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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, Dale Yuzuki, 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, Dale, Cindy, and
Sarantis. Hey
there, welcome to Proteomics in
Proximity where, today, we're talking
about a wonderful paper in Cell
Reports Medicine with Diaz-
Canestro and Aimin Xu out of Aimin
Xu's lab at Hong Kong
University. We're talking about the
characterization, their characterization of
inflammatory and cardiometabolic
proteins, in particular in response
to chronic exercise. And this is a
cohort of 36 overweight and
obese men with
prediabetes.
So the team looked at how
does response to exercise
work? And they used a machine learning
algorithm to actually characterize
response to exercise. So, this is
a very, very different paper
than we've talked about before. And I
think this is because we're seeing
proteomics being leveraged in these areas
that we haven't seen before. So very
exciting!
And you think about the
global burden of obesity,
being overweight, and diabetes. I mean, it's a
huge, huge problem, right? When
you look at the obesity
rates in developed countries, it's just
increasing at these breathtaking rates
and diabetes, right? I mean, how
many people that we know
are pre-diabetic or on diabetes
medication or what have you? Sarantis,
what can you tell us about this particular
group of 36 men?
First of all, I will start with the limitations
of the study and something that the
authors have discussed already, and we are
discussing before this meeting, right? It's
a small cohort. There are 36
individuals, but also
there are males, there are no females. And
not very diverse
in ethnicity as well.
And they pointed out that
probably in future studies, they will follow up
just to make it more strong, the evidence
of these biomarkers
for prediction. And I think
that's really important to keep in mind when
you design a study. Even in their age
range, right? They commented on how women,
the age range was like 20 to 60, and
longitudinally over the course of
twelve weeks. They're talking
about menstrual cycles,
and they're also talking about hormones and
how they're varying
in women. So therefore, you can
say that for this first study, men made
a simpler population. Just removing a
few variables, right? On the small
numbers.
And then the other one, which I
was thinking about, which is how difficult
it is to get
obese men to
volunteer for something like this.
So this group of men
were, on average, 40 years old
with a full BMI
average of 30. So we
have obese men, and
they were going through high-intensity
interval training, which
was over the
twelve-week period. And, Cindy,
how often did they do this? Yeah, they're
doing it three times a week. So I think that
this is 70 minutes per session. Right.
So they had the structure of the exercise
set up in several stations
to keep it novel. And
they maintained
the
exercise to
improve and to change as they
got used to the exercise. So
they tried to maintain engagement.
But like you mentioned, it's
hard to keep people
exercising over a twelve-week period that
frequently, right? Especially if they're
going from zero to three times a
week. Zero to
three times a week, 70 minutes
per session. It's too much.
It was a ten-
minute warm up. It was these stations
for about 15 minutes per station.
So four different stations and then a
cool-down period. And you think, okay, how
do we get enough people? But they
did this. They found
people with an average age 40, average
BMI of 30
willing to go through twelve weeks of
this. But then they went ahead and,
Sarantis, they took a look at their
blood samples at zero, four
weeks, and twelve weeks.
Unfortunately, the test itself,
to take the fasting insulin and glucose
tolerance tests, are not easy tests, actually.
Because they
really need a lot of preparation
and it's not a straightforward study then and it
has a lot of difficulties. Yeah, so the
glucose tolerance, Sarantis, what can you
tell me about that? I think most of
us go through this kind of diabetes
screening, but
from an insulin resistance
point-of-view, what does glucose
tolerance show?
The insulin resistance
actually is
a condition of
diabetic patients,
actually. And there are a lot of
actual causes of this
disease and most likely it is gene
related. But also the environment can
play a role. Epigenetics also could play a
role on this disease.
And at the end, or an outcome,
there is a tolerance of the genes and
there is resistance of the
cells to the insulin. And for this, we
have accumulation of glucose in the
blood. In simple
words, at the end,
we just see a
lot of glucose being in the
circulating blood. And
tests also, like C-peptide test or
OGTs or insulin fasting
and glucose fasting
tests, help people to
understand the condition of the disease.
