Web Conference with Dr. Andy McMahon

Cell Fate Regulation in the Neural Tube

Vokes SA, Ji H, McCuine S, Tenzen T, Giles S, Zhong S, Longabaugh WJ, Davidson EH, Wong WH, McMahon AP. 2007. Genomic characterization of Gli-activator targets in sonic hedgehog-mediated neural patterning.Development. 134(10):1977-89.


TRANSCRIPT

00:00 Thank you very much for taking the time to chat with us today. I know you were very busy with grants yesterday, I hope that went out okay.

00:54 Hopefully, yeah, I’ll find out in a few minutes.

– Alrighty, so my class, particularly the whole class, read two of you papers. One of ’em was on the recent paper on Gas1 and Cdo, and the other one was on the glee activator using Chip to identify down stream targets. We read through it, thought they were fantastic and figured out all the problems that you also generated as well. And so we have some series of questions here, mostly all student generated. I’d like to start off with the first one though, if you can introduce yourself, tell us a little bit about your career path over the years and then what specifically got you to studying hedgehog signal transduction and neural tube patterning.

– Yeah, so this is a question I’ve been asked a number of times, and… Can you hear that?

– Yeah.

– Okay, and I guess I’m not a good example for career guidance, so I just sort of followed what I’d been interested in over the years with no clear path or trajectory, and things just worked out okay. And I find that there’s a remarkable correlation amongst my colleagues that did exactly the same. So there’s a lot to be said for following your nose and interests and not thinking too hard about how that’s gonna take care of your life. In terms of the specific problem that how we got involved in the hedgehog problem, this really started when I first set up my group, and I was very influenced by work in the fly at the time, where people at first as screen, and then by genes that regulated anterior posterior polarity. We not even tried to do some things with our mouse to try and identify genes, and they all failed, and so with failure looking us in the face, we have to figure out how we could find some genes that may be important in regulating early mouse development, and we thought with genes that cause cancer might be a good class of genes, so we looked at those cancer genes, and we intrigued by some genes that encoded signals, but have no known patterns of expression during development. And, one of those was a signal which was called Week One, and it turned out to be their mammalian homolog of a gene called wingless in Drosophila, and so this, apply this inter Drosophila again, and we studied with one and its importance in brain development, and in thinking about how it might work in the brain, we thought that maybe the molecular mechanisms are conserved with the fly, and in the fly with one regular hedgehog, and so these fellas are looking for hedgehog camel parts invertebrate systems, and so one thing led to another in a very random way, which I think is a good illustration of my career.

03:26 Now, one of my students asked, after hearing your accent the first time we connected, where you were from, but I figured, and I said you would probably tell us, so you’re gonna have to tell us that.

– Hands up for Australia.

– There ya go.

– No, hands up for the United Kingdom.

– Okay.

– Oh, very good, very good. Most people actually say Australia, but it’s from the United Kingdom. If anybody supports soccer, they all have heard of a place called Liverpool.

– Yeah.

– If anybody’s heard of The Beatles, then they might of heard of Liverpool, too.

– Well, thanks.

– Hello.

– Hi.

4:05 Hi, you show in your paper that Gas1 functions as a sonic hedgehog pathway activator, but simultaneously is negatively regulated by sonic hedgehog, this seems counterintuitive. Can you please explain what benefit there is for neural tube patterning to have an activator of sonic hedgehog signaling be negatively regulated by that same system?

– Mm.

– Thanks.

04:26 So, I think what you’re going to hear is a recurring theme today, well, two recurring themes. One will be major amounts of hand waving because we don’t really understand how the system truly works, and the other is the word time. So, the description of how the hedgehog powerfully works is a very static system, description, and it doesn’t really integrate time in any meaningful way. So, let’s just think about how this kicks off. So, it kicks off by hedgehog signaling coming from the notochord to the overlying neural tube, and you might imagine that at the outset of this process, there’s very little signal, and it has to build that over time. The one way in which you can actually get the process working rapidly is to make those cells super sensitive to the first ligand that’s produced. So, they may, themselves, express components that are going to be very useful for low levels of ligand signaling, but you may want to, after you’ve got the process up and running, be able to silence that so signaling is no longer maximal in those subtypes. So, that could be one logic, which is to just get the signaling going at the appropriate time. The second logic could be that, whereas these proteins are negatively regulated at the leftward transcription by hedgehog input, they’re not silenced completely. So, they overlap the peripheral boundaries, the distal boundaries of the hedgehog target field. So, what this may do then is enable low levels of signal to the outer signal more effectively or more robustly by having different membrane components that combine the protein in those distal regions of its target field. So, I think various aspects about the initiation and the ongoing nature of the signaling process could explain why you would have positively acting factors that you negatively regulate at the transcriptional way.

