Selective integration of diverse taste inputs within a single taste modality
- Zuckerman Mind Brain Behavior Institute, Columbia University, United States
- Neuroscience Graduate Program, Emory University, United States
- Department of Biology, Emory University, United States
Figures
Figure 1
Models for bitter taste processing and Gal4 lines to target bitter neuron subsets.
(A) Schematic depicting three major taste organs in the fly. (B) Models for how different subsets of bitter-sensing neurons could be processed in the brain. (C–D) Expression patterns of Gal4 lines …
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Figure 1—source data 1
Expression pattern of Gal4 lines used to target bitter neuron subsets.
- https://cdn.elifesciences.org/articles/84856/elife-84856-fig1-data1-v1.xlsx
Figure 2 with 1 supplement
Bitter neuron subsets in different organs act in parallel to regulate feeding responses.
(A) Effect of activating bitter neuron subsets on proboscis extension response (PER) to 100 mM sucrose (n=5–8 sets of flies). Bar graphs for each neuronal subset (left) represent the fraction of …
Figure 2—figure supplement 1
Additional characterization of the effects of bitter neuron manipulations on feeding.
(A) Schematic of the microstructure of fly feeding behavior, as defined in Itskov et al., 2014. Feeding activity bouts are periods of time when the fly is frequently interacting with the food, while …
Figure 3 with 2 supplements
Bitter neuron subsets in three different organs elicit similar effects on locomotion.
(A) Arena used for tracking flies during optogenetic activation. (B) Schematic of protocol to test the effect of bitter neuron activation on locomotion. 5 s light stimulation was used at three …
Figure 3—figure supplement 1
Additional characterization of locomotor effects elicited by activating all bitter neurons.
(A) Fraction of flies moving when all bitter neurons were activated using Gr33a-Gal4 (n=11–12 trials). Traces show behavior with medium intensity light stimulation. Bar graphs show change in …
Figure 3—figure supplement 2
Additional characterization of locomotor effects elicited by subsets of bitter neurons.
(A) Change in forward speed (top) or turning (bottom) elicited by low or high intensity light stimulation (n=10–12 trials). Results for medium intensity stimulation are shown in Figure 3E–F. (B) …
Figure 4 with 2 supplements
Subsets of bitter neurons in multiple organs elicit innate and learned aversion.
(A) Protocol to test the effect of bitter neuron activation on innate positional aversion. (B–C) Effect of activating all bitter-sensing neurons (B) or subsets of bitter neurons (C) on innate …
Figure 4—figure supplement 1
Additional characterization of innate aversion elicited by activating all bitter neurons.
(A) Innate positional aversion elicited by the activation of all bitter-sensing neurons at medium or high light intensity (n=22–24 trials, 11–12 sets of flies). Traces for low light intensity and …
Figure 4—figure supplement 2
Effects of bitter neuron subsets on innate positional aversion at other light intensities.
Graphs show the preference index (PI) during the last 5 s of light presentation at medium (A) or high (B) light intensity (n=20–24 trials, 10–12 sets of flies). The effect of low intensity light is …
Figure 5
Summary of behavioral effects elicited by optogenetic activation of bitter neuron subsets.
(A) Effects are color-coded by strength, relative to the effect of activating all bitter neurons with Gr33a-Gal4. All observed effects went in the same direction. See Materials and methods for …
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Figure 5—source data 1
Summary of behavioral effects elicited by optogenetic activation of bitter neuron subsets.
- https://cdn.elifesciences.org/articles/84856/elife-84856-fig5-data1-v1.xlsx
Figure 6 with 1 supplement
Bitter inputs from different organs are relayed to overlapping downstream pathways.
(A) The entire population of second-order bitter neurons was labeled by trans-Tango tracing from Gr33a-Gal4-expressing cells. Arrows denote the three major tracts (lateral, mediolateral, and medial) …
Figure 6—figure supplement 1
Additional images of trans-Tango labeling.
