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Proteomics in Proximity discusses the intersection of proteomics with genomics for drug target discovery, the application of proteomics to reveal disease biomarkers, and current trends in using proteomics to unlock biological mechanisms. Co-hosted by Olink's Dale Yuzuki, Cindy Lawley and Sarantis Chlamydas.
Welcome
to the Proteomics and Proximity Podcast
where your co-hosts, Cindy Lawley
and Sarantis Chlamydas, from Olink Proteomics
talk about the intersection of proteomics
with genomics for drug target
discovery, the application of proteomics
to reveal disease biomarkers,
and current trends in using proteomics
to unlock biological mechanisms.
Here we have your hosts, Cindy and Sarantis.
Hello, everyone.
Thanks so much for joining
Proteomics in Proximity.
I'm one of your co-hosts, Cindy Lawley,
and I have with me my other co-host.
Hello, everybody.
I'm Sarantis Chlamydas, happy to be here.
Very good.
So so today we are joined by Mine Koprulu
and Claudia Langenberg.
We are talking today about
a wonderful paper that came out in Nature
Metabolism in March [2023] called "Proteo-
genomic links to human metabolic diseases."
Very exciting paper. By
by Claudia Langenberg and Mine
Koprulu standards,
a modest sample size but amazing findings,
so I think it's really enabling.
We'll dig into that,
but first, let's introduce our guests.
Sarantis,
do you want to do the honors?
Absolutely, we'll start with Mine.
Mine is a PhD
student on Gates Cambridge scholarship
and supervised by Dr.
Langenberg.
Before her PhD, she received a Bachelor
of Studies in Human Genetics at UCL [University of College London]
and then subsequently she completed
a Masters of Philosophy in Genomic
Medicine at University of Cambridge
working in genomics data and
proteomics data and dealing with population studies
and the UK Biobank data with us.
And we are really looking forward to hearing
about your project and the paper about proteogenomics.
Yeah,
and digging into how you got
where you are.
Anything you want to add to that, Mine,
that you want people to know going in?
No, I think that's a very clear
introduction and it's lovely
to be here and I'm looking forward
to sharing a bit more about
both our journey
and our recent publication.
Awesome, fantastic.
So then I have the privilege and and the
opportunity to introduce
Claudia Langenberg.
Now, Claudia is probably someone
who needs no introduction, but
the I think the impact
that she's had in the publication
space is huge.
Last I saw, she had I think this is in
2021 over
300 peer reviewed publications at her
very young age and and I think
over 40,000 citations at that point.
And that's already several years old.
So this is a
this is a world-renowned
scholar that is working with Mine,
And of course, many of her publications
are in
journal series
like Nature, JAMA, Lancet,
very, very
prestigious and very impactful,
working with massive datasets.
So I think of her as a large scale
population epidemiologist,
such a big word,
but it integrates many of the omics
and I think she keeps an eye
on the technology advances to be able to
build systematic approaches
to understanding human biology.
So Claudia, I think now you're in you're
at Queen Mary, University of London.
I think when I met you originally,
you were at the Berlin Institute of
of Health and Charité.
So I'd love for you to just tell us
a little bit
about your position now
and your affiliations, if you don't mind.
Well, thank you so much
for the extremely kind introduction.
And I think the most complimentary thing
was about the young age,
which is not only a compliment
but a lie,
so this
I think, puts everything in perspective.
And yes, also I should say the work
that we've done that
Mine is a student
at the University of Cambridge
where I've had a dual affiliation
and a long career.
And prior to me arriving
at the Berlin Institute of Health,
so originally be Germany
and training as a doctor in Germany.
I came to the UK and I was in London
originally where I did my Ph.D.
in epidemiology and a Master's of hygiene.
And before that I went to Cambridge
to focus more specifically on genomics
and then later other omics.
And so yeah, it's a huge privilege
to be here
and it's an even bigger privilege
to work with talented people like Mine
and others in our team.
So it's a real team effort
and a lot of fun there to do that.
So it does seem like you have fun.
And I'll tell you what, whenever
I talk to Mine and when I've seen her
present,
she's always got a big smile on her face.
