Genes, The Brain, and Behavior
Transcript of Part 2: Cracking the Circuits for Olfaction: Odors, Neurons, Genes and Behavior
00:00:00.00 Hi, I'm Cori Bargmann, 00:00:03.25 from the Rockefeller University in New York, 00:00:05.29 and the Howard Hughes Medical Institute. 00:00:08.02 And I'm going to talk today about work that we've been doing to try to crack circuits for olfaction, 00:00:13.27 to understand how you go from odors to neurons to genes to behavior. 00:00:19.24 Now, I'm going to talk about this in the context not of the noble human brain, 00:00:24.20 but of the noble brain of the nematode worm, Caenorhabditis elegans. 00:00:28.20 Why would we study a simple animal instead of studying humans? 00:00:31.29 The reason is that the human brain is almost unimaginably complex: 00:00:36.14 it has billions of neurons that are connected to each other by trillions of synapses. 00:00:42.03 By contrast, the nervous system of the nematode worm C. elegans has only 302 neurons 00:00:47.27 that are connected by 7000 synapses, and another 600 or so gap junctions. 00:00:54.15 Now, this much simpler nervous system nonetheless shares many components with the nervous system of a human. 00:01:01.19 So whereas humans have about 25,000 genes, 00:01:04.09 worms have about 20,000 genes, 00:01:06.09 many or which are shared between the species. 00:01:08.20 And when we look at the properties of the nervous system, 00:01:11.01 we find that many features of the nervous system are similar, 00:01:14.22 that worms use similar neurotransmitters, channels, and developmental genes, as humans. 00:01:20.07 Therefore, we think that some of the principles that underlie the function of the brain 00:01:24.08 and the function of brain circuits in behavior will also be similar between simpler animals like the worm 00:01:30.06 and complex animals like ourselves. 00:01:34.17 Now, with C. elegans, we also have, from the work of John White and his colleagues, 00:01:39.06 knowledge of how those 302 neurons communicate with each other, through a wiring diagram. 00:01:45.05 This wiring diagram contains only 6000 or 7000 connections, 00:01:48.26 but that's still too many, as you can see in this illustration, 00:01:52.24 to really understand the flow of information. 00:01:55.05 We need to directly test what the connections do, 00:01:57.28 we need to test what the neurons do, in order to understand behavior. 00:02:03.28 And the way that we try to understand behavior is using the behavior of the entire animal, 00:02:10.29 the functions of individual genes, and the functions of neurons, 00:02:14.18 and relate those to each other vertically, from the level of molecules 00:02:18.23 to the level of the entire organism. 00:02:21.07 Now, the starting point for this set of studies will be the fact that worms respond to odors 00:02:27.15 with robust behavioral responses, 00:02:29.24 that pose a set of questions we can ask about how behavior is generated. 00:02:33.24 So, if you put a lot of worms down in an environment where there's no odor, 00:02:36.27 they'll scatter around. 00:02:38.24 But if you them in an environment where there's a good odor on one side, 00:02:41.25 they'll quickly move to the source of that good odor and accumulate there. 00:02:46.29 Conversely, if you put them in an environment with a bad odor, 00:02:49.06 they'll go as far from it as they possibly can. 00:02:51.25 So we can see attraction, repulsion, or neutral responses in the behavior of the animal. 00:02:57.21 We can then ask: What parts of the worm brain are required for these different kinds of behaviors? 00:03:04.15 And we can ask this question through different kinds of approaches, 00:03:08.22 either loss-of-function approaches or gain-of-function approaches, 00:03:12.01 and both of those converge on the same answer, 00:03:15.03 which is that specific neurons detect odors and initiate behaviors in the animal, 00:03:20.20 and that the neurons that do this are reliably similar from worm to worm. 00:03:25.23 So, one way to determine that is to eliminate the functions of single neurons, 00:03:29.27 which we can do by killing them with a laser microbeam, 00:03:32.13 and when we do that, for example, for this neuron shown here in blue, the AWC neuron, 00:03:37.13 we find that the animals become defective in their ability to chemotax 00:03:40.26 to certain attractive odors and to search for food. 00:03:44.15 Now, if we kill the neuron right next to AWC, this red neuron, ASH, 00:03:48.20 there's no defect in odor chemotaxis and food search. 