🎙️ Episode 91: Genome-wide Association of Plasma N-Glycome Reveals Links to Liver Disease and Anti-Inflammatory Proteins
🧬 In this episode of PaperCast Base by Base, we explore a large-scale genome-wide association study that uncovers novel genetic regulators of plasma protein N-glycosylation and their connections to liver function and inflammatory pathways.
🔍 Study Highlights:
We conducted a meta-analysis of genome-wide association studies on 10,000 individuals from seven cohorts to identify genetic loci influencing plasma N-glycan structures. The study doubled the known glycosylation-associated loci by discovering 16 novel regions and prioritized 13 candidate genes, many of which are highly expressed in liver tissue and immune cells. Integration of glycomics, proteomics, transcriptomics, and Mendelian randomization analyses revealed genetic links between N-glycosylation, metabolic and liver diseases, and anti-inflammatory proteins such as HP, CFH, and SERPINA1. PheWAS and polygenic score analyses demonstrated pleiotropic associations between glycan-related genetic variants and complex traits including metabolic syndrome, liver disease, and immune-related conditions.
🧠 Conclusion:
This study provides a comprehensive resource for understanding the genetic regulation of plasma N-glycosylation and lays the groundwork for developing glycan-based biomarkers and therapeutic targets in liver and immune-related diseases.
📖 Reference:
Sharapov S, Timoshchuk A, Zaytseva O, Maslov DE, Soplenkova A, Elgaeva EE, Tiys ES, Mangino M, Wittenbecher C, Karssen L, Timofeeva M, Nostaeva A, Vuckovic F, Trbojević-Akmačić I, Štambuk T, Feoktistova S, Potapova NA, Voroshilova V, Williams F, Primorac D, Van Zundert J, Georges M, Suhre K, Allegri M, Chaturvedi N, Dunlop M, Schulze MB, Spector T, Tsepilov YA, Lauc G, Aulchenko YS. A genome-wide association study in 10,000 individuals links plasma N-glycome to liver disease and anti-inflammatory proteins. Nat Commun. 2025;16:5525. doi:10.1038/s41467-025-60431-y
📜 License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:14):
Welcome to Base by BASE, the paper cast that brings genomics
to you wherever you are. Imagine this.
More than half of all the proteins circulating in your
blood right now are carrying a kind of secret message.
They're decorated with these intricate sugar structures
called glycans. Think of them like tiny
personalized sugar codes. Now, here's the really
(00:37):
surprising part. What if these hidden codes could
actually offer profound insightsinto your health?
What if they could, say, predictserious conditions like liver
disease or chronic inflammation not just months ahead, but maybe
years, even decades before symptoms really start to show?
It's a truly fascinating area and you know, for a long time
we've seen these links when these sugar structures go wrong.
(00:58):
Aberrink. Glycol oscillation, we call it.
It's clearly linked to a whole host of diseases.
Liver, cardio, metabolic, immuneissues.
The list keeps growing. Right, But the how has been the
mystery. How our own genes actually
direct this whole process? Exactly our understanding of how
genes orchestrate these complex sugar patterns has been, well,
pretty limited. But that's what are changing
now, and that's what this deep dive is all about, how cutting
(01:21):
edge genomic research is finallystarting to crack these hidden
connections. So the big question really is
how could unlocking the genetic blueprint of these tiny sugar
molecules completely revolutionize, you know, our
whole approach to health and personalized medicine?
Today we're really celebrating some trailblazing work by a big
collaborative team. We're talking researchers like
(01:43):
Sabo Sharapov and A Timosh Chuk,Yuriya Solchenko and Gordon
Loaf. They come from great
institutions, the MSU Institute for Artificial Intelligence,
Genus Glycoscience Research Laboratory, King's College
London, a real international effort.
Yes, their work has really pushed forward our understanding
of how genes regulate this process, plasma and
(02:03):
glycosylation, and crucially, how it ties into major diseases.
OK, so let's zoom in on that term N glycosylation.
What exactly is it? Right.
So think of it as a critical post production step for
proteins, after your DNA tells the cell how to build a protein.
N glycosylation is where these complex carbohydrates, the
glycans, get precisely attached.And they're not just, you know,
(02:24):
sprinkled on randomly. They're specific structures,
covalently bonded. It's incredibly common too.
