The Interactive Fly

Genes involved in tissue and organ development

The Drosophila Brain

  • The Adult Brain - Index to brain structure and function
  • Genes expressed in Adult Brain Development and Function
  • The Larval Brain and Development of the Adult Brain
  • Odor detection and Processing - Odorant receptors and olfactory receptor neurons, and olfactory learning
  • The Visual System - Optic lobe and optic glomeruli
  • The Central Body Complex - Ellipsoid body, superior arch, fan shaped body and the protocerebral bridge
  • Mushroom Bodies - The site of olfactory and other learning
  • Neuroendocrine complex - Ring gland: prothoracic gland, corpus allatum, and corpus cardiac
  • Subesophageal Ganglion - Site of the taste system and feeding behavior
  • Behavioral Paradigms - Sexual Behavior, motor Behavior, photoperiod response and others




    The functional organization of descending sensory-motor pathways in Drosophila

    In most animals, the brain controls the body via a set of descending neurons (DNs) that traverse the neck. DN activity activates, maintains or modulates locomotion and other behaviors. Individual DNs have been well-studied in species from insects to primates, but little is known about overall connectivity patterns across the DN population. This study systematically investigated DN anatomy in Drosophila melanogaster and created over 100 transgenic lines targeting individual cell types. Roughly half of all Drosophila DNs were identified and connectivity between sensory and motor neuropils in the brain and nerve cord, respectively, were comprehensively mapped. The nerve cord was found to be a layered system of neuropils reflecting the fly's capability for two largely independent means of locomotion -- walking and flight -- using distinct sets of appendages. These results reveal the basic functional map of descending pathways in flies and provide tools for systematic interrogation of neural circuits (Namiki, 2018).

    This study systematically characterized the organization of DNs, a population of interneurons that conduct information from the brain of a fly to motor centers in the VNC. This analysis was based on the morphologies of 98 DN cell types, covering 190 bilateral pairs of neurons. To discern DN morphologies, individual neurons from driver lines targeting many cells were segmented, and also a library of 133 split-GAL4 lines were generated that sparsely target 54 DN types. By registering the morphology of all the DNs with standardized maps of the brain and VNC, three major sensory-motor pathways. One pathway links two neuropils on the posterior slope of the brain (IPS and SPS) to dorsal neuropils associated with the neck, wing, and haltere motor systems, and a second carries neurons with dendrites in the gnathal ganglion (GNG) to the leg neuromeres. The third pathway consists of DNs originating from an array of brain neuropils that converge to innervate the tectulum, a long thin region of the VNC sandwiched between the wing and leg motor neuropils (Namiki, 2018).

    The simple, tripartite anatomical pattern that was observed may reflect both the functional organization of the DNs as well as the evolutionary history of Drosophila. With the notable exception of insects (and the mythical horse, Pegasus), all flying animals use a modified foreleg as a wing. That is, an appendage originally evolved for walking was coopted for flight in pterosaurs, birds, and bats - a fact supported by the fossil record, comparative morphology, and the organization of the underlying motor circuitry. The evolution of flight was quite different in insects, because their wings and associated muscles, did not arise via sacrifice of an entire ancestral leg, and thus the novel aerial mode of locomotion did not strongly compromise the more ancient, terrestrial mode. As a result, insects are unique in possessing two somewhat independent motor systems, a fact that is elegantly manifest in the organization of the VNC and the pattern of DN innervation that was observed: the ventral leg neuromeres of flies resemble those of apterygote hexapods from which they derived, whereas the more recent wing neuropil sits atop the VNC like icing on a cake. It is speculated that the GNG-to-leg neuromere descending pathway represents a very ancient pathway and some of its member DNs may have deep homologies with other arthropod taxa, whereas the pathway linking the posterior slope neuropils to the dorsal motor neuropils of the neck, wing, and haltere are more recently evolved within insects (Namiki, 2018).

    Many behaviors such as grooming, courtship, take-off, and landing require the simultaneous use of both legs and wings. Thus, insects must have a means of coordinating activity across the two motor systems, a need that arose during or after the evolution of flight. As described more fully below, it is speculated that the teculum, and possibly the lower teculum, are neuropils that mediate this functional integration of motor actions between the two systems. The convergence of DNs into the tectulum from such a broad array of brain nuclei may reflect the high degree of sensory integration required to trigger and regulate these more complex, multi-appendage behaviors (Namiki, 2018).

    Based on PA-GFP labeling of neurons in the neck connective, ~350 DN pairs were counted. This is within the range of 200-500 DN pairs estimated in other insect species, but smaller than a value of ~550 pairs estimated in Drosophila based on backfills using a dextran dye. Part of this discrepancy can be explained by the fact that the current count excluded several specialized cell populations that were included in a previous study. These include a set of ~19 pairs of neck motor neurons, whose axons exit the neck connective posterior to the region illuminated for PA-GFP photoconversion, as well as 16 neurons selectively innervating the retrocerebral complex. One of these cells (DNd01), which innervates both the VNC and retrocerebral complex, was included. The current analysis is also likely an underestimate of the total because the nsyb-LexA driver line used to pan-neuronally express PA-GFP, may not label all neurons. For example, this line does not label the Giant Fiber. It is also possible that certain cells are harder to label using the PA-GFP approach as opposed to dextran backfills. The estimates from the two studies agree quite closely for DNs with cell bodies in the cerebral ganglia (172 in this study vs. 206 in Hsu and Bhandawat, 2016). Most of the discrepancy concerns DNs in the GNG group; this study counted 180 pairs, only 51% of the number reported by Hsu and Bhandawat. Taking the estimate of 350 as a lower bound and 550 an upper bound, it is estimated that the DNs described in this study represent between one third and one half of the entire population (Namiki, 2018).

    Identification of particular DN types in this study relied on the existence of a GAL4-line in the Rubin or Vienna (BrainBase) collection with sparse enough expression to recognize individual DN morphology. Additionally, most of the expression patterns that were screened were from female flies, thus the analysis would not include any potential male-specific DNs. As a result, some DNs were not found that have been reported in other studies, including the Moonwalker Descending Neuron (MDN), which controls backwards walking in flies, and pIP10/p2b, which are involved in the male courtship sequence (Namiki, 2018).

    A direct pathway was found linking the posterior slope of the brain to dorsal VNC neuropils. The posterior slope is innervated by lobula plate tangential cells (LPTCs) projecting from the optic lobe, which are excited by patterns of optic flow resulting from self-rotation. These optic flow patterns are especially relevant during flight, when the fly is able to move freely about all six degrees of freedom, and it has been suggested that LPTCs mediate both corrective steering maneuvers of the wings as well as gaze stabilization of the head. Most of the DNs in this pathway targeted all three segmental dorsal VNC neuropils, which contain neck (T1), wing (T2), or haltere (T3) motor neurons, sensory neuron projections from associated mechanoreceptors, and premotor interneurons. DN innervation of all three segmental dorsal neuropils is consistent with recent studies showing that neck and wing movements are highly correlated and suggests that the DNs of this major posterior slope-to-dorsal neuropil pathway are involved in flight control. This notion is confirmed by recent whole cell recordings from tethered flying flies showing that three members of this population are strongly correlated with compensatory visual responses, and another is involved with spontaneous turns and collision avoidance (Namiki, 2018).

    A similar pathway, in which DNs receiving inputs in the posterior slope target flight neuropil, has been observed in blowflies and flesh flies. These have been contrasted with other DNs in the protocerebrum that have anterior dendrites near the outputs of the lobula that project to ventral leg neuropils. They suggested that the posterior and anterior DN protocerebral pathways are parallel systems linked to separate photoreceptor channels that process different features of the visual scene (e.g. color vs. motion) and may be loosely analogous to the dorsal and ventral streams of the mammalian visual system. The dataset allowed evaluation of this hypothesis in Drosophila by examining the subset of 42 DNs with dendrites in the protocerebrum. In keeping with the observations from large fly species, examples were found in which a DN with more posterior dendrites (e.g. DNg02) projected to the dorsal part of the VNC, whereas a DN with anterior dendrites (e.g. DNg13) projected to the ventral leg neuropils. It was also found that the median location of a DN's dendrites along anterior-posterior axis largely predicted whether its axons targeted dorsal or ventral leg neuropil (although see exceptions DNb01, DNb06, DNp07, and DNp18). However, the dendritic locations of DNs projecting to the dorsal and leg neuropils of the VNC were not segregated into separable, parallel groups, but instead form a continuous pattern of innervation in the protocerebrum. That is, the DN representation is graded in the protocerebrum, at least at the level of resolution of the analysis. Furthermore, the dendritic arbors of many DNs are broad enough that they sample from both anterior and posterior regions of the protocerebrum, suggesting that many DNs integrate information from both the lobula and lobula plate. Rather than the two separate parallel pathways suggested previously - one carrying visual information from the lobula plate to the wing neuropil and the other carrying information from the lobula to the leg neuropil - it is proposed that there is a mixing of this visual information in the protocerebrum, possibly in a graded manner along the anterior-posterior axis. A similar divergence and convergence of connectivity has been described in the brainstem of mice. Brainstem nuclei differentially address spinal circuits, forming exclusive connections either with forelimbs, hindlimbs, or both with differing connection strength (Namiki, 2018).

    Among all DNs targeting the wing neuropil, evidence was found for at least two distinct control systems, one entering the neuropil from a dorsal tract and targeting the dorsal and medial portion of the wing neuropil layer, where power muscle motor neuron dendrites reside, and one entering the neuropil from a more ventral tract and invading primarily the ventral and medial wing neuropil, where many steering muscle motor neurons dendrites reside. In Drosophila, the power muscles comprise two sets of stretch activated muscles attached across the thorax orthogonally. Alternate deformation of the thoracic cavity by these muscles drives the wing stroke indirectly, powering both flight and courtship song. In contrast, the smaller steering muscles attach to the base of the wing hinge and act directly to coordinate the wing movements that change flight course, or actuate finer movement, such as the timing of song pulses. The results suggest separate descending control of the power and steering muscle systems. Outside of flight and song, flies perform a wide range of different behaviors with their wings, including grooming, aggressive displays, and preparation for takeoff. Although this study found that the posterior slope had the largest number of DNs innervating wing neuropil, a wide range of other brain neuropils, including the gnathal ganglia (GNG), VES, lobula plate (PLP), anterior mechanosensory motor center (AMMC), SAD, superior medial protocerebrum (SMP), and lateral accessory lobe (LAL), are also connected to the wing neuropil, albeit via a smaller number of DNs (see Anatomical compartments of the brain and VNC in Drosophila). These sparser pathways may be important for coordinating wing motion when the flies are not flying (Namiki, 2018).

    Despite the trend described in the previous section, in which DNs with more anterior dendrites in the protocerebrum tend to target leg neuropil, this analysis found that a different brain region, the GNG, had the strongest DN connectivity to the six ventral neuromeres of the VNC. This was true even after excluding the many DNs whose neurites are presynaptic in the GNG. Indeed, 90% (88/98) of the DN types have processes in the GNG, most of which are varicose terminals containing synaptogagmin, and thus likely output terminals. Only one-third (29/88) of DNs with processes in the GNG had dendrites in that region, two-thirds of which (18/29) target leg neuropil without any terminals in the dorsal wing, neck, or haltere neuropils (Namiki, 2018).

    Given the GNG's evolutionary history as a separate anterior segmental ganglion, it is perhaps not surprising that this neuropil is strongly connected to more posterior motor centers. The suboesophageal ganglion, which includes the GNG, is involved in a variety of behaviors, including walking, stridulation, flight initiation, head movement, and respiration. However, the GNG has been most specifically implicated in the temporal patterning of walking. For example, both supra- and subesophageal DNs are recruited in the preparatory phase before walking, whereas the activity of subesophageal DNs become predominant during the walking phase (Namiki, 2018).

    The terminals of DNs targeting the same layers of the VNC clustered together within the GNG. One intriguing possibility is that these foci represent regions in which efferent copies of descending commands to leg and wing motor centers are available to cephalic sensory circuits. This information could then be integrated directly with other descending commands within the GNG, or reciprocal connections could feed the information back to the cerebral ganglia. The GNG also receives ascending inputs from the leg neuropil, allowing further integration within this region of information regarding locomotor state or mechanosensory input. Given that the cerebral ganglia are known to have a strong inhibitory effect on walking in insects, another possibility is that some DN terminals in the GNG are inhibitory. Indeed, a recent study found that 37% of DNs express the inhibitory neurotransmitter GABA, compared to 38% that are cholinergic, and just such an inhibitory pathway from the cerebral ganglia to the GNG has been suggested based on prior behavioral experiments. For example, lesion studies have shown that walking persists when the cerebral ganglia are removed and spontaneous bouts are prolonged. In contrast, removal of the GNG reduces spontaneous walking, but prolongs flight duration. Thus it is possible that the DN pathway identified, linking the posterior slope to wing neuropil, maintains flight and inhibits walking, whereas the pathway linking the GNG to the leg neuropils maintains walking and inhibits flight. Thus, the connections within the GNG may play a critical role in action selection, at least at a coarse level (Namiki, 2018).

    DN terminals in the leg neuropils could be sorted into two major types: DNs projecting to the dorso-medial part of each neuromere (type-I) and DNs penetrating through the neuromeres via the oblique tract (type-II). Their terminal locations suggest that type-I and type-II leg DNs may have different access to leg motor neurons because the dendrites are known to form a rough myotopic map across the leg neuromere, with more proximal leg muscles having more proximal dendrites. Based on this arrangement, one possible function of the type-I leg DNs is to coordinate the direction of walking, which depends critically on the control of coxal muscles that protract and retract of the entire leg. Indeed, inverse activation of the thoraco-coxal muscle is required for switching from forward to backward walking in stick insects. In Drosophila, moonwalker DNs (MDNs) innervate the dorso-medial part of the leg neuropil and thus are classified as type-I. Activation of bilateral MDNs cause backward locomotion, whereas the unilateral activation cause backward turning toward the contralateral side. Type-II DNs running through the oblique tract have the opportunity to contact with the entire array of proximal and distal motor neurons and thus may be important for coordinated action of all leg segments. For example, the jumping part of escape takeoffs may require tension in all leg segments, even though the extrinsic muscle extending the trochanter is the primary actor for the fast takeoff mode. Consistent with this idea, type-II DNs are abundant in mesothoracic leg neuropil (DNp02, p05, p06 and p11), and it is the middle legs that flies extend during a jump. Similarly, in locust, the descending contralateral movement detector (DCMD), which is important for escape behavior, has terminals that resemble type-II and synapses directly on the motor neurons in the neuropil associated with the jumping legs (Namiki, 2018).

    A small population of nine DNs specifically project to an intermediate zone of the VNC, the lower tectulum, which occupies a volume distinct from wing and leg neuropils and which this study suggests can be distinguished from the other intermediate neuropil, the tectulum, that sits above it. Neuronal connectivity is not well described in this region, and its function is unknown. However, the observations suggest that, like the tectulum, it is an integrative area involved in both leg and wing control. For example, this region includes dendrites from both the tergotrochanteral leg motor neuron (TTMn) and a branch of a wing motor neuron that has been tentatively identified as III1. The lower teculum also contains the peripheral synapsing interneuron (PSI), which is presynaptic to motor neurons for the wing depressor muscles. The giant fiber (GF) descending neurons that drive a looming-evoked escape takeoff terminate with unbranched axons within the lower tectulum and form gap junctions with the TTMn and PSI. It was surmising that the lower tectulum may play a role during takeoff, which requires coordinated actions of the wings and legs. It is known that there are parallel pathways for take-off behavior in Drosophila, although the anatomical source has not yet been identified. A group of eight unique type DNs, in addition to the GF, were identified whose dendrites overlap with the terminals of visual projection neurons that detect looming. Most of these invade the lower tectulum and their axon terminals share some anatomical features with the GF. This population are candidates for parallel pathways for takeoff, as well as other looming-evoked evasive behaviors, and could represent circuits for wing-leg coordination (Namiki, 2018).

    No DNs were found that originate in the central complex (CX), consistent with studies in other insect species. Thus, information from the CX must be relayed to motor centers via other brain regions. A prime candidate is the the lateral accessory lobe (LAL), which has dense mutual connections with the CX and, together with the bulb (BU), is considered the CX primary output. However, many fewer DNs from the LAL were found than from other regions such as PS, PVLP or AMMC. In other insects such as silk moths, connections between the LAL and the PS are well documented. In Drosophila, connectivity between the LAL and PS is suggested by connectomics studies and the morphology of individual neurons connecting these regions has been recently described. Thus, it is suggested that information processed in the CX may descend to the VNC via a CX-LAL-PS pathway (Namiki, 2018).

    No DNs were found originating from the mushroom bodies (MB), important processing areas for olfactory and visual memory. However, there are 11 DN types innervating the superior medial protocerebrum (SMP), a major target of MB output neurons. The SMP is also well connected with the LAL, which suggests MB output also uses the major descending pathway from the posterior slope via the LAL (Namiki, 2018).

    Prior studies in insects have focused on DN function at the single neuron level. Thus, how DNs operate as a population is still unclear. Evidence in insects and other species suggests that motor directives are likely encoded across the DN population rather than in the activity of individual command neurons. For example, many DNs are active, albeit with different firing patterns, at the same time during walking in locusts, and there are multiple brain locations where electrical stimulation can trigger walking behavior in cockroaches. Also, population vector coding for object direction has been observed in the DNs of dragonflies. Zebrafish have also been shown to utilize population coding in the control of locomotion, despite having only ~220 DNs -- even fewer than Drosophila. In fact, there are very few neurons that fit the rigorous requirements of command neuron (i.e. necessary and sufficient). Even the giant fibers (a.k.a. DNp01), whose activation drives a stereotyped escape jump in response to looming stimuli, are necessary only for a particular 'fast mode' of takeoff, and the behavioral effect of their activation to naturalistic looming stimuli has been shown to depend on the timing of their spike relative to activity in other descending neurons (Namiki, 2018).