So I would love to dig into your journey
to end up in Claudia's lab,
Mine.
Do you mind giving us a little background
and how you got interested in science?
I mean, feel free to start
wherever you want.
Yeah, sure.
I've always been very interested
in science, and especially biology.
But especially I always wanted to do
something with my career, which improves
life, supports and contributes
to the society in a meaningful way.
And I've said given
I was quite interested in biology,
human genetics
especially quite interested me
because a lot of the sciences
are quite established in the sense
that, you know, the
human genome is very recent compared
to many other scientific fields
and there's so much unknown.
And I think that is especially
what interested me
as well as its potential to, you know,
improve the current healthcare system
and the clinical translational potential
of the human genetics findings.
So that was my inspiration
to go into that.
And unlike some other people,
I think I've been in that quite
narrow specific path ever since then.
So I did my bachelor's at UCL
as Sarantis mentioned in human genetics -
it was just strictly.
And after that I did my master's
in University of Cambridge
in genomic medicine, which is where I got
introduced to bioinformatics.
So I did my Masters project
at Sanger Institute working on UK Biobank
as well as whole genome sequencing data
for each population isolates
at least
in that lab at the time.
And after that I moved back to back home,
which is Turkey for me to work on rare
disease genetics.
So I spent two years back in Turkey
at a
university
researching rare disease genetics.
So that was more studying
whole exome sequencing data
from consanguineous marriages
or consanguineous parents having
disabled children trying to better
pinpoint the exact genetic changes
that caused such severe, rare disorders.
And in the meantime, while I was back
home, I was thinking about my PhD
and what I might want to pursue
and multi-omics integration.
So better understanding the different "omic" layers
and how that can actually improve
our understanding of genomic studies
and their clinical translation
was what interested me.
So I got in touch with Claudia
and we had a brief interview
and that was the beginning
of such a beautiful collaboration,
at least on my part.
And then starting a
little bit about your paper -
What were you able to understand about the data,
about this EPIC-Norfolk study?
The EPIC-Norfolk cohort. What are the characteristics
of this cohort and what makes it so special?
Sure, the EPIC-Norfolk study
is a population cohort
that was first established
in beginning of 1990s,
so it has approximately 30,000
participants that were followed up.
Phenotypic research in terms of different
behavioral and sort of health
characteristics for several years,
as well as linkage to their health records.
And we had proteomics samples
measured from 3000 of
EPIC-Norfolk participants.
But Claudia, if you have anything more
to add on the EPIC reference study ...
I think I can say one of the opportunities
this study has offered
and I think the beauty
of such perspectives cohorts such as EPIC-Norfolk,
and UK Biobank
and so on, things that were set up
in the past is that we benefit from the samples
that were stored at baseline
in liquid nitrogen.
And those samples are so valid
because anything you can do in the future
and now you measure 3000 [proteins].
And you measure 5000, you measure ten
and more thousand proteins.
So anything we use
now we won't be able to use in the future.
So we're always very cautious
of that and hence
cautious with our sample use.
So it's really important that proteomic
technologies now use such a little sample.
So you could have used more sample
in the past to measure three proteins.
So to be able to [measure] thousands
in such a small amount of blood
is absolutely amazing.
And to have the opportunity, you know,
PIs when our PIs of those
cohorts had the foresight of setting this up.
Nick Wareham in Cambridge and before that
Kay-Tee Khaw, others who let us study.
And other cohorts that we had
the privilege of using the samples for.
It's really,
really amazing to be able to do that.
That's one thing.
But the other thing
that's important is as time
elapses, these codes
become even more valuable because,
you know, sadly people have events, they
they develop diseases, they die.
So if you then look backwards
because you have samples stored,
provides the opportunity to have such
an efficient design of doing it,
you don't have to measure
everyone. But you can have a kind of a
very nifty design in choosing
people who have developed a disease, i.e.
new onset disease, but
the sample was stored before they had it.
So you avoid this
kind of reverse causation by which disease
impacts your proteome and hence
you can't really just associate.
This is what we exactly did
in this context.
Mine's study was one of the projects
that we did on context of that design.
