Session 5: Nanofabrication via Structural DNA
Transcript of Part 3: DNA-Nanostructure Tools
00:00:07.17 Welcome to the third part of this lecture 00:00:09.13 on structural DNA nanotechnology. 00:00:12.22 My colleague George Church said, 00:00:15.08 "The problem with your field is that it looks like 00:00:17.16 you're having too much fun." 00:00:20.00 And the reality is that learning how to build 00:00:23.13 with these structures is fun, 00:00:24.28 but we also firmly believe that the power behind it 00:00:28.07 also has to do with what kinds of applications 00:00:31.17 might be possible. 00:00:33.05 And in particular, we're very interested in applications 00:00:35.29 in molecular biophysics 00:00:37.26 and future therapeutics, 00:00:39.16 so I'm going to share with some of the research directions 00:00:42.04 in my laboratory trying to find useful applications 00:00:45.03 for these DNA nanostructures. 00:00:49.23 So the first application is... 00:00:52.01 we were able to create a tool that allows 00:00:55.04 the NMR structure determination 00:00:57.12 of an alpha helical membrane protein. 00:00:59.09 This is work that was done in collaboration 00:01:02.07 primarily with James Chou's group at Harvard Medical School. 00:01:07.11 So the story starts, again, with Ned Seeman, 00:01:09.18 the person who started the field 00:01:11.22 of structural DNA nanotechnology. 00:01:13.21 You might recall from the first part 00:01:15.18 that we had this vision of flying fish, 00:01:18.01 of the host-guest crystal, 00:01:20.04 and that maybe this would make structural biology 00:01:22.14 much easier if we had access to these host-guest crystals. 00:01:26.10 Ned Seeman's group made an important landmark discovery 00:01:29.20 along the road to this eventual goal. 00:01:32.11 In 2009, they reported this nice paper 00:01:34.17 where they rationally designed a unit cell 00:01:37.19 -- they called it a tensegrity triangle -- 00:01:40.05 that basically has three crisscrossing double helices 00:01:43.17 that define three different axes in 3-dimensional space. 00:01:46.11 And if they program with sticky ends to self-assemble, 00:01:49.06 then they could actually make a macroscale crystal 00:01:52.08 that has dimensions of about 0.2 mm per side 00:01:56.06 and diffracted X-rays down to 4 Ångstrom resolution. 00:02:00.08 So this is an important first step. 00:02:02.01 A next step will be to improve the resolution of the crystal, 00:02:05.08 and then another difficult step on top of that 00:02:07.22 will be to get the target proteins to dock into very ordered, 00:02:11.06 stereotyped positioned 00:02:12.23 within each unit cell. 00:02:14.23 So I think this is a fantastic goal for the field, 00:02:18.05 maybe because it's very hard. 00:02:20.06 I think it's important to have hard goals to reach for, 00:02:23.00 but it may still take a while before we have a workable object 00:02:26.11 that will actually help us with 3-dimensional protein crystallization. 00:02:31.28 In the meantime what James Chou's group and my group have been able to do 00:02:35.28 is to do something that's technologically much more modest, 00:02:39.29 and yet it achieves the same end goal of 00:02:42.05 enabling atomic resolution protein structure determination. 00:02:45.24 And this is a method that's know as weak ordering. 00:02:49.04 It's something that I'll explain in a little bit, 00:02:51.03 something that's been known about for a number of decades now, 00:02:54.13 but hasn't really been well applicable 00:02:56.28 to membrane proteins until recently, 00:02:59.09 we believe because of our tool. 00:03:02.05 So host-guest crystallization, 00:03:04.24 you can think of that as a very strong sort of ordering, 00:03:07.09 that you want to force the protein 00:03:09.07 into a very stereotyped translational position, 00:03:12.01 very stereotyped rotational orientation. 00:03:15.06 And if that becomes too messed up, 00:03:17.24 that's going to destroy the process of structure determination. 00:03:24.09 In contrast, with weak ordering what we're doing 00:03:26.13 is we're just barely trying to change the population 00:03:29.21 of solution tumbling molecules away from isotropic. 00:03:32.15 So what does that mean? 00:03:34.08 So in a nutshell, we have this animation to explain the concept. 00:03:37.24 So imagine that the little red dots floating around 00:03:39.27 represent our target protein of interest 00:03:42.06 that we're trying to solve the structure of. 00:03:44.27 And what we want to do is to introduce weak order 00:03:47.26 into those proteins by mixing it into a dilute liquid crystal 00:03:51.27 of these long molecular rods. 00:03:55.11 So we can zoom in here - the notion is that 00:03:57.12 -- this is supposed to represent a membrane, 00:03:59.17 this is the detergent micelle solubilizing the membrane protein -- 00:04:02.27 and the idea is that most proteins are not perfectly spherical in their shape, 00:04:07.12 and they'll have a higher tendency to bump into these molecular trees 00:04:11.25 if their long axis is perpendicular 00:04:13.23 to the long axis of the molecular trees - 00:04:17.16 is perpendicular. 00:04:19.18 And so, therefore, if you could 00:04:21.12 now mix your protein with an aligned sample of molecular trees, 00:04:24.19 that should now provide a slight orientational bias 00:04:28.18 so that the proteins tend to spend a little bit longer 00:04:31.25 with their long axis pointing parallel to the aligning material, 00:04:36.01 than perpendicular. 00:04:38.05 So in this case, usually you want these long trees 00:04:41.14 to be about 1-2% weight:volume, 00:04:44.11 and furthermore for the NMR experiment, 00:04:47.07 you want them to have a magnetic susceptibility 00:04:49.16 so that if we put them in a magnetic field, 00:04:51.22 then they'll basically globally align. 00:04:55.03 And it turns out if you can now get your proteins to be partially aligned, 00:04:59.12 you can now extract out information 00:05:01.09 that otherwise would be invisible, 00:05:03.12 and that information can be very precise 00:05:05.03 and allow you to calculate the atomic resolution structure of the protein. 