Cell fate decisions in hematopoiesis

February 22, 2021

Information

Marjorie Brand, PhD
Professor, Department of Cellular and Molecular Medicine, University of Ottawa
Senior Scientist, Ottawa Hospital Research Institute
YCCEH Invited Speaker
February 19, 2021

ID6217

To CiteDCA Citation Guide

00:00Why don't we go ahead and get started?
00:03So I'm Jeannie Hendrickson.
00:04I'm representing the enrichment
00:06program of the Yale Cooperative
00:08Center of Excellence in Hematology,
00:10or super excited on this Snowy Friday
00:12afternoon to have Marjorie Brands
00:14visiting us for a talk that's called
00:16cell fate decisions in hematopoiesis.
00:18She's visiting us,
00:19and virtually she's a professor in the
00:22Department of Cellular and Molecular
00:24Medicine and a senior scientist at the
00:27Ottawa Hospital Research Institute,
00:28and really enjoy the picture of her lab here.
00:31So we're thrilled.
00:33She's with us this afternoon.
00:35And at the end of her talk we
00:37should be able to have some
00:40questions and answers as well.
00:42So thanks again Marjorie for visiting.
00:44We really appreciate it.
00:46Thank you very much.
00:48It's a great pleasure to be here.
00:50Thanks Pat for the invitation and I really
00:53enjoyed my discussions that I had today,
00:56so I'm going to start by 10.
00:59So the as we all know,
01:01hematopoiesis is a multi step process
01:04through which the hematopoietic stem cell.
01:06Shades to give rise to all the cells
01:09that are present in the blood,
01:12and this process is highly regulated
01:14by a number of transcription
01:16factors and cofactors,
01:17and these factors work together as
01:20networks to promote differentiation to
01:22our specific lineages such As for example,
01:25the average ridlen age that I'm going
01:28to focus on today and at the same
01:32time these transcription factors.
01:34Inhibiting the other alternate
01:36hematopoietic lineages.
01:36Now what's interesting is that the
01:39transcription factors that drive
01:41every trade or other lineages
01:43drive differentiation.
01:44There are expressed at very high levels
01:47at the specific stage when they are
01:50important to drive differentiation,
01:52usually towards the later stages.
01:54But most of these factors are
01:57also expressed in hematopoietic
01:59stem and progenitor cells,
02:01although at very low levels.
02:03Which suggests that the amount also
02:07dosage of transcription factors play
02:09critical role for cell fate determination.
02:13Now the importance of the dosage
02:15of transcription factors for the
02:17process of cell fate decision
02:19has been established early on by
02:22reprogramming experiments in cell lines.
02:24So, for example,
02:25the lab of tumors graph in 1995
02:28was able to reprogram myeloblastic
02:30cell lines 2 hours different fate
02:33depending on the amount of the data.
02:36One footings that was Ectopically
02:38expressed so specifically,
02:39the lab showed that if that high
02:42levels of get a one.
02:44We drive differentiation to us every
02:46trade lineages or megakaryocytes,
02:48while intermediate levels of get
02:50a one will drive differentiation
02:53too as the elzina fear.
02:54Now these early experiment really
02:57demonstrated that different
02:58amounts of the data one put in
03:01can promote alternate cell fate.
03:03Now, more recently,
03:04linear programming experiment in
03:06in primary cells have also shown
03:09the importance of the dosage of
03:12transcription factors for the
03:13process of cell fate decisions.
03:15So for example, in this publication here,
03:19the authors have reprogrammed
03:21human fibroblasts into every trade
03:23progenitors by using this cocktail
03:26of four transcription factors get
03:28away until one element 2 and C myc,
03:31which they called the GTLM.
03:33Cocktail, and for these reprogramming
03:35experiments they have used a retroviruses,
03:37and each retrovirus expresses one
03:39of these transcription factors.
03:41And what's very interesting is they
03:43found that depending on the relative
03:46levels between their retroviruses
03:47that they use in their experiments,
03:50they can obtain completely different results.