But you can also look at
different diseases
based on the people who developed them
and then studied. Use that baseline sample
and then you use a big control cohort
of people who serve as the controls
for many different diseases.
It's called a nested case cohort study.
And it's a really beautiful design,
but that can only be used
if you had the foresight of setting up
a large prospective cohort like that.
So we were very grateful
to all the participants.
EPIC-Norfolk and the PIs, of course,
who's enabled the use of that,
and also coming to the cost
of some of these kind of very,
you know,
informative molecular technology
such as yours is.
Of course they're not cheap
and hence that means
if you can use a design that minimizes
the number of samples
you have to use for given purpose,
of course
that's incredibly useful for us. So
and I think I just
wanted to comment on this,
this liquid nitrogen aspect, this ability,
I think this foresight -
I work with a lot of groups
that do a lot of different things
with large population health studies.
There are very few cohorts
that had that much
attention
to reducing pre-analytical variation
and tracking it
as is documented in this cohort.
So that's exciting to see.
I think it's maybe less important
for proteomics
than metabolomics as an example,
but you work in all of that.
And so yeah,
I just wanted to underscore that point.
Yeah, sorry, Sarantis, please go ahead.
Okay.
I will like Mine
to summarize the take-home messages
of your paper.
and what's so exciting
about this paper.
Yeah.
First, and just also
clarify a bit [about] the paper,
I was the first author,
but of course it was a team work
and everyone contributed a lot
making this work possible,
including the EPIC-Norfolk participants
of course.
Just to summarize the paper,
I think I could just briefly say
we looked at the sort of systematically
linking genetic variants, blood protein
levels as well as disease risk data,
to be able to pinpoint causal genes
and proteins that underlie diseases.
So just to give a bit of background since
enablement
of the genotyping technology,
little studies have been conducted
associating genetic variants
with different disease risks or
disease susceptibility.
And today 200,000 genetic variant disease
associations have been established
and are publicly available.
Looking at such numbers and figures,
we would assume we know
the biological basis for all diseases.
However, we all know that's not at all
the case, and I think that's
where proteomics and different
sorts of omic layers come into play:
in helping us better understand
the disease mechanisms of
actually what's happening
underlying the diseases in our body.
So in this study
we had samples from 3000 individuals
measuring three approximately
3000 blood protein levels.
So we first looked at the genetic
regulation of the cis regions.
So the cis regions are the protein-
encoding regions
and sort of flanking regions around
the gene for the protein target itself,
because of our moderate sample
size, as we've already mentioned.
So we looked at the cis genetic regulation
or different blood
protein levels,
some of which had never been targeted
before, given the recent
Olink platform [Olink Explore 3072]
and then we have used that knowledge,
that sort of protean genomic knowledge,
to better understand
causal genes of proteins that
underlie diseases in a systematic manner.
So we have first
looked at shared genetic regulation
for different disease outcomes and blood
protein level regulation,
pQTLs as we call it -
protein Quantitative trait loci.
And we identified 224 targets
that regulates 500,
approximately 500 different traits.
And we also refined the causal genes
or proteins
for 40% of the previously established
genetic risk loci,
which was which sort of highlighted
that even moderately sized
proteogenomic studies can contribute
to our covering of novel biology
for consanguineous risk loci that were
published in the literature.
And finally, we looked at the convergence
of the pQTLs
studies and the regulation
where
the rare variant gene burden analysis.
So, on one hand, comparing the loss
of function of the genes with that sort
of genetic regulation of the proteins.
And so I wanted to click back on this:
the disease associations
that were enabled by genetics. I think the
the ability to make those associations
in small sample sizes was like
low hanging fruit in the early days
of that sort of GWAS era,
which I think of maybe early 2000s, right?
2005, 2006
that's when those things started
really ramping up discoveries
and then they became harder and harder
to make those associations
as those really strong,
strong associations
were discovered and documented
and then larger and larger populations
were needed to make those associations.
I think what you're saying
is that these other layers are helping you
to to do more
with those more modest populations.
And Claudia already made the point
about the costs of layering
on metabolomics or proteomics
or these additional omics.
Can you say something about what
what that enables and what you think
will happen in the future there?
