RESEARCH ARTICLE
A complex peripheral code for salt taste
in Drosophila
Alexandria H Jaeger1,2†, Molly Stanley1†, Zachary F Weiss1, Pierre-Yves Musso1,
Rachel CW Chan3‡, Han Zhang3, Damian Feldman-Kiss1, Michael D Gordon1*
1
Department of Zoology, University of British Columbia, Vancouver, Canada;
Graduate Program in Neuroscience, University of British Columbia, Vancouver,
Canada; 3Engineering Physics Program, University of British Columbia, Vancouver,
Canada
2
Abstract Each taste modality is generally encoded by a single, molecularly defined, population
of sensory cells. However, salt stimulates multiple taste pathways in mammals and insects,
suggesting a more complex code for salt taste. Here, we examine salt coding in Drosophila. After
creating a comprehensive molecular map comprised of five discrete sensory neuron classes across
the fly labellum, we find that four are activated by salt: two exhibiting characteristics of ‘low salt’
cells, and two ‘high salt’ classes. Behaviorally, low salt attraction depends primarily on ‘sweet’
neurons, with additional input from neurons expressing the ionotropic receptor IR94e. High salt
avoidance is mediated by ‘bitter’ neurons and a population of glutamatergic neurons expressing
Ppk23. Interestingly, the impact of these glutamatergic neurons depends on prior salt
consumption. These results support a complex model for salt coding in flies that combinatorially
integrates inputs from across cell types to afford robust and flexible salt behaviors.
DOI: https://doi.org/10.7554/eLife.37167.001
*For correspondence:
gordon@zoology.ubc.ca
†
These authors contributed
equally to this work
Present address: ‡Department
of Computer Science, University
of Toronto, Toronto, Canada
Competing interests: The
authors declare that no
competing interests exist.
Funding: See page 26
Received: 31 March 2018
Accepted: 14 September 2018
Published: 11 October 2018
Copyright Jaeger et al. This
article is distributed under the
terms of the Creative Commons
Attribution License, which
permits unrestricted use and
redistribution provided that the
original author and source are
credited.
Introduction
Sodium is essential to survival, but its intake must be carefully regulated to maintain ionic homeostasis. It is therefore unsurprising that taste systems have evolved robust mechanisms for detecting salt,
and that salt palatability depends on its concentration. In general, sodium concentrations below 100
mM tend to be attractive, while any salt present at higher concentrations becomes increasingly aversive (Chandrashekar et al., 2010; Lindemann, 2001; Oka et al., 2013)
Although there is considerable debate about modes of central taste coding, there is strong evidence that most taste modalities activate a single, molecularly defined, population of peripheral
taste receptor cells (Yarmolinsky et al., 2009). However, research in both mammals and insects has
favoured a dual-pathway model for salt taste: a low-threshold sodium-specific population of ‘low
salt’ cells mediates attraction, which is overridden at higher concentrations by ion non-specific ‘high
salt’ cells that drive avoidance (Ishimoto and Tanimura, 2004; Lindemann, 2001; Marella et al.,
2006; Oka et al., 2013; Zhang et al., 2013). Moreover, two distinct aversive taste receptor cell
(TRC) types (bitter and sour) contribute to high salt taste in mammals (Oka et al., 2013). Thus,
peripheral coding of salt taste appears more complex than other primary taste modalities.
The Drosophila labellum contains three types of gustatory sensilla, each of which harbors 2–4 gustatory receptor neurons (GRNs) (Singh, 1997; Stocker, 1994)(Figure 1A). Short (S-type) and long (Ltype) sensilla have four molecularly and physiologically distinct GRNs, while intermediate (I-type) sensilla have only two (Freeman and Dahanukar, 2015; Scott, 2018; Stocker, 1994). Extracellular ‘tiprecordings’ of different sensilla have identified four GRN types: a water (W) cell that responds to low
osmolarity; a sugar (S) cell that responds to sweet compounds; a low salt (L1) cell that is sodium-specific; and a high salt (L2) cell that responds to high ionic concentrations (>250 mM) (Fujishiro et al.,
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eLife digest Salt is essential for our survival, but too much can kill us. Our taste system has
therefore evolved two different pathways to help us maintain balance. Low concentrations (like the
salt on our chips) activate a pathway that makes us want to eat. But high concentrations (like the salt
in seawater) activate pathways that do the opposite.
The nervous system takes on the role of detecting salt and encoding the information in a way
that the brain can use. One specific type of cell detects each of the four other tastes: sweet, bitter,
sour, and umami. But salt, with its two sensing pathways, is the exception to this rule. Previous work
has examined salt taste responses in flies, but the picture is incomplete.
In flies, one type of taste neuron uses a different signaling mechanism to the others, suggesting
that it might play a special role. So here, Jaeger, Stanley et al. asked how fly sensory cells encode
salt information for the brain, and what those unusual neurons are for.
Mapping the taste receptor neurons in the tongue-like structure of the fly, the proboscis,
revealed that salt information is not restricted to one or two types of cell. In fact, all five types of
neurons tested (covering more than 90% of all the taste neurons present in flies) responded to salt
in some way. Of these, two ‘low salt’ cell types made the fly want to eat salt, and two ‘high salt’ cell
types made the fly want to avoid it. One of these high salt cell types was the unusual taste neuron
identified previously. Rather than always encoding high salt as ’bad’, the message from this type of
cell changed depending on the diet of the fly. Salt-deprived flies ignored the activity of that cell
type altogether. This complex way of encoding taste allowed the fly to change its behavior
depending on how much salt it needed.
This work opens new questions, like how do the fly’s neuronal circuits process this complex salt
code? And how do the ‘high salt’ cells achieve their negative effect only when the need for salt is
low? Understanding more about this system could lead to a better understanding of why our own
brains enjoy salty foods so much.
DOI: https://doi.org/10.7554/eLife.37167.002
1984; Hiroi et al., 2002; Ishimoto and Tanimura, 2004). S- and L-type sensilla are thought to have
one of each GRN type, with S-type L2 cells responding to bitter compounds in addition to high salt
(Meunier et al., 2003)(Figure 1B). I-type sensilla were shown to have an S/L1 hybrid cell that
responds to sugars and low salt, and an L2 cell that responds to bitters and high salt (Hiroi et al.,
2004)(Figure 1B).
The early physiological recordings have been mostly borne out by molecular characterization of
GRN types (Freeman and Dahanukar, 2015; Scott, 2018)(Figure 1B). S- and L-type sensilla each
have a single GRN that expresses the low osmolarity sensor Pickpocket28 (Ppk28) and corresponds
to the W cell (Cameron et al., 2010; Chen et al., 2010; Inoshita and Tanimura, 2006). The S cell is
labelled by the sugar receptor Gr64f, along with other members of the gustatory receptor (GR) family (Dahanukar et al., 2007; Fujii et al., 2015; Jiao et al., 2007; Slone et al., 2007; Thorne et al.,
2004; Wang et al., 2004). Similarly, Gr66a is co-expressed with other Grs in a single bitter responsive neuron per S-type and I-type sensillum, corresponding to the L2 cell (Marella et al., 2006;
Thorne et al., 2004; Wang et al., 2004; Weiss et al., 2011). The degenerin/epithelial sodium channel (Deg/ENaC) family member Ppk23, which is required for pheromone detection in leg gustatory
sensilla, is known to be expressed in a labellar neuron population that partially overlaps with Gr66a/
bitter GRNs (Thistle et al., 2012). Ppk23 neurons are necessary for calcium avoidance, but details of
the labellar Ppk23 expression map, as well as the physiology and function of these neurons are
largely unknown (Lee et al., 2018).
In contrast to water, sweet, and bitter tastes, the principles of peripheral salt coding in flies
remain unclear. Early calcium imaging experiments revealed low salt responses in Gr5a-Gal4 GRNs,
suggesting that sweet neurons may mediate low salt attraction (Marella et al., 2006). However,
Gr5a-Gal4 was later shown to label additional GRNs outside the sweet class (Fujii et al., 2015), and
the Ionotropic receptor (IR) family member IR76b was proposed to specifically mediate low salt taste
via a dedicated low salt cell distinct from sweet GRNs (Zhang et al., 2013). This view was challenged
by the recent demonstration that IR76b is also required for high salt taste, raising questions about
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Figure 1. A molecular map of the fly labellum. (A) A schematic of sensillum identities in the fly labellum. (B) A
summary of how GRN identities are currently viewed across the three sensillum types, with each color representing
a GRN class with its most notable molecular label (if known) and its ascribed response properties. (C–H) Single
labellar palps immunolabelled for VGlut-Gal4 driving UAS-tdTomato (magenta) alone (C) or in combination with
Figure 1 continued on next page
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Figure 1 continued
LexAop-CD2::GFP (green) under the control of ChAT-LexA (D), Ppk28-LexA (E), Gr64f-LexA (F), Gr66a-LexA (G), or
Ppk23-LexA (H). (I) Ppk23-LexA (green) and Gr66a-Gal4 (magenta) label partially overlapping populations. Arrows
indicate sensilla where two Ppk23 GRNs exist, one of which co-expresses Gr66a. (J–K) Ppk23 subpopulations
displayed by restricting Ppk23-Gal4 expression with VGlut-Gal80 (J), or Gr66a-LexA and LexAop-Gal80 (K). (L–N)
IR94e-Gal4 labellum expression (magenta), co-labelled with Ppk28-LexA (L), Gr64f-LexA (M), and Ppk23-LexA (N).
(O) Summary of newly defined GRN types following our mapping experiments. (P) Detailed map of each sensillum.
Colors of ‘+’ in chart indicate cell type. Grey denotes unknown identity. The VGlut +GRNs observed in I-type
sensilla were sporadic and small, which is why they are not considered in the summary.
DOI: https://doi.org/10.7554/eLife.37167.003
The following figure supplement is available for figure 1:
Figure supplement 1. Molecular mapping of characterized and uncharacterized labellar GRN types.
DOI: https://doi.org/10.7554/eLife.37167.004
its utility as a marker for one defined GRN population (Lee et al., 2017). Moreover, although Gr66a
GRNs showed calcium responses to high salt concentrations and electrophysiology suggested that
bitter and high salt are encoded by the same sensory neurons, genetically eliminating these cells left
behavioral aversion to high salt largely intact (Marella et al., 2006; Wang et al., 2004).
Here, we probe the logic of salt coding across the labellum by systematically characterizing the
physiological and behavioral roles of molecularly-defined GRN types covering the entire labellar
GRN map. We find that all GRN types show dose-dependent excitation or inhibition by salt, indicating a complex model for salt coding. Of particular interest is that, like mammals, flies have two distinct high salt cells. In addition to activating canonical bitter neurons, high salt concentrations excite
a glutamatergic GRN population expressing Ppk23. Salt responses of these ‘Ppk23glut’ GRNs require
IR76b, whereas those of bitter GRNs do not. Both bitter and Ppk23glut GRNs are necessary for
behavioral avoidance of high salt when flies have been reared on a salt-containing diet. However,
salt deprivation reduces high salt avoidance by specifically suppressing the impact of Ppk23glut neurons, suggesting that these GRNs mediate internal state-dependent modulation of salt consumption.
Consistent with this idea, closed-loop optogenetic activation of Ppk23glut neurons reduces feeding
by salt-fed flies, but not those that have been salt deprived. Our results support a model where the
combinatorial excitation and inhibition of various taste pathways mediates the behavioral valence of
salt, with one pathway conferring the ability to specifically modulate salt consumption based on
internal state.
Results
A comprehensive map of GRN classes in the labellum
Although several studies have mapped the expression of different receptors across the labellum
(Freeman and Dahanukar, 2015), a comprehensive map covering all GRN types was still lacking.
