Gwyneth Card uses high-throughput behavior tracking and new electrophysiological techniques to study the neural circuits that govern simple decision behavior during Drosophila escape responses.
How does an animal decide what to do and when to act? To navigate a complex environment successfully, an animal must transform an influx of sensory information into action. Inherent to this sensory-motor transformation is a decision regarding which behavior from the animal's combinatorially large repertoire of motor acts is most "appropriate." How are these decision processes enacted at the level of neuronal circuits to produce ecologically relevant behavior?
We study the fruit fly, Drosophila melanogaster, because the fly displays flexible, interesting behaviors and its nervous system is small enough to analyze at the level of individual neurons.
We record video of flies at 6,000 frames per second to characterize a naturalistic fly behavior—escape, and we are automating this process to achieve rapid throughput. We also use new genetic and electrophysiological techniques to identify cells involved in escape circuits and assess their functional roles.
Fly Escape Behaviors: Flexible and Modular
We have identified a set of escape maneuvers performed by a fly when confronted by a looming object. These escape responses can be divided into distinct behavioral modules. Some of the modules are very stereotyped, as when the fly rapidly extends its middle legs to jump off the ground. Other modules are more complex and require the fly to combine information about both the location of the threat and its own body posture. In response to an approaching object, a fly chooses some varying subset of these behaviors to perform. We would like to understand the neural process by which a fly chooses when to perform a given escape behavior.
Beyond an appealing set of behaviors, this system has two other distinct advantages for probing neural circuitry. First, the fly will perform escape behaviors even when tethered such that its head is fixed and neural activity can be imaged or monitored using electrophysiology. Second, using Drosophila as an experimental animal makes available a rich suite of genetic tools to activate, silence, or image small numbers of cells potentially involved in the behaviors.
Neural Circuits for Escape
Until recently, visually induced escape responses have been considered a hardwired reflex in Drosophila. White-eyed flies with deficient visual pigment will perform a stereotyped middle-leg jump in response to a light-off stimulus, and this reflexive response is known to be coordinated by the well-studied giant fiber (GF) pathway.
The GFs are a pair of electrically connected, large-diameter interneurons that traverse the cervical connective. A single GF spike results in a stereotyped pattern of muscle potentials on both sides of the body that extends the fly's middle pair of legs and starts the flight motor. Recently, we have found that a fly escaping a looming object displays many more behaviors than just leg extension. Most of these behaviors could not possibly be coordinated by the known anatomy of the GF pathway.
Response to a looming threat thus appears to involve activation of numerous different behavioral pathways, which the fly may decide if and when to employ. Our goal is to identify the descending pathways involved in coordinating these escape behaviors as well as the central brain circuits, if any, that govern their activation. This would provide a model system with which to investigate how the fly makes behavioral choices at the level of neuronal circuits.
Automated Single-Fly Screening
To pursue the neural substrate of fly decision behavior, we are developing a new kind of high-throughput genetic screen.
Historically, genetic screens involved perturbing specific areas of the nervous system, either by mutagenesis or by selective silencing of neurons using the UAS-GAL4 system, and then screening for altered behavioral phenotypes. This approach is limited by (1) selectivity of available GAL4 driver lines and (2) ability to detect subtle behavioral changes.
Gerry Rubin's lab at Janelia, with which we collaborate, has overcome the first limitation by creating a library of several thousand limited-expression GAL4 lines that target small groups of cells throughout the entire brain. These new GAL4 constructs enable much more specific perturbations of the fly's central nervous system.
Our lab is surmounting the second limitation with a new behavioral tracking system to automatically capture fly escape sequences and quantify individual behaviors. We will use this system to perform a high-throughput genetic silencing screen to identify cell types of interest. Automation permits analysis at the level of individual fly movements, while retaining the capacity to screen through thousands of GAL4 promoter lines. Single-fly behavioral analysis is essential to detect more subtle changes in behavior during the silencing screen, and thus to identify more specific components of the contributing circuits than previously possible when screening populations of flies.
Our goal is to identify candidate neurons involved in coordination and choice of escape behaviors.
Measuring Neural Activity During Behavior
Complementary to our automated screen, we use whole-cell patch-clamp electrophysiology to determine the functional roles of any identified candidate neurons.
Flies will still perform escape behaviors when their head and thorax are immobilized for physiological recording. In this case, we give the fly a small Styrofoam ball to hold. In response to a looming stimulus, the fly will kick the ball away and initiate flight. Thus we can record from candidate neurons while simultaneously observing the fly's behavior. This allows us to link a neuron's responses directly to a behavioral decision.
Our work is focused on finding the neural circuitry that determines how a fly chooses which escape behaviors to perform and when to perform them. We hope to show that the fly escape response, a behavior that was once the paragon of stereotyped reflexes, may instead be an ideal model system in which to study the neural bases of decision processes.
As of March 31, 2010