03:52So for example,
03:53if they use a GT LM ratio of 2111,
03:57then these these reprogram cells will
03:59produce exclusively red cell colony,
04:02which suggests that those are
04:04functionally which would progenitors.
04:06However, if they use a
04:07slightly different GT LM ratio,
04:09for example 1112,
04:11then the reprogram cells will
04:12not produce red cells colonies,
04:14suggesting that these are not functional.
04:17So these these experiments have
04:18shown that it's not only the dosage
04:21of the amount of transcription
04:23factors that's important,
04:24but really the whole ative levels,
04:27or just to comma tree of
04:29transcription factors.
04:30That is key for reprogramming efficiency,
04:32and it is the same for native hematopoiesis.
04:36Where it has been proposed that the
04:38transcription factors that drive
04:40differentiation towards alternate
04:42hematopoietic lineages Co expressing
04:45bipotential progenitors and that those
04:47changes in us to comma tree is what
04:50is important to drive differentiation
04:53to US1 fate or another phase.
04:57Now a number of gene regulatory
04:59networks have been established in
05:01an attempt to model this self,
05:03a choice that occur in this
05:05bipotential progenitors,
05:06and for example,
05:07here is a network model of the
05:09selfish choice that occur in the MVP,
05:12just the megakaryocytes every
05:14trade progenitors and those cells
05:16must choose between an arbitrate
05:18fate or a megakaryocytic faith.
05:20Now,
05:20this hematopoietic tree of
05:22differentiation that I have shown
05:24you so far was a commonly accepted
05:27model for hematopoiesis until
05:28about 6:00 or seven years ago,
05:30when the use of single cell RNA
05:33seek transformed it into something
05:35that looks more like this.
05:37Now,
05:37in this knew continuous model
05:39of hematopoiesis,
05:40there are no differentiation steps process,
05:42but instead the cells are gradually
05:45transitioning along hematopoietic lineages.
05:46And really in these types of models,
05:49the.
05:49Hematopoiesis is actually based on a
05:52continuum of changing failed probability.
05:54So in in that type of model they are
05:57known by potential opportunities,
05:59but they're only hematopoietic.
06:01For generators with sulfate
06:02probabilities that are becoming
06:04more and more restricted as the
06:05cells gradually transition along
06:07the hematopoietic trajectories.
06:08Now in his new types of models
06:11for him at a crisis,
06:13and the exact exact role of
06:15transcription factors and the
06:17importance of their quantitative
06:18changes for the process of self.
06:21Decision remains unclear,
06:22so our goal is to establish a dynamic
06:26model of erythropoiesis that can
06:29integrate those quantitative changes in
06:32the level of transcription factors overtime.
06:35Now I will.
06:36I will model system to study human.
06:38Every troop Oasis is an ex vivo
06:41differentiation protocol where we
06:42isolate the city certified positive
06:44stomach progenitor cells from cord
06:46blood or peripheral blood or bone
06:48marrow and those cells are then
06:50differentiated to observe Richard
06:52lineages by using a cocktail of
06:54growth factors and cytokines.
06:56Now here are the sum of the
06:58morphological changes that occur
06:59during this ex vivo electrophoresis.
07:01You can see at the at the beginning at
07:03Day zero we can see the hematopoietic
07:05stem and progenitor cells and then there
07:08are dramatic changes in morphology and
07:10at the end of differentiation we obtain
07:12complete indication on those cells.
07:14That we wanted to further study every
07:17true policies at the single cell level.
07:19And for this we decided to use
07:21my cytometry or site off,
07:23which allows one to measure
07:25putains in single cell.
07:27So for the site of experiment,
07:29we harvested cells at regular
07:31intervals during the crossover.
07:33Every trait differentiation and
07:34then we barcoded those cells are
07:37separately at different time points
07:39with Palladium isotopes prior to
07:41combining them and standing them
07:43together with a cocktail of antibodies
07:46contain including cell surface markers
07:48as well as transcription factors.