We began by asking whether the vesicular glutamate transporter (VGlut) may define a functionally
distinct population of GRNs. An enhancer trap upstream of VGlut, OK371-Gal4, labels an uncharacterized population of putatively glutamatergic neurons in the labellum, and a subset of pheromoneresponsive GRNs in the legs (Kallman et al., 2015; Mahr and Aberle, 2006). VGlutMI04979-Gal4,
which is a gene-trap inserted within a VGlut exon, showed expression in a single cell per S-type and
L-type sensillum in the labellum (Figure 1C). These cells do not overlap with those expressing a similar gene trap for choline acetyltransferase (ChAT), supporting the idea that VGlutMI04979-Gal4 labels
a bona fide population of glutamatergic GRNs (Figure 1D).
Co-labelling of VGlutMI04979-Gal4 with LexA reporters for known sensory neuron populations
revealed that VGlut is not expressed in water (Ppk28), sweet (Gr64f), or bitter (Gr66a) GRNs
(Figure 1E–G). However, all VGlut+ cells were positive for Ppk23 (Figure 1H). Further examination
of Ppk23 expression revealed that Ppk23 GRNs are comprised of two distinct subsets: most S-type
and all L-type sensilla contain a single GRN that expresses Ppk23 and VGlut; and six S-type sensilla,
roughly corresponding to those designated as ‘S-a’ sensilla (Freeman and Dahanukar, 2015;
Weiss et al., 2011), have a second Ppk23 GRN that is positive for Gr66a and ChAT (Figure 1I). We
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will refer to these two populations as Ppk23glut and Ppk23chat, respectively. We then used gene trap
insertions of Gal80 into VGlut and ChAT to isolate Ppk23chat and Ppk23glut GRNs. While this restriction largely agreed with our co-expression data, it was not perfect: VGlut-Gal80 restricted Ppk23Gal4 expression to four S-type GRNs instead of the expected six (Figure 1J); and ChAT-Gal80 suppressed Ppk23-Gal4 expression in most, but not all, Ppk23chat GRNs, leaving 1 – 2 s-type sensilla
with two Ppk23 neurons (Figure 1—figure supplement 1A). We suspect these results reflect minor
differences between expression of the Gal4, LexA, and Gal80 reporters, although we confirmed that
Ppk23-Gal4 and Ppk23-LexA labelled the same population of GRNs (Figure 1—figure supplement
1B). To create a more conservative representation of Ppk23glut, we constrained Ppk23-Gal4 activity
using Gr66a-LexA and LexAop-Gal80 (Figure 1K). This manipulation faithfully restricted expression
to only Ppk23glut GRNs, but also globally reduced expression levels. Thus, we retained both methods of isolating Ppk23glut cells for functional characterization.
Our analysis of Ppk23 expression nearly completed the labellar GRN map: s-type sensilla generally have one Ppk28 (water), one Gr64f (sweet), one Gr66a (bitter, some of which are Ppk23chat), and
one Ppk23glut GRN; I-type sensilla have one Gr64f and one Gr66a GRN; and L-type sensilla have one
Ppk28, one Gr64f, one Ppk23glut, and one unidentified GRN that has been proposed to express
IR76b and respond to low salt concentrations (Freeman and Dahanukar, 2015; Zhang et al., 2013).
To identify a marker for the last GRN class in L-type sensilla, we first examined IR76b. However,
IR76b-Gal4 is expressed in many neurons from all four known classes of labellar GRNs, limiting its
utility as a marker (Figure 1—figure supplement 1C–F). We therefore visually screened the Vienna
Tile (VT) and Janelia Rubin Gal4 collections for lines that sparsely label GRN projections in the brain,
and identified VT046252-Gal4, which drives Gal4 expression under the control of the genomic
region upstream of the IR94e locus. Because the labellar projections to the subesophageal zone
(SEZ) labeled by VT046252-Gal4 (Figure 1—figure supplement 1G) appear identical to those of a
previously published reporter for IR94e expression (Koh et al., 2014), we will henceforth simply refer
to it as IR94e-Gal4. IR94e-Gal4 is expressed in one cell per L-type sensillum, and does not overlap
with Ppk28, Gr64f, or Ppk23 (Figure 1L–N). This driver is therefore specific for the fourth GRN class
found in L-type sensilla and completes our molecular map of the labellum (Figure 1O–P).
All labellar GRN classes respond to salt
With a complete labellar GRN map in hand, we examined the salt responses across all identified
GRN classes. We expressed GCaMP6f under the control of each GRN class-specific Gal4 line and
performed imaging of GRN axon terminals in the SEZ while stimulating the labellum with a series of
tastants (Figure 2A–D). As expected, known GRN classes responded strongly to their cognate
modality – Ppk28 to water, Gr64f to sugar (sucrose), and Gr66a to bitter (lobeline) (Figure 2C–D).
As previously demonstrated, Ppk28 neurons show dose-dependent inhibition by salt, as with any
osmolyte (Cameron et al., 2010). In contrast, Gr64f and Gr66a both showed dose-dependent excitation by salt, with Gr64f GRNs activated at a lower threshold. Moreover, Gr64f responses were
sodium-specific, while Gr66a also responded to potassium chloride (Figure 2C–D). These results are
consistent with Gr64f operating as a ‘low salt’ cell type, and Gr66a acting as a ‘high salt’ cell type.
Strikingly, we found that the two relatively uncharacterized labellar GRN types – IR94e and Ppk23
– also showed salt-evoked activity (Figure 2C–D). IR94e displayed weak activation by 50 mM NaCl,
but no responses to higher concentrations. Further testing of different salts at 100 mM revealed
sodium-selective tuning, indicating that IR94e labels a second low salt cell type (Figure 2—figure
supplement 1A–B). The weak responses in IR94e neurons suggest a limited role in salt coding, but
it is possible that they account for the previously observed peak response to low salt in L-type sensilla (Zhang et al., 2013). On the other hand, Ppk23 neurons showed very strong dose-dependent
salt responses that were ion non-selective. In addition to sodium chloride, we observed robust activation by 1 M solutions of potassium chloride, sodium bromide, potassium bromide, cesium chloride, and calcium chloride (Figure 2—figure supplement 2A–B). As confirmation that the observed
activity is salt-evoked and not a response to high osmolality, we found that Ppk23 GRNs do not
respond to 1 M concentrations of sucrose (Figure 2—figure supplement 2A–B).
Since Ppk23 neurons on the leg are known to sense pheromones, we also tested labellar Ppk23
GRN responses to male and female cuticular hydrocarbons. We observed only very weak activation
of Ppk23 neurons in female flies to a mixture of two male pheromones, and no significant responses
in male flies (Figure 2—figure supplement 2C–D). Together, these data suggest that a primary
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Figure 2. Salt activates or inhibits every GRN class. (A) Schematic of calcium imaging preparation. Taste neurons are stimulated on the proboscis, while
GCaMP6f fluorescence is recorded at the synaptic terminals in the SEZ. (B) Representative heat map showing activation of Ppk23 GRNs with 1 M NaCl.
(C) GCaMP6f fluorescence changes over time, following stimulation of each GRN class with the indicated tastants. Lines and shaded regions represent
mean ±SEM, with stimulation occurring at 5 s. In each case, UAS-GCaMP6f is expressed under the control of the indicated GRN-Gal4, with the
exception of Ppk28, which is from Ppk28-LexA and LexAop-GCaMP6f. (D) Peak fluorescence changes during each stimulation. Bars represent
mean ±SEM. n = 8 – 37. Open circles indicate values that were higher than the y-axis maximum. Asterisks indicate significant difference from water by
one-way ANOVA with Bonferroni post hoc test, *p<0.05, **p<0.01, ***p<0.001.
DOI: https://doi.org/10.7554/eLife.37167.005
The following source data and figure supplements are available for figure 2:
Source data 1. Raw numerical data for Figure 2 and associated figure supplements.
DOI: https://doi.org/10.7554/eLife.37167.008
Figure supplement 1. IR94e neurons show weak, low sodium-specific responses to salt.
DOI: https://doi.org/10.7554/eLife.37167.006
Figure supplement 2. Ppk23 neurons respond strongly to all salts and only weakly to pheromones.
DOI: https://doi.org/10.7554/eLife.37167.007
function of labellar Ppk23 GRNs is to mediate a high salt response, and position Ppk23 and Gr66a
as markers of two high salt GRN classes.
Functional subdivision of Ppk23 GRNs
Our expression mapping revealed that Ppk23 GRNs encompass two subsets based on neurotransmitter expression: Ppk23chat and Ppk23glut. Given that Ppk23chat GRNs also express Gr66a, we suspected that this subpopulation may confer bitter responses to the Ppk23 population when
measured as a whole. Although we did not observe Ppk23 activation in response to 0.3 mM lobeline
(Figure 2C–D), we did see strong responses to caffeine (Figure 3A). Interestingly, we observed a
marked difference in the synaptic calcium signals in response to salt and bitter stimuli. While salt
stimulation of Ppk23 GRNs resulted in predominantly lateral activation of Ppk23 projections, bitter
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Figure 3. Morphological and functional distinction between Ppk23 subclasses. (A) Representative heat maps showing the activation Ppk23 GRNs in a
single fly stimulated with 1 M NaCl and 100 mM caffeine. Salt primarily results in lateral activation, while bitter activates medial projections. (B) The
projections of Ppk23 subsets targeting the SEZ, as revealed by immunofluorescent detection of GFP (green). Neuropil is labeled by nc82 (magenta).
The full Ppk23 population targets both medial and lateral regions (left panel). Ppk23glut GRNs, revealed by restriction of Ppk23-Gal4 with Gr66a-LexA
and LexAop-Gal80, target only lateral areas (middle panel). Ppk23chat projections, revealed by restriction of Ppk23-Gal4 with VGlut-Gal80, project to
medial targets (right panel). (C) GCaMP6f fluorescence changes over time, following stimulation of each Ppk23 subset with the indicated tastants. Each
fly has Ppk23-Gal4 driving UAS-GCaMP6f, restricted by either Gr66a-LexA and LexAop-Gal80 (top row) or Vglut-Gal80 (bottom row). Lines and shaded
regions represent mean ±SEM, with stimulation occurring at 5 s. (D) Peak fluorescence changes during each stimulation. Bars represent mean ±SEM,
n = 10–17. Asterisks indicate significant difference from water by one-way ANOVA with Bonferroni post hoc test, *p<0.05, **p<0.01, ***p<0.001.
DOI: https://doi.org/10.7554/eLife.37167.009
The following source data and figure supplement are available for figure 3:
Source data 1. Raw numerical data for Figure 3 and associated figure supplements.
DOI: https://doi.org/10.7554/eLife.37167.011
Figure supplement 1. Ppk23glut GRNs respond to high salt.
DOI: https://doi.org/10.7554/eLife.37167.010
stimulation activated medial ring-like projections characteristic of Gr66a (Figure 3A)(Kwon et al.,
2014; Thorne et al., 2004; Wang et al., 2004). These activation patterns matched closely with the
projections of the Ppk23glut and Ppk23chat subsets, revealed by restricting Ppk23-Gal4 activity with
Gr66a-LexA and LexAop-Gal80 (Ppk23glut) or VGlut-Gal80 (Ppk23chat)(Figure 3B). This suggests that
Ppk23glut and Ppk23chat both respond to salt, but that only Ppk23chat responds to bitter compounds.
