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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 in Proximity podcast,
where your co-hosts Cindy Lawley and
Sarantis Chlamydas from Oink 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 host,
Cindy, and Sarantis.
Hey there.
Welcome to Proteomics in Proximity
where Sarantis, and I will be talking to
Cornelia today.
I'll have Sarantis
introduce her in a moment.
But first I wanted to announce
a very exciting advance,
in Olink where we have now merged
with Thermo Fisher Scientific.
So we're part of the Proteomics Services
division within, Thermo Fisher.
And we're definitely going
to be talking about
the ability to sort of sequence
the proteome as well as genotype
the proteome in future episodes,
because these technologies are
incredibly complementary
under this umbrella of this exciting
Thermo Fisher Scientific parent company.
And with that, I'm going to allow Sarantis
to introduce our guest for today.
We're super excited to have Cornelia here.
Sarantis, please.
Thank you very much, Cindy for the introduction.
Thank you very much, Cornelia
for coming with us, it’s
the last episode before summer holidays.
We are really excited to have with us,
Professor Cornelia van Duijn.
She's a professor of epidemiology in the
Population Health Department of Oxford University.
And today, we’re
going to talk about your exciting work
and mainly dedicated to aging
and age-related diseases.
Cornelia,
would you like to start
telling us a little bit
about your background
and your scientific interest
and expertise?
Thank you very much for joining our group.
Sure it's my pleasure to be here.
It's the great pleasure.
But, yeah, my background, I think.
I work in epidemiology again,
I studied there, there's an epidemiologist
now one of 30 years ago
working on dementia,
which was in that time
still a forgotten epidemic.
I think everybody swarmed with dementia
in their families, I guess,
particularly parents and grandparents.
But in those days, people
hadn't heard of the disease, hardly.
Definitely not of Alzheimer's disease
and had difficulty
grabbing what Parkinson's disease is.
But,
I started out doing the epidemiology,
but then figured that out
pretty soon, that the only risk factor
that we could find in
those days was just family history.
So I switched to genetics. And for long.
I did my PhD
just waiting for the good old markers,
the genetic markers,
to do the linkage analysis in the family.
It's finding the genes.
So this was waiting for months
for a six RFLPs to arrive.
And then two had failed and I went into
another cycle of waiting and waiting.
So in the end of the day we found genes,
and in the end of the day, I was more
than happy that at the technology
emerged to do larger based studies.
And then I went into the genetic
associations studies genome wide.
And that was millions of millions
of genetic variants
to study in millions of people
and now finally arrived for metabolomics
for the age of proteomics.
So that's the background
and back to epidemiology, not anymore in
Rotterdam but now in Oxford.
So in epidemiology, I think about this
as a challenging field
because often
you're dealing with population level data
sets of community data
that are imperfect, that are messy,
that aren't as clean as I imagine
some of the genetic data sets enable.
Is that a factor in how you've
how you've evolved your career in bringing
in these omics that now you have something
to associate with that maybe is more
I don't know, it's just really hard
to collect environmental data, right?
And epidemiology is plagued with this.
Well, I totally agree with you because I think
if you look at epidemiology
and I'm not only the data analyst,
I also have been able to set up
the different epidemiological studies
and, one of them was a Rotterdam study,
with the
elderly people really followed over time.
And it's hard. It's a lot of effort.
And sometimes I wonder that people,
young people
who are dealing with all these data now
think why haven't they done it better?
But it's a huge effort.
Not only the Rotterdam study,
we set up large family
based studies, like the Erasmus Rettfeld study
and last but not least Generation R
Wasn’t the leading in there,
but I was working on there, setting up
and a study of, little children,
followed from utero.
And it is hard.
It's hard to get really a grasp
on how you capture,
to what people are exposed to.
And then, of course,
if you think about people,
the exposures that you have over
time are changing, they’re ever changing.
Your smoking habits,
your alcohol habits.
What your weight is and what you're
eating, incredibly changes over time.
And, I think it's the availability,
the cost, but also definitely,
what you know is healthy and unhealthy.
So, they're growing inside,
but what we’re learning to know now
that it's important to do these studies.
And they have been incredibly helpful
in making the genetics study happen.
It has enabled it
that it would have not been at the states
where it is now without it, but definitely
also,
a lot of the future of proteomics will be
in these studies
so we’re depending on them.
That's a great.
No, go a head, Sarantis.
I just wanted to follow up
on this question that you have posed,
know, for
genetics and proteomics.
Nowadays, for example, these complicated
diseases like Alzheimer's,
do you think one omic is enough
and how you see multi omics in this field?
How you see the challenge that people
that are facing of data integration.
What is your feeling on this?
Well, I think, we learn a lot from
genetics, and I think you can't deny that.
So people have had troubles with it
that once you start doing at scale
as genome wide
association studies,
we’re just going to the moon,
And then beyond,
we were almost going to Mars,
just finding new pathways
in the disease process.
