# Gene expression regulation by RNA modifications in cancer

February 14, 2023## Information

Yale Cancer Center Grand Rounds | February 14, 2023

Presented by: Dr. Sigrid Nachtergaele

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- 00:00First of all, I'd like to thank you all
- 00:02for attending our grand rounds today.
- 00:05It is my pleasure to present this special
- 00:09event, which is sponsored by the Chenevert
- 00:14family brain Tumor Center at Yale.
- 00:17And today we have the pleasure to
- 00:21welcome Doctor Sigrid Natural.
- 00:23And. She isn't an assistant
- 00:26professor of molecular,
- 00:27cellular and developmental biology.
- 00:30She received her PhD from Stanford
- 00:33University and during her graduate work
- 00:35in the laboratory of Rajat Rochetti,
- 00:38she developed novel chemical tools
- 00:40to study the Hedgehog signaling,
- 00:41uncovering new modes of steroid
- 00:43mediated regulation of this
- 00:45critical developmental pathway.
- 00:47She was a postdoc fellow which
- 00:48run hay at University of Chicago,
- 00:50where she set out to identify
- 00:53novel chemical modifications in M
- 00:55RNA and uncovered their functions.
- 00:57Her lifestyle is working on numerous
- 00:59aspects of RNA mediated regulation
- 01:01of cell signaling.
- 01:02And their current focus is on
- 01:04uncovering the regulation and function
- 01:06of chemical modifications on RNA.
- 01:08She was awarded a demo reunion postdoc
- 01:11Fellowship and subsequently a demo reunion.
- 01:14They fray award for breakthrough
- 01:16scientists for this work.
- 01:18And more recently,
- 01:19she was awarded the Distinguished Scientist
- 01:23Award from the Sontag Foundation in
- 01:28in to reward her work in brain tumors.
- 01:32So please welcome.
- 01:41Thank you so much for having me.
- 01:45Look, I need a phone book
- 01:46behind this podium. One second.
- 01:50Get rid of this.
- 01:53All right. Thank you so much for having me.
- 01:55And I'm really, really thrilled to
- 01:58be here particularly because of,
- 01:59I think part of the reason this came
- 02:02about is that because of the santag
- 02:04word application that I was submitting,
- 02:06I guess a couple of years ago now,
- 02:08Doctor Romero kindly not only
- 02:09answered my e-mail, but even agreed
- 02:11to chat over zoom for for a bit,
- 02:13which you know was incredibly generous
- 02:15as I prepared for for that application.
- 02:17And So what I'm excited to share
- 02:19with you today is a little bit about
- 02:20what our lab is doing with respect
- 02:22to gene expression regulation.
- 02:23By Arna modifications and I'm going to
- 02:26try to use the cursor on the screen so
- 02:29that the folks on zoom can also follow along.
- 02:33Now to start with,
- 02:34I don't really have any disclosures,
- 02:36but I will perhaps start with
- 02:37a bit of a disclaimer.
- 02:39You may have already realized that I'm a
- 02:41basic scientist, I am not a clinician.
- 02:44And you know, while I think,
- 02:47you know,
- 02:47our work is really rooted in sort of
- 02:49fundamental biological questions,
- 02:51I do consider myself sort of cancer
- 02:53biology adjacent.
- 02:54And I have been for quite a while.
- 02:56So my PhD work focused on the
- 02:58Hedgehog signaling pathway.
- 02:59So I sort of became quite familiar
- 03:01with the sort of hedgehog.
- 03:02Even medulloblastoma field and
- 03:03then during my postdoc I sort of
- 03:06collaborated with a few different
- 03:07cancer biologists sort of uncovered the
- 03:09roles of RNA modifications and cancer.
- 03:11So you know while we are very much
- 03:14a fundamental basic science lab,
- 03:16I,
- 03:16I really do not only enjoy but
- 03:18really sort of motivate the lab to
- 03:20think about sort of the applications
- 03:22and implications of our work for
- 03:24for cancer biology.
- 03:26But all of that being said,
- 03:27I rely a lot on collaborators and sort of
- 03:30dedicated dedicated clinicians to sort of.
- 03:32Would have you know helped motivate
- 03:33our and drive our work forward
- 03:35in in that respect.
- 03:36So today I'll give you a little bit of
- 03:38an introduction on our new modifications.
- 03:41It's not always sort of the the
- 03:43first thing you think about when
- 03:45it comes to cancer biology.
- 03:46So I will then sort of segue directly
- 03:49into some of the work that's already been
- 03:52done on RNA modifications and cancer.
- 03:54And then I'm going to describe
- 03:56actually the current limitations of
- 03:58a lot of that work including my own
- 04:00and and try to describe for you the
- 04:01sort of new approaches that the lab
- 04:03is taking to try to sort of improve
- 04:05our ability to really understand how
- 04:07these chemical marks might actually
- 04:09sort of regulate gene expression
- 04:11and how we might actually be able
- 04:13to translate that into the clinic.
- 04:16And so with that,
- 04:17I'll just dive right in.
- 04:18And I think,
- 04:19you know,
- 04:19when we think of the central dogma
- 04:21in terms of its sort of,
- 04:23you know,
- 04:24the fund,
- 04:24the foundations of gene expression
- 04:26regulation,
- 04:26this probably looks quite familiar.
- 04:28This is what I teach the
- 04:29undergraduates that I teach
- 04:30biochemistry over on the main campus.
- 04:32And so when we think about
- 04:33gene expression regulation,
- 04:34really what we're thinking about
- 04:35is the flow of information from
- 04:37the genome and DNA through RNA
- 04:39molecules and then on to proteins.
- 04:40But of course, you know,
- 04:42this is a rather simplified view and of
- 04:44course you know DNA needs to be replicated.
- 04:47We know that a lot of interesting
- 04:49reverse transcript cases exist to
- 04:51sort of move back from RNA to DNA
- 04:53and then of course protein based
- 04:54enzymes and factors of course
- 04:56regulate this at many steps.
- 04:58But on top of that, you know,
- 05:00even within each of these processes,
- 05:01of course there are many
- 05:03sort of intervening steps.
- 05:04And so as an RA biologist,
- 05:05I'm going to bias your view
- 05:07and say that the RNA,
- 05:08so to sort of protein transition
- 05:09is the most interesting.
- 05:10But of course some of my basic
- 05:12biology friends will disagree with me.
- 05:14But that being said, you know,
- 05:15even just to go from a functional
- 05:18MRI MRA molecule to a protein,
- 05:20we have to undergo many intermediate
- 05:22steps including things like capping,
- 05:24splicing, processing of other types,
- 05:27then translation into protein
- 05:28and then eventually.
- 05:30RNA decay.
- 05:31And as you know,
- 05:33you have probably already quite
- 05:35familiar at each of these steps DNA,
- 05:37RNA and protein.
- 05:38There are many different chemical marks
- 05:40that can regulate these processes.
- 05:41This is sort of a fundamental paradigm of of,
- 05:44you know,
- 05:45signaling and gene expression regulation.
- 05:48And so we know for instance that
- 05:49DNA can be methylated and that
- 05:51this is sort of a critical,
- 05:53you know,
- 05:54regulator of gene expression at
- 05:56the level of the genomic DNA.
- 05:58And then all the way on the other
- 06:00side we of course know that enzymes.
- 06:02Think of things like kinases,
- 06:04of course phosphorylated and modified,
- 06:06with many other post translational
- 06:08modifications that really dictate
- 06:09their activity and function,
- 06:11and in some cases localization.
- 06:13What some people are sometimes
- 06:15less familiar with is that in
- 06:17fact RNA molecules are also very
- 06:19heavily chemically modified.
- 06:20And this can sometimes be a little
- 06:22bit counterintuitive because when
- 06:24we think of our name molecules
- 06:26and particularly M RNA,
- 06:28we think of very transient
- 06:30sort of chemical molecules.