06:55 Any followup questions for that? Nothing? So, one quick followup, if you have the hedgehog signal, if you have distal targets, now being, having their sensitivity heightened, would you say that that sensitivity is still going to be below, or still result in a response that is graded from ventral to dorsal, or that the sensitivity is, in fact, so heightened that we almost have to throw out the gradient model?

07:27 As I would say that your, it maybe sustenance at the boundaries where cells are not strong whether they’re seeing, would see a signal, so it would be dropping off, and here, it might make it more uniform for those populations cells at the very low signaling input.

– I see.

– Hello.

– Hi.

07:50 My question for you is that you mentioned time as an important factor to consider when examining the co-expression of Gas1 and Cdo, which occurs during a brief temporal period. Can you explain what impact this has had on our current models of hedgehog signaling?

08:09 Right, so time, again is, just ask yourselves, when does a cell know that it’s received the information that it needs to make a decision? So, is it five minutes, is it 10 minutes, is it three hours, is it nine hours? What is the process by which a cell decides that its still a certain amounts of ligand for a certain amount of time, that it then stably locks in a cell’s state that’s now independent of new ligand input, and what we basically don’t understand that process very much at all. I’m just reminding myself of your question. So, we don’t know how long cells actually require to read a signal, and we don’t know the dynamics in conjunction with that of the distributions of the proteins that are required for cells to see and give that much signaling. So, I think what we were trying to get at there is that this is a dynamic process, and the current view is the static process that takes a moment in time and says what goes on, and until we can look at this as its dynamic process, then we really will not be able to understand what’s happening here. You could delay trusting experiments and show that if you add high concentrations of ligand for a short period of time, you can get a response that’s different than keeping that slight complication of ligand there for a longer period of time, so if you have a certain concentration of ligands for a long period of time, it may give you a more eventful cell identity than that same ligand for a short period to time. If you also take a different low concentration of ligands and just have that presence for a very long period of time, you can also get a similar response. So, chronological time, in some way, is not right. It’s some sort of a measure of integrating how much signaling has been perceived, and so that integration can integrate that amount of information with a high ligand over a short period of time that would take a low level of ligand to be integrated over a long period of time. We don’t understand this at all, and so I think this is a major problem, and I think in the end, of course, it’s these proteins get on, but they’re important in this, we really need to be able to bring those to the entire question.

– Any followup questions up here? Susan?

– So I wanna know is–

– You have to go up there and speak up.

– But, I couldn’t quite hear the question.

– Oh, I’m coming over here to say it.

– Okay, fine.

12:12 Has there been any work to show if it’s actually the amount of ligand that’s around, or if it’s like the steepness of the gradient that controls what the cells become?

12:25 We have recently made efforts, so you’re going to ask me at the very end, I think, about what do I think’s important, so we’ve recently made efforts to try and look directly at this issue. What we’ve done there is that we’ve engineered, by gene targeting, a tagged form that the sonic hedgehogs are real, so now it makes green sonic hedgehog, and so now we can actually just so simply look at the green sonic hedgehog, see where it is, and quantitate it over time. If one quantitated over time, you’ve got interesting results that are quite complicated. So, the protein builds up in two different sorts of ways. It builds up right in the ventral midline cells, which are right over the notochord, where it accumulates over time, so those cells, right above the notochord, have more of a hedgehog on them over time. Those cells, at the make line, actually switch types over time, so they start with a subtype that’s more dorsal, and then they’re promoted over time into a more ventral cell fate. Cells also divide over time, so cells move out from that region to now a more dorsal position, and if we then track the gene expression for a given cell state with those cells now in a more dorsal position, we actually find that the level of ligand goes down on those cells as they move to a more dorsal position, compared to the cells at the ventral midline. So, in our view, we’re backtracking a little bit on the idea that maybe there’s a long range signaling gradient to something whereby, maybe there is a signaling gradient, but maybe what happens in a small cluster of cells right at the ventral midline over time may be actually very critical in this process, and that its, that cell grew, that sees ligands and sees different levels of ligand, but then gives rise to many of the ventral cell identities. In an essence, it’s maybe actually a little closer in thinking to the limb system than we had imagined before.

– Great. Thanks for that information. Look forward to seeing those results, and seeing how you explain them.