    This study found that the VNC areas receiving the largest number of DNs are the dorsal neuropils associated with flight control (neck, wing, haltere neuropils and tectulum). It has been suggested that the number of DNs engaged during a behavior might relate to the precision of the control. In mammals, for example, the number of activated corticospinal tract neurons corresponds to the degree of digital dexterity. It is possible a large DN population target flight neuropils because flight requires a high level of precise control. For example, flies can execute sophisticated rapid aerial turns to evade a looming predator (Muijres et al., 2014), movements that are controlled by a combination of adjustments in firing phase of tonically active motor neurons and recruitment of phasically active cells (Namiki, 2018).

    In addition to the number of DNs putatively assigned to wing control, this study found that the organization of wing DNs is different than that of the DNs targeting leg neuropil. Several distinct clusters of DNs were identified with nearly identical morphologies and highly overlapped input and output projections, which are referred to as population type DNs because their similar morphology suggests they may function as a group (e.g. DNg01, g02, g03, g05 and g06). In most cases, these population DNs project to the wing neuropil or tectulum and are thus likely involved in flight. In contrast, only unique type DNs (identifiable single bilateral pairs) projecting to leg neuropil were found. This suggests that the strategy for controlling flight and walking may be fundamentally different. Because of the physics involved, even very small changes in wing motion during flight can result in large aerodynamic forces and moments. The necessity for fine control might account for the greater dependence on population coding in flight as compared to walking. Another difference between flight and walking is the temporal scale required for control. For example, wingbeat frequency is much faster than leg stepping frequency. The control of force generation by wing steering muscles depends on the precise timing of motor neuron spikes. The descending input during flight must have the capacity to regulate motor neuron firing phase on a precise temporal scale, a functionality that might be achieved via population coding. Another possibility is that the number of active DNs encodes the magnitude of a command signal to regulate continuous locomotor parameters such as speed. In larval zebrafish and lamprey, for example, more reticulospinal DNs are recruited with increasing swimming frequency. Further functional studies will be required to test whether DN encoding of flight and walking commands operates by different principles (Namiki, 2018).

    This study has analyzed the neuronal organization of descending motor pathways in Drosophila, with single-cell resolution. The wiring diagram revealed, in a genetically accessible model system, creates a framework for understanding of how the brain controls behavior. In combination with the Drosophila genetic toolkit, the driver lines created in the present study open up the possibility to directly probe the function of individual DNs during natural behavior (Namiki, 2018).

    Embryonic brain tract formation

    During embryogenesis in insects, the axon-scaffold of the brain is built around the embryonic foregut which separates the anlagen of the brain hemispheres. An investigation of this process was carried out in Drosophila and it appears that the major longitudinal and horizontal tracts of the embryonic brain form superficially near the interface between the foregut and embryonic brain hemispheres. Three types of cellular structures are identified that might be involved in tract formation: rows of glial cells at the medial brain margin, cellular bridges composed of neuronal somata and the epithelial surface of the foregut itself.

    • The first of these cellular structures is a set of longitudinal glial cells, generated at the medial edges of the brain hemispheres that separate the developing longitudial tracts of the brain from cells of the forgut. Differentiation of these glial cells begins at stage 12 of embryogenesis, when the first sign of glial cells missing expression can be detected. Subsequently, these cells express repo. The medial edges of the main longitudinal tracts, which become more massive due to the increase in axon number, are then clearly bordered by the rows of repo expressing longitudinal glial cells and separated by them from the foregut.
    • The neuronal somata, the second set of cellular structures involved in tract formation, span the interhemispheric space above the foregut. These cells express elav, a neuronal marker. One of these rows of neuronal cells extends from the medially located frontal ganglion anlage along the roof of the foregut into the left and right tritocerebral parts of the nervous system. This row of neuronal cells is closely apposed to the overlying neuronal cells of the brain hemispheres and also, at the midline, runs close to the underlying frontal ganglion. It is clearly separated from the roof of the foregut. The initial axon fascicles of the developing brain preoral commissure can be seen projecting out from each hemisphere along the row of neuronal cells across the midline.
    • The developing postoral tritocerebral commissure is not, in contrast to the preoral primary commissure, associated with a row of neuronal cells that project across the midline. The initial axonal fascicles of the tritocerebral commissure extend across the midline, along the ventral floor of the foregut. In doing so, these fascicles come into close contact with the foregut epithelium. In contrast to the close and persisting association and persisting association of the developing postoral tritocerebral commissure and the forgut epithelium, there is a clear separation - involving intercalated glial and neuronal cell bodies - between the nascent tritocerebral commissure and the adjacent commissure of the first s neuromere (mandibular commissure).

    The close proximity of the foregut to the outgrowing axons suggests that the structures at the brain-foregut interface may play a role in the morphogenesis of embryonic brain tracts in Drosophila (Wildemann, 1997).

    Excitatory and inhibitory switches for courtship in the brain of Drosophila melanogaster

    Courtship is the best-studied behavior in Drosophila melanogaster, and work on its anatomical basis has concentrated mainly on the functional identification of sexually dimorphic sites in the brain. Much less is known of the more expansive, nondimorphic, but nonetheless essential, neural elements subserving male courtship behavior. Sites in the CNS mediating initiation and early steps of male courtship in Drosophila melanogaster were identified by analyzing the behavior of mosaic flies expressing transgenes designed either to suppress neurotransmission or enhance neuronal excitability. Suppression of neurotransmission was accomplished by means of the dominantly acting, temperature-sensitive dynamin mutation shibirets1, whereas enhanced neuronal excitability was produced by means of a novel, dominantly acting, truncated eag potassium channel. By using a new, landmark-based procedure for aligning diverse expression patterns among the various mosaic strains, a comparison of courtship performance and affected brain sites in strains expressing the transgenes identified a cluster of cells in the posterior lateral protocerebrum that exerts reciprocal effects on the initiation of courtship, suppressing it when they are inactivated and enhancing it when they are hyperactivated, indicative of cells that normally play an excitatory, triggering role. A separate group of nearby cells, slightly more anterior in the lateral protocerebrum, was found to inhibit courtship when its activity is enhanced, indicative of an inhibitory role in courtship. It is concluded that a cluster of cells, some excitatory and some inhibitory, in the lateral protocerebrum regulates courtship initiation in Drosophila. These cells are likely to be an integration center for the multiple sensory inputs that trigger male courtship (Broughton, 2004).

    Male courtship is elicited by visual and chemosensory cues, either of which is sufficient to initiate courtship behavior in the presence of a virgin female. Projections from the antennal lobes to the lateral protocerebrum, independent of the mushroom bodies, are essential for courtship initiation. These data, which are consistent with the normal initiation of courtship in mushroom body-ablated and mushroom-body impaired males, suggest that courtship is initiated via a mushroom body-independent mechanism (Broughton, 2004).

    In the current study, only those GAL4 lines expressed in a common region of the lateral, posterior protocerebrum had a specific effect on courtship initiation. Of particular significance is the reciprocal effect on initation seen in MJ286 and MJ146 when expressing a transgene suppressing neuronal activity as compared with an enhancing transgene. The implication is that these cells exert an activating effect on courtship initiation. This region has previously been implicated sex specifically in male courtship, physiologically in male courtship, and in mediating the plasticity associated with courtship conditioning. As the recipient of many different kinds of sensory projections, it is likely to carry out a variety of integrative functions, as already suggested by a study of its olfactory inputs (Broughton, 2004).

    These lateral protocerebral cells in enhancer trap lines MJ286 and MJ146 lie just ventral to the anatomical neighborhood previously identified as the sex-specific focus for courtship initiation: the dorsal posterior brain. In fact, the marking technique used in the earlier studies detected cell bodies whose neuronal processes may well project into the region identified in the current study. The presence in MJ286 of sexually dimorphic function suggests that its dorsal, lateral, and protocerebral cells may even be part of the sex-specific focus for initiation, as well as being required for its physiological realization. The lack of FRUM expression in these cells may reflect the incomplete overlap in expression patterns between FRUM and the male-specific product of another gene in the sex determination cascade, doublesex (DSXM), each of which distinctively influences male courtship behavior. MJ146 does not show sex-specific function, despite its overlap, albeit limited, with FRUM expression (Broughton, 2004).

    In contrast, the one case in which the transgene that enhances neuronal activity exerted a suppressing effect on courtship, MJ63, is suggestive of an inhibitory circuit, though it is not associated with the GABA-ergic marker GAD-dsRED. In this instance, the effect was unidirectional: blocking neuronal activity in the same cells had no effect. One interpretation of this result is that this region normally acts in a modulatory fashion and does not act to continuously inhibit courtship behavior. When MJ63 is used to drive expression of a CaMKII inhibitory peptide during the courtship conditioning assay, memory is disrupted and regions defined by this line have been suggested to be involved in enabling memory of the inhibition by the mated female. These data raise the possibility that the inhibitory regions identified by MJ63 may normally act to mediate experience-dependent inhibition of male courtship behavior and the inappropriate activation of them suppresses courtship, thus mimicking the conditioning paradigm (Broughton, 2004).

    The opposing effects of c747/UAS-shits1 and c309/UAS-shits1 on wing extension and vibration, are likely to be due to the fact that the broader expression pattern in c747 disrupts inhibitory sites that are intact in c309 and that c309 affects excitatory sites. If this excitation were due to the mushroom body expression in c309, which would then be couteracted by additional expression in c747, it would be consistent with the previous finding of a sex-specific effect of this structure on the performance of wing extension and vibration. The song itself is controlled in the mesothoracic ganglion. Although the mushroom bodies have been suggested to be involved in mate discrimination, a recent finding that expression of UAS-shits1 in the mushroom bodies did not induce male-male courtship behavior shows that mushroom body activity is not required for the recognition of sex-specific pheromones that inhibit male-male courtship. Mushroom bodies have been implicated, however, in modulating other kinds of motor output (Broughton, 2004).

    Mapping the neural elements of male courtship is an essential step in understanding the functional circuitry and neural basis for this evolutionarily critical behavior. The fact that courtship consists of a series of stereotypical steps offers great advantages for assigning roles to particular circuits and will facilitate the merging of findings from studies of the sexual dimorphisms underlying courtship with those of genetic perturbations of physiology (Broughton, 2004).

    The Drosophila pheromone cVA activates a sexually dimorphic neural circuit

    Courtship is an innate sexually dimorphic behaviour that can be observed in naive animals without previous learning or experience, suggesting that the neural circuits that mediate this behaviour are developmentally programmed. In Drosophila, courtship involves a complex yet stereotyped array of dimorphic behaviours that are regulated by FruM, a male-specific isoform of the fruitless gene. FruM is expressed in about 2,000 neurons in the fly brain, including three subpopulations of olfactory sensory neurons and projection neurons (PNs). One set of Fru+ olfactory neurons expresses the odorant receptor Or67d and responds to the male-specific pheromone cis-vaccenyl acetate (cVA). These neurons converge on the DA1 glomerulus in the antennal lobe. In males, activation of Or67d+ neurons by cVA inhibits courtship of other males, whereas in females their activation promotes receptivity to other males. These observations pose the question of how a single pheromone acting through the same set of sensory neurons can elicit different behaviours in male and female flies. Anatomical or functional dimorphisms in this neural circuit might be responsible for the dimorphic behaviour. This study reports a neural tracing procedure that employs two-photon laser scanning microscopy to activate the photoactivatable green fluorescent protein. Using this technique it was found that the projections from the DA1 glomerulus to the protocerebrum are sexually dimorphic. A male-specific axonal arbor was observed in the lateral horn whose elaboration requires the expression of the transcription factor FruM in DA1 projection neurons and other Fru+ cells. The observation that cVA activates a sexually dimorphic circuit in the protocerebrum suggests a mechanism by which a single pheromone can elicit different behaviours in males and in females (Datta, 2008).

    In initial experiments, photoactivatable green fluorescent protein (PA-GFP) was expressed in flies in which the GAL4 enhancer-trap GH146 drives the expression of UAS-PA-GFP in 60% of the PNs that innervate most glomeruli in the antennal lobe. PA-GFP exhibits low-level fluorescence, sufficient to identify individual glomeruli, that is enhanced 100-fold after photoconversion with high-energy light. The PA-GFP was photoactivated with a two-photon laser scanning microscope to localize 710-nm light with submicrometre three-dimensional precision. Photoactivation of the antennal lobe neuropil, encompassing all glomeruli, results in intense labelling of the dendritic arbors of GH146 PNs. Diffusion of PA-GFP from the illuminated dendritic arbors allowed revealation of the cell bodies and axonal projections of the multiple GH146 PNs. Photoactivation of individual glomeruli (VM3 and DA1) reveals the dendritic arbors, cell bodies and projections of the subpopulation of GH146 PNs that innervate a single glomerulus (Datta, 2008).

    An approach was devised to allow the tracing of individual PNs that innervate identified glomeruli. The DA1 glomerulus was exposed to low levels of photoconverting light and then the antennal lobe was rapidly imaged to identify the PN cell bodies that show modest increases in fluorescence intensity. Under these limiting conditions of photoactivation, diffusion of PA-GFP into axonal projections was not observed. Next a single weakly labeled PN cell body was strongly photoactivated at higher light intensity to reveal the axonal projections of an individual PN that innervates the DA1 glomerulus. Thus, two-photon laser scanning microscope-mediated activation of PA-GFP provides sufficient spatial resolution and photoconversion energy to reveal the neuronal processes of defined neuronal populations as well as individual neurons in the fly brain (Datta, 2008).

    The development of a combined genetic and optical neural tracing method permits comparison of the topography of projections from Fru+ PNs that innervate the cVA-responsive DA1 glomerulus in male and female flies. Flies in which GAL4 is expressed under the control of the P1 fruitless promoter responsible for generating FruM (fruGAL4) were crossed with flies harbouring the UAS-PA-GFP transgene. P1 transcripts from the modified fruGAL4 allele do not undergo the sexually dimorphic splicing observed for the wild-type fru allele, and they therefore allow marking of Fru+ cells in both sexes. Unilateral photoactivation of the fly brain reveals many Fru+ cells, including neurons in the antennal lobe. Specific photoactivation of the DA1 glomerulus reveals six Fru+ PNs in both male and female flies that innervate this glomerulus. The cell bodies of these neurons reside in the lateral PN cluster, not the dorsal cluster as previously suggested (Datta, 2008).

    It is possible that the sex-specific behavioural responses to cVA result from different functional responses of the DA1 glomerulus in the two sexes despite there being no apparent difference in the number or location of Fru+ DA1 PNs. Therefore the Ca2+-sensitive fluorescent protein GCaMP was expressed in Fru+ neurons, and two-photon imaging was used to examine increases in Ca2+ in the DA1 glomerulus in response to cVA. Large increases in Ca2+ within the DA1 glomerulus were detected by two-photon imaging after exposure of an intact, behaving fly to cVA. However, no differences were observed between male and female responses over a broad range of cVA concentrations (Datta, 2008).

    These imaging experiments report local changes in the concentration of Ca2+ in both the presynaptic and postsynaptic compartments, because both Or67d-expressing neurons and DA1 PNs are Fru+. Therefore whether the electrophysiological properties of Fru+ DA1 PNs were sexually dimorphic was examined. The DA1 glomerulus was photoactivated to identify Fru+ DA1 PNs and the enhanced fluorescence was used to guide a patch electrode to the cell bodies. Recordings were made from Fru+ DA1 PNs in the loose patch configuration in an intact fly preparation and no significant difference was noted in the spike frequency or response kinetics between males and females when tested at several concentrations of cVA. These responses are comparable to those previously observed in whole-cell recordings of female DA1 PNs. This result demonstrates that male and female DA1 PNs show similar electrophysiological responses to cVA despite the previously noted dimorphism in the size of the DA1 glomerulus (Datta, 2008).

    Next the projection patterns of Fru+ DA1 PNs were examined in the two sexes. Photoconversion of the DA1 glomerulus allowed the projection patterns of the population of DA1 PNs to be revealed in the lateral horn in living brains. Despite significant similarity in the axonal arbors of DA1 PNs in males and females, an increase was observed in the density of ventral axonal branches in the male. Quantification of differences in branch patterns in multiple individual male and female flies was hampered by variations in the orientation of the live brain during microscopy. Therefore the approach was altered to employ fixed brains stained with the antibody nc82 to label the synaptic neuropil of the lateral horn. An image registration algorithm was used to first 'warp' the nc82 channel of individual brains onto a reference brain and then map the PA-GFP fluorescence onto this reference brain. The registration error averaged less than 2μm in any dimension when measured at the neuropil edge. It was observed that the projections from the DA1 glomerulus target the anterior ventromedial region of the LH. The projection pattern is triskelion-shaped, with ventral, lateral and dorsal branches. Fru+ DA1 projections from males have additional axonal branches that extend ventromedially. Superposition of the DA1 projections taken from ten male and ten female flies confirms this observation, indicating that information carried by Fru+ DA1 PNs is differentially segregated in the lateral horn of the two sexes. As a control a similar analysis of the PN projections from the Fru- glomerulus VM3, which responds to alcohols and acetates, was performed. Superposition of the projections from VM3 reveals no consistent differences in the pattern of axonal projections in the lateral horn between the two sexes. These observations show that the image alignment procedure does not introduce sex-specific biases in projection patterns and that the dimorphic projection patterns that were observe for the Fru+ glomerulus DA1 are not a general feature of projections from all glomeruli (Datta, 2008).

    The anatomical dimorphism observed at the level of the population of axons is also shown by the axons of single identified neurons. Tracing individual Fru+ DA1 neurons after warping revealed that the ventral axonal branches of male PNs define a male-specific region of protocerebral space (about 600 μm3). Each individual male in the data set sends at least one axon branch into this area. This area seems to partly overlap a region of neuropil in the lateral horn that was recently shown to be larger in male flies than in female flies. In addition, the total density of ventrally oriented axonal branches is significantly greater in males than in females. In contrast, the total innervation of the dorsal axonal arbor showed no statistically significant differences between sexes. No similar female-specific area was identified, although there are several smaller areas (particularly laterally) that appear to have an increased density of female axons. The data from single-axon tracing, along with observations from populations of DA1 neurons, indicate that DA1 PN projections are sexually dimorphic (Datta, 2008).