Over half the proteins in your blood plasma have these N
glycans. Wow, over half.
That includes some really important ones.
Right. Oh, absolutely.
We're talking essential players.Enzymes, hormones, antibodies,
receptors. Basically like a lot of the
workhorses that keep your body running smoothly.
(02:46):
So these sugar tags, they're notjust decoration then, they're
actually fundamental to how the protein works.
Precisely. Glycans can dramatically change
a protein's physical properties,its stability, how it folds, who
it interacts with, fundamentallyaltering its biological
function. For normal Physiology, you need
adequate glycosylation. It's essential.
(03:07):
But when it goes awry, this aberrant glycosylation, that's
when we see it implicated in so many diseases.
Like what kinds of diseases? Well, everything from cancer and
diabetes to inflammatory conditions and autoimmune
disorders, which of course makesthese glycans really
interesting. They're potential therapeutic
targets and maybe even more immediately, potentially
valuable biomarkers for early detection.
(03:29):
That makes a lot of sense, but if it's so important, why has it
been so hard to study genetically?
I mean, genes code for proteins directly, right?
This feels one step removed. Is it more complicated?
That's a great way to put it. It is more complicated.
Unlike proteins, where the primary amino acid sequence is
directly read from your DNA, thespecific patterns and amounts of
(03:51):
these N glycans are not directlyencoded.
Instead they emerge from this really complex interplay.
Think of a whole network of enzymes that build and trim the
glycans. The availability of the sugar
building blocks themselves othercellular processes.
Almost like an assembly line with lots of optional steps.
Kind of, yeah. And all of this is influenced by
your underlying genetics, sure, but also epigenetic factors,
(04:14):
your environment, your diet. It's a whole system.
It's definitely not a simple DNAto glycan blueprint.
Fascinating complexity. So given that, where do these N
glycosylated proteins we find inthe blood actually come from?
Are there specific factories? Primarily, yes.
The liver is a major source, andB cells, which are key immune
cells found in your lymphoid tissues, are the other big
(04:36):
producers of the glycoprotein circulating in your blood.
OK, so liver and B cells? Right, which means the N glycan
patterns we measure in a blood sample act like a really
important mirror. They reflect the health and
activity of those two critical tissues.
So understanding how glycosylation is regulated gives
us insights into liver diseases,B cell related conditions, but
also broader things like cardiometabolic and inflammatory
(04:59):
diseases. And before this study, we only
had a partial picture. A very partial picture.
Previous GWS, those genome wide scans had found maybe 15 to 19
genetic regions linked to N glycans.
But a really comprehensive map of the specific gene regulators
in health and disease that was missing, and that's the gap this
study aimed to fill. All right, So let's talk about
(05:20):
how they tackle this massive challenge.
The scale here is just remarkable.
This was the largest ever genomewide association study
specifically looking at N glycosylation of the entire
blood plasma proteome. And the numbers, over 10,000
people, right, 10,764 participants from 7
international cohorts. That's just huge.
(05:41):
It's an unprecedented scale for this kind of work.
And to handle it, they didn't just look at one or two lichens.
They meticulously measured 36 directly observed and glycan
structures. And then using those direct
measurements, they computationally derived another
81 related traits. So in total they had 117
different glycan traits, each representing different facets of
(06:01):
these complex biosynthesis pathways.
OK, one. 117 glycan traits. How do they connect that level
of detail back to our DNA? How do you map sugar patterns to
genes? They use a really powerful
technique, A genomewide Association meta analysis, or
DWAMA. Essentially, it lets you combine
genetic data from multiple studies, multiple cohorts, which
dramatically boosts your statistical power.
(06:22):
So you can find smaller effects,subtler links.
Exactly, they range dwama for single traits looking at genetic
links to each of the 117 glycan traits individually and also
multi trait GWS which is clever because it can spot genetic
variants that influence several glycan traits at once, maybe
pointing to genes acting earlierin the pathway.
(06:43):
But finding a genetic region isn't the same as finding this
specific gene responsible, right?
How did they pinpoint the actualculprits?
No, it's not. And that's where their gene
prioritization strategy was really sophisticated.
They didn't just stop at the broad location, they integrated
8 different lines of evidence toreally zoom in on the most
likely causal genes. 8 lines like what?