00:03:51.14 But now instead, there's a defect in nociception 00:03:55.16 and escape behavior that is triggered by noxious compounds that the worm hates. 00:04:00.20 So this tells us these neurons are required for different behaviors. 00:04:04.08 We can complement this loss-of-function analysis by gain-of-function analysis, 00:04:08.19 where we activate these neurons artificially and ask what behaviors the animal generates. 00:04:14.15 And the method that's used to do that currently in neuroscience 00:04:18.07 is to use a molecule called channelrhodopsin. 00:04:21.03 It's a light-activated ion channel from a unicellular organism. 00:04:25.23 The gene for channelrhodopsin can be introduced into different neurons in different animals, 00:04:30.25 and it will then make those neurons responsive to light, 00:04:33.15 so that when you shine light on them, the neurons become active. 00:04:36.15 You can then ask, in this gain-of-function configuration, 00:04:39.20 what happens when you activate one of these neurons? 00:04:42.26 And so for, example, as is shown in this movie here, when you activate the ASH 00:04:47.24 nociceptive neuron that mediates escape behaviors simply by turning a light on 00:04:52.26 and activating channelrhodopsin, the worm generates a reversal. 00:04:57.01 This is an escape behavior associated with a change of direction 00:05:00.16 that's exactly like what would happen if ASH detected one of its normal, 00:05:05.04 noxious stimuli that would also direct an escape behavior. 00:05:09.12 And so we can say here that ASH is both necessary and sufficient for generating escape behaviors. 00:05:18.13 Now, explaining escape behavior is pretty straightforward. 00:05:22.17 Escape behavior is deterministic; 00:05:24.26 that means that, when a worm encounters a noxious substance, 00:05:28.09 as illustrated by this series of panels, every worm generates a reliable response 00:05:33.09 to that noxious substance, in a way that's quite predictable, 00:05:37.08 where it will back up, turn away, and move in a new direction. 00:05:41.04 But when we try to understand chemotaxis behavior, we see that it has different properties. 00:05:46.01 It's a probabilistic behavior, 00:05:48.08 and what I mean by that is that, 00:05:49.29 while all of the worms will eventually reach the odor, 00:05:53.12 they get to the odor by what seems to be an unpredictable path. 00:05:57.00 Every worm seems to follow a different path to reach the odor source. 00:06:01.09 How can we explain this more complex trajectory, 00:06:04.21 which doesn't look like the reflex or deterministic action? 00:06:07.26 What we need is some kind of a model that would explain 00:06:11.00 how animals can approach an odor. 00:06:13.29 And in fact, exactly such a model was developed by Shawn Lockery and colleagues, 00:06:19.05 and what they showed was that worms approach the odor using a strategy 00:06:24.01 called a "biased random walk," which is the same strategy that bacteria use 00:06:29.06 to detect attractive chemicals in their environment. 00:06:32.12 A biased random walk occurs through a fascinating strategy where 00:06:37.19 animals don't point their nose straight up toward the odor like a weather vane; 00:06:42.11 instead, they simply move through their environment, 00:06:45.17 waiting to see whether conditions are changing, and if so, 00:06:51.05 whether they're getting better or worse. 00:06:53.18 And what the animals do is that they turn, changing directions, 00:06:56.29 at some constant rate in constant conditions. 00:06:59.25 But if conditions get better, if the odor increases, 00:07:05.23 then they make fewer turns. 00:07:08.04 If the conditions get worse, if the odor decreases, 00:07:11.12 they make more turns. 00:07:12.27 And the effect of this, is that animals will move in a good direction 00:07:17.00 where odors are increasing for a longer period of time, 00:07:21.04 and they'll move in a bad direction where odors are decreasing 00:07:24.00 for shorter periods of time. 00:07:25.20 And eventually, just changing direction at random, 00:07:28.15 this will lead them to accumulate at the odor through what appears to be a 00:07:32.13 more-or-less random path. 00:07:34.14 So the key feature of this strategy is that the animals aren't detecting the absolute levels of odors, 00:07:40.01 they're detecting the change in an odor level... 