– Hi.

– Hi.

15:34 Okay, so my question was, you show that Gas1 cooperates with Cdo to promote sonic hedgehog signaling, however, the phenotypes of Gas1, Cdo double knockouts are not as severe as sonic hedgehog nodes. First, what is the nature of the cooperation of Gas1 and Cdo? Do they directly direct and signal along the same pathway, or do they promote sonic hedgehog target activation through independent pathways? Second, what else compensates for the difference in phenotypes between the double gas Cdo knockout and the sonic hedgehog knockout alone?

16:07 Okay, so that’s good questions, and we’re very actively addressing those. So, what is the nature of their corporation of Gas1 and Cdo? And, that’s really has to be a biochemical question, so we shine it genetically, so now what we’d like to do is see what possibly the simplest model is like first, and the simplest model would be that there’s some co-receptor complex that includes these proteins, may even include the actual hedgehog receptor patch, and this, if you like signal receptor zone, is the entity that receives hedgehog information. So, the approach there is to try an affinity, purify these proteins in their native state, and then see what they’re associated with, and if they’re in a complex defined nature of that complex. So, the answers we don’t know, but that would be the simplest model. Then, when you go to the differences of the phenotypes, well, I should say at the outset that it may be that proteins like Gas1 and Cdo are not absolutely required for cells to be able to respond to the hedgehog signal. They may be required for a cell to respond to a certain level of hedgehog signal. So, there may not be an absolute requirement, so it may not be surprising then, but the loss of function of these two proteins is less severe than the loss of function of the ligands. That said, there may be other players, so we know for a fact that our awaited protein to Cdo is this protein Boc, and Boc and Cdo have many similarities in their expression counts, and so it could be that it’s not until we can remove all three of those proteins that we will see the maximum type of phenotype that could be generated, and then how that compares with the loss of function of the ligands, and indeed, there’s evidence to suggest, which I’ll possibly come to later, but this other protein Boc figures into this as well. So, it shorts out the risk, but we don’t know how these actually operate at the biochemical level, but the simplest hypothesis would be that they’re in some complex and we’re trying to look at that. And then, in terms of the phenotypes that are generated, it’s not necessarily the case, but these proteins are absolutely required for a hedgehog sinal, and may be required the level of hedgehog signaling. However, there’s also ample opportunity for redundency amongst at least three proteins that we know of to date. And now, you always have to have in the back of your mind something you don’t know about it.

– Hi again.

– Hello.

19:21 You suggest that the possible reasons why little to no defects are seen in the limb of the Gas1 Cdo double knockout embryo is because, one, Boc makes up for the loss of Cdo gas, or two, the limb structures that depend on sonic hedgehog in the lImb are made up of the sonic hedgehog secreting cells themselves, unlike in neural tube patterning. Has there been any work to support either of your proposed mechanisms?

19:49 Yeah, so there’s some work now that suggests the idea that this redundancy with these factors could embark is likely correct, so now if we can make genetic confirmations where we can remove, just to take it back, if you remove Gas1 function, you lose one digit, and that digit is likely a digit, too, which is the most anterior digit that actually requires a hedgehog signaling input. If you remove Cdo and Gas1, you only have exactly the same phenotype as with the Gas1 removal. If you removed Gas1 and Boc, now you get a more severe phenotype, and now digit three also requires direct sonic hedgehog action, so direct signaling action at the distance is also reflected. Digits four and five, which both come from a center that itself transcribes sonic hedgehog, unaffected in that combination. What we haven’t been able to do is, yet, is to be able to look at the triple view. So, we do have supportive evidence that places a Boc Gas1 interaction into the power play, and as predicted from the model where the cells, though they’re most likely to be effective than the ones that have obligatory tor receive the signal from another cell, then we see that those are the ones affected in this paradigm.

21:43 Any followups there? I just have one sort of clarification followup. I’m wondering, can you try to propose a model a little bit more directly of how Gas1, Cdo, Boc are influencing the actual signal to impact smoothened. So, if you can work in smoothened into this signaling mix with these other players just really briefly to see how it all works together.