    Fru mutant males court other males with high frequency. If the male-specific arbor contributes to the dimorphic behavioural response, it is expected that the DA1 PN projection patterns will be regulated by the fruitless gene. Therefore the axonal projections of single DA1 PNs were made visible in fru mutant males, and it was observed that DA1 PNs lack the characteristic male-specific axonal branches and exhibit a branching pattern more characteristic of wild-type females. However, the feminization is not complete in that the male-specific ventral axonal branches are significantly reduced but not completely eliminated in fru mutant males. Thus, the male pattern of projections of Fru+ DA1 PNs requires the male-specific isoform of fru, FruM (Datta, 2008).

    It was also shown that the ectopic expression of FruM in females masculinizes the axonal arbor of their DA1 PNs. Projections of single Fru+ DA1 PNs in female flies that express FruM (fruGAL4/fruUAS-FruM) exhibit a striking increase in axonal projections to the ventral male-enhanced area. Quantitative analysis of these branches reveals that expression of FruM in females renders their ventral axon branch pattern statistically indistinguishable from that of males. The innervation patterns of individual neurons are sufficient for a computational discrimination algorithm to effectively distinguish individual females from FruM-expressing females with 100% accuracy, and individual males from fru mutant males with more than 91% accuracy. Thus, analysis of the PN projections of both single defined neurons and populations of neurons reveal that Fru+ DA1 PNs project to different regions of the protocerebrum in male and female flies. Moreover, this anatomical dimorphism in the neural circuit is controlled by the dimorphic transcription factor, FruM (Datta, 2008).

    Next, whether the formation of the male-specific arbor requires the action of FruM in DA1 projection neurons was examined. The enhancer-trap MZ19 drives the expression of GAL4 in six DA1 PNs, about ten additional PNs that innervate two Fru- glomeruli, and 25 extrinsic neurons of the mushroom body. Flies harbouring fruGAL4, MZ19 or MZ19;fruGAL4 all reveal expression of PA-GFP in six DA1 PNs. This suggests that the six lateral DA1 neurons labelled by the MZ19 and fruGAL4 lines are identical. In accord with this observation, male and female DA1 neurons in MZ19 flies have a sexually dimorphic pattern of projections that closely resembles the dimorphic branching observed for Fru+ DA1 PNs. Therefore FruM expression was eliminated in male MZ19 neurons by expression of Tra, which directs the female-specific splicing of fruitless transcripts. Genetic feminization of male DA1 PNs in MZ19/UAS-tra flies results in two anatomical classes of DA1 projection neurons. Half of the genetically feminized DA1 PNs show a reduction in the male-specific arbor and closely resemble male DA1 projection neurons defective for FruM. The remaining genetically feminized neurons exhibit the wild-type male-specific branching patterns. Within a single male MZ19/UAS-tra fly, neurons of both anatomical classes were observed. These data suggest that FruM is required in DA1 PNs to generate a male-specific projection pattern, but its action in this genetic context is partly penetrant (Datta, 2008).

    Also, whether the expression of FruM in female DA1 PNs masculinizes the DA1 axon arbor was examined. DA1 PNs in female MZ19; fruUAS-FruM flies do not significantly innervate the male-specific area, although most send minor branches into the ventral region of the lateral horn. This is in contrast with observations with fruGAL4/fruUAS-FruM strains that exhibit a transformation of the female DA1 PN branching pattern into a complete male-specific arbor. Taken together, these results suggest that FruM is required in both DA1 PNs and in other Fru+ neurons to generate the male-specific pattern of ventral axon arborization in the lateral horn (Datta, 2008).

    In Drosophila, courtship behaviour is governed by pheromonal excitation of peripheral olfactory pathways that ultimately activate behavioural circuits in higher brain centres. One pheromone elaborated by the male, cVA, suppresses male-male courtship but in females enhances receptivity to courting males. cVA activates the DA1 glomerulus, which is innervated by PNs that have sexually dimorphic projections in the lateral horn. This dimorphic circuit is under control of the transcription factor FruM, a male-specific isoform of fruitless. Moreover, the dimorphism in this circuit correlates with behaviour. In males mutant for FruM, cVA no longer suppresses male-male courtship and males exhibit a feminized pattern of DA1 projections. In females that express FruM, DA1 PNs exhibit a male pattern of axonal arbors in the lateral horn, and these females show reduced sexual receptivity. These observations are in accord with a mechanism in which the anatomical differences observed in Fru+ DA1 projection neurons contribute to the distinct behaviours elicited by cVA in the two sexes. In Drosophila, dimorphism in the Fru+ SP2 and mAL neurons has been observed, but the behavioural function of these circuits is unknown (Datta, 2008).

    The anatomical dimorphism observed may be translated into a behavioural dimorphism if the connections between DA1 PNs and third-order neurons differ between the sexes. Third-order neurons whose dendrites innervate the ventral lateral horn may either receive greater input from male PNs or may restrict their synapses to the male-specific region of the DA1 axon arbor. The relatively small size of the male-specific arbor, about the volume of a glomerulus, implies a precision of connectivity in higher processing centres in the fly brain. The stereotyped and local precision of synaptic connections is an organizing principle in the antennal lobe and may be a common feature of invertebrate nervous systems (Datta, 2008).

    Characterization of specific neural circuits that may mediate behaviour, as described in this study for the pheromone-responsive DA1 pathway, requires the development of tracing approaches that label defined populations of neurons. The distinction between genetic approaches -- including MARCM, Flp-Out and PA-GFP-based tracing -- and the histological approaches of Golgi and Cajal 100 years ago is the ability to use genetic markers to identify partners in the neural circuit more precisely. The targeted illumination of PA-GFP permits non-random, optically guided labelling of individual neurons from either anatomically or genetically defined subsets of neurons. Moreover, PA-GFP can be photoactivated in neurons in the living brain and allows electrophysiological recordings of labelled cells. This approach to neural tracing and recording in a defined circuit can be readily adapted to other brain regions in both the fly and mouse (Datta, 2008).

    Cell class-lineage analysis reveals sexually dimorphic lineage compositions in the Drosophila brain

    The morphology and physiology of neurons are directed by developmental decisions made within their lines of descent from single stem cells. Distinct stem cells may produce neurons having shared properties that define their cell class, such as the type of secreted neurotransmitter. This study developed the transgenic cell class-lineage intersection (CLIn) system to assign cells of a particular class to specific lineages within the Drosophila brain. CLIn also enables birth-order analysis and genetic manipulation of particular cell classes arising from particular lineages. The power of CLIn was demonstrated in the context of the eight central brain type II lineages, which produce highly diverse progeny through intermediate neural progenitors. 18 dopaminergic neurons from three distinct clusters were mapped to six type II lineages that show lineage-characteristic neurite trajectories. In addition, morphologically distinct dopaminergic neurons are produced within a given lineage, and they arise in an invariant sequence. Type II lineages that produce doublesex- and fruitless-expressing neurons were identified, and whether female-specific apoptosis in these lineages accounts for the lower number of these neurons in the female brain was examined. Blocking apoptosis in these lineages results in more cells in both sexes with males still carrying more cells than females. This argues that sex-specific stem cell fate together with differential progeny apoptosis contribute to the final sexual dimorphism (Ren, 2016).

    The relationship between neuron classes and lineages is complex in the Drosophila brain, where analogous neurons of a given class may arise from distinct lineages and a single lineage can yield multiple neuron classes. Therefore, a method was developed that would enable mapping and and analysis of neuron classes with respect to lineage identity using intersectional transgenic strategies. Specifically, the neuron class of interest expresses the GAL4 transcriptional activator from a class-specific transgene, while the lineage(s) of interest expresses the KD recombinase from a lineage-specific transgene. The KD recombinase activity triggers production of another recombinase, Cre, under the control of the deadpan (dpn) promoter, which is active in all NBs. Cre recombinase activity then triggers the simultaneous production of the LexA::p65 transcriptional activator and loss of the GAL4 inhibitor, GAL80, in all subsequently born progeny within the lineage(s). The LexA::p65 activates reporter-A expression within lineages of interest via lexAop. Because all other neurons outside lineage(s) of interest express GAL80, GAL4 is only active in neurons of the LexA::p65-expressing lineage(s) and thus can positively mark these neurons by activating expression of a reporter-B under UAS control. One can therefore subdivide any complex set of neurons that express a class-specific GAL4 transgene based on their developmental lineage(s). Consequently, CLIn enables the unambiguous determination of the lineage origins of particular neuron classes, which is essential for understanding the development and organization of the Drosophila brain (Ren, 2016).

    The CLIn system unambiguously establishes the correspondence between cell classes and their lineage origins and enables the subdivision of a given neuronal class among certain NB lineages. It also allows interrogation of serially derived neuronal diversity. One can therefore map individual neurons of a given class with respect to their lineage and temporal origins in an effort to unravel the intricate neuron class-lineage relationships in the brain (Ren, 2016).

    Revealing diverse cell classes of a lineage, by carefully choosing different GAL4 drivers that each distinguish a particular cell class, will allow better characterization of progeny heterogeneity within a lineage. It is therefore possible to explore how cellular diversity is generated during development. For example, it will be interesting to determine whether a specific cell class develops from one fixed temporal window. Moreover, comparing the cell-class diversity of different lineages will provide insight into the developmental heterogeneity of stem cells (Ren, 2016).

    Conversely, for cell classes that originate from multiple lineages, CLIn analysis reveals the distribution of a cell class among different lineages. Vertebrate studies found that neurons of the same lineage origin, compared to neurons of the same class but different lineage origins, are more likely structurally connected via gap junction and have similar network functions. In Drosophila, lineage has been shown to be a developmental and a functional unit. Thus, assigning a cell class to different lineages may reveal the particular function of a neuronal subset within a cell class (Ren, 2016).

    Moreover, the CLIn system permits incorporations of additional effectors driven by the GAL4-UAS system or the LexA system to manipulate cell class or lineage, respectively. The toolkit of effectors for different purposes is growing rapidly over recent years. Multiple reporter constructs are available to label specific sub-domains of the cell (dendrite, axon, or synapse). Effectors that affect cell viability could eliminate or immortalize specific neurons or glia. Effectors that alter membrane activity can be used to modulate neural activity. In addition, CLIn enables distinguishing gene’s functions in whole lineage including stem and progenitor cells versus only in a subset of lineage progeny by independent gene manipulations via lexAop versus UAS systems (Ren, 2016).

    However, the CLIn system requires further improvement to reach its full potential. In particular, the drivers for targeting various NB subsets remain to be fully characterized. Moreover, their targeting efficiency and specificity could vary individually. Engineering drivers based on genes known to be expressed in defined subsets of embryonic NBs may provide an initial complete set of more reliable NB drivers. An additional challenge for the study of type II lineages is how to selectively target INP sublineages. Via the current dpn enhancer, the frequencies of INP1 sublineages are very low compared with that of NB lineages (Ren, 2016).

    Type II NBs yield supernumerary neurons plus glia, which are expected to be highly diverse in cell classes. CLIn unambiguously assigned various neuronal classes to common type II lineages. In this study, the majority of progeny remained negative for the drivers employed. Revealing the full spectrum of neuronal heterogeneity within type II lineages requires characterization of additional cell-class drivers (Ren, 2016).

    Diverse cell classes could arise from a single INP. Single-cell lineage analysis has shown that one INP can produce multiple morphological classes of neurons most likely pertaining to different functional classes. Temporal mapping by CLIn revealed the birth of both TH-GAL4 and dsxGAL4 neurons in early windows of larval type II lineages. This lends further support to the production of diverse neuronal classes by common INPs. Examining INP clones labeled by CLIn did validate that the first larval-born INP of the DM6 lineage makes one fruGAL4 neuron in addition to two TH-GAL4 neurons (Ren, 2016).

    Per the limited cell-lineage analysis along the NB axis of type II lineages, sibling INPs produce morphologically similar series of neurons that differ in subsets of terminal neurite elaborations. These phenomena indicate expansion of related neurons across sibling INP sublineages. Assuming production of about 50 sibling INPs and in the absence of apoptosis, one would expect composition of 50 cell units for each neuronal class made by one type II NB. Notably, rescuing apoptotic dsx- or fru-expressing neurons throughout lineage development did restore complements of 50 or so cells in several, but not all, type II lineages. However, most type II lineages yield very few, if any, TH-GAL4 neurons. For instance, the DL1 lineage produces two TH-GAL4 neurons that innervate the upper FB layers. Temporal mapping of the DL1 lineage reveals the existence of multiple (n > 3) morphologically distinct INP clones that contain neurons projecting to the FB upper layers, similar to the DL1 TH-GAL4 neurons. Thus, morphologically similar neurons may belong to different functional classes, highlighting the challenges in sorting out neuronal classes and their interrelationships in the brain (Ren, 2016).

    Pioneering studies in C. elegans showed that the acquisition of neurotransmitter identity could be achieved through distinct mechanisms. A shared regulatory signature consisting of three terminal-selector types of transcription factors regulates the terminal identity of all dopaminergic neurons. By contrast, different combinations of terminal selectors act in distinct subsets of glutamatergic neurons to initiate and maintain their glutamatergic identity. In the present study, it was observed that six type II lineages produce 18 dopaminergic neurons but all during early larval neurogenesis. The derivation of TH neurons from multiple neuronal lineages at similar temporal windows argues for their specification by combinations of different lineage-identity genes with common temporal factors (Ren, 2016).

    Previous analysis of fruGAL4 neurons has uncovered 62 discrete MARCM clones in the fly central brain that might arise from an equal number of lineages. Ten clones show dimorphic cell numbers, and 22 clones exhibit dimorphic trajectories. Contrasting the stochastic clonal labeling of only fruGAL4 neurons, CLIn allows determination among a collection of lineages of whether a given lineage yields any fruGAL4 neurons. Based on the additional lineage information, two clones (pIP-j and pIP-h) were attributed as being partial clones of another two full-sized clones (pIP-g and pMP-f). Moreover, a more focused approach reveals sexual dimorphism of fru-expressing neurons in all type II NB lineages (Ren, 2016).

    The presence of many more dsx- or fru-expressing neurons in male than female brains is proposed to result from selective loss of specific neurons in females through apoptosis. However, blocking apoptosis increased dsx- or fru-expressing neurons in both male and female lineages. This is consistent with a previous report showing that sex-independent apoptosis occur widely in type II lineages. Although the number of apoptosis-blocked female neurons was similar, if not identical, to that of the control male neurons, blocking apoptosis unexpectedly increased the number of male dsx- or fru-expressing neurons such that there were more neurons in the apoptosis-blocked male than female lineages. This unmasks the original potential of the male and female NBs to produce different numbers of dsx- or fru-expressing neurons in type II lineages (Ren, 2016).

    Distinct fates have been reported for male and female NBs in the abdominal ganglion of Drosophila CNS. In this study, the male isoform of Dsx, DsxM, promotes additional NB divisions in males relative to females. Similarly, it has been reported that DsxM specifies additional cell divisions in the male, relative to female, central brain NBs that give rise to the pC1 and pC2 clusters. The proliferation of Drosophila intestinal stem cells is also determined by their sexual identity, although this is controlled by genes other than dsx and fru. Consistent with the notion that male and female NBs may possess distinct fates, this study found that male type II lineages contain more neurons committed to express dsx or fru, which possibly results from the greater number of NB divisions in males, as shown in the previous study. Elucidating the underlying molecular mechanisms of sex-specific neuron numbers in the central brain will require additional studies of the sex-dependent production and specification of different dsx- or fru-expressing neurons in the apoptosis-blocked type II NB lineages (Ren, 2016).

    Lineage mapping based on morphology provides limited information about neuronal classes. Given the intricate relationship between neuronal classes and cell lineages, CLIn is needed to resolve the detail even in fly brains where invariant neuronal lineages exist. This is critical for fully understanding how cell lineages guide the formation of variant neural circuits with distinct combinations of neuronal classes and types (Ren, 2016).

    In mammalian brains, extensive neuronal migration obscures the roles of cell lineages in the global organization of neural networks. However, clonally related neurons preferentially make local connections. Moreover, ample evidence exists for the heterogeneity of mammalian neural stem cells and the control of neuronal identity by spatiotemporal patterning of neural progenitors. Untangling of a further sophisticated brain and its development may absolutely require examination of cell lineages and neuronal classes at the same time. Systems like CLIn with its emphasis on the relationship between cell class and lineage potentially aid greatly in the study of mammalian brain development, anatomy, and function (Ren, 2016).

    bHLH-O proteins balance the self-renewal and differentiation of Drosophila neural stem cells by regulating Earmuff expression
    Balancing self-renewal and differentiation of stem cells requires differential expression of self-renewing factors in two daughter cells generated from the asymmetric division of the stem cells. In Drosophila type II neural stem cell (or neuroblast, NB) lineages, the expression of the basic helix-loop-helix-Orange (bHLH-O) family proteins, including Deadpan (Dpn) and E(spl) proteins, is required for maintaining the self-renewal and identity of type II NBs, whereas the absence of these self-renewing factors is essential for the differentiation of intermediate neural progenitors (INPs) generated from type II NBs. This study demonstrates that Dpn maintains type II NBs by suppressing the expression of Earmuff (Erm). Evidence is provided that Dpn and E(spl) proteins suppress Erm by directly binding to C-sites and N-boxes in the cis-regulatory region of erm. Conversely, the absence of bHLH-O proteins in INPs allows activation of erm and Erm-mediated maturation of INPs. The results further suggest that Pointed P1 (PntP1) mediates the dedifferentiation of INPs resulting from the loss of Erm or overexpression of Dpn or E(spl) proteins. Taken together, these findings reveal mechanisms underlying the regulation of the maintenance of type II NBs and differentiation of INPs through the differential expression of bHLH-O family proteins (Li, 2017).

    This study demonstrates that similar to the canonical Notch signaling, Dpn maintains the identity and self-renewal of type II NBs at least in part by inhibiting Erm expression. Loss of Dpn leads to the ectopic activation of erm in type II NBs and that removing Erm not only prevents the transformation of dpn mutant or Dpn knockdown type II NBs into type I-like NBs but also largely inhibits their premature termination of self-renewal. The results from gel-shift assays and reporter assays provide evidence to support that Dpn and E(spl) proteins suppress Erm expression by directly binding to at least two of the three putative bHLH-O binding sites in the erm enhancer (Li, 2017).