Additional images of trans-Tango labeling, with presynaptic bitter-sensing neurons labeled in green and postsynaptic second-order neurons labeled in red. The top row represents the same brains shown …
Figure 7 with 2 supplements
Second-order mlSEZt bitter neurons receive input from multiple organs.
(A) Top: R29F12-Gal4 expression pattern (maximum intensity projection created from images generated by the Janelia FlyLight Project Team). Brain slices at the far anterior and posterior edges are …
Figure 7—figure supplement 1
Further characterization of mlSEZt responses.
(A) Image of R29F12-Gal4 expression (same image as Figure 7A) showing the approximate location of subesophageal zone (SEZ) and superior lateral protocerebrum (SLP) imaging examples in panels B and …
Figure 7—figure supplement 2
Responses of mlSEZt cells labeled by R55E01-Gal4 and R29F12-R55E01 split-Gal4.
(A) R55E01-Gal4 expression pattern (maximum intensity projection created from images generated by the Janelia FlyLight Project Team). Brain slices at the far anterior and posterior edges are not …
Figure 8 with 3 supplements
Second-order mlSEZt neurons regulate a subset of taste-related behaviors.
(A) Expression pattern of split-Gal4 line (R29F12-AD+R55E01-DBD) labeling mlSEZt neurons with high specificity. Pink arrowhead shows mlSEZt cell bodies; white and yellow arrows denote their …
Figure 8—figure supplement 1
Additional characterization of mlSEZt effects on feeding behavior.
(A–D) Additional characterization of the effects of activating (A–B; n=42–47 flies) or silencing (C–D; n=66–69 flies) mlSEZt neurons. These data are derived from the same experiments shown in Figure …
Figure 8—figure supplement 2
Additional characterization of mlSEZt effects on locomotor and preference behaviors.
(A–D) mlSEZt neurons were activated using R29F12-R55E01 split-Gal4 driving UAS-Chrimson. (A, C) Effects of low and medium intensity light stimulation on forward velocity (A) or turning (C) (n=11–12 …
Figure 8—figure supplement 3
Effects of silencing mlSEZt neurons on locomotor and preference behaviors elicited by bitter neuron activation.
(A–D) Effects of activating bitter-sensing neurons (Gr66a-lexA driving Aop-Chrim) while silencing mlSEZt neurons (mlSEZt split-Gal4 driving UAS-Kir). Flies lacking Aop-Chrim were used as a negative …
Figure 9 with 2 supplements
Downstream pathways from mlSEZt cells.
(A) Colabeling of mlSEZt cells with markers for GABAergic (top) or cholinergic (bottom) neurons. Each row shows mlSEZt labeling (red), the neurotransmitter marker (green), and both channels …
Figure 9—figure supplement 1
Morphology of mlSEZt neurons in male brains.
Examples of mlSEZt cells in male brains labeled with the split-Gal4 line (R29F12-AD+R55E01-DBD). No reproducible differences in morphology were identified between mlSEZt neurons in male and female …
Figure 9—figure supplement 2
Additional images of mlSEZt colabeling with neurotransmitter markers.
(A–B) Additional images showing colabeling of mlSEZt cells with markers for GABAergic (A) or cholinergic (B) neurons. Each column shows mlSEZt labeling (red), the neurotransmitter marker (green), …
Tables
Table 1
List of 3Ns receiving at least 20 synapses from mlSEZt cells.
