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. 2021 May 5;10:e67878. doi: 10.7554/eLife.67878

Ir56d-dependent fatty acid responses in Drosophila uncover taste discrimination between different classes of fatty acids

Elizabeth B Brown 1, Kreesha D Shah 1,2, Justin Palermo 1, Manali Dey 3, Anupama Dahanukar 3,4,✉, Alex C Keene 1,✉
Editors: Ilona C Grunwald Kadow5, Piali Sengupta6
PMCID: PMC8169106  PMID: 33949306

Abstract

Chemosensory systems are critical for evaluating the caloric value and potential toxicity of food. While animals can discriminate between thousands of odors, much less is known about the discriminative capabilities of taste systems. Fats and sugars represent calorically potent and attractive food sources that contribute to hedonic feeding. Despite the differences in nutritional value between fats and sugars, the ability of the taste system to discriminate between different rewarding tastants is thought to be limited. In Drosophila, taste neurons expressing the ionotropic receptor 56d (IR56d) are required for reflexive behavioral responses to the medium-chain fatty acid, hexanoic acid. Here, we tested whether flies can discriminate between different classes of fatty acids using an aversive memory assay. Our results indicate that flies are able to discriminate medium-chain fatty acids from both short- and long-chain fatty acids, but not from other medium-chain fatty acids. While IR56d neurons are broadly responsive to short-, medium-, and long-chain fatty acids, genetic deletion of IR56d selectively disrupts response to medium-chain fatty acids. Further, IR56d+ GR64f+ neurons are necessary for proboscis extension response (PER) to medium-chain fatty acids, but both IR56d and GR64f neurons are dispensable for PER to short- and long-chain fatty acids, indicating the involvement of one or more other classes of neurons. Together, these findings reveal that IR56d is selectively required for medium-chain fatty acid taste, and discrimination of fatty acids occurs through differential receptor activation in shared populations of neurons. Our study uncovers a capacity for the taste system to encode tastant identity within a taste category.

Research organism: D. melanogaster

Introduction

Animals detect food primarily through taste and olfactory systems. Across phyla, there is enormous complexity in olfactory receptors and downstream processing mechanisms that allow for detection and differentiation between odorants (Keller et al., 2017; Nara et al., 2011; Parnas et al., 2013). By contrast, taste coding is thought to be simpler, with most animals possessing fewer taste receptors and a diminished ability to differentiate between tastants (Freeman and Dahanukar, 2015; Scott, 2018; Yarmolinsky et al., 2009). Most early studies in different species have focused on characterization of a limited number of taste modalities largely defined by human percepts (sweet, bitter, sour, umami, salt), though there is growing appreciation that additional taste pathways are likely to influence gustatory responses and feeding (Chaudhari and Roper, 2010; Scott, 2018). Between studies of Drosophila and mammals, cells or receptors that are involved in sensing water, carbonation, fat, electrophiles, polyamines, metal ions, and ribonucleotides have been identified, suggesting a previously underappreciated complexity in the coding of tastants (Cameron et al., 2010; Kang et al., 2010; Mishra et al., 2018; Zhang et al., 2013). Elucidating the underlying mechanisms of tastant detection can provide fundamental insight into the molecular and cellular basis of tastant recognition and taste processing.

In flies and mammals, tastants are sensed by dedicated gustatory receptors that are expressed in gustatory receptor neurons (GRNs) or taste cells, respectively. In both systems, distinct subsets of taste sensory cells are responsive to compounds belonging to distinct taste modalities such as sweet or bitter, and convey information to discrete areas of higher order brain structures (Vosshall and Stocker, 2007; Yarmolinsky et al., 2009; Zhang et al., 2003). Given the conserved logic of taste processing, flies provide a powerful system for studying sensory processing and principles of taste circuit function (Freeman and Dahanukar, 2015; Scott, 2018; Yarmolinsky et al., 2009). Further, a number of genes and biochemical pathways that regulate feeding behavior are conserved across phyla (Vosshall and Stocker, 2007; Yarmolinsky et al., 2009). Notably, the gustatory system of Drosophila is amenable to in vivo Ca2+ imaging and electrophysiology, both of which can be coupled with robust behavioral assays that measure reflexive taste responses and food consumption (Wisotsky et al., 2011). Taste neurons are housed in gustatory sensory structures called sensilla, which are located in the distal segments of the legs (tarsi), in the external and internal mouth organs (proboscis and pharynx), and in the wings. Each sensillum contains dendrites of multiple GRNs, each of which can be distinguished from the others based on its responses to various categories of tastants. Two main classes of non-overlapping gustatory neurons that have been identified are sweet-sensing and bitter-sensing neurons. Sweet-sensing GRNs promote feeding, whereas bitter-sensing GRNs act to deter (Marella et al., 2006; Thorne et al., 2004). Both sweet and bitter GRNs express subsets of 68 G-protein-coupled gustatory receptors (GRs) (Clyne et al., 2000; Scott et al., 2001). In addition, the Drosophila genome encodes 66 glutamate-like ionotropic receptors (IRs), a recently identified family of receptors implicated in taste, olfaction, and temperature sensation (Benton et al., 2009; Rytz et al., 2013). GRNs predominantly project to the subesophageal zone (SEZ), the primary taste center, but the higher order circuitry downstream of the SEZ contributing to taste processing is poorly understood (Flood et al., 2013; Marella et al., 2012; Pool et al., 2014; Wang et al., 2004). Determining how tastants activate GRNs that convey information to the SEZ and how these signals are transmitted to higher order brain centers is central to understanding the neural basis for taste and feeding.