- 06:31So why would you sort of want to
- 06:33fine tune something that is such,
- 06:35you know,
- 06:35such a transient molecule in nature,
- 06:37but it turns out that RNA actually
- 06:40carries over 150 now known RNA.
- 06:43Qualifications,
- 06:44and I'm showing you just a
- 06:46very small subset of them here,
- 06:48but these range from everything from
- 06:50the addition of methylation marks to
- 06:53isomerization ins around different bonds.
- 06:56And you know.
- 06:57On the other end,
- 06:59we have sort of double methylations,
- 07:01but this is actually a very
- 07:02sort of limited view.
- 07:03And there's all kinds of incredible
- 07:05chemistry that can happen on
- 07:07RNA and particularly T RNA's
- 07:08to regulate gene expression.
- 07:11And so if we sort of overlay
- 07:13this on what
- 07:14we actually encounter in the cell,
- 07:16which is not a very linear pathway,
- 07:18we get a very complex picture like
- 07:21this where we have modifications that
- 07:23are in fact regulating literally every
- 07:26step of an M RNA during its life cycle.
- 07:28And so many of these chemical
- 07:30modifications are added,
- 07:31sort of as the RNA is being made.
- 07:33Not all of them are, but many of them are.
- 07:36And so the RNA modification machinery
- 07:39will in fact interact with.
- 07:41My cursor is frozen.
- 07:43Well, actually interact with
- 07:44the transcription machinery as
- 07:45the RNA is being made.
- 07:47The RNA.
- 07:48Most MRA's carry a 5 prime
- 07:50cap that is in fact derived of
- 07:53numerous modifications and then
- 07:55every step along the way from
- 07:58splicing to polyadenylation export,
- 08:00all of this sort of,
- 08:02you know,
- 08:02modifications have been implicated
- 08:03in many of these processes.
- 08:05They're exact functions.
- 08:06We don't always know,
- 08:07but we have sort of evidence to
- 08:10suggest that modifications can
- 08:11regulate every step of this process.
- 08:13Once an MRA gets exported,
- 08:16modifications can impact the
- 08:18association with translation machinery.
- 08:20So the ribosome itself can in fact
- 08:22detect sometimes modifications
- 08:23out or in your start codons,
- 08:25and then something that's going
- 08:26to come up a few times today.
- 08:29Our name modifications can
- 08:30also regulate M RNA decay.
- 08:32And now this picture is actually
- 08:34only focused really on M RNA.
- 08:35It turns out really all RNA's are
- 08:37modified really across all kingdoms of life,
- 08:39but for the purposes of today,
- 08:41we're mostly going to be focused on M RNA.
- 08:44And so when you zoom in very,
- 08:46very far down,
- 08:47really at the sort of chemical level,
- 08:49really what's going on here is that,
- 08:51you know,
- 08:51these small little chemical
- 08:52moieties can in fact dramatically
- 08:54change base pairing patterns.
- 08:56And so just as an example at the
- 08:57top here I'm showing you an AU
- 08:59base pair and a few examples of
- 09:01different modifications and how
- 09:02they would sort of impact base
- 09:04pairing at the chemical level.
- 09:06And so the addition of a methylation
- 09:09group here an N6 methyl adenosine,
- 09:12which is abbreviated M6A.
- 09:15Can slightly destabilize this base pair.
- 09:18If you actually shift that
- 09:20methylation group over just a bit,
- 09:22you actually get something that
- 09:24completely impedes base pairing
- 09:26altogether and then so on and so forth.
- 09:28You can imagine that as you
- 09:29increase the chemical diversity,
- 09:30you can really impact on RNA
- 09:32base pairing and structure and
- 09:34a really wide variety of ways.
- 09:36But at the sort of functional level,
- 09:38zooming back out a little bit,
- 09:39what does this really mean?
- 09:41And So what do these modifications
- 09:44actually do?
- 09:45So we're going to focus mostly
- 09:46today on one RNA methylation
- 09:48mark and six methyl adenosine,
- 09:50which I'm showing on the top left here.
- 09:53And it's a great example for
- 09:54a lot of reasons,
- 09:55but one of them is that we can
- 09:57actually ascribe some very specific
- 09:59M RNA regulatory mechanisms
- 10:00to this specific mark.
- 10:02So this methylation Mark is
- 10:04installed by a complex of
- 10:06a methyl transferase complex that
- 10:08contains metal three and metal
- 10:1014 proteins as well as some other
- 10:13accessory factors and it can be removed.
- 10:16By demethylase SES.
- 10:17Two of which I'm showing you here,
- 10:18called FTO&amp;L PH-5, essentially making
- 10:21this sort of a reversible process.
- 10:23You can add a methylation mark,
- 10:24you can remove it back and forth.
- 10:27But the consequences of of
- 10:29this particular methylation,
- 10:30particularly near the three prime end
- 10:32of M RNA's is is really interesting
- 10:34because in fact that mark alone is
- 10:37sufficient to sort of recruit the
- 10:39adenylation and decay machinery to M RNA's.
- 10:41And so essentially what happens is
- 10:43the this methyl mark is specifically
- 10:46recognized by multiple different proteins,
- 10:48but in many cases this protein
- 10:50called YTHD F2.
- 10:52And in that process,
- 10:54YTHD F2 then recruits the DND
- 10:56adenylation and decay complex,
- 10:58which effectively means that
- 10:59this M RNA is destabilized not
- 11:01necessarily at the chemical level,
- 11:03but because the actual decay machinery
- 11:06is getting actively recruited to this
- 11:08M RNA to decay it more quickly than
- 11:11it would if it was not methylated.
- 11:13And so this,
- 11:14it turns out,
- 11:15can then coordinate with other modifications.
- 11:18So what I'm showing you here is an
- 11:20example actually from development.
- 11:22This is for those of you that are
- 11:24interested in developmental biology,
- 11:25the maternal zygotic transition
- 11:27and zebrafish development.
- 11:29But really this is applicable to
- 11:31many other situations as well.
- 11:33And So what can happen is you can have for
- 11:35instance in this case a subset of genes,
- 11:37in this case the the maternal genes
- 11:40that are marked by this M6A mark.
- 11:42That decay machineries and recruited at
- 11:44the appropriate time and development,
- 11:46causing a drop in the maternal the
- 11:49levels of these maternal transcripts at
- 11:52the time at which the zygotic genes are
- 11:54the ones that are supposed to be activated.
- 11:57So to facilitate this transition,
- 11:59essentially maternal genes are being
- 12:00decayed by the presence of this mark.
- 12:03But there's a subset of transcripts
- 12:05in this context that actually
- 12:07need to be maintained and
- 12:08stabilized through this process,
- 12:10and it turns out that these the subset of.
- 12:13Transcripts is actually marked
- 12:14by a different modification,
- 12:16in this case 5 methyl cytidine.
- 12:18And in this case in fact the modification
- 12:21is recruiting a different set of
- 12:23machinery that is preventing these
- 12:24these transcripts from being decayed
- 12:26and allowing for sort of longer half
- 12:28lives and stability of those transcripts
- 12:31through this developmental transition.
- 12:33Now,
- 12:33it's important to note actually that
- 12:35we have arrived at this sort of
- 12:38very simplified picture by numerous
- 12:39studies in many different contexts,
- 12:41all studying different modifications
- 12:43on their own,
- 12:44separately.
- 12:44So we have sort of synthesized this
- 12:47information together to postulate
- 12:49a model whereby you could have
- 12:51different modifications coordinating
- 12:52these events all in one system.
- 12:54But I will point out that we actually
- 12:56don't have the power for the most
- 12:58part yet to actually detect all of
- 13:00this sort of in real time, you know,
- 13:02with multiple modifications.
- 13:03At once.
- 13:04But you can imagine that this kind of
- 13:06decay mechanism would not only be
- 13:08important for something like development,
- 13:10but also in cases of disease.