Percent input from mlSEZt refers to the percent of total input synapses the cell receives that come from mlSEZt cells. Target regions for each cell refer to areas where the cell has output synapses. …
| 3N cell type | Cell ID | # mlSEZt input cells | mlSEZt input types | # mlSEZt synapses | % Input from mlSEZt | Target regions |
|---|---|---|---|---|---|---|
| SLP191 | 420973599 | 7 | mAL3A, mAL3B | 64 | 6.6 | AVLP, SCL, SIP, SLP |
| SLP132 | 359892669 | 6 | mAL3A, mAL4 | 54 | 2.1 | AVLP, LH, SCL, SIP, SLP |
| SMP389 | 575197482 | 8 | mAL3A, mAL3B,mAL4 | 48 | 3.4 | AVLP, PLP, SCL, SIP, SLP, SMP |
| SLP191 | 421313563 | 8 | mAL3A, mAL3B, mAL4 | 47 | 5.0 | AVLP, LH, SCL, SIP, SLP |
| aSP-g3B | 421650982 | 5 | mAL3A | 46 | 12.9 | AVLP, PLP, SCL, SIP, SLP, SMP, SMP(C) |
| LHAV4l1 | 360236724 | 4 | mAL4 | 41 | 3.3 | AVLP, LH, SCL, SIP, SLP |
| SLP179_b | 420623873 | 5 | mAL3B, mAL4 | 38 | 3.7 | SCL, SIP, SLP |
| LHAV2f2_b | 574710121 | 5 | mAL3A, mAL3B, mAL4 | 38 | 7.9 | AVLP, LH, SCL, SLP |
| LHAV2f2_a | 604735525 | 4 | mAL3B, mAL4 | 36 | 9.4 | AVLP, LH, SCL, SLP |
| LHAV2f2_b | 573346324 | 5 | mAL3A, mAL4 | 35 | 5.5 | AVLP, LH, SCL, SLP |
| aSP-g3A | 329919036 | 5 | mAL3A, mAL3B, mAL4 | 35 | 6.3 | SIP, SLP, SMP(C), SMP |
| LHAV1e1 | 390271033 | 4 | mAL3A, mAL3B, mAL4 | 35 | 2.2 | AVLP, LH, SCL, SIP, SLP |
| SLP015_e | 393340402 | 4 | mAL3B, mAL4 | 33 | 6.0 | SIP, SLP |
| SLP011 | 297519736 | 7 | mAL3A, mAL4 | 33 | 2.2 | LH, SCL, SIP, SLP |
| SLP179_b | 391311186 | 5 | mAL3B, mAL4 | 32 | 3.3 | SIP, SLP |
| SLP015_c | 359240144 | 5 | mAL3B, mAL4 | 28 | 3.1 | AVLP, SCL, SIP, SLP |
| aSP-g3B | 485430336 | 5 | mAL3A | 27 | 9.2 | AVLP, SCL, SIP, SLP, SMP |
| SLP187 | 578521941 | 2 | mAL3B | 27 | 11.8 | SIP, SLP |
| SLP187 | 607820937 | 2 | mAL3B | 26 | 7.3 | AVLP, SCL, SLP |
| SLP376 | 298254384 | 4 | mAL3B, mAL4 | 25 | 0.6 | SIP, SLP, SMP |
| SLP216 | 5813011119 | 7 | mAL3A, mAL3B, mAL4 | 25 | 1.0 | AOTU, AVLP, GOR, GOR(C), IB, ICL, LH, PLP, SCL, SIP, SLP, SMP, SMP(C) |
| SLP215 | 608534097 | 3 | mAL3B, mAL4 | 24 | 1.1 | AVLP, PLP, SCL, SLP, VES |
| SLP015_c | 359926923 | 4 | mAL3B, mAL4 | 23 | 5.4 | SIP, SLP |
| SLP187 | 483711811 | 3 | mAL3B | 23 | 8.9 | SLP |
| SLP259 | 5813040707 | 4 | mAL3B, mAL4 | 23 | 1.7 | AVLP, SCL, SIP, SLP, SMP |
| AVLP024 | 420965117 | 3 | mAL3B | 22 | 1.2 | AVLP, LH, PVLP, SCL, SIP, SLP |
| LHCENT1 | 328861282 | 4 | mAL4 | 22 | 0.2 | AVLP, LH, MB, SCL, SIP, SLP, SMP |
| LHAV6a10 | 5813047255 | 3 | mAL4 | 22 | 6.5 | LH, SIP, SLP |
| SLP015_e | 329206628 | 2 | mAL4 | 21 | 3.2 | SIP, SLP |
| SMP550 | 452689494 | 3 | mAL3B, mAL4 | 21 | 0.4 | AVLP, LH, PLP, SCL, SIP, SLP, SMP |
| SLP018 | 451663172 | 4 | mAL3A, mAL3B, mAL4 | 20 | 3.1 | AVLP, LH, SCL, SIP, SLP |
| SLP057 | 5813019955 | 4 | mAL3B, mAL4 | 20 | 0.5 | AVLP, LH, PLP, SCL, SIP, SLP |
Table 2
List of top 30 4Ns.
