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. 2016 Apr 15;5:e14009. doi: 10.7554/eLife.14009

Direct neural pathways convey distinct visual information to Drosophila mushroom bodies

Katrin Vogt 1,2,†,, Yoshinori Aso 3,, Toshihide Hige 4,§, Stephan Knapek 1, Toshiharu Ichinose 1,2, Anja B Friedrich 1, Glenn C Turner 4,§, Gerald M Rubin 3,*, Hiromu Tanimoto 1,2,*
Editor: Mani Ramaswami5
PMCID: PMC4884080  PMID: 27083044

Abstract

Previously, we demonstrated that visual and olfactory associative memories of Drosophila share mushroom body (MB) circuits (Vogt et al., 2014). Unlike for odor representation, the MB circuit for visual information has not been characterized. Here, we show that a small subset of MB Kenyon cells (KCs) selectively responds to visual but not olfactory stimulation. The dendrites of these atypical KCs form a ventral accessory calyx (vAC), distinct from the main calyx that receives olfactory input. We identified two types of visual projection neurons (VPNs) directly connecting the optic lobes and the vAC. Strikingly, these VPNs are differentially required for visual memories of color and brightness. The segregation of visual and olfactory domains in the MB allows independent processing of distinct sensory memories and may be a conserved form of sensory representations among insects.

DOI: http://dx.doi.org/10.7554/eLife.14009.001

Research Organism: D. melanogaster

Introduction

Rewarding or punitive stimuli modulate behavioral responses to sensory stimuli. In insects, such associative modulation takes place in the mushroom body (MB) (Heisenberg, 2003). In the fruit fly, the MB receives distinct dopaminergic inputs that signal reward and punishment, and this valence circuit in the MB is shared for associative memories of different modalities: olfaction, gustation, and vision (Masek et al., 2015Aso et al., 2012; Liu et al., 2012Perisse et al., 2013). The role of MB output during memory acquisition and testing is similar in visual and olfactory memories (Vogt et al., 2014). However, the required subsets of MB Kenyon cells (KCs) are not the same (Aso et al., 2014a; Vogt et al., 2014), raising the possibility that memory-relevant visual and olfactory information may be represented by different KC subsets in the MB.

Studies of the neuronal circuits underlying olfactory learning have benefitted from well-characterized neuronal pathways conveying olfactory information to the MB (Turner et al., 2008; Butcher et al., 2012). Beyond visual associative memory, the MB was shown to be important in various vision-guided behavioral tasks (Liu et al., 1999; Brembs, 2009; Zhang et al., 2007). Yet, how visual information is conveyed and represented in the Drosophila MB is totally unknown. MBs of hymenoptera receive visual afferents in their calyces (Ehmer and Gronenberg, 2002; Gronenberg and Hölldobler, 1999; Paulk and Gronenberg, 2008), while such direct visual input from the optic lobes to the MBs has never been observed in dipteran insects (Mu et al., 2012; Otsuna and Ito, 2006). Therefore, indirect, multisynaptic pathways have been proposed to convey the visual input to the Drosophila MB (Farris and Van Dyke, 2015; Tanaka et al., 2008). In this report, we demonstrate the existence of two visual projection neurons that directly connect the optic lobes to the MB.

Results

To understand the representation of visual memory in the MB, we blocked different subsets of KCs using split-GAL4 drivers and UAS-shits1 (Aso, 2014a) and behaviorally screened the flies for color discrimination memory using an aversive reinforcer (Figure 1C). Memory was consistently impaired when we used drivers labelling the γ-lobe neurons, confirming their importance for visual memory (Figure 1—figure supplement 1A) (Vogt et al., 2014). Strikingly, this screen further suggested a subset of the γ neurons (γd) to be specifically responsible (Figure 1, Figure 1—figure supplement 1A).

Figure 1. The γd KCs are required for visual memory.

(A) γd neurons labeled by MB607B-GAL4. (B) 3D reconstruction of γd neurons labeled by MB607B-GAL4 (purple) in the entire MB (yellow). Arrow indicates atypical dendritic protrusion of the γd neurons. Scale bars: 50 µm (A) and 20 µm (B). (C) Schematic diagram of color discrimination learning and test. (D) Average time courses of conditioned color avoidance in the test for flies with the blockade of the γd neurons with MB607B-GAL4 (red) and the parental controls (black and gray). (E) Pooled conditioned color avoidance. Blocking the γd neurons with MB607B-GAL4 impairs aversive color discrimination learning (one-way ANOVA, post-hoc pairwise comparison, p<0.05; n = 8–12). (F) The same Shits1 blockade of the γd neurons does not impair immediate aversive olfactory memory (one-way ANOVA, post-hoc pairwise comparison, p>0,05; n = 9–10). Throughout this study, bars and error bars display mean and SEM, respectively.

DOI: http://dx.doi.org/10.7554/eLife.14009.002

Figure 1.

Figure 1—figure supplement 1. Behavioral screen identifies the requirement of the γd KCs in aversive visual conditioning but not olfactory conditioning.

Figure 1—figure supplement 1.

(A) Subsets of KC types labelled in GAL4 lines and the resulting learning defects in the behavioral screen using UAS-shits1. Expression levels (represented in the gray scale) are determined as previously described (Paulk and Gronenberg, 2008); stochastic expression. Stars in the left column indicate the statistical significance of memory impairment compared to the UAS-shi/+ control (Dunn’s multiple pairwise comparison); n = 8–119. (B) Expression pattern of MB419B-GAL4 which specifically labels γd KCs. Scale bar represents 50 µm. Grey background staining: Discs large. (C) Blocking output of the γd neurons with MB419B-GAL4 (B; one-way ANOVA, post-hoc pairwise comparison, p<0.05; n = 18–60) impairs aversive color discrimination learning. (D) The same blockade does not significantly affect aversive olfactory conditioning (one-way ANOVA, p>0.05; n = 8–10).

The γd neurons are embryonic born KCs consisting of ca. 75 cells in the adult brain (Butcher et al., 2012; Aso et al., 2009; Aso et al., 2014b). Two split-GAL4 drivers MB607B and MB419B showed strong expression in the γd neurons but had no detectable expression in other KCs (Figure 1A, Figure 1—figure supplement 1B). The blockade of the γd neurons using these lines severely impaired visual memory (Figure 1D,E, Figure 1—figure supplement 1C). In contrast, olfactory memory of these flies was not significantly affected (Figure 1F, Figure 1—figure supplement 1D). Given that both visual and olfactory memories were reinforced by the same aversive stimulus - electric shock punishment - the selective γd requirement for visual memory suggests that these KCs represent visual stimuli.

We characterized γd cell morphology using the MB607B-GAL4 and MB419B-GAL4 drivers to express axonal and dendritic markers. Their axons run in parallel to those of the other KCs in the peduncle, and project to the dorsomedial tip of the γd lobe (Figure 1B, 2A). The γd neurons are atypical in that their dendrites are highly enriched ventrolaterally outside the main calyx, where olfactory projection neurons (OPNs) terminate (Figure 1B, 2B), and form the ventral accessory calyx (vAC) (Butcher et al., 2012; Aso et al., 2014b). Nevertheless, the γd neurons are equipped with claw-like dendritic endings forming microglomeruli similar to those in the main calyx (Figure 2C,D).

Figure 2. γd neurons are atypical KCs and respond to visual stimuli.

Figure 2.

(A–B) Main output and input sites labeled by Syt::GFP (green) and DenMark::mCherry (red) are differentially localized to the dorsal γ lobe and the vAC (arrow). The MB lobe (A) and main calyx (B) are outlined. P: MB peduncle (C–D) The γd dendrites (green) enwrap presynaptic terminals (gray; arrows). A single optical slice of the inset in the projection in C is magnified in D-D’’. P: MB peduncle. Scale bars: 50 µm (A–B); 20 µm (C); 2 µm (D-D’’). (E) Responses to light and odor stimulation in γd KCs measured with whole-cell current-clamp recordings. Data from four representative neurons are shown (each column corresponds to the data from one cell). Voltage traces of individual trials (gray lines, 5–7 trials) are overlaid with the mean (colored line). Raster plots below the traces represent spikes. Stimulus presentation is indicated below each trace (duration = 1 s). For odors, three of five tested odors are displayed (OCT: 3-octanol; MCH: 4-methylcyclohexanol; HEP: 2-heptanone). (F) Responses in two representative α/β KCs. (G) Modality segregation by γd (n = 12 cells) and α/β KCs (n = 11 cells). Each of the pie charts represents 24 (γd) or 22 (α/β) light-cell pairs measured in 6 flies and 60 (γd) or 55 (α/β) odor-cell pairs measured in 3 flies. The distributions of all four response categories are significantly different between γd KCs and α/β KCs with respect to both visual (p<10–5, Fisher’s exact test) and odor responses (p<10–6) See Materials and methods for details.