- 13:13And so as soon as this sort of
- 13:15decay mechanism was discovered,
- 13:17we had an explosion of work in many,
- 13:20many different areas, but particularly
- 13:22in various sort of forms of cancer.
- 13:26And so I'm showing you just a subset
- 13:27of papers here, most of them not mine,
- 13:29but one of them is and I will sort
- 13:31of use that as an example, but.
- 13:34Essentially by studying these
- 13:36different modifications in
- 13:37multiple different types of cancer,
- 13:39it was sort of realized that it might
- 13:41be that these modifications are also
- 13:43regulating transcript stability of
- 13:45critical oncogenic or tumor suppressor
- 13:47transcripts in the context of cancer.
- 13:49And So what I'm going to do today is sort
- 13:52of use one of these studies as an example,
- 13:54this one here,
- 13:55which is actually done in AML,
- 13:57but for reasons that hopefully
- 13:59will become clear are sort of
- 14:01relevant to some of the brain cancer
- 14:03work we're hoping to do as well.
- 14:04But really this is just sort of
- 14:06to give you a snapshot of sort
- 14:08of what we have learned,
- 14:09but also the significant limitations
- 14:11that we still encounter in this field.
- 14:15And so this story started,
- 14:17this is a collaborative effort.
- 14:19I was sort of, I was a postdoc in
- 14:22Juan he's lab and John Jin Chen was
- 14:24working on a big project essentially
- 14:26trying to characterize the roles
- 14:28of N6 methyl adenosine in AML.
- 14:31And they stumbled on sort of a really
- 14:34interesting observation with respect
- 14:36to R2 HYDROXYBUTYRATE or R2 HG,
- 14:39which at the time was sort of
- 14:41thought to be an uncle metabolite.
- 14:43And that was, there was sort of
- 14:45various lines of evidence for this.
- 14:46But essentially there was sort of a
- 14:49long history of of this metabolite
- 14:51being described as an Aqua metabolite.
- 14:53But there were some sort of weird
- 14:55results that I'll get into that sort
- 14:57of suggested that that might not be
- 14:59the case in every in every situation.
- 15:02And so this metabolite is actually
- 15:04derived from a mutant form
- 15:07of isocitrate dehydrogenase.
- 15:08And so I DH isn't the enzyme that's
- 15:11responsible for transforming isocitrate.
- 15:14To Alpha Ketoglutarate,
- 15:15which is a critical factor for
- 15:17many many different enzymes,
- 15:18including RNA modification
- 15:20enzymes in the cell.
- 15:22But it turns out that
- 15:24with specific mutations,
- 15:25as many of you might be aware,
- 15:27I DH can generate sort of a different
- 15:29a different form of this molecule card,
- 15:32excuse me, called R2 hydroxy glutarate.
- 15:35So it turns out that this
- 15:38particular form of this metabolite,
- 15:42R2 HD can in fact compete with
- 15:44alpha ketoglutarate at many of the
- 15:47sort of enzyme sites that require
- 15:49alpha ketoglutarate for activity.
- 15:51This includes cofactors for many
- 15:54different enzymes including histone
- 15:56DNA and RNA demethylase hes,
- 15:58which, you know,
- 15:59if you've encountered any sort of
- 16:02gene expression analysis of cancers,
- 16:03we know that many of these are dysregulated.
- 16:06And in many different cancers.
- 16:09And so sort of combined,
- 16:10we can sort of imagine a scenario
- 16:13where the presence of suddenly,
- 16:15you know,
- 16:15a much higher level of this metabolite
- 16:17R2 HG could significantly impact gene
- 16:19expression patterns based on the fact
- 16:22that it would sort of compete with
- 16:24these enzymes that are modulating
- 16:25all of these different groups.
- 16:28And so it was interesting about this
- 16:30particular study and when they sort
- 16:32of like roped myself and a few other
- 16:34people in my postdoc lab and on this
- 16:36project was the sort of interesting
- 16:38observation that when they just
- 16:40took a panel of AML cell lines.
- 16:42So these are grown in dishes
- 16:43and culture in the lab.
- 16:45There was a pretty striking difference
- 16:47in how sensitive they were to
- 16:50the presence of this metabolite.
- 16:52And so there was actually a
- 16:54panel of about maybe three times
- 16:55the size in the paper.
- 16:57And because this is published,
- 16:58I'm not going to go through
- 16:59sort of all the data,
- 17:00but I'm going to try to give
- 17:01you sort of the snapshots to
- 17:03sort of give you the idea.
- 17:04So in just this subset of cells,
- 17:08we can see that there is quite
- 17:10a few that are sensitive in the
- 17:13sense that the size of the circle
- 17:15means. How many cells are still viable
- 17:16and so at an early time point which is in
- 17:19Gray you can see all the cells are viable.
- 17:21But when you treat with R2 HG the
- 17:23the different colors are different
- 17:24time points and you can see that
- 17:26dramatically the circle gets really small
- 17:28really quickly and so this suggests
- 17:30a cell line that when you add this
- 17:33uncle metabolite a lot of cells die.
- 17:35But on the flip side,
- 17:36you can see in the bottom row here
- 17:38that there are actually quite a few
- 17:39that are also quite resistant to this.
- 17:41So despite using the same time course,
- 17:43the same dosing we have, you know,
- 17:45different cell lines that have
- 17:46ostensibly are the same thing,
- 17:47but of course we know that they're not.
- 17:49They're responding quite differently
- 17:51to the presence of this metabolite.
- 17:53And so this sort of started us
- 17:55off on a quest to sort of figure
- 17:57out why that might be true.
- 17:59And through this process,
- 18:01the Chen Lab noticed that one of
- 18:03the enzymes that was particularly
- 18:06disregulated across cell lines that
- 18:08were sort of sensitive and resistant
- 18:10to this metabolite was this enzyme FTO,
- 18:13which you might recall was one of the
- 18:15demethylase hes that I highlighted.
- 18:17So FTO removes the M6A mark from many
- 18:20different M RNA's and the sort of
- 18:23predicted outcome of this would be that
- 18:26you would have more M6A left on transcripts.
- 18:29Because you've sort of
- 18:31inhibited the demethylase.
- 18:32So that's a relatively simple thing to test.
- 18:34And so we can measure these
- 18:36modifications by various different means.
- 18:38The data I'll show you is a
- 18:40mass spec based measurement,
- 18:41but we did this by by other means as well.
- 18:44And so when you measure the
- 18:47levels of this M6A modification,
- 18:49we always normalize relative
- 18:51to the unmodified a.
- 18:53So that's sort of what this
- 18:55ratio here on the Y axis shows,
- 18:57can see that when you treat
- 19:00with drug for sensitive lines,
- 19:02you see this small but sort of reproducible
- 19:04increase in M6A levels like we predicted,
- 19:07whereas it was much more variable in
- 19:09the resistant lines and there was
- 19:11certainly no sort of consistent effect.
- 19:13So this is potentially intriguing,
- 19:15but it still didn't really
- 19:16explain everything.
- 19:17And so we sort of turned to like what,
- 19:20why would the presence of this
- 19:22metabolite actually have such an impact?
- 19:25And we got a big clue from this
- 19:27Western blot that was done in the
- 19:29Chen lab in which they showed that
- 19:32the levels of this demethylase FT O
- 19:34were vastly different in the sensitive
- 19:37lines versus the resistant lines.
- 19:39And conversely levels of Mick
- 19:42were dramatically lower.
- 19:44In the sensitive lines,
- 19:45then in the resistant lines.
- 19:47And that the levels of these two
- 19:50key factors actually change when
- 19:52you add this metabolite RHG.
- 19:55So this was intriguing because of
- 19:57course Mick is a critical factor
- 19:59that drives a lot of not just AML,
- 20:01but a lot of other cancers.
- 20:03And so we wondered whether we
- 20:04could sort of turn
- 20:06a sensitive line into a resistant
- 20:081 purely by increasing the
- 20:09levels of making the cell.
