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Patterning Development in the Early Embryo: The Role of Bicoid

Transcript of Part 2: Stability of Morphogen Gradients & Movement of Molecules

00:00:02.15		So I'm Eric Wieschaus, I'm a HHMI investigator and a professor at Princeton University.
00:00:08.15		And in the first part of this presentation, I already talked to you about how the Drosophila
00:00:13.19		embryo develops and in particular, how spatial patterns of gene expression
00:00:19.08		and cell behavior arise, and one of the things we saw was the importance of maternal RNAs
00:00:26.24		and maternal proteins that are localized to particular regions of the egg,
00:00:31.06		and provide positional information to the cells that are found in individual positions.
00:00:38.15		What I'd like to do in this second part of the lecture is to actually focus in a little bit more
00:00:44.26		on these maternal RNAs and in particular, I'd like to talk about some work that
00:00:49.23		a graduate student, Thomas Gregor has done in the lab in collaboration
00:00:56.18		with two biophysicists at Princeton, Bill Bialek and David Tank. And I think the reason that
00:01:02.07		I wanted to talk about these experiments is that they provided a wonderful example
00:01:08.29		I think of the importance to us as biologists right now, beginning to try to view
00:01:18.00		problems more quantitatively, to try to establish actual numbers for the biological
00:01:24.18		phenomenon that we've come to understand in a vague-ish kind of cartoon way, but that
00:01:28.28		if we want to bring our knowledge to the next level. If we want to test our understanding
00:01:36.10		one of the things that we really have to be able to do is to measure and supply numbers.
00:01:41.02		And the experiments that I'm going to present, although you'll see their not fully complete
00:01:45.27		in the numbers, in several cases even when one has numbers one still has to struggle with
00:01:52.09		the meaning for those numbers, I think the important thing
00:01:55.12		is the direction that they point out. So, what we saw and what we've learned
00:02:05.23		in the past twenty years in Drosophila development is that patterning
00:02:10.16		along the anterior to posterior, that is head to tail axis of the embryo depends
00:02:16.13		on the presence of a maternal protein called bicoid that's graded such that
00:02:22.20		individual genes like hunchback are activated at particular times and at particular places
00:02:31.07		in the embryo. Now the interesting thing about this information rich maternal gradient
00:02:41.06		of the bicoid protein is that it arises from the synthesis of the bicoid protein from
00:02:47.14		the localized RNA at the anterior end of the egg. And it's that process
00:02:51.27		that I'd like to talk about. What we were interested in, what Thomas in particular
00:02:55.07		was interested in was in this simple cartoon sense, of how an information gradient
00:03:03.25		like bicoid arises, you have a localized RNA, synthesis, and then movement
00:03:10.19		of the protein. And most of our knowledge and all the pictures
00:03:13.20		that you see back there were made in fixed embryos where you fix an embryo
00:03:19.11		you stain it with something that allows you to see particular proteins like bicoid protein
00:03:23.27		or do in situ hybridizations to identify the localization of the RNA, and that approach
00:03:32.18		of fixed material has been extremely valuable in almost all of developmental biology
00:03:38.04		over the past 20 years, but it has two essential flaws. One is that most of these
00:03:44.07		techniques are indirect, so you don't see the molecule itself, you see something
00:03:48.17		that binds to something that binds to something that binds
00:03:51.22		to the molecule, and you are never really sure then about the levels of staining
00:03:57.03		or the intensity of the staining that you see and how that relates to absolute
00:04:00.28		concentrations. And in a model, like the one we are dealing with here, where
00:04:05.24		the fates of cells are controlled by concentrations that you want to measure you'd really
00:04:11.15		like to have, as direct as possible, a measure of actual concentration in the given
00:04:17.27		region of the embryo. The other problem with fixed material obviously is that it's fixed.
00:04:21.29		You look at a given embryo, it is fixed at a given stage, even if you could measure it
00:04:26.18		at that stage, you don't really know what the levels were before or afterwards.