(07:04):
Well, things like checking if the gene encoded a known
glycosylation enzyme or if it was linked to rare congenital
glycosylation disorders. But crucially, they used
colloquialization analysis. Colloquialization.
What's that check looking for? It's looking for overlaps.
Does the same genetic variant that affects A glycan level also
affect the expression level of anearby gene that's an EQTL, or
(07:25):
the level of the protein that gene produces of PQTL?
Ah, so if the signals for glycanlevels, gene expression, and
protein levels all peak at the exact same genetic spot.
Bingo. It strongly suggests that
variant is driving all three through that specific gene.
They also looked at detailed variant annotation.
Does the variant actually changethe protein?
(07:46):
This multi pronged approach gavethem much higher confidence in
naming the likely causal genes. OK.
That's some serious genetic detective work.
So they found these genes linkedto glycans.
How did they then connect that to actual human health and
disease? How do you make that leap?
That's the critical next step. They used several advanced
methods. 1st a phenom wide association study or fiwas.
(08:09):
Phenom wide so looking across thousands of traits.
Exactly. They checked if the genetic
variants they found linked to glycans also showed associations
with over 1000 different diseases and quantitative
traits, basically scanning across the entire human
phenomena for connections. Second, they calculated
polygenic scores for the N glycan traits.
Polygenic scores remind me what those are.
(08:30):
Think of it as a single score summarizing someones overall
genetic tendency towards a particular glycan pattern based
on contributions from many many genetic variants across the
genome. OK.
They then took these glycan and polygenic scores and tested if
they were associated with diagnosed diseases using I CD10
codes in that huge UK Biobank data set, nearly 374,000 people.
(08:54):
That gives you real world disease relevance.
Wow. OK.
And 3rd and this is really important for getting at cause
and effect, they used bidirectional Mendelian
randomization or? Or MSN Onndelian randomization
that uses genes as sort of natural experiments, right?
Precisely. It helps you infer causality.
Does changing the glycan level caused the disease, or does
having the disease cause changesin the glycan levels?
(09:15):
Or is there just some shared underlying factor?
Mr. helps untangle that. Right.
So after deploying all this methodology, this huge scale,
these clever analysis, what did they actually find?
What are the headline results? The biggest headline They more
than doubled the number of knowngenetic loci associated with
blood and glycosylation. They found 16 entirely new
(09:37):
genomic regions linked to these sugar patterns. 16 new ones.
Yes, and from those, they prioritize 13 novel candidate
genes that likely play a causal role.
This is a massive leap in our fundamental understanding of how
this process is genetically controlled.
That really is a huge jump. Can you give us some examples of
these newly implicated genes? Sure, some of the key new
(09:57):
players include genes like GCKR,Trip One, HP, Serpent 1, and
CFH. What's really striking is that
many of these are primarily active in the liver.
Connecting back to the liver being a major source.
Exactly, and this work established really for the first
time a direct genetic link between plasma and
glycosylation, metabolic diseases and liver health.
(10:17):
For instance, common genetic variants near GCKR and TRIG 1
genes we already knew increased risk for fatty liver disease
MASLD while those variants colloquialize with glycan
levels. Meaning the same genetic
variations influence both fatty liver risk and the sugar codes.
Precisely, it suggests a shared pathway.
Same for Serpano one which is linked to A1 antitrypsin
(10:39):
deficiency and liver problems. Again, a direct genetic link to
glycan patterns emerged. OK, so a clear genetic bridge
between glycans and liver health.
You also mentioned anti-inflammatory proteins.
Yes, another key finding. They linked 4 novel genes that
code for important anti-inflammatory proteins, HP,
heptoglobin, HPR, heptoglobin related protein serpin A1 again
(11:02):
and CFH complement factor H directly to north glycosylation.
And crucially the Mendelian minimization showed causality.
Variations in the levels of proteins like CFH and HP were
shown to causally influence their own and glycan structures.
It's not just correlation. The amount of protein directly
impacts its sugar decorations. That's quite a direct feedback
loop. It is, and maybe one of the most
(11:23):
profound insights was the tissuespecificity they uncovered.
The genetic control of plasma and glycosylation isn't uniform
across the body. It's high, highly specific,
primarily happening in lymphoid tissue, so B cells and liver
tissue, hepatocytes. So the genetic switches are
different in different places. Even for the same gene, take
glycosil transferase genes like FUT 8 and FUT 6.