00:07:42.27 are things getting better or are things getting worse? 00:07:46.03 They're looking at the change in concentration over time. 00:07:51.05 So, we would like to test this model. 00:07:53.14 How do you go about testing a model like this, about odor concentrations over time? 00:07:58.20 The way you have to test this model is to generate a temporal gradient, 00:08:03.12 an odor environment that changes only over time and not over space, 00:08:08.12 to test the predictions of this particular quantitative model. 00:08:12.13 And the way that this can be done is by generating small chambers 00:08:16.11 in which animals can be exposed to odors flowing past them rapidly, 00:08:20.13 and then examine for their different kinds of behavioral responses. 00:08:24.07 And a chamber to carry out this task was designed by Dirk Albrecht. 00:08:30.05 So, what Dirk did was to find a small environment in which he could provide pulses of odors 00:08:35.20 at a known concentration at a known schedule, 00:08:38.10 and examine the responses of the worms in these environments. 00:08:41.23 And as is seen in the movie here, when you watch worms moving through this chamber, 00:08:46.02 sometimes they move in straight lines, and sometimes they change directions, 00:08:49.11 generating different kinds of turns. 00:08:51.28 Now, this light color here are worms in the absence of an odor. 00:08:55.12 Some of them are turning, some of them are moving in straight lines. 00:08:58.07 When the dark color appears, that will signal the appearance of an attractive odor. 00:09:02.25 When the light colors appears, the odor will disappear. 00:09:05.25 And what you should be able to see is that, 00:09:07.17 when the odor appears, the worms move in long, straight lines, 00:09:11.08 and when the odor disappears, they turn, they change direction. 00:09:15.02 Again, attractive odor... long, straight lines. 00:09:18.28 Disappearance... turning. 00:09:21.13 This is exactly the behavior that is predicted in the biased random walk model: 00:09:26.22 An increase of turning when conditions are getting worse. 00:09:30.14 So here we can see that at a visual level. 00:09:33.05 But in order to understand behaviors, we need to quantify those behaviors, 00:09:37.08 not just look at them qualitatively. 00:09:40.16 And to do that, we can use methods to automatically analyze the turning behaviors 00:09:45.08 using computers to monitor the position of worms over time. 00:09:49.04 We can then assign to each of the worms a description of what it's doing at any particular time: 00:09:54.23 Is it moving forward, here in gray? 00:09:57.02 Is it pausing or reversing, here in black? 00:09:59.24 Or is it generating different kinds of turns, called pirouettes, here in red? 00:10:04.17 This analysis can be done for many hundreds of animals over different kinds of stimulus protocols, 00:10:10.20 leading to the kinds of data shown here, where animals are exposed to pulses of odors in blue, 00:10:17.25 and odor being removed (replaced by buffer) in white. 00:10:21.25 And then here, hundreds of animals are monitored for their behavior in response 00:10:26.04 to that sequence of odor and buffer pulses. 00:10:29.04 Now what you should be able to see is that there's a lot of red and black material in the presence of buffer, 00:10:34.26 but much less when odor is present. 00:10:38.04 These hundreds of traces can then be quantified to generate the one trace underneath, 00:10:42.29 which shows the probability of turning under different conditions. 00:10:47.12 And what you can see is that, when odor is present, as it is here, 00:10:51.08 the probability of turning is quite low, but it's not zero. 00:10:55.04 And when odor is removed, as is shown here, 00:10:57.17 the probability of turning shoots up, but it doesn't go up to 100%... 00:11:02.03 it eventually returns again to the basal probability of turning. 00:11:06.11 So from this we can say a couple of different things: 00:11:08.29 We can confirm the biased random walk model, we can say that, yes, 00:11:12.14 turning rates do change based on odor history, 00:11:16.01 whether odor has been added or removed. 00:11:19.02 And we can also notice that this is indeed a probabilistic behavior, 00:11:23.29 that the probability of turning changes, but it's never 0%, and it's never 100%. 