00:05:09.26 So I mentioned that this weak alignment method 00:05:11.19 has been around for a while, 00:05:13.15 unfortunately it's only been accessible to people 00:05:15.29 studying soluble proteins, 00:05:18.07 because the most popular alignment media 00:05:21.02 turns out to be a natural bacteriophage 00:05:23.06 related to the M13 bacteriophage. 00:05:26.11 And these work quite well for soluble proteins, 00:05:29.05 we can make large amounts of them in bacteria, 00:05:32.01 they have magnetic susceptibility, 00:05:34.04 they'll line up with each other, works great. 00:05:36.19 But the problem is that these bacteriophage 00:05:40.19 will denature in the detergents that you need to 00:05:42.16 solubilize membrane proteins, 00:05:44.21 and therefore we haven't been able to use the bacteriophage 00:05:47.15 to weakly align membrane proteins, 00:05:49.19 because they just fall apart. 00:05:51.21 That's where we came in. 00:05:53.10 We decided we wanted to build DNA nanostructures 00:05:57.03 that would be a shape mimetic of these filamentous bacteriophage, 00:06:02.16 but because they're self-assembled from DNA, 00:06:04.14 they should be impervious to denaturation by the detergents, 00:06:07.27 and therefore we should now make this method of weak alignment 00:06:10.28 available to detergent-solubilized membrane proteins. 00:06:18.28 On the upper left-hand panel what we have are 00:06:21.10 electron micrographs of our DNA nanotubes. 00:06:24.01 In this case, we designed M13s 00:06:26.20 to fold into a 6-helix DNA nanotube 00:06:28.29 that's about 400 nm long and about 7 nm in diameter. 00:06:32.16 We actually programmed two of them to come together 00:06:34.15 to make a structure that is almost a micron in length. I 00:06:38.15 t turns out if you can make the structures longer 00:06:41.11 then they'll do a better job as these molecular trees. 00:06:45.07 They'll actually line up more easily. 00:06:47.21 And what we find is that when we concentrate them 00:06:50.13 to about 2% weight:volume, 00:06:52.18 then in fact they do start lining up. 00:06:54.26 So this is an entropic phenomenon 00:06:57.01 that's behind liquid crystal formation. 00:06:59.17 And one signature of liquid crystal formation is birefringence, 00:07:03.13 so if we look at our DNA nanotubes under crossed polars, 00:07:07.11 then we see this beautiful birefringence pattern 00:07:09.18 which is indicating that we're forming the liquid crystal. 00:07:12.05 So when the sample is much more dilute you don't see anything, 00:07:14.24 but then now when the sample is concentrated 00:07:16.16 it forms a liquid crystalline phase 00:07:18.11 and then you can see this nice pattern. 00:07:20.25 But most importantly, now if we take a test protein of known structure, 00:07:24.23 so this is a transmembrane domain from the 00:07:27.16 -- zeta transmembrane domain from the T-cell receptor -- 00:07:30.17 we know what the structure is and based on that known structure 00:07:33.04 what we can do is we can calculate what 00:07:36.02 the kind of magnetic response will be 00:07:38.05 for a partially aligned structure, 00:07:40.20 so these are called dipole-dipole couplings. 00:07:43.17 And then we can compare those predicted couplings 00:07:46.07 against the ones that we experimentally measure 00:07:49.28 when we now mix the protein with the dilute liquid crystal 00:07:53.27 in the magnetic field. 00:07:55.28 And then on the lower left-hand side 00:07:57.25 what we're doing is we're comparing 00:08:00.19 on the y-axis the predicted couplings, 00:08:03.21 and on the x-axis the observed couplings, 00:08:06.08 and what we get is a very nice correlation between what we predict 00:08:09.10 and what we observe, 00:08:10.17 with high signal-to-noise. 00:08:13.00 We published this result in 2007, 00:08:15.11 we were very excited about it because we knew that this meant that 00:08:18.12 we had a tool that really works. 00:08:19.28 That we could use this to solve the structure 00:08:22.08 of membrane proteins. 00:08:24.28 However, there's of course many different hurdles 00:08:28.21 that still have to be overcome in order to solve the structure, 00:08:30.28 but just to review, 00:08:32.10 what we want to do is have these molecular trees, 00:08:34.14 our DNA nanotubes, 00:08:36.06 that when you concentrate them to 2% weight:volume 00:08:37.19 they start lining up. 00:08:39.17 We put them in an external magnetic field, 00:08:41.06 so we get global lining up. 00:08:43.07 We mix that with our protein of interest, 00:08:44.26 the protein bounces off the rods, 00:08:46.19 and we get that weak alignment. 00:08:48.10 So we get that bias in the orientation of the population of molecules. 00:08:52.28 Introducing that bias allows us to measure these 00:08:55.00 dipole-dipole couplings that encode precise 00:08:57.29 structural information about the protein, 00:09:00.18 which we can then throw into a computer program that, 00:09:03.14 if it has enough data, it can calculate the atomic resolution structure, 00:09:07.13 at least of the backbone chain. 00:09:09.18 Currently, we are not able to use this method 00:09:12.07 to experimentally measure the configuration of the sidechains. 00:09:16.01 But even if you can just generate the atomic resolution backbone, 00:09:19.25 then there are several very nice algorithms 00:09:22.13 that will allow you to predict 00:09:23.20 how the sidechains are going to pack onto that backbone. 00:09:29.12 So Marcelo Berardi in James Chou's lab 00:09:33.13 was able to use our DNA nanotubes 00:09:35.26 in order to solve the structure of a protein from the UCP family. 00:09:40.22 So this is the uncoupler proteins 00:09:42.28 that exist in the inner mitochondrial membrane, 00:09:45.12 and they're known to have an activity of translocating protons 00:09:49.23 back into the mitochondrion. 00:09:53.25 UCP1 i the most famous member of this family, 00:09:57.13 it's present in brown fat, and what it's doing is it's 00:09:59.04 just leaking protons across that membrane 00:10:01.19 and that generates heat. 00:10:03.10 So it's a mechanism to generate heat in brown fat. 00:10:07.04 Marcelo solved the structure of UCP2, 00:10:09.21 it's a related family member that's thought to be involved 00:10:11.27 in energy source selection, 00:10:15.01 so whether fatty acids vs. amino acids vs. pyruvate 00:10:19.26 should be metabolized for energy, as a source of energy. 