07:51And then we did a clustering analysis
07:53on the 27 markers at a different time
07:56points and this allowed us to identify
07:5818 clusters or cell populations.
08:01And because we did a temporal
08:02because we did re bar coded the
08:05samples at different time points,
08:07we could do a temporal analysis of
08:10those cell populations which allowed us
08:12to reconstruct the dynamic trajectory
08:14from the early March put on progenitor
08:16cells to the differentiated auto chromatic,
08:18which replies.
08:19And then we looked at.
08:21The transcription factors expression
08:23in single cells within these different
08:26populations along the retreat trajectory.
08:28And our first question was whether
08:30there is Co expression of antagonist
08:33transcription factors in early progenitors.
08:35So we looked at fly one which
08:37promotes differentiation towards
08:39megakaryocytes and PLF,
08:40one which promotes differentiation
08:41to repair itself,
08:43and we looked at them in the MVP
08:45populations that we gated with this
08:48combination of seven cell surface
08:50markers and what we found is that the
08:53vast majority of the MVP Dooku Express
08:56is antagonist transcription factors,
08:57KLF one and fly one.
09:00In single serve.
09:01So next we wanted to follow the
09:03changes in put in levels of KLF one
09:05and fly one as the sales proceeds
09:08towards irritated differentiation
09:10and you can see these results at
09:12the single cell level here and when
09:15we aggregate the results we found
09:17that there is a gradual increase
09:19in put in levels of KLF one and
09:22at the same time gradual decrease
09:24in foot in levels of fly one.
09:26But there is no abrupt switch
09:28after a specific population.
09:30What happens is that those
09:32gradual changes actually.
09:33Taking several populations to occur
09:36belongs in between the trajectory.
09:40And then we we decided to Ectopically
09:43Express a flag tag version of fly,
09:46one in early progenitors.
09:48And then again,
09:50we followed differentiation by
09:51site of analysis and this time we
09:54identified not one but two trajectories
09:56in addition to the average rate
09:58trajectory that you can see here.
10:00By high level of expression of the
10:02Alpha globin we also found a second
10:05trajectory here which correspond to
10:07the megakaryocytic trajectory as
10:08shown by high level of expression of
10:11the city 41 marker.
10:13And then when we followed the cells
10:16that express flag fly one by including
10:18a flag antibody in our cocktail,
10:21we could determine that only the
10:23cells that express flag fly one shown
10:26in blue are differentiating towards
10:28the megakaryocytic trajectory,
10:30while the cells that do not
10:32express factor one shown here in
10:34green continue to differentiate
10:36along the imagery trajectory.
10:38So this experiment showed that the
10:41ectopic expression of Flight 1 is
10:43able to deviate the cells from their
10:45preferred every trade tragic tree
10:47to take on a megakaryocytic faith.
10:50So overall,
10:50these results are supports the
10:53concepts that quantitative changes
10:54in transcription factor put in
10:57levels in individual hematopoietic
10:59progenitors are key determinants
11:00of the cell fate decisions.
11:02Now in our site of experiment we
11:05could account for all older cell
11:07populations that were present
11:09in our differentiation media and
11:12that includes the hematopoetic
11:14progenitors erythroid cells.
11:15Also some megakaryocytes and Milo itself.
11:18However,
11:19there was one major cell population here
11:22for which the identity was less clear.
11:26And as you can see,
11:28the cell population starts to accumulate
11:30from day four of differentiation and
11:32then it increases until the 11th
11:34and then starts to progressively
11:36decrease and then completely
11:38disappears from our differentiation.
11:42Such a medium.
11:43Now looking at the cell surface
11:45markers for this population,
11:47we notice that it expresses
11:48very high levels of CD 44,
11:50which you can see here on the heat map,
11:54but also on this Disney plot.
11:56And we also notice that this
11:58sector population expresses
11:59intermediate levels of CD123.
