The development of topographic maps in the primary visual system is thought to rely on a combination of EphA/ephrin-A interactions and patterned neural activity. Here, we characterize the retinogeniculate and retinocollicular maps of mice mutant for ephrins-A2, -A3, and -A5 (the three ephrin-As expressed in the mouse visual system), mice mutant for the 2 subunit of the nicotinic acetylcholine receptor (that lack early patterned retinal activity), and mice mutant for both ephrin-As and 2. We also provide the first comprehensive anatomical description of the topographic connections between the retina and the dorsal lateral geniculate nucleus. We find that, although ephrin-A2/A3/A5 triple knock-out mice have severe mapping defects in both projections, they do not completely lack topography. Mice lacking 2-dependent retinal activity have nearly normal topography but fail to refine axonal arbors. Mice mutant for both ephrin-As and 2 have synergistic mapping defects that result in a near absence of map in the retinocollicular projection; however, the retinogeniculate projection is not as severely disrupted as the retinocollicular projection is in these mutants. These results show that ephrin-As and patterned retinal activity act together to establish topographic maps, and demonstrate that midbrain and forebrain connections have a differential requirement for ephrin-As and patterned retinal activity in topographic map development.
In mammals, retinal ganglion cell projections initially intermingle and then segregate into a stereotyped pattern of eye-specific layers in the dorsal lateral geniculate nucleus (dLGN). We show here that, in mice deficient for ephrin-A2, ephrin-A3, and ephrin-A5, eye-specific inputs segregate but the shape and location of eye-specific layers is profoundly disrupted. In contrast, mice that lack correlated retinal activity do not segregate eye-specific inputs. Inhibiting correlated neural activity in ephrin mutants leads to overlapping retinal projections located in inappropriate regions of the dLGN. Thus, ephrin-As and neural activity act together to control patterning of eye-specific retinogeniculate layers.In the mammalian visual system, retinal ganglion cells (RGCs) project to their main forebrain target, the dLGN of the thalamus, in an orderly and stereotypical manner. This order is established during development and can be described by two main components. First, the projection pattern from each eye is topographic, with neighboring RGCs connecting to neighboring positions in the dLGN. The Eph family of receptor tyrosine kinases and their cell surface-bound ligands, the ephrins, have been shown to act as graded labels that are required for topographic mapping in multiple areas in the CNS, including the two main targets of RGCs: the dLGN and the superior colliculus (SC) 1-5 .The second organizing feature of visual connectivity is the segregation of projections from each eye, a phenomenon thought to depend mechanistically on neural activity. Early in development the retinogeniculate projections of the two eyes overlap but then segregate and form eye-specific layers postnatally 6,7 . Inhibiting activity in the retina or in the whole brain prevents the segregation of RGC axons 8-10 . Neural activity has been theorized to drive axonaxon competition for dLGN territory between the two eyes, and indeed, when the balance of activity levels in the two eyes is altered, inputs from the more active eye occupy a larger area within the dLGN 10-14 . It is thought that activity-based competition relies on the ability of inputs from each eye to cooperate with one another to strengthen synaptic connections in a Hebbian manner, although exactly how activity functions in this context remains controversial 15,16 . While activity-dependent models can account for how eye-specific inputs segregate, they cannot explain the stereotypical placement of the layers within the dLGN. Segregation models based solely on neural activity predict that layer placement should be stochastic, such that in some animals a given layer of the dLGN would be innervated from the left eye while in other animals it would be innervated by the right eye, or alternatively, that axons from each eye would segregate in a "salt and pepper" pattern 17,18 . Multiple theories have been proposed to explain the stereotypical placement of the eye-specific layers, including temporal differences between ipsi-and contralateral ingrowth into the dLGN, layer-specific m...