Top 30 4Ns were selected and sorted based on the total number of synapses from top 3Ns. Percent input from 3Ns refers to the percent of total input synapses the cell receives that come from the top …
| 4N cell type | Cell ID | # 3N input cells | 3N input cell types | # synapses from 3Ns | % input from 3Ns | Target regions |
|---|---|---|---|---|---|---|
| SMP548 | 297580589 | 16 | AVLP024, LHAV1e1, SLP015_c, SLP015_e, SLP057, SLP179_b, SLP191, SLP216, SMP389, SMP550, aSP-g3A, aSP-g3B | 363 | 6.3 | AVLP, LH, PLP, SCL, SIP, SLP, SMP |
| SLP279 | 360591860 | 8 | LHAV1e1, LHCENT1, SLP015_e, SLP018, SLP179_b, SLP216, SMP550, aSP-g3A | 354 | 8.4 | SCL, SIP, SLP, SMP, SMP(C) |
| LHCENT4 | 517506265 | 4 | LHAV4l1, LHCENT1, SLP057, SMP389 | 297 | 4.6 | AVLP, LH,, MB, PLP, SCL, SIP, SLP, SMP, mALT |
| DM1_lPN | 542634818 | 1 | LHCENT1 | 247 | 2.4 | AL, LH, MB, SCL, SLP, mALT |
| LHMB1 | 5813020988 | 2 | LHCENT1, SLP057 | 238 | 3.5 | CRE, LH, MB, SCL, SIP, SLP, SMP |
| SLP388 | 298258611 | 11 | AVLP024, SLP015_e, SLP018, SLP179_b, SLP191, SLP216, SMP389, aSP-g3B | 203 | 3.1 | SCL, SIP, SLP, SMP, SMP(C) |
| SMP550 | 452689494 | 7 | AVLP024, LHAV1e1, SLP018, SLP216, SMP389, aSP-g3B | 201 | 4.2 | AVLP, LH, PLP, SCL, SIP, SLP, SMP |
| oviIN | 423101189 | 2 | SMP389, SMP550 | 190 | 0.8 | CAN, CRE, CRE(C), GOR, IB, LAL, SIP, SIP(C), SMP, SMP(C), SPS, VES |
| LHCENT9 | 330268940 | 10 | AVLP024, LHAV1e1, LHCENT1, SLP132, SLP191, SMP389, aSP-g3A, aSP-g3B | 177 | 1.4 | AOTU, AVLP, LH, MB, SCL, SIP, SLP, SMP |
| SLP212 | 5812980529 | 14 | AVLP024, SLP015_e, SLP018, SLP057, SLP132, SLP191, SLP216, SMP389, SMP550, aSP-g3A, aSP-g3B | 145 | 6.7 | SIP, SLP, SMP, SMP(C) |
| SMP108 | 298258513 | 3 | SLP057, SMP389, SMP550 | 145 | 0.6 | CRE, CRE(C), LAL, LH, MB, MB(C), SCL, SIP(C), SIP, SLP, SMP, SMP(C) |
| oviDNa | 550655668 | 2 | SLP216, SMP550 | 137 | 12.3 | CRE, SCL, SIP, SLP, SMP, SMP(C), VES |
| MBON18 | 5813020828 | 1 | LHCENT1 | 132 | 1.0 | LH, MB, SCL, SIP, SLP |
| LHPD4c1 | 421641859 | 9 | LHAV1e1, LHAV4l1, LHCENT1, SLP011, SLP018, SLP057, SLP132, SLP187 | 130 | 2.5 | LH, SCL, SIP, SLP, SMP |
| SLP113 | 390589591 | 6 | AVLP024, LHAV2f2_b, LHAV4l1, LHCENT1, SLP132 | 127 | 10.5 | LH, SIP, SLP, SMP |
| LHPV10b1 | 604709727 | 2 | LHCENT1, SLP057 | 125 | 2.8 | CRE, LH, MB, PLP, PVLP, SCL, SIP, SLP, SMP |