12:00But when we look closer on a Disney product,
12:03you can see the expression
12:05of CD123 is heterogeneous,
12:06with some cells expressing
12:08extremely high levels an other
12:10cells are moderate levels of CD123.
12:12We also notice that these cells
12:14express very high levels of data.
12:16Two,
12:16it's actually the cell population that
12:19expresses the highest level of of getting 2.
12:22In in our experiment,
12:24and it does express graded levels
12:27of the transferring receptor CD
12:3071 but is completely negative
12:33for the early marker CD 34.
12:36So based on these combination
12:38of cell surface markers,
12:40it suggested that these populations
12:42could represent battlefields which
12:44at first we found a surprising
12:46because basil fees are supposed
12:48to emerge from the granulocytic.
12:50So the model with branch of differentiation,
12:52not the average rate branch.
12:54However,
12:55these results are consistent with
12:57a single scientific data that have
12:59been published both from mouse bone
13:01marrow and from human bone marrow
13:04that have identified a cell population.
13:06In close proximity to the error rate,
13:09cells that with the gene expression
13:10profile that that is consistent with the
13:13battlefield identity transcription factors.
13:15Now why do we want to
13:17know Putin's documetary?
13:18First of all,
13:19because we want to better understand
13:21how this changes in stock.
13:23Yama tree, Dr,
13:24Cell fate decisions.
13:25But also because despite everything
13:27that we have learned from genomic
13:29studies and cheap seek about
13:31the binding of transcription
13:32factors to their target side,
13:34we still don't know how
13:36many transcription factors.
13:37Are there compared to the number of
13:39binding sites and we still don't
13:42know if cofactors are limiting
13:43compared to transcription factors,
13:46in which case the transcription
13:48factors must compete to recruit them,
13:50or whether cofactors are
13:52present in large excess,
13:53in which case there equipment would
13:56be highly facilitated.
13:58Now, if one wants to measure
14:00documetary between Putin's,
14:02it is necessary to use approaches
14:04that provide an absolute
14:05quantification of these proteins.
14:07So we decided to use a targeted
14:10mass spectrometry approach,
14:12which is called selected reaction
14:14monitoring our SRM, which,
14:15when coupled with the spiking of known
14:18amounts of isotopically labeled peptides,
14:20can provide an absolute
14:22quantification of footings.
14:23Now recently,
14:24a number of publications came
14:26out using quantitative mass
14:28spectrometry approaches.
14:29Which we are not SRM,
14:31so I just wanted to take a few
14:34minutes to explain why we have
14:37decided to use this SRM approach
14:39for our for our own purposes.
14:42So first of all I will start by
14:44saying that my spectrometry is not
14:46inherently quantitative an what it
14:48means is that the intensity of the
14:51signal that is measured by the mass
14:53spectrometer does not only depend
14:55on the abundance of the peptide,
14:57but also on a number of other criteria,
15:00such as the amino acid composition
15:02or the ionization efficiency,
15:03or other parameters that are
15:05not fully understood and what it
15:08means is that it's not unusual to
15:10be in situation like this one.
15:12When you have a very high abundant
15:14peptide like the blue peptide
15:16producing a low intensity signal by
15:19mass spectrometry or lower opponents
15:21peptide like the red peptide,
15:23producing a very high intensity signal.
15:27Now to overcome this limitation and
15:29still being able to extract quantitative
15:31information from my spec data,
15:33a number of strategies have been proposed.
15:36Anna very popular strategy is
15:38called the Proteomic Ruler Method,
15:40which has been established by the
15:42lab of Matthias Mann to estimate
15:44copy number of proteins preserve,
15:46so this method is using the mass
15:49spectrometry signals from histones
15:50as an internal standard to quantify
15:52all the other footings,
15:54and this is this is a very effective
15:57method to estimate putting copy numbers.
16:00It's also high throughput.