So by pre-diabetic,
meaning their glucose levels weren't
so high, but they were
approaching that threshold. With the glucose
tolerance test, literally they're
coming in in a fasted
state and they're
drinking straight glucose
and then
measuring
their glucose levels. Or maybe
they're also measuring insulin levels. But
certainly seeing whether your glucose
levels shooting straight up and
not coming down over a period
of time. And if they're not coming down
because your body is not
releasing what it needs to
move that sugar from your bloodstream into
your cells, then you
are portraying, you're
demonstrating some insulin resistance.
That's my understanding of it.
The remarkable thing about the study is
the intervention was the
exercise, right? The intervention
was these overweight
individuals coming in three times a week to
a center where they were
monitored, told what to do, walk through
the steps three times a week for
twelve weeks. Must not have been
pleasant. And yet, at the same
time, they had had no
intervention with regard to diet.
Basically said you eat the way you
normally eat. And
through the exercise, I think one of
the interesting things about the cohort,
in addition to the biology, which we'll get
to in just a minute, was they
lost weight. I mean, their average BMI
went from 30.05 to
that time frame, not adjusting
for any kind of diet. And it
made me think, hey, there's hope.
There's hope for the rest
of us, as
long as we do high-
intensity training. I think the
paper also points out it was not
moderate or low intensity. It
had to be, had to be
intense. Then they went
ahead and took a look at blood
at baseline, at four
weeks, and at twelve weeks.
What did they
use to take a look at the protein
levels? They use an
Olink platform. Yeah, I can answer that. So this
group decided to use two
Explore panels. So, just as a
reminder to our audience,
Olink came out with a
qPCR readout in the
founding of the technology to measure proteins
called the proximity extension assay,
which is the namesake of our
podcast. Then
in 2020, Olink
expanded its product portfolio to have an
NGS, or a next generation sequencing,
readout. And the advantage of that
is that, in a
run of a sequencing
instrument, you can get more
molecules read out at
one time. And so, with our
NGS readout, that's our Explore
technology, we're able to, today, measure
about 3000 proteins
in each of
using an Illumina NovaSeq instrument, just
as an example.
Here, they used not
the full 3000,
they used, what was it?
Do I remember that? Right.
So they decided, kind of like you were
saying earlier, Sarantis, focusing,
it in to
see, in these
extreme cases of men,
trying to reduce
your variability, reduce your number
of variables that you control,
and see what the
signals are. They also
focused in on cardiometabolic and
inflammation proteins.
We roughly categorized them
in these buckets,
right? And they stuck with
those two sets of
proteins. We have
a full 3000 and, in fact,
you mentioned their future
suggestions for where to
go in the future, and they suggested
expanding it beyond that, because we
actually have two cardiometabolic panels and
two inflammation panels in our Explore 3072 platform.
And so it would be really interesting to
see this study expanded.
And I think, just from
seeing the patterns
within just these
the machine learning algorithm, we should
talk a little bit about that. This machine
learning algorithm that was able
to integrate protein data and
understand, sort of, and predict
someone's response to
exercise, whether they're going to be
what they called a responder or non-
responder. Sounds really
compelling around precision
or individualized training, or
individualized
prevention of
diabetes. And when you
talk about responder versus non-
responder, you're actually saying
that there was a subset of men
who did not respond
biologically
to the sort of pre-
diabetes down to sort of a
normal level, yet there was a whole
other group going through the same exercise
regimen that responded. And
that's really interesting, the findings.
Yeah, and the parameter they
used, and this is not something I'm familiar
with, so I'm not going to portray that
I'm an expert at all, but
a clinical parameter called
H-O-M-A-I-R. So it's an
insulin resistance metric:
HOMA-IR.