00:11:29.19 To understand behavior, we have to think quantitatively and statistically 00:11:33.28 about what animals are doing at any given time. 00:11:39.17 So, using these kinds of assays and simpler assay that resemble these, 00:11:44.09 it's been possible to map out neurons that are required for odor chemotaxis and food search. 00:11:50.18 I told you that the AWC neuron, an olfactory neuron, is required for odor detection. 00:11:55.23 AWC forms synapses onto three different classes of interneurons, 00:12:00.16 neurons that collect information from a variety of sensory neurons, 00:12:04.23 and these neurons are connected to each other and with a fourth neuron. 00:12:09.04 All four of these neurons, that are one synapse away from the AWC neuron, 00:12:13.26 regulate turning probabilities. 00:12:16.15 Two of them, shown in blue, 00:12:18.15 act to increase the rate of turning when odor is removed, and two of them, show in red, 00:12:24.09 act to decrease the the rate of turning. 00:12:26.14 So they're both positive and negative signals in this circuit that are mediating odor information. 00:12:32.27 Now, once a turn is being generated, 00:12:36.08 the worm has to decide what kind of turn it's going to be. 00:12:39.00 The neurons shown here in gray at the bottom of the slide 00:12:42.02 are neurons that help interpret this turning frequency information and 00:12:45.19 turn it into different kinds of output motor behaviors. 00:12:48.22 I won't talk about those further in this talk. 00:12:51.02 I'll just concentrate on the first step: 00:12:53.08 How is the problem of detecting odor transformed through the neurons 00:12:57.11 that collect this information from the sensory neuron, to regulate turning rates? 00:13:04.28 So, one way to answer that question is to start to get a dynamic picture 00:13:09.18 of what the neurons are doing in response to odors. 00:13:13.10 We want to visualize what's happening in these neurons. 00:13:16.21 So what are the tools we can use to understand when neurons are active? 00:13:20.25 In C. elegans, one of the tools we like to use are genetically encoded calcium indicators. 00:13:27.23 These are fluorescent proteins based on the "green fluorescent protein" 00:13:32.05 that include within them a calcium-binding protein "calmodulin," 00:13:35.29 as well as a peptide that will bind to calmodulin when calcium is present. 00:13:40.25 Through genetic engineering and biochemical studies, 00:13:43.13 Junichi Nakai and others have generated versions of these proteins that increase fluorescence 00:13:49.06 when they are bound to calcium, and are less fluorescent when they are not bound to calcium. 00:13:53.28 This is useful to us because calcium is a good reporter of when a neuron is active. 00:13:59.20 When neurons are depolarized, they open voltage-gated calcium channels, 00:14:04.07 leading to an increase of calcium within the cell. 00:14:07.03 And therefore, an increase in fluorescence of a protein associated with 00:14:11.07 an increase of calcium will tell you when a neuron is depolarized. 00:14:16.04 To monitor a specific neuron, 00:14:17.27 we then take advantage of the powerful transgenic tools in C. elegans 00:14:22.04 to express this genetically encoded fluorescent protein 00:14:25.02 only in a single kind of neuron of interest, 00:14:27.23 in this case, in the AWC neuron, to ask when that neuron is active. 00:14:35.20 Now there's a third component required to monitor the activity of these neurons, 00:14:39.22 and that is that we need to be able to hold the worm still and 00:14:42.29 deliver odors in precise patterns while monitoring the fluorescence intensity of the AWC neuron. 00:14:50.05 We do that by borrowing a technology back from the engineering, 00:14:54.05 from the silicon chip, industry, into biology, called microfabrication. 00:14:58.23 And we build special worm traps that are worm dimension, 00:15:02.21 that enable us to hold a worm in an optically transparent environment, 00:15:07.23 while restraining it in three dimensions, and then flowing different kinds of fluids 00:15:11.20 past the nose of the worm while monitoring fluorescence intensity. 00:15:15.12 This microfluidic chamber then permits us to combine the genetic tools 00:15:20.00 with chemical tools to monitor neural activity. 