16:01You can measure thousands of
16:03proteins in a single experiment,
16:05and it is quite easy to do and it
16:08has been used in and in a lot of
16:11different publications and I have
16:13highlighted some of these publications
16:16here for high throughput studies.
16:18So,
16:19however,
16:19in our case we want it to be
16:21able to distinguish even subtle
16:24differences in the circulatory
16:26between transcription factors.
16:28So so instead we decided to
16:30use a different approach,
16:32which is based on the spiking of
16:35known amount of isotopically labeled
16:37peptides that are used as internal controls.
16:40So in this approach,
16:42each individual peptide is
16:44quantified using an isotopically
16:45labeled version of itself,
16:47as illustrated here.
16:49Which we called the the silk
16:51peptide and this approach has been
16:54shown to be extremely sensitive
16:56to dynamic range,
16:57is very large and it's currently
16:59considered as a gold standard for putting
17:03quantification by mass spectrometry.
17:05So to use to establish SRM assay
17:08for putting quantification,
17:09we collaborated with Jeff and Ishan
17:12Margulis pee from the Institute
17:14for Systems Biology in Seattle,
17:16and we developed SRM assay and
17:19optimize Sri massive over 100 Putin's
17:21and we decided to use a different
17:24types of proteins, including.
17:26DNA binding transcription factors,
17:28but also compressors, coactivators,
17:30some chromatin remodeling enzyme,
17:32as well as some subunits of the
17:35general transcription machinery.
17:36And overall we quantified 103
17:38proteins at 13 time points during the
17:41course of erythroid differentiation.
17:43Now I should mention that even though
17:46these SRM assay are quite expensive
17:48to develop and they take time,
17:51but once they have been developed
17:55they can be reused.
17:57Much cheaper way and fast way to be
18:00able to quantify the same protein in
18:04in a different cellular environment.
18:06So because we wanted to build
18:09gene regulatory network,
18:10we also had to measure changes
18:12in our near levels.
18:13So again,
18:14we have just excels at regular intervals
18:17during the course of their true policies,
18:19and we measured our in a level by RNA
18:24sequencing and put in levels by SRAM.
18:27And first,
18:27focusing on the RNA data.
18:29Here I'm showing this irony Sickbay
18:32Suretrade trajectory overtime along
18:33these two principal components,
18:35and as you can see,
18:37there is a nice purple nice progression
18:39from the hematopoietic stem and
18:42progenitor cells at day zero to the
18:44differentiated erythroid cells shown here.
18:47Now looking at the proteins that we
18:49have measured by hospital clustering,
18:51you can see that there are different
18:54clusters and these clusters appear
18:57to be a temporary Co regulated.
19:00Now the first question we wanted
19:02to ask was whether there is a good
19:05correlation between putting an M
19:06RNA during erythropoiesis and 1st.
19:09We looked at global correlation
19:11as illustrated here.
19:12Looking at M RNA level versus
19:14protein levels and what we found is
19:17that there is really quite a good
19:19correlation in differentiating cells.
19:21However,
19:22the correlation is extremely low in
19:24hematopoietic stem and progenitor cells,
19:26which is consistent with published
19:28data that have shown that the
19:30protein translation rate.
19:32Is extremely low in the hematopoietic
19:34stem and progenitor cells.
19:36So next we wanted to look at the
19:39correlation between our input in
19:41changes overtime during differentiation
19:43as illustrated here and what we
19:45found is that for most genes there
19:48is a positive correlation.
19:49However,
19:49for some genes there is low correlation
19:52and even sometimes a negative correlation.
19:54So for example here if we look
19:57at the fly one or get the two.
20:00She and you can see that there is
20:02a very good correlation between
20:04changes in putting an M RNA levels.
20:07However,
20:08for other genes such as get our Nortel one,
20:11you can see the Putin level is
20:13increasing much faster than the our name,
20:16suggesting an important contribution
20:17of the post transcriptional
20:19regulatory mechanisms.