To confirm this and reveal any functional differences in salt coding between the Ppk23 subpopulations, we measured the tuning of Ppk23glut and Ppk23chat using calcium imaging. As expected,
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Ppk23chat, but not Ppk23glut, GRNs exhibited bitter responses; however, both subpopulations
showed strong dose-dependent excitation by salt (Figure 3C–D). The salt responses in Ppk23glut
GRNs appeared smaller than those of Ppk23chat, but we suspected this was due to very low
GCaMP6f expression in Ppk23glut, which reduced the signal-to-noise in those measurements. We
therefore repeated the Ppk23glut imaging by restricting Ppk23-Gal4 expression with ChAT-Gal80.
Consistent with the imperfect restriction we observed in the labellum (Figure 1—figure supplement
1A), these flies had small, although insignificant, caffeine responses (Figure 3—figure supplement
1A–B). However, this primarily Ppk23glut population exhibited very strong activation by salt (Figure 3—figure supplement 1A–B).
Taken together, our anatomical and functional studies support a salt coding model with two functionally distinct high salt GRN populations: Gr66a and Ppk23glut. We currently lack evidence of any
functional distinctions between Gr66a GRNs that are positive or negative for Ppk23. Therefore, for
the purposes of salt coding, we will consider Gr66a GRNs as a uniform population that includes
Ppk23chat.
IR76b is necessary for low and high salt responses
A previous report suggested that IR76b is specifically required for low salt responses in an L-type
GRN class distinct from Gr64f (Zhang et al., 2013). However, more recent evidence points to a role
in both high and low salt taste (Lee et al., 2017). Since we observed widespread IR76b-Gal4 expression in many GRN classes, we sought clarity on the role of IR76b in salt taste responses across the
labellum.
Calcium imaging in IR76b mutants revealed that IR76b is absolutely required for salt-evoked
activity in Gr64f GRNs (Figure 4A–B). By contrast, the salt responses of Gr66a GRNs were only
mildly decreased in the mutants, showing that these neurons have a mostly IR76b-independent
mechanism for detecting high salt. Ppk23 salt responses had a much stronger dependence on
IR76b, with significantly decreased peak values, compared to controls, at all concentrations tested
(Figure 4—figure supplement 1A–B).
Given the IR76b-independent salt responses in Gr66a GRNs, it was unsurprising that IR76b
mutants showed some Ppk23 GRN activity in the medial region targeted by Ppk23chat (Gr66a-positive) projections (Figure 4C). We therefore reanalyzed the Ppk23 dataset by quantifying fluorescence change in a region-of-interest restricted to the lateral areas characteristic of Ppk23glut
projections (Figures 3B and 4C). IR76b mutants exhibited essentially no salt-evoked activity in this
target region, suggesting that IR76b is necessary for both the sodium and potassium salt responses
of the Ppk23glut population (Figure 4A–B).
Since IR25a is expressed in GRNs and thought to be another broadly acting co-receptor
(Ahn et al., 2017a; Benton et al., 2009; Cameron et al., 2010; Chen and Amrein, 2017; Lee et al.,
2018), we also tested its involvement in salt taste. We found that IR25a mutants have GRN response
profiles similar to those of IR76b mutants, suggesting that perhaps IR25a and IR76b act in a complex
to mediate gustatory salt responses (Figure 4—figure supplement 2). However, in contrast to
IR76b and IR25a, mutations in Ppk23 and the related ENaC Ppk29 had no observable effect on the
salt-evoked calcium responses of Ppk23 GRNs, consistent with previously reported behavioral tests
(Figure 4—figure supplement 3A–B, [Thistle et al., 2012]). Thus, the Ppk23 gene marks a saltresponsive GRN population but does not appear to be involved in salt detection.
The fact that IR76b mutants lack salt responses in the primary low salt GRN class and one of two
high salt GRN classes provides an explanation for observed defects in both low salt attraction and
high salt avoidance (Lee et al., 2017; Zhang et al., 2013). Before further dissecting the cellular contributions of different GRN classes to salt behaviors, we wanted to establish behavioral assays that
replicated these phenotypes. To test low salt attraction, we used a binary choice assay where flies
were given the option to feed on either 50 mM salt mixed with low sugar (2 mM sucrose), or the
same concentration of sugar alone (LeDue et al., 2015; Tanimura et al., 1982; Zhang et al., 2013).
As previously reported, control flies are strongly attracted to the salt-containing option, while IR76b
mutants lose this attraction (Figure 4D). We used a similar assay to probe high salt avoidance. In this
case, control flies avoid 250 mM salt mixed with 25 mM sucrose in favor of plain sucrose at a lower
concentration (5 mM). Much like their defects in low salt attraction, IR76b mutants are severely
impaired in high salt aversion (Figure 4E).
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Figure 4. IR76b is necessary for Gr64f and Ppk23glut, but not Gr66a, salt responses. (A) GCaMP6f fluorescence changes over time for each indicated
GRN type, following stimulation with the denoted tastants. Black lines are for control genotypes (IR76b1/+ background), red lines for IR76b1/IR76b2
mutants. ‘20%’ scale bar refers to all curves except for the Gr66a caffeine curves, which are scaled by the ‘60%’ bar. For Ppk23glut, GCaMP6f was
expressed under the control of Ppk23-LexA, but only the lateral regions corresponding to Ppk23glut projections were quantified. The pre-stimulus and
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Figure 4 continued
post-stimulus periods were truncated in all curves to more clearly illustrate stimulus phase. (B) Peak fluorescence changes during each stimulation. Bars
represent mean ±SEM, n = 15 for each stimulus. Filled grey circles (control), open red circles (IR76b mutants), and filled red circles (cell-specific IR76b
rescue in imaged GRN population) indicate values for individual replicates. Asterisks indicate significant difference between control and mutant or
mutant and rescue responses for each stimulus by two-way ANOVA with Bonferroni post hoc test, *p<0.05, **p<0.01, ***p<0.001. (C) Representative
heat maps of salt-evoked activity in Ppk23 neurons from control (top) and IR76b mutant (bottom) animals. Activation is predominantly lateral in controls
and medial in mutants. Dotted line demarks representative area quantified to measure Ppk23glut-specific response (see Materials and methods). (D) Low
salt attraction of IR76b mutants, heterozygous controls, and cell-specific rescue of IR76b in Gr64f sweet neurons. Preference measured in binary choice
assay with 50 mM NaCl plus 2 mM sucrose versus 2 mM sucrose alone. Bars represent mean ±SEM. n = 30 – 40 groups of 10 flies each, with filled grey
circles (controls), open red circles (IR76b mutants), or filled red circles (rescues) indicating values for individual groups. Asterisks denote significance by
one-way ANOVA with Bonferroni post hoc test, ***p<0.001. (E) High salt avoidance of IR76b mutants, heterozygous controls, and cell-specific rescue of
IR76b in Gr66a bitter neurons or Ppk23 neurons. Preference measured in binary choice assay with 250 mM NaCl plus 25 mM sucrose versus 5 mM
sucrose alone. Bars represent mean ±SEM. n = 22 – 52 groups of 10 flies each, with filled grey circles (controls), open red circles (IR76b mutants), or
filled red circles (rescues) indicating values for individual groups. Asterisks denote significance by one-way ANOVA with Bonferroni post hoc test,
***p<0.001.
DOI: https://doi.org/10.7554/eLife.37167.012
The following source data and figure supplements are available for figure 4:
Source data 1. Raw numerical data for Figure 4 and associated figure supplements.
DOI: https://doi.org/10.7554/eLife.37167.016
Figure supplement 1. Ppk23 GRN salt responses require IR76b.
DOI: https://doi.org/10.7554/eLife.37167.013
Figure supplement 2. A subset of GRN salt responses requires IR25a.
DOI: https://doi.org/10.7554/eLife.37167.014
Figure supplement 3. Ppk23 GRN salt responses do not require Ppk23 or Ppk29.
DOI: https://doi.org/10.7554/eLife.37167.015
The cellular basis for salt attraction and avoidance
To probe the cellular basis of salt behaviors, we conditionally silenced different GRN populations
using Kir2.1 expression temporally restricted with Gal80ts. As expected, both Ppk23glut and Ppk28
GRNs were dispensable for low salt attraction (Figure 5A). Focusing on the two GRN classes with
low salt tuning properties, we found that Gr64f GRN activity is necessary for attraction to 50 mM
NaCl, but expression of Kir2.1 in IR94e GRNs had no effect (Figure 5A). Further, silencing Gr64f and
IR94e neurons together resulted in behavior indistinguishable from Gr64f silencing alone. Puzzled by
the apparent lack of a role for IR94e GRNs in salt attraction, we expressed a different effector – tetanus toxin (TNT) – in these neurons without any temporal restriction with Gal80ts, and observed
reduced low salt attraction (Figure 5—figure supplement 1). Moreover, attraction was virtually eliminated when TNT was expressed in both Gr64f and IR94e GRNs (Figure 5—figure supplement 1).
Thus, we conclude that sweet GRNs likely mediate the bulk of low salt attraction, with additional
input from the IR94e class.
Interestingly, Kir2.1 expression in IR76b-Gal4 GRNs had a similar effect to Gr64f silencing, suggesting that Gr64f mediates the bulk of IR76b-dependent low salt attraction. However, this phenotype appears less severe than that of IR76b mutants, which display mild low salt avoidance
(Figure 4D). This could reflect incomplete silencing from Kir2.1, as suggested by the lack of observable effects in IR94e GRNs, or weak IR76b-independent low salt responses in Gr66a (bitter/high salt)
GRNs that further reduce salt preference in IR76b mutants. In any case, restoring IR76b selectively
to Gr64 neurons rescues low salt attraction in IR76b mutants, further supporting the role of sweet
neurons in salt attraction (Figure 4D).
Since Gr64f neurons are necessary for sugar detection, we sought verification that the Gr64f salt
attraction phenotype was not from an inability to sense the low concentration of sucrose in both
food options. Indeed, Gr64f silencing caused a similar reduction in salt attraction in the absence of
sugar (Figure 5B).
We then tested the role of each high salt GRN class in high salt avoidance and found that Gr66a
and Ppk23glut GRNs are both necessary for this behavior (Figure 5C). To confirm the novel role for
Ppk23glut in behavioral salt avoidance, we tested its impact on the Proboscis Extension Reflex (PER),
which is an acute measure of gustatory palatability. Consistent with our binary choice assay, silencing
Jaeger et al. eLife 2018;7:e37167. DOI: https://doi.org/10.7554/eLife.37167
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A
B
50 mM NaCl + 2 mM sucrose
ns
ns
50 mM NaCl
***
***
***
1.0
Preference Index
1.0
Preference Index
ns
***
0.5
0.0
-0.5
0.5
0.0
-0.5
-1.0
-1.0
2 mM sucrose
C
D
salt fed flies
U
AS
-K
ir
G
r6 2.1
/+
4
G f-G
a
r6
4f l4/+
>
Ki
r2
.1
U
AS
-K
ir
G
r6 2.1
/+
4f
G -G
a
r6
4f l4/+
>
IR
K
94 ir2
e- .1
IR
G
94 al4
Pp e > /+
K
k2
3 glu ir2.
Pp
1
t
G
k2
3 glu al4
/+
t
Pp > K
k2 ir2.
Pp 8-G 1
k2 al4
8
/+
>
I
Ki
R
G
r
7
r6
6b 2.1
4f
-G IR -G
76 al4
al
4
b
/+
+
>
G
I
r6
K
4f R94 ir2
.1
+
e
IR -G
94 al4
e
/+
>
Ki
r2
.1
water
salt deprived flies
250 mM NaCl + 25 mM sucrose
250 mM NaCl + 25 mM sucrose
***
1.0
1.0
Preference Index
***
0.0
-0.5
ns
0.5
0.0
-0.5
-1.0
-1.0
U
AS
-K
ir
G
r6 2.1
6a
/+
-G
G
r6
a
6a l4/
+
Pp
>
Ki
k2
r
PP 3 glut 2.1
K2 -G
3 glu al4
/+
t
>
Ki
r2
.1
5 mM sucrose
5 mM sucrose
U
AS
-K
ir
G
r6 2.1
6a
/+
G
r6 Gal
4
6
Pp a > /+
Ki
k2
r
2.