    Although Dpn and canonical Notch signaling could function through a similar mechanism, these factors do not appear to be completely functionally redundant as previously suggested. First, during early 1st instar larval stages when type II NBs are still quiescent, the maintenance of type II NBs may mainly rely on Dpn in that Notch is not activated in quiescent type II NBs, as evidenced through the findings showing that the loss of Dpn at early 1st instar larval stages leads to ectopic Erm-mediated transformation and the premature loss of type II NBs. Second, after reactivation of type II NBs, both Dpn and Notch signaling are required to suppress the ectopic Erm expression in type II NBs because both the loss of Dpn and the components of the canonical Notch signaling pathways alone lead to ectopic Erm expression in type II NBs. However, the Notch signaling likely plays a dominant role in suppressing ectopic Erm expression and maintaining type II NBs. It has been previously shown that the loss of components of the canonical Notch pathway, including E(spl) proteins, leads to ectopic Erm expression and the transformation and premature loss of type II NBs, despite the presence of Dpn in the NBs, whereas the knockdown of Dpn after the reactivation of NBs only results in the weak ectopic activation of erm but not transformation or premature loss of type II NBs. Therefore, Dpn and Notch signaling may not be completely functionally redundant in suppressing the ectopic Erm expression or maintaining type II NBs, and their functions might be dependent on developmental stages. Furthermore, a recent study reported that Klu could also bind to the R9D11 enhancer to repress the expression of Erm. Thus, type II NBs likely utilize multiple mechanisms to ensure that erm will not be prematurely activated (Li, 2017).

    Previous studies suggested that all E(spl) proteins share similar DNA sequences. However, results from the present study suggest that this similarity may not always be the case. Gel-shift assays show that only members of the E(spl) family, including E(spl)mγ, mβ, mδ, m3, and m7, can bind to the bHLH-O binding sites in the erm regulatory region, whereas the other two, E(spl)m5 and m8, cannot. The difference in their DNA binding specificity is consistent with differences in the amino acid sequences of their bHLH domains and their overexpression phenotypes in type II NB lineages. Therefore, although multiple E(spl) proteins have been shown to be expressed in larval NBs and at least two of them, E(spl)mγ and m8, are activated by Notch, these E(spl) proteins may bind to different DNA sequences and regulate the expression of different target genes, which may in turn determine their functional specificity (Li, 2017).

    In contrast to the maintenance of type II NBs, the maturation of imINPs requires the activation of erm by PntP1 and shutdown of Dpn expression and Notch signaling. It has previously been shown that the loss of Erm or aberrant activation of dpn or Notch signaling in imINPs both lead to the dedifferentiation of imINPs and overproliferation of type II NBs. However, the functional relationship between the activation of erm and the absence of Dpn or Notch signaling in imINPs has never been established. This study demonstrates that the absence of Dpn and Notch signaling is essential for the activation of erm and subsequent Erm-mediated maturation of INPs. First, the results show that aberrant activation of dpn or Notch signaling inhibits the activation of erm in imINPs. Second, maintaining Erm expression in imINPs largely blocks the overproliferation of type II NBs resulting from the misexpression of E(spl) or Dpn proteins, suggesting that one main reason for the dedifferentiation of imINPs caused by Dpn or E(spl) overexpression is the suppression of Erm. However, the overproliferation of type II NBs resulting from the overexpression of Nintra or Numb knockdown can only be partially suppressed by concomitant Erm expression. Therefore, in addition to functioning through the canonical pathway to activate E(spl) expression, Notch may also act through noncanonical pathways, such as the mTORC2/Akt pathway, to regulate type II NB proliferation (Li, 2017).

    How does Erm promotes INP maturation and prevents the dedifferentiation of imINP? It has previously been suggested that Erm prevents the dedifferentiation of INPs by activating pros expression and attenuating the response of INPs to self-renewing factors such as Dpn and E(spl) proteins. However, two pieces of evidence argue against this notion. First, the loss of Pros only induces the overproliferation of INPs but not the dedifferentiation of imINPs into type II NBs . Second, Erm is only expressed in imINPs, which do not express Dpn or E(spl) proteins. In the present study, evidence is provided demonstrating that Erm likely promotes INP maturation in part by inhibiting the expression and/or function of PntP1. These results show that the overproliferation of type II NBs resulting from the loss of Erm or overexpression of Dpn or E(spl) proteins, which leads to suppression of Erm expression, could be significantly inhibited by knocking down PntP1. These data strongly argue that the dedifferentiation of imINPs and generation of extra type II NBs resulting from the loss of Erm is in part due to de-repression of PntP1 expression and/or function in imINPs, which is consistent with the PntP1 function in specifying type II NBs and suppressing the activation of ase. Similar to other Ets family proteins that are commonly involved in tumorigenesis, PntP1 may also activate the expression of cell cycle regulators that promote nonproliferative imINPs to enter the cell cycle and initiate unrestricted tumorigenic overproliferation. However, PntP1 may not be the only target of Erm in imINPs. As shown in a recent study, in addition to PntP1, Erm also directly inhibits the expression of Grh-O in imINPs (Janssens, 2017). Therefore, Erm likely promotes the maturation of INPs by regulating the expression/function of multiple target genes (Li, 2017).

    In conclusion, this study demonstrates here that similar to Notch signaling, Dpn maintains the identity and self-renewal of type II NBs in part by inhibiting Erm expression. Whereas in imINPs, the absence of Dpn and E(spl) proteins allows PntP1-mediated activation of erm, which in turn promotes INP maturation by inhibiting the expression and/or function of PntP1 and Grh-O in imINPs. Thus, the present study elucidates the mechanistic details of the maintenance of type II NBs and maturation of INPs (Li, 2017).

    Structural long-term changes at mushroom body input synapses

    How does the sensory environment shape circuit organization in higher brain centers? This study has addressed the dependence on activity of a defined circuit within the mushroom body of adult Drosophila. This is a brain region receiving olfactory information and involved in long-term associative memory formation. The main mushroom body input region, named the calyx, undergoes volumetric changes correlated with alterations of experience. However, the underlying modifications at the cellular level remained unclear. Within the calyx, the clawed dendritic endings of mushroom body Kenyon cells form microglomeruli, distinct synaptic complexes with the presynaptic boutons of olfactory projection neurons. Tools were developed for high-resolution imaging of pre- and postsynaptic compartments of defined calycal microglomeruli. This study shows that preventing firing of action potentials or synaptic transmission in a small, identified fraction of projection neurons causes alterations in the size, number, and active zone density of the microglomeruli formed by these neurons. These data provide clear evidence for activity-dependent organization of a circuit within the adult brain of the fly (Kremer, 2010).

    Odors encountered in the environment and detected by olfactory sensory neurons are initially processed in a first olfactory center, the antennal lobe in insects or the olfactory bulb in mammals. This olfactory information is then conveyed by insect antennal lobe projection neurons or mammalian mitral/tufted cells to secondary centers for odor recognition and the formation of olfactory memories. These two functions appear to be accomplished by two regions in the fly brain, the lateral horn and the mushroom body, respectively. Changes in the olfactory environment are reflected by changes in activity at the mushroom body input synapses. Furthermore, because projection neurons can house an appetitive memory trace, the mushroom body input synapses might be potentially involved in olfactory memory formation. It is unknown, though, whether alterations of presynaptic activity or formation of memories induce structural changes in the mushroom body (Kremer, 2010).

    In the Drosophila brain, most antennal lobe projection neurons send axonal projections terminating with bulbous boutons into the mushroom body calyx. Here they form specialized synaptic complexes, called microglomeruli (Yasuyama, 2002; Leiss, 2009), with the dendrites of mushroom body Kenyon cells, which are essential for the formation and retrieval of olfactory memories. Within a microglomerulus, a projection neuron bouton is enwrapped by actin-rich, claw-like, dendritic endings of more than one Kenyon cell and forms multiple synapses with these Kenyon cells' claws, each including a presynaptic active zone and a postsynaptic density. In social insects, the size and number of microglomeruli are modified in correlation with changes in the sensory environment. This study asked directly whether silencing olfactory projection neuron presynaptic activity or blocking their synaptic transmission alters the organization of calycal microglomeruli of Drosophila (Kremer, 2010).

    To this end, genetic tools were generated to identify the microglomeruli and the sites of synaptic contact within them. Projection neurons are the only reported cholinergic input to the mushroom body calyx. Therefore, a fusion of the MB247 fragment of the D-mef2 promoter, active in a large subset of Kenyon cells, and the Dα7 subunit of the acetylcholine receptor tagged with eGFP were constructed (MB247-Dα7-GFP) to visualize the postsynaptic rim formed by claw-like dendritic endings of multiple Kenyon cells around each projection neuron bouton. The localization of Dα7-GFP within the calyx appeared to be specific for the postsynaptic densities of the Kenyon cell claws, and it closely matched the active zone labeling in the projection neuron boutons. Importantly, expressing this construct did not affect the active zone number in the calyx (Kremer, 2010).

    Attempts were made to label the presynaptic active zones associated with postsynaptic densities. The active zone protein Bruchpilot (BRP) shapes the presynaptic active zone T bar and is essential for proper active zone function. A fluorescently tagged fragment of BRP, which depends on endogenous BRP for localization (UAS-brp-shortcherry), represents a reliable marker for active zones. Upon specific expression of UAS-brp-shortcherry in projection neurons, discrete BRP-shortcherry dots lined the inner rim of the Kenyon cell claw, closely matching the Dα7-GFP signal at putative sites of synaptic contact (Mz19-Gal4). The number of active zones is not affected by the expression of UAS-brp-shortcherry (using Mz19-Gal4) (Kremer, 2010).

    Previous data suggested that simple complete sensory deprivation experiments did not elicit detectable changes in the calyx. Therefore, a competitive situation in the calyx was constructed between silenced and nonsilenced projection neurons and among dendrite claws of the same Kenyon cell receiving normal or no presynaptic input. Thus a defined subset of microglomeruli was manipulated and differentially labeled, highlighted by the Mz19-Gal4 driver. Mz19-Gal4 is expressed after 18 hr after puparium formation in 10-13 projection neurons whose dendrites are restricted to three glomeruli in the antennal lobe (DA1, VA1d, and DC3; and form boutons in a confined area of the calyx. Mz19-Gal4-driven UAS-brp-shortcherry signal was used to identify the subset of microglomeruli formed by these projection neurons. Whereas calycal microglomeruli were highlighted with MB247-Dα7-GFP, the Mz19-positive subpopulation was identified by BRP-shortcherry and MB247-Dα7-GFP (Kremer, 2010).

    Within each calyx, the Mz19-positive microglomeruli were compared with those in which no Mz19-Gal4-driven expression of brp-shortcherry was observed. This experimental setup also allowed the problems posed by the large variability of overall brain volume and calycal size among animals, to be obviated independently of their genotype (in these experiments, approximately 10% of the calycal volume), and by potential differences in image acquisition and calycal labeling among animals. These factors could, in principle, hamper the detection of small morphological changes (Kremer, 2010).

    It was reasoned that the structurally repetitive organization of the calyx, the introduction of a competitive situation within the calyx, and the specificity of the developed markers might facilitate revealing alterations induced by changes in presynaptic activity (Kremer, 2010).

    To address whether the size and number of microglomeruli and the active zone distribution in adult calyces depend on presynaptic activity, attempts were made to silence or at least drastically reduce firing of the Mz19-positive projection neuron population. Using the Mz19-Gal4 driver, UAS-dORK1.ΔC, which functionally acts as a constitutively open K+ selective pore or leak and should lead to hyperpolarization of the resting membrane potential, was expressed; UAS-dORK1.ΔNC, a nonconducting version of the same channel, served as a control. Whole-cell current-clamp recordings from the somata were performed in an isolated, intact brain preparation to analyze the intrinsic firing properties of the projection neurons. Seven-day-old adult males were used, and the recorded neurons were labeled with biocytin to confirm their identity. Mz19-positive projection neurons of both genotypes did not show spontaneous activity in the cell-attached configuration or in the whole-cell configuration (Kremer, 2010).

    Next, it was asked whether the microglomeruli formed by boutons of the silenced Mz19-positive projection neurons were altered (Kremer, 2010).

    For every experimental group, the two calyces of at least six 7-day-old adult males weew analyzed, and 60-100 confocal optical sections were obtained per calyx. To obtain an unbiased identification of microglomeruli in high numbers of single optical sections, software was developed for the automated detection of microglomeruli. In every optical section, microglomerular rings were identified based on the intensity of the Dα7-GFP signal, the size and shape of the structure, and whether the Dα7-GFP-positive structure surrounded a darker lumen. A microglomerulus was defined as the sum of a Dα7-GFP-positive ring object and the lumen it contained. The software detected more than 30% of the manually identified microglomeruli, including only 3% false positives. Importantly, the overall size distribution of the microglomeruli was not significantly different between manual and software-based identification, suggesting that the software detection is unbiased. Furthermore, by using this approach, it was possible to detect alterations in the number of Mz19-positive microglomeruli obtained by overexpression of PI3K, a manipulation that induced bouton sprouting of ellipsoid body projection neurons and served as a positive control (Kremer, 2010).

    Thus the Mz19-positive neurons were silenced by expressing dORK1.ΔC. It was found that the fraction of Mz19-positive microglomeruli per calyx was significantly increased compared to the nonsilenced dORK1.ΔNC control. It should be noted that, in motorneurons, hyperactivation -- and not suppression of activity -- leads to bigger axonal elaborations (Kremer, 2010).

    Moreover, it was observed that the relative size of the microglomeruli was increased. This general enlargement of microglomerular size upon silencing correlated with an increase of the relative size of the Dα7-GFP-positive ring, which was, however, not significant (Kremer, 2010).

    Hence, reducing the activity of the Mz19-positive projection neurons led to an increase in the number of the microglomeruli formed by those neurons. Additionally, the relative size of the microglomeruli increased (Kremer, 2010).

    Therefore, it was next asked whether the number of individual presynaptic active zones per microglomerulus would increase as well or whether only size changes in the postsynaptic densities were taking place (Kremer, 2010).

    Individual synaptic release sites are characterized by a presynaptic active zone, identifiable by BRP. Thus, the BRP-shortcherry puncta weew analyzed in calyces of flies expressing dORK1.ΔNC or dORK1.ΔC and brp-shortcherry under the control of Mz19-Gal4. The BRP-shortcherry puncta were counted using software for semiautomated detection applied to 3D reconstructions of the whole calyx (Kremer, 2010).

    Strikingly, silencing the presynaptic neurons with dORK1.ΔC induced a clear increase in the number of BRP-shortcherry puncta per calyx compared to the control. This increase could simply be due to the above-described higher number of Mz19-positive microglomeruli obtained upon silencing. Therefore the active zone density in the Mz19-positive terminals was determined. Silencing those projection neurons resulted in a total increase in synaptic density in the Mz19-positive terminals; no significant increase was seen in the total area of the presynaptic terminals. The size of single active zones was not modified in silenced projection neurons with respect to the control. Hence, silencing or strongly reducing the generation of action potentials in projection neurons induced increased active zone density (Kremer, 2010).

    In summary, the microglomeruli formed by silenced projection neurons were more numerous compared to the control. Additionally, the postsynaptic domain was larger and the density of presynaptic active zones was higher in the silenced rather than in the unaffected microglomeruli (Kremer, 2010).

    The overexpression of dORK.1ΔC in projection neurons led to hyperpolarization, decreased input resistance, and subsequent inhibition of action potential firing, and thus, presumably, suppression of action potential evoked synaptic transmission. To dissect the contribution of these components, synaptic vesicle fusion was specifically suppressed using tetanus toxin (UAS-TNT) under the control of Mz19-Gal4. As a consequence, the relative size of the microglomeruli was significantly increased. Because this result was similar to the effect of silencing the presynaptic neurons with dORK1.ΔC, increase of the synaptic complex size might be caused by the loss of synaptic transmission in both situations. In contrast to the effect caused by dORK1.ΔC expression, however, the fraction of Mz19-positive microglomeruli was decreased upon expression of TNT. Also, the number and density of active zones were clearly diminished upon TNT expression. Thus, the number of microglomeruli and the active zone density are distinctly regulated depending on the manipulation (Kremer, 2010).

    It is suggested that antagonistic mechanistic components might confront each other here. First, a mechanism seems to sense activity within projection neurons. If neuronal activity is suppressed, a coherent 'compensatory' response is triggered, increasing bouton size and number, as well as the density of active zones at the affected terminals. In mammals, neuronal activity was found to effectively control neuronal gene transcription and translation. Second, there appears to be a homeostatic compensation within the microglomerular microcircuit of the loss of synaptic transmission, leading to increased bouton and postsynaptic ring size. Finally, loss of transmission per se induces a reduction in the active zone density and in the number of microglomeruli. How these phenomena interact throughout physiological adaptations will be interesting to address in the future (Kremer, 2010).

    This study has described changes in the number of calycal microglomeruli, as well as in their pre- and postsynaptic composition, that depend on the activity state of the presynaptic neuron and on transmission. Thus, this study has revealed that the structural and synaptic organization of the adult mushroom body calyx of Drosophila requires appropriate presynaptic activity and synaptic transmission (Kremer, 2010).

    Microglomeruli are more ill defined in calyces of just-eclosed males, suggesting a reorganization of the circuit during early adult life. In line with this hypothesis, the microglomerular circuit might be refined after eclosion in Apis. It is thus possible that microglomeruli form normally but that projection neuron input is required during a hypothetical refinement phase. Alternatively, the initial formation of microglomeruli may be affected by the absence of appropriate presynaptic activity and/or synaptic transmission. In support of this second scenario, modification of synaptic input alters the dendritic differentiation of a motor neuron in fly embryos (Tripodi, 2008). At this point, these two possibilities cannot be distinguished (Kremer, 2010).

    Previous evidence indicated that, in the adult fly brain, the establishment of correct connectivity is largely independent of activity. Nonetheless, the activity-dependent component of circuit organization might be difficult to detect above interanimal variability. Thus, it is proposed that the type of approach described in this study, including internal controls, high-resolution imaging of pre- and postsynaptic elements, and software-based analysis, will be necessary to reveal similar phenomena in other regions of the fly brain. In the calyx, this analysis was facilitated by the organization in microglomeruli, recognizable repetitive structural elements (Kremer, 2010).