22:15 Okay, so the key aspect in the end is to remove the repression that patch has on smoothened. There’s some evidence from working in 3T3 cells in culture from our collaborators that Cdo of Boc could possibly have independent of patch in regulating the hedgehog pathway. So, I think you’ll always have to bear in mind there may be other ways in which these factors can work. However, we’ve seen nothing in our invevo studies to suggest that they operate any other way than through the conventional pathway. So, with that in mind, we would think that the most likely thing they do is work to present ligands as a certain concentration, and more effectively detached, and therefore, to repress patches repression and smoothened. So, what that actually means is not well understood, so people don’t know how patch communicates with smoothened, and there are various models out there based upon homologies that patch has with other types of proteins, which are these ABC transporter proteins that maybe, perhaps, is involved in trafficking some small molecule that could have positively or negatively in terms of smoothened regulation, so for example, it may traffic into the cell some small molecule that’s within , and so it gets smoother than in off-state, and so when hedgehog is presented to patch, it starts that trafficking process, and now that inhibitor path goes to smoothened. Alternatively, my compounds to the cell, some small molecule that’s an agnes, the stimulator of smoothened, and now, if it’s silenced, now that can go to smoothened, and then there’s the recent observation that these proteins have expression domains, but appear to be mutually exclusive on psyllium, so whether that small molecule activates a protein that’s involved in trafficking of these so that patch can come off the psyllium smoothened can go on. Really, there’s no good mechanistic understanding at all of the communication between patched and smoothened, and it’s clearly the most critical shortcoming of our knowledge of this pathway.

– It was nice when all we knew was hedgehogs, patched and smoothened, huh?

– Right, so I think in general, things start off simple, and then in biology they become complex, and then when you know how it works, they become simple again.

– That’s what we’re working towards, okay.

– It’s complex at the moment.

– Hello.

– Hi.

25:33 When you examine the possible cooperation of Gas1 and Cdo, the double knockout mutant showed no sonic hedgehog expression at the midline, not in the floor plate or even in the notochord. However, when you label the notochord with CA3, notochord development, while not completely normal, still continued, can you explain what this suggests and how is it that Gas1 and Cdo actually influence the expression of sonic hedgehog in the notochord? Thank you.

26:05 Okay, so, just from a developmental perspective, there’s a number of studies that suggest that the notochord and the floor plate are our, have considerable similarities in their properties, and in fact, people have suggested that they actually come from a common lineage in sub-resistance, in fact, that that definitely is the case if you look into zebra fish, where the medial cells of the floor plate are actually from the same source that gives rise to the notochord, and in fact, those medial cells it may have floor plate are cells, but I’m not actually induced by hedgehog. It’s only the cells in that course of action. So, if we just think about, okay, the floor plate and the notochord shares a common property. They both actually transcribe sonic hedgehog. How is that transcription regulated? So, in all likelihood, they share a common mechanism, a regulation, which is probably through a transcript more regulated cord foxade two, and foxade two actually binds to sonic hedgehog’s six regulatory regions, it’s actually activated and stopped by sonic hedgehog in the floor plate that may be transiently maintained by sonic hedgehog in the notochord, and it could be through that regulatory circuit, if they share a common mechanism by which foxade two is required at some point in time to maintain hedgehog’s expression, or to activate hedgehog’s expression within the notochord. So, part of it is likely a shared regulatory loop through foxade two.

28:08 So then, you’re saying that, so then you’re implying that Gas1 and Cdo ultimately operate through activating foxade two?

28:15 So, that’s right, so we would think that the notochord in a gas kick out mutants. It’s still likely responding to sonic hedgehog, and the reason for that is that, compared to a sonic hedgehog mutant, it’s inlocked out of shape, so the sonic hedgehog mutant and notochord is degenerating very rapidly, whereas, in the Cdo Gas1 mutant, it’s actually still a fairly normal looking notochord, so we think that there’s probably some level of hedgehog signaling going on there, when not sufficient level of sonic hedgehogs stay behind to activate its own expression at the highest levels. And then, that requires this regulate recircuit through foxate two.

– Interesting.

– Hi.

– Hi!

29:10 My question is, in your paper, genomic characterization of glee activator targets and sonic hedgehog mediated neural patterning, you used an epitope tag, glee1, for chip analysis. This being a relatively new technique, can you please walk us through your experimental design? You used embryo bodies for this study. Is there any chance it can be done with neural tube tissues, and if it could, could you predict the results, or would they be any different?