3
gl
PP
ut
1
K2 -G
a
3 glu l4
/+
t
>
Ki
r2
.1
Preference Index
***
0.5
Figure 5. Specific GRN contributions to salt attraction and avoidance. (A) Low salt attraction in binary choice
assay, following silencing of different GRN populations with Kir2.1. Positive values indicate preference for 50 mM
NaCl plus 2 mM sucrose; negative values indicate preference for 2 mM sucrose alone. Bars represent mean ±SEM.
n = 40 groups of 10 flies each for all genotypes except the UAS-Kir2.1/+ control, where n = 200. Filled grey circles
indicate values for individual groups. Asterisks denote significant difference from both UAS-Kir2.1/+ and
corresponding Gal4/+ controls by one-way ANOVA with Bonferroni post hoc test, ***p<0.001. (B) Low salt
attraction tested in the absence of sugar. Bars represent mean ±SEM. n = 40 groups of 10 flies each. Asterisks
denote significant difference from both UAS-Kir2.1/+ and corresponding Gal4/+ controls by one-way ANOVA with
Bonferroni post hoc test, ***p<0.001. (C–D) Requirements of aversive GRNs in high salt avoidance under salt fed
(C) or salt deprived (D) conditions. Positive values indicate preference for 250 mM NaCl plus 25 mM sucrose;
negative values indicate preference for 5 mM sucrose. Bars represent mean ±SEM. n = 25 (C) or 30 (D) groups of
10 flies each for all genotypes except the UAS-Kir2.1/+ control in (D), where n = 60. Filled grey circles indicate
values for individual groups. Asterisks denote significant difference from both UAS-Kir2.1/+ and corresponding
Gal4/+ controls by one-way ANOVA with Bonferroni post hoc test, ***p<0.001. For all panels, ‘Ppk23glut-Gal4’
indicates Ppk23-Gal4 with Gr66a-LexA and LexAop-Gal80; and ‘>’ denotes indicated Gal4 driving UAS-Kir2.1 with
temporal restriction by tub-Gal80ts.
DOI: https://doi.org/10.7554/eLife.37167.017
The following source data and figure supplements are available for figure 5:
Figure 5 continued on next page
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Figure 5 continued
Source data 1. Raw numerical data for Figure 5 and associated figure supplements.
DOI: https://doi.org/10.7554/eLife.37167.021
Figure supplement 1. Silencing with tetanus toxin reveals a role for IR94e in low salt attraction.
DOI: https://doi.org/10.7554/eLife.37167.018
Figure supplement 2. Ppk23glut and Gr66a GRNs differentially function in PER suppression by high salt,
depending on internal state.
DOI: https://doi.org/10.7554/eLife.37167.019
Figure supplement 3. Ppk23glut calcium responses are not modulated by salt deprivation.
DOI: https://doi.org/10.7554/eLife.37167.020
Ppk23glut GRNs severely impaired the inhibition of PER by high salt (Figure 5—figure supplement
2A). Moreover, rescue of IR76b expression in either Gr66a or Ppk23glut GRNs partially restores high
salt avoidance to IR76b mutants (Figure 4E).
Ppk23glut mediates state-dependent modulation of salt behaviors
Fly gustatory responses are frequently modulated by need for specific nutrients (Kim et al., 2017).
However, modulating salt behaviors presents a complex problem because two of the three GRN
classes exhibiting strong salt-evoked activity – Gr64f (sweet) and Gr66a (bitter) – have prominent
roles in the detection of other modalities. These are therefore poor candidates for need-dependent
modulation of salt responses, unless plasticity is achieved by regulating a salt-specific receptor. We
therefore speculated that Ppk23glut GRNs, which to our knowledge specifically respond to salt, may
tune the fly’s salt behaviors based on need.
The high salt assay shown in Figure 5C was performed on flies under salt fed conditions (three
days with food containing 10 mM NaCl) to maximize salt avoidance. We subsequently repeated this
experiment with flies deprived of salt for three days and observed the expected weakening of salt
aversion in controls (Figure 5D; p<0.0001 compared to Figure 5C). Strikingly, while silencing Gr66a
GRNs further reduced salt avoidance, silencing Ppk23glut GRNs had no effect (Figure 5D). This suggests that the aversiveness of Ppk23glut GRN activation is suppressed by salt deprivation. To verify
this result, we again turned to PER and found that salt deprivation reduced high salt inhibition of
PER and suppressed the role of Ppk23glut GRNs (Figure 5—figure supplement 2A). Interestingly,
Gr66a GRN silencing produced only weak effects on PER inhibition by high salt, which were significantly manifested only in the salt deprived state (Figure 5—figure supplement 2B).
We next asked whether the observed behavioral modulation by salt deprivation would be evident
in the calcium responses of these neurons; however, salt deprivation led to only a very mild and statistically insignificant reduction in Ppk23glut salt responses (Figure 5—figure supplement 3). Therefore, modulation is likely to occur downstream of GRN output.
To further explore this idea, we built a closed-loop system for real-time optogenetic activation of
neurons during feeding behavior. Developed as an add-on to the fly Proboscis and Activity Detector
(FlyPAD; [Itskov et al., 2014]), our system triggers illumination of a red LED immediately upon
detecting a fly’s interaction with one of the two food sources (Figure 6A). We call this system the
Sip-TRiggered Optogenetic Behavior Enclosure (STROBE). The STROBE is similar in concept to
another recently described optogenetic FlyPAD (Steck et al., 2018), but implements sip detection
and light triggering in a different way to minimize latency and achieve illumination during sips, with
LED activation tightly locked to sip onset and offset.
As expected, sip-induced triggering of Gr64f GRN activation makes a tasteless food source
attractive compared to the same food without light stimulation, and this effect is independent of salt
deprivation (Figure 6B). Similarly, Gr66a activation is strongly aversive for both salt fed and salt
deprived flies. Consistent with their lack of a strong phenotype when silenced, activation of IR94e
neurons did not produce a detectable phenotype in either condition. However, stimulating Ppk23glut
GRNs is aversive, but only when flies have been pre-fed on a salt-containing diet (Figure 6B). This
supports a model where salt need modulates salt avoidance downstream of Ppk23glut GRN
activation.
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Figure 6. Salt deprivation modulates salt avoidance downstream of Ppk23glut. (A) Schematic of closed-loop optogentic feeding assay, called the ‘sip
triggered optogenetic behavior enclosure’ (STROBE). Each food is presented as a small drop containing 1% agar. Interactions with Food 1 that are
recorded as ‘sips’ trigger illumination of an LED in the roof of the enclosure, above Food 1. Light triggering is temporally coupled to sip onset with
minimal latency. Sips on Food 2 are recorded, but no light is triggered. (B) STROBE results for optogenetic stimulation of each salt-responsive GRN
class. CsChrimson was expressed in each indicated GRN class, and the preference for Food 1 (light triggering) vs Food 2 (no light triggered) is plotted.
Food 1 and Food 2 were otherwise the same in each experiment: plain agar (‘water’) for Gr64f and IR94e activation, and 1M sucrose for Gr66a and
Ppk23glut activation. 1 M sucrose was used for aversive GRN tests because we observe more robust avoidance in this context. Bars represent
mean ±SEM. Colored bars represent flies fed retinal (active CsChrimson) and gray bars represent flies not fed retinal (inactive controls). n = 19 – 24 for
Gr64f, IR94e and Gr66a experiments, and n = 34 – 42 for Ppk23glut experiment. Filled (retinal fed) or open (not retinal fed) circles indicate values for
individual flies. Asterisks indicate significant differences between retinal and no retinal groups for each condition by two-way ANOVA with Bonferroni
post hoc test, ***p<0.001. Asterisks between fed and deprived conditions for Ppk23glut experiment represent a significant interaction between salt
feeding and ±retinal conditions, ***p<0.001.
DOI: https://doi.org/10.7554/eLife.37167.022
The following source data is available for figure 6:
Source data 1. Raw numerical data for Figure 6.
DOI: https://doi.org/10.7554/eLife.37167.023
Discussion
Our results suggest a complex model for how the fly peripheral gustatory system encodes salt taste.
In contrast to other known taste modalities, which typically activate a single, molecularly defined
population of sensory neurons, salt taste is encoded by the combined activity of most to all GRN
classes (Figure 7). By identifying markers that cumulatively cover virtually every labellar GRN, we
find that two cell types – Gr64f and IR94e – display low salt tuning properties and mediate salt
attraction. Although Gr64f neurons are also activated by higher concentrations of salt, their relatively
low threshold for activation and specificity for sodium are consistent with a low salt GRN identity.
Two other cell types – Gr66a and Ppk23glut – act as high salt GRNs, responding ion non-selectively
to high concentrations of salt and driving avoidance. Moreover, the impact of Ppk23glut activation is
suppressed upon salt deprivation, providing a means to reduce salt avoidance when need is
elevated.
Prior salt coding models have primarily relied on correlations between neural activity and behavioural responses to different salt stimuli, as well as changes to those properties in mutants that may
Jaeger et al. eLife 2018;7:e37167. DOI: https://doi.org/10.7554/eLife.37167
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have wide ranging effects (Ishimoto and Tanimura, 2004; Lee et al., 2017; Zhang et al.,
2013). These studies led to the important idea
Low salt
High salt
that there are distinct low salt and high salt cells
present, and that at increasing salt concentrations, the aversive high salt cell progressively
dominates over the attractive low salt cell. However, without the molecular tools to identify and
manipulate individual GRN classes, the identity
and number of salt-responsive cell types were
unclear. Our examination of salt coding across a
glut
Ppk28 IR94e Gr64f Gr66a Ppk23
comprehensive set of molecularly defined GRN
classes provides new insight into the complexity
salt
salt
of salt coding in flies, but is not without limitafed
deprived
tions. Most notably, we cannot detect possible
heterogeneity within defined GRN classes. For
?
Attraction
Avoidance
example, a Gr64f (sweet) neuron is present in
each bristle of all sensillum types. Because we
Figure 7. Model for salt encoding across different GRN looked at the population as a whole, we cannot
classes in the labellum. Line thickness indicates
confidently conclude that every Gr64f cell acts as
strength of the excitatory (arrows) or inhibitory (bars)
a low salt cell; we know only that there are
effects of high and low salt on each GRN class, as well
sodium-specific salt responses from the Gr64f
as the impact of each cell type on behavior.
population, and that this population drives low
DOI: https://doi.org/10.7554/eLife.37167.024
salt attraction.
Ppk23 labels a new high salt cell
Electrophysiological recordings of individual
labellar taste sensilla identified high salt responses in the bitter-sensing neurons of S- and I-type sensilla, and previous GRN calcium imaging confirmed that Gr66a neurons respond to 1 M NaCl and
KCl (Marella et al., 2006; Meunier et al., 2003). However, two key results suggested that bitter
GRNs did not account for all high salt taste. First, high salt neurons have been identified in L-type
sensilla (which don’t have Gr66a neurons) via tip recordings, although this has subsequently been
debated (Hiroi et al., 2002; Ishimoto and Tanimura, 2004; Zhang et al., 2013). Second, genetically
ablating Gr66a GRNs did not block the inhibition of PER by high salt (Wang et al., 2004). The existence of Ppk23glut high salt cells likely explains both of these observations and provides a mechanism by which flies can specifically modulate their salt behavior in response to need.