- 20:11And it turns out that you
- 20:12can actually do that.
- 20:13And so I'm just showing you.
- 20:14One snapshot here, but there's no more.
- 20:17One cell line was one sensitive
- 20:19cell line where essentially the
- 20:21red lines represent just no sort
- 20:24of additional mix just at baseline.
- 20:26And when you add the R2HG,
- 20:29you can see that there's a drop in
- 20:31the ability of the cells to proliferate.
- 20:33When you exogenously just add a
- 20:36bunch of mic expression to the cells,
- 20:38you can see that you can make
- 20:40them more resistant to it.
- 20:42So this is just one snapshot,
- 20:43but this is sort of.
- 20:45The model that we sort of,
- 20:47you know,
- 20:47took out from this and I will say that
- 20:49there was a whole bunch of sort of
- 20:50mouse work done as well that I was not
- 20:51involved in and that I'm not showing.
- 20:53If you're interested,
- 20:54you are welcome to check out the paper.
- 20:56But all together we sort of settled
- 20:59on this model where perhaps the
- 21:02relative levels of the demethylase
- 21:04and Mick were sort of essentially
- 21:07dictating where on a spectrum of R2
- 21:10HG sensitivity these cell lines were.
- 21:12So the idea would be if you have.
- 21:15A lot of mixed transcripts floating around.
- 21:18You add R2 HG that inhibits FTO.
- 21:22You have less demethylation.
- 21:25You get a mic target gene expression
- 21:28because essentially M6A levels are lower.
- 21:32Transcript decay is lower,
- 21:34meaning overall mic levels are
- 21:36high and so the mic itself and its
- 21:39target genes are highly expressed,
- 21:41however.
- 21:41In the opposite scenario where maybe
- 21:44you just have a lot of mic present or
- 21:47FTO is able to demethylase and remove a
- 21:50lot of that methyl that methylation mark,
- 21:53you can overcome this by essentially just
- 21:56boosting the amount of Mick transcript.
- 21:59So essentially the
- 22:01methylation facilitates decay,
- 22:03loss of methylation facilitates
- 22:04stability and essentially by toggling
- 22:06both of these variables you're able
- 22:08to get sort of the spectrum of R2
- 22:11HD sensitivity even in what is.
- 22:12Ostensibly cell lines from sort
- 22:14of the same cancer.
- 22:15Now there's a lot of limitations to this.
- 22:18I'm going to go into some
- 22:20of them in great detail.
- 22:21But of course a big one is that a lot of
- 22:23this is done in cell lines and mouse models.
- 22:25And we're sort of zooming in on
- 22:27one particular aspect of this.
- 22:29We know that this metabolite does many,
- 22:31many other things including regulation of
- 22:33like way more metabolites than you know.
- 22:36We could even particularly cover
- 22:37in this talk.
- 22:38But I hope this is sort of highlighted
- 22:40one way in which sort of the presence
- 22:42or absence of this methylation.
- 22:43They can sort of dictate where you are,
- 22:46particularly in the context of cancers
- 22:49in which there are ID H mutations.
- 22:52And you know,
- 22:53this sort of set me on a path of a
- 22:56little bit of a rabbit hole and.
- 22:59It turns out that I didn't know
- 23:00this at the
- 23:01time. I'm sure all of you already know this,
- 23:03that you know glioblastoma is another
- 23:05instance in which ID H mutation
- 23:07status can dramatically impact
- 23:09sort of prognosis for patients.
- 23:11And one of my students in
- 23:12the lab is working on this.
- 23:13Emily actually found this sort
- 23:16of lovely table that summarizes
- 23:18a lot of what we know about N 6
- 23:21methyl adenosine in glioblastoma,
- 23:23not even taking into account
- 23:25ID H mutation status.
- 23:27And if you just read a few lines,
- 23:29you can already start to see.
- 23:30There's a lot of conflicting data on this.
- 23:33So for instance depending on you
- 23:36know which paper we want to look
- 23:38at all sort of published within
- 23:40the same approximate time frame.
- 23:42We have many different M6A
- 23:44related factors we can look at.
- 23:47But even when we're looking at the same ones,
- 23:49we can find papers that say the presence
- 23:51of that or absence of and says oncogenic.
- 23:54Another one says it's tumor suppressive.
- 23:56We different,
- 23:57you know different target genes
- 23:59are being explored mix socks.
- 24:01To Fox M1, essentially there's very
- 24:03little concordance among any of these.
- 24:05And that's not to say that they're wrong,
- 24:07but I think it sort of reflects that.
- 24:09Depending on exactly,
- 24:10let's say what cell line or
- 24:12what system you're using,
- 24:13if you have something like a
- 24:15spectrum of sensitivity or just
- 24:16differences in gene expression,
- 24:18you can arrive at this very confusing
- 24:20pattern of data that I am not a clinician,
- 24:22like I said.
- 24:23But I would assume that this doesn't
- 24:24exactly scream confidence to you,
- 24:25that this is something that,
- 24:26you know,
- 24:27maybe would be interesting to
- 24:28pursue in the clinic.
- 24:30And so this sort of motivated me to
- 24:32think a little bit more about why we
- 24:34might be getting it not necessarily so wrong,
- 24:37but why are we so confused by this.
- 24:39And I think a lot of this comes
- 24:41down to the approaches that we're
- 24:42using to study these problems.
- 24:44So M6A in particular has been a very
- 24:47popular modification to study because
- 24:49it's actually relatively simple to
- 24:51sequence where it is in the transcriptome,
- 24:53you know,
- 24:54in any cell linear system you want.
- 24:56The reason for that is that
- 24:58there's an antibody based approach
- 24:59to to sequence M6A sites.
- 25:00And essentially what you do is
- 25:03you take an antibody and your
- 25:05favorite RNA sample of interest,
- 25:07you mix them together,
- 25:09you pull down your M6A containing
- 25:12RNA's on your antibody and then you
- 25:14use high throughput sequencing which
- 25:16is now so run-of-the-mill that you
- 25:17can really do this with even very,
- 25:19very small amounts of input RNA.
- 25:22So this has been fantastic in terms of
- 25:24driving the field forward and sort of.
- 25:26Giving us the ability to sequence and
- 25:28say in a lot of different contexts,
- 25:30but it's essentially driven people
- 25:33to to this sort of top down approach
- 25:37of trying to identify unifying
- 25:38sequence features and gene groups
- 25:40and use that to try to sort of
- 25:42generate functional themes.
- 25:43So let's identify the most
- 25:45dysregulated sort of genes when
- 25:47we, you know lose M6A and and see
- 25:49if that tells us anything about
- 25:51how it might be functioning in
- 25:53our favorite system of interest.
- 25:55But it turns out that this can be
- 25:57a little bit tricky to interpret.
- 25:59So when you do an experiment like this,
- 26:01you will get many, many genes and
- 26:02you will do all kinds of analysis.
- 26:04And that's sometimes very illuminating.
- 26:06But in some cases it's also worth thinking
- 26:09about what exactly do those data look like?
- 26:11And so it turns out,
- 26:12when you do an experiment like this,
- 26:14you get, let's say,
- 26:15a section of your favorite gene.
- 26:16It could be Mick,
- 26:17could be anything else.
- 26:18And you will get a whole bunch of
- 26:20sequencing reads at different places
- 26:22along your favorite transcripts.
- 26:24But what this does not tell you is
- 26:26whether you have a situation like
- 26:28this where you have multiple M RNA's
- 26:31where half of them have multiple M6A
- 26:33sites and half of them have none.
- 26:36Or you could have a situation
- 26:38like this where actually all of
- 26:40your transcripts have M6A on them,
- 26:42but in slightly different locations.
- 26:44You might think that seems like a bit
- 26:46of a detail, like, why do we care?