00:04:32.01		And so for those reasons, what Thomas Gregor decided to do in the lab was to develop
00:04:37.20		a living probe, a probe that could allow us to follow the bicoid protein in living embryos
00:04:44.26		and to do that he established a fusion protein of bicoid to the fluorescent EGFP protein
00:04:53.04		and followed its development in embryos and I'll show you that in the next images
00:04:59.14		and these are actually living photographs at different levels of a single Drosophila embryo
00:05:05.06		that expresses this EGFP bicoid transgene that Thomas Gregor made. Now the transgene
00:05:12.27		that he made is under control of the normal bicoid transcription controls during
00:05:18.29		oogenesis. It also has the normal 3'UTR of the bicoid RNA, which is the part of the RNA
00:05:25.15		that is involved in localizing that RNA to the anterior end of the egg. And so
00:05:30.03		the EGFP-bicoid is expressed as RNA in the mother, localized appropriately, and in fact if you
00:05:39.15		put it into a mutant embryo, mutant for bicoid, it will rescue and produce perfectly
00:05:45.02		normal development, indistinguishable from that which you see in wild-type embryos.
00:05:50.14		Now, while I've been talking, this embryo has established a bicoid gradient, and I think
00:05:59.16		what we need to do is to go back one time and watch this movie one more time.
00:06:06.02		So what we're going to see is the bicoid gradient in this living embryo and in the embryo
00:06:14.16		immediately after fertilization you can see some movement in the cytoplasm here.
00:06:18.18		What you can't see yet is an obvious difference in the concentration of fluorescence
00:06:25.11		at the anterior end and the posterior end. And over time though, you can begin to see
00:06:29.27		over the two hour period of time when this, this is the nuclei now have made it out to the surface
00:06:36.17		and you can see in those nuclei at the anterior end of the egg, you can see an
00:06:42.20		accumulation of bicoid. You can see that when the nuclei divide, bicoid protein goes out
00:06:47.25		of the nuclei. When the nuclei reform after each division the bicoid protein goes back in.
00:06:54.00		What you can clearly see in this embryo is that along the anterior/posterior axis of the egg
00:06:59.16		is a graded distribution. So the nuclei at this end have much more bicoid protein
00:07:04.18		and the protein levels drop off, such that a cell really could figure out where it was
00:07:10.25		in the egg. Whether it was the hundredth cell at this end or the zero cell or the one cell
00:07:16.27		or the tenth cell just based on what its position is here. Now, using this construct
00:07:28.07		because we can accurately time and say exactly how old the embryo is how many minutes
00:07:36.19		it's been since it's done any particular division, we can image these embryos with
00:07:42.03		two photon microscopy that allows us to get very good in-depth resolution. We're able to
00:07:49.11		and because we're actually looking at EGFP bicoid, we're looking at the protein that is
00:07:54.28		actually responsible for the pattern, we can measure the intensity and we can measure
00:08:00.22		the profile of the gradient. So what it allows us to do is to ask relatively simple questions.
00:08:06.24		If the RNA is localized at the anterior end of the embryo and it begins translation at
00:08:14.20		fertilization, protein is being made, translation is continuous if the embryo is making more
00:08:22.12		and more bicoid protein, then how is it, how does the profile of bicoid concentration
00:08:31.21		along the length of the embryo change? Does it continue to rise? Does it stabilize?
00:08:36.09		Stabilization would actually be really important because if the nucleus wants to know
00:08:41.17		where am I along the anterior/posterior axis of the egg, the easiest way of doing that is if
00:08:47.23		the concentration at that point, 30% egg length really were constant and were constant
00:08:53.10		over time. So one of the things that Thomas did by following individual embryos from
00:08:59.08		the time when the nuclei first migrate out to the surface, and you can measure the bicoid
00:09:03.06		concentration. Measuring exactly the same position, even when nuclei divide, looking
00:09:09.24		at the daughter nucleus that comes to lie closest to the position of the mother nucleus
00:09:16.07		and ask when does the concentration in a given area stabilize? And what Thomas found,
00:09:22.04		and this was quite remarkable, is that at the earliest points when we could measure the RNA,
00:09:26.24		that is at cycle 10, just after 10 cycles to just when the nuclei made it out to the surface
00:09:32.12		the concentration in a given nucleus was fixed for that position and remained constant
00:09:41.04		at that position and remained constant throughout all the remaining cleavage divisions,
00:09:47.11		that is the concentration is stable all the way up to cycle 14 when we see massive
00:09:53.07		visible expression of the downstream targets. The other thing that was really important
00:09:58.16		for us was to look at many embryos on the same slide and ask in 10 different embryos,
00:10:05.21		if you look at the bicoid concentration, how similar is this concentration at 30% from one
00:10:12.27		embryo to the next to the next. Because we know that ultimately all 10 embryos
00:10:16.18		will develop with exactly the same gene expression patterns, and so if the concentration
00:10:21.22		of bicoid really is directly reflecting or is directly controlling those gene expression patterns,
00:10:29.29		then in individual embryos somehow at 30% egg length you've got to have the same bicoid
00:10:35.27		concentration in all those different embryos and quite remarkably
00:10:39.07		and something that we had never really been able to convincingly see in fixed material
00:10:45.08		because of the variabilities of fixation and staining was there's very very little
00:10:50.05		variation from one embryo to the next. And all of those observations were really
00:10:55.17		important because it pointed out to us that bicoid and the nuclear concentration
00:11:02.15		of bicoid can provide stable sufficient information about position to activate precise
00:11:11.27		patterns of gene expression and that it could do this from cycle 10 all the way
00:11:17.12		up to cycle 14. Now, another thing that you can do if you believe that
00:11:26.18		you can actually measure the concentration of the molecule, you can actually measure
00:11:30.20		and describe the distribution. And if you look at the bicoid distribution in embryos what
00:11:38.10		you can see is that it's as expected, highest at the anterior end here and falls
00:11:44.05		and you can show that this fall can be fitted to an exponential decay which is what one
00:11:50.07		would predict if you had a source and you had simple diffusion from that source.