And they had this
criterion that it needed to
reduce by two-fold in order for them
to be categorized as responders.
And so, yeah exactly, Dale
And
regardless, one of the
figures that was really interesting was the
trajectory of the proteins. So
we're looking at 688
proteins from an Explore 384
panel - oh, I'm sorry - two Explore 384
panels. Cindy, correct me if I'm wrong ...
You don't need the NovaSeq capacity? That is
true. You can do individual 384
panels in each run of
a NextSeq. That's right. So they
did two of these panels on a
NextSeq.
I have to mention something for the assays
that's really clear and
really nice because they orthogonally
validated these assays with MSD's [Mesoscale Diagnostics platform].
And they see, in 19
individuals, the really nice correlation with
MSD. That's another way that shows
specificity and how specific are the assays
in our detected proteins, right? Because they can
validate it with other orthogonal
technologies and have really great data from
your data. And to be clear, MSD only
had, I think, an overlap of 15
inflammatory proteins. Okay. So
they only looked at 15
proteins, a very small subset.
But nonetheless, across the 19
individuals, they found a very nice
correlation to the vast majority
of those proteins.
Eleven out of those
of 15 were moderately correlated.
So you just say, hey, that's a
very small subset of only
But good enough,
right? Yeah. And I'll also emphasize
these proteins that they were identifying
are low abundant proteins.
These are ones where
Olink has really shined a light
because of the ability for us to
come in and hook out these low abundant
proteins. These are
certainly able to be seen through a mass
spec approach, but it's
harder to do that
because of high abundant
proteins being
so strong in a mass spec
readout. I think you'd have to
do some sort of subtraction to
remove that component. And so what we're
finding is that Olink has
a nice way to
reveal some of these low
abundant signatures that we just weren't
easily able to see
using existing methods. And I
think you point out something really
important, Sarantis: that there
just aren't that many low abundant assays
out there that are commercially available.
So
it's exciting
to me to see.
And to look at some of these
results now over time, they clustered
the response over week zero,
week four, and week twelve
in Figure 2, which
the more I think about it, the more
interesting those figures become.
Why? Because, for example,
cluster one was steadily increasing
over time. It starts out at a very
low level, goes to an intermediate
level at four weeks, and then
high at 12 weeks. And
those proteins were what? EPO
and something they call myokines, which
I'd never heard of before. They are a
subset of cytokines
that are released by
the muscle, is how they defined it. But I
agree, it's an interesting
term. Actually, in the abstract, they call it
exerkines, although I haven't heard of
exerkines.
The
myokines are a subset of
the exerkines. Exactly.
That's great.
And IL-6 is there.
And
IL-6 produced
by muscle having a different
function than IL-6 produced by
the immune system. It's like, wow.
Fascinating, right? And didn't we talk
about that, or didn't Tthe two of you talked about that
with Katerina? She had an example of
IL-6 as an assay, which
was compelling and very
specific and actually, there was a
corroborative
assay
where Olink was
able to show these IL-6
signatures that other assays weren't.
Not to just detract from the main message,
but I'm thinking, okay, how can you
determine that this IL-6
molecule, which is identical to the molecule
of IL-6 produced by the immune system, how
can you tell it's from the muscle?
Right, because it's in
circulation. But then it was well, no, this
is in response to exercise. Right?
Yeah.
Well, I think the bottom
line is this is a signature
you're seeing in response to
an exercise intervention.
And so we're speculating that
that's due to exercise. But I think
that's a pretty good bet, as
you say. Yeah. And then there was
another cluster that was also interesting,
cluster three, where you had protein expression levels starting at
a high level and then steadily
decreasing. Okay, so this is another really
simple example. But not necessarily
the same proteins, right? No. I think that's
important to point out.