00:15:25.13 And that's exactly what's happening in this image here. 00:15:28.17 So this is a single AWC neuron expressing a genetically encoded calcium indicator, 00:15:33.20 and you will see when the movie starts, the neuron starts with a yellow level of fluorescence 00:15:39.05 and a relatively low level of fluorescence in the process of the neuron. 00:15:42.26 Ten seconds into the movie, a switch in odor stimuli will occur, and the neuron will become brighter. 00:15:49.22 The brighter color, the more intense color, the larger white color in the cell body of the neuron over here, 00:15:54.21 all reflect the fact that calcium has gone up, and the neuron has become active. 00:15:59.17 So, indeed, we can see that the AWC neuron responds to odors by changing its activity. 00:16:06.23 But it responds in a way that we did not expect, 00:16:10.06 because the AWC neurons are not activated when odors are presented to the worm. 00:16:15.26 In fact, when we look at the fluorescence intensity and graph it in the presence of odor, 00:16:20.05 it is, if anything, a little less intense than it would have been in the absence of odor. 00:16:26.27 Instead, the AWC neurons become active when odor is removed. 00:16:31.21 This leads to a large increase in the fluorescence intensity, 00:16:34.21 indicating depolarization and the presence of calcium. 00:16:38.08 So these neurons seem to work in reverse. 00:16:41.13 They are inhibited by odors, their natural stimuli. 00:16:44.28 They are active when odors are removed. 00:16:47.24 And I just want to remind you that the worm has to generate a behavior when odor is removed. 00:16:53.02 When odor is removed, the worm is going to start turning. 00:16:56.00 So the activity of the neuron is correlated with the behavioral output, not with the input stimulus. 00:17:05.18 So we can now say something about this first neuron that interacts with odors. 00:17:10.25 How does it communicate with the target neurons that then convert this information into behavior? 00:17:17.10 The way that we study this is by studying the process of synaptic transmission. 00:17:21.15 Neurons connect to each other at specialized structures called synapses, 00:17:25.08 where a presynaptic neuron, the upstream neuron, in this case AWC, 00:17:29.28 will release vesicles filled with a neurotransmitter, and these neurotransmitters 00:17:33.24 will interact with receptors on the postsynaptic neuron, here shown in gray. 00:17:39.01 One kind of neurotransmitter that neurons release is glutamate, an amino acid, 00:17:45.25 and glutamate is packaged into special synaptic vesicles by a molecule called the 00:17:49.25 "vesicular glutamate transporter," or EAT-4 in C. elegans. 00:17:54.25 We can use this EAT-4 molecule to probe the action of synapses in the AWC neuron. 00:18:02.27 We can do that by using mutants in EAT-4 to inactivate the transporter 00:18:07.26 and therefore the ability of AWC to release glutamate. 00:18:11.19 And we can ask then, 00:18:13.09 what kinds of behavior can the animal generate in the absence of this glutamate transmitter? 00:18:18.15 And remember that turning is a reflection of the response to odor removal, 00:18:23.22 an important component of chemotaxis behavior, and that we can quantify this. 00:18:26.27 So a high level here of "1" is a high level of turning. 00:18:31.13 In red here is an eat-4 mutant. 00:18:33.15 The eat-4 mutant does not turn efficiently when odor is removed, 00:18:37.21 indicating to us that glutamate is required as a neurotransmitter for this turning behavior. 00:18:43.04 And when we restore EAT-4 just in the AWC neurons using a specific transgene, 00:18:48.22 we restore most of the turning behavior. 00:18:51.01 And so we can say that glutamate from AWC promotes turning. 00:18:57.25 So we now have insight into the first step of how AWC communicates with its target: 00:19:03.10 It uses EAT-4 to package glutamate into vesicles, it releases glutamate, 00:19:08.06 and this must then act on target neurons. 00:19:10.23 How does it communicate with the target neurons? 00:19:12.26 How does it communicate with these three different neurons with which it forms connections? 00:19:17.00 Well, it has to do that through glutamate receptors, 00:19:20.04 proteins that are expressed on the target neurons that enable them to detect the released glutamate. 00:19:25.11 And we found that there are two classes of glutamate receptors 00:19:28.22 that are important for this particular behavior. 