00:10:23.29 So he wanted to solve the structure, 00:10:25.16 he tried for a long time using crystallography, 00:10:27.18 using conventional NMR, 00:10:29.15 but wasn't having a lot of luck. 00:10:31.14 Of course, for any kind of structural biology problem, 00:10:33.07 you first need to solve the problem of 00:10:35.21 being able to overexpress your protein, 00:10:38.00 being able to fold it to high homogeneity. 00:10:40.25 That's always going to be difficult 00:10:42.12 and it took many years to do that, 00:10:44.06 but to make a long story short, 00:10:45.21 once he was able to generate a large amount of the protein 00:10:48.17 in a homogeneous state, 00:10:50.15 then he was able to mix it with our DNA nanotubes, 00:10:53.18 weakly align the protein, 00:10:55.13 use that in order to measure the dipole-dipole couplings, 00:10:58.25 and then use that to calculate the atomic resolution 00:11:01.11 backbone structure of the protein. 00:11:03.10 And so this is here the crystal structure of this protein, 00:11:06.04 it's a 6-transmembrane helix protein. 00:11:08.22 Looking at the structure doesn't immediately tell you 00:11:11.18 the mechanism of proton transport, 00:11:14.09 but now James Chou's group is very interested in 00:11:18.09 using this structure as a foundation 00:11:20.15 for further structure/function exploration of 00:11:23.09 what the mechanism might be. 00:11:24.26 So their current hypothesis is that 00:11:26.22 you actually have proton transported by these fatty acids 00:11:30.05 that are flipping through the membrane, so when it's neutral... 00:11:32.18 when it's protonated it can flip through the membrane, 00:11:35.00 but now when it's deprotonated on the other side it can't flip back, 00:11:37.16 so you're not actually going to get net proton transfer. 00:11:40.09 So the idea is that the ionized fatty acid 00:11:42.28 can now diffuse into the inside 00:11:45.20 of this 6-helix barrel 00:11:47.14 and then you have a hydrophilic environment on the inside of that 00:11:49.14 where the fatty acid can flip back, 00:11:51.11 and therefore this might provide a mechanism 00:11:53.17 for multiple turnover of transfer of protons 00:11:57.07 across the membrane by fatty acids. 00:12:02.09 So that was a very satisfying experiment 00:12:04.14 because we were able to demonstrate utility 00:12:06.21 to a very urgent need in structural biology, 00:12:10.18 and we're looking forward to further developments 00:12:13.16 in the technology to make it more general, 00:12:16.24 both for NMR experiments, also for cryo-EM, 00:12:20.07 maybe for X-ray crystallography as well. 00:12:22.18 So the next area of applications that I'd like to describe 00:12:26.17 are involving single molecule biophysics. 00:12:29.12 So this is work making rigid handles for optical tweezing experiments 00:12:33.25 that was done in the lab of Hendrik Dietz. 00:12:36.24 He actually initiated this experiment when he was 00:12:39.21 doing his postdoctoral training with my group at Harvard, 00:12:42.29 and now finally was able to carry the project to fruition. 00:12:47.11 It's going to be a great tool and he was kind enough 00:12:48.20 to include me on the author list. 00:12:51.29 So the basic idea is let's say that you want to 00:12:54.27 use your optical traps in order to monitor single molecule 00:12:58.27 conformational changes of your molecule of interest. 00:13:02.17 So let's say that it's a DNA hairpin 00:13:04.27 that is opening and closing, and you want to be able to observe this. 00:13:08.06 So ordinarily this could be kind of hard to watch, 00:13:11.11 but you also want to be able to watch this 00:13:13.14 as a function of the force that you're applying 00:13:15.13 on the ends of the hairpin. 00:13:17.08 So you want to be able to dial in greater and greater amounts of force 00:13:19.23 on my elbows to peel it away 00:13:21.26 and watch what the effect is of that force 00:13:24.27 on the binding and peeling and unpeeling kinetics 00:13:28.09 of this DNA hairpin. 00:13:30.28 So the way that you actually observe this 00:13:32.24 is you now want to place your molecule of interest 00:13:36.21 in between two large microspheres, 00:13:41.06 and it turns out for technical reason you can't have these two microspheres 00:13:44.16 too close together, 00:13:46.02 so you need to give you some space, 00:13:48.20 and in order to create that space, 00:13:50.08 people like to use these double-stranded tethers 00:13:52.29 that are on the order of 300 nm long. 00:13:56.03 And so what you do is you have your beads, your tethers, 00:13:59.03 and then your molecule of interest, 00:14:00.18 and then you start to pull the beads apart, 00:14:02.26 that generates a force on the molecule in the middle. 00:14:05.25 And so if we now pull the beads further and further apart 00:14:07.21 that generates a greater and greater force 00:14:09.05 on the molecule in the middle, 00:14:10.27 and in principle we can watch the opening and closing 00:14:13.12 of that molecule in the middle 00:14:15.26 by watching how the distance between the beads changes. 00:14:18.24 So for example, we can imagine that 00:14:21.20 if you have the molecule now opens up, 00:14:24.28 now the beads should move further apart. 00:14:27.15 If the molecule in the middle now snaps shut, 00:14:29.17 then those two large beads should move closer together. 00:14:32.13 And so by measuring the distance between the beads, 00:14:34.28 in principle we can infer the conformational state 00:14:37.14 of the molecule in the middle. 00:14:39.15 Alright, sounds good, so where's the problem? 00:14:42.14 The problem is that you have Brownian motion. 00:14:44.29 So these large microspheres are actually 00:14:47.09 undergoing a lot of motion, 00:14:49.04 and it can be difficult to tell what is 00:14:51.06 just random motion and what is reporting on 00:14:53.15 something that's actually opening up in the middle. 00:14:56.03 And the problem becomes especially bad 00:14:58.11 when you have a very low force, 00:15:00.01 because at low force these double-stranded tethers 00:15:03.06 are going to be very floppy 00:15:04.23 and you're going to get lots and lots of Brownian motion. 