really got motivated
and started coming together in the field of
proteogenomics which led to the cancer moonshot
USA program.
The next intention was cancer has no boundaries.
Can we expand this project to the international
community and they form international cancer
proteogenome consortium to have more association
and collaboration from other countries.
We are very fortunate and proud to be part
of this program of ICPC where India joined
the 12 country consortium and we are also
going to look at the proteo genome analysis
of different complex cancer of India and then
share the data and analyze the data with different
countries and try to obtain more meaningful
information for these difficult to treat cancers.
An image shown here for one of the ICPC retreat
where different countries are participating
in this activity.
So, within the picture, I have shown Dr. Henry
Rodriguez, he is one of the director of NIH,
NCI who has really led the path for the ICPC
program.
And I am going to show you a video clip of
sharing his perspective about the ICPC program
and cancer moonshot as well as what are the
guidelines for doing research in this area
and the way forward his vision.
Speaker 3 Dr. Henry Rodriguez ; So, what I
could speak to is to more from the national
cancer institute which is the effort that
I leave.
I think the part that is very exciting I have
watched now the evolution as I would call
it and the maturity of the science of proteomics.
So, 10 years ago when we started this effort
at the cancer institute referred to as now
the clinical proteomic tumor analysis consortium
CP TAC those were an acknowledgement out of
NCI while we saw the potential for proteo
genomics.
We knew proteomics would be a very complementary
field, but at that time we felt that technology
was being developed in this case mass spectrometry
yet while it is extremely a very powerful
technique there were items that we needed
to address in terms of the rigor and reproducibility
if it were to one day be a complementary technology
to the genomics landscape.
So, 10 years ago while at cancer genome atlas
was the very first one large scale biology
that moved forward in cataloguing all the
perturbations inside cancerous cells from
a genomic perspective.
The proteomics field which is already take
a little different approach.
We went after the analytical rigor of those
technologies take about 5 years.
But once we got that finally, on hand the
question became what would we do next.
There was the opportunity that we seized we
went after those same tumor types that should
not that the genomics community went after
the TCGA and we applied those proteomic base
technologies mass spectrometry onto them.
When we saw the beauty of the additional biology
is going to be generated from the combination
of these two disciplines that is when we saw
a lot more potential.
So, today this program now in it is next iteration
in ways it is it will be expanding we will
go after additional cancer types from the
first program.
So, the first program we went after colorectal,
breast and ovarian cancer.
Now we will be cataloging approximately additional
5 cancer types, but the most exciting part
that I would have to say for the first time,
we are going to be teaming up a proteomics
laboratory expertise with an ongoing NCI sponsored
clinical trial.
And these clinical trials are typically genomically
driven, but we recognize that genomics is
still advanced, but there is an opportunity
to fill in the void of the biology of trying
to understand why patients either respond
well or do not respond well.
Based on a genomic driven information to the
treatment that they were just administered
to.
I would say the best way that I would look
at it is what is driving precision medicine
today.
In oncology which is my specialty I think
clearly genomics is making tremendous strides
there are exquisite examples today where we
are able to identify and be in a very great
position and how to treat a patient.
Simply rely on genomics; however, you kind
of flip your coin there are still many instances
in oncology we do not fully understand the
biology and how these individuals are responding
to the treatments that is where I see now
proteomics filling a very critical piece of
that information.
So, what we are trying to do at the NCI through
our initiatives is begin to converge these
disciplines.
And quite frankly partly, I think the reason
has been is the evolution of technology.
Genomics technology has matured and is still
maturing at a faster rate, but as proteomics
technologies are also maturing and also we
could go after smaller samples it is just
a matter of time that these disciplines do
begin to converge.
So, that is why I see these two areas being
very complementary to one another.
Specifically an area precision based medicine
for oncology as opposed to the common problem
of mass effect I would say what is needed.
Because I really do not see the most problems
I just see them as the evolution of technology.
My one wish I think which is a natural wish
that you have of all technologies a common
ones you want automation, you want it to be
very simplistic for the average user to use.
You also want that the system as technologies
of all to potentially using less sample material
up front.
But keep in mind I think this question is
one that people tend to ask on is well how
much material do you need to do the analysis
that you wish to go after.
The question that people should be asking
is what do you what is the question you are
trying to ask with biology.
That question will dictate what you want the
technology to do.
And in most instances you find out when people
really begin to ask the question they would
like to see if the experiment technologies
themselves are quite mature from what I have
seen from my perspective proteomics the way
we know it today would not exist without mass
spectrometry pure and simple genomics gave
us a blueprint what people did with in the
specialty is there was an acknowledgment here
is a mass spectrometer.
Mass spectrometry has been used in clinics
for over 30 years for different purposes.
It is mainly used for small molecule screening,
but then a bit, but then there was a brilliant
idea that came into play.
Can you take a big protein break it apart
into small pieces into these peptides and
informatically, just like an eloquent puzzle
stitch it back up into a protein without mass
spectrometry, proteomics as we know it today
simply would not exist.