- 26:47But it turns out that that can
- 26:49actually have very profound
- 26:50functional consequences in terms
- 26:52of how you interpret the data.
- 26:54And so for instance,
- 26:55you know,
- 26:55this might actually represent functionally
- 26:58two different transcript pools.
- 27:00It could mean that one of these pools is
- 27:02getting localized to a specific place
- 27:04or getting decayed and one pool is not.
- 27:06And conversely,
- 27:07this could actually just mean that,
- 27:09you know,
- 27:10the exact site of the M6A doesn't matter,
- 27:12it just has to be somewhere in this vicinity
- 27:14and then it'll have the same outcome.
- 27:16But we can't sort of disentangle
- 27:19these two things with a simple
- 27:21IP sequencing experiment.
- 27:23And that's not to say we haven't made
- 27:24improvements to this these methods.
- 27:26We have made a lot of improvements to
- 27:28IP based sequencing that allow you to
- 27:30get more precise sort of location data,
- 27:32but a lot of those are not easily applicable
- 27:35necessarily to very low abundance.
- 27:37Examples which might sort
- 27:38of be clinically relevant.
- 27:40So.
- 27:42This sort of leads me to sort of
- 27:44posit to you that part of the
- 27:45reason we get so much confusing
- 27:47data is because we are actually sort
- 27:48of the data itself is is not as
- 27:51clarifying as maybe we would think.
- 27:53And so how do we change our
- 27:55approaches to sort of deconvolve
- 27:56some of these variables.
- 27:58And this is really sort of at the crux
- 28:00of essentially everything my lab studies,
- 28:02but we we sort of apply this in a
- 28:04lot of different ways.
- 28:08So just to sort of summarize the
- 28:10approach that I just gave you because
- 28:12I'm going to try to sort of convince
- 28:14you that our approach sort of
- 28:16provides an interesting alternative.
- 28:17The approach that I just described is
- 28:20what I would call a top down approach.
- 28:22So we do transcriptome wide sequencing
- 28:24usually for one specific modification.
- 28:27We, you know, sequence everything and
- 28:28then we try to pick out features that
- 28:31are common across all the transcripts
- 28:33that have our favorite modification.
- 28:35And this is in fact what led to our,
- 28:37you know, the identification of this decay.
- 28:39Mechanism that I described earlier.
- 28:40So it is definitely not a useless approach.
- 28:42It has been very productive,
- 28:44but I will argue that we sort of hit
- 28:46a wall in terms of really getting a
- 28:48mechanistic understanding of of how
- 28:50some of these modifications work,
- 28:51which has led to some confusing results.
- 28:55So what I have tried to do,
- 28:57you know in setting up the research
- 28:59program in our lab is to try to
- 29:01develop what I would call more of
- 29:02a bottom up approach.
- 29:03So we identify specific transcripts and
- 29:05then we do a deep dive to figure out,
- 29:08OK, what are all the different
- 29:11modifications on this transcript.
- 29:12Once we know that,
- 29:13can we identify the regulatory
- 29:15enzymes are there,
- 29:16you know variations across
- 29:18different diseases etcetera.
- 29:20My argument is sort of that if
- 29:22we can do this,
- 29:23you can then go and look in your next
- 29:25favorite transcript and say it does
- 29:26it also apply here and essentially
- 29:28build out the network from there.
- 29:30So the idea is to sort of not limit
- 29:32ourselves necessarily to these
- 29:34RNA that we're interested in,
- 29:35but to use them to identify rules
- 29:37and then try to figure out if
- 29:39those rules apply elsewhere.
- 29:41And so I'm going to drive describe
- 29:43sort of the two approaches we're
- 29:45using to sort of make maps like this.
- 29:47The first one is a mass spectrometry
- 29:50based approach,
- 29:51and this has really been pioneered in
- 29:53my lab by a student, Lauren Wilson,
- 29:54aided by many other people in the
- 29:57lab including Josh and undergraduate.
- 30:00But the essence essentially the idea is this.
- 30:02So what if we just took our favorite
- 30:04transcript and we designed a whole
- 30:06bunch of probes that are complementary
- 30:08to that transcript and we just
- 30:10purified it out of cells?
- 30:11This sounds crazy,
- 30:12but Lauren has actually gotten this to work,
- 30:16arguably for more abundant transcripts.
- 30:17But nevertheless, you know,
- 30:19it's actually working quite well,
- 30:20as I'll show you in a second.
- 30:22And the idea here is,
- 30:23OK,
- 30:23let's just hypothetically say that
- 30:24we can purify the transcripts
- 30:26that we're interested in.
- 30:28And then digest them into individual
- 30:31nucleosides down to sort of
- 30:33the individual module level and
- 30:35then analyzed by mass spec sort
- 30:37of everything that's in there.
- 30:39And so I'm happy to discuss the
- 30:41details of the mass spec with
- 30:43anyone who's interested.
- 30:44But the crux of it is that
- 30:46essentially between the
- 30:47the fragmentation patterns and the retention
- 30:50time on a specific column that we use,
- 30:53we can distinguish between even very
- 30:55closely related chemical species.
- 30:57So that we can distinguish even two
- 30:59different singly methylated species
- 31:01like and one methyl adenosine and and
- 31:03six methyl adenosine and we can go
- 31:05down the line and look at sort of any
- 31:07modifications we might be interested in.
- 31:10And so this has turned out to be
- 31:12relatively fruitful as I just alluded to.
- 31:14And so we're starting to do this
- 31:16admittedly with very abundant transcripts.
- 31:18And so the data I'll show you
- 31:19is for a very abundant but very,
- 31:22very big long non coding RNA called neat one.
- 31:25It's got some very interesting biology
- 31:27that I won't really go into at the moment.
- 31:31But essentially we can purify neat 1
- 31:34transcripts to really high enrichment
- 31:37relative to sort of baseline total RNA.
- 31:40So the exact numbers that you get for
- 31:42enrichment depend a little bit on
- 31:45what you're comparing it to, right?
- 31:47So are you comparing to an M RNA
- 31:49or a very abundant ribosomal RNA?
- 31:51But for instance,
- 31:52if we look at sort of a favorite
- 31:54housekeeping M RNA.
- 31:55We can enrich this long non coding
- 31:59RNA many thousand fold over what
- 32:01we would have in just sort of the
- 32:04baseline pool of RNA.
- 32:05And importantly,
- 32:06Lauren can now do this to sort of a
- 32:09level of abundance that we can actually,
- 32:11you know,
- 32:12get enough RNA out of this to
- 32:14digest and do mass spec analysis.
- 32:16And so the idea here is that we take this,
- 32:19you know,
- 32:20RNA of interest and we just look
- 32:22for our favorite modifications.
- 32:23I'm just showing you a few of them here.
- 32:26You can look at different cell lines,
- 32:27you know,
- 32:28whatever cell lines you might be
- 32:30interested in and then basically
- 32:32profile the relative abundance
- 32:34of different modifications.
- 32:35In this sample. In different cell lines.
- 32:39And so for instance here we have a 549
- 32:42and HeLa cells and you can see that
- 32:45the M1A levels are a little bit different,
- 32:48which maybe it's interesting,
- 32:49maybe it's not.
- 32:50We'll have to find out.
- 32:52The M6A levels are relatively
- 32:54stable and so on and so forth.
- 32:57And so this is just a snapshot,
- 32:59but I hope you can sort of appreciate that,
- 33:01you know,
- 33:01this is one way to get a much more
- 33:04unbiased picture of what might be
- 33:06present in your long and encoding.
- 33:08RNA of interest I will say in
- 33:10the interest of Full disclosure
- 33:11we cannot do this yet for MRA's.
- 33:13MRI's are much less abundant.
- 33:15Maybe someday we will get there,
- 33:17but at the moment we can at least
- 33:18take a stab at taking our favorite
- 33:20long non coding our names of interest
- 33:22and trying to see what modifications
- 33:24are sort of sprinkled throughout.