00:15:07.24 So for example, on the lower right-hand trace, 00:15:12.05 what we see is a time trace of the distance between the beads 00:15:16.18 for the example on the top in B, 00:15:19.17 where we're looking at a DNA hairpin 00:15:21.18 that presumably is opening and closing at some specific force. 00:15:26.20 And I'm not going to explain 00:15:28.24 what's going on in the lower left-hand corner, 00:15:30.24 I encourage you to check out the publication, 00:15:32.28 but suffice it to say, 00:15:34.23 just looking at this bottom trace in E, 00:15:36.25 you can't really tell when that hairpin is opening or closing. 00:15:40.14 You can just see a lot of noise. 00:15:44.17 In contrast, if we now replace those long tethers 00:15:47.25 with a DNA Origami bundle of helices, 00:15:51.00 it's going to be much, much more rigid, 00:15:53.02 so even at those lower forces, 00:15:55.01 the Brownian noise is going to be suppressed. 00:15:57.16 And so if we look at the same force, 00:15:59.12 at the opening and closing of this object in the middle, 00:16:02.07 so D represents with these DNA Origami handles, 00:16:05.28 now you can hopefully tell that the noise is greatly suppressed, 00:16:09.09 and we have some more confidence 00:16:10.27 about assigning when the hairpin 00:16:13.13 is opening and closing. 00:16:16.06 So we think that this is a very nice application 00:16:18.15 of these rigid DNA nanorod elements 00:16:22.17 that will be useful for force spectroscopy 00:16:25.13 looking at single molecule dynamics 00:16:27.13 and energetics of biomolecules. 00:16:32.01 So the next area of application 00:16:34.01 I like to call Breadboard Biochemistry, 00:16:36.08 the notion that we can constrain the position of 00:16:38.29 many different protein actors in a molecular play 00:16:42.13 in order to understand and tease out 00:16:44.07 their individual roles in the process 00:16:46.00 - how are they interacting with each other? 00:16:47.26 What are the stoichiometric requirements? 00:16:49.21 What are the geometric requirements of this process? 00:16:53.12 So the first example of this Breadboard Biochemistry 00:16:56.00 I'd like to discuss is work that was 00:16:58.04 led in the lab of Sam Reck-Peterson, 00:17:00.13 my colleague at Harvard Medical School. 00:17:02.10 It was done primarily by two students: 00:17:06.15 Nate Derr and Brian Goodman. 00:17:08.11 Nate is a student that was co-advised by myself and by Sam. 00:17:12.15 And here we were interested in studying 00:17:15.06 how ensembles of cytoskeletal or microtubule motors 00:17:18.18 could antagonize each other 00:17:20.16 in terms of determining direction of motion 00:17:23.11 on a microtubule. 00:17:25.04 And the motivation is that it's been observed 00:17:27.07 that vesicles often times 00:17:28.16 bear both kinesins and dyneins, 00:17:31.08 which of course move in opposite directions along a microtubule, 00:17:34.05 and yet within a cell one can often observe that 00:17:36.24 the vesicles will choose one direction to go and then, 00:17:39.23 remarkably, will often times switch directions, 00:17:41.25 but what they don't typically do is just to stall. 00:17:44.22 So how can we try to study this in a reduced system, 00:17:47.06 an in vitro system? 00:17:49.05 And as a first step, what we did was we self-assembled 00:17:51.20 a chassis that is a 12-helix DNA nanotube 00:17:55.02 that's about 200 nm in dimensions. 00:17:58.16 And what we did was we decorated this chassis 00:18:01.00 with single-stranded DNA handles 00:18:03.00 that would come at regular intervals, 00:18:04.26 at 7 different positions. 00:18:07.07 And we can control and have any sequence we want 00:18:08.28 come out at any one of these positions. 00:18:11.19 In this particular case, what we did was 00:18:13.07 we had two different sequences: 00:18:15.01 one sequence for capturing dynein, 00:18:17.04 and another sequence for capturing kinesins. 00:18:19.15 In this example, 2 dyneins and 5 kinesins. 00:18:22.03 And the way that we capture 00:18:23.18 is that we express the protein, 00:18:26.00 let's say dynein, with a SNAP-tag 00:18:28.02 and then the SNAP-tag is used 00:18:30.00 to capture an oligonucleotide with a SNAP-tag ligand, 00:18:33.12 and in that way we're able to generate a protein-DNA conjugate. 00:18:38.05 And we purposely choose the sequence of that conjugate 00:18:40.23 so that it will be complementary 00:18:42.18 with the single-stranded DNA sequence 00:18:44.13 that comes out of the DNA Origami. 00:18:47.05 So in this example, we're creating 00:18:48.21 a chain gang of 2 dyneins and 5 kinesins, 00:18:51.13 and we wanted to see what happens 00:18:53.15 when we put this on a microtubules 00:18:55.03 - which way is it going to go? 00:18:57.05 And what Nate and Brian found is that 00:18:59.21 they basically stalled: 00:19:01.14 there was an irreversible tug-of-war, 00:19:03.14 at least in this first study, 00:19:06.02 and furthermore they were able to demonstrate 00:19:08.00 if they introduced photocleavable elements 00:19:10.14 either to release the dynein dynamically, 00:19:13.07 or else to release the kinesins dynamically, 00:19:15.27 then they could resolve this tug-of-war. 00:19:18.05 So for example, in one experiment, 00:19:20.09 if they released the dyneins, 00:19:22.09 then now that stall would be relieved 00:19:24.09 and the chassis would move towards the 00:19:25.19 plus end of the microtubules. 00:19:27.24 Likewise, if they cause the kinesins to be cleaved off, 00:19:31.17 then now the complex would no longer stall, 00:19:33.23 and move towards the negative end of the microtubule. 00:19:37.17 So we think this is a promising experimental platform 00:19:41.06 for now further trying to find out: 00:19:43.21 what else do we need to add in order to recapitulate 00:19:46.08 the very interesting behavior we see inside of a cell, 00:19:48.23 where the vesicles, they don't just stall, 00:19:50.17 but in fact they can move in one direction or the other, 00:19:52.22 and even more interestingly, change in direction. 00:19:56.02 So here's another application of Breadboard Biochemistry 00:19:59.05 where we're trying to study SNARE-dependent membrane fusion. 00:20:02.10 So in this process, 00:20:04.19 we have cells that are trying to fuse the vesicles 00:20:07.24 with let's say the plasma membrane, 00:20:09.27 and it's known that there are these transmembrane proteins 00:20:12.04 that are mediating this called SNARE proteins. 