00:11:57.01		Because the concentrations are so reproducible, particularly in the region right here
00:12:02.23		where we're activating hunchback gene expression you can measure those concentrations
00:12:10.04		measure the concentration of bicoid in a given nucleus and the output
00:12:15.29		of that concentration in terms of hunchback protein expression. One of the interesting
00:12:23.16		things is that if you look in these embryos at precisely at the region where hunchback
00:12:30.16		is being expressed right here, and you ask how much of a difference is there
00:12:38.26		in bicoid concentration in nuclei between those that are going to express hunchback
00:12:45.08		and those that aren't going to express. It's basically a 10% difference. The cells that are
00:12:51.13		making the choices, their neighbors either will not express hunchback protein
00:13:01.13		or express hunchback protein based on only a 10% change in the concentration of bicoid.
00:13:07.25		So what that means is that cells have an extraordinary ability to measure accurately
00:13:12.21		what these concentrations are. You can plot out that response. You can see that it's highly
00:13:18.11		precise you see hunchback expression within one or two cell diameters at 48% egg length
00:13:26.05		and that it's also highly non-linear, meaning that a small change in bicoid concentration
00:13:34.04		results in a huge on/off change in the response in terms of gene activities. So you could
00:13:43.06		think about how this non-linearity arises and there's a number of experiments from
00:13:48.28		a number of different labs that indicate that if you look at the control regions of the
00:13:53.20		hunchback gene, that there are multiple bicoid binding sites and you can begin to think
00:13:57.20		about this in terms of cooperativity of bicoid binding. And when you do that you model
00:14:03.03		the non-linearity and you come up with a Hill coefficient for this cooperativity of 5,
00:14:08.00		which is a very high degree of cooperativity, which means that very little change
00:14:13.13		in bicoid results in massive changes in gene expression. Now all that means
00:14:22.13		is that the relative numbers, because really if you think about what we are doing
00:14:25.03		where we are looking at these embryos is that we are looking at EGFP, we're measuring
00:14:28.29		the EGFP that's attached to individual bicoid molecules, measuring the intensity
00:14:34.06		of the signal and trying to extrapolate concentrations of functional protein
00:14:40.12		in the nucleus that are activating genes. What we'd really like to be able to do is not to talk
00:14:46.24		about bicoid concentration in terms of intensity, but to actually talk about it
00:14:50.16		in terms of number of molecules, in terms of absolute concentration. And the experiment
00:14:55.02		that Thomas did to begin to approach this is something that you can do uniquely in flies
00:15:01.14		in part, or at least it was easier to do in flies, is that we're using EGFP and many argue
00:15:08.21		that you can use type proteins in many organisms, but fly embryos develop
00:15:14.17		inside a water impermeable but completely transparent egg shell called the
00:15:21.07		vitelline membrane and so what Thomas was able to do was to take these EGFP-labeled
00:15:26.29		bicoid embryos and immerse them and image them in a solution that had
00:15:34.03		defined molarity of bicoid, in fact 36 nM bicoid and so what this meant is that he could
00:15:39.20		look at the concentration or measure the intensity of the bicoid EGFP signal
00:15:45.24		inside the embryo and compare it to an absolute concentration of bicoid
00:15:51.06		immediately outside the embryo. And this allowed him then to show that the actual
00:15:55.14		concentration of bicoid in the nucleus in those nuclei that are actually making
00:16:00.28		the choice whether to activate hunchback expression or not, was of the range
00:16:05.06		something like 8 nM. That's an interesting number because it's close to what might be
00:16:11.16		predicted from in vitro binding studies for what would be required
00:16:15.13		for activating and binding to and activating the hunchback gene. But it's actually interesting
00:16:21.11		from an additional standpoint, and one that raises I think one of
00:16:26.15		the really fundamental questions about transcription. If you take the concentration