DOI: http://dx.doi.org/10.7554/eLife.14009.004

To examine if the γd neurons are tuned to visual stimuli, we measured electrophysiological responses using whole-cell patch-clamp recordings. In line with the specific requirement of γd neurons for visual conditioning, we found spiking responses to blue and green light stimuli in some of these cells (Figure 2E,G). Stimulating flies with 5 different odors did not lead to excitatory responses of the γd neurons; olfactory stimulation rather evoked slow inhibitory responses, implying the existence of feedforward inhibition through other odor-responsive KC populations (Figure 2E,G). This response profile is in sharp contrast to what we observed with typical KCs (e.g. α/β neurons), many of which responded to odors but none to visual stimuli (Figure 2F,G, [Turner et al., 2008]). Thus, there is clear modality segregation between γd and α/β neurons.

The selective tuning of the γd neurons to visual stimuli prompted us to ask if Drosophila MBs receive direct visual input from the optic lobes in the γd dendrites. Performing an anatomical screen of a GAL4 driver collection (Jenett et al., 2012), we identified two types of neurons with arbors in the optic lobes and projections in the area of the vAC, making them candidates for visual projection neurons (VPNs) to the MB (Figure 3—figure supplement 1). We named these two types of VPNs VPN-MB1 and VPN-MB2.

To precisely map the morphology of these VPNs, we generated split-GAL4 drivers, MB425B and MB334C. These drivers have strong expression in VPN-MB1 and VPN-MB2 with little other expression (Figure 3A,C). MB425B-GAL4 has predominant expression in VPN-MB1 (Figure 3B) projecting from the medulla to the vAC with many cell bodies on the anterior surface of the optic lobe. In contrast, MB334C-GAL4 strongly labels 1–3 cells of VPN-MB2 as well as the MB output neurons MBON-α1 (Figure 3C,D) (Aso et al., 2014a; 2014b). We found that the majority of VPN-MB1 and VPN-MB2 dendrites are localized to the optic lobes (Figure 3E,G). The VPN-MB1 neurons have dendritic arbors in the medulla enriched in layer M8 (Figure 3F). They cover a large field of the medulla with a conspicuous elaboration in the ventral half (Figure 3A,E,F). Single-cell labeling revealed that each VPN-MB1 cell samples input from approximately 20 optic cartridges in the medulla (Figure 3B). Dendrites of a single VPN-MB2 cover a large field of the ventral medulla, arborizing in layer M7 (Figure 3C,D,G,H). The output sites of both VPNs are restricted to the lateral protocerebrum, including the vAC, forming pre-synaptic boutons in microglomeruli (Figure 3E,G; Figure 3—figure supplement 2).

Figure 3. VPNs directly convey optic lobe inputs to the MB vAC.

(A) VPN-MB1 neurons labeled by MB425B-GAL4. (B) A single VPN-MB1 neuron, generated by heat shock flip out, connects the medulla and the central brain. (C) VPN-MB2 neurons labeled by MB334C-GAL4. (D) VPN-MB2 neurons connect the medulla and the central brain. (E) VPN-MB1 has dendrites (DenMark::mCherry, red) in the ventral medulla and presynaptic terminals in the central brain (Syt::GFP, green). (F) The dendrites of VPN-MB1 (green) arborize in the M8 layer. (G) VPN-MB2 has dendrites (DenMark::mCherry, red) in the ventral medulla and presynaptic terminals in the central brain (Syt::GFP, green). (H) The dendrites of VPN-MB2 (green) arborize in the M7 layer. (I–J) Reconstituted GFP signals visualize contacts between KCs and VPN-MB1 in the vAC. (K–L) Reconstituted GFP signals visualize contacts between KCs and VPN-MB2 in the vAC. J and L are magnifications of the insets in I and K. Scale bars represent 50 µm.

DOI: http://dx.doi.org/10.7554/eLife.14009.005

Figure 3.

Figure 3—figure supplement 1. 3D reconstruction of VPNs and γd neurons (purple: MB419B-GAL4) registered in a standard brain reveals overlapping processes in the vAC (arrow).

Figure 3—figure supplement 1.

Yellow: entire MB. (A) Blue: MB425B-GAL4. (B) Green: MB334C-GAL4. Scale bar: 50 µm.

Figure 3—figure supplement 2. VPN axons overlap with KC processes in the vAC labeled with DC0.

Figure 3—figure supplement 2.

(A–C) The DC0 antibody highlights KCs including the γd neurons labeled by MB419B-GAL4. (D and E) The γd dendrites overlap with the processes of VPN-MB1 (MB425B-GAL4) (D) and VPN-MB2 (MB334C-GAL4) (E) Scale bars represent 50 µm (A and B) and 5 µm (D and E).

Figure 3—figure supplement 3. VPNs connect to the γd vAC.

Figure 3—figure supplement 3.

(A–A”) Double labeling of VPN-MB1 (MB425B-GAL4, red) and KCs (R13F02-LexA for labeling most KCs, green) reveals overlap in the vAC (arrows). (B–B”) Double labeling of VPN-MB2 (MB334C-GAL4, red) and KCs (R13F02-LexA, green) reveals overlap in the vAC (arrows). Scale bar: 5 µm.

To visualize possible connections between the VPNs and KCs in the vAC, we performed counterstaining of VPNs and the catalytic subunit of PKA that marks KCs (Wolff and Strausfeld, 2015) and found that the VPN terminals are enwrapped by the dendrites of the γd neurons (Figure 3—figure supplement 2). Differential labeling of KCs and the VPNs was consistent with their proposed connection (Figure 3—figure supplement 3). We also detected a GRASP signal between the VPNs and the ventrolateral projection of the γd dendrites (Figure 3I–L).

We examined the requirement of the VPNs as well as OPNs in visual memory by blocking their output using the driver lines MB425B-GAL4, MB334C-GAL4 and GH146-GAL4 (Figure 4A–C). Memory was not significantly altered upon the blockade of the OPNs (Figure 4A). In contrast, the blockade of VPN-MB1, but not VPN-MB2, significantly impaired color discrimination memory (Figure 4B,C). To further substantiate the results with MB425B-GAL4, we employed another driver line (VT008475) that labels similar VPNs to VPN-MB1. These VPNs in VT008475 project to the lateral protocerebrum but do not reach the vAC (Figure 4—figure supplement 1). The blockade of these VPNs left color discrimination memory intact (Figure 4—figure supplement 1). These results strongly suggest the existence of modality-specific input pathways to the MB and that VPN-MB1 is a key component of the visual learning pathway.

Figure 4. VPN-MB1 and VPN-MB2 convey distinct visual features.

(A) OPNs labeled by GH146-GAL4 are not required for visual color conditioning (one-way ANOVA, p>0.05), n = 8. (B–C) VPN-MB1 (MB425B-GAL4; B), but not VPN-MB2 (MB334C-GAL4; C), are required for color discrimination learning (one-way ANOVA, post-hoc pairwise comparison, p<0.01). n = 9–12. (D) γd neurons labeled by MB419B-GAL4 are required for green intensity learning (one-way ANOVA, post-hoc pairwise comparison, p<0.05), n = 9–11; these neurons are also required for color discrimination learning (Figure 1). (E–F) In contrast to the requirement in color discrimination learning, the blockade of VPN-MB2 (MB334C-GAL4; F), but not VPN-MB1 (MB425B-GAL4; E), significantly impaired intensity discrimination learning (one-way ANOVA, post-hoc pairwise comparison, p<0.05). n = 8–13. (G) Schematic of memory circuits in the MB. Visual and olfactory information is first processed in the optic lobe and antennal lobe, respectively. Components of sensory information (e.g. brightness and color) are separately processed there and conveyed to corresponding KC subtypes in the MB directly through distinct projection neurons Ca: calyx, vAC: ventral accessory calyx. These segregated representations of visual and olfactory information undergo the same dopaminergic (DA) valence modulation to operate acquired behavior (e.g. conditioned avoidance) via shared circuits.