- 33:25And it will say another RNA we
- 33:27have applied this to is Merlot,
- 33:28one which many of you might be familiar with.
- 33:31It's another sort of abundant
- 33:33long non coding RNA that's
- 33:34been of interest in in a few different
- 33:36cancers and so we're taking a stab
- 33:38at looking at the modifications.
- 33:39From that arena as well.
- 33:42But again, I told you I
- 33:43would tell you limitations.
- 33:44So mass spec is a very powerful tool,
- 33:48particularly to get really specific
- 33:51chemical identity information.
- 33:52But you lose location information
- 33:54because we're digesting all of
- 33:56this up into tiny little pieces
- 33:57so that we can really identify the
- 33:59chemical species that are present.
- 34:01So that's a bit of a problem
- 34:03and as I already alluded to,
- 34:04it's also limited at the moment
- 34:07much more abundant transcript.
- 34:08So things that we can actually
- 34:09purify to a degree to where we can
- 34:11actually do Mass Effect on them.
- 34:13It is also limited in the sense that,
- 34:15you know,
- 34:16we certainly wouldn't be able
- 34:17to do this from something like
- 34:18a biopsy or a patient sample,
- 34:19which I'll get to later.
- 34:21But that's sort of, you know,
- 34:22the dream down the line.
- 34:24So what do we do to complement this approach?
- 34:27So this is where we turn to a
- 34:29sequencing based method and this
- 34:31is actually where I've had the
- 34:33tremendous good fortune to work with
- 34:35another member of the department.
- 34:36And Anna Marie Pyle,
- 34:38her lab is just down the hall
- 34:40and we've got a fantastic sort
- 34:41of RNA wing in the Yale Science
- 34:44Building and they discovered and
- 34:46characterized this interesting reverse
- 34:48transcriptase enzyme called marathon.
- 34:51And Marathon is sort of interesting
- 34:54from a few different aspects.
- 34:55So first, as the name suggests,
- 34:57it can reverse transcribe
- 34:59really long transcripts.
- 35:00I mean,
- 35:01I actually have never seen anything quite
- 35:03as processive as this particular enzyme.
- 35:06The other thing that it does,
- 35:07though that's particularly useful for us,
- 35:09is it tends to install mutations
- 35:11when it encounters a modified
- 35:12modification in the RNA.
- 35:14That's not to say every single modification,
- 35:16but many of them,
- 35:17and even some that are a bit more subtle.
- 35:20So you might already be starting to
- 35:21piece together that this would be
- 35:23relatively useful if we could just
- 35:25reverse transcribe an RNA and use
- 35:26mutations to sort of identify where
- 35:28possible modification sites might be.
- 35:31Now the one caveat here is that
- 35:33we can't always tell the specific
- 35:35modification just based on the
- 35:37fact that there's a mutation there.
- 35:39There's a lot of different ways
- 35:41you can get mutations,
- 35:42but the massive benefit of this
- 35:43type of approach is that you can
- 35:45do it even for M RNA's that are
- 35:47not very abundant because for a
- 35:49reverse transcription reaction.
- 35:50Need much less than something
- 35:52like from aspec.
- 35:53So Dorothy in the lab has been the
- 35:55one really pioneering this approach.
- 35:57And as you can see here,
- 35:59so this time I'm using BRCA 2 as sort
- 36:01of an example of an MRA of interest.
- 36:04And you can see that as you
- 36:06reverse transcribe with marathon,
- 36:07you see some specific peaks where
- 36:11you get mutation signatures.
- 36:13We thought we knew what these
- 36:14mutation sites represented were
- 36:15actually a little bit less short now,
- 36:17which is why I'm not telling you the
- 36:18identity, the identity of the modification,
- 36:20because we're still trying to work that out.
- 36:22But what's interesting is that you can see
- 36:25that there's actually different levels of
- 36:27mutation depending on the specific site.
- 36:30As I said though, an important
- 36:31caveat here is you can get mutations
- 36:33by a lot of different routes,
- 36:34including of course because the
- 36:35genomic DNA might be a little bit
- 36:37different than your reference.
- 36:38And so here you can see that we've
- 36:41actually just picked up a snip.
- 36:43You can tell that actually you can
- 36:45almost predict that based on the
- 36:47fact that it's so highly modified.
- 36:48But then these others you can see
- 36:50there aren't really sort of concordant
- 36:52DNA based mutations and so we're
- 36:54sort of trying to follow up what
- 36:56those modifications might be.
- 36:57And so you can see that there's sort
- 36:59of an iterative process where you
- 37:02could essentially either identify
- 37:03potentially interesting modifications
- 37:05by mass spec in the favorite RNA and
- 37:07then try to use sequencing based on that
- 37:10knowledge to identify where they are.
- 37:11Or you could start with sequencing,
- 37:14identify specific sites on
- 37:15a transcript of interest,
- 37:17and then try to work out what
- 37:18the modification is either by
- 37:19mass spec or sort of another,
- 37:21you know, orthogonal method.
- 37:23But by iterating this process,
- 37:25the idea is that basically
- 37:26you can get sort of a map of.
- 37:28And RNA of interests and really
- 37:29that RNA could be anything.
- 37:31I'll tell you a little bit
- 37:32about the ones that you know,
- 37:33we're working on at the moment.
- 37:34But the idea is that if you can
- 37:36get this sort of more complete
- 37:38picture of what's actually on there,
- 37:40you can then go in and look at,
- 37:41OK, if I do perturbation X or
- 37:43if I look in disease Y,
- 37:45you can actually start to look at specific
- 37:48changes in those specific locations.
- 37:50And So what are we actually trying?
- 37:53Whoops,
- 37:54trying to do with this so.
- 37:57There's a lot of different applications
- 37:58I envision and this is where I probably
- 38:01should have put disclosure at the
- 38:02beginning that says you know I would
- 38:05love always perspective from clinicians
- 38:06and more translational researchers
- 38:08sort of interesting targets to look at.
- 38:10But I'll tell you a little bit about
- 38:11the ones that that we are looking at.
- 38:13And so I've mentioned the long non
- 38:16coding RNA's neat one and maillot one.
- 38:19These are sort of our first model
- 38:22transcripts let's say for the mass spec
- 38:24approach because they are abundant,
- 38:25they are big and there's
- 38:27some interesting biology.
- 38:28Associated with them,
- 38:29which means that once we have
- 38:31modifications of interest,
- 38:32we can go in and try to perturb them.
- 38:33We can go look in hopefully either
- 38:37sort of more disease relevant samples
- 38:39than just cell culture lines and
- 38:41actually go and look at whether
- 38:44these changes actually translate to,
- 38:46you know,
- 38:47samples that are a bit more
- 38:49directly translationally
- 38:50relevant. We're also interested
- 38:51in sort of M RNA's like Braca 2,
- 38:54but many others that we can use to sort
- 38:56of highlight the sequencing approach
- 38:57and essentially again go in and find
- 39:00some interesting modifications and try
- 39:01to perturb them and monitor how they
- 39:04change under under different conditions.
- 39:07But I think thinking sort of bigger picture,
- 39:09what these approaches sort of allow us
- 39:10to think about is to actually monitor
- 39:12changes and modifications in real time.
- 39:14And so a problem that we're,
- 39:17we're sort of starting to think a little
- 39:18bit about this is driven really by Emily.
- 39:20In our lab is can we actually, you know,
- 39:23look at instances of things like drug
- 39:25resistance where we're sort of used
- 39:27to thinking about DNA based mutations
- 39:29that are making a protein resistant
- 39:30to the drug that's targeting it.
- 39:32But are there other changes beyond
- 39:34that that we're missing that we could
- 39:37actually sort of use either as biomarkers
- 39:39or diagnostics or something else that
- 39:41we could actually either you know,
- 39:44target or at least use to monitor
- 39:47the development of disease sort
- 39:48of at the RNA level?