So in these
they're looking at pro-apoptotic
proteins, meaning these are proteins
that encourage cell death. And, Sarantis, you
want to comment on that? Why,
as a result of exercise,
you have the ones that encourage
apoptosis
decreasing? That's a great
point. I mean, I can
speculate. I'm not an expert
of apoptosis. I think it has to do
probably with different signaling
pathways so that they're regulating
different gene expressions that are related, and
auto metabolism, or could be due to
regeneration. Or something
for cell tissue regeneration.
But that's the only some
speculation that I have.
I mean, these are obese
individuals. The body mass index
is really high, and you can say
they actually have a higher level
of apoptotic activity. And to be clear,
apoptosis is programmed cell
death. This is sort of
cells being pruned from your
organism as a whole. And you just
say, well, maybe this is a
function of
inflammatory processes.
Or homeostasis, Dale. They
could be that you shift the homeostasis
somehow and could be different
pathways that may affect apoptosis.
But yeah, it's certainly an interesting finding,
that's true. And then to move onto
cluster number four, this is
appetite stimulating
that bounces. It starts at a certain
level, goes really low, and then goes
up to normal again. And this is
appetite stimulating. And
I thought ... Well, it's appetite
stimulating hormones that are
decreased over time. So the
idea might be that you're
reducing appetite stimulation.
That's really interesting. I agree. Yeah.
They basically get less
hungry, which explains
why that, even though they're not
modulating their diet
consciously, their body is saying, hey, you
don't need to eat so much.
And probably this is interesting, there is a
crosstalk, probably, with neuro genes in
the brain. Probably there's
some, if you do
some CSF [cerebrospinal fluid]
proteomics, you may see some hormones
released that are changing in the
brain. Probably, there's for sure, there's a
crosstalk with the brain. But
remember, these returned to baseline after
four weeks. So these dropped within four
weeks, but by twelve weeks, they were back
to baseline in this cluster
four. And I think there's been some strong
evidence that appetite
increases when you take
on an exercise program.
I don't think it's known whether it's a
biological increase or a
psychological increase that we tend to eat
more because we think that we're exercising.
But it's really an interesting
finding. And then
the proapoptotic,
like you say, Dale,
these functions
of cell turnover,
it makes sense to
me if you're breaking down muscle and
then reforming muscle, which I would expect
them to be doing. They saw that
pattern in cluster one and cluster six
in particular, which I thought was
really compelling.
And related to
appetite was cluster
five. And that was where it
dropped and then stayed relatively
low. And among those proteins
was Leptin.
And, Cindy, what can you tell me about
Leptin? Isn't that involved with appetite as
well? Instead of appetite
stimulating isn't
Leptin
suppressing? Yeah, that's
my understanding of Leptin. But you
know, that's in isolation. So what is
Leptin doing when it's
interacting with all these other proteins?
Right? I also remembered in that one, what
I'd written down was MSTN,
right? So another protein within
that cluster, which I think had to do with
musculogenesis, but I'd have to go pull it
up again.
And then going back to
cluster two, which I skipped at the
beginning on purpose. And this is anti-
inflammatory
proteins, right? Like, was it IL-10,
where it starts
low, goes a little lower, and then spikes
up after twelve weeks. And what does that
imply? Where this
high level
inflammation that people who are
obese generally have
after twelve weeks,
we see a marked improvement
in terms of this high
level of inflammatory activity,
which I thought was really interesting and
informative the very fact that
the body is a system. All these particular
clustered changes that we're
monitoring over time as a
direct result of intervention.
So I think the high level message is
keep exercising. Well,
exercise has so many benefits
other than just weight. Right? I mean, it's
got clear benefits to
your brain, to depression. I mean, it's
one of the most important
interventions that you can do
in psychological,
many
psychiatric
cohorts. So
it's the most powerful intervention
we all have at our fingertips, right?
Yes. And this
one is just a really interesting
experiment where they're able to just change
one variable, which is the amount of
exercise.
Although from zero to
three times a week, I might want to
suggest maybe start with one time a
week, 30 minutes.