DOI: http://dx.doi.org/10.7554/eLife.14009.009

Figure 4.

Figure 4—figure supplement 1. The blockade of similar VPNs without vAC connection does not impair color discrimination learning.

Figure 4—figure supplement 1.

(A and B) MB425B-GAL4 (A) and VT008475-GAL4 label VPN-MB1 and similar VPNs that do not extend to the vAC (arrows). Scale-bars represents 50 µm. (C) Blocking output of neurons labeled by VT008475-GAL4 does not impair color learning (one-way ANOVA, p>0.05), n = 8.

Figure 4—figure supplement 2. The blockade of MBON-a1 does not impair intensity discrimination learning.

Figure 4—figure supplement 2.

(A) MB331C-GAL4 labels MBON-a1 (arrows) in addition to VPN-MB2. (B) MB310C-GAL4 labels MBON-a1 (arrows) but not VPN-MB2. Scale-bars represents 50 µm. (C) Blocking output of neurons labeled by MB310C-GAL4 does not impair intensity learning (one-way ANOVA, p >0.05), n = 8–11.

In our standard learning assay, flies discriminate the chromatic information of blue and green LED stimuli. In addition, flies can learn different light intensities of a single chromatic cue (Schnaitmann et al., 2013). We asked if the γd neurons and same VPNs are also required for brightness discrimination learning. We found that while the γd neurons were also necessary for this task, the blockade of VPN-MB1 did not significantly alter performance (Figure 4D,E). In contrast, learning was significantly impaired by blockade of VPN-MB2 (Figure 4F). Because MB334C-GAL4 has expression not just in VPN-MB2 but also in MBON-α1, we tested and ruled out the involvement of MBON-α1 by showing that its blockade did not significantly impair brightness discrimination learning (Figure 4—figure supplement 2). The differential requirements for VPN-MB1 and VPN-MB2 indicate that information about color and intensity of a visual cue is separately conveyed to the MB via distinct VPNs.

Discussion

Previous studies have shown the importance of MB circuits for visual memories and other visually guided behaviors in Drosophila (Vogt et al., 2014; Liu et al., 1999; Zhang et al., 2007); however, the MB had been thought to receive visual input from the optic lobe through an unknown indirect pathway, because direct connections had not been observed (Farris and Van Dyke, 2015; Tanaka et al., 2008). Our identification of direct pathways reveals similarity between dipteran and hymenopteran circuit design for visual processing, and provides experimental evidence for the behavioral roles of these circuits (Figure 4G).

VPN-MB1 and VPN-MB2 receive differential inputs in the medulla and convey distinct visual features - color and brightness - to the MB. The medulla layer M8 where VPN-MB1 arborizes (Figure 3F) contains presynaptic terminals of Tm5 neurons that are involved in color vision (Karuppudurai et al., 2014; Gao et al., 2008). VPN-MB2 is the first Drosophila interneuron shown to convey brightness information, and the position of its dendrites suggests that light intensity may be encoded in the medulla layer M7 (Figure 3H). Interestingly, the dendrites of VPN-MB1 and VPN-MB2 are preferentially distributed in the ventral half of the medulla, an arrangement consistent with the specialization of the ventral retina for color processing of landmarks during foraging (Giger and Srinivasan, 1997Kinoshita et al., 2015; Wernet et al., 2015). The target region of the MB-projecting VPNs is separated from other described VPNs in the central brain, and largely segregated from other VPNs mediating innate spectral processing and motion vision (Mu et al., 2012; Otsuna and Ito, 2006; Zhang et al., 2013a). Parallel processing of different visual features with segregated projections may be a conserved circuit strategy for visual processing across phyla (Livingstone and Hubel, 1988).

Our results provide evidence that the Drosophila MB represents distinct sensory modalities in different KC subsets whose dendrites are segregated in subdomains of the calyx. Olfactory inputs project to the main calyx and visual stimuli to the vAC, while gustatory stimuli have been recently shown to project to yet another calyx domain (Kirkhart and Scott, 2015). In the fly MB, dopamine neurons including those encoding positive and negative valences divide the long axon terminals of KCs into distinct compartments (Aso et al., 2014a; Tanaka et al., 2008). As KCs send parallel axon fibers, a single dopamine neuron locally modulates the corresponding axonal compartment of multiple KCs (Hige et al., 2015Cohn et al., 2016; Boto et al., 2014). Distinct KC subsets, γd and γm for example, can therefore share the same dopaminergic valence modulation, even if these KCs are devoted to different sensory modalities (Figure 4G) (Vogt et al., 2014). These local modulations in turn affect the information conveyed by shared MB output pathways (Figure 4G) (Aso et al., 2014; Vogt et al., 2014). Our results can thus explain the circuit mechanism by which the Drosophila MB processes memories of different modalities with shared modulatory and output pathways (Figure 4G).

There appears to be a close correlation between the ecological specialization of different insects and the organization of their MB calyces (Ehmer and Gronenberg, 2002; Lin and Strausfeld, 2012; Yilmaz et al., 2016; Groh et al., 2014), with the functional subdivision of the calyx reflecting the salient sensory environment. Olfactory processing, which is the dominant sensory modality in Drosophila subject to associative modulation, utilizes ~1800 of the 2000 KCs (Quinn et al., 1974). While fruit flies perform a wide range of behaviors driven by visual input, few could be modified by associative learning (Borst, 2009), given that fewer KCs subserve visual memory formation. The MB calyces of other insects also possess modality segregation, and different sets of KCs are presumably assigned to each sensory space (Gronenberg and Hölldobler, 1999; Kinoshita et al., 2015; Mobbs, 1982). Our results here are the first to show that individual KCs indeed have unimodal responses. The segregated sensory representation in the MB enables independent formation of different sensory memories, while allowing interaction among distinct KC populations that may underlie complex forms of learning involving multimodal integration (Zhang et al., 2013b).

Materials and methods

Flies and genetic crosses

Flies were reared at 25°C, at 60% relative humidity under a 12-12-hr light-dark cycle on a standard cornmeal-based diet. Flies were sorted by genotype at least two days prior to experiments. Each behavioral experiment used 30–40 flies of mixed gender under dim red light in a custom-made plastic box, containing a heating element on the bottom and a fan for air circulation.

For behavioral experiments, we used F1 progeny of crosses between females of w+;;pJFRC100-20x pJFRC100-20XUAS-TTS-Shibire-ts1-p10 in VK00005 (Pfeiffer et al., 2012) or WT-females and males of genotypes MB607B-GAL4 (Aso et al., 2014b), MB419B-GAL4 (Aso, 2014b), GH146-GAL4 (Stocker et al., 1997), MB425B-GAL4, MB334C-GAL4, MB310C-GAL4 (Aso et al., 2014b), VT008475-GAL4 (VDRC, Vienna, Austria) or Canton-S males. Split-GAL4 lines were generated using described vectors (Pfeiffer et al., 2010): MB425B carries 28F07-p65ADZp in attP40 and 10E05-ZpGdbd in attP2, MB334C carries 52G04-p65ADZp in attP40 and 49F03-ZpGdbd in VK00027. Split-GAL4 lines used in the KC screening (Figure 1—figure supplement 1) and for intensity conditioning (Figure 4—figure supplement 2) are as described in (Aso et al., 2014b). As all transgenes were inserted into the w- mutant genome, the X chromosomes of the shits effector strain was replaced with that of wild-type Canton-S (w+).

Aversive visual conditioning

We used LEDs to present visual stimuli (green [520 nm] and blue light [465 nm]) from the bottom of the arena as previously described in (Schnaitmann et al., 2013). The intensities were controlled by current and calibrated using a luminance meter BM-9 (Topcon Technohouse Corporation) or a PR-655 SpectraScan Spectroradiometer: 19.4 mW/m2 (blue) and 8.58 mW/m2 (green) (Schnaitmann et al., 2013). To train flies with different light intensities, blue and green visual cues were replaced by different intensities of green light (1:10 ratio; 27.8 mW/m2 (bright-green), and 2.77 mW/m2 (dark-green).

For aversive electric shock conditioning, we used an arena with a transparent shock grid as previously described (Vogt et al., 2014). During the test phase, the shock arena was video recorded from above with a CMOS camera (Firefly MV, PointGrey, Richmond, Canada) controlled by custom-made software (Schnaitmann et al., 2010). Four setups were run in parallel.