In addition to the modulation of their behavioral impact, Ppk23glut neurons display some notable
characteristics, the most conspicuous being they are the only GRN class to express a marker for glutamatergic, rather than cholinergic, neurons. This adds a potential new dimension to the gustotopic
GRN map formed in the fly brain and may be a key mechanism by which the output of Ppk23glut neurons remains functionally distinct from other GRN classes targeting postsynaptic neurons in the
same area. Indeed, the aversive nature of Ppk23glut output stands in contrast to what one would predict from their projection morphology, which looks qualitatively similar to known appetitive (Gr64f,
Ppk28), rather than aversive (Gr66a) GRNs. It is possible that Ppk23glut aversiveness is mediated
through inhibition of appetitive taste pathways, as glutamate can have excitatory or inhibitory postsynaptic effects, depending on the receptor present (Liu and Wilson, 2013).
Although Ppk23glut defines a novel high salt cell, it is important to note that the Ppk23 channel is
not required for its salt responsiveness. This raises questions about what Ppk23-dependent
responses these cells may exhibit. Since Ppk23 is required for leg GRN pheromone-evoked activity
that regulates courtship, a related function for Ppk23 labellar GRNs cannot be excluded. Indeed,
weak, but significant Ppk23-dependent pheromone responses have been observed in labellar GRNs,
although it’s unclear whether these were from Ppk23glut or Ppk23chat cells (Thistle et al., 2012).
Moreover, the interaction between salt taste and mating suggests that perhaps there is a need to
co-modulate salt and social cues based on salt diet (Walker et al., 2015).
In contrast to the strong salt responses in Ppk23glut cells, the other uncharacterized GRN class we
identified, IR94e, displayed only weak salt-evoked activity. We therefore expect that this class
Jaeger et al. eLife 2018;7:e37167. DOI: https://doi.org/10.7554/eLife.37167
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primarily responds to other, yet unidentified, taste ligands. Given the lack of strong effects we
observe upon activation of IR94e GRNs in the STROBE, we also suspect that the behavioral impact
of IR94e activation is, like Ppk23glut, state- or context-dependent.
IR76b has widespread roles in chemosensation
To date, IR76b has been shown to be necessary for gustatory responses to low salt, high salt, calcium, acids, amino acids, fatty acids, and polyamines (Ahn et al., 2017b; Chen and Amrein, 2017;
Hussain et al., 2016b; Lee et al., 2017; Zhang et al., 2013). Consistent with these widespread roles
in the taste system, we find expression of IR76b-Gal4 in every GRN type tested. Nonetheless, we
felt it important to clarify the role of IR76b in salt taste, given the apparent complexities in salt
responses across labellar GRN types, and the previous demonstration that IR76b can function as a
sodium leak channel (Zhang et al., 2013).
We find that Gr64f salt responses are completely dependent on IR76b, consistent with its proposed role in low salt taste. Ppk23glut salt responses also require IR76b, but those in Gr66a GRNs do
not, indicating two different salt transduction mechanisms in these two high salt cells. This may
explain why prior reports differed on whether high salt responses remain intact in IR76b mutants
(Lee et al., 2017; Zhang et al., 2013). Interestingly, the IR76b-dependent salt responses in Ppk23glut
GRNs are not sodium specific, as we see loss of high sodium and potassium salt-evoked activity. This
suggests that, although IR76b is primarily permeable to sodium when expressed in heterologous
cells (PNa: PK = 1: 0.4), it may function in complexes with other subunits that confer different ion
selectivity in different GRN classes (Zhang et al., 2013).
Recently, Ppk23 GRNs were identified as underlying IR76b-dependent calcium taste avoidance
(Lee et al., 2018). Although it isn’t clear whether Ppk23glut or Ppk23chat (or both) subpopulations
are responsible, our results indicate that this effect is not specific to calcium, but rather a general
salt avoidance mechanism. Indeed, Ppk23 GRNs respond to high concentrations of all salts tested.
Moreover, we find that IR25a, which was implicated in Ppk23-mediated calcium taste (Lee et al.,
2018), is necessary for salt responses in Gr64f and Ppk23glut GRNs, similar to the requirements for
IR76b. This stands in contrast to results reported by Zhang et al. (2013), which suggested that
IR25a did not play a role in sodium taste. The similar requirements for IR76b and IR25a also suggest
that these two receptors may act in a complex to mediate salt taste, which is consistent with previous evidence that IR25a is a broadly expressed coreceptor (Ahn et al., 2017a; Benton et al., 2009;
Cameron et al., 2010; Chen and Amrein, 2017; Lee et al., 2018).
Modulation of salt avoidance by internal state
Changes in gustatory sensitivity based on internal state are a widespread feature of the fly taste system: starvation potentiates sweet GRN sensitivity and suppresses bitter GRN responses; mating
increases taste peg GRN sensitivity to polyamines and behavioral sensitivity to low salt in females;
and protein deprivation sensitizes taste peg GRNs to yeast and increases behavioral sensitivity to
amino acids (Hussain et al., 2016a; Inagaki et al., 2012; Inagaki et al., 2014; LeDue et al., 2016;
Steck et al., 2018; Toshima and Tanimura, 2012; Walker et al., 2015). Although modulation of salt
taste has not been previously examined in flies, salt depletion in humans increases salt palatability
(Beauchamp et al., 1990). In line with all these results, we observe significant modulation of fly salt
taste behavior by salt deprivation.
In contrast to most taste modalities, which activate a single GRN population, modulation of salt
taste presents a complicated problem, because tuning the gain of Gr64 or Gr66a GRN output would
have side effects on sweet and bitter taste sensitivity that may be situationally inappropriate. Here,
we have presented evidence that the fly gustatory system solves this problem by specifically modulating the effects downstream of Ppk23glut activation. Salt deprivation suppresses the aversiveness of
these neurons, allowing the fly to be less repulsed (or more attracted) to salty foods.
Thus, the fly taste system appears to encode salt as a complex mixture of attractive and repulsive
sensory responses. Two GRN classes – Gr64f and Gr66a – provide a baseline level of attraction or
avoidance, and this response is then adjusted to need via modulation of a third class of salt-responsive GRNs, Ppk23glut. The apparent specificity of labellar Ppk23glut GRNs to salt may also provide an
important neural substrate for discrimination between salt and other taste modalities. Continued
exploration of how salt, and other, taste signals are integrated higher in the brain will provide insight
Jaeger et al. eLife 2018;7:e37167. DOI: https://doi.org/10.7554/eLife.37167
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into how an apparently low-dimensional sensory system can successfully encode a variety of diverse
chemical cues.
Materials and methods
Key resources table
Reagent type (species)
or resource
Designation
MI04979
Source or
reference
Identifiers
Genetic reagent
(D. melanogaster)
vGlut
-Gal4
Diao et al. (2015)
BDSC: 60312;
RRID:BDSC_60312
Genetic reagent
(D. melanogaster)
ChATMI04508-Gal4
Diao et al. (2015)
BDSC: 60317;
RRID:BDSC_60317
Genetic reagent
(D. melanogaster)
vGlutMI04979LexA::QFAD
Diao et al. (2015)
BDSC: 60314;
RRID:BDSC_60314
Genetic reagent
(D. melanogaster)
ChATMI04508LexA::QFAD
Diao et al. (2015)
BDSC: 60319;
RRID:BDSC_60319
Genetic reagent
(D. melanogaster)
vGlutMI04979Gal80
Diao et al. (2015)
BDSC: 60316;
RRID:BDSC_60316
Genetic reagent
(D. melanogaster)
ChATMI04508Gal80
Diao et al. (2015)
BDSC: 60321;
RRID:BDSC_60321
Genetic reagent
(D. melanogaster)
DPpk23
Thistle et al. (2012)
Flybase:
FBal0277047
Genetic reagent
(D. melanogaster)
DPpk29
Thistle et al. (2012)
Flybase:
FBal0277049
Genetic reagent
(D. melanogaster)
Gr66a-LexA
Thistle et al. (2012)
Flybase:
FBal0277069
Genetic reagent
(D. melanogaster)
ppk28-LexA
Thistle et al. (2012)
Flybase:
FBal0277050
Genetic reagent
(D. melanogaster)
ppk23-Gal4
Thistle et al. (2012)
Flybase:
FBal0277044
Genetic reagent
(D. melanogaster)
Gr64fLexA
Miyamoto et al. (2012)
Flybase:
FBti0168176
Genetic reagent
(D. melanogaster)
ppk23-LexA
Toda et al. (2012)
Flybase:
FBst0051311
Genetic reagent
(D. melanogaster)
IR76b-Gal4
Zhang et al. (2013)
Flybase:
FBtp0085485
Genetic reagent
(D. melanogaster)
IR76b1
Zhang et al. (2013)
Flybase:
FBst0051309
Genetic reagent
(D. melanogaster)
IR76b2
Zhang et al. (2013)
Flybase:
FBst0051310
Genetic reagent
(D. melanogaster)
UAS-IR76b
Zhang et al. (2013)
Flybase:
FBtp0085485
Genetic reagent
(D. melanogaster)
IR25a1
Benton et al. (2009)
Flybase:
FBst0041736
Genetic reagent
(D. melanogaster)
IR25a2
Benton et al. (2009)
Flybase:
FBst0041737
Genetic reagent
(D. melanogaster)
UAS-IR25a
Abuin et al., 2011
Flybase:
FBst0041747
Genetic reagent
(D. melanogaster)
Gr66a-Gal4
Wang et al. (2004)
Flybase:
FBtp0014660
Genetic reagent
(D. melanogaster)
Gr64f-Gal4
Dahanukar et al.