00:19:31.29 There's a glutamate-gated cation channel; it's an excitatory receptor called GLR-1. 00:19:38.01 And there's also a glutamate-gated chloride channel, 00:19:41.05 an anion channel that is an inhibitory receptor called GLC-3. 00:19:45.14 These two glutamate receptors, 00:19:47.11 which can generate two different kinds of responses in target neurons, 00:19:50.20 are important for AWC's communcation with its targets. 00:19:56.26 We can demonstrate that both through quantitative behavioral assays 00:20:02.18 and through direct observation of the activity of target neurons, 00:20:06.18 which we do using genetically encoded calcium indicators. 00:20:10.15 Now, instead of expressing them in AWC, we express them in downstream neurons, 00:20:15.24 such as AIB. 00:20:17.18 AIB is one of the neurons that receives synapses from AWC, 00:20:21.16 and we see that AIB, like AWC, responds to odor removal by an increase in calcium. 00:20:29.06 This response disappears if the AWC neuron is killed, 00:20:33.21 and it also disappears in an animal that lacks the glutamate receptor GLR-1. 00:20:38.17 GLR-1 is required in AIB for AIB to sense the glutamate signal from AWC. 00:20:46.09 This excitatory glutamate receptor transmits an excitatory signal from sensory neuron to interneuron. 00:20:56.03 Next, we looked at the AIA and AIY interneurons. 00:21:01.09 These neurons also respond to odors, 00:21:04.05 but these neurons respond oppositely to AWC. 00:21:08.12 AIA and AIY respond with an increase in calcium to odor addition, 00:21:13.22 there's been a change in the sign of the signal between the sensory neuron and the interneuron. 00:21:18.21 They don't respond to odor removal. 00:21:21.19 Now this response to odor addition still requires AWC, 00:21:25.19 and it requires a glutamate receptor. 00:21:28.04 It requires GLC-3, the glutamate-gated chloride channel. 00:21:32.21 This inhibitory receptor serves to transmit a signal from an excited AWC 00:21:38.18 into a signal that will inhibit the downstream neurons, 00:21:42.06 so the downstream neurons AIA and AIY respond oppositely 00:21:47.00 to odors than the upstream neuron AWC. 00:21:52.24 So putting this information together, here on the left, 00:21:56.09 we can assemble a C. elegans odor circuit. 00:21:59.26 We can say that attractive odors inhibit the AWC olfactory neurons, 00:22:04.16 that the AWC olfactory neurons now release glutamate 00:22:08.03 onto two classes of downstream neurons through two classes of receptors. 00:22:12.16 They excite one class of neurons, the AIB neurons, 00:22:15.25 through an excitatory glutamate receptor. 00:22:18.14 They inhibit other classes of neurons, AIA and AIY neurons, 00:22:22.17 through an inhibitory glutamate receptor. 00:22:25.15 By splitting the information in this way, 00:22:27.15 the AWC neurons have now transformed information into two streams: 00:22:32.00 One signals the appearance of odor, an "odor ON" response; 00:22:35.17 the second stream signals the disappearance of odor, an "odor OFF" response. 00:22:40.26 Remarkably, when we examine this circuit, 00:22:43.12 it looks similar to another sensory circuit that's been well characterized, 00:22:47.14 and that is the circuit that is used to collect light in the vertebrate retina, 00:22:51.18 in your own eye. 00:22:53.14 So in your eye, light is collected by the rod and cone photoreceptors. 00:22:58.15 Rods and cones are active in the dark; 00:23:00.29 they are inhibited by light, their natural stimulus, 00:23:04.03 just as AWC neurons are inhibited by odors. 00:23:08.12 Rods and cones release glutamate to communicate with their targets, 00:23:12.04 and they have two major classes of target neurons. 00:23:14.28 The target neurons are called bipolar cells. 00:23:17.24 One connection is through an excitatory glutamate receptor, and therefore, 00:23:22.24 these neurons have the same pattern of activity as the photoreceptors. 00:23:27.01 They're what are called "OFF" bipolar cells; they signal when lights go off. 00:23:31.26 The other class of neurons are connected through inhibitory glutamate receptors. 00:23:36.01 Therefore, these neurons are called "ON" bipolar cells; they signal when lights come on. 00:23:43.05 So comparing these different neural circuits, 00:23:45.19 we can say that in a worm olfactory system and in a vertebrate visual system, 00:23:50.23 some of the same principles are used to process sensory information. 