Differential conditioning was followed by binary choice without reinforcement (Vogt et al., 2014; Schnaitmann et al., 2010). Briefly, in a single experiment, approximately 40 flies were introduced into the arena using an aspirator. During a training trial, the entire arena was illuminated with alternating visual stimuli (60 s each) with one stimulus paired with aversive reinforcement. A 1-s electric shock (AC 60 V) was applied 12 times spaced over 60 s during presentation of the punished visual stimulus. Training trials were repeated four times per experiment. In the test, administered 60 s after the end of training, flies were allowed to choose between the two visual stimuli, which were each presented in two diagonally opposed quadrants of the arena. The distribution of the flies was video recorded for 90 s at 1 frame per second. No US was presented in the test period; however, a 1-s shock pulse (90 V) was applied 5 s before the beginning of the test to arouse the flies. Two groups were trained with reciprocal pairings and tested consecutively in the same setup, respectively. The difference in visual stimulus preference between the two groups was then used to calculate a learning index for each video frame. Reinforcement was paired with the first visual stimulus in half of the experiments, and with the second in the remaining experiments, to cancel any effect of order. The whole experimental setup was kept at 33°C for the temperature-induced effect of Shits1.

Aversive olfactory conditioning

Aversive olfactory conditioning was performed as described in (Aso et al., 2014a). A group of about 50 flies in a training tube alternately received octan-3-ol (OCT; Merck, Darmstadt, Germany) and 4-methylcyclohexanol (MCH; Sigma-Aldrich, MO) for 1 min in a constant air stream. OCT and MCH were diluted to 0.6% and 2%, respectively, in paraffin oil (Sigma-Aldrich) and presented in a cup with a diameter of 30 mm. For odor presentation, two of 3/2-way solenoid valves (MFH-3-3/4-S, FESTO, Germany) were used. Each valve was connected to two cups, one of which contains the diluted odor and the other of which contains pure paraffin oil. Four training tubes were connected to the valves. Twelve 1.5 s 90 V electric shocks (DC) were paired with one of the odor presentations. The delivery of electric shocks and the odors was controlled by a custom-made computer program. In the test, the trained flies were allowed to choose between MCH and OCT for 2 min in a T-maze. The odor cups used were identical to the ones for the conditioning. The distribution of the flies was imaged by cameras (FFMV-03M2M, Point Grey), and the preference index was calculated by taking the mean indices of the last 10 s in the 2 min choice. The learning index was then calculated by taking the mean preference of the two reciprocally trained groups. Half of the trained groups received reinforcement together with the first presented odor and the other half with the second odor to cancel the effect of the order of reinforcement. Temperature and humidity were 60% and 33ºC, measured with a digital thermo-hydrometer (6011000, Venta Luftwäscher, Germany). Behavioral experiments were performed in dim red light for training and in complete darkness for test.

Statistics

Statistical analyses were performed with Prism5 software (GraphPad). Groups that did not violate the assumption of normal distribution (Shapiro-Wilk test) and homogeneity of variance (Bartlett’s test) were analyzed with parametric statistics: one-sample t-test or one-way analysis of variance followed by the planned pairwise multiple comparisons (Bonferroni). Experiments with data that were significantly different from the assumptions above were analyzed with non-parametric tests, such as Mann-Whitney test or Kruskal–Wallis test followed by Dunn’s multiple pair-wise comparison (Figure 1—figure supplement 1). The significance level of statistical tests was set to 0.05. Only the most conservative statistical result of multiple pairwise comparisons is indicated. Visual conditioning bar graphs show pooled data over total duration of test. Olfactory conditioning bar graphs show pooled data over last 10 s of test.

Electrophysiology

In vivo whole-cell recordings from KCs were performed as previously reported (Turner et al., 2008). Specific cell types were visually targeted using GFP signals, with a 60X water-immersion objective (LUMPlanFl/IR; Olympus) attached to an upright microscope (BX51WI; Olympus). γd and α/β KCs were specifically labeled with GFP by crossing flies bearing UAS-2eGFP (Bloomington) with GAL4 lines, MB607B (γd KCs) or MB008B (α/β KCs). Adult F1 females were used at 2–3 days after eclosion. The patch pipettes were pulled for a resistance of 6–7 MΩ and filled with pipette solution containing (in mM): L-potassium aspartate, 125; HEPES, 10; EGTA, 1.1; CaCl2, 0.1; Mg-ATP, 4; Na-GTP, 0.5; biocytin hydrazide, 13; with pH adjusted to 7.3 with KOH (265 mOsm). The preparation was continuously perfused with saline containing (in mM): NaCl, 103; KCl, 3; CaCl2, 1.5; MgCl2, 4; NaHCO3, 26; N-tris(hydroxymethyl) methyl-2-aminoethane-sulfonic acid, 5; NaH2PO4, 1; trehalose, 10; glucose, 10 (pH 7.3 when bubbled with 95% O2 and 5% CO2, 275 mOsm). Whole-cell current-clamp recordings were made using the Axon MultiClamp 700B amplifier (Molecular Devices). Cells were held at around -50 mV by injecting a small hyperpolarizing current, typically less than 2 pA. Signals were low-pass filtered at 5 kHz and digitized at 10 kHz. Spikes were automatically detected by custom-written scripts in Matlab (R2008b, MathWorks) based on their amplitude, after first removing slow membrane potential deflections with bandpass filtering (100 to 1000 Hz); for each recording, we verified the accuracy of this automatic detection algorithm by visual inspection. To detect subthreshold responses, the peak amplitude during a response time window (0 to 0.5 sec after stimulus onset for light stimulation and 0.2 to 3 s for odor stimulation) was calculated using mean voltage traces smoothed with moving average. When amplitudes exceeded 4.2 SDs of the membrane potential fluctuations during baseline (a 1-sec or 3-sec period prior to stimulus onset), we called this an excitatory or inhibitory response. This criterion accurately reflected our visual impression of what was a significant subthreshold response.

For light stimulation, an LED (520 and 468 nm peak wavelength; WP154A4SUREPBGVGAW, Kingbright) was directed at a fly’s head from an angle 45 degrees below and directly in front of the animal, at a distance of 11cm. Light intensities were adjusted to match those used in the behavioral experiment by changing the duty cycle of the LED through Arduino Uno (Arduino). All experiments were performed in a semi-dark room. Odors were presented through a custom-built device as described previously (Honegger et al., 2011). Saturated vapors of pure odorants were diluted with air at a 1: 20 ratio. Final flow rate was 1 L/min. Odors were presented in a pseudo-random order so that no odor was presented twice in succession. The following five chemicals were used as stimuli: 2-heptanone (CAS# 110-43-0), 3-octanol (589-98-0), 4-methylcyclohexanol (589-91-3), isoamyl acetate (123-92-2) and apple cider vinegar (Richfood).

We observed distinct response profiles of γd KCs and α/β KCs with respect to both light and odor responses (Figure 2G). To see if excitatory drive from the two different modalities is segregated between the cell types, we performed similar statistical analysis (Fisher’s exact test) after combining the counts of 'Spike' with 'Excitatory', and 'Inhibition Only' with 'No Response' in Figure 2G. This analysis also showed highly significant differences both in light (p<10–5) and odor responses (p<10–7).