    In addition, it is reckoned that the effect of activity on circuit organization might be best revealed by unbalancing the circuit as this study did by silencing only a defined fraction of olfactory projection neurons. As an example, monocular deprivation experiments, rather than binocular elimination of visual input, were instrumental to the understanding of the role of activity in shaping the visual circuit in mammals (Kremer, 2010).

    Projection neuron activity delivers a representation of the olfactory environment to the calyx. The effect of the manipulations of projection neuron activity in this study suggests that olfactory experience modulates the calycal circuit. In line with this hypothesis, sensory experience modifies properties of microglomeruli in the honeybee and ant. The functional outcome of these adaptations remains to be investigated. Importantly, in Drosophila, alterations of olfactory experience determine volumetric changes of adult antennal lobes. Furthermore, because projection neurons can house an appetitive memory trace, the mushroom body input synapses might be potentially involved in olfactory memory formation. Given the high resolution of the system that this study has established, it is envisaged that the next challenge will be to address directly whether the structure of defined microglomeruli can be modulated upon the establishment of long-term appetitive memories (Kremer, 2010).

    A dimorphic pheromone circuit in Drosophila from sensory input to descending output

    Drosophila show innate olfactory-driven behaviours that are observed in naive animals without previous learning or experience, suggesting that the neural circuits that mediate these behaviours are genetically programmed. Despite the numerical simplicity of the fly nervous system, features of the anatomical organization of the fly brain often confound the delineation of these circuits. This study identified a neural circuit responsive to cVA, a pheromone that elicits sexually dimorphic behaviours. Neural tracing using an improved photoactivatable green fluorescent protein (PA-GFP) was combined with electrophysiology, optical imaging and laser-mediated microlesioning to map this circuit from the activation of sensory neurons in the antennae to the excitation of descending neurons in the ventral nerve cord. This circuit is concise and minimally comprises four neurons, connected by three synapses. Three of these neurons are overtly dimorphic and identify a male-specific neuropil that integrates inputs from multiple sensory systems and sends outputs to the ventral nerve cord. This neural pathway suggests a means by which a single pheromone can elicit different behaviours in the two sexes (Ruta, 2010).

    The male pheromone 11-cis-vaccenyl acetate (cVA) elicits male-male aggression and suppresses male courtship towards females as well as males. A single class of olfactory neurons mediates behavioural responses to a Drosophila sex pheromone. In females, cVA activates the same sensory neurons to promote receptivity to males. cVA-induced aggregation behaviour is shown by both sexes. What neural circuits permit a single pheromone acting through the same set of sensory neurons to elicit several distinct and sexually dimorphic behavioural responses? (Ruta, 2010).

    The sensory neurons that express the odorant receptor Or67d respond to cVA, and these neurons converge on the DA1 glomerulus in the antennal lobe. Projection neurons (PNs) that innervate the DA1 glomerulus terminate in the lateral horn of the protocerebrum. Comprehensive maps of Drosophila higher olfactory centers: spatially segregated fruit and pheromone representation. Previous experiments showed that the DA1 axons are sexually dimorphic and reveal a male-specific ventral axonal arborization in the lateral horn (Datta, 2008). This dimorphism by itself might explain the sexually dimorphic behaviours or, alternatively, it might presage iterative anatomical dimorphisms at each stage in the circuit to descending output. Therefore, a neural circuit was characterized that transmits information from the DA1 PNs to the ventral nerve cord (see Photoactivation identifies dimorphic lateral horn neurons). The analysis was restricted to neurons that express the sexually dimorphic transcription factor fruitless (FruM). FruM is expressed in both Or67d-expressing sensory neurons and DA1 PNs and governs the development of dimorphic neural circuitry including the male-specific axonal arborization of DA1 PNs. In addition FruM specifies many male-specific behaviours, including those that are mediated by cVA (Ruta, 2010).

    In initial experiments PA-GFP, a photoactivatable GFP, was used to identify Fru+ third-order neurons whose dendritic processes are closely apposed to DA1 axon termini. A strategy was developed in which two-photon photoactivation is restricted to a small, circumscribed region of a neuron's axonal arborization with the expectation that this would label the postsynaptic cells by photoconversion of PA-GFP in their dendrites. To ensure that this limited activation could produce sufficient signal from the photoconverted fluorophore to illuminate third-order neurons and their most distal processes, two new enhanced PA-GFPs were generated, namely C3PA-GFP and SPA-GFP (Ruta, 2010).

    Photoconversion of the DA1 glomerulus in flies expressing C3PA-GFP or SPA-GFP under the control of fruGAL4 readily identified the axonal arborizations of the DA1 PNs. Then the volume of neuropil circumscribing the DA1 axon termini was photoactivated and four clusters of presumptive third-order neurons were reproducibly labelled in the lateral horn of male flies. Labelling of the two dorsal clusters, DC1 and DC2, was observed only in males; the clusters were either absent in the female or lacked projections into the ventral lateral horn. The lateral cluster LC1 was present in the two sexes but was dimorphic in both number and projection pattern. LC2 did not show an apparent numeric or anatomical dimorphism. Photoactivation of DA1 axon terminals in male flies that express C3PA-GFP pan-neuronally labelled few additional neurons and suggests that these four Fru+ clusters constitute the major potential recipients of DA1 input (Ruta, 2010).

    These photoactivation experiments identify clusters of third-order neurons in the lateral horn that are anatomically poised to propagate dimorphic responses to cVA. However, anatomical proximity does not ensure functional connectivity. Therefore a method was developed to specifically activate individual glomeruli and simultaneously record from presumptive downstream neurons to determine whether the lateral horn clusters that were identified receive excitatory input from DA1 PNs. DA1 PNs were selectively stimulated by positioning a fine glass electrode in the centre of the DA1 glomerulus and iontophoresing acetylcholine, the neurotransmitter that excites PNs, into the glomerular neuropil. Varying the iontophoretic voltage allowed variation of the frequency of elicited action potentials systematically in DA1 PNs up to 250, a value close to the upper limit of cVA-elicited responses measured in these PNs (Datta, 2008; Schlief, 2007). Activation of the DA1 glomerulus over this voltage range excited DA1 PNs specifically and elicited no response in PNs innervating other glomeruli in the antennal lobe. Stimulation of the neighbouring glomeruli, VA1d and VA1lm, similarly elicited the specific excitation of their cognate PNs but did not activate DA1 PNs (Ruta, 2010).

    Next, whether stimulation of the DA1 glomerulus would result in the excitation of neurons within the four clusters in the lateral horn that were identified, a result indicative of functional synaptic connections with DA1 PNs, was examined. The genetically encoded calcium indicator GCaMP3 was examressed in Fru+ neurons in male flies and two-photon imaging was used to monitor increases in Ca2+ concentration in the lateral horn clusters in response to DA1 excitation. Stimulation of the DA1 glomerulus elicited large increases in Ca2+ in neurons within the DC1 and LC1 clusters, with a far weaker response being observed in LC2. The small DC2 cluster is difficult to identify reliably because of the low basal fluorescence of GCaMP3; it was therefore not examined by optical imaging. The Ca2+ response in DC1 was specific for DA1 activation and was not observed when the stimulating electrode was repositioned in two neighbouring glomeruli, VA1d and VA1lm. These optical imaging experiments demonstrate that neurons within the DC1 and LC1 clusters extend processes in anatomical proximity to the DA1 axons and receive excitatory input from DA1 PNs. Immunostaining indicated that neurons within the LC1 cluster produce the inhibitory neurotransmitter GABA (γ-aminobutyric acid). Electrophysiological experiments suggested that DC1 neurons are excitatory but the neurotransmitter remains unknown (Ruta, 2010).

    Focused was placed on the male-specific DC1 neurons to define a cVA-responsive circuit. The DC1 cluster consists of ~19.7 cell bodies (n = 10) in a spatially stereotyped location in the dorsal aspect of the anterior protocerebrum. Double labelling experiments revealed that the DC1 processes interdigitate richly with DA1 axons in the lateral horn. Photoactivation of single DC1 cell bodies indicated that the cluster is composed of several anatomical classes of neurons characterized by distinct branch patterns within the protocerebrum that are likely to receive and integrate inputs from both olfactory and non-olfactory brain centres (Ruta, 2010).

    Electrophysiological recordings were performed to examine the response of DC1 neurons to both DA1 stimulation and cVA exposure. Selective stimulation of the DA1 glomerulus evoked action potentials in 66% of male-specific DC1 neurons recorded in the loose patch configuration. Among responsive DC1 neurons, it was observed that the sensitivity to DA1 stimulation differed. This functional heterogeneity within the DC1 cluster observed by both electrical and optical recording was consistent with the anatomical heterogeneity of dendritic fields in the lateral horn observed for single DC1 neurons (Ruta, 2010).

    In accord with the imaging experiments, the electrophysiological response of DC1 neurons is selectively tuned to DA1 input. After recording the response of a DC1 neuron to DA1 stimulation, the stimulating electrode was repositioned into 6-11 other superficial glomeruli located throughout the antennal lobe. DC1 neurons activated by minimal DA1 stimulation were either weakly excited or unresponsive to strong stimulation of other glomeruli. Stimulation of the Fru+ VA1lm glomerulus failed to excite DC1 neurons despite the close proximity of DA1 and VA1lm axons (Jefferis, 2007). These observations demonstrate the specificity of glomerular excitation and reveal that olfactory input to DC1 is mediated largely by the DA1 glomerulus and not by the activation of at least 11 other glomeruli, suggesting that DC1 neurons receive olfactory stimulation only from cVA. Next cVA-evoked responses from DC1 neurons were recorded in an intact fly preparation. It was observed that 62% of DC1 neurons were responsive to cVA over a range of concentrations. The input-output relationship of DC1 neurons was similar whether action potentials were evoked in DA1 PNs through direct glomerular stimulation or by pheromonal excitation of the antenna, suggesting that DC1 neurons are excited primarily by means of DA1 input. Both Or67d-expressing sensory neurons and DA1 PNs have been shown to be selectively tuned to cVA. DC1 neurons showed similar odorant selectivity and fired only weakly in response to stimulation of the antenna with a cocktail of ten fruit-derived odorants that excite a majority of glomeruli. Thus, DC1 neurons are likely to receive direct excitatory feedforward input from DA1 PNs and respond selectively to cVA (Ruta, 2010).

    Photoactivation of PA-GFP in presynaptic DA1 axonal arborizations, in concert with electrophysiology, has identified postsynaptic third-order neurons in the lateral horn that are responsive to cVA. The iterative use of this strategy could allow definition of the complete cVA circuit from sensory input to descending output. Tracing of photoactivated DC1 axons revealed that they terminate proximally within a triangular neuropil in the lateral protocerebrum (the lateral triangle) and extend distal processes to a previously uncharacterized tract within the superior medial protocerebrum (the SMP tract). The lateral triangle and SMP tract are sexually dimorphic neuropils that are absent in females (Ruta, 2010).

    Photoactivation of the terminal arborizations of DC1 axons was performed to identify neurons innervating the lateral triangle and SMP tract. Dense labelling was observed in these structures arising from the rich male-specific projections of multiple classes of Fru+ neurons. Dimorphic LC1 neurons that receive direct innervation from DA1 PNs send inhibitory projections to the lateral triangle and SMP tract. Dimorphic mAL neurons were also observed extending from the subesophageal ganglion (SOG) and terminating within these neuropils. In addition, these neuropils are innervated by male-specific P1 interneurons implicated in the initiation of male courtship behaviour. Thus, the lateral triangle and SMP tract receive dimorphic projections from several brain regions including other sensory processing areas, suggesting that these neuropils may integrate sex-specific information from multiple sensory systems (Ruta, 2010).

    Several neurons that innervate the lateral triangle and SMP tract also extend processes that descend into the ventral nerve cord, suggesting that these potential fourth-order descending neurons may transmit information from cVA-responsive sensory neurons to the ganglia of the ventral nerve cord. Descending neurons that innervate the lateral triangle and SMP tract were characterized by photoactivation of the cervical connectives conveying neural signals from the brain to the ventral nerve cord. In the brain, the processes of these descending neurons showed a marked dimorphism that was apparent in their extensive innervation of the male-specific SMP tract and lateral triangle. A descending neuron, DN1, absent in females, was observed in the ventral posterior aspect of the male brain, at the midline. Labelling of this male-specific cell body revealed short processes terminating within the lateral triangle and SMP tract, and a long descending process entering the ventral nerve cord and terminating within the thoracic and abdominal ganglia. Electroporation of DN1 with Texas Red dextran, followed by photoactivation of the DC1 cluster, revealed extensive intermingling of the green DC1 axons with the red dendrites of the descending neuron. This suggests that this descending neuron is anatomically poised to make direct synaptic contacts with third-order, cVA-responsive DC1 neurons (Ruta, 2010).

    Whole-cell patch clamp recordings were performed on DN1 to discern whether it transmits pheromonal information to the ventral nerve cord. In response to either exposure of the antenna to cVA or direct stimulation of the DA1 glomerulus, DN1 received a barrage of excitatory postsynaptic potentials (EPSPs) bringing its membrane potential close to or past threshold. To determine whether this response was mediated by DC1 neurons, a microlesion technique was devised exploiting the spatial precision of a two-photon laser to effectively sever DC1 inputs into the lateral triangle and SMP tract. Optical recordings revealed that microlesioning of DC1 dendrites resulted in the immediate and selective loss of DC1 responses to DA1 stimulation without affecting the excitation of neighbouring LC1 dendrites and cell bodies. Severing the connections between DA1 and DC1 resulted in an almost complete loss of the response of DN1 to stimulation of DA1. The response of this descending neuron was far weaker than the response of early neural participants in this circuit. However, the observation that two-photon-mediated microlesions in DC1 resulted in a decrease of more than 70% in the DN1 response to stimulation of DA1 suggests that, despite its weak excitation, DN1 is a component of this circuit. A more potent response may require a more natural setting that integrates pheromonal input with other sensory signals. Taken together, these experiments suggest that male-specific DC1 neurons excite the male-specific DN1 through synaptic connections within the dimorphic lateral triangle and SMP tract. Thus, olfactory information may be processed by as few as three synapses within the brain before descending to initiate motor programs within the ganglia of the ventral nerve cord. Although a behaviour elicited by this circuit cannot yet be defined, it is presumed that it mediates a component of the innate behavioural repertoire initiated by cVA (Ruta, 2010).

    This cVA-responsive circuit provides insights into the mechanism by which sensory information received by the antenna may be translated into motor output. First, the circuit is concise: as few as four neuronal clusters and three synapses bring pheromonal signals from the periphery to the ganglia of the nerve cord. This minimal circuit assumes monosynaptic connections between the neurons that that were identified. This circuit is shallow but seems to include adequate synaptic connections to permit the integration of olfactory and non-olfactory information. Third-order lateral horn neurons reveal a capacity for multisensory integration with inputs to the DC1 cluster from the SOG and from the optic lobe. The lateral triangle and SMP tract also integrate sensory inputs from DC1 and LC1 as well as inhibitory projections from the SOG. This integration provides the opportunity for other sensory signals emanating from a cVA-scented fly to modulate the response to the pheromone (Ruta, 2010).

    Second, multiple neural components within the circuit are anatomically dimorphic, and this could explain the different behaviours elicited by cVA in males and females. The initial neural components of the circuit, Or67d-expressing sensory neurons and DA1 PNs, are dedicated to the receipt of a singular olfactory stimulus, cVA, and are equally responsive to the pheromone in the two sexes. However, dimorphisms are observed in the synaptic connections between the PNs and the third-order lateral horn neurons and define a node from which sex-specific neural pathways emanate. The DA1 PNs reveal dimorphic axon arborizations, but this dimorphism is only one component of a highly dimorphic circuit. These dimorphic arborizations synapse with male-specific DC1 neurons that send axons to a male-specific neuropil (the lateral triangle and SMP tract). One output of this neuropil is a male-specific descending neuron, DN1. This circuit is likely to participate in the generation of cVA-elicited behaviours observed only in males. The identification of a sex-specific circuit including extensive neuropils present only in males suggests pathways for dimorphic behaviours that differ from earlier proposals that invoke the differential activation of circuits that are common to the two sexes. DA1 PNs also synapse onto the cluster of LC1 neurons that are present in both sexes but are numerically and anatomically dimorphic. The multiple dimorphic targets of a singular olfactory input could explain how a pheromone acting through the same sensory inputs may elicit different behaviours in the two sexes (Ruta, 2010).

    Functional maps of mechanosensory features in the Drosophila brain

    Johnston's organ is the largest mechanosensory organ in Drosophila. It contributes to hearing, touch, vestibular sensing, proprioception, and wind sensing. This study used in vivo 2-photon calcium imaging and unsupervised image segmentation to map the tuning properties of Johnston's organ neurons (JONs) at the site where their axons enter the brain. The same methodology was then applied to study two key brain regions that process signals from JONs: the antennal mechanosensory and motor center (AMMC) and the wedge, which is downstream of the AMMC. First, a diversity of JON response types was identified that tile frequency space and form a rough tonotopic map. Some JON response types are direction selective; others are specialized to encode amplitude modulations over a specific range (dynamic range fractionation). Next, it was discovered that both the AMMC and the wedge contain a tonotopic map, with a significant increase in tonotopy-and a narrowing of frequency tuning-at the level of the wedge. Whereas the AMMC tonotopic map is unilateral, the wedge tonotopic map is bilateral. Finally, a subregion of the AMMC/wedge was identified that responds preferentially to the coherent rotation of the two mechanical organs in the same angular direction, indicative of oriented steady air flow (directional wind). Together, these maps reveal the broad organization of the primary and secondary mechanosensory regions of the brain. They provide a framework for future efforts to identify the specific cell types and mechanisms that underlie the hierarchical re-mapping of mechanosensory information in this system (Patella, 2018).