00:16:30.13		of bicoid, this 8 nM value, and you know the actual volume of the nucleus
00:16:37.27		because you can measure that optically from the images that you have,
00:16:42.18		you can calculate the total number of bicoid molecules in the nucleus,
00:16:46.26		in a nucleus that is making a decision. When Thomas did that, he found that
00:16:50.08		there were about 697 molecules per nucleus. This is a remarkably small number
00:16:57.22		if you think about it, 600 or 700 molecules in a nucleus if we consider that
00:17:05.17		the neighboring nucleus that is choosing not to activate hunchback
00:17:13.00		or choosing to activate hunchback only varies by 10%, and so what it requires
00:17:21.29		is that individual nuclei distinguish between whether in their total nuclear volume
00:17:28.23		there's 690 molecules or 630 or 770. The relatively small number of bicoid molecules
00:17:39.19		that are present in the nuclei, that are making accurate decisions raises a lot of questions
00:17:45.21		about how bicoid or how any transcription factor controls or activates transcription
00:17:53.15		in a concentration dependent way.  Part of the question is that we don't really know,
00:17:58.04		we assume that bicoid activates transcription of hunchback by binding to
00:18:04.16		a hunchback control region and activating transcription by interacting with
00:18:09.18		other proteins that are the actual transcriptional activators. What we don't know
00:18:14.00		about bicoid and hunchback is the extent of this binding and occupancy
00:18:21.06		how long it lasts, and whether it's permanently associated when genes are active
00:18:27.06		whether bicoid molecules that are bound or that bicoid molecules responsible
00:18:32.13		for that activation remain permanently bound or whether there's a dynamic
00:18:36.07		exchange between bicoid and individual transcription factors. And that becomes important
00:18:43.15		because actually hunchback is not the only gene in the Drosophila genome that can
00:18:48.21		respond to bicoid transcription, and there are various informatics estimates that suggests
00:18:54.03		that there's anywhere from several thousand based on informatics to several hundred
00:19:01.16		based on biochemical studies, several hundred genes capable of binding to
00:19:07.06		and responding to bicoid. So if all of these genes are trying to measure bicoid concentration
00:19:12.22		and binding bicoid concentration at the same time, and there are only 630 molecules
00:19:17.19		in the nucleus, the question of occupancy becomes really important and it drives
00:19:22.15		us to models where what's actually required is, for cells to make choices,. The
00:19:35.01		concentrations are based in some way by averaging collisions over time, averaging
00:19:44.13		interactions between bicoid molecules over time, such that molecules of bicoid diffusing
00:19:51.29		throughout the nucleus will bind or interact with a promoter and then leave and be able to
00:19:57.21		interact with other genes in the genome. But to measure concentration in those molecules
00:20:03.18		what you have to be able to do is you have to have some memory, some way of recording
00:20:10.06		individual collisions and averaging them over time to come up with an accurate
00:20:15.00		measurement of concentration. The problem is actually, to make the problem graphic just
00:20:23.08		one last cartoon like illustration, but what a nucleus or what a hunchback gene, what any
00:20:32.12		gene in a nucleus has to do with response to transcription is similar to what a piece of DNA
00:20:40.03		or say my knuckle here, would have to do if sitting in the middle of this room
00:20:45.03		where I'm giving this talk, if there were 690 molecules of bicoid flying around
00:20:50.19		and it's trying to judge from collisions how many molecules are actually in this room,
00:20:56.06		and trying to figure out whether it is in a room with 690 or 630 because it's activation
00:21:04.17		is going to depend on that. And other pieces of DNA, if we accept
00:21:12.06		the transcriptional threshold model, accept the idea that the bicoid gradient is providing
00:21:17.18		pattern along the entire access of the embryo, other genes responding and
00:21:22.25		measuring concentrations are sensitive to activation at other concentrations.