00:20:14.16 They have one domain that's... 00:20:18.05 let's say this is the vesicle membrane, 00:20:19.24 they have a transmembrane and they have a cytoplasmic domain 00:20:22.10 that's a coiled-coil, 00:20:24.07 and then in the target membrane you have something analogous, 00:20:26.09 you have a transmembrane domain and then a complementary 00:20:28.15 coiled-coil domain. 00:20:30.02 There's two other helices that are involved as well, 00:20:33.02 and so you can actually zipper up to form a 4-helix bundle, 00:20:36.25 and that's thought to provide the energy 00:20:39.01 for driving a vesicle in close proximity 00:20:41.29 to the plasma membrane, 00:20:43.13 because otherwise that's an energetically unfavorable process. 00:20:47.16 And then once the two vesicle membranes 00:20:49.00 are brought close together, 00:20:50.19 then some other process happens 00:20:52.25 -- that's not very well understood -- 00:20:54.14 causing the membranes to fuse. 00:20:57.08 And there's some outstanding biophysical questions about this process 00:21:01.10 that can be very simply articulated 00:21:02.29 but difficult to nail down very precisely. 00:21:06.04 For example, how many different SNARE proteins 00:21:08.21 does it take to trigger the fusion event? 00:21:11.13 And then, more subtly, 00:21:13.10 how does the geometry of these proteins 00:21:15.06 affect the kinetics of this process? 00:21:18.07 And there have been some different measurements 00:21:20.21 of the number of SNAREs required, 00:21:23.06 and depending on the context it seems 00:21:25.07 to vary between 1 and 10. 00:21:27.08 In our case, what we're interested in 00:21:29.00 is generating a more robust method 00:21:30.29 for measuring these stoichiometric requirements, 00:21:33.02 and therefore we think we can use our system 00:21:35.04 to study this problem at higher resolution. 00:21:39.20 So here's the idea: 00:21:40.26 imagine you have a supported bilayer with your t-SNAREs down there, 00:21:45.19 and you're trying to now down a vesicle on there, 00:21:50.14 but you want to only have a controlled number of t-SNAREs 00:21:53.15 that could possibly participate in the reaction. 00:21:56.29 So what if we were to create a molecular corral 00:21:59.17 where we had 3 and only 3 of the t-SNAREs 00:22:02.01 in the corral. 00:22:03.26 And now what we could ask, 00:22:05.11 "Well, is 3 SNAREs enough for membrane fusion?" 00:22:07.25 So the idea here is that we chemically synthesize, 00:22:11.18 we chemically link to the N-terminus of this SNARE protein, 00:22:14.23 this white oligonucleotide in one test tube, 00:22:17.23 and then in another test tube we self-assemble this 00:22:20.09 red DNA nanostructure. 00:22:22.14 And the red DNA nanostructure again is decorated with these 00:22:24.14 single-stranded DNA handles 00:22:26.18 that are complementary to the white anti-handles. 00:22:29.06 Now when we mix the two together, 00:22:30.26 we should get a controlled stoichiometry 00:22:33.09 of the greens and whites onto the red, 00:22:35.09 in this case three. 00:22:36.29 And now we can say, 00:22:38.09 "Well, what happens when a vesicle tries to dock into the molecular corral? 00:22:42.01 What happens when we have 3 SNAREs in the corral? 00:22:44.06 What happens when we have 2? 00:22:45.25 What happens when we have 1?" 00:22:47.12 So certainly if we have no SNAREs in the corral, 00:22:49.12 then you wouldn't expect anything about background, 00:22:51.05 and then as we start to increase the number of SNAREs in the corral, 00:22:54.08 hopefully the proteins now will be able to cooperate 00:22:56.18 to trigger the membrane fusion above background. 00:23:00.24 So I should mention that this is work that's still in progress. 00:23:04.12 It's a collaboration between our lab 00:23:06.15 and the lab of Jim Rotheman. 00:23:09.00 Chenxiang Lin was a postdoctoral fellow in the group, 00:23:11.11 he initiated the project 00:23:13.07 along with Weiming Xu in Jim's lab. 00:23:15.15 Now Chenxiang is an assistant professor at Yale, 00:23:17.21 and in our lab the project has been taken over 00:23:20.07 by Bhavik Nathwani at the time of this taping. 00:23:30.27 So again, what's thought to happen is that 00:23:32.20 you have engagement of the t-SNAREs and the v-SNAREs 00:23:36.15 and then that brings the membranes close together, 00:23:39.00 some magic happens, 00:23:40.16 membranes fuse. 00:23:42.12 And the details of that biophysical process are very interesting, 00:23:45.06 but we're starting off by asking a simpler questions 00:23:47.16 of just, how many SNAREs are involved and how does the geometry affect that? 00:23:51.28 So just to prove our ability to 00:23:53.20 decorate our DNA rings with different guests, 00:23:56.01 we started with gold nanoparticles 00:23:57.22 instead of SNARE proteins, 00:23:59.20 they're just easier to see in the electron microscope. 00:24:02.09 And we can demonstrate that we can now 00:24:04.18 decorate our DNA rings with 3 gold particles on the side, 00:24:07.18 3 on the outside, 00:24:09.07 6 on the inside, 00:24:11.06 4 on the inside, 00:24:12.28 8 on the inside. 00:24:15.06 And what we've observed so far is that we can get 00:24:17.20 something on the order of 90% occupancy of our sites. 00:24:21.13 We'd like to get higher than that, 00:24:22.29 something more like 99.99%, 00:24:25.12 it's something we're working on, 00:24:27.21 but currently we think even with a 90% occupancy rate, 00:24:30.14 this is still useful because it provides an upper bound 00:24:33.11 on the number of SNARE proteins, or whatever guest, 00:24:36.00 that could be bound in our assembly. 00:24:40.02 So here what we're doing is we're actually 00:24:41.06 binding SNARE proteins now, detergent-solubilized SNARE proteins 00:24:44.02 instead of gold nanoparticles. 00:24:46.14 And again, what we're doing is 00:24:48.29 we have these green SNARE proteins 00:24:51.00 conjugated to a white oligonucleotide 00:24:53.10 that are now self-assembling with this complementary red oligonucleotide 00:24:56.21 being displayed on the outside of this DNA nanostructure. 00:25:00.06 And in the electron microscope, 00:25:01.26 we can see enough contrast from the proteins 00:25:03.21 to count the number of guest molecules 00:25:05.22 on the DNA nanostructure. 