(2007)
Flybase:
FBti0162678
Genetic reagent
(D. melanogaster)
Gr64f-Gal4
Dahanukar et al
. (2007)
Flybase:
FBtp0057275
Genetic reagent
(D. melanogaster)
Ppk28-Gal4
Cameron et al. (2010)
Flybase:
FBtp0054514
Additional information
Continued on next page
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Continued
Reagent type (species)
or resource
Designation
Source or
reference
Genetic reagent
(D. melanogaster)
LexAop-CD2::GFP
Lai and Lee (2006)
Flybase:
FBti0186090
Genetic reagent
(D. melanogaster)
UAS-Kir2.1
Baines et al. (2001)
Flybase:
FBti0017552
Genetic reagent
(D. melanogaster)
tub-Gal80ts
McGuire et al
. (2004)
Flybase:
FBti0027797
Genetic reagent
(D. melanogaster)
IR94e-Gal4
Tirián and Dickson, 2017
VDRC: v207582
Genetic reagent
(D. melanogaster)
w1118
Bloomington
Drosophila Stock
Center
BDSC: 3605;
RRID:BDSC_3605
Genetic reagent
(D. melanogaster)
LexAop-Gal80
Bloomington
Drosophila Stock
Center
BDSC: 32214;
RRID:BDSC_32214
Genetic reagent
(D. melanogaster)
LexAop-GCaMP6f
Bloomington
Drosophila Stock
Center
BDSC: 44277;
RRID:BDSC_44277
Genetic reagent
(D. melanogaster)
UAS-GCaMP6f
Bloomington
Drosophila Stock
Center
BDSC: 42747;
RRID:BDSC_42747
Genetic reagent
(D. melanogaster)
UAS-GCaMP6f
Bloomington
Drosophila Stock
Center
BDSC: 52869;
RRID:BDSC_52869
Genetic reagent
(D. melanogaster)
UAS-CsChrimson
Bloomington
Drosophila Stock
Center
BDSC: 55135;
RRID:BDSC_55135
Genetic reagent
(D. melanogaster)
UAS-TNT
Bloomington
Drosophila Stock
Center
BDSC: 28838;
RRID:BDSC_28838
Genetic reagent
(D. melanogaster)
UAS-impTNT
Bloomington
Drosophila Stock
Center
BDSC: 28840;
RRID:BDSC_28840
Antibody
anti-GFP
Abcam, Cambridge,
UK,
#13970;
RRID:AB_300798
(1:1000 dilution)
Antibody
anti-RFP
Rockland
Immunochemicals,
Pottstown, PA,
#600-401-379;
RRID:AB_2209751
(1:200 dilution)
Antibody
anti-chicken
Alexa 488
Abcam
#150169;
RRID:AB_2636803
(1:200 dilution)
Antibody
anti-rabbit
Alexa 647
Thermo Fisher
Scientific, Waltham,
MA,
#A21245;
RRID:AB_2535813
(1:200 dilution)
Antibody
anti-brp
Developmental
Studies Hybridoma
Bank
#nc82;
RRID:AB_2314866
(1:50 dilution)
Antibody
anti-rabbit Alexa 568
Thermo Fisher
Scientific, Waltham,
MA,
#A11036;
RRID:AB_10563566
(1:200 dilution)
Chemical
compound, drug
All trans-Retinal
Sigma-Aldrich
#R2500
Chemical
compound, drug
Sucrose
Sigma-Aldrich
#S7903
Chemical
compound, drug
NaCl
Sigma-Aldrich
#S7653
Chemical
compound, drug
KCl
Sigma-Aldrich
#P9541
Identifiers
Additional information
Continued on next page
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Continued
Reagent type (species)
or resource
Designation
Source or
reference
Identifiers
Chemical
compound, drug
NaBr
Sigma-Aldrich
#S4547
Chemical
compound, drug
KBr
Sigma-Aldrich
#221864
Chemical
compound, drug
CsCl
Sigma-Aldrich
#289329
Chemical
compound, drug
CaCl2
BDH chemicals
#BDH4524
Chemical
compound, drug
Lobeline hydrochloride
Sigma-Aldrich
#141879
Chemical
compound, drug
Caffeine
Sigma-Aldrich
#C0750
Chemical
compound, drug
7,11-heptacosadiene
(7,11-HC)
Caymen
chemical company
#10012567
Chemical
compound, drug
7,11-nonacosadiene
(7,11-NC)
Caymen
chemical company
#9000314
Chemical
compound, drug
7-tricosene (7 T)
Caymen
chemical company
#9000313
Chemical
compound, drug
Cis-vaccenyl acetate
(c-VA)
Caymen
chemical company
#10010101
Chemical
compound, drug
Erioglaucine
Spectrum chemical
#FD110
Chemical
compound, drug
Amaranth
Sigma-Aldrich
#A1016
Software,
algorithm
STROBE
executable
Chan, 2018a
github: https://github.com/rcwch
an/STROBE_software/ (copy archived
at https://github.com/elifesciencespublications/STROBE_software)
Software,
algorithm
STROBE postprocessing
Chan, 2018a
github: https://github.com/rcw
chan/STROBE_software/ (copy archived
at https://github.com/elifesciencespublications/STROBE_software)
Software,
algorithm
STROBE VHDL code
Chan, 2018b
github: https://github.com
/rcwchan/STROBE-fpga (copy archived
at https://github.com/elifesciencespublications/STROBE-fpga)
Software,
algorithm
ImageJ
Schneider et al. (2012)
https://imagej.nih.gov/ij; RRID:SCR_003070
Software,
algorithm
Prism 6
Graphpad
RRID:SCR_002798
Software,
algorithm
Photoshop
Adobe
RRID:SCR_014199
Software,
algorithm
Illustrator
Adobe
RRID:SCR_010279
Additional information
Fly genotype table
Figure panel
Genotype
Figure 1C
+/+; vGlutMI04979-Gal4/+; UAS-CD8::tdTomato/+
Figure 1D
+/+; vGlutMI04979-Gal4/
LexAop-CD2::GFP ; UAS-CD8::tdTomato/ChATMI04508- LexA::QFAD
Continued on next page
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Continued
Figure panel
Genotype
Figure 1E
+/+; vGlutMI04979-Gal4/
LexAop-CD2::GFP;
UAS-CD8::tdTomato/Ppk28-LexA
Figure 1F
+/+; vGlutMI04979-Gal4/
LexAop-CD2::GFP;
UAS-CD8::tdTomato/Gr64fLexA
Figure 1G
+/+; vGlutMI04979-Gal4/
LexAop-CD2::GFP; UAS-CD8::tdTomato/Gr66a-LexA
Figure 1H
+/+; vGlutMI04979-Gal4/
LexAop-CD2::GFP; UAS-CD8::tdTomato/Ppk23-LexA
Figure 1I
+/+; Gr66a-Gal4/
LexAop-CD2::GFP; UAS-CD8::tdTomato/Ppk23-LexA
Figure 1J
+/+; vGlutMI04979-Gal80
/UAS-GCaMP6f;
Ppk23-Gal4/+
Figure 1K
Gr66a-LexA/+; LexAop-Gal80/
UAS-GCaMP6f;
Ppk23-Gal4/+
Figure 1L
+/+; UAS-CD8::tdTomato
/LexAop-CD2::GFP;
IR94e-Gal4/ppk28-LexA
Figure 1M
+/+; UAS-CD8::tdTomato
/LexAop-CD2::GFP; IR94e-Gal4/Gr64fLexA
Figure 1N
+/+; UAS-CD8::tdTomato
/LexAop-CD2::GFP; IR94e-Gal4/ppk23-LexA
Figure 1—figure supplement 1A
+/+; ChATMI04508-Gal80
/UAS-GCaMP6f; Ppk23-Gal4/+
Figure 1—figure supplement 1B
+/+; UAS-CD8::tdTomato
/LexAop-CD2::GFP; Ppk23-Gal4/Ppk23-LexA
Figure 1—figure supplement 1C
+/+; IR76b-Gal4/
LexAop-CD2::GFP; UAS-CD8::tdTomato/Ppk28-LexA
Figure 1—figure supplement 1D
+/+; IR76b-Gal4/
LexAop-CD2::GFP; UAS-CD8::tdTomato/Gr64fLexA
Figure 1—figure supplement 1E
+/+; IR76b-Gal4
/LexAop-CD2::GFP; UAS-CD8::tdTomato/Gr66a-LexA
Figure 1—figure supplement 1F
+/+; IR76b-Gal4/LexAop
-CD2::GFP; UAS-CD8::tdTomato/
Ppk23-LexA
Figure 1—figure supplement 1G
+/+; UAS-CsChrimson/+; IR94e-Gal4/+
Figure 2C and D
+/+; LexAop-GCaMP6f/+;
Ppk28-LexA/+
+/+; UAS-GCaMP6f/
Gr64f-Gal4; +/+
+/+; UAS-GCaMP6f/+; IR94e-Gal4/+
+/+; UAS-GCaMP6f/
Gr66a-Gal4; +/+
+/+; UAS-GCaMP6f/+;
Ppk23-Gal4/+
Figure 2—figure supplement 1A–D
+/+; UAS-GCaMP6f/+;
Ppk23-Gal4/+
Figure 2—figure supplement 2
+/+; UAS-GCaMP6f/+;
IR94e-Gal4/+
Figure 3A
+/+; UAS-GCaMP6f/+; Ppk23-Gal4/+
Continued on next page
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Continued
Figure panel
Genotype
Figure 3B
+/+; UAS-GCaMP6f/+; Ppk23-Gal4/+
Gr66a-LexA/+; LexAop
-Gal80/UAS-GCaMP6f; Ppk23-Gal4/+
+/+; vGlutMI04979-Gal80
/UAS-GCaMP6f; Ppk23-Gal4/+
Figure 3C and D
Gr66a-LexA/+; LexAopGal80/UAS-GCaMP6f; Ppk23-Gal4/+
+/+; vGlutMI04979-Gal80/
UAS-GCaMP6f; Ppk23-Gal4/+
Figure 3—figure supplement 1A and B
+/+; ChATMI04508-Gal80/
UAS-GCaMP6f;
Ppk23-Gal4/+
Figure 4A and B
+/+; Gr64f-Gal4/UASGCaMP6f; IR76b2/+
+/+; Gr64f-Gal4/UASGCaMP6f; IR76b1/IR76b2
+/+; Gr64f-Gal4, UASGCaMP6f/UAS-IR76b; IR76b1/IR76b2
+/+; Gr66a-Gal4/UASGCaMP6f; IR76b2/+
+/+; Gr66a-Gal4/UASGCaMP6f; IR76b1/IR76b2
+/+; Gr66a-Gal4, UASGCaMP6f/UAS-IR76b; IR76b1/IR76b2
+/+; Ppk23-LexA/LexAopGCaMP6f; IR76b2/+
+/+; Ppk23-LexA/LexAopGCaMP6f;
IR76b1/IR76b2
+/+; UAS-GCaMP6f/UASIR76b; IR76b1,
Ppk23-Gal4/IR76b2
Figure 4C
+/+; Ppk23-LexA/LexAopGCaMP6f; IR76b2/+
+/+; Ppk23-LexA/LexAopGCaMP6f; IR76b1/IR76b2
Figure 4D
+/+; +/+; IR76b1/+
+/+; +/+; IR76b2/+
+/+; +/UAS-IR76b;
IR76b1/IR76b2
+/+; Gr64f-Gal4/+;
IR76b1/IR76b2
+/+; Gr64f-Gal4/UAS-IR76b;
IR76b1/IR76b2
Figure 4E
+/+; +/+; IR76b1/+
+/+; +/+; IR76b2/+
+/+; +/UAS-IR76b; IR76b1/IR76b2
+/+; Gr66a-Gal4/+;
IR76b1/IR76b2
+/+; Gr66a-Gal4/UAS-IR76b; IR76b1/IR76b2
+/+; +/+; Ppk23-Gal4,
IR76b1/IR76b2
Continued on next page
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Continued
Figure panel
Genotype
+/+; +/UAS-IR76b; Ppk23Gal4, IR76b1/IR76b2
Figure 4—figure supplement 1A and B
+/+; Ppk23-LexA/LexAop
-GCaMP6f; IR76b2/+
+/+; Ppk23-LexA/LexAopGCaMP6f; IR76b1/IR76b2
+/+; UAS-GCaMP6f/
UAS-IR76b; IR76b1,
Ppk23-Gal4/IR76b2
Figure 4—figure supplement 2
+/+; IR25a1/+; Gr64fGal4/UAS-GCaMP6f
+/+; IR25a1/IR25a2;
Gr64f-Gal4/UAS-GCaMP6f
+/+; UAS-IR25a,
IR25a1/IR25a2; Gr64f-Gal4/UAS-GCaMP6f
+/+; IR25a1/+; Gr66aGal4/UAS-GCaMP6f
+/+; IR25a1/IR25a2;
Gr66a-Gal4/UAS-GCaMP6f
+/+; UAS-IR25a,
IR25a1/IR25a2;
Gr66a-Gal4/UAS-GCaMP6f
+/+; IR25a1/+;
Ppk23-Gal4/UAS-GCaMP6f
+/+; IR25a1/IR25a2;
Ppk23-Gal4/UAS-GCaMP6f
+/+; UAS-IR25a,
IR25a1/IR25a2;
Ppk23-Gal4/UAS-GCaMP6f
Figure 4—figure supplement 3A and B
DPpk23/DPpk23;
DPpk29/DPpk29; Ppk23-Gal4/UAS-GCaMP6f
Figure 5A
+/+; +/+; UAS-Kir2.1,
tub-Gal80ts/+
+/+; Gr64f-Gal4/+; +/+
+/+; Gr64f-Gal4/+;
UAS-Kir2.1, tub-Gal80ts/+
+/+; +/+; IR94e-Gal4/+
+/+; +/+; IR94e-Gal4/
UAS-Kir2.1, tub-Gal80ts
Gr66a-LexA/+;
LexAop-Gal80/+; Ppk23-Gal4/+
Gr66a-LexA/+;
LexAop-Gal80/+; Ppk23-Gal4/UAS-Kir2.1, tub-Gal80ts
+/+; +/+; Ppk28-Gal4/+
+/+; +/+; Ppk28-Gal4/
UAS-Kir2.1, tub-Gal80ts
+/+; IR76b-Gal4/+; +/+
+/+; IR76b-Gal4/+;
UAS-Kir2.1, tub-Gal80ts/+