Immunohistochemistry

Adult fly brains were dissected, fixed, and stained using standard protocols (Aso et al., 2009). MB419B-GAL4, MB607B-GAL4, MB425B-GAL4, MB334C-GAL4, MB310C-GAL4, and VT008475-GAL4 were crossed to UAS-mCD8::GFP (Figure 1, Figure 2, Figure 3, Figure 1—figure supplement 1, Figure 3—figure supplement 1, Figure 3—figure supplement 2, Figure 4—figure supplement 1, Figure 4—figure supplement 2) and stained with anti-GFP antibody (AB) (rabbit anti-GFP polyclonal, Invitrogen, 1:1000) followed by Alexa Fluor 488 (goat anti-rabbit IgG highly cross absorbed, Invitrogen, 1:1000). For neuropil labeling, we used mouse anti-dskl AB (4F3, DSHB, 1:50) followed by Cy3 anti-mouse (Jackson ImmunoResearch, 1:250) or mouse anti-synapsin AB (DSHB, 1:100, (Klagges et al., 1996) followed by Cy3 anti-mouse (Dianova, 1:250) or Alexa 633 anti-mouse (Invitrogen, 1:250). MB419B-GAL4, MB425B-GAL4 and MB334C-GAL4 (Figure 2D,E, 3C,D) crossed to UAS-DenMark::mCherry; UAS-Syt::GFP (Nicolaï et al., 2010) and double labeling of w-;UAS-myr-CD8::Cherry,R13F02LexA/CyO;LexAop-GFP/TM2 crossed to MB425B-GAL4 or MB334C-GAL4 (Figure 3, Figure 3—figure supplement 3) were stained with anti-GFP AB (rat anti-GFP AB, Chromotek, 1:100) followed by Alexa Fluor 488 (anti-rat AB, Invitrogen, 1:250) and anti-dsRed AB (rabbit anti-dsred, Clontech, 1:100) followed by Alexa Fluor 568 (anti-rabbit AB, Invitrogen, 1:250). DC0-positive neurons (PKA-C1, Figure 3—figure supplement 1) were visualized using anti-DC0 AB staining (anti-DC0 rabbit, 1:2000, (Skoulakis et al., 1993) followed by Cy3 anti-rabbit (Jackson ImmunoResearch, 1:200). To visualize connections between VPNs and the vAC we crossed w-; mb247-DsRed; mb247-splitGFP11, UAS-splitGFP1-10 (Pech et al., 2013) to MB425B-GAL4 and MB334C-GAL4. For visualization of reconstituted GFP (Figure 3I–J), we used mouse anti-GFP AB (Clone N86/38, Neuro-Mab, Antibodies Inc., 1:100) followed by goat anti-mouse Alexa 488 (Invitrogen, 1:200). For MB labeling (Figure 3I–J), we used rabbit anti-dsRed AB (Clontech, 1: 100) followed by Cy3 anti-rabbit (Jackson ImmunoResearch, 1:200). For neuropil labeling, we used rat anti-N-cadherin staining (anti-N-cad DN-Ex no.8, Developmental Studies Hybridoma Bank, 1:100) followed by Alexa 633 anti-rat (Invitrogen, 1:200). R7/R8 neurons (Figure 3F,H) were visualized using mouse anti-MAb24B10 AB (DSHB, (Zipursky et al., 1984) followed by Cy3 anti-mouse (Jackson ImmunoResearch, 1:250). Optical sections of whole-mount brains were sampled with a confocal microscope (Olympus FV1000). Images of the confocal stacks were analyzed with the open-source software Fiji (Schindelin et al., 2012) and rendered using Fluorender (Schnaitmann et al., 2010). We applied the 3D mean filter (r = 2 pixels) followed by deconvolution to high magnification image stacks in Figure 3D–D”.using Fiji plugins Iterative Deconvolve 3D.

To obtain single-cell flp-out staining, males of the MB425B-GAL4 were crossed with females of y-w-, hsp70-flp [X]; UAS>CD2 y+>mCD8::GFP/CyO; TM2/TM6b (Wong et al., 2002) to obtain F1 progeny carrying GAL4 insertion, hsp70-flp and UAS>rCD2,y+>mCD8-GFP. Crosses were raised at 25°C. One to six days before eclosion a mild heat shock was given by placing the vial into a 32°C incubator to remove the FLP-out cassette (rCD2, y+) in a subset of the neurons. The duration of the heat shock was 60–90 min. The eclosed flies were then transferred into a fresh vial and 2- to 5-day-old flies were used for dissection.

Acknowledgements

We thank Aljoscha Nern for sharing unpublished information on the anatomy of VPNs and critical reading of the manuscript; Irina Sinakevitch for anti-DC0. This work was supported by Max-Planck Gesellschaft (HT), Deutsche Forschungsgemeinschaft (TA 552/5-1 to HT), MEXT/JSPS KAKENHI (26250001, 26120705, 26119503, 15K14307 to HT) and Postdoctoral Fellowship for Research Abroad (TH), the Strategic Research Program for Brain Sciences “Bioinformatics for Brain Sciences” (HT), Naito Foundation (HT), Howard Hughes Medical Institute (GMR and YA), NIH (R01 DC010403-01A1 to GCT), and a Postdoctoral Fellowship from the Uehara Memorial Foundation (TH).

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Funding Information

This paper was supported by the following grants:

  • Howard Hughes Medical Institute to Yoshinori Aso, Gerald M Rubin.

  • Japan Society for the Promotion of Science Postdoctoral Fellowship for Research Abroad to Toshihide Hige.

  • Uehara Memorial Foundation Postdoctoral Fellowship to Toshihide Hige.

  • NIH Blueprint for Neuroscience Research R01DC010403-01A1 to Glenn C Turner.

  • Max-Planck-Gesellschaft to Hiromu Tanimoto.

  • Deutsche Forschungsgemeinschaft TA552/5-1 to Hiromu Tanimoto.

  • Ministry of Education, Culture, Sports, Science, and Technology KAKENHI 26120705 to Hiromu Tanimoto.

  • Ministry of Education, Culture, Sports, Science, and Technology The Strategic Research Program for Brain Sciences to Hiromu Tanimoto.

  • Japan Society for the Promotion of Science KAKENHI 26250001 to Hiromu Tanimoto.

  • Naito Foundation to Hiromu Tanimoto.

  • Ministry of Education, Culture, Sports, Science, and Technology KAKENHI 26119503 to Hiromu Tanimoto.

  • Japan Society for the Promotion of Science KAKENHI 15K14307 to Hiromu Tanimoto.

Additional information

Competing interests

The authors declare that no competing interests exist.

Author contributions

KV, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article.

YA, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents.

TH, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents.

SK, Acquisition of data, Analysis and interpretation of data.

TI, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article.

ABF, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article.

GCT, Conception and design, Drafting or revising the article.

GMR, Conception and design, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents.

HT, Conception and design, Analysis and interpretation of data, Drafting or revising the article.