00:25:08.18 And then what we do is we, 00:25:10.11 so in this case what we're trying to do is create liposomes 00:25:13.09 with controlled numbers of SNARE proteins 00:25:15.04 and see how they behave in a fusion assay. 00:25:18.13 And so the next step here is that we mix our DNA rings 00:25:22.00 with protein guest 00:25:23.28 with giant liposomes 00:25:25.23 in the presence of slight amounts of detergent, 00:25:28.14 and through some process we don't quite understand, 00:25:30.24 these DNA nanostructures with hydrophobic groups 00:25:34.20 somehow take a bite out of the giant liposomes 00:25:37.11 and end up with smaller liposomes 00:25:39.19 basically filling the interior of the ring. 00:25:43.18 So in this way, through a process we don't quite understand, 00:25:45.24 we're able to capture liposomes on the inside of our DNA nanorings, 00:25:49.25 with controlled numbers of SNARE proteins. 00:25:53.01 Then here's a fusion assay that we use, 00:25:55.18 it was developed by Erdem Karatekin at Yale. 00:25:58.21 It's a microfluidic assay where we're 00:26:01.11 fluorescently labeling these vesicles that we've captured 00:26:04.14 in the DNA ring 00:26:06.18 and then when we get fusion, 00:26:08.18 what will hopefully happen is that those dyes 00:26:10.25 will then diffuse out into the supported bilayer. 00:26:13.28 So initially what happens is the dyes 00:26:15.22 are quenching each other somewhat, 00:26:17.28 the first thing is actually the spot should grow brighter 00:26:20.16 as the dyes de-quench, 00:26:22.13 but then as the dyes diffuse away from each other, 00:26:24.11 then you should now get rapid 00:26:27.12 dilution of the fluorescence response. 00:26:30.03 And so we have here a microfluidic setup 00:26:32.04 where we have an Eppendorf tube 00:26:34.05 with our labeled vesicles 00:26:36.18 that are being pulled through this microfluidic chamber. 00:26:39.13 We're observing it using TIRF microscopy, 00:26:41.20 and then trying to see whether or not those vesicles 00:26:45.03 can fuse to the supported bilayer, 00:26:46.27 as a function of number the number of SNARE proteins. 00:26:49.15 So this is just an example of the assay in action. 00:26:53.18 Your eye might be drawn to this giant blob on the bottom, 00:26:56.16 but I'd like you to try to ignore that. 00:26:58.06 Instead, focus on this red circle here, where we can see 00:27:01.19 -- it's on a loop -- 00:27:03.01 so we can see a vesicle docking and shortly after docking 00:27:05.24 then we get fusion. 00:27:07.12 So that's the kind of event that we're trying to score, 00:27:10.06 so far we're still in the process of trying to see 00:27:12.25 whether or not our scaffolded DNA rings 00:27:15.02 actually can increase the rate of liposome docking and fusion. 00:27:19.05 Hopefully we'll be able to make some progress on that 00:27:21.05 for the next lecture. 00:27:25.15 So the other class of applications 00:27:27.15 I'd like to discuss with you involve 00:27:29.20 potentially using DNA nanostructures 00:27:32.07 as therapeutic delivery devices. 00:27:34.19 And as a first step towards that, 00:27:36.25 Franziska Graf, who was a student co-advised 00:27:39.13 by myself and Don Ingber, 00:27:41.07 we set out to do some pilot studies 00:27:43.06 to look at how the shape of DNA nanostructures 00:27:45.18 might affect their uptake into a model cell line. 00:27:48.23 In this case, these are umbilical vein endothelial cells. 00:27:52.29 So how the shape of DNA Origami 00:27:54.16 affect their tastiness to these cells? 00:27:56.27 And if you think about it, you could come up with 00:27:58.05 a long list of potential descriptors 00:28:00.27 that might make a difference, 00:28:03.06 and it would require very a systematic study 00:28:07.01 to look through all of these, 00:28:08.16 but we just started out with a pilot study 00:28:10.04 looking at aspect ratio. 00:28:12.17 So in this case, we wanted to ask the question: 00:28:15.07 if we have bunch of particles of the same mass, 00:28:17.22 but then we made them in different shapes, 00:28:20.08 then what would be the relatively uptake? 00:28:22.13 So let's first look at these 3 blue shapes 00:28:24.18 that are highlighted in yellow. 00:28:26.13 So they're the same mass, 00:28:27.22 they're about 5 megaDaltons, 00:28:29.11 but they're made into a very long spindly rod that's a 6-helix bundle, 00:28:32.23 and then we have this 24-helix bundle, 00:28:35.22 and this is a 48-helix bundle, 00:28:37.14 that are successively more compact. 00:28:39.24 So if we predict that structures 00:28:41.17 with a longer aspect ratio 00:28:43.13 should get into cells better, 00:28:44.29 then we'd predict that this longer one 00:28:47.15 should do the best in terms of getting into the cell, 00:28:49.19 being taken up. 00:28:51.11 In contrast, if we hypothesize 00:28:53.11 that the more compact structure should get in better, 00:28:55.16 then we expect to observe the opposite behavior: 00:28:58.07 that this more compact structure 00:29:00.00 should get in more quickly. 00:29:01.22 And we also made some other shapes. 00:29:03.08 So here we made a wireframe octahedron, 00:29:06.13 we also made three different orange shapes that are analogues, 00:29:10.06 but now just 40% the mass. 00:29:12.25 And so we can say, 00:29:14.01 "Well, with this starting panel of eight structures, 00:29:16.06 what's the relative rate of uptake into these HUVEC cells?" 00:29:22.04 And what we observed 00:29:24.13 -- so I have a very busy slide here 00:29:27.05 but I'll try to give you the overview -- 00:29:30.04 so first thing is if you kind of squint your eyes 00:29:33.22 you might notice that the bars, 00:29:35.21 which represent some kind of uptake, 00:29:37.19 tend to be taller on the right-hand side of the slide 00:29:41.09 than one the left-hand side of the slide. 00:29:44.15 And also, if you look at it for a little bit longer, 00:29:47.14 you might notice that the structures 00:29:49.15 that are on the right-hand side 00:29:51.11 tend to be more compact 00:29:53.03 than the structures on the left-hand side. 00:29:54.24 So just from a simple eyeballing of the figure, 00:29:57.10 we can get the feeling that, 00:29:59.06 at least for this class of particles, 00:30:01.01 when the structures are more compact, 00:30:03.10 then they get in more easily into cells. 