Yeah, of course.
So basically, as I mentioned
and as you've mentioned already, thousands
of genetic variants,
a disease associations are being made
and those were very valuable
and contributed to our understanding
of diseases to some extent.
However, the majority of those associations
fell into noncoding regions of the genome,
meaning it was quite difficult
to actually interpret which causal gene
or protein were acting in
causing the disease.
Hence, it was quite difficult
to understand the pathways
and the mechanisms.
And, well, if
we don't have a functional target,
it is difficult to actually build
more effective and safer
therapies or repurpose existing ones.
So in that sense, having the initial layer
of the proteomics states actually
helps us to pinpoint a functional entity
that plays a role in the disease.
And so
I said there's
certain statistical methods
that help us better understand
the shared genetic regulation
of both the disease risk
and the blood protein levels
or abundance of certain proteins.
And we see strong statistical evidence
for a shared genetic
regulation done within a large region
of many candidate genes.
We can actually refine
that to a single candidate gene or protein
for particular diseases
in a systematic way
which can then be used
for as I mentioned, intervention
or more targeted
therapy, safer therapies.
So in that sense, even moderately sized,
more regular pQTL studies
can really help us better understand
the disease and pathways that are involved
and also build more effective therapies.
I think we're on a path.
I think this is a path for discovery
that is, I think just going to ramp up.
I think very similarly to what we've seen,
those discoveries enabled by genetics.
I'm really excited about that.
I always think about
these different diseases
that might share pathways in common,
almost like a Venn diagram
and understanding the complexities of
of why certain proteins or protein
pathways are related to like,
I don't know, caspase,
I think is like showing up
in breast cancer and asthma. It's
an example. I think
those diseases are so disparate to me.
I wonder if you
think that proteins or pathways
that are showing up as causal
and I think you make a good point about
why causality is so important
for therapies.
Do you expect those same proteins
to be causal in other diseases?
Like what do
you think about that?
You're much more versed
in seeing the data.
So I think that is sort of the beauty
of our study design
that we sort of approach
with different layers
of biological data
starting from the genome
all the way to the phenome
in a hypothesis-free manner.
So doing that and looking at the data
systematically without any sort of prior
hypothesis
allows us to see what the data tells us.
So as you mentioned, certainly in looking
at what we have discovered in our papers,
that genetically anchored
proteogenomics studies so the proteomics
can help us discover
molecular hubs.
All associations of proteins
with diseases that we wouldn't
have predicted otherwise from just
the general literature or prior knowledge.
And that is actually quite interesting
to follow up
because, yeah, the data uncovers
something that was unexpected.
But likewise we can also see
quite specific protein disease pairs,
which can also allow us
to have quite specific therapies
for potentially not intervened
diseases before.
But just one more thing to add
is that we
currently do not have not a very full complete
picture
of the proteomics, so we're only able
to do that for the proteins
we are able to target.
So for future directions
you asked about,
having a more complete idea
about the full landscape of proteomics
would be ideal to better understand
those molecular hubs
that we have just talked about.
But so maybe
I can add one more thing to this,
because as Mine's work
other proteogenomics work
than we previously did in the past
has shown is exactly the beauty is that, as
you scale up the number of proteins
and as you scale up
the number of diseases,
you can look at this kind of proteo-
genomic approach that doesn't require
that each of these layers is measured
in the same sample size.
You can utilize the power that
you have across any biggest genomic study
for diseases that you have to get
a reaction, a proteogenomic study.
And that is the beauty of looking exactly
like you say, Cindy,
at the overlap of a specific gene to protein
across any disease that you can look at
because the community is sharing
data openly or the summary statistics
for each of these diseases is shared
openly for most diseases. Cancer is
lagging behind sadly,
a little bit in terms of the openness.
But for many diseases that is there,
it enables us to do exactly
that: to draw a whole map
from gene to protein to disease
and is that link coincidental
or is possibly causal
and a rare genetic signal?
And that's such a good way
of prioritizing what you then use for
experimental work downstream
because of course
this is in the computational approach
and not an outcome of proof,
but it's such a good and data driven way
of prioritizing
links between
diseases, links for where we have already,
you know, specific drug targets, new ones,
potentially adverse effects.