And then of course, people said,
“well, we knew this, we always get this.”
But we finally have it established.
And that is what you do with genomics.
I mean, you can hypothesize
that the complement,
system as one of the immune systems,
that is one of the defenses against that
invasive swarm of bacteria and viruses
that you can have the hypothesis.
And it was there already before,
the theory was, that it’s implicated
in your pathogenesis,
the development of dementia.
But, you know, genetics nailed it.
It benchmarked it.
It says, well,
if we have genes, I'm not sufficient
there, you’re not going to make it.
In genetics of dementia,
we went through the whole series of
we though it's a neuronal disease
because your neurons don’t
function anymore
and therefore you're demented.
You forget things,
you can’t even comprehend things,
how to put your shoes on
and where you should put them.
You put them on your head.
All the things for your brain to work,
of course it's the neurons that die
and that, give you the disease
that will make you forget things
and not understand things.
But then in the end of the day,
what we learned from GWAS
is that the microglia, the helper cells
of your neurons, were much more important.
So definitely we learned a lot of it,
what we did not learn.
And that's always
as the scientist, for young scientist,
that's even more important, right?
It wasn't the endpoint because what
we learned from genetics, for instance,
that the apolipoprotein E4 variant
more or less splits
the population in half,
who gets the disease and determines
who gets the disease early or late.
But you know, it doesn't tell you
whether you get it that 16, 17 or 18.
That is so important for people
and for that, you need these proteins
or the metabolites, that will tell you.
And that's what we see
now that B tau is telling you that.
But we see also, that other proteins
like GFAP and that NFL
that you can measure
easily that there's also doing that.
And that is incredibly important
and that is what we need to know.
And that is what we need to take further.
So I’ll ask a question now
along that genetics line.
Along with Rotterdam study,
Generation R, certainly CHARGE initiatives.
And all the cohorts
that are involved in that.
You have been involved
in a lot of really pivotal work
in that population health area.
One of the other Biobanks
I've seen you involved with
is the China Kadoorie Biobank.
That's incredibly important
for our understanding
of East Asian populations
and how they're very different from
what we see in the UK Biobank
as just another example.
And I just saw in Oxford
a presentation given by,
I believe it was Alfred,
who talked about, GWAS
leveraging proteomics
in the context of genomics
with the clinical data that are
that are available for these cohorts.
Can you talk a little bit about the outliers
and liars that we talked about there?
And just explain how proteins are
showing signals about lifestyle factors
that I think is pretty compelling.
Yeah, sure I think what has been
a breakthrough in that, not with my head
as a geneticist, but with the other head
as an epidemiologist,
because after all, I'm a genetic
epidemiologist by training.
Is that what the proteomics is giving us
is really the mirror of what happens
if you have an exposure that is,
in the case of smoking,
I think nobody doubts anymore
that that is shortening your lifespan,
is giving you increased risk of cancer,
but also lung diseases,
cardiovascular diseases,
and definitely in the end of the time
also it's related to
many a neurological diseases
and neurodegenerative
diseases like dementia.
But measuring
these exposures is a nightmare.
And it's difficult for smoking.
And there’s people specialized
in how to asses how much you smoke.
But it's quite a difficult task.
So you have to ask,
when did you start smoking?
When did you stop smoking?
How much the smoke over time.
Because that everybody thinks, oh,
I smoke half a packets or a packet today.
I only have smoke today, 24 cigarettes.
I do have to take another one, would I?
So it's approximation.
Nobody will live like that.
People stop smoking when they're pregnant
or the first child is born.
You think I'd have to be more healthy now?
It's quite an effort.
And don't get us
started as epidemiologists
on something more complex like,
alcohol use.
Because alcohol use, we have the month
that we're all asked to be sober October
or dry January.
And that becomes even more difficult.
Definitely there is the pregnancy issue.
Definitely there is,
once you start being older,
you can't deal with it
anymore as well as before.
So what do we do now?
Well,
we really ventured out targeted smoking
because it is the major determinant
of your life expectancy
and all the diseases that you'll
encounter with the old age.
So the question was, what is really
the proteomic profile
associated with smoking?
And see how [---]
really ventured out on this
he had an interesting cancer.
And of course lung cancer,
very well known as the major outcome.
And what we did
see in the very simple experiment,
seeing whether we could discriminate
those who were never smokers
or told us that were never smokers,
and those who were currently smoking
and had been honest about that.
We saw that we would set the data to,
I mean, really quite well
or using the proteomics,
and then you really talking about
the discrimination of 0.95,
you hardly see that in any
epidemiological setting.
Well, that was fantastic.
But we still saw overlap
between the two groups.
And I know that is the major question.
So if you are a never smoker,
you declare yourself as a never smoker,
and then
you still have a proteome profile
that looks like
you are quite a heavy smoker.
It raises questions
and that is the fantastic thing.