References

  1. Aso Y, Grübel K, Busch S, Friedrich AB, Siwanowicz I, Tanimoto H. The mushroom body of adult drosophila characterized by GAL4 drivers. Journal of Neurogenetics. 2009;23:156–172. doi: 10.1080/01677060802471718. [DOI] [PubMed] [Google Scholar]
  2. Aso Y, Herb A, Ogueta M, Siwanowicz I, Templier T, Friedrich AB, Ito K, Scholz H, Tanimoto H. Three dopamine pathways induce aversive odor memories with different stability. PLoS Genetics. 2012;8:e14009. doi: 10.1371/journal.pgen.1002768. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Aso Y, Sitaraman D, Ichinose T, Kaun KR, Vogt K, Belliart-Guérin G, Plaçais P-Y, Robie AA, Yamagata N, Schnaitmann C, Rowell WJ, Johnston RM, Ngo T-TB, Chen N, Korff W, Nitabach MN, Heberlein U, Preat T, Branson KM, Tanimoto H, Rubin GM. Mushroom body output neurons encode valence and guide memory-based action selection in drosophila. eLife. 2014a;3:1–42. doi: 10.7554/eLife.04580. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Aso Y, Hattori D, Yu Y, Johnston RM, Iyer NA, Ngo T-TB, Dionne H, Abbott LF, Axel R, Tanimoto H, Rubin GM. The neuronal architecture of the mushroom body provides a logic for associative learning. eLife. 2014b;3:1–47. doi: 10.7554/eLife.04577. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Borst A. Drosophila's view on insect vision. Current Biology. 2009;19:R36–47. doi: 10.1016/j.cub.2008.11.001. [DOI] [PubMed] [Google Scholar]
  6. Boto T, Louis T, Jindachomthong K, Jalink K, Tomchik SM. Dopaminergic modulation of camp drives nonlinear plasticity across the drosophila mushroom body lobes. Current Biology. 2014;24 doi: 10.1016/j.cub.2014.03.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Brembs B. Mushroom bodies regulate habit formation in drosophila. Current Biology. 2009;19:1351–1355. doi: 10.1016/j.cub.2009.06.014. [DOI] [PubMed] [Google Scholar]
  8. Butcher NJ, Friedrich AB, Lu Z, Tanimoto H, Meinertzhagen IA. Different classes of input and output neurons reveal new features in microglomeruli of the adult drosophila mushroom body calyx. The Journal of Comparative Neurology. 2012;520:2185–2201. doi: 10.1002/cne.23037. [DOI] [PubMed] [Google Scholar]
  9. Cohn R, Morantte I, Ruta V. Coordinated and compartmentalized neuromodulation shapes sensory processing in drosophila. Cell. 2015;163:1742–1755. doi: 10.1016/j.cell.2015.11.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Ehmer B, Gronenberg W. Segregation of visual input to the mushroom bodies in the honeybee (apis mellifera) The Journal of Comparative Neurology. 2002;451:362–373. doi: 10.1002/cne.10355. [DOI] [PubMed] [Google Scholar]
  11. Farris SM, Van Dyke JW. Evolution and function of the insect mushroom bodies: Contributions from comparative and model systems studies. Current Opinion in Insect Science. 2015;12:19–25. doi: 10.1016/j.cois.2015.08.006. [DOI] [Google Scholar]
  12. Gao S, Takemura SY, Ting CY, Huang S, Lu Z, Luan H, Rister J, Thum AS, Yang M, Hong ST, Wang JW, Odenwald WF, White BH, Meinertzhagen IA, Lee CH. The neural substrate of spectral preference in drosophila. Neuron. 2008;60:328–342. doi: 10.1016/j.neuron.2008.08.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Giger A, Srinivasan M. Honeybee vision: Analysis of orientation and colour in the lateral, dorsal and ventral fields of view. The Journal of Experimental Biology. 1997;200:1271–1280. doi: 10.1242/jeb.200.8.1271. [DOI] [PubMed] [Google Scholar]
  14. Groh C, Kelber C, Grübel K, Rössler W. Density of mushroom body synaptic complexes limits intraspecies brain miniaturization in highly polymorphic leaf-cutting ant workers. Proceedings. Biological Sciences / the Royal Society. 2014;281 doi: 10.1098/rspb.2014.0432. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Gronenberg W, Hölldobler B. Morphologic representation of visual and antennal information in the ant brain. The Journal of Comparative Neurology. 1999;412:229–240. doi: 10.1002/(sici)1096-9861(19990920)412:2&#x0003c;229::aid-cne4&#x0003e;3.0.co;2-e. [DOI] [PubMed] [Google Scholar]
  16. Heisenberg M. Mushroom body memoir: From maps to models. Nature Reviews. Neuroscience. 2003;4:266–275. doi: 10.1038/nrn1074. [DOI] [PubMed] [Google Scholar]
  17. Hige T, Aso Y, Modi MN, Rubin GM, Turner GC. Heterosynaptic plasticity underlies aversive olfactory learning in drosophila. Neuron. 2015;88:985–998. doi: 10.1016/j.neuron.2015.11.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Honegger KS, Campbell RA, Turner GC. Cellular-resolution population imaging reveals robust sparse coding in the drosophila mushroom body. Journal of Neuroscience. 2011;31:11772–11785. doi: 10.1523/JNEUROSCI.1099-11.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Jenett A, Rubin GM, Ngo TT, Shepherd D, Murphy C, Dionne H, Pfeiffer BD, Cavallaro A, Hall D, Jeter J, Iyer N, Fetter D, Hausenfluck JH, Peng H, Trautman ET, Svirskas RR, Myers EW, Iwinski ZR, Aso Y, DePasquale GM, Enos A, Hulamm P, Lam SC, Li HH, Laverty TR, Long F, Qu L, Murphy SD, Rokicki K, Safford T, Shaw K, Simpson JH, Sowell A, Tae S, Yu Y, Zugates CT. A gal4-driver line resource for drosophila neurobiology. Cell Reports. 2012;2:991–1001. doi: 10.1016/j.celrep.2012.09.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Karuppudurai T, Lin TY, Ting CY, Pursley R, Melnattur KV, Diao F, White BH, Macpherson LJ, Gallio M, Pohida T, Lee CH. A hard-wired glutamatergic circuit pools and relays UV signals to mediate spectral preference in drosophila. Neuron. 2014;81:603–615. doi: 10.1016/j.neuron.2013.12.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Kinoshita M, Shimohigasshi M, Tominaga Y, Arikawa K, Homberg U. Topographically distinct visual and olfactory inputs to the mushroom body in the swallowtail butterfly, papilio xuthus. The Journal of Comparative Neurology. 2015;523:162–182. doi: 10.1002/cne.23674. [DOI] [PubMed] [Google Scholar]
  22. Kirkhart C, Scott K. Gustatory learning and processing in the drosophila mushroom bodies. Journal of Neuroscience. 2015;35:5950–5958. doi: 10.1523/JNEUROSCI.3930-14.2015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Klagges BR, Heimbeck G, Godenschwege TA, Hofbauer A, Pflugfelder GO, Reifegerste R, Reisch D, Schaupp M, Buchner S, Buchner E. Invertebrate synapsins: A single gene codes for several isoforms in drosophila. Journal of Neuroscience. 1996;16:3154–3165. doi: 10.1523/JNEUROSCI.16-10-03154.1996. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Lin C, Strausfeld NJ. Visual inputs to the mushroom body calyces of the whirligig beetle dineutus sublineatus: Modality switching in an insect. The Journal of Comparative Neurology. 2012;520:2562–2574. doi: 10.1002/cne.23158. [DOI] [PubMed] [Google Scholar]
  25. Liu L, Wolf R, Ernst R, Heisenberg M. Context generalization in drosophila visual learning requires the mushroom bodies. Nature. 1999;400:753–756. doi: 10.1038/23456. [DOI] [PubMed] [Google Scholar]
  26. Liu C, Plaçais PY, Yamagata N, Pfeiffer BD, Aso Y, Friedrich AB, Siwanowicz I, Rubin GM, Preat T, Tanimoto H. A subset of dopamine neurons signals reward for odour memory in drosophila. Nature. 2012;488:512–516. doi: 10.1038/nature11304. [DOI] [PubMed] [Google Scholar]
  27. Livingstone M, Hubel D. Segregation of form, color, movement, and depth: Anatomy, physiology, and perception. Science. 1988;240:740–749. doi: 10.1126/science.3283936. [DOI] [PubMed] [Google Scholar]
  28. Masek P, Worden K, Aso Y, Rubin GM, Keene AC. A dopamine-modulated neural circuit regulating aversive taste memory in drosophila. Current Biology. 2015;25:1535–1541. doi: 10.1016/j.cub.2015.04.027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Mobbs PG. The brain of the honeybee apis mellifera. I. the connections and spatial organization of the mushroom bodies. Philosophical Transactions of the Royal Society B: Biological Sciences. 1982;298:309–354. doi: 10.1098/rstb.1982.0086. [DOI] [Google Scholar]
  30. Mu L, Ito K, Bacon JP, Strausfeld NJ. Optic glomeruli and their inputs in drosophila share an organizational ground pattern with the antennal lobes. Journal of Neuroscience. 2012;32:6061–6071. doi: 10.1523/JNEUROSCI.0221-12.2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Nicolaï LJ, Ramaekers A, Raemaekers T, Drozdzecki A, Mauss AS, Yan J, Landgraf M, Annaert W, Hassan BA. Genetically encoded dendritic marker sheds light on neuronal connectivity in drosophila. Proceedings of the National Academy of Sciences of the United States of America. 2010;107:20553–20558. doi: 10.1073/pnas.1010198107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Otsuna H, Ito K. Systematic analysis of the visual projection neurons of drosophila melanogaster. I. lobula-specific pathways. The Journal of Comparative Neurology. 2006;497:928–958. doi: 10.1002/cne.21015. [DOI] [PubMed] [Google Scholar]
  33. Paulk AC, Gronenberg W. Higher order visual input to the mushroom bodies in the bee, bombus impatiens. Arthropod Structure & Development. 2008;37:443–458. doi: 10.1016/j.asd.2008.03.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Pech U, Pooryasin A, Birman S, Fiala A. Localization of the contacts between kenyon cells and aminergic neurons in the drosophila melanogaster brain using splitgfp reconstitution. The Journal of Comparative Neurology. 2013;521:3992–4026. doi: 10.1002/cne.23388. [DOI] [PubMed] [Google Scholar]
  35. Perisse E, Burke C, Huetteroth W, Waddell S. Shocking revelations and saccharin sweetness in the study of drosophila olfactory memory. Current Biology. 2013;23:R752–763. doi: 10.1016/j.cub.2013.07.060. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Pfeiffer BD, Ngo TT, Hibbard KL, Murphy C, Jenett A, Truman JW, Rubin GM. Refinement of tools for targeted gene expression in drosophila. Genetics. 2010;186:735–755. doi: 10.1534/genetics.110.119917. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Pfeiffer BD, Truman JW, Rubin GM. Using translational enhancers to increase transgene expression in drosophila. Proceedings of the National Academy of Sciences of the United States of America. 2012;109:6626–6631. doi: 10.1073/pnas.1204520109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Quinn WG, Harris WA, Benzer S. Conditioned behavior in drosophila melanogaster. Proceedings of the National Academy of Sciences of the United States of America. 1974;71:708–712. doi: 10.1073/pnas.71.3.708. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez JY, White DJ, Hartenstein V, Eliceiri K, Tomancak P, Cardona A. Fiji: An open-source platform for biological-image analysis. Nature Methods. 2012;9:676–682. doi: 10.1038/nmeth.2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Schnaitmann C, Vogt K, Triphan T, Tanimoto H. Appetitive and aversive visual learning in freely moving drosophila. Frontiers in Behavioral Neuroscience. 2010;4 doi: 10.3389/fnbeh.2010.00010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Schnaitmann C, Garbers C, Wachtler T, Tanimoto H. Color discrimination with broadband photoreceptors. Current Biology. 2013;23:2375–2382. doi: 10.1016/j.cub.2013.10.037. [DOI] [PubMed] [Google Scholar]
  42. Skoulakis EM, Kalderon D, Davis RL. Preferential expression in mushroom bodies of the catalytic subunit of protein kinase A and its role in learning and memory. Neuron. 1993;11:197–208. doi: 10.1016/0896-6273(93)90178-T. [DOI] [PubMed] [Google Scholar]
  43. Stocker RF, Heimbeck G, Gendre N, de Belle JS. Neuroblast ablation in drosophila P[GAL4] lines reveals origins of olfactory interneurons. Journal of Neurobiology. 1997;32:443–456. doi: 10.1002/(SICI)1097-4695(199705)32:5&#x0003c;443::AID-NEU1&#x0003e;3.0.CO;2-5. [DOI] [PubMed] [Google Scholar]
  44. Tanaka NK, Tanimoto H, Ito K. Neuronal assemblies of the drosophila mushroom body. The Journal of Comparative Neurology. 2008;508:711–755. doi: 10.1002/cne.21692. [DOI] [PubMed] [Google Scholar]
  45. Turner GC, Bazhenov M, Laurent G. Olfactory representations by drosophila mushroom body neurons. Journal of Neurophysiology. 2008;99:734–746. doi: 10.1152/jn.01283.2007. [DOI] [PubMed] [Google Scholar]
  46. Vogt K, Schnaitmann C, Dylla KV, Knapek S, Aso Y, Rubin GM, Tanimoto H. Shared mushroom body circuits underlie visual and olfactory memories in drosophila. eLife. 2014;3:1–22. doi: 10.7554/eLife.02395. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Wernet MF, Perry MW, Desplan C. The evolutionary diversity of insect retinal mosaics: Common design principles and emerging molecular logic. Trends in Genetics. 2015;31:316–328. doi: 10.1016/j.tig.2015.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Wolff GH, Strausfeld NJ. Genealogical correspondence of mushroom bodies across invertebrate phyla. Current Biology. 2015;25:38–44. doi: 10.1016/j.cub.2014.10.049. [DOI] [PubMed] [Google Scholar]
  49. Wong AM, Wang JW, Axel R. Spatial representation of the glomerular map in the drosophila protocerebrum. Cell. 2002;109:229–241. doi: 10.1016/S0092-8674(02)00707-9. [DOI] [PubMed] [Google Scholar]
  50. Yilmaz A, Lindenberg A, Albert S, Grübel K, Spaethe J, Rössler W, Groh C. Age-related and light-induced plasticity in opsin gene expression and in primary and secondary visual centers of the nectar-feeding ant camponotus rufipes. Developmental Neurobiology. 2016 doi: 10.1002/dneu.22374. [DOI] [PubMed] [Google Scholar]
  51. Zhang K, Guo JZ, Peng Y, Xi W, Guo A. Dopamine-mushroom body circuit regulates saliency-based decision-making in drosophila. Science. 2007;316:1901–1904. doi: 10.1126/science.1137357. [DOI] [PubMed] [Google Scholar]
  52. Zhang X, Liu H, Lei Z, Wu Z, Guo A. Lobula-specific visual projection neurons are involved in perception of motion-defined second-order motion in drosophila. The Journal of Experimental Biology. 2013a;216:524–534. doi: 10.1242/jeb.079095. [DOI] [PubMed] [Google Scholar]
  53. Zhang X, Ren Q, Guo A. Parallel pathways for cross-modal memory retrieval in drosophila. Journal of Neuroscience. 2013b;33:8784–8793. doi: 10.1523/JNEUROSCI.4631-12.2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Zipursky SL, Venkatesh TR, Teplow DB, Benzer S. Neuronal development in the drosophila retina: Monoclonal antibodies as molecular probes. Cell. 1984;36:15–26. doi: 10.1016/0092-8674(84)90069-2. [DOI] [PubMed] [Google Scholar]
eLife. 2016 Apr 15;5:e14009. doi: 10.7554/eLife.14009.012