00:21:28.00		So one of the other things that we don't know, and I think it's really important to know
00:21:31.16		about the whole response is to get a more global sense of what genes respond to bicoid,
00:21:40.02		how do they respond, and do they show the same levels of accuracy
00:21:49.07		and their ability to measure molecules. One possibility would be that the embryo
00:21:53.27		uses certain genes like hunchback as guideposts, establishes them with great accuracy
00:21:59.20		and then determines other genes which appear to respond, like these genes
00:22:05.01		that I've listed here, this orthodenticle, giant, or Krüppel, obviously are also responding
00:22:12.20		in some way to the bicoid gradient, but whether those responses
00:22:20.10		are necessarily as accurate as hunchback, or whether part of the accuracy if
00:22:24.16		they are as accurate, whether part of that accuracy depends on interactions or
00:22:29.26		subsequent interactions that depend heavily on the interactions of guidepost genes
00:22:34.20		like hunchback. The way that the bicoid gene activates transcription
00:22:42.20		is a fascinating problem but it’s not the only thing that we don't know.
00:22:45.19		Another really interesting problem is how it is that the gradient is actually established
00:22:54.06		in a stable form. How does bicoid protein move. If it's being made at the anterior end
00:22:59.23		of the egg, how does that movement of bicoid protein from the site of its synthesis
00:23:07.11		how is that able to establish a stable gradient. And in particular, in that simple
00:23:13.09		cartoon models that we have, that those protein molecules unlike the RNAs
00:23:19.03		are not anchored would be able to move by diffusion. So what we'd like to know is what are the
00:23:24.22		mechanisms that actually move bicoid molecules newly made from the anterior end
00:23:31.24		and establish the gradient. In simple kinds of experiments where you follow
00:23:37.14		the establishment of the gradient over time. Look at how soon you can begin
00:23:44.12		to detect molecules at different distances from the anterior end of the egg,
00:23:48.12		and you model the kinds of diffusion constants, the rates that molecules have to move
00:23:54.07		within the egg to establish a stable gradient. Most of the modeling that's been done
00:24:01.03		suggests that you need diffusion constants of the order of
00:24:05.17		about 4-8 microns squared per second. Once we had an EGFP-bicoid molecule
00:24:14.09		though, that allowed us to do photobleaching experiments
00:24:17.07		or to tag individual molecules and follow them. What Thomas Gregor
00:24:21.29		was able to do was to actually measure the movement of bicoid molecules
00:24:27.14		in small little spaces using photobleaching experiments over small volumes
00:24:32.16		and over small times to ask how fast does bicoid molecules actually move in the surface,
00:24:40.14		and the remarkable conclusion, the one that was surprising to all of us was that
00:24:45.08		if you look at those movements and those measurements that you can measure
00:24:48.28		you find that they are very small, that the diffusions constants are very small.
00:24:52.18		The molecules move very slowly. The best measurements that we have
00:24:58.18		from Thomas's data and constructs suggest that the diffusion constants are
00:25:04.02		on about the order of 0.3 microns squared per second, which is ten-fold, twenty-fold
00:25:08.20		less than what you'd actually need to visibly establish the gradient.
00:25:13.07		So a value of 0.3 microns squared per second is too small, molecules would move too
00:25:21.05		slow to, it would require several hours to produce a gradient of the kind that we see
00:25:27.17		in the Drosophila egg, and yet we know that the gradient is already there
00:25:33.00		and stable by cycle 10 and already the effects of that gradient in terms of
00:25:40.21		gene expression patterns, are also already clearly visible at that stage.