So we thought, if may be that these
people have not been fully honest
or they forgot
that they ever smoked
or they didn't want to be reminded
of the fact that they ever smoked.
And that is certainly the case.
And we noticed for instance of alcohol
that people say, I’m not drinking alcohol.
And they turned out to be ex users
that have to stop because some problem
that was related to alcohol,
for instance, the liver.
But there's also
alternative explanations.
And that was the important thing
that, we really soon found out
that if you look at this profile,
it's really determined
at least half of it
in the general population by smoker.
I used smoker that determines
how high your score is in
what we call P -SIN, how much you've seen
in terms of your smoking habits.
But,
if you really, look at to other factors
that may determine this score,
how can it be
if we talked to a genetic epidemiologist,
we looked at the genes and there's some
contribution of the genes but not big.
If you'll look at the exposures.
Well see all the exposures.
So one of the most fantastic thing is that
we found that your maternal smoker,
whether you're not a smoker,
was popping up.
Whether you were passively smoking,
popped up.
How much air pollution was
around you, popped up.
But there's also all these factors
that we thought, hey,
also obesity pops up.
And if you know a little bit
about smoking, it's
it’s one of the strange things
is if you smoke you'll
usually have a lower weight
than nonsmokers.
If you stop smoking, a lot of people
say I go obese and I don't want that,
I don't fit in my dress anymore, and
I don't look as beautiful as I did before.
So that is affected.
That did not surprise us.
And if think about how to explain this,
we also started seeing that
there are probably common pathways which
go to aging and age related diseases.
With overlap
for instance for obesity and smoking.
That is really what you expect
also because and we don't think
that smoking has a unique pathway.
It may be in your lungs,
I mean, in direct exposure.
The oesophagus also, right.
We all know, that is a problem.
But really if you start thinking
how it causes aging, of course,
we all know
that if you ask your pathologist, well,
you will not ask your own pathologist,
but that of a another person.
And if you look at the skin,
really,
if you look at the in the microscope,
you really see something
awkward in the smokers.
The skin ages and we all see that
your throat, you're voice.
Usually, people who are 80 years and
have smoked all their life,
you hear, oh, this is a course voice.
So we do see differences.
But the processes
that are ongoing in your body overlap.
So we also saw that of course
we think some people don't tell us anymore
whether they smoke.
And how much they smoke.
But, we also
think that there are other reasons.
But some of the reasons are, you know,
we can't put our finger on it.
But the other common ones, like obesity,
it's the major problem worldwide, so
we see it.
I’ll also correct myself.
It wasn’t Alfred.
Alfred talked about GWAS in the
China Kadoorie Biobank,
but it was Sihao that actually
presented this.
Sihao is a PhD student
who has been working with these data
and looking in the UK B data as well
as corroborating in China Kadoorie
Biobank, B data, super, super interesting.
So that that piece and that this idea
of having a smoking signature
and an ability to determine
and maybe it's, you know,
secondhand smoking and heavy secondhand
smoking or something like that.
But I think being able to parse this out
and corroborate the genetics
and the proteomics in any way with,
the epidemiological data
and vice versa is super exciting.
And then, of course, we've talked
on this podcast before about using
genetics to corroborate proteomics
and proteomics to corroborate,
what we're seeing in the, in the genetics
that have maybe supported
drug programs, for example.
So can we, and this is
Sarantis’ absolute area of expertise,
if we could transition to aging,
That's a great point, actually.
You know, I’m intriguing for the fact
we say the mothers when they are pregnant
and they're smoking,
you see effects on the babies.
There are a lot of studies like that.
That means apart genetics,
there are a lot of other factors,
probably epigenetics
that may influence all of this transition.
And we know for measuring the aging
epigenetic clocks are really
the gold standard so far.
But proteomics takes a really big
attention and really go to nail
down the details of aging
and aging related disease.
Right.
And you have seen these
with your own data and with amazing work
we had with Austin together.
And It would be soon published.
Would you like to say a few words
about the biological age
and how proteomics clock enable
the study of biological age?
That'd be great.
Yeah.
I think one of the the golden grails
we're all looking for is how to live long
and how to not to become older
looking than you are, right.
And it's a it's a golden grail.
And I think this longevity
research, has been
what has baffled me for
always and I’ve been really
working on aging
and dementia now
already 30 or something more.
That there was a lot of progress
in the field of animal
based experimental studies.
And they had wonderful findings,
whether it was telomeres.
Whether it was on
the basis of
protein homeostasis or metabolites.
IGF 1 was a notorious one.
And all these things seem to fit, right.
All the animals,
if you look at the animal kingdom
except for the birds, but the smaller
animals live longer than the other animals
and the wonderful study,
dogs in science with undercover
a big dog life expectancy 6 to 8 years,
if it’s a Danish dog or a big pointer,
a small dog with a very long life
expectancy of 15 years, 20 years.
But it never translated to humans,
and that has bothered me forever.
So even something like telomeres again,
the Nobel Prize, right.