What's interesting, too, because they had
to go to a particular place right there
in Hong Kong, an exercise
physiology center, where they had
monitors and all this, and
encouragement and coaches,
they basically say they
were encouraged. They use
that language, right? To do this, to do
this, do this. And they don't give any more
details as far as the compliance.
What did
they use? I don't know.
What kind of rewards? I don't
know. How much do they pay these
volunteers? I don't know.
Well, I think the
really interesting take-home
message is, to dance
through several figures on volcano
plots and significantly up and down
regulated proteins, was Figure
six about the differential changes
being able to distinguish
between responders and non-
responders. And that has a
potential clinical kind of
implications. Sarantis,
you want to comment on Figure 6?
Yeah, I think not
just in this figure, but among
these 23 proteins that
significantly change in responders
versus non-responders, I think they
also mentioned that there are proteins like
TFF-2 that
regulate mucosal gastro-
intestinal immunity.
And this could be a direction through
the interconnection with gut
macrobiome. And
they don't have really
direct data to show this, but
I understand
the fact that identifying regulating genes like
TFF-2, you may
indirectly influence the expression of
immune response genes in
mucosa. And with that reason, you
can regulate, for example, gut
microbiome. That's really important also to
your metabolism. It's really important when
you have to deal with exercise and diabetes.
And you think of the gut
microbiome as a part of
your system, right? Yeah. Here
it is, we're intervening with
exercise. An extra organ.
Exactly. We're intervening with exercise.
And the gut microbiome is
changing, and they're unpicking some
of the biology using this
TFF-2. And then they use
several other molecules, which I thought
was pretty deep.
I'm not an expert on the
microbiome, but they talked
about a prior study where they looked
at exercise intervention.
This is so important. I'm
so glad you brought this up, Sarantis. This
same team out of Dr.
Zhu's lab found
a role for gut
microbiota in conferring the
metabolic benefits of exercise. In other
words, mediating they saw a
signature in
responders of exercise that was specific
in this gut
microbiota, which would suggest and
they talk about this in this paper that
there might be an opportunity in
responders versus non-
responders to do an intervention
with those that you predict to be non-
responders. Do an intervention, see if you
can't nudge the microbiome in a
direction that might make
the body more responsive
to the metabolic benefits of exercise,
which I thought was super
compelling. I mean, it's not easy to
nudge the microbiome, but it is
possible. And so
bringing these two bodies of work
together in a preliminary
way and think about what they might do
in the future, I think is super
fun, super exciting.
Yeah, it's a system.
The particulars on the mechanism
gets super complicated in
terms of exactly how
the effects of the immune system
in the gut microbiome,
exactly how that mechanism works. I'm
sure that's sort of an area of
active interest. But to
think that to be able to get a
say, all right, you
group here with these
of benefiting from high-intensity exercise.
And then, I guess,
thinking about it now ... well,
there are other benefits anyway.
But then the endpoint
was pre-
diabetes. The whole endpoint
was, how do we lower that
risk of diabetes?
Such a big health care burden, right?
Yeah. Any concluding comments
from either of
you? Sarantis? Cindy?
Sarantis, I'll let you go first
if you had closing
ideas. I think,
first of all, the use of a
few biomarkers like
machine learning
algorithm, it's again and again coming
to our attention.
And I think it will really help a
lot of diagnoses for
a lot of diseases from now on. And, of course,
the exercise is, at the end,
the best medicine for a lot of things,
right? There you go. That should be our
final statement right there. I
would just double-click on that.
Exercise is the best
medicine. This particular paper in Cell
Reports Medicine was published just in
February 2023. The title is, "A machine learning
algorithm integrating baseline
serum proteomic signatures predicts
exercise responsiveness in overweight
males with prediabetes." Well,
thank you for joining us today. See you
next time.
Thank you for listening to the
Proteomics in Proximity podcast brought to
you by Olink Proteomics. To
contact the hosts or for further
information, simply email
info@olink.com