So it's a really versatile way
of looking at all of that.
And then just maybe to add one thing
again, as you said, is
you can kind of ramp this up
as you increase the number of proteins
or as you increase the density
of your genomic array
or the coverage of rare variants
and by sequencing,
and so on. Of course
an important part of really making
this more useful
is increasing the phenotypic spectrum.
And that really is only possible
if we move away from diseases. We all
study opportunistically to diseases
that you can't really easily measure
or nobody's interested in, but they're still
really important for patients and a lot
less headway has been made in terms
of understanding that genetics
releasing the summary statistics
for those studies, and that's really where
huge studies come in that have electronic
health record data from GPs [general practitioners],
from hospitals, from death certificates,
and bring all of this together.
And that's why, for example, UK Biobank
but also Fingen
and many other endeavors around the world
can really help us
to not just increase the molecular side
but the phenotypic side.
And that is so important to also
link diseases which have not really been
so much in the center of attention
and but need to be.
Can you give an example of one of those?
Are we thinking rare disease here?
So I think it can be
across the frequency spectrum,
it can be across specialties.
I think these disease
examples that I would choose,
others that possibly are not as easily
diagnosed, are not severe enough
to always require
hospitalization,
because I think most people around
the world have tried to get
their hospitalization data
I see decoded is a relatively easy,
but the data, for example, in the UK
that comes from England, from primary care
records, is harder to map,
is kind of bit more diversity in terms
of systems, the data structures and so on.
So diseases that are predominantly managed
and diagnosed in
primary care are more
where we need to move towards.
Oh, that's so interesting.
Yeah. I have a bit of a
technical question here.
How do you make the selection
of false positives
and how do you define
these in your analysis
compared to
a lot of technologies, for example,
that you applied before?
You're
asking about false positives? Yes.
So in our analysis, we try to be careful
given we're working
with data and diseases, as you mentioned.
So in terms of our analysis,
we always use quite a rigorous
threshold to report
what is statistically significant.
So in terms of our statistical threshold,
we always called for genome wide
significance times the number of proteins
that we are including in the study
to sort of minimize the error, including
thousands of targets in this case.
So we usually go for quite
a rigorous statistical threshold
as well as good
QC before we feed in the data,
of course.
So that's great.
So some of these pQTLs
may fall in non-coding regions.
Do you have any idea about the type of these regions?
Maybe have a promoters there,
I don't know the answers.
Do you have an idea and can you map these regions?
So we mapped
all the variants in terms of what
they're predicted to be in the genome.
So we look at the proportion of protein
altering variants, those variants
that fall into the protein
coding region itself and alter
the shape or the structure of
the protein.
And the percentage
that fall into non-coding regions.
However, there's quite limited knowledge
about the functional characterization
of the non-coding variants themselves
acquired from particular groups studying
particular genes and more sort of cellular
or functional models.
So in terms of our work so far
we have only computationally annotated
the predicted consequence of the variants.
However, of course further follow up
of analytes and what exactly those
non-coding variants do in terms
of cis regulation of the protein itself.
That's great.
So I think a lot of studies
and then from the literature,
as you as you mentioned, for different
proteins from different
mice knockouts,
you can have an idea
about how they are regulated.
Is there any plan to follow up, like more
mice knockouts
in the future to follow
some of these targets or collaborations
in this respect?
If you can share this?
I mean, we of course always welcome
any sort of collaborations
on the functional characterization.
The idea of doing these studies
is not to put in a long Excel sheet,
as we have done so far
in terms of our supplementary table,
but rather genuinely understand
the biological mechanisms
that underlie diseases
and how that can contribute
to clinical translation
of some of the findings
and the sort of longer runs.
So, with that, an open call
to anyone who finds any of
that targets
that we highlight interesting.
We are of course
very keen to functionally characterize
and better understand the mechanisms.
I think
in the paper you mentioned something about -
I was just looking for the reference,
something about cellular models
and the history of
of demonstrating functional associations
or understanding
a function around cellular models
and how this approach
can be a complementary way
to add an understanding of function.