(11:45):
They are expressed in both liverand B cells.
They help attach sugars in both places, but the specific genetic
variants controlling their impact on end glycosylation were
different in each tissue. It's like having the same tool
but different instruction manuals for how to use it
depending on whether you're in the liver workshop or the B cell
workshop. This level of tissue specific
genetic regulation for glycosylation wasn't really
(12:08):
appreciated before. That's incredibly detailed.
So bringing you back to health outcomes, how did these
molecular findings translate? What did the polygenic scores
show for actual diseases? Well, that part was tracking
too. Polygenic scores for having
higher levels of high Mannos, glycans, things like M9, M total
were positively associated with several cardiovascular diseases,
(12:28):
essential hypertension, ischemicheart disease, angina, high
lipids. So more high Mannos, higher
cardiovascular risk. That's what the association
suggests. Also linked positively to type 1
diabetes and asthma, but then conversely, scores for higher
levels of galactosylated glycans.
Different sugar structures were negatively associated with
obesity, lipoprotein disorders, primary hypertension, and type 2
(12:50):
diabetes. So the type of glycan pattern
really matters for disease risk association, and the Mendelian
randomization added that causal layer.
For instance, they found evidence that disorders of
lipoprotein metabolism, basically problems processing
fats, actually causally increasethose hymenoglycans M9 and M
total. So the metabolic problem
directly drives changes in thosespecific sugars.
(13:10):
That's what the Mr. suggests, yes.
It could be a consequence of thedisrupted lipid processing, or
maybe even contribute to it. They also found a causal effect
of increased M6 glycan on asthmarisk.
Specifically M6 on asthma, Yes. And another interesting liver
link, certain glycan patterns, specifically higher a 3G3S3 and
lower antennaary fucosylated glycans were associated with
(13:31):
lower levels of AST, which is a marker of liver cell damage.
This hints these glycans might play a protective role in liver
Physiology. OK, let's try to synthesize
this. This study really blows open our
understanding of the genetic playbook controlling our plasma
and glycan. And crucially, it draws these
direct genetic lines connecting glycan patterns to liver disease
(13:52):
and inflammation for the first time.
Right. And it's not just drawing lines,
it's suggesting shared mechanisms.
When you see glycans linked to GCKR and Tribe one, it means the
genetic risk for fatty liver disease is biologically
intertwined with how proteins get glycosylated and elevated
Hymanos glycans. That might reflect problems
inside the cell, like in the Golgi apparatus where proteins
(14:14):
get processed. Or maybe it reflects higher
levels of certain carrier proteins like APO B100, which
itself is tied to lipid disorders and heart disease.
It gives us clues about the underlying biology.
So this opens up real possibilities for the future,
doesn't it? For diagnostics, maybe even
therapies. Absolutely.
This knowledge is fuel for discovering new glycan based
biomarkers. You can't imagine blood tests
(14:35):
looking at specific sugar codes to predict disease risk much
earlier, or track how someone's responding to treatment.
And beyond biomarkers, it pointstowards new research avenues.
If we know which genes control specific glycan features linked
to disease, we can investigate those pathways much more
closely. That could potentially lead to
new therapeutic targets, ways tointervene, and maybe correct
(14:56):
problematic glycosylation. And that tissue specificity
finding seems critical for future work, too.
It's fundamental. Knowing that the genetic
regulation is different in the liver versus B cells, even for
the same enzymes, is huge. It tells us we need to think
about these processes in a very context specific way.
It's a much more nuanced picturethan we had before.
Of course, no study is perfect. Were there limitations or areas
(15:19):
where more research is clearly needed?
Sure. While the study was massive,
they noted statistical power wasstill a limitation for
establishing causal links for everything.
For example, they didn't see strong causal evidence linking
glycans to some other common liver enzymes like ALTALPGGT or
NAFLD specifically. That might just need even larger
(15:39):
studies or different approaches.And it highlights the need for
future work to look at the glycosylation of individual
proteins, not just the whole mixin the plasma.
That would give even finer detail.
Plus, there's potential for computational methods to sort of
deconvolve the bulk data we havenow.
