Gene expression regulation by RNA modifications in cancer

Transcript

• 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&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.