Do you mind talking a little bit
about that,
like where this fits
into our traditional approaches?
Like where it might fit in
in understanding
what we're learning from single cell
or spatial
work that some of those technologies
are really advancing right now?
I'm curious your thoughts on those.
Of course.
So what we do in terms of our team
is more bioinformatics.
So what we work with is what the data and
sort of predictions
and better understanding the computational
and statistical conclusions
we can draw from the data.
However, as we all know
and as you've highlighted,
they are complementary ways
in better understanding these.
And basically we are very interested
to see whether
what we observe computationally
and statistically actually
translates into biology,
starting from cellular models,
building up to animal models
and sort of human biology.
So what we're observing almost
quite isolated
looking at singular
sort of targets and singular
genetic variants. It will be quite interesting
in whether our conclusions hold or
sort of variations from
our conclusions we see in the different models.
So I definitely agree
that they're complementary
and there's ways of integrating those
knowledge to build a more comprehensive
or sort of holistic understanding
of the biological mechanisms.
Oh, I love it.
I love the background
you have both in an understanding
of epidemiology
and your mad bioinformatics skills.
Where does machine learning
fit into all of this?
Is it something you've -
yeah, I'll just stop with the question.
No, of course, machine
learning and artificial intelligence
is certainly fields
that are very quickly developing
and are grabbing a lot of attention
at the moment. So what
we use are considered basic machine
learning models themselves.
But in terms of employing machine
learning or artificial intelligence to
sort of prettier genomics for these
or more biological studies at the moment
is rather challenging
because as we all spoken about, this large
high triplet biological data
is recently being generated
and they are so recent,
but we're spending a lot of time
better understanding what they mean
and what they actually are.
And frankly, the biological data
the way they are actually violates
a lot of assumptions that machine
learning and artificial intelligence
models make.
So I think in the future,
they can certainly be very useful,
but we certainly need to be cautious
and better understand
them
before feeding them into the models,
I personally say.
And maybe I can add something to this.
So I'm currently sitting in
what's called the DERI.
So when I came to QMU, or Queen
Mary University of London last September,
I had this opportunity to set up this
new institute for precision healthcare.
And it's kind of quite unique
because it's a cross functional institute.
So it really draws in a very broad
range of expertise and so [there are] big plans
and developments for a new life sciences
building, and that's all quite away.
So we have to wait for this.
So we need an interim space and we're very
lucky and it's actually not coincidence
Where
I'm sitting right now is called the DERI.
It's the only other cross functional
institute that QMU has and DERI stands
for the Digital Environment Research
Institute, which is focused on AI.
And so it's not just in healthcare, it's
AI across a broad range of applications.
You know, from games - we're sitting on
the second floor with the games people,
which my my kids think I have the best job
in the world.
Actually what they don't know
when they have conversations
about board games is
I have not heard of a single one of them,
so I can't mention really anything else.
But anyway, having said that, this is the
environment in which it can flourish.
So we focus on health
and on healthcare data
and DERI has
as part of its remit as well.
So it's really important
to bring this together.
And, you know,
going away from,
you know, this pitch on
how it's important, the concrete thing
that we're already doing is
the molecular data, which is so complex,
you need efficient
and unbiased ways of data reduction.
That, at the moment, is most of the use
for machine learning in our
arena of work.
So where we kind of try to, let's say,
predict diseases,
how are you going to prioritize?
Nobody wants to go into the clinic
and measure 3000 proteins
to predict a disease.
Do you even have any benefit of
what's the incremental value
of measuring proteins
once you have a core set of ten
most important ones?
So those are all questions
that machine learning can help us to
to address more systematically,
and that's incredibly useful.
So is a steep learning curve for us.
This one, it's not our bread and butter.
We're not the methodological experts
of developing it,
but being in an institute
like this one, for example, means
that we're only very a short way removed
from the people who do develop these
methods and that we at the right time
can employ them, can test other useful,
could they be biased.
And so that's really, really
yeah, it's a great opportunity, I think.
Well,
I think those people that are developing
those methods aren't going to have
the biological know-how to know
when they've overtrained those methods
and are going off on a spurious tangent
so the context I think is essential.
