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.