    This study imaged activity in specific neuropil regions in the Drosophila brain. That focus was placed on the neuropil, rather than neural somata, may require explanation for readers unfamiliar with Drosophila neuroanatomy. Briefly, most of the Drosophila brain volume is exclusively neuropil, i.e., axons and dendrites. Axons and dendrites with similar tuning properties are often co-localized, and so pan-neuronal GCaMP imaging can reveal orderly maps of sensory stimulus features in Drosophila brain neuropil. By contrast, neural somata are excluded from the neuropil and are instead confined to a thin rind around the neuropil core; there is no simple rule that relates the location of a neuron's soma to the location of its axon and dendrites. Thus, similarly tuned cells often have dissimilar soma locations, and a sensory stimulus typically evokes activity in widely dispersed somata on the surface of the brain. The goal of this study was to visualize maps of mechanosensory stimulus features in the brain, and so neuropil imaging was an appropriate choice (Patella, 2018).

    One limitation of neuropil imaging (as compared to somatic imaging) is that signals are being measured from groups of neurons, not single neurons. Cell type diversity may disappear due to optical mixing. Alternatively, diversity might actually be overestimated. Consider a hypothetical scenario with only two cell types, occurring in 10 different ratios in 10 different neuropil subregions; the algorithm would identify 10 response types because of optical mixing. It is reasonably certain that diversity is not being overestimated diversity because of optical mixing, because sparse Gal4 lines (where only scattered cells are labeled) collectively revealed the same sort of diversity seen with a broad Gal4 line. It seems more likely that diversity is being underestimated rather than being overestimated. Calcium imaging also has limited temporal resolution. This is especially relevant to mechanosensation, which is the fastest known sensory modality. Both JONs and AMMC neurons can phase-lock to vibrations as fast as 500 Hz. GCaMP6f signals will reveal only the amplitude modulation envelope of these responses, not their fine structure (Patella, 2018).

    All the stimuli transduced by Johnston's organ can be reduced to a single variable, namely, the rotational angle of the distal antennal segment, and its evolution over time. This single variable fully describes the rich content of the natural stimuli that impinge on Johnston's organ. These include stimuli as diverse as courtship song, wind, self-generated wingbeat patterns, vestibular cues, and tactile stimuli. In much the same way, the rich content of human speech and music is also described by a single variable, i.e., the position of the tympanal membrane (Patella, 2018).

    Any one-dimensional time-varying signal can be described in terms of three fundamental features: frequency, amplitude, and phase. The results show that mechanosensory neurons in this system show specializations for encoding all three of these fundamental features. Collectively, they divide up frequency space, amplitude space, and phase space. Below each of these features is discussed in turn (Patella, 2018).

    Almost all of the neural response types that were identified were tuned to frequency. Moreover, frequency tuning was consistently related to spatial position. In other words, tonotopy was found at every level of this system, from JONs to AMMC to WED (Patella, 2018).

    Tonotopy may be useful because it allows co-tuned neurons to interact with each other using a minimal expenditure of 'wire'. This arrangement should maximize speed and minimize metabolic costs. Tonotopic maps are a prominent feature of vertebrate auditory systems in peripheral cells and CNS neurons. In insects, tonotopic maps have been described in peripheral cells, but there is less evidence for tonotopy in CNS neurons. Coarse tonotopy has been reported in some CNS neurons, but in other cases there is a lack of tonotopy. Indeed, it has been proposed that the insect CNS generally discards the tonotopic organization of the periphery. Surprisingly, this study found that tonotopy is a prominent feature of the AMMC and WED in the Drosophila brain, suggesting there may be more functional similarity than previously suspected in the auditory systems of insects and vertebrates. The prominence of tonotopy in the AMMC and WED (and the narrowness of frequency tuning in the WED) is also surprising for yet another reason: spectral cues are reportedly irrelevant for determining behavioral responses to courtship song in Drosophila. However, courtship behaviors are not the only behaviors that depend on Johnston's organ. Spectral cues may be important for other Drosophila behaviors that are much less well studied, e.g., suppression of locomotion by turbulent wind, flight-steering maneuvers, or defensive reactions to predator sounds (Patella, 2018).

    Different neural channels were found that were specialized to encode stimulus amplitude over different ranges. At one extreme, some channels responded to vibrations as small as 225 nm. This is close to the smallest vibration amplitude that elicits a detectable electrophysiological or behavioral response (Patella, 2018).

    Interestingly, the most sensitive coding channels were already approaching saturation at low-stimulus amplitudes. These channels should be relatively insensitive to amplitude modulations at high amplitudes. Indeed, when the stimulus is the sound of the fly's own flight (which is a loud buzz, from the fly's perspective), high-sensitivity JONs are saturated, and so they cannot follow the sound amplitude modulation envelope. This illustrates the need for low-sensitivity channels as well. Accordingly, this study found low-sensitivity coding channels that did not saturate at high-stimulus amplitudes. Together, these findings illustrate the principle of dynamic range fractionation: different stimulus intensity ranges are allocated to different coding channels. This principle applies to many peripheral sensory cells, including insect auditory receptors and proprioceptors. In other sensory systems, distinct cell types comprise low- and high-threshold subtypes. For example, in the vertebrate somatosensory system, each patch of skin contains low- and high-threshold afferents. In the vertebrate auditory system, each frequency band contains low- and high-threshold auditory fibers. Similarly, in the Drosophila brain, each frequency band is found to contain both high- and low-sensitivity channels. In other words, these neural channels tile both frequency space and amplitude space (Patella, 2018).

    Phase is the third fundamental feature of time-varying signals. Like frequency and amplitude, phase is also represented systematically in JONs and downstream CNS neurons. The push/pull channels are a case in point. When the antenna is pushed and pulled, the push/pull channels respond ~180 degrees out of phase. This is equivalent to saying that these two channels have opposing preferred directions (Patella, 2018).

    Direction sensitivity has obvious utility in wind sensing. Walking flies use wind direction as a guidance cue. Johnston's organ is a wind-sensing organ, and so it is interesting to compare it with the best-studied insect wind-sensing organ, the cricket cercus. The mechanisms of direction sensitivity are quite different in the cricket cercus and the fly antenna. The cercus is covered by tiny hairs. Due to the asymmetric structure of the hair socket, each hair has a preferred direction of movement, and wind from different directions will maximally deflect different hairs. Each hair is innervated by one mechanoreceptor neuron, which only spikes when the hair is pushed in its preferred direction. By contrast, in the wind-sensing system of Drosophila, there is a single mechanical receiver (i.e., the arista, which is rigidly coupled to the distal antennal segment); this stands in contrast to the many mechanical receivers on the cricket cercus (i.e., the many hairs that move independently). Whereas each receiver in the cricket cercus is innervated by a single neuron, the single receiver in Johnston's organ is innervated by many neurons (JONs), with some cells having opposing responses to the same receiver movement. Thus, in the cercus peripheral complexity is mechanical, whereas in Johnston's organ peripheral complexity is neural (Patella, 2018).

    Direction sensitivity is also potentially useful in sound sensing, because the relative phase (direction) of right and left antennal movements can carry information about the sound source location in the azimuth. Thus, direction sensitivity may exist in vibration-preferring JONs. Indeed, direction sensitivity does exist in certain vibration-preferring neurons in the AMMC that project to the WED. However, in this study, it was not possible resolve direction sensitivity in vibration-preferring JONs or AMMC/WED subregions, because GCaMP signals cannot fluctuate rapidly enough to capture any direction sensitivity (phase preferences) in vibration responses (Patella, 2018).

    This study provides the first physiological evidence of bilateral integration downstream of Johnston's organ. Notably, it was found that each strip of the WED tonotopic map receives convergent input from both antennae. Within each strip, ipsi- and contralateral frequency preferences are matched. One potential function of bilateral integration is sound localization. In vertebrates and large insects, lateralized sound produces a detectable difference in the amplitude and/or timing of sound pressure cues at the two auditory organs. However, as body size decreases, sound pressure differences become difficult to resolve. For example, in the fly Ormia ochracea, the two auditory organs are only ~500 μm apart; this species has evolved mechanisms for amplifying left/right differences in sound pressure. Drosophila melanogaster is even smaller than Ormia ochracea, meaning left/right differences in sound pressure are correspondingly smaller as well. Accordingly, Drosophila has evolved an auditory organ that does not sense sound pressure: instead, the distal antennal segment blows back and forth with air particle velocity fluctuations (like a flag), rather than expanding and compressing with air pressure fluctuations (like a balloon). Thus, each antenna has intrinsic direction sensitivity. Because the two aristae are positioned at different angles, they have different preferred directions. Bilateral comparisons would still be needed for true directional hearing, because one organ alone could not tell the difference between a quiet sound coming from a preferred direction and a loud sound coming from a nonpreferred direction. The key point is that each organ is inherently directional, so there is no need for them to be separated by a large distance (Patella, 2018).

    In crickets, bilateral integration for sound localization occurs in cells directly postsynaptic to peripheral auditory afferents. These cells receive antagonistic input from ipsi- and contralateral auditory organs. By contrast, in Drosophila, bilaterality does not seem to emerge in cells directly postsynaptic to JONs (AMMC neurons). Instead, this study found the first evidence for bilaterality in the WED. In addition to finding bilaterality in vibration-preferring subregions, bilaterality was also found in one subregion of the CNS that preferred steady antennal displacements. This subregion spans the border between the AMMC and WED. This subregion is particularly interesting because it has antagonistic directional preferences for ipsi- and contralateral displacements: it responds best when the ipsilateral antenna is pulled while the contralateral antenna is pushed. This pattern of bilateral antagonism confers selectivity for wind directed at the ipsilateral side of the head (Patella, 2018).

    Of course, bilateral integration does not necessarily involve left/right antagonism. Instead, excitatory signals from the two auditory organs may simply be added together. This sort of bilateral pooling could improve the accuracy of behavioral decisions based on the temporal or spectral features of sound stimuli (Patella, 2018).

    Drosophila neurobiologists refer to the little-studied regions of the fly brain as terra incognita. New tools have recently opened these brain regions to functional characterization. Like explorers in an unknown land, Drosophila neurobiologists are now facing the task of mapmaking (Patella, 2018).

    This study illustrates a general approach to mapmaking that makes no assumptions about the scale or shape of functional compartments or the functional properties that distinguish them. This approach yielded fine-grained maps of mechanosensory feature representations. Maps like these will complement new bioinformatic tools that allow researchers to search genetic driver lines using fine-grained anatomical criteria. Together, these tools will enable detailed investigations of specific cell types and the neural computations they implement (Patella, 2018).

    P1 interneurons promote a persistent internal state that enhances inter-male aggression in Drosophila

    How brains are hardwired to produce aggressive behavior, and how aggression circuits are related to those that mediate courtship, is not well understood. This large-scale screen for aggression-promoting neurons in Drosophila identifies several independent hits that enhance both inter-male aggression and courtship. Genetic intersections reveal that P1 interneurons, previously thought to exclusively control male courtship, are responsible for both phenotypes. The aggression phenotype is fly-intrinsic, and requires male-specific chemosensory cues on the opponent. Optogenetic experiments indicate that P1 activation promotes aggression vs. wing extension at low vs. high thresholds, respectively. High frequency photostimulation promotes wing extension and aggression in an inverse manner, during light ON and OFF, respectively. P1 activation enhances aggression by promoting a persistent internal state, which could endure for minutes prior to social contact. Thus P1 neurons promote an internal state that facilitates both aggression and courtship, and can control these social behaviors in a threshold-dependent manner (Hoopfer, 2015).

    This study describes the first large-scale neuronal activation screen for aggression neurons in Drosophila. Using the thermosensitive ion channel dTrpA1, a collection of over 3,000 GAL4 lines was screened for flies that exhibited increased fighting following thermogenetic neuronal activation. Among ~20 hits obtained, three exhibited both increased aggression and male-male courtship behavior. Intersectional refinement of expression patterns using split-GAL4 indicated that both social behaviors are controlled, in all three hits, by a subpopulation of ~8-10 P1 neurons per hemibrain. P1 cells are male-specific, FruM+ interneurons that integrate pheromonal and visual cues to promote male courtship behavior. The results indicate, surprisingly, that at least a subset of P1 neurons, previously thought to control exclusively courtship, can promote male aggression as well. Moreover, it was shown that they exert this influence by inducing a persistent fly-intrinsic state, lasting for minutes, that enhances these behaviors. These data define a sexually dimorphic neural circuit node that may link internal states to the control of mating and fighting, and identify a potentially conserved circuit 'motif' for the control of social behaviors (Hoopfer, 2015).

    A neural command circuit for grooming movement control
    Animals perform many stereotyped movements, but how nervous systems are organized for controlling specific movements remains unclear. This study used anatomical, optogenetic, behavioral, and physiological techniques to identify a circuit in Drosophila melanogaster that can elicit stereotyped leg movements that groom the antennae. Mechanosensory chordotonal neurons detect displacements of the antennae and excite three different classes of functionally connected interneurons, which include two classes of brain interneurons and different parallel descending neurons. This multilayered circuit is organized such that neurons within each layer are sufficient to specifically elicit antennal grooming. However, differences were found in the durations of antennal grooming elicited by neurons in the different layers, suggesting that the circuit is organized to both command antennal grooming and control its duration. As similar features underlie stimulus-induced movements in other animals, there is a possibility of a common circuit organization for movement control that can be dissected in Drosophila (Hampel, 2015).

    Parallel transformation of tactile signals in central circuits of Drosophila

    To distinguish between complex somatosensory stimuli, central circuits must combine signals from multiple peripheral mechanoreceptor types, as well as mechanoreceptors at different sites in the body. This study investigated the first stages of somatosensory integration in Drosophila using in vivo recordings from genetically labeled central neurons in combination with mechanical and optogenetic stimulation of specific mechanoreceptor types. Three classes of central neurons were identified that process touch: one compares touch signals on different parts of the same limb, one compares touch signals on right and left limbs, and the third compares touch and proprioceptive signals. Each class encodes distinct features of somatosensory stimuli. The axon of an individual touch receptor neuron can diverge to synapse onto all three classes, meaning that these computations occur in parallel, not hierarchically. Representing a stimulus as a set of parallel comparisons is a fast and efficient way to deliver somatosensory signals to motor circuits (Tuthill, 2016).

    Uncoupling of brain activity from movement defines arousal states in Drosophila

    An animal's state of arousal is fundamental to all of its behavior. Arousal is generally ascertained by measures of movement complemented by brain activity recordings, which can provide signatures independently of movement activity. The relationships were examined among movement, arousal state, and local field potential (LFP) activity in the Drosophila brain. This study measured the correlation between local field potentials (LFPs) in the brain and overt movements of the fruit fly during different states of arousal, such as spontaneous daytime waking movement, visual arousal, spontaneous night-time movement, and stimulus-induced movement. The correlation strength between brain LFP activity and movement was found to be dependent on behavioral state and, to some extent, on LFP frequency range. Brain activity and movement are uncoupled during the presentation of visual stimuli and also in the course of overnight experiments in the dark. Epochs of low correlation or uncoupling are predictive of increased arousal thresholds even in moving flies and thus define a distinct state of arousal intermediate between sleep and waking in the fruit fly. These experiments indicate that the relationship between brain LFPs and movement in the fruit fly is dynamic and that the degree of coupling between these two measures of activity defines distinct states of arousal (van Swinderen, 2004).

    Sleep in fruit flies shares key characteristics with sleep in all other animals. In Drosophila, sleep is homeostatically regulated and is defined by increased arousal thresholds, and molecular changes correlated with sleep mirror similar changes occurring in mammals. Sleep measures, which include rest/activity data, arousal thresholds, and sleep rebound following deprivation, are most often quantified behaviorally by locomotor activity; an awake fly shows a high probability of walking within a 5 min period. Sleep in Drosophila is accompanied by decreased brain activity (10-100 Hz), as measured by local field potentials (LFPs) in the medial protocerebrum (mpc). In the preparation developed for these studies, sleep is defined by lack of fly movement for periods of more than 5 min and an increased arousal threshold. In this study movement was not monitored in freely moving flies. Instead, flies were tethered by their head and thorax, and movement was monitored by an infrared beam directed across their legs. Alternatively, a wire implanted into their thorax served as a movement detector. By these techniques, gross movements from the fly's wings, legs, and abdomen were monitored continuously to determine sleep states and arousal thresholds, and these were correlated with simultaneous brain activity recordings. A central finding (Nitz, 2002) is that that sleep in Drosophila is correlated with significantly decreased brain LFP activity, and by the same token, that waking is correlated with increased brain LFP activity in the medial protocerebrum (mpc). However, waking LFP activity is not well correlated with fly movement on a short time scale; moment-to-moment correlation between brain activity and movement potentials is insignificant and increases only moderately for longer (5 s) correlation bins. This observation is important because it uncouples the waking levels of brain activity from every movement and suggests that increased brain activity is truly a correlate of waking rather than a correlate of just the movement that accompanies most waking states. Similarly, van Swinderen (2003) showed that 20-30 Hz brain activity in response to visual salience (noticable visual stimulation) can be uncoupled from flight or movement behavior in the fruit fly. Increased arousal, as measured by increased responsiveness to a visual or mechanical stimulus, can therefore be manifested in the fly brain without necessarily being accompanied by gross behavioral changes. Behavior is usually correlated with states of arousal, especially over circadian time scales, but changes in arousal, as evidenced by neural signatures in the brain, can occur without changes in behavior. In the current study, the dynamic relationship between brain activity, movement, and arousal has been explore more closely in Drosophila. This study seeks to define how arousal is manifested over short time scales by examining the ongoing correlation between two parameters central to describing arousal: brain activity and movement (van Swinderen, 2004).

    Spontaneous fly movement was monitored in 5 s bins with an electrode implanted into the thorax and brain activity was simultaneously recorded from electrodes inserted 75-100 μm into the medial protocerebrum with a reference electrode in the eye. Dye released from the mpc electrode tip showed that this brain-recording position in adult CS females is level with the base of the mushroom bodies, above the esophagus and in the vicinity of the central complex. The simultaneous recordings of spontaneous movement from the thoracic electrode and brain activity from the mpc revealed a correlation profile for this recording position. The correlation level is not equal for all frequencies (1-100 Hz) of brain activity. The higher frequencies (60-100 Hz) are more strongly correlated to movement than the lower frequencies in the 10-50 Hz range. The very lowest frequency range examined, 1-10 Hz, also shows a stronger correlation to movement than the 10-50 Hz bracket does. Among the lower frequencies, the 20-30 Hz bracket shows the lowest correlation to movement during spontaneous, daytime waking activity at this recording position (van Swinderen, 2004).