29:38 Right. So, the actually, if you want to do this technique, chromotin amino precipitation, so you want to precipitate a transcriptional regulator bound to its target it’s just regulatory sequences using an antibody. As you can imagine, a very significant technical aspect of this is the quality of your antibody. And so, the idea here for epitope tacking your transcription that wasn’t an idea we had, but actually it was an idea that came originally from people who first started to look at regulating circuits in use. Now, in these, it’s always struck me that those people found it arguably easy to generate good antibodies against their favorite proteins of interest. Since our immune systems are pretty worldwide to attack these proteins. However, what they found is that, if they actually put in epitope tag, they could get much better results and could pick up targets that they couldn’t see with the negative antibodies, simply because of the very high affinity antibodies that are being developed for those appetites. So, we challenged that approach to epitodes have the glee process. The but if you generally , and you’re interested in a number of the transcriptional regulators, now you can use one kind of strategy for all those transcriptional regulators if they all carried the same appetite. So, it’s a general approach then that would be applicable to multiple types of fat, ’cause otherwise you’d have to have an antibody for every single factor, and then you’d have to be characterizing all of those independently. Then, the approach is, okay, how do we generate the appropriate cell types and the appropriate response to hedgehog signal, and as you rightly said, the most obvious way is to go in vivo and actually go to the normal target zones in vivo, but this provides a technical challenge because you have to have enough cells to be able to get enough DNA from the chromotin amino precipitation to be able to identify those priorities. So, this is my surrogate system. What people, actually, this is a beautiful example of applying the logic of an understanding of development to the differentiation of embryo stem cells. So, there’s a lot of junk in the literature about things done with embryo stem cells, but this is how they should be used, so we have a good understanding of the pathways that lead to the development of neural cells. We understand how those neural projectors get posteriorized, and we know the signals that ventrilize, and so just apply those to the styles, and now at the end of this should pop your sonic hedgehog dependent style types, and I think that’s the case. An important thing that you might wanna note here too, so when you’re talking to your local congress people, and talking about the issues of why you should use embryo stem cells and not neural stem cells is that they don’t work. So, for the neural stem cells that have been isolated, and people use throughout the world, do not respond to sonic hedgehog to make these ventral cell parts. Only if you make the very earliest neural cells from an embryo stem cell can you get the most neurons, but you might want to study invetro, or you might want to transform invevo to do clinical applications towards things like amyotrophic lateral sclerosis or spinal muscular atrophy. This model is, I think, a fantastic recapitulation of what really goes on invevo, but the proof of the pudding would be to be able to recapitulate this same approach invevo, and so what we need to do there is have an approach by which we can easily isolate the cells we’re entrusting. So for example, if we’re entrusted in understanding how hedgehog induces a mountain neuron, we label the mountain neuron in some way that we can easily purify those cells at the maringes and then look at hedgehog’s regulation of the target change it marks up, and most things we will do in time. I should say that we actually have moved invevo, but in a different context, so Steve Oaks, who pioneered the work in the paper that you read, has now performed a whole genoprofilum of hedgehog’s actions in the developing limb, so he’s taken the limb bumps from developing mass embryos and directly looked at glee bands to its target’s firm. So, this is the first, to my knowledge, the first effort in the mammalian system to look in normal development at the complete context of all transcriptional regulation within a particular pathway. It’s a massive amount of information. 8,000 DNA sequences are bound with the standard deviation of greater than eight above the mean, or scattered throughout the genome, and we’re trying to make sense of this.

Followup questions there? So, what’s your plan to make sense of all that?

35:50 Well, at the moment what we’re trying to do is to understand which of the data we should pay the most attention to, and so a lot of the stuff at the moment, it’s really functional verification of the data. So for example, it predicts a particular sysregulatory region, close to a genus of plausible hedgehog target, that is its expression, seemingly could be that, and then we test those in transgenic marks.

– It’s hard to do for 8,000.

It is hard to do for 8,000, so you have to do that with a subset of things that you think is reflected in the data set as a whole. Even doing it with a few tells you a lot, actually. So for example, the classic view of hedgehog limb patterning is that it’s all lots of hedgehog repressed. No real for hedgehog activated.

– Really?

36:58 Well, I think that’s not true, so we can find regulatory modules that are activated driven modules, and so, it’s likely to feature both the loss of repressor and the gain of activator response to give you the appropriate outcome.

– Wow.