So wrapping this up, the big take home message is what in a
nutshell, this massive study revealed that these complex
(16:00):
sugar structures on our blood proteins, the N glycans, are
under surprisingly strong and very tissue specific genetic
control. And crucially, it established
clear genetic links between these glycans and major health
issues like metabolic disorders,liver disease and inflammation.
Right. And what that really means is
your genes don't just decide which proteins you make, but
(16:22):
also provide instructions for how they get decorated with
these vital sugar molecules. It's a whole other layer of
biological information, and unlocking that layer opens up
totally new possibilities for biomarkers, predicting risk
earlier, and potentially for guiding new therapies by
understanding and maybe even manipulating these intricate
sugar codes. It's quite something to think
(16:43):
about a future where a simple test of your sugar code could
give early warnings or guide personalized treatments.
That's the direction this pointstowards.
A fascinating look at a hidden layer of our biology.
What might your own sugar code reveal about your health journey
ahead? Definitely food for thought.
This episode was based on an Open Access article under the
(17:03):
CCBY 4 Point O license. You can find a direct link to
the paper and the license in ourepisode description.
If you enjoyed this analysis, the best way to support Base by
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show. Thanks for listening and join us
(17:23):
next time as we explore more science base by Base.
Welcome to Base by BASE, the paper cast that brings genomics
to you wherever you are. Imagine this.
More than half of all the proteins circulating in your
blood right now are carrying a kind of secret message.
They're decorated with these intricate sugar structures
called glycans. Think of them like tiny
personalized sugar codes. Now, here's the really
(00:37):
surprising part. What if these hidden codes could
actually offer profound insightsinto your health?
What if they could, say, predictserious conditions like liver
disease or chronic inflammation not just months ahead, but maybe
years, even decades before symptoms really start to show?
It's a truly fascinating area and you know, for a long time
we've seen these links when these sugar structures go wrong.
(00:58):
Aberrink. Glycol oscillation, we call it.
It's clearly linked to a whole host of diseases.
Liver, cardio, metabolic, immuneissues.
The list keeps growing. Right, But the how has been the
mystery. How our own genes actually
direct this whole process? Exactly our understanding of how
genes orchestrate these complex sugar patterns has been, well,
pretty limited. But that's what are changing
now, and that's what this deep dive is all about, how cutting
(01:21):
edge genomic research is finallystarting to crack these hidden
connections. So the big question really is
how could unlocking the genetic blueprint of these tiny sugar
molecules completely revolutionize, you know, our
whole approach to health and personalized medicine?
Today we're really celebrating some trailblazing work by a big
collaborative team. We're talking researchers like
(01:43):
Sabo Sharapov and A Timosh Chuk,Yuriya Solchenko and Gordon
Loaf. They come from great
institutions, the MSU Institute for Artificial Intelligence,
Genus Glycoscience Research Laboratory, King's College
London, a real international effort.
Yes, their work has really pushed forward our understanding
of how genes regulate this process, plasma and
(02:03):
glycosylation, and crucially, how it ties into major diseases.
OK, so let's zoom in on that term N glycosylation.
What exactly is it? Right.
So think of it as a critical post production step for
proteins, after your DNA tells the cell how to build a protein.
N glycosylation is where these complex carbohydrates, the
glycans, get precisely attached.And they're not just, you know,
(02:24):
sprinkled on randomly. They're specific structures,
covalently bonded. It's incredibly common too.
Over half the proteins in your blood plasma have these N
glycans. Wow, over half.
That includes some really important ones.
Right. Oh, absolutely.
We're talking essential players.Enzymes, hormones, antibodies,
receptors. Basically like a lot of the
workhorses that keep your body running smoothly.
(02:46):
So these sugar tags, they're notjust decoration then, they're
actually fundamental to how the protein works.
Precisely. Glycans can dramatically change
a protein's physical properties,its stability, how it folds, who
it interacts with, fundamentallyaltering its biological
function. For normal Physiology, you need
adequate glycosylation. It's essential.
(03:07):
But when it goes awry, this aberrant glycosylation, that's
when we see it implicated in so many diseases.
Like what kinds of diseases? Well, everything from cancer and
diabetes to inflammatory conditions and autoimmune
disorders, which of course makesthese glycans really
interesting. They're potential therapeutic
targets and maybe even more immediately, potentially
valuable biomarkers for early detection.
(03:29):
That makes a lot of sense, but if it's so important, why has it
been so hard to study genetically?