I think Mine makes that point.
Yeah, exactly.
It's this exactly.
It's a team effort, right?
And you need the people, have
the samples, and the clinical knowledge.
You need the people who have
some other kind of bioinformatics,
computational and you need the,
you know, method developers.
So it's beautiful.
Nobody can do it all.
So, you know, I think it's a great.
It's CQ, right?
It's collaboration quotient, right?
You have your IQ, your EQ
and CQ. From my experiences,
once you
develop your collaboration quotient,
which you're teaching your students
and your postdocs and all of that
if they don't already have it,
Claudia, is once you have it,
you can't go back?
I don't think
once you experience
the joy and the ability to work together
cross-functionally, it's really
remarkable what can be accomplished.
Well, coming back to the question that
Sarantis was asking,
and I think it's a very good one
and Mine has said so.
The problem that we have encountered
is, you know, is Mine
has come up
with these beautiful candidates.
And it's almost there's two things
that are important to kind of what
she said
so well, which is that now using humans
as the model organism
is a great way of prioritization.
That's number one.
But the second one is kind of, you know,
if you come from a Bayesian framework,
you really want to find out what's
your greatest chance of success?
How do you increase your prior
of this having success?
And given that the experimental procedure
is expensive, it's lengthy
and it can go wrong in so many ways,
It is really a great opportunity.
to prioritize
on the basis of human proteomics
as Mine has said.
The problem is: how do you get people
who have the experimental set up
to come to our results and take it?
Because that's not as easy as we thought.
We think, "Oh, here!"
We're so excited to see it.
We go to the world expert in this
and bring them our example,
it's hard.
It's hard to motivate people
to step out of what they're already doing
and focus on your finding.
It's hard if you have somebody
who's focused their whole life
on a given pet protein to say,
how about this one?
So we need help,
I think, to try and learn
how we engage the relevant
clinicians and molecular biologists
and other people to also use our
results rather and at least,
you know,
complement what they're already doing
in order to be able to follow some of it
up, because we do depend
on the functional value
and the expertise of those people.
So how do we reach out?
How can you help us
to reach out to the relevant people
to really make it worthwhile?
What are the titles of those people?
I mean, what are their jobs?
You know, like,
I know there's more than one, but
I think of translational,
certainly translational scientist
or implementation scientist
or maybe those are pieces of it.
But you know, who are the ones
that we need to
listen to this?
So I think it depends on the stage
of translation
that you're at. For really early
experimental work,
it's a very different set of people
than it would be for the people
if you have a different kind of work
where you really are ready to maybe,
possibly already consider an initial trial,
then you would need a relevant clinician
or a head of that clinical department
to enable that kind of set up within that,
let's say, hospital.
So I think it really depends
on what level you're talking.
The initial level I think is
what's the experimental follow up
and validation, because what we do is
yeah, the statistics and probability an an error,
but it's still an association
where we're under no doubt we do not prove causality.
And so that kind of functional
follow-up is crucial.
And those are the people at the first stage.
Think actually you don't know
the cause of some of these diseases,
you don't know
the cause of the effect, right?
You don't know if this is what you see
in the protein level. Is it a cuase
or the effect of the disease?
It could be both. The last comment
to me I really like when
you see the co-localization with eQTLs
from different tissues
like does what we see in the plasma proteome
reflect what happens in the tissues?
And do you have any comment
on how you see these correlate
and what is the distribution
of the singular feature
to the plasma proteome?
You have some examples,
some ideas on that?
And a shout out to gnomAD and GTEX
I will say fantastic resources. Yes.
No, we are very grateful
for all the publicly available resources,
ranging from the eQTL studies
all the way to the GWAS summary statistics,
which have made our work possible as well.
In terms of the eQTL overlap,
that is rather challenging
in our field in the sense that now
we see, for example, in our study
that only approximately 4% of the pQTLs
that we see
are in close linkage disequilibrium
that we deal with an eQTL
and certain examples,
we see beautiful overlap, right?
Like we find an example
with a disease for that
act in a particular tissue
and we see co-localization,
quite strong co-localization
with a tissue of interest
where the story becomes quite beautiful
and everything makes sense.