- 39:50The consequences of IH1 status are
- 39:52really unknown here and this is
- 39:55really where I think where we've
- 39:56become sort of really interested in
- 39:58glioblastoma for a few different reasons.
- 40:00One of them is the sort of huge
- 40:02dichotomy with with DH one.
- 40:04But another is that, you know,
- 40:06it's a disease in which,
- 40:07at least based on what I've read
- 40:09and I'm happy to be corrected,
- 40:10there's actually a fair amount of
- 40:11evidence that a lot of drug resistance
- 40:13isn't really happening based on
- 40:15mutations at the DNA level suggesting
- 40:17that there might be some really
- 40:18interesting things we can look at at the.
- 40:20RNA level including things like modifications
- 40:23that aren't really driven by you know,
- 40:25DNA mutations specifically.
- 40:27And so hopefully I've convinced you that
- 40:29that you know what we're doing is sort
- 40:32of somewhat feasible and interesting.
- 40:34I think moving forward,
- 40:35you know,
- 40:36we've definitely have a really strong
- 40:38interest in glioblastoma and this is
- 40:40sort of what the Sontag Foundation has
- 40:43sort of funded us to look into Umm,
- 40:45thankfully with the brain tumor
- 40:47centers as support and guidance,
- 40:49but we're really interested in sort of.
- 40:51Applying this across all different
- 40:53types of disease and other cancers
- 40:55in particular because I don't think
- 40:57that our approaches are necessarily
- 40:58going to be limited to you know,
- 41:00glioblastoma specifically.
- 41:01But you know,
- 41:03I'm hoping that with a little bit of
- 41:05discussion or maybe I've peaked some
- 41:07interest in some of you here that maybe
- 41:09you can give us some great new ideas
- 41:10and spark some new collaborations to,
- 41:13to work on this moving forward.
- 41:16And so with that I would just like
- 41:18to acknowledge the people that are
- 41:19actually doing the work in our lab.
- 41:21And so I think this is a up to
- 41:24date picture for the most part.
- 41:26So we are mostly graduate student lab,
- 41:28but we have undergraduates and
- 41:30postgraduate researchers working with us
- 41:33as well. We're supported by a tremendous
- 41:36team of collaborators both in in a piles lab
- 41:40but also Brent gravely and Emmanuel Saliba.
- 41:42We have a joint NHR I grant to sort of study.
- 41:46Um, you know, develop methods to sort
- 41:48of sequence modifications more broadly.
- 41:50We're also very grateful to our mass
- 41:53spec support and the Yale Chemical
- 41:56Biology Instrumentation Center.
- 41:58And of course our funding sources through
- 42:01the NIH, the Hood Foundation and the Sontag
- 42:04Foundation that I already mentioned.
- 42:06We've got also great support
- 42:08from the RNA center at Yale,
- 42:10which is a a great community.
- 42:12But of course I would definitely
- 42:13like to thank the Cancer Center,
- 42:15not only for the invitation to actually
- 42:16come in and talk to you a little bit today,
- 42:18but I was truly,
- 42:19it was the most pleasant surprise
- 42:21when I emailed Doctor Amura about
- 42:22this sort of like random hey,
- 42:24I'm writing a grant about brain cancer.
- 42:26Like, would you be willing to chat and and
- 42:28maybe talk through some of these ideas?
- 42:30It was a fantastic interaction
- 42:32and great conversation.
- 42:33I hope this is sort of a productive
- 42:35collaboration in the future.
- 42:37And with that,
- 42:37I'll just leave it there and I'll
- 42:39take any questions you might have.
- 42:50Well, let's check.
- 42:53I don't think so.
- 42:54In the meantime, let's get some
- 42:55questions from the audience.
- 43:03I have two questions though kind of like.
- 43:08The first one being I was wondering
- 43:10if you if your studies especially
- 43:12when they looked at like locations
- 43:14which does obviously cause an
- 43:16increase in strain escalation,
- 43:18seeing whether using methylation can
- 43:20change body affects or potentially
- 43:23changes the amount of modifications
- 43:25tomorrow night, I'm not saying.
- 43:28It's a really great question.
- 43:29That's actually a PhD project in our lab
- 43:33essentially not exactly that but yes,
- 43:34so that's a fantastic question.
- 43:36So for the people on zoom that
- 43:37may not have caught the question,
- 43:38the question is essentially about the
- 43:40sort of effects of methylation status of
- 43:42DNA on sort of RNA modification status.
- 43:45So at the moment there's a lot of
- 43:48correlative evidence suggesting that
- 43:50either DNA methylation and or histone
- 43:53methylation could impact sort of
- 43:54the presence or absence of specific
- 43:56modifications and then that will of course.
- 43:58Relate with sort of gene expression,
- 44:00but we have a bit of a chicken
- 44:02and egg problem.
- 44:03I think intuitively you would assume that
- 44:05the DNA level stuff would impact the RNA
- 44:07level stuff and only in One Direction.
- 44:09But it turns out that that's actually
- 44:11maybe not true and that there's sort of
- 44:14interactions between sort of chromatin
- 44:17transcription machinery modification
- 44:18machinery that maybe actually going
- 44:20back and impacting chromatin.
- 44:22So the short answer is yes,
- 44:24it likely is impacting it.
- 44:25We don't exactly know how,
- 44:27but one way that we're trying to study.
- 44:30Just to look at the code transcriptional
- 44:31regulation of different modifications
- 44:32and if we can figure out kind of
- 44:33exactly where they're put on,
- 44:34then we can go and tinker with the
- 44:36DNA and then figure out if it's
- 44:38still happening or. Yeah, yeah.
- 44:41Question is.
- 44:44Retaliating.
- 44:47It's like I don't know.
- 44:56Yes. So we we always try to validate
- 44:58sort of in multiple different ways,
- 45:00not only sort of loss of the enzyme but
- 45:02also loss of the modification it turns out.
- 45:04So the shorter answer is yes, we do.
- 45:06The tricky part is that oftentimes we're
- 45:08dealing with very small changes and
- 45:09actually in the case of metal three,
- 45:11it's even worse because it's an essential
- 45:13gene and if you lose it for too long,
- 45:15cells are dead.
- 45:17So we do our best to sort of.
- 45:20Tune the perturbation so we can get
- 45:22a change in modification but not,
- 45:23you know, lose everything.
- 45:25But I will say that actually one
- 45:26thing that we've been trying to
- 45:28work out though it's been very,
- 45:30very difficult is to use these
- 45:32sort of crisper cast based systems
- 45:34to sort of target enzymes to
- 45:36specific modification sites.
- 45:37So the idea would be you take a dead
- 45:39cast 9 or a dead cast whatever.
- 45:41There's like 10 of them now I think
- 45:43that are essentially trying to tether
- 45:45enzymes using this this machinery to a
- 45:47specific place using a guide RNA and
- 45:48then the enzyme is around and should be.
- 45:50Essentially just be removing a
- 45:52modification at that specific site.
- 45:55We're trying some of these systems.
- 45:57We haven't gotten all of them
- 45:59to work very well yet,
- 46:00but that would be sort of a much more
- 46:02targeted and much better way to look at
- 46:04sort of very specific modifications.
- 46:07So,
- 46:07yeah, good questions. Yeah.
- 46:15Yes. So yes, so the question is have
- 46:18we looked at IMIDAZOLINE and LH3? Yes,
- 46:21I actually didn't talk about the M1A work.
- 46:25I have a long history with that
- 46:26modification from my postdoc. Actually,
- 46:29it's been a very tricky one to study.
- 46:31And if you're familiar with that literature,
- 46:33you might know there's been a lot of
- 46:34arguments about sort of prevalence,
- 46:35presence, location, etcetera.
- 46:37And so I think are a lot
- 46:39without the H3 as a postdoc.