| SMP156 | 673776769 | 3 | SLP216, SMP389, SMP550 | 124 | 2.1 | CRE, GOR(C), IB, ICL(C), ICL, LAL, MB, SMP, SMP(C), SPS, SPS(C) |
| SMP385 | 5813083780 | 2 | SMP389, SMP550 | 124 | 2.0 | CRE, CRE(C), LAL, LAL(C), SCL, SIP, SIP(C), SMP, SMP(C), |
| SLP440 | 328870472 | 13 | LHAV1e1, SLP015_c, SLP015_e, SLP018, SLP057, SLP179_b, SLP191, SLP376, aSP-g3B | 123 | 3.0 | LH, SCL, SIP, SLP, SMP, SMP(C) |
| LHPV5e1 | 328611004 | 5 | LHAV1e1, LHAV4l1, LHCENT1, SLP015_e, SLP132 | 123 | 1.1 | CRE, CRE(C), LH, MB(C), SCL, SIP, SIP(C), SLP, SMP, SMP(C) |
| SMP029 | 604070433 | 3 | SLP216, SMP389, SMP550 | 120 | 6.7 | AVLP, LH, MB, PLP, SCL, SIP, SLP, SMP |
| SMP311 | 5813049378 | 2 | SMP389, SMP550 | 117 | 7.5 | AVLP, ICL, PLP, SCL, SIP, SLP, SMP |
| PPL201 | 328533761 | 6 | LHAV1e1, LHCENT1, SLP011, SLP015_e, SLP057, SLP179_b | 117 | 1.4 | AVLP, CRE, LH, MB, PLP, POC, SCL, SIP, SLP, SMP, mALT |
| SMP109 | 5813009620 | 2 | SMP389, SMP550 | 107 | 1.7 | AOTU, CRE, CRE(C), LAL, LAL(C), MB, SIP, SMP, VES |
| SMP029 | 541347811 | 4 | SLP215, SLP216, SMP389, SMP550 | 106 | 5.8 | AVLP, MB, PLP, SCL, SIP, SLP, SMP |
| SMP503 | 361312808 | 12 | AVLP024, LHCENT1, SLP011, SLP015_e, SLP018, SLP132, SLP187, SLP215, SMP389, SMP550, aSP-g3B | 102 | 1.5 | AVLP, CRE, LH, MB, PLP, SCL, SIP, SIP(C), SLP, SMP, SMP(C) |
| SLP113 | 421271305 | 8 | LHAV2f2_a, LHAV2f2_b, LHAV4l1, LHCENT1, SLP132, SLP187 | 102 | 10.0 | LH, SIP, SLP, SMP, SMP(C) |
| MBON18 | 457196444 | 1 | LHCENT1 | 97 | 3.6 | LH, MB(C), SCL, SIP, SIP(C), SLP |
| SLP149 | 5813009312 | 9 | LHAV1e1, LHAV4l1, LHCENT1, SLP015_c, SLP018, SLP057, SLP179_b, SLP376 | 97 | 4.3 | SIP, SLP, SMP |
| SLP441 | 5813078542 | 13 | LHAV1e1, LHCENT1, SLP011, SLP015_c, SLP015_e, SLP057, SLP179_b, SLP191, SLP259, SLP376, aSP-g3B | 96 | 3.3 | AVLP, SCL, SIP, SLP, SMP |
Key resources table
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|---|---|---|---|---|
| Genetic reagent (Drosophila melanogaster) | Gr33a-Gal4 | Moon et al., 2009 | BDSC: 31425 | |
| Genetic reagent (Drosophila melanogaster) | Gr58c-Gal4 | Weiss et al., 2011 | BDSC: 57646 | |
| Genetic reagent (Drosophila melanogaster) | Gr59c-Gal4 | Weiss et al., 2011 | BDSC: 57650 | |
| Genetic reagent (Drosophila melanogaster) | Gr22f-Gal4 | Weiss et al., 2011 | BDSC: 57610 | |
| Genetic reagent (Drosophila melanogaster) | Gr9a-Gal4 | Weiss et al., 2011 | BDSC: 57596 | |