I mean, genes code for proteins directly, right?
This feels one step removed. Is it more complicated?
That's a great way to put it. It is more complicated.
Unlike proteins, where the primary amino acid sequence is
directly read from your DNA, thespecific patterns and amounts of
(03:51):
these N glycans are not directlyencoded.
Instead they emerge from this really complex interplay.
Think of a whole network of enzymes that build and trim the
glycans. The availability of the sugar
building blocks themselves othercellular processes.
Almost like an assembly line with lots of optional steps.
Kind of, yeah. And all of this is influenced by
your underlying genetics, sure, but also epigenetic factors,
(04:14):
your environment, your diet. It's a whole system.
It's definitely not a simple DNAto glycan blueprint.
Fascinating complexity. So given that, where do these N
glycosylated proteins we find inthe blood actually come from?
Are there specific factories? Primarily, yes.
The liver is a major source, andB cells, which are key immune
cells found in your lymphoid tissues, are the other big
(04:36):
producers of the glycoprotein circulating in your blood.
OK, so liver and B cells? Right, which means the N glycan
patterns we measure in a blood sample act like a really
important mirror. They reflect the health and
activity of those two critical tissues.
So understanding how glycosylation is regulated gives
us insights into liver diseases,B cell related conditions, but
also broader things like cardiometabolic and inflammatory
(04:59):
diseases. And before this study, we only
had a partial picture. A very partial picture.
Previous GWS, those genome wide scans had found maybe 15 to 19
genetic regions linked to N glycans.
But a really comprehensive map of the specific gene regulators
in health and disease that was missing, and that's the gap this
study aimed to fill. All right, So let's talk about
(05:20):
how they tackle this massive challenge.
The scale here is just remarkable.
This was the largest ever genomewide association study
specifically looking at N glycosylation of the entire
blood plasma proteome. And the numbers, over 10,000
people, right, 10,764 participants from 7
international cohorts. That's just huge.
(05:41):
It's an unprecedented scale for this kind of work.
And to handle it, they didn't just look at one or two lichens.
They meticulously measured 36 directly observed and glycan
structures. And then using those direct
measurements, they computationally derived another
81 related traits. So in total they had 117
different glycan traits, each representing different facets of
(06:01):
these complex biosynthesis pathways.
OK, one. 117 glycan traits. How do they connect that level
of detail back to our DNA? How do you map sugar patterns to
genes? They use a really powerful
technique, A genomewide Association meta analysis, or
DWAMA. Essentially, it lets you combine
genetic data from multiple studies, multiple cohorts, which
dramatically boosts your statistical power.
(06:22):
So you can find smaller effects,subtler links.
Exactly, they range dwama for single traits looking at genetic
links to each of the 117 glycan traits individually and also
multi trait GWS which is clever because it can spot genetic
variants that influence several glycan traits at once, maybe
pointing to genes acting earlierin the pathway.
(06:43):
But finding a genetic region isn't the same as finding this
specific gene responsible, right?
How did they pinpoint the actualculprits?
No, it's not. And that's where their gene
prioritization strategy was really sophisticated.
They didn't just stop at the broad location, they integrated
8 different lines of evidence toreally zoom in on the most
likely causal genes. 8 lines like what?
(07:04):
Well, things like checking if the gene encoded a known
glycosylation enzyme or if it was linked to rare congenital
glycosylation disorders. But crucially, they used
colloquialization analysis. Colloquialization.
What's that check looking for? It's looking for overlaps.
Does the same genetic variant that affects A glycan level also
affect the expression level of anearby gene that's an EQTL, or
(07:25):
the level of the protein that gene produces of PQTL?
Ah, so if the signals for glycanlevels, gene expression, and
protein levels all peak at the exact same genetic spot.
Bingo. It strongly suggests that
variant is driving all three through that specific gene.
They also looked at detailed variant annotation.
Does the variant actually changethe protein?
(07:46):
This multi pronged approach gavethem much higher confidence in
naming the likely causal genes. OK.
That's some serious genetic detective work.
So they found these genes linkedto glycans.
How did they then connect that to actual human health and
disease? How do you make that leap?
That's the critical next step. They used several advanced
methods. 1st a phenom wide association study or fiwas.
(08:09):
Phenom wide so looking across thousands of traits.