So as a Nobel Prize on it,
it works the most well ever.
And in the animal it works.
Except in humans you do see associations,
you do see suggestions.
You don't see a lot, a lot, a lot,
if you translate it to diseases
has been the breakthrough.
If we look at the proteomics clock now,
and if you look how it
predicts, projects
to diseases, it's phenomenal.
And in that sense if you compare it
with the methylation clock.
Well the first thing I did
you say well whatever we're going to do,
compare first what the overlap is
with the methylation clock.
And I was really understanding that
whatever you find in methylation
also very much goes to this.
I was already up to date
that, you know, a lot on the cancer field
and methylation, huge progress,
it' seen as a very helpful
and promising field.
But I was actually surprised how
few evidence there is
for direct links between the
proteomics group and diseases,
and definitely, as with so many diseases
as we see now with the proteomics.
So we were talking a lot
the methylation folks,
and we were just arguing like, okay,
we worked a bit on it,
and definitely [---] worked on it
in relation to psychiatric diseases.
But and we were a little bit amazed
that the
overlap between the proteomics and the
methylation clocks isn't big.
But what you also saw
that in the methylation clocks
what you usually have to tweak that the
the methylation clocks
only associate to disease.
If you are any focusing all
coding proteins at the methylation
that is related
to genes that are known to be involved
in diseases.
It's not so strange
because if you really start thinking
what the what methylation does,
it will be agnostic.
It's just going all over the genome.
The CPT unit.
And what we know of the genome,
only a small fraction
is involved in coding protein.
Now of course
we all think that in a translation
and RNA regulation is important
in the development of the disease.
But in the end of the day,
it's still the protein
who does a lot of the job.
Exactly.
In Alzheimer's
and dementia and vascular dementia,
it's the most important the proteins there.
But I think what we are seeing that
the proteins are also mentioned in
cardiovascular disease.
And it's not unexpected, is it?
It's it's more I expect that the,
the metabolome for instance, did
much less than the proteome.
And that that brings us back to work that
that this is probably the field to be in.
It feels like it's
the druggable aspect of the omics as well.
So the fact that we do have antibody
therapies that are able to target
pathways, I think means that
the translation feels like
it will be more straightforward.
But I think
we're only scratching the surface.
I think well, what I tell any
all young people in my group,
and also others that I come across now,
is that you really has to invest in this.
And I confess to you
and to the world, I always was
a metabolomics fan and I thought
that is going to make it happen.
And that is the place to be
because it's the active compounds, it's
the activated part
and if you compare that now
to the development in proteomics,
I do agree with you, Cindy, it's more
the druggable part in it,
but it's also the part that explains
for us, the thing is, and that makes you
wonder a little bit what's happening.
It's the phenotype, right?
The proteins are really depicting the real phenotype.
Yeah, definitely.
If you go to CPGs, they are, like, more upstream,
like more going to the mechanistic.
That will be other factors
that may influence.
But at the end, end point is the protein.
The real phenotype is what
happened at the protein level, right?
And that's the real picture.
What worries me also a
little bit if you are looking at
expression data in the brain, now
and there's often
not a correlation between the two.
And they often go opposite direction.
So that makes us worry a little bit
what's going on there.
I mean you should ask ourselves
what will be the height of the day
in five years.
But the idea now is that,
it's the proteomics
that matters more than anything else.
Exactly.
It's nice to hear that it's adding value
to the data sets we've got already.
I think there's
the in -depth pathway analysis trying
to dig into why RNA would go one direction
and proteins would go the other direction.
If we can at least come up with some hypotheses
for any given system why that would be,
for example, maybe the
products are being cleared out to move to a
different place where they're being used.
Maybe they're in vesicles or something like that.
Being able to sort of dig in to provide hypotheses
for testing the mechanism is exciting.
And it means that if people are listening to this podcast
thinking they wanna go do their PhD,
there are so many questions to answer
and they should consider going to Oxford,
I will say.
Definitely, definitely.
So I echo that.
I think that, I, I noticed that,
and it really is the same as genetics.
I mean, we weren't doing the genetic,
the genome wide association study
that we had found
three genes for diabetes.
And then people said, oh,
we got to find out what these
genes do, and this is probably it.
There's no other genes to be found.
Well, afterwards we found hundreds more.
I mean, that is what we are
at the stage with the proteomics.
I mean, this is the start.
It looks fantastic. It looks great.
But we are at the start,
this will be an effort of 10, 15 years
like it was with genome association studies
We’ve been working on it,
and we still haven't finalized it,
but we have now, genetic risk factors
that we all add together,
the picture is becoming
completely more and more clear.
And in is work in progress.
I mean, we know that from the genetics.
We were staring at the genome
-wide association studies.
We said, oh, we don't see amyloid
at all in working in the genome
Five years later we go into GWAS and
that was the first pathway was amyloid.
The second pathway,
the third one was pathway.
And we asked, what is happening here?