20:20So if you are interested
20:22in looking at other genes
20:24that we have measured,
20:25we have we have done a website here
20:28which I'm going to try to access and I
20:31hope it is going to work so I'm just
20:35gonna stop sharing for a few seconds.
20:38And try to share.
20:46My other sites. OK,
20:47so I hope you can see it so so perfect.
20:52Thank you so on these websites we
20:54have to drop down menu here with the
20:57proteins that you have measured.
20:59So I just wanted to show you an
21:01example of proteins that going up
21:03during differentiation where there
21:05is extremely good correlation so
21:07you can see so on the left side.
21:09Here we have put in copy number per
21:12cell and the blue graph correspond to
21:15put in an on the right we have the RNA.
21:18And from left to right we have
21:21a differentiation from MPP to
21:22Basel figurative glass,
21:23so you can see this two hour and then
21:26put in correlate extremely well for six,
21:296, but actually for for a lot of
21:31proteins there is a very low correlation
21:33or or even a negative correlation.
21:36I just want to show you one more one.
21:39Another example kept away which
21:41is a histo nasty transfer is also
21:44called GCN 5 and you can see for
21:46this put in the RNA and put in.
21:49Growth curves have almost
21:51nothing to do with each other,
21:54suggesting a highly complex
21:57posttranscriptional regulation mechanism.
21:59So.
22:01I will just go back to my presentation now.
22:06OK, perfect,
22:06so as you could see,
22:08we found that there are really
22:11major discrepancies between
22:13mRNA and put in and put in.
22:15Abundance is an.
22:16This is for master regulators
22:18of erythropoiesis,
22:19suggesting that gene regulatory networks
22:21should not be limited to M RNA but
22:25should also integrate put things.
22:27And this is what we tried to
22:29do in collaboration with the
22:31computational biology step.
22:32Perkins and Daniel Sanchez start
22:34level from our Institute and
22:36basically they they wanted to build
22:38a dynamic network model of every
22:40trade commitments that incorporates
22:41the quantitative changes in
22:43transcription factor protein levels.
22:45So in the model,
22:46M RNA are considered as the targets and
22:49Putin's are considered as the regulators.
22:51Putins can be activated so
22:53they can be repressors.
22:55And basically what they did is to
22:59use ordinary differential equations
23:01to try to predict the kinetic
23:03of the change in M RNA levels by
23:07the changes in the quantitative
23:09changes in protein levels.
23:10And here is the the models that
23:13they they established around 14
23:16transcription factors at different
23:18days during differentiation.
23:20Now what distinguishes this model
23:22from previously published model is
23:24that in quantifies the strength of
23:27the identified regulatory relationships
23:29as a measure of the contribution of
23:32all proteins and this is shown by
23:35different levels of transparency.
23:37So for example here,
23:38if we look at ranks one and get it to,
23:41you can see that the activation
23:43of getting 2 by ranks one is much
23:45stronger than the activation of ranks.
23:47One by getting 2.
23:49The model is also dynamic in
23:51that it reveals changes in these
23:54regulatory relationships overtime,
23:55so you can see, for example,
23:58at day zero,
23:59the strongest regulatory relationships.
24:01Involve proteins that are important
24:03in progenitor cells,
24:04but those links are progressively
24:07decreasing in strength and they
24:09are fading away and that
24:11they tend in committed cells.
24:13They have been replaced by other links.
24:17That that binds proteins that are known
24:19to be important in differentiated cells.
24:22We also notice that our model is able to
24:25correctly recapitulates the timing of the
24:27transcription factor across antagonisms
24:29that underlies the cell fate decisions.
24:32So here you can see at day zero,
24:35the model was able to capture the P1,
24:38get a one across antagonism,
24:39which regulates a very early self,
24:42a choice between my
24:44Louisiana Richard lineage.
24:45And that they too were able to capture
24:48a second subsequent cross antagonism,
24:50the KLF one,
24:52Fly 1 cross antagonism that regulates
24:54cell fate decision that took on later
24:57between the erythroid cells Anna
24:59megakaryocytes and both of these
25:01cross antagonisms have completely
25:03disappeared at day 10 in committed cells.