| Genetic reagent (Drosophila melanogaster) | R29F12-Gal4 | Jenett et al., 2012 | BDSC: 49495 | |
| Genetic reagent (Drosophila melanogaster) | R55E01-Gal4 | Jenett et al., 2012 | BDSC: 39117 | |
| Genetic reagent (Drosophila melanogaster) | R29F12-AD | Dionne et al., 2018 | BDSC: 71164 | |
| Genetic reagent (Drosophila melanogaster) | R55E01-DBD | Dionne et al., 2018 | BDSC: 69662 | |
| Genetic reagent (Drosophila melanogaster) | Gr66a-lexA | Thistle et al., 2012 | BDSC: 93024 | |
| Genetic reagent (Drosophila melanogaster) | VGAT-lexA | Deng et al., 2019 | BDSC: 84441 | |
| Genetic reagent (Drosophila melanogaster) | ChAT-lexA | Deng et al., 2019 | BDSC: 84379 | |
| Genetic reagent (Drosophila melanogaster) | UAS-Chrimson-TdT | Duistermars et al., 2018 | N/A | |
| Genetic reagent (Drosophila melanogaster) | UAS-GTACR1-TdT | B Noro | N/A | |
| Genetic reagent (Drosophila melanogaster) | UAS-Kir2.1 | Baines et al., 2001 | BDSC: 6595 | |
| Genetic reagent (Drosophila melanogaster) | trans-Tango reporter (UAS-Myr-GFP, QUAS-mtdTomato; trans-Tango) | Talay et al., 2017 | BDSC: 77124 | |
| Genetic reagent (Drosophila melanogaster) | UAS-GCaMP6f | Chen et al., 2013 | BDSC: 42747 | |
| Genetic reagent (Drosophila melanogaster) | UAS-TdTVK5 | D Hattori | N/A | |
| Genetic reagent (Drosophila melanogaster) | UAS-TdTp40 | G Rubin and B Pfeiffer | BDSC: 32222 | |
| Genetic reagent (Drosophila melanogaster) | lexAop-GCaMP6f | D Kim; Hattori et al., 2017 | BDSC 44277 | |
| Antibody | Anti-GFP (chicken polyclonal) | Aves Labs | Cat# GFP-1020; RRID: AB_10000240 | 1:1000 |
| Antibody | Anti-DsRed (rabbit polyclonal) | Clontech | Cat# 632496; RRID: AB_10013483 | 1:500 |
| Antibody | Anti-bruchpilot (nc82; mouse monoclonal) | Development Studies Hybridoma Bank | Cat# nc82; RRID: AB_2314866 | 1:10 |
| Antibody | Alexa Fluor 488 (goat anti-chicken polyclonal) | Life Technologies | Cat# A11039; RRID: AB_2534096 | 1:500 |
| Antibody | Alexa Fluor 568 (goat anti-rabbit polyclonal) | Life Technologies | Cat# A11036; RRID: AB_10563566 | 1:500 |
| Antibody | Alexa Fluor 633 (goat anti-mouse polyclonal) | Life Technologies | Cat# A21052; RRID: AB_2535719 | 1:500 |
| Software, algorithm | Prism, version 9 | GraphPad | N/A | |
| Software, algorithm | MATLAB | Mathworks | N/A | |
| Software, algorithm | FlyTracker | Caltech; Eyjolfsdottir et al., 2014 | N/A | http://www.vision.caltech.edu/Tools/FlyTracker/ |