00:25:48.04		So we don't simply have enough time in development for a gradient to be established
00:25:54.00		with diffusion constants that we see. And there are other theoretical considerations
00:25:58.04		which one argues that the establishment of a stable gradient in some way balances,
00:26:06.20		the shape of that gradient somehow balances the movement of molecules versus
00:26:11.21		their constant degradation and under those circumstances you would need
00:26:15.10		half-lives to produce the visible shapes of the bicoid gradient that we see
00:26:20.23		you would require half-lives that are extremely long for the bicoid molecule
00:26:28.17		to produce through gradients, at any time in development, to produce
00:26:34.01		the gradients that we see with a diffusion constant of 3.5. So all of that basically
00:26:39.23		just raises the basic problem of that we don' t know really how molecules
00:26:46.23		move in the egg, and we don't really have good handles or good strategies
00:26:51.26		for tracking individual molecules or even whole populations of molecules
00:26:56.22		and knowing at what scale and over what time frames we have to
00:27:00.06		measure molecular movement. One of the other strategies that Thomas Gregor
00:27:06.02		took to follow movements of molecules involves experiments where
00:27:13.16		rather than looking at EGFP and making assumptions about its translation
00:27:19.07		RNA distribution and movement from the eggs, what Thomas did was to take embryos
00:27:25.27		wild-type embryos and inject fluorescently labeled compounds into
00:27:30.05		the anterior ends of the eggs, and you can see in this panel right here
00:27:34.07		the consequence of when you inject a dextran into the anterior end of the egg,
00:27:38.29		you can see this biologically inert but fluorescent molecule moving through
00:27:44.29		the whole volume of the egg and you can follow the change in the distribution of
00:27:52.03		these molecules over time you can model them. And what Thomas was able to show
00:27:56.07		is that when you take and inject dextran into the egg, it moves, it can be modeled
00:28:01.14		by its distribution over time, and it follows what would be expected of
00:28:08.00		simple diffusion and allowed Thomas to calculate for dextrans of different sizes
00:28:16.00		the diffusion constants for molecules. What this graph here shows is just some of
00:28:25.05		Thomas's data for trying to model bicoid movement by using dextrans
00:28:34.06		of approximately the size of bicoid. So bicoid with EGFP would be about
00:28:40.03		a 70 kDa protein, and so if you inject a dextran of 70 kDa into the egg
00:28:47.11		and ask how fast does it move compared to what he was measuring with EGFP bicoid,
00:28:53.11		what Thomas saw was that in contrast to the 0.3 microns squared per second
00:29:00.12		diffusion constants that he obtained for bicoid at the surface of the egg and
00:29:05.26		when photobleaching small volumes of cytoplasm, the dextrans apparently move with
00:29:13.18		diffusion constants with speeds of 15 microns squared per second. 15 microns is
00:29:21.08		more than enough of what you need to make a gradient. And so this means that
00:29:25.06		at least some molecules moving from the anterior end of the egg
00:29:29.09		can produce gradients of the kind that we see with bicoid.
00:29:40.09		Now, strictly speaking, what Thomas's measurements argue for dextran is that
00:29:49.09		you can model the movement of injected dextran as though it were diffusion.
00:29:54.17		It doesn't actually tell you that molecules are moving by diffusion rather than
00:29:59.18		being transported or binding to something or being moved by other mechanisms.
00:30:05.12		One of the approaches, one of the strategies, for testing whether
00:30:08.12		the molecular movement that you are looking at in a biological system is due to
00:30:14.10		diffusion or to Brownian motion if you will, is its size dependence in that the
00:30:20.24		Stokes Einstein relationship argues that small molecules will diffuse faster,
00:30:28.02		larger molecules will diffuse more slowly and that the diffusion constant
00:30:32.08		that you measure will depend on the size of the molecules. And so the curve
00:30:36.29		as you see here, you see the behavior of the diffusion constants that you measure
00:30:43.10		for dextrans that have small radii versus large radii and you can see
00:30:52.05		the size dependence of those diffusion constants which argues that
00:30:55.29		a significant component of the movement that he's looking at really fits
00:31:00.09		Stokes/Einstein, really is a physical diffusion. But one of the other interesting things
00:31:05.01		that arose from Thomas's analysis and by fitting these curves is that if you follow
00:31:11.20		the shape of this curve out as molecules get bigger and bigger, what you find
00:31:21.12		is that while the curve can be fitted to a Stokes/Einstein relationship
00:31:26.22		it doesn't go down to zero.  There's a floor, a fraction of the movement
00:31:33.27		of every one of these particles, which is size independent, meaning that
00:31:38.27		about 6 microns squared per second of the movement of a molecule of about
00:31:43.07		the size of the radius of bicoid is independent of its actual size and all molecules
00:31:53.09		are going to move in the egg at about 6 microns squared per second
00:31:58.07		independent of their size. This is actually a very intriguing observation for us
00:32:03.05		because it suggests that a molecule, even a molecule like bicoid
00:32:08.22		that may show very slow diffusion constants perhaps because it’s interacting
00:32:19.21		with or being bound to other molecules and therefore ultimately we can explain that
00:32:25.10		as having an increased size. If you look at values at the behavior here,
00:32:30.26		this size independent movement might actually account for the movement of bicoid.