00:30:06.25 So the second order piece of information that we learned is - 00:30:10.05 so you might notice here that there's hollow bars and solid bars, 00:30:12.03 so what does that mean? 00:30:13.27 So the hollow bar represents the amount of 00:30:16.22 fluorescently labeled nanostructures 00:30:18.21 that were taken up by the cell 00:30:20.25 after some incubation time, 16 hours, 00:30:24.04 and then the solid bar represents the same experiment, 00:30:27.19 but what we did was we treated the cells with DNase I 00:30:31.21 before we did the fluorescence analysis. 00:30:33.27 So what that's going to do is to remove 00:30:35.22 any membrane-bound DNA nanostructures 00:30:38.18 and now the assay will only report on those structures 00:30:41.05 that actually have been internalized, 00:30:43.07 that are now protected from DNase digestion. 00:30:46.04 And so what we observe is something quite interesting. 00:30:48.11 That for these compact structures, 00:30:50.01 the height of the bars is very similar, 00:30:52.03 so that says that most of the particles 00:30:53.27 that stick to the cells immediately go in, 00:30:58.03 or are getting in quite efficiently. 00:30:59.25 But for these extended structures, 00:31:02.00 you might notice that the hollow bar 00:31:03.26 is actually much taller than the solid bar, 00:31:06.13 and what that suggests is that if you're really extended, 00:31:09.02 now maybe you can resist getting internalized, 00:31:11.28 which we can rationalize by saying for this class of cells, 00:31:14.27 they're going to have a hard... 00:31:16.08 they don't really have good mechanisms 00:31:17.23 for engulfing structures that are 400 nm long. 00:31:22.00 So this might present an interesting opportunity therapeutically, 00:31:24.16 that we could design structures 00:31:26.19 that have the tendency to persist on the outside of the cell, 00:31:29.29 that can resist internalization. 00:31:32.08 It might be useful for creating 00:31:34.18 a sentinel or a beacon for other nanoparticles 00:31:37.01 to then deliver their contents to that particular cell, 00:31:39.18 because if you just get swallowed, 00:31:41.04 then you're not going to be able to act as that sentinel. 00:31:46.07 Here what Franziska has done is she's taken one of the structures, 00:31:49.06 this nanocylinder, 00:31:50.27 and she's decorated the outside of the nanocylinder 00:31:53.23 with a bunch of ligands, in this case 00:31:55.20 cyclic RGD ligands that are known to bind the 00:31:58.17 alpha V beta 3 integrin receptors 00:32:00.03 that are overexpressed on this cell line. 00:32:02.18 So the prediction is that if we decorate our nanoparticles 00:32:06.04 with a high density of these ligands, 00:32:08.08 then we're going to increase the rate of internalization. 00:32:12.06 And then we can also do a control with cyclic RAD peptides 00:32:15.23 that should not interact with those receptors. 00:32:18.25 And in fact that's basically what we observe, 00:32:22.01 that when we have the cyclic RGD-labeled structures 00:32:26.11 we get an order of magnitude greater uptake of these particles 00:32:31.07 compared to controls that had no peptide 00:32:33.29 or controls that have the mock peptide sequence cyclic RAD. 00:32:39.14 And so what this says is that we have the ability 00:32:42.05 to make these particles of different shapes 00:32:44.02 to either help their uptake 00:32:46.07 or help prevent their uptake. 00:32:47.28 We can decorate these particles with a high density, controlled density, 00:32:51.28 of ligands to help further modulate that process, 00:32:54.21 either get faster uptake or not. 00:33:01.12 So far I've shown you naked DNA particles. 00:33:06.20 Again, we might be worried about things 00:33:08.18 like nuclease digestion of these particles, 00:33:11.10 we might have a desire to encapsulate 00:33:13.21 soluble factors in these structures. 00:33:15.24 So if we try to make a wireframe cage 00:33:18.18 and then we put soluble factors on the inside of the cage, 00:33:20.27 then those soluble factors 00:33:23.11 might just diffuse out through those windows. 00:33:25.08 So how do we keep those factors on the inside? 00:33:27.19 So Steve Perrault in the group 00:33:29.08 has pioneered a method for encapsulating 00:33:31.19 these DNA nanostructures within liposomes 00:33:34.11 to create something that's structurally analogous 00:33:36.15 to an enveloped virus. 00:33:38.14 So the way that he's done this is 00:33:40.06 he again self-assembles a DNA octahedron 00:33:42.19 with single-stranded DNA handles coming, 00:33:45.14 and then he hybridizes on a complementary oligonucleotide anti-handle 00:33:49.26 that has a lipid conjugate covalently linked to it, 00:33:52.14 solubilized by detergent. 00:33:54.18 And so through base pairing interaction, 00:33:56.07 he basically gets these detergent solubilized lipids 00:33:59.28 to cover his DNA octahedron, 00:34:02.18 he has something like on the order of 50 copies of these lipids 00:34:05.09 covering his octahedron, 00:34:07.18 solubilized by detergent. 00:34:09.27 The next step is that he mixes this... 00:34:11.19 he dilutes this into a solution of giant liposomes 00:34:14.04 and then dialyzes out the remaining detergent. 00:34:17.07 Through a process that we still don't quite understand, 00:34:19.23 we get shrink-wrapping of the liposomes 00:34:21.28 around our DNA nanostructures, 00:34:24.03 creating something, again, that resembles 00:34:25.20 under the transmission electron microscope, 00:34:28.05 envelope viruses. 00:34:29.18 So here we can see on the left the naked DNA octahedra, 00:34:33.09 they're about 50 nm in diameter, 00:34:36.11 and then on the right 00:34:38.07 we can see the liposome-encapsulated DNA octahedra. 00:34:42.25 Again, very reminiscent of an envelope virus. 00:34:46.04 So we think this is an important step 00:34:48.09 towards the versatile use of DNA nanostructures 00:34:51.19 for delivery, for example, of soluble factors, 00:34:54.18 or if we wanted to simply protect the DNA from nucleases 00:34:58.16 or factors that would try to digest it. 00:35:00.21 We of course still want to be able to 00:35:03.02 decorate the surface of the structures 00:35:05.02 with functionalities, 00:35:06.27 so now we're working on the ability 00:35:08.13 to present transmembrane features, 00:35:10.16 starting from the inside, 00:35:12.25 going through the membrane, 00:35:14.13 and then basically controlling the spatial orientation of ligands 00:35:17.04 through the puppet master on the inside. 