- 46:41And and the sort of odd thing that
- 46:44I stumbled on and I think this is
- 46:46like buried in the supplementary
- 46:48figure whatever of that paper is.
- 46:50It was strange because I when I
- 46:53overexpressed out PH3IN cell lines,
- 46:54you can see an ink.
- 46:56So you see an increase in LH3,
- 46:57you would expect a decrease in M1A and
- 47:00we did see that but when we knocked down
- 47:02LH3 we did not see the sort of reverse.
- 47:06But interestingly other labs have
- 47:07and so I don't know if this is
- 47:09sort of a methods based thing or
- 47:11not I will say in an in vitro.
- 47:12Experiment,
- 47:13it absolutely will demethylated M1A.
- 47:16It will do that probably on RNA and DNA.
- 47:18The question is more just if in the
- 47:20cell itself they're encountering
- 47:22each other to the extent that you
- 47:24would need for it to regulate M RNA.
- 47:26So we're not sure we're we're sort
- 47:28of going to still try to work that
- 47:30out in a more targeted way if we can.
- 47:33We actually would love to target LPH
- 47:343 with a cast system and sort of
- 47:37specifically demethylated specific places,
- 47:39specific sites but that's been a tricky 1.
- 47:42For a lot of reasons, yeah.
- 47:46So
- 47:47I was fascinated by the use
- 47:49of antibodies for enrichment.
- 47:52Have those antibodies ever been used inside
- 47:54to try to identify location
- 47:56of the marks or any kind of?
- 47:59Yeah, that's a great question.
- 48:00So I don't know about Umm.
- 48:04So yes, a little bit in sort
- 48:08of cell culture based systems,
- 48:10it's often a little bit tricky unless you
- 48:12can get a very specific concentration of
- 48:14a modification in a very specific place.
- 48:17For something like M6A that's sort
- 48:19of diffuse on many different RNA,
- 48:21it can be a little tricky.
- 48:22That being said, for some things,
- 48:24you know particularly if you
- 48:27have very concentrated sort of.
- 48:29You know, a nuclear body or
- 48:30a cytoplasmic body.
- 48:31You might be able to detect them,
- 48:32I don't know.
- 48:33Various people have tried and
- 48:35to sort of varying success.
- 48:37I think it's something we need
- 48:38to look at a little bit more.
- 48:39The the other tricky thing with
- 48:41these antibodies is it turns out
- 48:42they're not all super specific and
- 48:44so then we can't interpret whether
- 48:46the signal we're getting is actually
- 48:48because of that modification or just
- 48:50it's binding to a lot of things.
- 48:53So something we still need to work out
- 48:55but I there's people there have been
- 48:57some interesting methods trying to
- 48:59sort of use proximity based you know,
- 49:01akin to a proximity ligation
- 49:03based strategy where you know
- 49:04if you can bridge two things.
- 49:05Together to kind of combine an antibody
- 49:08with something else to tell you that
- 49:10that Mark is there with some success.
- 49:12But the imaging based methods are
- 49:13a little bit behind I would say,
- 49:15yeah.
- 49:19At the beginning.
- 49:24I was wondering if that's drastic
- 49:26enough to affect the RNA confrontation,
- 49:30but you could essentially have proteins
- 49:31that couldn't or wouldn't be picked up.
- 49:35Yeah, that's a great question.
- 49:36So the question is for those of you on zoom,
- 49:38whether the impact of modifications on
- 49:40base pairing would be sufficient to
- 49:42maybe even impact essentially decoding I
- 49:44guess is what you're asking and and sort
- 49:46of introduce mutations into proteins.
- 49:48There is certainly evidence that the
- 49:51presence or absence of modifications
- 49:53can impact at least at the very least
- 49:56sort of start code on usage and to
- 49:58some extent a little bit of decoding.
- 50:01Not as familiar with the
- 50:02translation literature.
- 50:03There's certainly evidence for it in sort
- 50:04of in vitro translation systems in vivo.
- 50:07It's a bit harder to tease apart,
- 50:09but if you're interested
- 50:10in that literature at all,
- 50:11actually Shalini Oberdorfer's lab
- 50:13has done some really amazing work
- 50:15on a C4C and how it sort of dictates
- 50:18start code on usage through sort
- 50:21of base pairing interactions.
- 50:22So that's not our work,
- 50:24but it's beautiful and I would
- 50:25encourage you to take a look at
- 50:26it if you're interested,
- 50:27because yes,
- 50:27there there's certainly some evidence
- 50:29for that.
- 50:39So the question is the time scale
- 50:41of our modifications and the
- 50:42and how that sort of relates to
- 50:44chromatin and things like that.
- 50:46So this is something that we're
- 50:48still trying to work out so.
- 50:50We've been working on actually a totally
- 50:52different strategy I haven't talked about.
- 50:54We're trying to work out a dual
- 50:55labeling strategy in our labs.
- 50:57This is a different graduate student,
- 50:58Luke, who is working on this
- 51:00where we're essentially trying
- 51:01to combine nascent RNA labeling,
- 51:03something like an EU approach or
- 51:05a fourth European labeling with
- 51:07essentially trying to both Mark
- 51:09naison RNA and nascent methylation
- 51:12with a deuterated Sam analog.
- 51:13We haven't been able to pin
- 51:16down the kinetics.
- 51:17I have some very preliminary
- 51:18data from like way back in my
- 51:20postdoc that this can happen.
- 51:21Within minutes.
- 51:22But it's very hard to catch
- 51:24the dual sort of label.
- 51:26And so that's something that
- 51:27we're still working out.
- 51:29We're hoping that that will be working soon,
- 51:31but that's exactly the question we
- 51:33want to answer because we don't know
- 51:35if the timescales are relevant yet
- 51:36because we can't pin them down yet,
- 51:38if that makes any sense.
- 51:46Hmm.
- 51:51The.
- 51:57Yeah. So the question is whether the,
- 51:58the mutation rate sort of essentially
- 52:00correlates with what's actually
- 52:02happening at the modification level.
- 52:03So we're literally working with
- 52:05Anna Kyle's lab to try to generate
- 52:07the standards to figure that out.
- 52:08So what we essentially need to make
- 52:11is a calibration curve with known
- 52:13sort of modified oligos because
- 52:15based on some previous work with a
- 52:18different reverse transcriptase,
- 52:20we know that there is some correlation
- 52:21between the level of modification.
- 52:23And sort of the mutation rate in terms
- 52:26of it's somewhat accurate reflection
- 52:28because you know it's reverse
- 52:30transcribing not just the modified
- 52:31pool but also the unmodified pool.
- 52:32So there should be some concordance.
- 52:34It is however probably not perfect and
- 52:36so we kind of need to make a calibration
- 52:39curve essentially getting you know
- 52:40known modified oligos at zero percent,
- 52:4225 percent,
- 52:4350% and generating a curve to try to
- 52:45correlate where those where those
- 52:47are based on some really early work
- 52:50in our first M1A paper that for
- 52:52M1A we know that mutations.
- 52:54Um are relatively usually relatively strong.
- 52:57And in that case M1A tended to occur at
- 53:00about 20% of its given transcript pool.
- 53:02So like if you had a favorite
- 53:04transcript that was 1A methylated on
- 53:05average it was about 20% methylated.
- 53:07We never figured out what that means though.
- 53:09We don't know if that means most
- 53:11of it's being decayed or you know,
- 53:12anything like that,
- 53:13but we're still working that out.
- 53:20Thank you very much.
- 53:22Sacred support is amazing talk.
- 53:24It's really very rewarding to see
- 53:27this high end science coming to
- 53:29visit her here on the other side.
- 53:31Let's hope that this will be the
- 53:33beginning of many more collaborations.
- 53:36And on that note,
- 53:37I would like to remind you all
- 53:40that we do have an RFA out for
- 53:43funding laboratory research.
- 53:44There has more of a translational character.
- 53:47So you might want to check it out.
- 53:48Thank you very much.