I asked my friend's colleague
and he said, well, we looked at it too,
but what happened is that the people
doing more research in the biochemistry
and start linking those genes to amyloid
completely.
Now we can go the reverse way.
We we can look at the proteins
associated with the disease.
And of course with now checking
whether they also associate
to the genes of the disease
and the exposures related to the disease.
So it's one of the most exciting tangles,
if you are interested in the disease
and understanding disease,
but also predicting disease,
it's the breaking point,
but don't see it as end points yet.
We are still on the way.
It's a journey. We’re moving up.
I think, you know,
I think genetics pay off in the
pharma space has been pretty clear.
I think it's, Matthew Wilson.
I shouldn't say the name,
but I think his publication outlined that.
When you have genetic evidence
going into a program,
you're more than twice as likely to have
a successful exit of that of that target.
So I think we're still early days
with proteomics, but I'm very optimistic
that having proteomics
evidence will further help us with
with demonstrating that,
a program is likely to be successful.
So it's
then we'll have to be able to juggle
all these hugely successful programs
and get them out into the market
with the health care system
that maybe unprepared to
pay for them. But we'll see.
But that's, that's
different problems for
different health care systems.
But yeah.
So so both of you, I'd love to understand
where you see
an ability to have a subset of proteins
that really help us understand
biological age and how biological age
may not be reflective
of chronological age,
how might that actually be useful
in the future as a clinical tool,
It's a great point.
as a direct to consumer tool?
If the ancestry.coms or 23andMe's of the world
build something like this, how might people use it?
What are your thoughts there?
And also to add something here before
Cornelia, you're of course
the best person to answer this,
but also to add the fact that now we're not
talking about single proteins or single genes,
we're talking about pathways,
we're talking about signatures at the end.
And, we see a lot of inflammation
coming with aging.
And I think probably
we have to deep dive a little bit more
in inflammation mechanism to understand aging.
But yeah, I'm happy to hear your thoughts
how you see going to the clinics
or how do you see go to the prognosis,
for example, from your prospective.
Well, I think well, again,
we learn from the genetics.
I think the 23andMe people are interested in
in their genes,
either at the risk of the disease,
but it was also in their heritage.
I think if you look in, the UK,
we have the ZOE program
where people I'm very much interested
in their microbiome.
Again, it's a field in action.
I can't believe that,
people getting the tools
and the final tools in there,
but they get an impression
how well their gut microbiome is functioning
based on the state of the art
and and the truth on that.
So I, I definitely think
that in the direct consumer field,
this is exciting.
This will be interesting.
I can imagine that if you link
your microbiome to
your aging profile that,
that it's even going be more interesting.
And that is where I see
the field also going.
What we trying to do
is starting out with the smoking data
What we have to try out now is
to what extent you can
revert back your aging profile.
And to me,
based on what my gut feeling is
in there specifically,
is that you probably can hold the processes
as long as you intervene early.
And old age, it's not clear,
but I think we have to find that out now.
We don't know. Does it pay the price?
If you are 90 plus to start doing
physical activity.
Well, you ask me,
there's also dangers associated with it.
I mean, we all know that
if your hip breaks,
you have a broken hip after the age of 85,
it's one of the strongest
predictors of dying.
But I think that
is what we are facing at the
I think, well, the beauty is of
our analysis,
it will give you a readout of interventions
that we always missed.
I mean, if one of the interventions
that has been
well pursuited is of course,
chlorectristration.
Now, we all know that
that is quite a harsh job,
because you really have to eat
less than you're supposed to eat
Lika a third or something.
It is quite harsh.
And it really goes to this idea
that small animals live longer,
than large animals.
Really small men and women live longer,
than tall men than women.
And, there is a point to that and,
that is really targeted at this system.
It's IGF one signaling.
And in all animals
that is a problem
for living long.
So I think that is one of the outcomes.
But I think it gives us hands and feet now,
to have a readout that
think about the monkey studies,
in caloric restriction.
There's only three, four done.
You have to wait for ages
before these monkeys age.
And now we have a readout
that that is a little bit closer
The readout seems
to work already by age 40,
and probably also age 20, 30.
So hey, that must accelerate research also.
And it must give us
an insight whether intervention
stopping smoking,
don't wait for it just do it.
Too much alcohol.
Stop that too.
But physical activity was
if you talk to people in the aging field,
some people are saying, well,
maybe good, but wait a minute,
if you're doing other physical activity,
also generating a lot of oxidative stress
is that not also cause of aging?
So I think we read it out now.
We can read it out.
It doesn't look that way in our hands.
So it means that totally, you know,
some physical activity is good, and
at least also for not only for vascular
but also for the brain.
And I think that kind of opportunities,
the multitude to use it now as an outcome.
We have to prove it
but it looks that way that it is working.
Well, you hear it here.
Smoking, stop smoking,
drink less alcohol, eat less food,
and do exercise,
but not to the extreme, right?
