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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.

This material is based upon work supported by the National Science Foundation and the National Institute of General Medical Sciences under Grant No. MCB-1052331. Any opinion, finding, conclusion, or recommendation expressed in these videos are solely those of the speaker and do not necessarily represent the views of iBiology, the National Science Foundation, the National Institutes of Health, or other iBiology funders.

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