However, there is also the flip of the coin
where we see in some examples
quite limited overlap
between the pQTL and eQTL data.
And although our current paper
hasn't really focused on that,
I think that is something that we
as a community need to better understand
where there is overlap
and where there's a lack of
and what might be the underlying
reasons that would be.
I think this is a great
but to mention the really interesting
link to type two diabetes
that you found in here that that we want
someone to follow up on, as Claudia mentioned.
Do you want to just summarize
that really quickly, that pathway?
Of course,
one of the examples that we have noticed
or we have highlighted in our paper
is the gastrin-releasing
peptide, or GRP for short
and its link with type two diabetes.
So what we saw in our paper is that
beautifully different layers
of biological data,
so evidence from my studies,
evidence from human data as we have been talking
about and different sort of similar models
all overlapped with the same conclusion.
So what we've identified
was that the higher levels of GRP in human
plasma were co-localizing
with lower risk of type two diabetes.
And when we integrated
in the different sort of
intermediate layers,
which are different body sets and
distribution traits,
so where we actually accumulate
fat in our body
as well as the overall fat,
what we observed
was that higher levels of GRP
was leading to less fat
accumulation overall
in our body, leading to lower
risk of type two diabetes.
And as I mentioned,
there were previous studies
that were published
where human recombinant GRP
led to actually reduced
intake and weight loss.
So we have highlighted in our paper
that GRP can be a new example
or a potential therapy for type
two diabetes by decreasing or
lowering the fat accumulation
in our body.
It's actually a hot topic
for the weight loss.
A lot of companies now
are trying to follow this
game of weight-loss strategy.
I think that weight-loss
drugs are really interesting,
and I am looking forward
to people following up
on this.
And I love the use of
the word beautiful, right.
But it's beautiful
because these layers are all agreeing.
It's giving us a preponderance of evidence
that gives us a lot of confidence,
that gives us confidence
in the other results that you see, too.
Right.
So the fact that you've built
this systematic map
of these potential causalities.
Yeah.
This corroborates, I think, the approach
which is
certainly beautiful.
At this point,
I would like to thank Mine and Claudia.
We could just stay here talking
like this for hours or for days on some
amazing paper and amazing data.
I mean, we learned so much today
and I would like to invite if you have to
add something to our audience. Mine, Claudia,
from your perspective, and where
do you see this going in the future,
that would be great.
I'd love to hear your thoughts about.
Yeah, anything you'd like to add?
We'd love to hear it.
No, I think frankly, as I mentioned,
the increasing large scale, high
throughput generation
of additional layers of biological
data is frankly very exciting
and I'm very excited to be in a field
where we are better understanding
their potential translational capacities.
And with that,
I also would like to thank both
my colleagues,
which have enabled all the work that we do
possible, as well as the first instance
of EPIC-Norfolk study
and the past and present team members
which have made this study possible.
Anything more from you, Claudia?
Yeah, I just
I think I said already, looking forward,
how it'd be very valuable
to increase the kind of phenotypic
space in the diseases we can look at.
I think also moving forward,
I certainly think the beauty
of these large-scale population
based studies is one thing.
That's certainly one of the reasons
why I love sitting here in East London
and being like just a hundred reaches away
from one of the largest hospitals
is because the next step really is how
do we enable in clinical proteomics, i.e.
proteomics in patient studies,
how do we design that well
and in a way that enables flexibility
of different research questions?
So that's kind of what I want to focus on
and which I think is very exciting.
And for that we would need technologies
that are kind of ready
to move to that
standard relatively quickly.
And so I think that's an exciting new area
and doing that in a way
that is affordable even within the context
of a national health system.
So it's exciting,
It's really amazing times
and working together with talented
people like Mine
and other people in our team. just as you
As one of my mentors used to say:
if it's not fun, it's not epidemiology.
I love that.
Well, you
certainly make it look fun,
that's for sure.
Now, thank you so much for the opportunity
to talk today.
It was a pleasure to have you guys.
All right.
Thanks, everyone.
Thank you.
Thank you for listening to the Proteomics
in Proximity
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