+/+; Gr64f-Gal4/+;
IR94e-Gal4/+
Continued on next page
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Continued
Figure panel
Genotype
+/+; Gr64f-Gal4/+;
IR94e-Gal4/UAS-Kir2.1,
tub-Gal80ts
Figure 5B
+/+; +/+; UAS-Kir2.1,
tub-Gal80ts/+
+/+; Gr64f-Gal4/+; +/+
+/+; Gr64f-Gal4/+;
UAS-Kir2.1, tub-Gal80ts/+
Figure 5C
+/+; +/+; UAS-Kir2.1,
tub-Gal80ts/+
+/+; Gr66a-Gal4/+; +/+
+/+; Gr66a-Gal4/+;
UAS-Kir2.1, tub-Gal80ts/+
Gr66a-LexA/+;
LexAop-Gal80/+; Ppk23-Gal4/+
Gr66a-LexA/+;
LexAop-Gal80/+; Ppk23-Gal4/UAS-Kir2.1, tub-Gal80ts
+/+; +/+; UAS-Kir2.1, tub-Gal80ts/+
Figure 5D
+/+; Gr66a-Gal4/+; +/+
+/+; Gr66a-Gal4/+;
UAS-Kir2.1, tub-Gal80ts/+
Gr66a-LexA/+;
LexAop-Gal80/+; Ppk23-Gal4/+
Gr66a-LexA/+; LexAop-Gal80/+; Ppk23-Gal4/UAS-Kir2.1, tub-Gal80ts
Figure 5—figure supplement 1
+/+; UAS-impTNT/+; +/+
+/+; UAS-TNT/+; +/+
+/+; +/+; IR94e-Gal4/+
+/+; UAS-impTNT/+; IR94e-Gal4/+
+/+; UAS-TNT/+; IR94e-Gal4/+
+/+; Gr64f-Gal4/+; IR94e-Gal4/+
+/+; UAS-impTNT/Gr64f-Gal4; IR94e-Gal4/+
+/+; UAS-TNT/Gr64f-Gal4; IR94e-Gal4/+
Figure 5—figure supplement 2A
Gr66a-LexA/+; LexAop-Gal80/+;
Ppk23-Gal4/UAS-Kir2.1, tub-Gal80ts
Gr66a-LexA/+; LexAop-Gal80/+; Ppk23-Gal4/+
+/+; +/+; UAS-Kir2.1, tub-Gal80ts/+
+/+; Gr66a-Gal4/+; UAS-Kir2.1, tub-Gal80ts/+
Figure 5—figure supplement 2B
+/+; Gr66a-Gal4/+; +/+
+/+; +/+; UAS-Kir2.1, tub-Gal80ts/+
Figure 5—figure supplement 3
+/+; UAS-GCaMP6f/+; Ppk23-Gal4/+
Figure 6B
+/+; UAS-CsChrimson/Gr64f-Gal4; +/+
+/+; UAS-CsChrimson/+; IR94e-Gal4/+
+/+; UAS-CsChrimson/Gr66a-Gal4; +/+
Gr66a-LexA/+; LexAop-Gal80/UAS-CsChrimson; Ppk23-Gal4/+
Flies
Flies were raised on standard cornmeal fly food at 25˚C in 70% humidity. The following genotypes
were used: vGlutMI04979-Gal4, ChATMI04508-Gal4, vGlutMI04979-LexA::QFAD, ChATMI04508- LexA::
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QFAD, vGlutMI04979-Gal80, ChATMI04508-Gal80 (Diao et al., 2015); Gr66a-LexA, ppk28-LexA, ppk23Gal4, UAS-CD8::tdTomato (Thistle et al., 2012); Gr64fLexA (Miyamoto et al., 2012); ppk23-LexA
(Toda et al., 2012); IR76b-Gal4, IR76b1, IR76b2, UAS-IR76b (Zhang et al., 2013); IR25a1, IR25a2
(Benton et al., 2009); UAS-IR25a (Abuin et al., 2011); Gr66a-Gal4 (Wang et al., 2004); Gr64f-Gal4
(Dahanukar et al., 2007); Ppk28-Gal4 (Cameron et al., 2010); LexAop-CD2::GFP (Lai and Lee,
2006); UAS-Kir2.1 (Baines et al., 2001); tub-Gal80ts (McGuire et al., 2004); IR94e-Gal4 (Tirián and
Dickson, 2017)(Vienna Drosophila Resource Center: v207582); LexAop-Gal80 (32214), LexAopGCaMP6f (44217), UAS-GCaMP6f (42747 and 52869), UAS-CsChrimson (55135), UAS-TNT (28838),
UAS-impTNT (28840) (Bloomington Stock Center).
Tastants
The following tastants were used: Sucrose, NaCl, KCl, NaBr, KBr, CsCl, CaCl2, Lobeline hydrochloride, Caffeine (Sigma-Aldrich); 7,11-heptacosadiene (7,11-HC), 7,11-nonacosadiene (7,11-NC), 7-tricosene (7 T), and cis-vaccenyl acetate (c-VA) (Cayman Chemical Company, Ann Arbor, MI). Tastants
were mostly kept as 1 M stocks and diluted as needed. Lobeline hydrochloride was kept as a 1.25
mM stock. 7,11-heptacosadiene (7,11-HC), 7,11-nonacosadiene (7,11-NC), and 7-tricosene (7 T)
were diluted in water to desired 0.0001 mg/ul. Cis-vaccenyl acetate (c-VA) was diluted to stock solution of 0.01 mg/ul in EtOH, and then diluted in water. All hydrocarbons stocks were kept at 20˚C,
diluted as needed, and stored at 4˚C for up to seven days. 1% of each Ethanol and Hexanol were
diluted in a mix with water and kept at 4˚C as control solution for pheromone imaging.
Immunohistochemistry
Immunofluorescence on labella was carried out as described (Jeong et al., 2016). Labella were dissected and fixed for 25 min in 4% paraformaldehyde in PBS + 0.2% Triton. After washing with
PBS + triton (0.2%; PBST), labella were blocked in 5% NGS diluted with PBST for 40 min. The following primary antibodies were applied and incubated at 4˚C overnight: chicken anti-GFP (1:1000,
Abcam, Cambridge, UK, #13970) and rabbit anti-RFP (1:200, Rockland Immunochemicals, Pottstown,
PA, #600-401-379). After washing for 1 hr, the following secondary antibodies were added for 2 hr:
goat anti-chicken Alexa 488 (1:200, Abcam #150169) and goat anti-rabbit Alexa 647 (1:200, Thermo
Fisher Scientific, Waltham, MA, #A21245). Labella were washed again for 40 min, placed on slides in
SlowFade gold (Thermo Fisher Scientific), with small #1 coverslips as spacers.
Brain immunofluorescence was carried out as described previously (Chu et al., 2014). Primary
antibodies used were chicken anti-GFP (1:1000, Abcam #13970) and mouse anti-brp (1:50, DSHB
#nc82). Secondary antibodies used were goat anti-chicken Alexa 488 (1:200, Abcam #150169) and
goat anti-rabbit Alexa 568 (1:200, Thermo Fisher Scientific #A11036).
All images were acquired using a Leica SP5 II Confocal microscope with a 25x water immersion
objective. Images were processed in ImageJ (Schneider et al., 2012) and Adobe Photoshop.
Labellar expression map annotation
To annotate the expression of different markers in the labellum, each sensillum was analyzed in 4 – 8
labella stained for each combination of markers. Confocal z-stacks were examined to identify how
many neurons in each sensillum were positive for the different drivers, and which neurons overlapped with the respective co-labelled population. The most common result for each neuron in each
sensillum was reported. Sensilla S0, I0, I9, and I10 were the most difficult to score because of viewing difficulties. At times there were duplications of specific sensilla on a labellum, in which case both
sensilla were considered.
GCaMP imaging
For calcium imaging experiments, female or male flies were aged from 2 to 10 days in groups of
both sexes. Females were used for all experiments except where indicated (pheromones). Prior to
imaging, flies were briefly anesthetized using CO2, legs amputated for full access to the proboscis,
and placed in custom chamber suspended from their cervix. To ensure immobilization, a small drop
of nail polish was applied to the back of the neck and the proboscis was pulled to extension and
waxed out on both sides. A modified dental waxer was used to apply wax on each side of the chamber rim, making little contact with the feeding structure. Flies were left to recover in a humidified
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chamber for 1 hr. The antenna were removed from the fly and a small window of cuticle was
removed from the top of the head, exposing the SEZ. Adult Hymolymph Like (AHL) buffer was
immediately applied to the preparation (108 mM NaCl, 5 mM KCl, 4 mM NaHCO3, 1 mM NaH2PO4,
5 mM HEPES, 15 mM ribose, pH 7.5). The air sacs, fat, and esophagus were clipped and removed to
allow clear visualization on the SEZ. Once ready to image, AHL buffer was added that includes Mg2+
and Ca2+ (108 mM NaCl, 5 mM KCl, 4 mM NaHCO3, 1 mM NaH2PO4, 5 mM HEPES, 15 mM ribose,
2 mM Ca2+, and 8.2 mM Mg2+).
GCaMP6f fluorescence was observed using a Leica SP5 II Confocal microscope with a 25x water
immersion objective. The relevant area of the SEZ was visualized at a zoom of 4x, a line speed of
8000 Hz, a line accumulation of 2, and resolution of 512 512 pixels. The pinhole was opened to
2.98 AU. For each taste stimulation, data was acquired during a baseline of 5 s prior to stimulation,
1 s during tastant application, and 9 s following the stimulation.
Tastant stimulations were done using a pulled capillary pipette that was filed down to match the
size of the proboscis and fit over all taste sensilla on both labellar palps. The pipette was filled with
1 – 2 ml of a tastant and positioned close to the proboscis labellum. At 5 s a micromanipulator was
used to apply the tastant to the labellum manually. Between taste stimulations of differing solutions,
the pipette was washed with water. All NaCl solutions were applied in the order of increasing concentration, finishing with 1M KCl. All other solutions were applied in random order to control for
potential inhibitory effects between modalities.
The maximum change in fluorescence (DF/F) was calculated using the peak intensity (average of 3
time points) minus the average intensity at baseline (10 time points), divided by the baseline. Quantification of fluorescence changes was performed in ImageJ and graphed in GraphPad Prism6.
For quantification of Ppk23glut projections, the caffeine response for each fly was used to create a
region of interest starting below the ‘bitter ring’ and extending across to encompass the lateral projections. This same region of interest was applied to the salt responses of that fly to exclude the
Ppk23chat population overlapping with Gr66a in this ‘bitter ring’.
Salt deprivation
Flies were placed in one of two conditions for 2 – 3 days: 1% agar, 5% sucrose, and 10 mM NaCl
(salt fed); or 1% agar and 5% sucrose (salt deprived).