    In a study of Drosophila LFP responses to visual stimuli, it has been shown that 20-30 Hz brain activity is associated with salience effects evoked by novelty, conditioning, and selective discrimination of visual stimuli (van Swinderen, 2003). In that study, the 20-30 Hz effects were found to be independent of spontaneous, gross movement. Such movement-independent changes in 20-30 Hz activity may account for some of the 10-50 Hz trough in the profile correlating brain activity and movement. Therefore two distinct frequency ranges emerge from these results: the lower range centered around 20-30 Hz (this is less correlated with unstimulated, waking movement but is associated with salience-related arousal), and the higher frequencies, which are more strongly correlated with unstimulated, waking movement but less associated with salience effects (van Swinderen, 2004).

    Both visual and movement paradigms were combined in order to test the effect of visually induced arousal (van Swinderen, 2003) on the correlation profile coupling brain activity and ongoing movement. Introducing a visual stimulus (a rotating dark bar layered onto an unchanged lit background) to the fly uncouples brain activity from movement. That this uncoupling was most significant in the higher frequency range is surprising because these higher frequencies are not coupled to the visual response either. Neither low (10-50 Hz) nor high (60-100 Hz) frequencies by themselves change significantly in average power for the duration of the experiment in comparison to the imageless control, and average movement was unchanged as well. Rather, it appears that visual salience (which has a characteristic 20-30 Hz response (van Swinderen, 2003) uncouples most brain LFP activity (at this medial recording position) from ongoing movement activity. The correlation level between movement and brain LFP activity appears to depend on the fly's arousal state as well as on the frequency bracket examined. In the following experiments the relationship between the correlation phenotype and arousal was examined more closely by focusing on movement activity and two LFP frequency ranges: 20-30 Hz because of its association to salience effects and 80-90 Hz as a contrasting range that is more correlated to movement (van Swinderen, 2004).

    These correlation studies were all performed for short time periods (200 s, or 40 five-second bins) during the day. In the absence of salient, rotating, visual stimuli, the correlation between brain LFP activity and movement was consistent for these daytime experiments. Whether such consistency persists throughout much longer recording sessions (12 hr) extending through the animal's night time, when characteristic changes in arousal state are endogenously generated, was examined. Epochs without movement more than 5 min long are associated with decreased power for all frequencies in the brain. However, such immobile epochs do not constitute the majority of a tethered fly's night time behavior, in contrast to the behavior of a freely walking fly. In fact, tethered flies move most of the time, even at night, and will be rendered immobile by sleep for only about 20% of the night, not necessarily contiguously. Whether the correlation profile (between movement and brain LFP activity) changed during spontaneous night time movement was investigated (van Swinderen, 2004).

    The correlation was analyzed for six flies kept in sealed and humidified chambers during overnight (12 hr) experiments performed in complete darkness. All animals were still alive and moving in the morning after lights were turned back on. Average movement activity during the first daylight hour after the experiments was not significantly different from pre-experiment levels. For each hour of the night, the correlation coefficient between brain activity (20-30 Hz, 80-90 Hz) and movement was calculated from averages of 5 s activity data. In all six flies, the correlation between brain activity and movement decreased during several consecutive hours of the night compared to the first two hours of the night. Both frequency ranges showed a proportionally similar decrease in correlation to movement. These decreases did not necessarily occur during the same contiguous hours in all animals. This 'correlation trough' was maximal in hours 6-7 after dark for four flies and hours 3-4 for two flies. Average correlation levels increased later in the night) to pre-trough levels, before the lights were turned back on (van Swinderen, 2004).

    Average hourly movement and average LFP amplitude at both 20-30 and 80-90 Hz also decrease during the night, but the decrease of either is not significant during the respective 'correlation trough' hours compared to the first two hours of the experiments. Thus, the loss of correlation between brain activity and movement cannot be accounted for by any significant changes in the hourly averages of either brain activity or movement. The epoch in which both average hourly movement and LFP power do attenuate significantly, during the last hours of the night, exhibits a correlation between the two statistics that is as high as pre-trough levels (van Swinderen, 2004).

    Closer inspection of hourly averages through the night reveals that average movement can increase while average brain activity does not. Indeed, combined data from all six flies show that the variance in 20-30 Hz activity decreases significantly during the respective trough hours compared to the first hours of the night. Variance in 80-90 Hz brain activity was also significantly decreased during the 'correlation trough' hour. In comparison, the variance of movement activity was not significantly decreased during the trough hours compared to hours 1-2. Such unmatched changes in variance may partially explain why a decreased correlation with brain activity is seen during consecutive hours of the night (van Swinderen, 2004).

    Because the records also show evidence of sleep, the relationship between the LFP/movement correlation dynamics and epochs of extended immobility embedded throughout the overnight records was investigated. Long bouts of quiescence (>5 min) were immediately preceded by significantly lower levels of correlation to movement (for both 20-30 Hz and 80-90 Hz), compared to the correlation levels seen immediately after the resumption of movement. Brain activity and movement are thus more uncoupled immediately before quiescence as well as during contiguous hours of the night (van Swinderen, 2004).

    Sleep is typically associated with immobility, but determining sleep in an animal is also contingent on testing arousal thresholds by measuring behavioral responsiveness to a stimulus. Because the average correlation between movement and brain activity is dynamic during the night and because sleep is preceded by uncoupling, it was questioned whether the loss of correlation between brain and body was similarly associated with altered behavioral responsiveness to a test stimulus, as was shown for sleep (van Swinderen, 2004).

    Six additional flies were prepared for testing arousal thresholds throughout the night. Responsiveness to arousing stimuli (measured by increased movement following mechanical taps or light flashes) was analyzed in terms of the preceding level of correlation between movement and brain activity. Using an automated online paradigm, the thoracic channel was periodically scanned for brief epochs (5 s) of immobility before delivering a stimulus, so that all subsequent behavioral responses were compared with this baseline immobility. Responsive animals displayed, on average, a greater correlation between movement and 20-30 Hz brain activity during the 3 min preceding the test stimulus. The level of correlation between 80-90 Hz brain activity and movement was also predictive of responsiveness to the test stimulus. By the same token, unresponsive flies displayed a significantly decreased correlation between brain activity at both frequencies and ongoing movement in the 3 min preceding the test stimulus (van Swinderen, 2004).

    As in the overnight 'correlation troughs', correlation level in these arousal experiments was not dependent on the amount of movement displayed by the animal. Different levels of movement showed similar levels of correlation to brain activity when considered irrespective of responsiveness to stimuli. Unresponsiveness to a test stimulus is therefore predicted by a decreased correlation between brain LFPs and movement as well as by prolonged immobility in these tethered preparations. These predictors of arousal level are distinct but complementary. As in Nitz (2002), unresponsiveness was also associated with less average movement in the minutes preceding a test stimulus because these cases included instances of extended (> 5 min) immobility. The predictive value for arousal level of the correlation between movement and LFPs was significant, however, even when these few cases were removed from the analysis (van Swinderen, 2004).

    In summary, the correspondence between movement and brain LFP activity in the mpc decreases significantly during overnight recordings, and such uncoupling is characterized by increased arousal thresholds. These results demonstrate that ongoing changes in arousal levels in the fly do not necessarily parallel spontaneous movement activity. Rather, changes in arousal are marked by changes in the coupling dynamics between brain activity and movement (van Swinderen, 2004).

    These studies demonstrate, by simultaneously monitoring brain LFPs and gross movement in a tethered Drosophila preparation, that brain activity can be uncoupled from the body activity typically used as the measure of arousal state in nonhuman animals. It was thus possible to examine arousal in terms of the correlation between two relevant yet distinct measures of fly activity. The results suggest that ongoing changes in arousal in the fly can be effectively studied as a function of the degree of coupling between brain LFP activity and movement (van Swinderen, 2004).

    In humans and other mammals, most states of heightened arousal (waking, attention), as well as 'paradoxical' (REM) sleep, are accompanied by increased high-frequency (40-80 Hz) field activity in the brain, whereas deep sleep is associated with slow activity. Such brain signatures can be uncorrelated to bodily movement, as in paradoxical sleep and in sleep disorders such as narcolepsy/catalepsy or somnambulism, although most sleep is indeed accompanied by quiescence. In fruit flies, extended immobility also correlates with sleep and associated changes in brain activity. Yet, changes in fly brain LFP activity are not always associated with movement on shorter time scales. Because behavioral changes, evidenced by movement of some kind, are the primary way of ascertaining arousal states in nonhuman animals, the relationship between arousal and movement can be difficult to disentangle. Brain activity may be closer to reflecting ongoing changes in arousal in the fruit fly, and the uncoupling between brain activity and movement appears to be a useful indicator of a change in arousal state (van Swinderen, 2004).

    20-30 Hz brain activity plays a crucial role in visually directed arousal (van Swinderen, 2003). In the current study, 20-30 Hz brain activity is less coupled to spontaneous, waking movement than are other frequencies, including the 80-90 Hz range contrasted throughout this study. The higher frequencies (of which 80-90 Hz is just representative) may represent a variety of stimuli coming from the body, whereas the 20-30 Hz signature might represent the fly's version of a 'spotlight.' In support of this idea, the level of correlation between movement and 20-30 Hz brain activity increases up to 80-90 Hz correlation levels during the initial hours of the overnight experiments, immediately after epochs of extended quiescence, and also throughout overnight arousal-testing experiments. This contrasts with the lower correlation (approximately 0.2) found during the day for the 20-30 Hz range in spontaneously moving flies. 20-30 Hz activity can be selectively correlated with visual stimuli (van Swinderen, 2003). For an awake fly in complete darkness at night, when visual (as well as auditory and vibrational) stimuli are lacking, this signal may associate with a different set of stimuli, such as those engendered by the fly's own movement (van Swinderen, 2004).

    The most surprising outcome of overnight studies is the finding that fruit flies display a distinct behavioral state intermediate between sleep and waking; this state is defined by heightened arousal thresholds and is characterized by the loss of correlation between ongoing movement and LFP activity. In unperturbed flies during the course of overnight experiments, such loss of correlation is consolidated during several contiguous hours of the night. Additionally, periods of low correlation between brain activity and movement immediately precede epochs of extended quiescence. During sleep, already well characterized in this organism, animals become immobile, and all brain frequencies attenuate to equal extents (Nitz, 2002). The uncoupled state in moving animals may enable subsequent sleep or may itself accomplish certain key sleep functions. Beyond increased arousal thresholds, both behavioral states are also similar in the uniformity of their effects on the different frequency ranges. In the low-arousal, moving state, correlation to movement is decreased proportionally for both 20-30 Hz and 80-90 Hz frequency ranges, and during sleep, both frequency ranges decrease proportionally in terms of overall power. In contrast, in awake flies, frequencies between 1 and 100 Hz are partitioned by salience effects in the 20-30 Hz range (van Swinderen, 2003) and movement effects in the higher frequencies (e.g., 80-90 Hz). When flies are in either of the two states characterized by increased arousal threshold, there is a corresponding decrease in the variance or amount of information in the entire LFP signal (van Swinderen, 2004).

    Experiments with visual stimuli show that a form of arousal directed to salient images (van Swinderen, 2003) also uncouples brain activity from movement, even at the higher frequencies not associated with visual salience. This brings up the possibility that during the uncoupled state at night flies may still be partially aroused (as suggested, after all, by their ongoing movement), despite their higher arousal thresholds. This paradox may be partially understood if one considers some common features between sleep and selective attention, both arousal states with behavioral and neural correlates in the fruit fly. Although humans perceive sleep and attention as clearly different states of arousal, both are defined to a certain extent by uncoupling. During sleep, most external stimuli are rendered less accessible, thereby uncoupling the brain from those sensory modalities. During selective attention, an animal may be seen as having partitioned its arousal between a high level directed at the salient stimulus and a low level directed at everything else. The current demonstration of uncoupling between brain activity and movement is consistent with Drosophila's ability to suppress brain responses to simultaneous unattended stimuli (van Swinderen, 2003). Similarly, responses to visual stimuli persist in the optic lobes during sleep, whereas the 20-30 Hz response in the medial brain is attenuated (van Swinderen, 2003). These ideas are extended, in this study, to propose that such uncoupling is a common feature of different arousal states and that fly brain activity might be uncorrelated from movement at night by a similar mechanism as that which suppresses visual stimuli. Altogether, these findings suggest that arousal states in the fly are a function of the degree of coupling within the nervous system and that changes in arousal can be defined more accurately by such criteria in the fly when considered in conjunction with the standard behavioral measures of responsiveness (van Swinderen, 2004).

    Arousal in Drosophila, like consciousness in humans, is unlikely to be localized to a unique set of cells in the brain. Rather, arousal probably recruits dynamic networks extending throughout the brain, a phenomenon that may be accessible to a combined genetic and electrophysiological approach in Drosophila (van Swinderen, 2004).

    Sleep in Drosophila is regulated by adult mushroom bodies

    Sleep is one of the few major whole-organ phenomena for which no function and no underlying mechanism have been conclusively demonstrated. Sleep could result from global changes in the brain during wakefulness or it could be regulated by specific loci that recruit the rest of the brain into the electrical and metabolic states characteristic of sleep. This study addresses this issue by exploiting the genetic tractability Drosophila, which exhibits the hallmarks of vertebrate sleep. Large changes in sleep are achieved by spatial and temporal enhancement of cyclic-AMP-dependent protein kinase (PKA) activity specifically in the adult mushroom bodies of Drosophila. Other manipulations of the mushroom bodies, such as electrical silencing, increasing excitation or ablation, also alter sleep. These results link sleep regulation to an anatomical locus known to be involved in learning and memory (Joiner, 2006).

    To determine whether specific brain loci regulate sleep, the GAL4/UAS (upstream activating sequence) system was used to express a catalytic subunit of PKA in various regions of the fly brain. PKA was first expressed under the control of the RU486-inducible pan-neuronal driver elavGeneSwitch. Restricting the expression of PKA to adult neurons decreased daily sleep, supporting earlier studies with mutants such as dunce that increase PKA levels, and showing that PKA directly regulates sleep rather than a developmental process that might affect sleep. PKA was expressed under the control of different GAL4 drivers, and the changes in total daily sleep were examined in the different driver/transgene combinations relative to driver/background and background/transgene controls. When both controls were taken into account, the expression of PKA by only two drivers led to changes in sleep that exceeded 2 s.d. These were 201Y, which increased sleep by 75 +/- 3% and 93 +/- 4% respectively, and c309, which decreased sleep by 46 +/- 11% and 43 +/- 14% per day compared with the two sets of controls. Changes in sleep caused by all other GAL4 drivers remained within 1 s.d. of the mean (Joiner, 2006).

    Next, whether activity levels during wake periods were affected by the 201Y and c309 drivers was examined. Many GAL4 driver/UAS-PKA lines were hypoactive, but line 201Y had normal waking activity. Similarly, activity normalized to waking time in c309 was not significantly higher in PKA-driven animals than in either control, indicating that c309 was not hyperactive. It is concluded that the sleep phenotypes of animals expressing PKA under control of the 201Y and c309 drivers are not associated with abnormal waking activity. Interestingly, both these drivers are known to be expressed in the mushroom bodies (MBs), a brain region implicated in associative learning (Joiner, 2006).

    Given the strong, yet opposite, effects that 201Y and c309 had on sleep, their expression patterns in the fly brain were further characterized by crossing them into animals bearing a UAS transgene for green fluorescent protein (GFP). It was found that 201Y is expressed largely in the γ lobes and the core region of the α/β lobes of the MBs, whereas c309 is expressed in the α/β and γ lobes but not in the core region of the α/β; lobes. This differential expression pattern within the MBs indicates that PKA might affect the regulation of sleep by the MBs in both a positive and a negative fashion by using anatomically distinct classes of neurons. Consistent with this notion of heterogeneous cell types within the MBs, some MB drivers, such as 30Y and 238Y, promoted sleep during the day but inhibited sleep during the night, leading to only marginal overall changes in daily sleep. This effect was not observed with any driver that was expressed exclusively outside the MBs. A small increase in daytime sleep was also frequently produced by the pan-neuronal elavGeneSwitch driver, which decreased overall sleep. The expression patterns of 238Y and 30Y overlap those of 201Y and c309, supporting the idea that 238Y and 30Y are expressed in both sleep-promoting and sleep-inhibiting areas (Joiner, 2006).

    To test the hypothesis that PKA expression in MBs regulates adult sleep, the PKA transgene was expressed under the control of an RU486-activatable MB GAL4 driver, P{MB-Switch}. It was confirmed selective expression of this driver in the MBs by coupling it to a GFP reporter, and inducible expression was found in the MBs. Sleep was significantly reduced in response to RU486 in MB-Switch/PKA animals but was unaffected by the drug in control animals harbouring either the driver or the transgene alone. Thus, PKA overexpressed preferentially in specific neurons of adult MBs is sufficient to reduce sleep (Joiner, 2006).

    Next sleep structure in the hyposomnolent animals was compared with that of controls. In both MB-Switch/PKA animals and c309/PKA animals, loss of sleep was caused by a shortened sleep bout duration without a concomitant increase in the sleep bout number. The underlying cause of reduced sleep in both sets of animals therefore seems to be impaired sleep need, because the alternative-normal sleep need, but an inability to maintain the sleep state-would be expected to produce an increase in sleep bout number. In contrast, in 201Y sleep bout duration remained unchanged (Joiner, 2006).

    It was then asked whether the reduction of sleep in MB-Switch/PKA animals was due to an impaired accrual of a sleep-inducing signal. If this were so, then a hallmark of sleep, homeostatic rebound-sleep that exceeds baseline to compensate for lost sleep-should not occur on relief of induced PKA expression. However, when RU486 was withdrawn after about three days of sleep deprivation, an average rebound of 156 +/- 38 min was observed. This is a robust rebound, comparable to that produced when genetically identical but uninduced flies were submitted to a standard 12 h of mechanical deprivation (137 +/- 26 min). Behavioural rebound was also observed in animals expressing elavGeneSwitch-driven PKA, after withdrawal of RU486, and was accompanied by a decrease in PKA activity in fly heads. Rebound after withdrawal of RU486 indicates that PKA might not prevent the accrual of sleep-promoting signals but might suppress homeostatic output (Joiner, 2006).