Well,
but going back to the point of Sarantis,
I think that inflammation
we're all interested in it.
But we also get now other proteins.
That's also interesting and,
what is the other thing
that is pushing us.
And I definitely think that this was the start
for a lot of diseases
and aging,
but also age-related diseases,
but also exposures,
you know, the plastic exposure.
Nobody knows what it does.
I used to
like if you have a readout for that,
that will inform us a little bit
what goes on in the body
and how worried we should be.
Yeah, PFOS, PFAS,
these sort of forever molecules.
Would you.
Would you like to comment,
a little bit about the drug interventions
I mean old drugs.
Old dog, new tricks,
like rapamycin for example.
Hg2 inhibitors, now we hear
that they are player or...
What is your feeling about that?
Targeting everything
is targeting aging actually?
Or vice versa?
Why do you mention this, Sarantis?
Because we won’t need to study that.
So we have this week a break for
what is our low hanging fruit.
Because I knew if you join this field it's
not for the faint hearted.
There's big competition, stiff competition
that we usually,
we've always been reasonable about it,
that we say, okay, if we see already a
publication.
What is our lease?
What is are what is the low hanging fruit?
And we definitely have
everything lined up there
with Sihao
and Austin to do this aging clock.
So but one of the things
that we are getting moving to
as a field, of interest
is also the clinical application.
We have already done a study
that liver and alcohol are big problem.
A big problem is also
that people don't know
how much alcohol they use, and they don't
want to know how much alcohol they use.
And the produce.
So can we just,
distinguished for liver diseases
can we not use this profile for that,
then predict how long we will do this?
And I of course,
it's used lots of alcohol
and you get the usual diseases,
but you also get the cirrhosis
and you get liver cancer.
so here you go
so that is what we take as a benchmark.
The other benchmark
we definitely we're to use is
how to, serve the certian drugs,
how do they act what we know that.
But also what is that unexpected actions.
So this will be negative side effects.
But we all know that some drugs,
think about statins you know,
there was time this is,
we are working here in the group
that did the most statins research
and you know except that
some people get some muscle pain
and some very severe ones
there is quite an argument to almost
put it in the drinking water, right.
So of course you shouldn’t do that.
But there's also positive effects, side
effects of the drugs which were never in
the notes you get if you take the drugs
but it's very interesting.
It's very interesting on this act
for instance on inflammation and how
so definitely
that is in part our target
and that's also with the way
we're working population health and
we should really resolve these issues.
There's so much opportunity
to understand mechanism,
rapamycin,
like Serhant has mentioned,
we don't really understand
metformin has some beneficial effects,
but it can also alter
how exercise is, is benefiting us too.
So understanding the mechanism of that,
G... what are the GLP 1s?
I mean, those are acting in the brain.
That's fascinating. Right?
We're really just parsing all that out
and it's already almost in the water
for many.
For many populations. Right.
There's just so much opportunity
that I hope proteins can help.
At least,
like I said, point to some hypotheses
that can then be tested
by groups like yours, Cornelia
So definitely that is a field of interest
and I.
But on the other hand,
the exposures of two exposures
to that shouldn’t be there.
The plastics that are built,
the pesticides.
I think we see them.
We see that the, you know, that.
And, the fact that there's
air pollution in the region,
pops up
as having the similar effect of smoking.
And that is not good.
So I think there's a lot of opportunities
and we need a lot of hands,
but also a lot of brains to do that.
And technologies.
And technologies to do that.
and that definitely,
we need more of the protein.
We know that there’s lots more proteins.
We need more, the different isoforms.
We need to know more
about the phosphorylation and the,
processes of processing of these proteins.
But it it isn't that a field
that, you know, we
I'm not I'm not young anymore,
but I think yeah, I think we definitely
the future will tell a lot about what
we always have been wondering about.
To that.
To the point around
the needs for this area.
What are the cohorts that come to mind
that are collecting
environmental information
that you think are ones
we want to highlight and promote?
Because it's like I said, it's
hard to collect these sort
of environmental variables.
Are there ones that you particularly
like that you want to make sure,
are successful in the future, continue
to collect data, that sort of thing?
I think that there are many cohorts now.
That,
of course, has, has really dedicated
their life to look at multiple exposures.
I really favor
the epidemiological setting.
And the reason for that is that,
what you probably, if you single out
one exposure,
right, it's unlikely that in your life
you only have one exposure
you need a broader picture.
So I, I'm brought up in
a department
in Rotterdam
where we always, looked and try
to look at the complete picture
with the view that in the end of the day,
you're asking yourself
what is the effect of smoking?
Oh, but,
you know, if you smoke, you often
more likely to drink a lot of coffee,
a lot of alcohol.
You're more likely to use oral contraceptives.
Hey, there’s a lot more things you do.
And, I think that these studies
have been incredibly powerful.
And incredibly important, the UK Biobank
is a is a fantastic example on that,
that also data have been gathered,
you know, they been adding of data
stacked onto each other.