25:07Now I will another interesting
25:09aspect of the model is that it allows
25:11us to compare the strength of the
25:13different transcription factors to
25:15the regulation of their target genes.
25:17So for example,
25:18if we look at the data 1 gene,
25:21you can see that the contribution of
25:23tell one to the activation of get
25:26a one is about twice as important
25:28compared to the contribution of GATA 2.
25:31And surprisingly,
25:32this is not because tell one is a
25:34stronger activator of this gene,
25:36because when we look.
25:38Get the activation strength and one
25:40is in fact a weaker activator of the
25:42ghetto engine compared to get that too.
25:44But what happens is that 10 one is
25:463 times more abundant than get a 2.
25:49So basically what this shows is
25:51that Taiwan is a week,
25:53but opponents of activators of the
25:55data 1 gene and get a 2 even though
25:58it's a stronger activator of the gene.
26:01Overall it contributes less to its
26:03activation because it is less abundant.
26:05So with this type of models we
26:07are really able to dissect the
26:09quantitative relationships between
26:11these transcription factors.
26:12And that's very important if we
26:15want to be able to control every
26:18trapezes either in vitro or in vivo.
26:21So because they built different gene
26:24regulatory networks at different time points,
26:26we pieced them all together and
26:29made little movies where you can see
26:32the quantitative changes in this
26:35regulatory relationships as the cells
26:37proceed towards every trait differentiation.
26:40So you can see that this early
26:43regulatory here relationships
26:44decreases with time and are replaced
26:47by other regulatory relationships
26:49in more differentiated cells.
26:54OK, So what about cofactors?
26:56So everything I showed you so far was about
26:59the DNA binding transcription factors,
27:02but we know that these proteins works
27:04with the recruitment of cofactors,
27:06so we also wanted to measure cofactors by SM.
27:11And we started with the
27:13histone acety transfers P300.
27:15Now, according to the current literature,
27:17P300 has been shown to interact with
27:20dozens of transcription factors,
27:22so we expected it to be highly abundant
27:24in the nucleus, but much more,
27:27well, surprise.
27:27We found that not only P 300 is not in
27:30excess of the transcription factors,
27:33it is in fact present at a lower
27:35opponents than almost every single
27:38transcription factor taken individually.
27:40So then we looked at the other histone
27:44acetyltransferase is CBP and catuara
27:46GCN 5 and as you can see those are
27:50just as well in the nucleus as P300.
27:53So the next logical thing was
27:55to look at the antagonist,
27:57enzymes, histone deacetylase,
27:58and as you can see,
28:00those are much more abundant than
28:02the histo nasty transparency.
28:03So there is a quantitative
28:05imbalance between the edge ducks
28:07and their hearts in the nucleus.
28:09And then we looked at a broader
28:12range of cofactors.
28:13And what we found is all the
28:15coactivators that we have measured
28:17are actually really low abundance
28:19compared to the compressors.
28:21So for example,
28:22if we focus on CHD for sociology,
28:24far is one of the enzymatic subunits
28:26of the Nerd core oppressor complex.
28:29And as you can see,
28:31there is an almost 50 fold excess
28:33of CHD 4 compared to the to
28:35the Co activators in nucleus,
28:37and these results likely explain the
28:39fact that they're not complex has been
28:42identified as an interacting Putin.
28:44In almost every single put on the screen,
28:47now that have been published so far.
28:50So we found that the compressors
28:52are highly opponent.
28:53The coactivators are rare,
28:55so where do the transcription
28:58factor of fit on that scale?
29:00And the answer is that they
29:02fit somewhere in the middle.
29:04So here on this graph we have the
29:06copy number of Putin's in log scale,
29:09and as you can see,
29:11there is about an order of magnitude
29:13difference in your balance between Co
29:15activators and transcription factors.