00:35:21.27 And then the final thing that I'd like to show you 00:35:23.24 is actually not from my laboratory, 00:35:26.03 it's from Shawn Douglas, 00:35:28.09 when he was a postdoctoral fellow in George Church's group, 00:35:31.05 and he did this fascinating... 00:35:33.04 along with Ido Bachelet, 00:35:34.19 they did this fascinating pilot study 00:35:36.16 where they generated something they called 00:35:37.25 a DNA Origami nanorobot, 00:35:40.19 that was designed, at least in this test tube example, 00:35:44.08 to specifically recognize cancerous lymphocytes and kill those off 00:35:50.00 -- program them to commit suicide -- 00:35:52.10 while leaving the healthy cells alone. 00:35:54.12 So how is this supposed to work? 00:35:55.28 The idea is that Shawn designed this DNA barrel 00:35:58.25 that's about 60 nm in diameter, 00:36:01.28 and he placed on the inside 00:36:04.05 antibodies that are known to bind to receptors 00:36:07.24 and cause receptor clustering 00:36:09.13 that then leads to apoptosis. 00:36:13.04 So kind of like what a natural killer cell might do. 00:36:15.21 But in this case, because the antibodies are sequestered 00:36:17.22 on the inside of the barrel, 00:36:19.06 they're not actually accessible to the cells. 00:36:21.26 The cells have fat fingers, if you will, 00:36:23.21 so they can't actually reach in 00:36:26.02 and touch those antibodies. 00:36:28.08 And the notion is that Shawn and Ido 00:36:29.27 wanted to program this robot 00:36:32.00 to open up when it encountered the cancerous cell 00:36:34.27 but not when it encountered the healthy cell. 00:36:37.23 And if the robot were to open up, 00:36:39.22 then now the antibodies that trigger apoptosis 00:36:42.17 could now be accessible to the cell surface. 00:36:45.12 So how do they get this robot to open up 00:36:47.00 only in the presence of the cancerous cell 00:36:49.01 but not in the healthy cell? 00:36:50.22 So what they did was they designed 00:36:52.08 this lock-and-key mechanism 00:36:54.03 where they had single-stranded DNA 00:36:56.13 on the two ends that would hybridize together, 00:36:59.20 and they designed this sequence 00:37:01.05 with something called a structure-specific aptamer. 00:37:05.15 And what happens is this aptamer 00:37:08.18 recognizes some protein ligand, 00:37:12.02 and so basically that protein ligand 00:37:13.22 is competing for interaction between the partner strand, 00:37:19.11 so in other words these two strands will interact with each other, 00:37:23.04 but that can be competed off by some specific protein ligand. 00:37:26.20 And they used sequence on one of the locks 00:37:29.04 that was derived from the literature, 00:37:30.20 a sequence that somebody else had isolated 00:37:33.06 through a SELEX experiment, 00:37:35.04 that sequence was known to bind to EGFR, 00:37:37.23 which is overexpressed on their sample cancer cell line. 00:37:41.06 And then they found in the literature another sequence 00:37:44.14 that was found to recognize something that's enriched 00:37:47.14 on their cancer cell line 00:37:48.28 but not on the healthy cell line. 00:37:50.28 So they're able to do a logical AND statement here. 00:37:53.27 Only if the target cell can open both locks 00:37:57.02 would the shell open up 00:37:59.03 and then reveal the antibodies to the cell surface. 00:38:03.00 So in this way you could argue that 00:38:04.21 it's an intelligent robot 00:38:06.19 in that it can do this more complicated logical argument. 00:38:10.29 And they were able to show, at least in the test tube, 00:38:13.01 that in fact this nanorobot 00:38:14.27 seems to trigger the apoptosis 00:38:16.29 a couple of orders of magnitude more easily 00:38:19.06 than in the healthy cell. 00:38:21.16 Of course, moving this into an actual therapeutic environment 00:38:24.27 requires several hurdles, 00:38:26.29 because if you wanted these things circulating in your blood, 00:38:29.22 you'd want them to avoid nuclease digestion, 00:38:32.08 you'd want them to avoid clearance by the immune system. 00:38:36.07 So, of course, there are going to be many hurdles to overcome 00:38:39.00 in order to translate this to the clinic, 00:38:41.11 but we think this is a fascinating first step in that direction. 00:38:48.04 So to conclude, DNA nanotechnology 00:38:51.28 is more than just smiley faces, we think. 00:38:54.26 In particular, we're very interested in two classes of applications. 00:38:59.13 One is as tools for molecular biophysics, 00:39:01.29 whether it be in tools for structural biology 00:39:04.10 or especially single molecule biophysics. 00:39:06.29 And secondly, we are exploring the notion that 00:39:10.00 these DNA nanostructures might be useful 00:39:13.07 as therapeutic delivery devices, 00:39:15.14 and of course there's many different platforms that 00:39:18.07 scientists are trying to explore for delivery, 00:39:20.24 but we're motivated by the insight 00:39:23.16 that our immune systems, in fact, 00:39:25.26 can be thought of as very complicated nanotechnologies. 00:39:29.17 They can process lots of information, 00:39:31.25 they can actuate all kinds of things, 00:39:33.13 they can punch holes into cells, 00:39:35.12 they can program each other to expand, 00:39:37.18 they can squeeze through tight spaces, 00:39:39.14 and the only way to achieve this really diverse, 00:39:42.19 sophisticated behavior 00:39:44.09 is by creating complex nanoscale objects. 00:39:47.03 At the moment, we believe that 00:39:49.12 the DNA nanotechnology platform 00:39:51.17 provides a very powerful method in that direction. 00:39:55.14 So I'd like to thank the following sources for support, 00:39:59.02 especially NIH and the Office of Naval Research. 00:40:02.10 We also got some support from the Wyss Institute 00:40:03.26 for Biologically Inspired Engineering. 00:40:06.07 I've tried to acknowledge 00:40:07.26 the different folks who have been doing the work 00:40:10.00 on the slides describing the work. 00:40:12.02 Thanks a lot.