And that allows you to do
multi-omics studies
in a very valid way,
but also weigh in exposures.
Now, one of the examples
I would give that convinced me totally
is that you have to look at,
multi-omics.
Is that what, we did
is look at metabolomics,
and we started thinking, why?
Well, the idea is metabolomics
is genetically determined, but so environment,
is the active component?
And you're really getting quite overwhelmed
if you look at the how strong medication
also influences metabolomics.
We're now going back the same
as Sarantis on proteomics
and for some it’s really overwhelming
how it's medication is, influencing your proteome.
Now look in the most of the epidemiologists
have been wise
and have been gathering data
of a lot of exposures
and that will be helpful.
And definitely the medication
you need to take that into account.
But, on the other hand,
they should look at medication.
The smokers also turned off to be,
a confounding factor for that.
But, you know,
the fact that both metabolomics
but also proteomics
even more is associated to medication,
suggests what we already have hypothesized
that a lot of medication
is somehow targeting proteome.
Yeah. It's.
It's the messy part of the data. Right?
But it's because we are collecting it across
ideally large numbers of people
that signal can emerge
even even though there's challenges
in collecting those data.
I think more and more we should include
it also proteomics in in trials.
We should do that.
And it's in clinical trials
in which you test medication.
But please if we do
these intervention trials also show me
that you have an effect of the proteins
that develop the disease.
and there is our aging work is important,
but there's a lot of more,
profiles that we need for dementia
in the early phase.
So not the fact that you have P tau,
which is just a signal that your head is
full of tau and if your head if full of tau...
It’s one only biomarker, right?
We need something earlier.
We need more.
I don't think that if, physical activity
protects you against dementia,
you shouldn’t start with it at age 85.
You should start with that early.
And we've I've read out of studies
that show death.
That convinced me. And of course, the...
Yeah, a little advice
is that we have on the exposures
are interesting, but we need much more.
We need much more.
On nowadays what
we are exposed to that even the fact that
our sleep is different, that
we are exposed to light at night,
that we never were exposed to.
There's a lot to be learned.
And I think that type of trials,
there’s two things on trials for exposures,
is the first of all,
they have to be big,
even for caloric restrictions.
You see all these smaller studies,
people lose weight.
I mean,
we have the better outcome, right?
of course we lose weight
if you don't eat the calories.
it is obvious
that that will happen.
But we need the readouts of that,
that shows us
that it takes
really that it stops aging.
And the trials, I came to Oxford
to the Oxford department, partly
because the trials are so big,
but partly I like the spirit
about the trials here, that they have
to be big in order to show things,
because that affects sometimes I, I mean,
are still subtle. I think we got used
to that in the genetics too.
Of course you have genes with big effects,
but a lot of them
will not have that big effect.
It's the aggregate of all the genes
and if it's the aggregate of the genes,
it has to be
the aggregate of the proteins to.
Otherwise the effect of these
all these genes don't make sense.
So I think that is what we’re facing that,
we have to start thinking of trials
with complex outcomes.
And we have had a lot of benefit
that coming to Oxford I really wanted
to start looking at machine learning too.
And that gave us also a
very much of a boost, I should say.
I'm not saying that machine learning
solves everything, and a definitely not.
You don't hear me say that.
But if you look at the in a simple,
even simple
machine learning models, it can deal
with the complexity a bit easier.
And I think we we nailed that down.
And for strong associations
like the proteomics age group,
it really doesn't matter what you take
a more classical approach
like elastic net or gradient boosting,
which is kind of a random forest
or you take a neural network,
but in the end of the day,
it may be that that some of these methods
may be more powerful
to pick up these aggregates and also
translate it back that you get into your hands.
Which plotting is doing what?
If it becomes completely obscure
in the neural network,
what has done what?
Are you really going to invest
hundreds of millions
to develop in therapy for that?
No, you want to first know,
not too many proteins,
and what is doing what, tell me. Right?
And that is when you have to be able to start out.
And machine learning, it's giving us a lot.
Yeah, yeah. And.
But we have to be careful
about overtraining.
But that's where having this
growing field of
machine learning is informing us.
But I think parsing out,
what's the genetic contribution from ancestry?
What's the contribution from gender?
Yeah.
What are the signals
in the proteins that confer gender
that you can then use to stratify that?
There's so much complexity
that machine learning is helping us to parse out.
Yeah, and that we were lucky.
I think we've been wonderful here in Oxford.
That we have multiple
all these cohorts
We have the China Kadoorie Biobank,
Yeah.
you saw that it’s fantastic
what they are setting up.
We have bigger cohorts in the million
women study, but we have also,
the large trials that have been done.
And, you know, even in a trial,
you can do now, start thinking
of a silico experiments
that if the trial has been done
with a certain drug
that you want to repurpose,
you can just measure in that trial
what the effects of the proteins are.
I think you really have to go.