29:17And then another order of magnitude
29:19difference between the transcription
29:21factors and the Co oppressors.
29:22And Interestingly,
29:23we could detect these differences
29:25in abundance only because we
29:27looked at Putin's because when we
29:29do the same analysis with M RNA.
29:31As you can see,
29:33there is no statistically significant
29:35differences in the abundance of
29:37the transcripts that code for
29:39these different classes of factors,
29:41which suggests that these very
29:43large differences in abundance are
29:45regulated post transcriptionally.
29:47And indeed, consistent with that,
29:49we found that when we inhibits
29:51protein translation by using by
29:53treating the cells with cycloheximide,
29:55you can see that the coactivators
29:57appear to be much less stable compare.
30:00This occurred pressers.
30:02So at least one of the reason why
30:05the coactivators are so where in
30:08a cell is
30:09because those appears to be
30:12extremely unstable footing.
30:14And this is true throughout every trip Oasis.
30:17Here I have plotted the Putin
30:20transcript average and as you can see
30:23the coactivators here are present
30:25between 100 and 1000 copies per
30:28Nicholas there transcription factors.
30:31On average, between 1000 and 10,000 and
30:35the oppressors between 10,000 and 100,000.
30:39So overall, these results suggest that
30:41the nucleus is a highly repressive
30:43environment where coactivators are limiting,
30:46which implies that the DNA bound
30:49transcription factors must compete with
30:51each other to mediate the recruitment
30:53of this limited number of coactivators.
30:56Now, one way through which the
30:59transcription factors can increase their
31:01likelihood of being able to recruit them,
31:04the limiting coactivators is through
31:06assembling themselves onto enhancers.
31:08So we wanted to ask the question,
31:11how do the number of active enhancers
31:13and coactivator molecules compare?
31:15So we looked at Co activators that are
31:18known to associate to active enhancers,
31:20which are the histone acetyl transferases,
31:22CPP 300 and the HTK for monometer
31:26transfer is is a mileage 3 MLN 4?
31:29And we have estimated the number of
31:32active enhancers by doing a taxic
31:34experiments which we have analyzed by
31:36hint Attack to identify transcription
31:39factor footprint and then intersected
31:42these with predicted enhancers from database.
31:44And this is the number of estimated
31:48active enhancers that we have obtained
31:50and as you can see these numbers are
31:54in the same order of magnitude as the
31:57number of molecules of coactivators.
31:59Suggesting that maybe the formation of
32:02these active in home sales depend on
32:05the availability of these coactivators.
32:07So this is of all our model where we have
32:10the transcription factors recruiting
32:12the RECO activators and the rest of
32:16the nucleus containing corepressor's.
32:18And we think that this.
32:23Hello hello hi how are you doing?
32:28Well, not there are you in
32:30the wilderness or in the camp.
32:32Sorry, my treat doctor Wiseman.
32:34Can you hear me OK?
32:37Actually going out on Sunday.
32:39So OK, so you missed some buddies
32:42working before Doctor Wiseman.
32:44Please mute yourself.
32:46I muted him. Thank you.
32:49I I'm almost done anyway.
32:52Thank you so so we think basically
32:55that this restriction of abundance
32:57of coactivators could be an
32:59important mechanism for concerted
33:01gene regulation and we think it
33:04may be particularly important in
33:06multipotent progenitors to prevent
33:08high levels of Co expression of
33:10genes from different lineages.
33:12So basically decreasing the
33:14transcriptional noise as well.
33:18And I will stop here.
33:20And I've knowledge people who did the work,
33:24so I already acknowledged all
33:26our collaborators an from my lab.
33:28This project was done mainly by two
33:31very talented postdoctoral fellow,
33:32common Poly who did most of the wet lab work.
33:36Xena Motory, who did the informatic.
33:39Analysis, With some help
33:41from a Kathy Silverman,
33:42who also did the informatic analysis,
33:44and I thank you all for listening and
33:47I'll be happy to take any question.