Sleep is homeostatically regulated, such that sleep drive reflects the duration of prior wakefulness. However, despite the discovery of genes important for sleep, a coherent molecular model for sleep homeostasis has yet to emerge. To better understand the function and regulation of sleep, we employed a reverse-genetics approach in Drosophila. An insertion in the BTB domain protein CG32810/insomniac (inc) exhibited one of the strongest baseline sleep phenotypes thus far observed, a ∼10 h sleep reduction. Importantly, this is coupled to a reduced homeostatic response to sleep deprivation, consistent with a disrupted sleep homeostat. Knockdown of the INC-interacting protein, the E3 ubiquitin ligase Cul3, results in reduced sleep duration, consolidation, and homeostasis, suggesting an important role for protein turnover in mediating INC effects. Interestingly, inc and Cul3 expression in post-mitotic neurons during development contributes to their adult sleep functions. Similar to flies with increased dopaminergic signaling, loss of inc and Cul3 result in hyper-arousability to a mechanical stimulus in adult flies. Furthermore, the inc sleep duration phenotype can be rescued by pharmacological inhibition of tyrosine hydroxylase, the rate-limiting enzyme for dopamine biosynthesis. Taken together, these results establish inc and Cul3 as important new players in setting the sleep homeostat and a dopaminergic arousal pathway in Drosophila.
Adult behavioral assays have been used with great success in Drosophila melanogaster to identify circadian rhythm genes. In particular, the locomotor activity assay can identify altered behavior patterns over the course of several days in small populations, or even individual flies. Commercially available, highly efficient automated systems allow for continuous data collection from large numbers of individuals, and analytical tools make it possible to quickly analyze multiple aspects of circadian behavior from each experiment. These features make the locomotor activity assay useful for high-throughput analyses, leading to the rapid discovery and functional characterization of many Drosophila circadian rhythm genes. The locomotor assay described here can simultaneously assess both circadian and sleep behavior, and several methods can be used to analyze the data generated from such assays. This protocol details the use of the Drosophila Activity Monitoring (DAM) System from TriKinetics. Briefly, the system records activity from individual flies maintained in sealed tubes placed in activity monitors. An infrared beam directed through the midpoint of each tube measures an "activity event" each time a fly crosses the beam. Events detected over the course of each consecutive sampling interval are summed and recorded over the course of the experiment for each fly. The general approaches described here can be applied to a wide range of behavioral activity experiments, including sleep deprivation analyses and general studies of hypoactivity and hyperactivity.
Topographic maps are the primary means of relaying spatial information in the brain. Understanding the mechanisms by which they form has been a goal of experimental and theoretical neuroscientists for decades. The projection of the retina to the superior colliculus (SC)/tectum has been an important model used to show that graded molecular cues and patterned retinal activity are required for topographic map formation. Additionally, interaxon competition has been suggested to play a role in topographic map formation; however, this view has been recently challenged. Here we present experimental and computational evidence demonstrating that interaxon competition for target space is necessary to establish topography. To test this hypothesis experimentally, we determined the nature of the retinocollicular projection in Math5 (Atoh7) mutant mice, which have severely reduced numbers of retinal ganglion cell inputs into the SC. We find that in these mice, retinal axons project to the anteromedial portion of the SC where repulsion from ephrin-A ligands is minimized and where their attraction to the midline is maximized. This observation is consistent with the chemoaffinity model that relies on axon-axon competition as a mapping mechanism. We conclude that chemical labels plus neural activity cannot alone specify the retinocollicular projection; instead axon-axon competition is necessary to create a map. Finally, we present a mathematical model for topographic mapping that incorporates molecular labels, neural activity, and axon competition. M ost sensory information is mapped topographically, meaning that the neighbor relationships among neurons are maintained when choosing synaptic partners in their target area. The visual projection from the retina to the superior colliculus (SC) has been widely used as a model to ascertain the mechanisms by which topographic maps develop. In the retinocollicular projection, the dorsal-ventral (D-V) axis of the retina maps topographically onto the medial-lateral (M-L) axis of the SC, and the temporal-nasal (T-N) axis maps onto the anteriorposterior (A-P) axis of the SC (1, 2). These two axes are mapped by independent mechanisms (3).Topographic mapping along each axis is believed to rely upon counterbalanced forces (4, 5). Dual molecular gradient models propose that separate repellent or attractant molecules coexist in opposing gradients and that the balance point of the two gradients determines the appropriate termination locus for retinal axons (hereafter called the dual gradient model) (4, 6-8). Servomechanism models posit that a single graded molecule can have both positive and negative effects that serve to guide retinal axons to their correct position (9-13). Still other models invoke a counterbalancing force that is generated via axonal competition for space or positive factors in the target (5,(14)(15)(16)(17)(18)(19). Although all of these models include competition as a factor to ensure that axons spread to fill the available target space, only competition models invoke competi...
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