37:26 So, I could actually at this point give you the ultimate answers to how we’ll make sense of all 8,000 of those binding sites at this time. It’s an emerging field, and the reason we chose the limb here to do this whole genome analysis and not the CNS, is that conceptually, the limb is a much tougher problem, and so we set ourselves the tougher task first. So, why to the most tougher problem, so in the CNS, the problem reduces to a much simpler concept. In the CNS, you just make different types of neurons in response to hedgehog, and those different types of neurons are different, they express different transcriptional regulators, they give rise to recognizably different cell types, so actually figuring out how you make a cell different is a much more conceptually straightforward problem. In the lens, the hedgehog regulated response doesn’t make a difference to our parts. Every digit in your finger is made up of exactly the same cell parts. What it does is it regulates the numbers of digit and ligand at form, the sizes of those, the growth of those, and so it’s conceptually a much more difficult problem. And so, it’s a much harder work from these to actually say how we’re going to figure this out, but it’s running which is being completely roadblocked for since the early days of Louis , and his group actually coming up with models as to understanding the fundamental nature of how you make different numbers of organizations and styles.

– Well I look forward to that. We’re about to cover limb development soon next week, so you’re–

– Alright!

– You’re introducing it perfectly, thank you.

– Good, so you’ll have fun with limbs.

– Hello.

– Hi.

39:41 The MCA analysis predicts multiple glee sites within a given locus where only a subset were bound by other glee factors. You state that you are not sure if this is because different sites may have differential affinities, and that there may be a mechanism by which one could impart specificity. Can you explain this mechanism and what potential impact this new information may have?

40:06 Right. I guess there are two aspects to this. So, the algorithms predict that X-site is a potential glee start, but it doesn’t actually state how strong the potential is for that to be a glee site. So, one thing that we do observe is that if we see a region where we can detect glee is bound, and it predicts a number of glee sites in that region, and one of those glee sites is a much better match to the actual consensus glee sites, which has a certain prediction of the probability of one in the four bases in any particular positions for the timber of the glee site. The one that has the highest probability prediction is oftentimes the site where glee is bound. The other sites may not have a high enough affinity binding, or their binding may be somewhat dependent upon binding to this higher site. So, not all sites are equal. That said, there are perfectly good examples throughout the genome of the sites that have a sequence identical to the ones that do bind glee in one position, but are not going binding glee somewhere else in the genome, and so there has to be something that controls the accessibility of glee factors so that it binds one site in lots of other sites. So, that could be another thing, though. The chromatin in some position may be in a state where it cannot be seen and bound, and the other important thing to remember is that it’s not as simple as simply binding glees, so glees operate in a context dependent fashion. They operate in neural cells to activate neural regulator readings, and they operate in lens that to make genes associated with limb regulation, got what have you, and so with any of these cyst regulating modules are very likely to have inputs from a number of different regulators, and the combined cooperative interactions amongst those regulators may well signal into whether glee binds or not, for example, for a particular neural element, it might require some generally distributed neural transcriptional regulator that has a cyst diving site close to one of those glee sites for glee to be able to interact with that site, unless those two occurred close to one another in compound. And, that’s a general regulatory principle that we see time and time again where there’s been well studied regulatory elements. Then, cooperative interactions are much the number of factors actually required in the end for binding and firing of the gene’s expression.

43:24 Our last question to wrap everything up here is largely, if you can sort of tell us a little bit about what your perceptions are as being the most pressing questions in the field today, for both hedgehog signaling and perhaps neural tube patterning, and then what your lab is doing to try to address those, thanks.

43:48 Yeah, I would say that that’s, I would guess that is a sort of a slightly philosophical question in a sense that… Some people may say the guts of the problem slap tabs. Okay, you’ve got this ligand concentration dependent signaling, different cellular outcomes. What else do you need to know? Some people may say they want to be able to induce this through a set of equations where they can account for the concentration of all components, and have it reduced to a mathematical problem. I lie somewhere in between in my case. So, I would like to understand the process and understand it in the context of how it works in an embryo over time, so I’d like to understand the dynamics of this, I’d like to understand the component parts of this that play the major role, I’d like to understand whether this is truly a classic more for doing gradient model, whereby we’ve got long range signaling happen in the classic model, or whether we have a modification of that model where we have local accumulation of the protein over time, and moving out of cells from that domain that gives rise to different kind. And in the end, the level of signaling is transformed into a level of transcriptional outputting. The only way we can understand the transformation of signals to transcriptional outputs is by knowing the targets of the pathway, so we’d like to integrate what happens at the top end of the cell with what happens at the nuclear end of the cell to get the appropriate outlook to an appropriate input.

46:06 Alrighty. well, thank you very much for your time. We really appreciate it, and let’s give Dr. McMann a round of applause. Thank you very much, good luck on all your work and your grants, and we look forward to seeing more of those papers.

– Thanks very much, and thanks for taking the time to look at our papers.

– Our pleasure.

– Appreciate it.

– Take care.

– Bye.

– Bye.

– Bye.