In Drosophila, GRNs in the labellum and tarsi detect hexanoic acid (Masek and Keene, 2013). Mutation of ionotropic receptor 56d (IR56d) disrupts hexanoic acid taste, implicating IR56d as a fatty acid receptor, or as part of a complex involved in fatty acid taste (Ahn et al., 2017; Sánchez-Alcañiz et al., 2018). IR56d is co-expressed with GR64f (Ahn et al., 2017; Tauber et al., 2017), which broadly labels sweet GRNs (Dahanukar et al., 2007; Jiao et al., 2008; Slone et al., 2007). IR56d-expressing GRNs are responsive to both sugars and fatty acids, suggesting that these neurons may respond to diverse appetitive substances including multiple classes of fatty acids (Tauber et al., 2017). Notably, overlapping populations of sweet GRNs are responsive to different appetitive modalities and confer feeding behavior.

Are flies capable of differentiating between tastants of the same modality or is discrimination within a modality exclusively dependent on intensity? Taste discrimination can be assayed by training flies by pairing a negative stimulus with an appetitive tastant and determining whether the acquired aversion generalizes to another tastant (Keene and Masek, 2012; Masek and Scott, 2010). A previous study employing such experiments found that flies are unable to discriminate between different sugars (Masek and Scott, 2010). Conversely, we reported that flies can discriminate between sucrose (sugar) and hexanoic acid (fatty acid), revealing an ability to discriminate between appetitive stimuli of different modalities (Tauber et al., 2017). Here, we find that flies are capable of discriminating between different classes of fatty acids, despite broad tuning of fatty-acid-sensitive neurons to short-, medium-, and long-chain fatty acids.

Results

Sugars and medium-chain fatty acids are sensed by an overlapping population of gustatory neurons, and flies can discriminate between these two attractive tastants (Ahn et al., 2017; Tauber et al., 2017). To test whether flies are capable of discriminating within a single modality, we measured the ability of flies to discriminate between different types of fatty acids of the same concentration. We used an aversive taste memory assay in which an appetitive tastant applied to the proboscis is paired with application of bitter quinine immediately afterwards, resulting in an associative memory that inhibits responses to the appetitive tastant (Masek et al., 2015). A modified version of this assay, in which training with one tastant is followed by testing with another, allows us to determine whether flies can discriminate between these tastants (Figure 1A).

Figure 1. Drosophila can discriminate between, but not among, short-, medium-, and long-chain fatty acids.