So, the cancer moonshot I would say is one
of the most incredible things that is been
developed off the united states over the past.
Now what approximately 1 year.
So, I will see one of the main drivers here
is the 47 vice president of the united states
Joe Biden it is been a huge honor to be part
of that effort two programs that I could specifically
speak to myself on one of them we actually
did which is a partnership between the to
the national institutes of health specifically
NCI, then we partner with the department of
defense.
We also part of and we also partner with the
veterans administration.
This program is referred to now as Apollo
this effort along with another program that
we have now launched under that cancer moonshot
umbrella is an international program.
This one I was referred to as the international
cancer proteogenome consortium or ICPC.
That program now which is an incredible effort
in itself.
Actually, now involves 27 institutes, spans
11 countries.
What is nice about it fills one of the key
promises that our former vice president wanted.
And that is the data that we generate in this
international effort will be placed in the
public domain.
But the part that is nice about these two
initiatives is that each one of them.
ICPC and also the Apollo, there will also
be given up the data in the public.
But what brings them together it is the foundation
of what CPTAC actually did and that is a recognition
converged genomics with proteomics with the
hope that only you will identify a new biology
that is in the Apollo case.
They really want to push it much more faster
in the translational space potentially in
the near term future begin to apply it towards
patient care.
Dr. Sanjeeva Srivastava :Another interesting
development happening in the field is integrative
personalized omics profiling.
Essentially this path was led by Dr. Mike
Schneider from Stanford University who took
his own samples and analyze longitudinal manner
using various multi omic technologies which
really led.
First time idea that you know he might be
prone for diabetes which was detected clinically
very late, but looking at the biomolecule,
they could sense this much ahead of time.
And this really constitutes that how looking
at the multi omics analysis from the same
individual can provide more meaningful information.
And likewise, there are many success stories
many different case studies are now coming
forward which are showing the utility of looking
at personalized omics profiling.
There are also groups in Michigan who are
looking at Michigan center for translational
pathology Dr. Rocha Naen and team.
They are doing from the same patient looking
at the genome data, their proteome data, their
metabolome data, how one could start finding
the right cure for the given tumor type which
is otherwise not possible.
And they are able to provide you know at a
much comprehensive level this kind of analysis
and directly reaching to the patient care.
Now Dr. Mike Schneider and his team has launched
a major program on human personalized omics
profiling or hPOP and idea is to provide much
deeper understanding of the genetics of the
human body and provides the new clues of different
diseases which was otherwise not possible.
Let us hear Dr. Mike Schneider about his program
and vision for the hPOP project.
Speaker 4 Dr. Mike Snyder: what is the overall
goal of the hPOP project hPOP project is really
trying to understand what it means to be healthy
and how do people differ all around the planet.
So, what we are doing is we are actually taking
peoples blood and urine and also their stool
samples.
And what we are doing is?
Doing deep omics profiling the proteome, the
metabolomes, the transcriptome to make as
many measurements as possible to see at a
molecular detail how people differ from one
another.
So, we are going to see this, we are going
to sample people from all around the globe
at these human proteome meetings and then
we are going to see what their molecular diversity
looks like how two different ethnic groups
and different locations have an impact on
peoples molecular composition.
Do you think omics will be used in clinical
self situations, I think at some level absolutely
that is to say we will be making thousands
of not more measurements in the future.
Right now when you go to a doctor they make
about 15 measurements on you and in the future
I think we will be making hundreds of not
thousands using various omics methods.
Especially proteomics and metabolomics will
be very very powerful for looking peoples
molecular state which I think will say a lot
about their health and also what happens when
they get sick.
Dr. Sanjeeva Srivastava: let me now take you
to the next project which is wellness project.
We have been talking mainly about how to compare
the healthy individuals with disease.
But can we look at the wellness or the health
status of healthy individual or seemingly
healthy individual in the longitudinal manner.
So, can we look at their entire omics profile
at different time interval from 0 time to
6 months to 1 year, 2 years and start looking
at are the changes happening in dynamic manner
inside the body which might give us the clues
for what disease the individual might be having
or whether the individual is still healthy.
So, the individual dynamic data clouds has
to be integrated and that field is known as
P4 medicine, which is led by Dr. Leroy Hood
from institute for Systems Biology in US in
Seattle.
This big project of wellness was also published
2 years ago in nature biotechnology has shown
the agility.
And many similar projects have not started
in different countries which are looking at
the omics data set to build the wellness.
Let us hear the views of the Dr. Leroy Hood
on the wellness project and P4 medicine.
Speaker 5 Dr. Leroy Hood : I am very excited
by the possibility Indian, India would consider
a scientific wellness program starting with
a pilot project and it is own population.
Scientific wellness I think is the keystone
for the revolution in medicine because through
a longitudinal analysis of populations using
what I term personal dense dynamic data clouds
that is the analysis of genomics of proteomics
of metabolites of clinical chemistries of
digital assessments of activity and sleep
and things like this.