Exactly. They checked if the genetic
variants they found linked to glycans also showed associations
with over 1000 different diseases and quantitative
traits, basically scanning across the entire human
phenomena for connections. Second, they calculated
polygenic scores for the N glycan traits.
Polygenic scores remind me what those are.
(08:30):
Think of it as a single score summarizing someones overall
genetic tendency towards a particular glycan pattern based
on contributions from many many genetic variants across the
genome. OK.
They then took these glycan and polygenic scores and tested if
they were associated with diagnosed diseases using I CD10
codes in that huge UK Biobank data set, nearly 374,000 people.
(08:54):
That gives you real world disease relevance.
Wow. OK.
And 3rd and this is really important for getting at cause
and effect, they used bidirectional Mendelian
randomization or? Or MSN Onndelian randomization
that uses genes as sort of natural experiments, right?
Precisely. It helps you infer causality.
Does changing the glycan level caused the disease, or does
having the disease cause changesin the glycan levels?
(09:15):
Or is there just some shared underlying factor?
Mr. helps untangle that. Right.
So after deploying all this methodology, this huge scale,
these clever analysis, what did they actually find?
What are the headline results? The biggest headline They more
than doubled the number of knowngenetic loci associated with
blood and glycosylation. They found 16 entirely new
(09:37):
genomic regions linked to these sugar patterns. 16 new ones.
Yes, and from those, they prioritize 13 novel candidate
genes that likely play a causal role.
This is a massive leap in our fundamental understanding of how
this process is genetically controlled.
That really is a huge jump. Can you give us some examples of
these newly implicated genes? Sure, some of the key new
(09:57):
players include genes like GCKR,Trip One, HP, Serpent 1, and
CFH. What's really striking is that
many of these are primarily active in the liver.
Connecting back to the liver being a major source.
Exactly, and this work established really for the first
time a direct genetic link between plasma and
glycosylation, metabolic diseases and liver health.
(10:17):
For instance, common genetic variants near GCKR and TRIG 1
genes we already knew increased risk for fatty liver disease
MASLD while those variants colloquialize with glycan
levels. Meaning the same genetic
variations influence both fatty liver risk and the sugar codes.
Precisely, it suggests a shared pathway.
Same for Serpano one which is linked to A1 antitrypsin
(10:39):
deficiency and liver problems. Again, a direct genetic link to
glycan patterns emerged. OK, so a clear genetic bridge
between glycans and liver health.
You also mentioned anti-inflammatory proteins.
Yes, another key finding. They linked 4 novel genes that
code for important anti-inflammatory proteins, HP,
heptoglobin, HPR, heptoglobin related protein serpin A1 again
(11:02):
and CFH complement factor H directly to north glycosylation.
And crucially the Mendelian minimization showed causality.
Variations in the levels of proteins like CFH and HP were
shown to causally influence their own and glycan structures.
It's not just correlation. The amount of protein directly
impacts its sugar decorations. That's quite a direct feedback
loop. It is, and maybe one of the most
(11:23):
profound insights was the tissuespecificity they uncovered.
The genetic control of plasma and glycosylation isn't uniform
across the body. It's high, highly specific,
primarily happening in lymphoid tissue, so B cells and liver
tissue, hepatocytes. So the genetic switches are
different in different places. Even for the same gene, take
glycosil transferase genes like FUT 8 and FUT 6.
(11:45):
They are expressed in both liverand B cells.
They help attach sugars in both places, but the specific genetic
variants controlling their impact on end glycosylation were
different in each tissue. It's like having the same tool
but different instruction manuals for how to use it
depending on whether you're in the liver workshop or the B cell
workshop. This level of tissue specific
genetic regulation for glycosylation wasn't really
(12:08):
appreciated before. That's incredibly detailed.
So bringing you back to health outcomes, how did these
molecular findings translate? What did the polygenic scores
show for actual diseases? Well, that part was tracking
too. Polygenic scores for having
higher levels of high Mannos, glycans, things like M9, M total
were positively associated with several cardiovascular diseases,
(12:28):
essential hypertension, ischemicheart disease, angina, high
lipids. So more high Mannos, higher
cardiovascular risk. That's what the association
suggests. Also linked positively to type 1
diabetes and asthma, but then conversely, scores for higher
levels of galactosylated glycans.