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Thank you for submitting your work entitled "Direct neural pathways convey distinct visual information to Drosophila mushroom bodies" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by Mani Ramaswami as Reviewing Editor and K VijayRaghavan as the Senior Editor.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

In honey bees and other insects, it is well known that the mushroom body receives visual information. In the original Research Article, the authors showed with convincing behavioral genetic analyses that mushroom body γ-lobe Kenyon cells are required for visual memory. In this "Research Advance", the authors use a refined split GAL4 line to show that a subpopulation of about 75 γ-lobe KCs (γd) are required for visual memory. These neurons have dendrites in the region called the ventral accessory calyx (vAC). With whole-cell patch clamp, they found that these γd KCs respond to visual but not olfactory stimulation. Furthermore, they generated new split GAL4 lines that label two classes of visual projection neurons, VPN-MB1 and VPN-MB2. VPN-MB1 and VPN-MB2 neurons both send axons to innervate the vAC region, and have dendrites in the optical medulla M8 and M7 layers, respectively. Interestingly, VPN-MB1 is required for a color discrimination task but not an intensity discrimination task. Conversely, VPN-MB2 is required for an intensity discrimination task but not a color discrimination task. Based on these observations, the authors conclude that VPN-MB1 and VPN-MB2 neurons receive and convey color and brightness information, respectively, to the MB.

The demonstration and identification of direct visual inputs to the mushroom bodies in Drosophila is important and novel since these MB neurons modulate contextual visual learning and the identification of key visual projection neurons involved will enable further research to understand how the mushroom bodies control responses to different sensory modalities. Thus, on balance, the works constitutes a material and useful advance on the original article.

There is however some room for improvement in manuscript.

1) Although a Research Advance, it would be valuable to structure this article more as a short stand-alone unit: with an informative Introduction, a Results section focused on this particular study, and a much deeper Discussion that interprets these new findings. Overall, the manuscript could be written such that there would be minimal need for the reader to go back to Vogt et al., 2014. Such a structure could also serve to contextualize this new work and present it from a broad perspective, not limited to the field of Drosophila neurogenetics.

2) The split GAL4 lines labeling the VPN-MB1 or the VPN-MB2 neurons also label other neurons in the central brain. Therefore, it would be excellent if additional electrical recordings from these two cell types could be used to demonstrate that they do indeed respond to distinct features of the visual stimulation. This would greatly strengthen the interpretation of the behavioral genetic experiments. However, the reviewers agree that this is an optional but not obligatory experiment.

3) The learning scores are represented as histograms. The authors should consider adding some actual behavioural traces to exemplify how these scores were derived, and how the mutant flies fail to learn, for example in Figure 1. Also in Figure 1: Please provide a schema of the behavioural assay, at the very least as is presented in Figure 4.

4) Figure 1—figure supplement 1: Was olfactory conditioning performed for all of these MB lines, or only for a few? Can this be clarified in the table? Can information for MB607B be included in this table?

5) Figure 2: The identified features in panels B-D are not easy to see. Consider especially improving versions of D-D'. The authors should clarify whether the trials are performed in the same cell? And in how many animals? G) Include the value for the proportion of the different reactions observed, and also a number of experimental animals.