00:23:55.02 Differential signaling of the appearance and the disappearance of a stimulus, 00:23:59.16 differential signaling through different classes of glutamate receptors, 00:24:03.01 to split information through different circuits. 00:24:05.22 This kind of insight helps convince us that there may be principles 00:24:09.10 for neural circuits that apply across different systems, 00:24:12.16 that will help us understand information processing. 00:24:16.01 What I've told you is that AWC communicates with three downstream neurons, 00:24:20.19 using glutamate to send complex information about the input stimulus 00:24:24.25 to different downstream sets. 00:24:28.23 In addition, AWC has another way of communicating with its targets, 00:24:33.04 because AWC doesn't just release glutamate, 00:24:35.23 it releases a second transmitter, a neuropeptide neurotransmitter called NLP-1. 00:24:41.15 NLP-1 is related to neuropeptides called buccalin in other animals, 00:24:46.02 and NLP-1 signals through a G protein-coupled receptor, called NPR-11. 00:24:52.04 NPR-11 is expressed on some of the downstream neurons from AWC, 00:24:57.18 but not all, including the AIA neurons. 00:25:01.09 So glutamate is released from AWC onto several neurons, and in addition, 00:25:05.29 a neuropeptide is released from AWC onto a subset of those neurons. 00:25:12.29 What is the function of NLP-1? 00:25:15.18 We can ask that by examining animals that are mutant for the NLP-1 neuropeptide 00:25:21.00 or mutant for its receptor, 00:25:22.27 and then comparing their behaviors to the behaviors of wild-type animals. 00:25:27.13 And what we find is that the function of NLP-1 is to antagonize 00:25:32.17 the glutamate signal from the same AWC neuron. 00:25:36.17 So, this is illustrated here in the quantitative turning behaviors that measure AWC output. 00:25:42.11 So a wild-type animal, shown here in white, 00:25:44.29 will turn about once a minute in response to odor removal. 00:25:48.22 These turns are absolutely dependent on the glutamate signal from AWC. 00:25:52.29 There are simply no turns when AWC glutamate is absent, as shown by this mutant. 00:26:00.06 But when we look at the nlp-1 mutant, we see that there are turns. 00:26:03.25 In fact, there are more turns than there would be in a wild-type animal. 00:26:07.21 So AWC is both sending a signal to stimulate turning (the glutamate signal), 00:26:12.20 and it's sending a second signal that inhibits turning (the NLP-1 signal). 00:26:17.23 It's limiting its own output by generating these two antagonistic signals. 00:26:24.08 We next asked how this signal interacts with the circuit 00:26:29.12 to affect the activity of different neurons. 00:26:32.20 And here there was a large surprise. 00:26:35.14 So we examined the nlp-1 mutant, and mutants in its receptor NPR-11, 00:26:40.17 to see where activity in the circuit was changed compared to the activity of wild-type animals. 00:26:45.25 We saw changes in the activity of the neurons not just in downstream target neurons; 00:26:51.16 we saw changes in AWC itself. 00:26:54.23 The olfactory neuron responds differently to odors 00:26:58.01 depending on the activity of this peptide system. 00:27:01.21 So we can see this here in calcium imaging experiments showing 00:27:05.08 the response of AWC neurons to odor removal. 00:27:08.23 In wild-type, they show a sharp, short response. 00:27:12.06 In animals that lack the NLP-1 peptide or its receptors, 00:27:16.27 we instead see a longer-lasting response and repeated responses, 00:27:20.21 indicating that the AWC neuron is staying active for longer after odor has been removed. 00:27:28.17 Now, AWC is releasing this signal, the receptor for this signal in on a downstream neuron... 00:27:34.23 How does that information come back to AWC? 00:27:38.15 The answer is that the downstream neuron releases another signal, a feedback signal, 00:27:44.20 that is an insulin-like peptide, that returns to the AWC neuron to modify its activity. 00:27:50.26 So, a signal from AWC talks to a target neuron, 00:27:54.07 the target neuron then sends a signal back to AWC, 00:27:57.13 and again, the use of that signal limits the activity of the AWC neuron. 00:28:02.12 The feedback keeps AWC from generating these longer 00:28:06.03 or repetitive responses to odor removal. 