Different sugar structures were negatively associated with
obesity, lipoprotein disorders, primary hypertension, and type 2
(12:50):
diabetes. So the type of glycan pattern
really matters for disease risk association, and the Mendelian
randomization added that causal layer.
For instance, they found evidence that disorders of
lipoprotein metabolism, basically problems processing
fats, actually causally increasethose hymenoglycans M9 and M
total. So the metabolic problem
directly drives changes in thosespecific sugars.
(13:10):
That's what the Mr. suggests, yes.
It could be a consequence of thedisrupted lipid processing, or
maybe even contribute to it. They also found a causal effect
of increased M6 glycan on asthmarisk.
Specifically M6 on asthma, Yes. And another interesting liver
link, certain glycan patterns, specifically higher a 3G3S3 and
lower antennaary fucosylated glycans were associated with
(13:31):
lower levels of AST, which is a marker of liver cell damage.
This hints these glycans might play a protective role in liver
Physiology. OK, let's try to synthesize
this. This study really blows open our
understanding of the genetic playbook controlling our plasma
and glycan. And crucially, it draws these
direct genetic lines connecting glycan patterns to liver disease
(13:52):
and inflammation for the first time.
Right. And it's not just drawing lines,
it's suggesting shared mechanisms.
When you see glycans linked to GCKR and Tribe one, it means the
genetic risk for fatty liver disease is biologically
intertwined with how proteins get glycosylated and elevated
Hymanos glycans. That might reflect problems
inside the cell, like in the Golgi apparatus where proteins
(14:14):
get processed. Or maybe it reflects higher
levels of certain carrier proteins like APO B100, which
itself is tied to lipid disorders and heart disease.
It gives us clues about the underlying biology.
So this opens up real possibilities for the future,
doesn't it? For diagnostics, maybe even
therapies. Absolutely.
This knowledge is fuel for discovering new glycan based
biomarkers. You can't imagine blood tests
(14:35):
looking at specific sugar codes to predict disease risk much
earlier, or track how someone's responding to treatment.
And beyond biomarkers, it pointstowards new research avenues.
If we know which genes control specific glycan features linked
to disease, we can investigate those pathways much more
closely. That could potentially lead to
new therapeutic targets, ways tointervene, and maybe correct
(14:56):
problematic glycosylation. And that tissue specificity
finding seems critical for future work, too.
It's fundamental. Knowing that the genetic
regulation is different in the liver versus B cells, even for
the same enzymes, is huge. It tells us we need to think
about these processes in a very context specific way.
It's a much more nuanced picturethan we had before.
Of course, no study is perfect. Were there limitations or areas
(15:19):
where more research is clearly needed?
Sure. While the study was massive,
they noted statistical power wasstill a limitation for
establishing causal links for everything.
For example, they didn't see strong causal evidence linking
glycans to some other common liver enzymes like ALTALPGGT or
NAFLD specifically. That might just need even larger
(15:39):
studies or different approaches.And it highlights the need for
future work to look at the glycosylation of individual
proteins, not just the whole mixin the plasma.
That would give even finer detail.
Plus, there's potential for computational methods to sort of
deconvolve the bulk data we havenow.
So wrapping this up, the big take home message is what in a
nutshell, this massive study revealed that these complex
(16:00):
sugar structures on our blood proteins, the N glycans, are
under surprisingly strong and very tissue specific genetic
control. And crucially, it established
clear genetic links between these glycans and major health
issues like metabolic disorders,liver disease and inflammation.
Right. And what that really means is
your genes don't just decide which proteins you make, but
(16:22):
also provide instructions for how they get decorated with
these vital sugar molecules. It's a whole other layer of
biological information, and unlocking that layer opens up
totally new possibilities for biomarkers, predicting risk
earlier, and potentially for guiding new therapies by
understanding and maybe even manipulating these intricate
sugar codes. It's quite something to think
(16:43):
about a future where a simple test of your sugar code could
give early warnings or guide personalized treatments.
That's the direction this pointstowards.
A fascinating look at a hidden layer of our biology.
What might your own sugar code reveal about your health journey
ahead? Definitely food for thought.
This episode was based on an Open Access article under the
(17:03):
CCBY 4 Point O license. You can find a direct link to
the paper and the license in ourepisode description.
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