Behavioral assays
Binary choice preference tests were similar to those previously described (LeDue et al., 2015).
Female flies aged 2–5 days were sorted into groups of 10 and placed in conditions of either salt
feeding or salt deprivation (see above) and shifted to 29˚C for 48 hr to induce expression of Kir2.1 in
the cells of interest. For the low salt assay, salt deprived flies were then tested directly. Flies for the
high salt assay were subjected to a subsequent 12 hr on medium without sugar (but with the same
salt content) to increase sugar attraction. For both assays, flies were then transferred into testing
vials containing six 10 mL dots of agar that alternated in color. For most low salt attraction assays,
the food choices were: 1% agar with both 2 mM sucrose and 50 mM NaCl (Food 1), and 1% agar
with 2 mM sucrose (Food 2). The experiment in Figure 5B was done without sucrose. For the high
salt avoidance assays, the food choices were: 1% agar with both 25 mM sucrose and 250 mM NaCl
(Food 1), and 1% agar with 5 mM sucrose (Food 2). Each choice contained either 0.125 mg/mL blue
(Erioglaucine, FD and C Blue#1) or 0.5 mg/mL red (Amaranth, FD and C Red#2) dye, and half the
replicates for each experiment were done with the dyes swapped to control for any dye preference.
Flies were allowed to feed for 2 hr in the dark at 29˚C and then frozen and scored for abdomen
color. Preference index (PI) was calculated as ((# of flies labeled with Food 1 color) – (# of flies
labeled with Food 2 color))/(total number of flies that fed).
For PER, 2 – 5 day old females were collected and treated exactly as described above for salt fed
and deprived conditions. Flies were then mounted inside pipette tips that were cut to size so that
only the head was exposed. The tubes were sealed at the end with tape, positioned on a glass slide
with double-sided tape. After a 1 – 2 hr recovery, flies were stimulated with water and allowed to
drink until satiated. Each fly was then stimulated on the labellum with increasing concentrations of
salt (0 mM salt, 250 mM NaCl, 500 mM NaCl, 1 M NaCl, and 1 M KCl) mixed with 100 mM sucrose
using a 20 mL pipette attached to a 1 mL syringe. Stimuli were presented three times each per fly.
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Four groups of 10 flies for each genotype were tested over four days. The order of genotypes tested
on each day was randomized.
STROBE system
The STROBE builds on the FlyPAD system’s hardware (Itskov et al., 2014) by adding a lighting circuit and opaque curtain (to prevent interference from outside lighting) to each of 16 FlyPAD arenas.
Thus, together, a functioning STROBE system consists of a field programmable gate array (FPGA)
controller attached to a multiplexor board, adaptor boards, fly arenas equipped with capacitive sensors, and lighting circuits.
The arrangement of the STROBE chambers mirrors the design of the FlyPAD, except each of the
eight adaptor boards connects to two FlyPAD arenas and two lighting units instead of the original
four FlyPAD arenas. These adaptor boards link the chambers to the FPGA, which is a Terasic DEV0Nano mounted onto a custom multiplexor board with a FTDI module allowing data transfer over
serial communications with a computer. The multiplexor board has eight 10-pin ports, each of which
connects to an adaptor board that splits the 10-pin line into four 10-pin ports connecting to two fly
arenas and two lighting circuits. The fly arena consists of two annulus shaped capacitive sensors and
a CAPDAC chip (AD7150BRMZ) that the main multiplexer board communicates with to initiate and
collect data (and ultimately stop collecting data). The CAPDAC interprets data from the two capacitive sensors on the fly-arena (Itskov et al., 2014).
The lighting circuit consists of connectors for power from an external power supply and for signaling from the FPGA controller via the intermediate components, a 617 nm light emitting diode (LUXEON Rebel LED – 127lm @ 700mA; Luxeon Star LEDs #LXM2-PH01-0060), two power resistors (TE
Connectivity Passive Product SMW24R7JT) for LED current protection, and two metal oxide semiconductor field effect transistors (MOSFETs; from Infineon 634 Technologies, Neubiberg, Germany,
IRLML0060TRPBF) allowing for voltage signal switching of the LEDs.
When the signal from a capacitive sensor rises during a fly sip (or other food interaction), the
CAPDAC on the fly arena propagates a signal through the adaptor board via the multiplexor to the
FPGA controller. The FPGA processes the capacitive sensor signal using code built atop the original
VHDL code from the FlyPAD (Itskov et al., 2018). The STROBE VHDL code (Chan, 2018b) implements a running minima filter that operates in real-time to detect when a fly is feeding or otherwise
interacting with the food. The filter determines the minimum signal value in the last 100 ms and compares the current signal value with this minimum. If the current signal value is greater than the minimum, and the difference between them is greater than a threshold set to exceed noise (100 units for
all experiments), this is considered a rising edge and the filter will prompt the lighting activation system to activate the LED (or keep it on if it is already on). By design, this means that the control system will send a signal to deactivate the lighting upon the falling edge of the capacitance signal, or if
the capacitance signal has plateaued for 100 ms, whichever comes sooner. At this point, a low signal
is sent to the MOSFET which pinches off the current flowing through the lighting circuit, turning off
the light. The signal to lighting response transition times are on the order of tens of milliseconds,
providing a nearly instantaneous response.
The system automatically records the state of the lighting activation system (on/off) and transmits
this information through USB to the PC, where it is received and interpreted by a custom end-user
program (built using Qt framework in C++) which can display both the activation state and signal
measured by the STROBE system in each fly arena in real-time.
All STROBE software is available for download from Github:
FPGA code: https://github.com/rcwchan/STROBE-fpga
All other code: https://github.com/rcwchan/STROBE_software/
STROBE experiments
Flies were place in vials for three days under ‘salt fed’ or ‘salt deprived’ conditions described above.
All flies were 5 – 9 days old at the time of the assay. For retinal groups, food was supplemented with
all trans-Retinal at a final concentration of 1 mM (Sigma-Aldrich).
Both channels of STROBE chambers were loaded with 4 ml of 1% agar (GR64f and IR94e experiments) or 1M sucrose mixed in 1% agar (Gr66a and Ppk23glut experiments). Acquisition on the
STROBE software was started and then single flies were transferred into each arena by mouth
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aspiration. Experiments were run for 60 min, and the preference index for each fly was calculated as:
(sips from Food 1 – sips from Food 2)/(sips from Food 1 + sips from Food 2).
Statistical analysis
Statistical tests were performed using GraphPad Prism six software. Descriptions and results of each
test are provided in the figure legends. Sample sizes are indicated in the figure legends. Sample
sizes were determined prior to experimentation based on the variance and effect sizes seen in prior
experiments of similar types. Whenever possible, all experimental conditions were run in parallel and
therefore have the same or similar sample sizes. However, in some cases this was impossible, due to
the concurrent availability of different genotypes or the size of the experiment. These situations
account for instances where some control genotypes have very large sample sizes, since they were
run in parallel with multiple experimental groups (e.g. Figure 5A).
All replicates were biological replicates using different flies. Data for all quantitative experiments
were collected on at least three different days, and behavioral experiments were performed with
flies from at least two independent crosses. Specific definitions of replicates are as follows. For calcium imaging, each data point represents the response of a single fly to the indicated stimulus. A
given fly was stimulated with a specific tastant only once. For binary choice behavioral tests, each
data point represents the calculated preference for a group of 10 flies. For PER, each replicate is
composed of 10 independent flies tested in parallel. For STROBE experiments, each data point is
the calculated preference of an individual fly over the course of the experiment.
Outliers were occasionally observed but were not removed from the datasets. For example, in
Figure 2D, two flies had strong water responses in Gr64f sweet neurons. Although these appear to
be outliers, we left them in the dataset because there was no other justification for removing them.
There were two conditions where data were excluded that were determined prior to experimentation and applied uniformly throughout. First, in calcium imaging experiments, all the data from a
fly were removed if either: a) there was too much movement during stimulation to reliably quantify
the response; or b) there was no response to a known, robust, positive control (rare). Second, for
STROBE experiments, the data from individual flies were removed if the fly did not pass a set minimum threshold of sips (15), or the data showed hallmarks of a technical malfunction (rare).
A third condition for data exclusion arose during pilot experiments and was then applied subsequently: A subset of flies expressing GCaMP in IR94e neurons (~20%) showed a large response to
water alone. These flies were removed from the analysis.
All the quantitative data used for statistical tests can be found as supplements for each figure.
Acknowledgements
We thank Emily LeDue for preliminary PER experiments and GCaMP imaging, Simon Roome for preliminary experiments on salt deprivation, Rachael Bartlett for PCR screening of Ppk23-Gal4, DIR76b
recombinant, Anupama Dahanukar for assistance with labellar bristle annotation, and members of
the Gordon lab for comments on the manuscript. We also thank Kristin Scott, Barry Dickson, Hubert
Amrein, the Bloomington Stock Center, and the Vienna Drosophila Resource Center for fly stocks.
Carlos Ribeiro and Pavel Itskov kindly provided their original FlyPAD VHDL code, which was instrumental in developing the STROBE system. This work was funded primarily by the Canadian Institutes
of Health Research (CIHR) operating grant FDN-148424, with the STROBE development funded by
Natural Sciences and Engineering Research Council (NSERC) grants RGPIN-2016 – 03857 and
RGPAS 492846 – 16, and with infrastructure funded by the Canadian Foundation for Innovation (CFI)
grant 27290. M.D.G. is a CIHR New Investigator and a Michael Smith Foundation for Health
Research Scholar.
Additional information
Funding
Funder
Grant reference number
Author
Canadian Institutes of Health
Research
FDN-148424
Michael D Gordon
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Natural Sciences and Engineering Research Council of
Canada
RGPIN-2016-03857
Michael D Gordon
Natural Sciences and Engineering Research Council of
Canada
RGPAS-492846-16
Michael D Gordon
The funders had no role in study design, data collection and interpretation, or the
decision to submit the work for publication.
Author contributions
Alexandria H Jaeger, Formal analysis, Investigation, Visualization, Writing—review and editing, Carried out the labellum expression mapping and much of the GCaMP imaging of wild-type GRNs;
Molly Stanley, Formal analysis, Investigation, Visualization, Writing—review and editing, Performed
imaging of mutant GRNs, Contributed to wild-type GRN imaging, Contributed to behavioural testing; Zachary F Weiss, Formal analysis, Investigation, Visualization, Writing—review and editing, Carried out GRN silencing and mutant behaviour; Pierre-Yves Musso, Formal analysis, Investigation,
Visualization, Writing—review and editing, Performed most of the STROBE experiments; Rachel CW
Chan, Resources, Software, Methodology, Designed and built the STROBE; Han Zhang, Resources,
Methodology, Designed and built the STROBE; Damian Feldman-Kiss, Formal analysis, Investigation,
Identified the IR94e-Gal4 driver and performed the IR94e STROBE experiment; Michael D Gordon,
Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization,
Methodology, Writing—original draft, Project administration, Writing—review and editing, Performed Ir94e labellum staining
Author ORCIDs
Rachel CW Chan
https://orcid.org/0000-0003-1009-6379
Michael D Gordon
http://orcid.org/0000-0002-5440-986X
Decision letter and Author response
Decision letter https://doi.org/10.7554/eLife.37167.027
Author response https://doi.org/10.7554/eLife.37167.028
Additional files
Supplementary files
. Transparent reporting form
DOI: https://doi.org/10.7554/eLife.37167.025
Data availability
All data generated or analysed during this study are included in the manuscript and supporting files.
Source data files have been provided for Figures 2-6.
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