We have to be intelligible.
And more intelligent
on how to repurpose,
and reuse the studies that we had.
But the fact I,
I totally agree with people that say,
if you split two data in the training
and the test set,
if there's structure in your data,
then it’s in your training
and your test set and then
in my early days using machine learning
in team discovery is that,
we figured out the hard way.
We had the test set
and training set replicated,
but when we finally looked,
what the neural network was using,
it was using missing data to predict
things like, how is it possible?
How is that possible
that you can predict with missing data?
There must be something that you can’t input
in that range region well,
or there must be a reason for that.
But if the problem with the missing
data is in your training set
it’s also in your test set.
So for us, it's
so important that you can use
data across studies that we could use UK
Biobank as a powerhouse
and a powerful tool,
but that we can replicate it in other studies
that are completely independent
and that will be important in genetics.
It will also be important in exposures.
And there are more and more studies
that are integrating, I know of,
Olink proteomics,
of course, that are integrating these data
with genetic data that will offer
opportunities for collaboration.
Yeah. Awesome.
Well, so I think this is a great place
for us to sort of wrap up.
I'd love to give you a chance to say any
last thoughts that you'd like to share?
Cornelia.
No. Anything that it’s...
The only take home is
well, I already mentioned
the race is not the realm.
I think it's not technology.
You guys are still finding,
better ways to quantify the protein.
You are finding better ways
to describe the proteome.
That will be ongoing.
I think it's quite exciting
to be in this field.
The other way around is that for us,
in the data science and
the epidemiology,
I think there's a lot of work to do.
A lot of thinking to do
how to analyze the data,
how to integrate it
over the exposome, the genome.
And then, it's
going to be very exciting on that field
telling people how to prevent the disease
better, giving them tools to monitor.
And nobody would have thought,
20 years ago
first of all,
that we were going not outside
of or house
without taking the phone.
But, you know,
that we would be having Apple Watches.
and Fitbits
Yeah, how many computers do we carry?
For them it was difficult to understand
that we landed on the moon.
And to accept that.
But you know nowadays
this is the field that’s going to develop
and we’re going to be boosted also
on the data analytics,
on the integration of data,
the use of machine learning,
the abuse, but also correct that again
And how to translate that back.
You know, it's not only the data science
that is relevant.
In the end of the day, it's relevant,
what you do, the impact that you have in
in curing people,
in presenting the disease
because you know, if anything in your life
you don't will to become diseased,
you want to prevent it.
Definitely with dementia,
but also with many other diseases.
It's that translational that counts.
And that is important.
And it's important
that we all keep that in mind.
Couldn't agree more.
And you all heard it here,
this is the place to go,
Oxford is the place to go for large
data sets.
They're amazing cohorts here
and amazing scientists to work with.
So for those of you who are thinking
about post-docs or PhDs, think about that.
Sarantis, I'll give you a chance to please
It was great.
Any last thought?
It was great to hear from Cornelia,
about the aging and aging-related diseases
I think proteins play
a really important role on that.
Plasma proteome is on spot now
and we can,
using this plasma proteome,
we can understand the biology of disease
from different tissue types.
We've also we can also understand
of different tissue types, phenotypes
screening only for plasma proteomics.
I think that's the take home message here.
And really nice
to have you, Cornelia.
Great, I enjoyed it a lot.
Thank you very much, Cindy.
And yeah I mean the
the last word is for you, Cindy.
Super fun. Super fun!
So then
I'll just go back and double click on.
So I mentioned, the study that
that demonstrated that having genetic data
going into a clinical trial helps
improve success by at least two times.
That was actually Matt Nelson.
So apologies for that, 2015,
and that was a Nature Genetics paper
really pivotal paper.
And then of course
AstraZeneca has also published
on their ways of filtering and leveraging
genetic data in different ways.
Gives them a seven times
improvement in clinical trial outcomes,
which is which I just wanted to highlight.
And then we also mentioned Austin Argentieri.
We mentioned, Sihao Zhao, a PhD student
and then we also talked
about China Kadoorie Biobank,
but we didn't mention Zhengming.
So I want to I want to, give a shout out
to the amazing biobank that he's built.
As I understand it, really,
a lot of the UK Biobank structure,
was founded in how Zhengming,
built out the China Kadoorie Biobank.
So those two are great ones.
And people use them a lot
for corroboration and combining data.
And Austin is a great example
of someone who's done that so well.
Well, in the future. Get him.
Get him on the podcast.
Perhaps once that paper comes out
and that paper will probably be out
by the time we get this podcast,
published, I hope so.
We can use this.
This is an opportunity
to promote that important work.
And so with with all of that exciting,
content that we've talked about today.
And I want to thank you, Cornelia,
so much for agreeing to
to come on and trust us with
some of your story.
Thank you very much.
Thank you for having me.
Well, that wraps up
this episode of Proteomics in Proximity.
Huge thanks to our guests and authors
of such impactful publications.
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