6) Figure 4: The model presented in G is quite compelling but still speculative: the authors are suggesting that dopaminergic effects leading to avoidance behaviour are equally applied to different inputs regardless of sensory modality? This warrants further discussion. Also please clarify acronyms in the legend (vAC, Ca).

7) Results presented in Figure 2 need to be statistically tested. For other figures when statistical significance is provided, it would be easier for the readers if the specific statistical method were provided in the legend. However, the reviewers note that the specific tests are stated in the Methods section.

8) Further discussion points:

A) What about the indirect pathways? For a broader audience, perhaps elaborate on what is known about indirect visual pathways to the mushroom bodies? Is there any feedback from the other central brain neuropils?

B) Do the authors think that only brightness and colour is processed in the "learning centre" of the MB and not other visual cues (or have these not been tested)? Is this perhaps linked to earlier finding about contextual visual learning in the MB? This should be explicitly considered.

9) Introduction, first paragraph: instead of have: has.

10) In the subsection “Electrophysiology”, last paragraph: was the saline solution in the fruit fly / around the fruit fly cooled somehow since the LEDs probably produced additional heat that might affect recordings?

11) Please include a table/ supplementary table all of the olfactory learning data for these MB lines, if such are available.

eLife. 2016 Apr 15;5:e14009. doi: 10.7554/eLife.14009.013

Author response


1) Although a Research Advance, it would be valuable to structure this article more as a short stand-alone unit: with an informative Introduction, a Results section focused on this particular study, and a much deeper Discussion that interprets these new findings. Overall, the manuscript could be written such that there would be minimal need for the reader to go back to Vogt et al., 2014. Such a structure could also serve to contextualize this new work and present it from a broad perspective, not limited to the field of Drosophila neurogenetics.

We revised the text by structuring it into full-fledged Introduction, Results and Discussion sections. Introduction and Discussion now contain sufficient background information as a stand-alone paper.

2) The split GAL4 lines labeling the VPN-MB1 or the VPN-MB2 neurons also label other neurons in the central brain. Therefore, it would be excellent if additional electrical recordings from these two cell types could be used to demonstrate that they do indeed respond to distinct features of the visual stimulation. This would greatly strengthen the interpretation of the behavioral genetic experiments. However, the reviewers agree that this is an optional but not obligatory experiment.

We agree that the Split-GAL4 lines used in this study, although highly specific, label neurons other than the target VPN-MBs. To further substantiate our conclusions, we performed behavioral experiments with new driver lines.

To further substantiate the role of VPN-MB1 in MB425B, we identified a GAL4 line VT8475 that labels similar VPNs that have projections from the medulla to the lateral protocerebrum, but not to the vAC region. The blockade of these neurons did not impair color discrimination learning (Figure 4—figure supplement 1). These new data further support the hypothesis that the specific connection of the medulla and the vAC by VPN-MB1 is required to convey color information to the MB. We added the description of these results in the seventh paragraph of the Results section.

The splitGAL4 line MB334C, which we used to manipulate VPN-MB2, also labels a mushroom body output neuron (MBON-α1; Aso et al., 2014). Since MB output is required for visual learning (Vogt et al., 2014), we controlled for the requirement of the MBON-α1 using a SplitGAL4 line specific for MBON-α1 but not VPNs (MB310C; Aso et al., 2014) during blockade with shits. Upon shi blockade with MB310C, we did not find any impairment in intensity learning experiments (Figure 4—figure supplement 2), further supporting the role of VPN-MB2 for conveying brightness information. We added the description of these results in the ninth paragraph of the Results section.

Electrophysiological recording from these cells is technically very difficult due to the positions of their cell bodies. Recording would require developing a new preparation to improve accessibility; this is not feasible within a few months.

3) The learning scores are represented as histograms. The authors should consider adding some actual behavioural traces to exemplify how these scores were derived, and how the mutant flies fail to learn, for example in Figure 1. Also in Figure 1: Please provide a schema of the behavioural assay, at the very least as is presented in Figure 4.

For better comprehension of the behavioral protocol of visual learning, we added a schematic drawing of conditioning to Figure 1 (Figure 1C). As suggested, we now show the time course of color choice behavior in the test for flies with blocked γd and for the respective parental control flies (Figure 1D).

4) Figure 1—figure supplement 1: Was olfactory conditioning performed for all of these MB lines, or only for a few? Can this be clarified in the table? Can information for MB607B be included in this table?

Split-GAL4 line MB607B was employed only after the initial screen (Figure 1—figure supplement 1). Given the expression pattern as clean as that of MB419B in the screen, we chose it as the best secondary line for confirmation of the requirement of the γd neurons for visual learning.

We understand the potential usefulness of comparing the results from the same GAL4 lines in the screens for olfactory and visual learning. We indeed tested all listed drivers for olfactory conditioning, however with a significantly different protocol (e.g. memory retention time) that precluded us from comparing these datasets.

Instead, we tested the Shi blockade with MB607B for immediate olfactory memory to further substantiate the differential requirement of the γd KCs. This blockade, as well as with MB419B, failed to impair olfactory memory. We included these new data in Figure 1.

5) Figure 2: The identified features in panels B-D are not easy to see. Consider especially improving versions of D-D'. The authors should clarify whether the trials are performed in the same cell? And in how many animals? G) Include the value for the proportion of the different reactions observed, and also a number of experimental animals.

We improved the clarity by adjusting the brightness/contrast for Figure 2B, and by reselecting the optical section, applying 3D deconvolution for the panels 2D-D”.

In the legend of Figure 2E-F, we now say: “each column corresponds to the data from one cell”.

We added the actual numbers of recordings and animals directly in Figure 2G as well as in the legend.

6) Figure 4: The model presented in G is quite compelling but still speculative: the authors are suggesting that dopaminergic effects leading to avoidance behaviour are equally applied to different inputs regardless of sensory modality? This warrants further discussion. Also please clarify acronyms in the legend (vAC, Ca).

Thanks to this comment, we added substantial explanation of the proposed circuit model into the corresponding part of the Discussion. We also clarified the acronyms in the legend.

7) Results presented in Figure 2 need to be statistically tested. For other figures when statistical significance is provided, it would be easier for the readers if the specific statistical method is provided in the legend. However, the reviewers note that the specific tests are stated in the Methods section.

We performed statistical analysis of the distribution of visual versus odor-responding cells in the two types of KCs using Fisher’s exact test. We added this to the legend and Materials and methods.

8) Further discussion points:

A) What about the indirect pathways? For a broader audience, perhaps elaborate on what is known about indirect visual pathways to the mushroom bodies? Is there any feedback from the other central brain neuropils?

No direct or indirect visual pathways to the MB had been previously found in Drosophila. Dipteran MBs have long been proposed to receive indirect visual input as had been described in the cockroach (Strausfeld and Li, J Comp Neurol 1999), where no direct VPN has been identified either. We provided this discussion in the first paragraph of Discussion.

Although we described that the MB contains many feedback circuits (Aso et al. eLife 2014), it is unknown which one is involved in visual processing. Direct connections from the central complex (or other prominent neuropils) to the MB have never been described.

B) Do the authors think that only brightness and colour is processed in the "learning centre" of the MB and not other visual cues (or have these not been tested)? Is this perhaps linked to earlier finding about contextual visual learning in the MB? This should be explicitly considered.

From the rich body of literature, it is clear that color and brightness are not the only visual cues processed in the Drosophila MB. A famous example is the information on visual contexts is processed in the MB (Liu et al., Nature 1999), which we mentioned in the text. However, we hesitate to go in to great depth regarding the link between our results and other visual functions of the MB, since differences between the behavioral tasks and experimental conditions across these studies are too large for a direct comparison.

9) Impact statement, second paragraph: instead of have: has.

The text has been changed accordingly.

10) In the subsection “Electrophysiology”, last paragraph: was the saline solution in the fruit fly / around the fruit fly cooled somehow since the LEDs probably produced additional heat which might affect recordings?

Our visual stimulation was performed with a single small LED, 11cm away from the fly. The stimulation time was only 1 second with 15s pause between trials. Thus, there shouldn’t be any significant temperature effect on the recordings. Furthermore, the saline was continuously perfused over the fly throughout the experiment, which would minimize any fluctuations in temperature.

11) Please include a table/ supplementary table all of the olfactory learning data for these MB lines, if such are available.

See our response to point 4.


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