00:28:11.29 So, it seems curious that a neuron would be generating 00:28:14.21 both positive and negative responses. 00:28:16.29 What could be the purpose of generating a negative feedback signal? 00:28:21.13 To understand this, you should understand that, 00:28:24.02 in animals, odor preference is modified by its experience with odor. 00:28:28.10 And this can be illustrated in a variety of ways, 00:28:31.15 but one simple way is that, when animals are exposed to odor in the absence of food, 00:28:35.25 they slowly adapt to the odor, so that they are no longer attracted to it. 00:28:40.15 This causes animals to prefer new odors, 00:28:43.18 or odors that have been paired with food, 00:28:45.14 to odors that have been seen in the absence of food, 00:28:49.02 and it represents an obvious good behavioral strategy for finding odors 00:28:53.11 that might be predictive of food in the future. 00:28:56.00 This can be quantified here, where the attraction to odor, shown here in black, 00:28:59.22 drops after 60 minutes of seeing an odor without food, 00:29:03.04 and drops even further after two hours of seeing the odor without food. 00:29:09.03 This change in the odor-dependent activity requires the neuropeptide feedback loop 00:29:16.10 that limits AWC activity. 00:29:19.06 If you remove either NLP-1 or its receptor NPR-11 00:29:24.13 or the feedback signal INS-1 that converts that information back to AWC, 00:29:29.19 then animals that have been exposed to odor, adapted animals, as shown here, 00:29:34.05 continue to respond to odor even after a long time of pairing of odor at the absence of food, 00:29:40.07 where wild-type animals would lose their response. 00:29:44.09 Adaptation requires the function of NLP-1 in the AWC neurons 00:29:49.22 and the function of NPR-11 and of INS-1 (the feedback signal) in the AIA neurons. 00:29:56.04 And so we can map this particular negative feedback signal to a particular 00:30:01.09 negative feedback that must occur to drive a useful olfactory behavior: 00:30:06.10 olfactory adaptation. 00:30:09.14 The activity of this feedback loop is observed not only at the behavioral level, 00:30:13.23 but also at the level of neuronal responses, 00:30:17.01 because when we examine the activity of AWC neurons after a long time of exposure to high odor, 00:30:23.06 as shown here in black, they simply stop responding to the odor 00:30:27.17 if the odor was present in the absence of food. 00:30:30.26 And this suppression of their response is defective in animals 00:30:36.05 that lack the neuropeptide feedback signal, as shown here in red, 00:30:39.29 which continue to respond to the odor even when it no longer predicts the presence of food. 00:30:50.07 So the conclusion of this part of the talk is that neuropeptide feedback, 00:30:55.04 superimposed on the basic function of the circuit, shapes sensory dynamics: 00:31:01.06 That sensory neurons like AWC respond to odors not in one way, 00:31:05.17 but in different ways depending on the activity of a feedback circuit; 00:31:09.20 that if that feedback circuit is lost, the sensory neurons respond for longer and with multiple stimuli; 00:31:16.14 that if the feedback circuit is present, they respond with a short stimulus; 00:31:20.07 and that if the feedback circuit is strongly activated through olfactory adaptation, 00:31:24.25 the sensory neurons stop responding, 00:31:26.23 allowing the animals to suppress the response to that odor, and to respond to new odors. 00:31:34.08 And the conclusion of this talk is that circuits change over time, that circuits are not fixed, 00:31:41.09 that they actively shape and transform sensory information. 00:31:45.05 They don't just passively receive that information. 00:31:48.03 And furthermore, circuits change their own properties 00:31:51.05 based on sensory information in real time. 00:31:55.12 This process, this dynamic and active interpretation of information, 00:32:00.18 allows circuits to perform complex computations and calculations. 00:32:05.14 If you take just what I told you about this small circuit of just a few C. elegans neurons, 00:32:11.06 you can realize that, if you multiply that by the billions of neurons in a human brain, 00:32:15.21 it can start to explain why a human brain can generate an 00:32:19.17 infinite number of perceptions, memories, and behaviors. 00:32:23.18 Thank you.