    To determine whether PKA affects sleep by regulating synaptic output in MB neurons, either of two K+ channels, Kir2.1 or EKO, were inducibly expressed under the control of the MB-Switch driver. Such transgenic expression should suppress action-potential firing by hyperpolarizing neurons and decreasing membrane resistance, thus leading to reduced synaptic transmission. It was found that induction of either Kir2.1 or EKO caused a significant increase in sleep. Because the opposite was observed with PKA expression in the same neurons, it indicates that PKA might decrease sleep by increasing either excitability or synaptic transmission. To address this issue further, a sodium channel (NaChBac), which depolarizes neurons and increases excitability, was inducibly expressed. When expressed under the control of the MB-Switch driver, the sodium channel caused a decrease in sleep, similar to that produced by PKA, confirming that PKA increases the output of these neurons (Joiner, 2006).

    The MB-Switch driver is expressed in a subpopulation of MB neurons similar to those labelled by c309, and both drivers had sleep-inhibiting effects. As noted above, this pattern of expression differed from that of other drivers, which had sleep-promoting or bidirectional effects on sleep, thus leading to a proposal that the MBs contain sleep-inhibiting and sleep-promoting neurons. To determine the overall effect of MBs on sleep, they were ablated with hydroxyurea, and sleep and activity were examined in adult flies. An overall increase in activity was observed. However, normalization of this activity to waking time indicates that the phenotype derives less from hyperactivity than from a reduction in sleep. Even so, the reduction in sleep was much less than that seen with other manipulations of the MBs or in short-sleep mutants such as minisleep. This supports the conclusion that MBs exert both positive and negative influences on sleep that are integrated to produce the overt behavioural state. A model takes into account these results; notably the integrator downstream of the MBs promotes activity in the default state. Thus, when MBs are ablated the overall effect is increased wakefulness (Joiner, 2006).

    Opposing effects of the c309 and 201Y drivers are also observed in a different behavioural model. They parallel MB-dependent changes in brain activity during the sleep/wake cycle that are associated with salience, a behavioural trait that may correspond to arousal. Consistent with the data was the observation that reducing synaptic transmission using the c309 driver inhibited salience, whereas the 201Y driver in the same type of experiment yielded no change. Increased arousal wouldbe predicted with 201Y, but in those experiments the animals were already awake (Joiner, 2006).

    Because MBs receive and transduce considerable sensory, particularly olfactory, input to the fly brain, it is speculate that they promote arousal or sleep by allowing or inhibiting the throughput of sensory information. In addition, given the major function that MBs have in regulating plasticity in the fly brain, it is likely that this is linked to their role in sleep. In mammals, sleep deprivation suppresses the performance of learned tasks, and sleep permits memory consolidation. Sleep and sleep deprivation also differentially affect cortical synaptic plasticity. In Drosophila, MBs participate in the consolidation or retrieval of memories involving olfactory cues, courtship conditioning and context-dependent visual cues by mechanisms that include cAMP signalling. Distinct anatomical regions of the MBs have been shown to be important for at least some forms of memory, as has now also been shown for sleep. Thus, memory and sleep may involve similar molecular pathways (cAMP signalling) and anatomical regulatory loci (MBs) (Joiner, 2006).

    A multilevel multimodal circuit enhances action selection in Drosophila

    Natural events present multiple types of sensory cues, each detected by a specialized sensory modality. Combining information from several modalities is essential for the selection of appropriate actions. Key to understanding multimodal computations is determining the structural patterns of multimodal convergence and how these patterns contribute to behaviour. Modalities could converge early, late or at multiple levels in the sensory processing hierarchy. This study shows that combining mechanosensory and nociceptive cues synergistically enhances the selection of the fastest mode of escape locomotion in Drosophila larvae. In an electron microscopy volume that spans the entire insect nervous system, the multisensory circuit was reconstructed supporting the synergy and spanning multiple levels of the sensory processing hierarchy. The wiring diagram revealed a complex multilevel multimodal convergence architecture. Using behavioural and physiological studies, functionally connected circuit nodes were identified that trigger the fastest locomotor mode, and others were identified that facilitate it. Evidence is provided evidence that multiple levels of multimodal integration contribute to escape mode selection. It is proposed that the multilevel multimodal convergence architecture may be a general feature of multisensory circuits enabling complex input-output functions and selective tuning to ecologically relevant combinations of cues (Ohyama, 2015).

    Different combinations of nociceptive and mechanosensory stimulation induced different likelihoods of the key escape sequences: rolling followed by fast crawling versus fast crawling alone. Nociceptor activation alone evoked a relatively low likelihood of rolling and a high likelihood of fast crawling. Vibration alone evoked only fast crawling and essentially no rolling. Combined with nociceptor activation, vibration increased the likelihood of rolling; the effect is dose-dependent and super-additive (synergistic). This vibration-induced facilitation of rolling is mediated through the mechanosensory chordotonal neurons (Ohyama, 2015).

    It is suspected that the information from the two modalities converges onto central neurons involved in the selection of rolling. To identify such neurons and thus determine where in the sensory processing hierarchy multisensory convergence occurs, a was performed behavioural screen for neurons whose thermogenetic activation triggers rolling. A 'hit' was identified in the R72F11 Drosophila line, that drove GAL4 expression in neurons potentially early in the sensory processing hierarchy. Activating the neurons in R72F11 triggered rolling in a significant fraction of animals, and inhibiting them significantly decreased rolling in response to bimodal stimulation (Ohyama, 2015).

    R72F11 drives expression selectively in four lineage-related, segmentally repeated projection neurons with basin-shaped arbors in the ventral, sensory domain of the nerve cord; therefore they were named Basins-1-4. The dendrites of Basin-1 and Basin-3 span a ventrolateral domain of the nerve cord, where the mechanosensory chordotonal terminals are located. The dendrites of Basin-2 and Basin-4 span both the ventrolateral chordotonal domain and a ventromedial domain where the nociceptive MD IV terminals are located. It was therefore asked whether the mechanosensory chordotonal and the nociceptive MD IV neurons directly converge on Basin-2 and Basin-4 (Ohyama, 2015).

    In an electron microscopy volume that spans 1.5 nerve cord segments, the chordotonal and MD IV arbors were scanned. The left and right Basin-1, -2, -3 and -4 among the reconstructed neurons (Ohyama, 2015).

    Basin-1 and Basin-3 received many inputs (each >25 synapses and >15% of total input, on average) from chordotonal neurons, but very few (no more than 1% of total input synapses) from MD IV neurons. Basin-2 and Basin-4 received many inputs from both chordotonal neurons (on average >20 synapses and >10% total input) and MD IV neurons (on average >20 synapses and >10% total input), each on distinct dendritic branchesl Of all the 301 partners downstream of MD IV and chordotonal neurons, only Basin-2 and Basin-4 reproducibly received >5 synapses from both chordotonal and MD IV neurons, suggesting that they are probably key integrators of chordotonal and MD IV inputs (Ohyama, 2015).

    To investigate whether the observed anatomical inputs from the sensory neurons onto Basins were functional and excitatory, calcium transients were imaged in response to MD IV or chordotonal activation collectively in all Basins or in individual Basin types, using lines that drive expression selectively in Basin-1 or Basin-4 (Ohyama, 2015).

    In Basin-1, calcium transients were observed in response to vibration, but not in response to MD IV activation, consistent with the large number of synapses it receives from chordotonal neurons and the relatively few from MD IV neurons. In Basin-4, calcium transients were observed in response to both vibration and MD IV activation, consistent with the large number of synapses it receives from both sensory types. Basin-4 integrated the inputs from the two modalities, responding significantly more to bimodal than to unimodal (Ohyama, 2015).

    Next, it was asked whether the multisensory Basin-4 interneurons contribute to rolling selection. Silencing Basin-4 neurons significantly decreased rolling in response to bimodal stimulation, indicating these neurons are involved in triggering rolling. Selective activation of the multisensory Basin-4 interneurons triggered rolling in a dose-dependent way, with strongest activation triggering rolling in 45% of animals (Ohyama, 2015).

    It was also asked whether a second level of multimodal integration (that is, integration of information from distinct Basin types, that receive distinct combinations of chordotonal and MD IV inputs), enhances the selection of rolling. Indeed, co-activation of Basin-1 with the bimodal Basin-4 facilitated rolling, resulting in a significantly higher likelihood of rolling compared to activation of Basin-4 alone (70% versus 45%). Thus, information from distinct Basin types may converge again onto downstream neurons involved in triggering rolling (Ohyama, 2015).

    To identify potential sites of convergence of information from the different types of first-order Basin interneurons a 'hit' was examined from the thermogenetic activation screen, R69F06, a Drosophila line that drove GAL4 expression in neurons that project far from the early sensory processing centres. Thermogenetic activation of neurons in R69F06 triggered rolling in a high fraction of larvae, and inhibiting them significantly decreased rolling in response to bimodal stimulation (Ohyama, 2015).

    R69F06 drives expression in a few neurons in the brain, in the sub-oesophageal zone (SEZ) and in a pair of thoracic neurons whose axons descend through the dorsal, motor domain of the nerve cord. Selectively activating the single pair of thoracic neurons triggered rolling in 76% of larvae. These command-like neurons were named Goro (a romanization of the Japanese for rolling) (Ohyama, 2015).

    Activation of Basins evoked strong calcium transients in the Goro neurons, indicating that these cell types involved in the same behaviour are functionally connected. To identify the shortest anatomical pathways from Basins to Goro that might support the observed functional connectivity and to determine whether the information from distinct Basin types converges onto Goro, electron microscopy reconstruction was again used (Ohyama, 2015).

    A second electron microscopy volume (from a second larva) was used that spans the entire larval nervous system and therefore also includes Goro neurons. In the new volume, chordotonal, MD IV and Basin neurons were reconstructed from segment A1, as well as the Goro neurons (Ohyama, 2015).

    To find putative pathways from distinct Basin types to Goro neurons, all neurons downstream of all axonal outputs were reconstructed from the four left and right Basin homologues from segment A1. Thirty-one pairs of reproducible downstream partners were identified. Among these second-order nerve cord interneurons were identified that constitute the shortest pathways from Basins to Goro neurons (called A05q and A23g where 'A' stands for abdominal neuron). They receive inputs from distinct Basin types and synapse onto Goro neurons. Thus, information from distinct Basin types, that receive distinct combinations of MD IV and chordotonal inputs, converges onto Goro neurons -providing a second level of multimodal convergence (Ohyama, 2015).

    Ten distinct second-order projection neuron types downstream of Basins ascend to the brain. Some of these integrate Basin information across multiple distal segments of the body, either exclusively from a single Basin type, or from distinct Basin types (that receive distinct combinations of sensory inputs; for example, A00c-a4 and A00c-a5). Then, distinct second-order PNs, that receive distinct combinations of Basin inputs (and therefore distinct combinations of mechanosensory and nociceptive inputs), re-converge again on third-order interneurons in the brain. Thus, following convergence of local mechanosensory and nociceptive information from a single segment onto multisensory Basins, global mechanosensory and multisensory information from multiple segments is integrated within the brain pathway (Ohyama, 2015).

    By tracing upstream of Goro dendritic inputs, brain neurons were identified that send descending axons that synapse onto Goro neurons. Tracing downstream of a multisensory second-order ascending projection neuron (A00c-a4), third-order projection neurons were identified connecting the ascending pathways from Basins to a descending path onto Goro neurons. Thus, the activity of the command-like Goro neurons may be modulated by the more local multisensory and unisensory information via the nerve cord Basin-Goro pathway and by the global body-wide nociceptive and mechanosensory multisensory information via the brain Basin-Goro (Ohyama, 2015).

    A third-order SEZ feedback neuron was identified that receives convergent body-wide mechanosensory and multisensory information and descends through the nerve cord sensory domain. The SEZ feedback neuron synapses onto the first-order (Basins) and second-order neurons from both the nerve cord (A05q) and brain (A00c) Basin-Goro pathways. Both the nerve cord and brain Basin-Goro pathways may therefore be jointly regulated based on integrated global multisensory information (Ohyama, 2015).

    Next, the functional role of the nerve cord and brain Basin-Goro pathways was explored. Basin activation could activate Goro neurons in the absence of the brain, suggesting the nerve cord Basin-Goro pathway is excitatory and sufficient for activating Goro neurons and triggering rolling. Consistent with this idea, Basin activation evoked calcium transients in their nerve-cord targets, the A05q neurons, and A05q activation evoked calcium transients in Goro neurons. Furthermore, thermogenetic activation of the neurons in a line that drives expression, among others, in the A05q neurons triggered (Ohyama, 2015).

    Calcium imaging in the terminals of three Basin-target neurons that ascend to the brain (A00c-a6, A00c-a5 and A00c-a4)) revealed that, collectively, they respond to vibration, to MD IV activation, and to Basin activation, suggesting that this connection is also excitatory (Ohyama, 2015).

    Silencing the A00c neurons decreased rolling in response to bimodal stimulation, and their co-activation with Basin-4 facilitated rolling. Therefore, downstream from early local multisensory integration by Basin-4, additional levels of integration of global mechanosensory and multisensory information appear to further facilitate the transition to rolling behaviour (Ohyama, 2015).

    The rolling response triggered by multisensory cues (or by strong nociceptive cues alone) is followed by fast crawling. Similarly, optogenetic activation of the first-order multisensory Basin-4 neurons triggered both locomotor modes; rolling followed by fast crawling. However, optogenetic activation of the Goro neurons triggered only rolling, but not fast crawling, suggesting that they act as dedicated command-like neurons for rolling. This also suggests that the act of rolling itself is insufficient to trigger fast crawling. In the future it will be interesting to determine how all of the 31 novel neuron types directly downstream of the Basins identified in the electron microscopy reconstruction contribute to the selection of the two locomotor modes, rolling and crawling, in a defined sequence (Ohyama, 2015).

    By combining behavioural and physiological studies with large-scale electron microscopy reconstruction this study has mapped a multisensory circuit that mediates the selection of the fastest mode of escape locomotion (rolling) in Drosophila larva. Mechanosensory and nociceptive sensory neurons were found to converge on specific types of first-order multisensory interneurons that integrate their inputs. Then, interneurons that receive distinct combinations of mechanosensory and nociceptive inputs converge again at multiple levels downstream, all the way to command-like neurons in the nerve cord. Activating just a single type of first-order multisensory interneuron triggers rolling probabilistically. Co-activation of first-order interneurons that receive distinct combinations of mechanosensory and nociceptive inputs increases rolling probability. Thus, action selection starts at the first-order multisensory interneurons and multiple stages of multimodal integration in the distributed network enhance this selection (Ohyama, 2015).

    Given that spurious firing from distinct sensors is uncorrelated, whereas event-derived signals will be temporally correlated across the sensory channels, multimodal integration even at a single level improves the signal-to-noise ratio. Multilevel multimodal integration can offer additional advantages. Theoretical studies show that a multilevel convergence architecture enables more complex input-output relationships. Similarly, the multilevel multimodal convergence architecture described in this study could offer better discrimination between different kinds of multisensory events. The weights in such networks could be tuned either through experience or through evolution to respond selectively to highly specific combinations of two cues. Using a simple model, it can be demonstrated that compared to early-convergence a multilevel architecture could specifically enhance the selection of the fastest escape mode in the most threatening situations, either in response to weak multimodal or strong unimodal nociceptive cues (Ohyama, 2015).

    The multilevel multimodal convergence architecture may be a general feature in multisensory integration circuits, enabling complex response profiles tunable to specific ecological needs. For example, physiological studies in mammals have identified multisensory neurons that integrate the same cues at several stages in the sensory processing hierarchy, although it is unclear whether the multisensory neurons at distinct levels are causally related to the same behaviour. Due to the size of networks involved, synaptic-level resolution studies of the underlying convergence architecture across multiple levels were unattainable (Ohyama, 2015).

    In addition to the multilevel multimodal feed-forward convergence motif, electron microscopy reconstruction revealed higher-order and local feedback neurons. Recent theoretical models of multisensory integration suggest that the output of individual multisensory neurons is normalized by the activity of other multisensory neurons in that population, but the anatomical implementation of such feedback has not been identified. Some of the feedback neurons in the multisensory circuit described in this study may have roles in such normalization computations (Ohyama, 2015).

    Another circuit motif revealed by the study is the divergence of sensory information into nerve cord and ascending brain pathways and subsequent re-convergence of the shorter and the longer pathway onto the same command-like neurons in motor nerve cord (Goro). The nerve cord pathway integrates nociceptive and mechanosensory information from a local region of the body (few segments), whereas the ascending brain pathway integrates the information across all body segments and provides a means of modulating command-like neuron activity based on global body-wide nociceptive and mechanosensory information. The multisensory circuit described in this study in a genetically tractable model system provides a resource for investigating in detail how multiple brain and nerve cord pathways interact with each other and contribute to the selection of different modes of locomotion (rolling and crawling) in a defined sequence (Ohyama, 2015).

    The electron microscopy volume spanning the entire insect nervous system acquired for this study can be used to map circuits that mediate many different behaviours. Combining information from a complete wiring diagram with functional studies has been very fruitful in the 302-neuron nervous system of C. elegans. Recently, similar approaches have been applied to microcircuits in smaller regions of larger nervous systems. This study has demonstrated that relating local and global structure to function in a complete nervous system is now possible for the larger and more elaborate nervous system of an insect (Ohyama, 2015).

    Whole-brain calcium imaging reveals an intrinsic functional network in Drosophila

    A long-standing goal of neuroscience has been to understand how computations are implemented across large-scale brain networks. By correlating spontaneous activity during "resting states", studies of intrinsic brain networks in humans have demonstrated a correspondence with task-related activation patterns, relationships to behavior, and alterations in processes such as aging and brain disorders, highlighting the importance of resting-state measurements for understanding brain function. This study developed methods to measure intrinsic functional connectivity in Drosophila. This study measured intrinsic functional connectivity in Drosophila by acquiring calcium signals from the central brain. An alignment procedure was developed to assign functional data to atlas regions and correlate activity between regions to generate brain networks. This work reveals a large-scale architecture for neural communication and provides a framework for using Drosophila to study functional brain networks (Mann, 2017).

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    Glia and axonogenesis

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