All of these together when analyzed properly
can lead to actionable possibilities to improve
wellness and or to avoid disease.
But I think the really transformational possibility
scientific wellness presents is that if you
create a population of people that follow
the scientific wellness program in a longitudinal
matter.
And if that population begins to right reach
a certain critical level of thousands in and
we begin to see wellness to disease transitions.
That you can use the dense dynamic data cloud
to get biomarkers to mark say the earliest
transition for diabetes.
And then you can begin to think about the
systems technologies and systems driven strategies
that can be used to identify the disease perturb
networks that will reveal candidates for creating
drugs to reverse these diseases that are at
a very earliest stage.
And if for example, over a 20 year period,
we can reverse all major diseases before they
ever really manifest themselves as a disease
and become irreversible.
We will save the health care system an enormous
amount of money.
In the US, for example, 86 percent of our
enormous healthcare budget goes to chronic
diseases suppose in 20 years.
There very few chronic diseases left we have
transformed a healthcare system that thinks
only about disease to a health care system
entirely focused on optimizing wellness for
their population.
This is the opportunity that India has and
I would be delighted to help you and if you
decide to make the commitment.
Dr. Sanjeeva Srivastava: So, finally, coming
back to the proteomics.
And the field of proteomics is now very well
led with the efforts of human proteome organization
which tagline is translating the code of life.
Idea is many scientists working in the field
of proteomics and integrated omics how to
come together they start sharing the data,
you start sharing the ideas, exchange their
various information build collaboration and
come forward for the big projects.
And human proteome project is one of the ambitious
project which is still happening which is
looking at the chromosome centric human proteome
project or disease and biology centric human
proteome project and now there are new pilllars
added for the pathology and data driven human
proteome projects.
So, let us hear the detail of HPP project
with ex-president of HUPO Dr. Mark Baker.
Speaker 6 Dr. Mark Baker : the human proteome
project has two major goals.
The first is to map the expression of the
human genome in terms of which proteomes are
made and which post translational modifications
are on those proteomes that affect it is activity.
And the second is to springboard discovery
in new drugs, discovery around biomarkers
for human disease and an understanding of
human health and wellness are one of the major
activities of the human proteome project has
been to determine how many of those proteomes.
We have observed in human health and human
disease.
So, far we have taken an approach that looks
at about 85 percent of the total proteome
content of the human proteome, but out of
the 15 percent that is missing about 7 percent
of them we have some evidence for them that
is either mass spectrometry evidence or evidence
that comes from other types of scientific
experiments, biochemistry, physiology, pharmacology,
genetics.
But there are still about a 1000 proteins
in the human proteome that we have absolutely
no scientific evidence for their existence
whatsoever.
Proteomics is not a word that everyone in
the street knows if I was to explain what
proteomics was it is a effectively the proteome
complement or the machines that are made from
the human genome from the coding instructions
into something real that makes a human body
work.
So, the proteomes are those things that are
important in the machinery of life in the
reproduction of life and in the development
of disease that is why it is important for
us to understand the proteomes as well as
the genes.
In order for us to understand human biology
and how that goes away in human disease.
Dr. Sanjeeva Srivastava : Additionally, it
will be interesting to also listen the last
3 HUPO presidents; Mark Baker, Mike Schneider
and Stephen Pennington and hear from their
perspective what proteomics and HUPO has achieved
so far.
And what are their visions for the future
guidelines for doing research in this area.
I hope this will be interesting conversation
for you to know the perspective of these leaders
who are leading the field of proteomics.
So, finally, I will stop here to show you
the slide which is a motivation for many people
working in this field of omics and life sciences
where lot of you know the actual problem.
We want to find the clues and the mechanism
and the leads understanding them at the omics
level at the molecular level and then want
to translate our bench side findings to the
bedside for the actual patient care or for
the actual welfare system to really make impact
of that.
And that is where all of this understanding
all of this technologies are really going
to help us to achieve that major goal.
I hope you this whole course the different
type of live sessions, different demonstrations
were helpful for you to appreciate the power
of these omic technologies.
It has definitely given you more confidence
that you can start doing many of these experiments
and projects even in this small colleges and
other universities, where you may not have
availability of these big gadget and platforms
with the access of so much data available
being shared by the entire community.
I think you can start looking at and appreciate
the data in a very different manner.
I hope the whole course has given you more
understanding much more new knowledge about
the field of interactomics more you know in
detail as well as the broad understanding
of the field of proteomics and other omics
sciences also you are now more confident to
start doing some experiment and doing project
in this field.
We will be happy to have more interaction
with you which we will be doing further.
And I hope that you know some of these understanding
and knowledge is going to help you not only
in your research, but also in your future
projects and for your career.
Thank you very much for attending this lecture
and attending this course and wish you all
the best for your future endeavors.
Thank you.