00:32:37.04		And we don't know what the nature of the size independent movement is,
00:32:40.14		but one model, all that we know is that it can't be explained by Brownian diffusion.
00:32:47.20		But one of the interesting models may come back from
00:32:52.27		the biological understanding of the phenomenon, because if you actually
00:32:56.29		look at eggs during the process when this gradient is being established,
00:33:06.07		in a simple sense, the way we've talked about it before,
00:33:10.17		we've had a localized RNA, which is translated into a protein and you have diffusion
00:33:14.28		of this molecule through what you can think of as a stable cytoplasm
00:33:18.17		such that diffusion could establish a stable gradient. If you actually look though,
00:33:23.22		at embryos during the process when this bicoid gradient is being established,
00:33:30.06		and you look at the cytoplasm, we're looking here at an early cleavage division
00:33:33.06		in embryos that carry a GFP-histone so we'll be able to eventually
00:33:39.29		to see the nuclei, but you'll also see some GFP-histone in the cytoplasm of these
00:33:44.12		syncytial embryos. You can see the nuclei migrating out to the surface
00:33:49.18		and you can see this pattern of division of the individual nuclei
00:33:55.04		that I talked about in the first lecture that give rise to these synchronous divisions
00:33:58.27		that give rise to the syncytial blastoderm, but one of the things that you can also see
00:34:07.17		and I'll point this out to you when we watch the move one more time,
00:34:15.11		is that associated with these nuclear replications are massive movements
00:34:21.02		of the cytoplasm. The cytoplasm moves forward and moves back and this swishing
00:34:26.13		kind of turbulent patterns that appear to have no overall directionality
00:34:34.27		are not going to move molecules in any particular direction, but
00:34:39.22		will we believe contribute, if bicoid is being associated with the cytoplasm,
00:34:46.14		will contribute to the movement of bicoid in a non-Brownian, non-diffusion
00:34:51.18		sense, but in a non-directed sense that will be essentially equivalent
00:34:56.04		to the random walks that are produced by diffusion. So what we're
00:35:01.08		beginning to think now is that the bicoid gradient that arises in the egg
00:35:06.20		does not arise necessarily directly from diffusion or from the diffusive movements
00:35:14.29		of bicoid molecules per say, but is actually established by random movements
00:35:20.07		in the cytoplasm. If that is true then what it will argue is that the establishment
00:35:30.25		of the gradient can't be understood from simple biophysical properties
00:35:37.17		like diffusion. That it requires that the egg cytoplasm and the motors in the cytoplasm
00:35:43.11		maybe in a totally undirected way but still that these cytoplasmic flows
00:35:48.28		establish and move molecules like bicoid and that the stable gradients that we see
00:35:56.24		are the products more of that movement. We're beginning to try and test those
00:36:01.23		models by asking can we inhibit this movement, can we follow the establishment
00:36:06.25		of gradients in unfertilized eggs where we see different patterns of movements
00:36:10.19		but the possibility that cytoplasmic movements rather than simple physical diffusion
00:36:18.23		establish the bicoid gradient is an intriguing possibility for us because it suggests
00:36:23.29		that if biological parameters like cytoplasmic flows control the ultimate shape
00:36:33.27		and distribution of bicoid, then those biological parameters
00:36:38.03		in fact can become immediate targets in a biological process
00:36:43.20		if you wanted to change the shape of the bicoid gradient or have the bicoid gradient
00:36:47.26		move during the course of evolution. As eggs change or times change,
00:36:57.04		if you want to maintain or continue bicoids usefulness to use bicoid as
00:37:04.00		a morphogen, as a molecule whose concentration establishes gene expression
00:37:09.07		you possibly want to be able to manipulate its distribution beyond those things
00:37:16.11		that are possible by simple physical parameters like diffusion. So in the last
00:37:20.07		part of this lecture, I'll talk a little bit about what we learned about how
00:37:25.21		bicoid distributions have changed during evolution.

This material is based upon work supported by the National Science Foundation and the National Institute of General Medical Sciences under Grant No. 2122350 and 1 R25 GM139147. Any opinion, finding, conclusion, or recommendation expressed in these videos are solely those of the speakers and do not necessarily represent the views of the Science Communication Lab/iBiology, the National Science Foundation, the National Institutes of Health, or other Science Communication Lab funders.

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