(A) An aversive taste memory assay was used to assess fatty acid taste discrimination in female w1118 flies at a 1% concentration. First, initial responses to a short-, medium-, or long-chain fatty acid were assessed (Pretest). Next, flies were trained by pairing this fatty acid with quinine presentation immediately following tastant application (Training). Proboscis extension response (PER) in response to either the same or different fatty acid was then tested in the absence of quinine (Test). In control experiments (Naïve), the same procedure was followed, but quinine was not applied to the proboscis. (B) The pairing of medium-chain hexanoic acid (6C) and quinine (red) results in a significant reduction in PER compared to naïve flies. After training, PER response to 6C was significantly lower in trained flies compared to naïve flies (p<0.0001), but there was no difference in PER to short-chain valeric acid (5C; p=0.6864). Restricted maximum likelihood (REML): F1,80 = 7.329, p=0.0003, with Sidak’s test for multiple comparisons; N = 40–42. (C) The pairing of medium-chain hexanoic acid (6C) and quinine (red) results in a significant reduction in PER compared to naïve flies. After training, PER response to 6C was significantly lower in trained flies compared to naïve flies (p<0.0001), but there was no difference in PER to long-chain nonanoic acid (9C; p=0.3346). REML: F1,64 = 6.296, p=0.0146, with Sidak’s test for multiple comparisons; N = 33. (D) The pairing of short-chain valeric acid (5C) and quinine (red) results in a significant reduction in PER compared to naïve flies. After training, PER response to 5C was significantly lower in trained flies compared to naïve flies (p=0.0014), but there was no difference in PER to long-chain nonanoic acid (9C; p=0.0789). REML: F1,46 = 2.721, p=0.0105, with Sidak’s test for multiple comparisons; N = 24. (E) The pairing of short-chain butanoic acid (4C) and quinine (red) results in a significant reduction in PER compared to naïve flies. After training, PER to both 4C and short-chain valeric acid (5C) was significantly lower in trained flies compared to naïve flies (4C: p<0.0001; 5C: p<0.0001). REML: F1,38 = 33.67, p<0.0001, with Sidak’s test for multiple comparisons; N = 20. (F) The pairing of medium-chain hexanoic acid (6C) and quinine (red) results in a significant reduction in PER compared to naïve flies. After training, PER to both 6C and medium-chain heptanoic acid (7C) was significantly lower in trained flies compared to naïve flies (6C: p<0.0001; 7C: p<0.0001). REML: F1,81 = 45.88, p<0.0001, with Sidak’s test for multiple comparisons; N = 41–42. (G) The pairing of medium-chain hexanoic acid (6C) and quinine (red) results in a significant reduction in PER compared to naïve flies. After training, PER to both 6C and medium-chain octanoic acid (8C) was significantly lower in trained flies compared to naïve flies (6C: p<0.0001; 8C: p<0.0001). REML: F1,65 = 32.76, p<0.0001, with Sidak’s test for multiple comparisons; N = 33–34. (H) The pairing of medium-chain heptanoic acid (7C) and quinine (red) results in a significant reduction in PER compared to naïve flies. After training, PER to both 7C and medium-chain octanoic acid (8C) was significantly lower in trained flies compared to naïve flies (7C: p<0.0001; 8C: p<0.0001). REML: F1,72 = 33.67, p<0.0001, with Sidak’s test for multiple comparisons; N = 37. (I) The pairing of long-chain decanoic acid (10C) and quinine (red) results in a significant reduction in PER compared to naïve flies. After training, PER to both 10C and long-chain nonanoic acid (9C) was significantly lower in trained flies compared to naïve flies (10C: p<0.0001; 9C: p=0.0015). REML: F1,38 = 33.23, p<0.0001, with Sidak’s test for multiple comparisons; N = 20. Error bars indicate ± SEM. **p<0.01; ***p<0.001; ****p<0.0001.

Figure 1—source data 1. Raw taste discrimination data between short-, medium-, and long-chain fatty acids.

Figure 1.

Figure 1—figure supplement 1. Drosophila can discriminate between, but not among, short-, medium-, and long-chain fatty acids.

Figure 1—figure supplement 1.

Aversive taste memory was measured as described in Figure 1A. The tastants used during training and to assess taste discrimination are reciprocal to those in Figure 1 and were tested at a 1% concentration. (A) The pairing of short-chain valeric acid (5C) and quinine (red) results in a significant reduction in proboscis extension response (PER) compared to naïve flies. After training, PER response to 5C was significantly lower in trained flies compared to naïve flies (p<0.0001), but there was no difference in PER to medium-chain hexanoic acid (6C; p=0.2102). Restricted maximum likelihood (REML): F1,45 = 15.73, p=0.0003, with Sidak’s test for multiple comparisons; N = 23–24. (B) The pairing of long-chain nonanoic acid (9C) and quinine (red) results in a significant reduction in PER compared to naïve flies. After training, PER response to 9C was significantly lower in trained flies compared to naïve flies (p=0.0031), but there was no difference in PER to medium-chain hexanoic acid (6C; p=0.9811). REML: F1,123 = 4.254, p=0.0413, with Sidak’s test for multiple comparisons; N = 14–38. (C) The pairing of long-chain nonanoic acid (9C) and quinine (red) results in a significant reduction in PER compared to naïve flies. After training, PER response to 9C was significantly lower in trained flies compared to naïve flies (p=0.0077), but there was no difference in PER to short-chain valeric acid (5C; p=0.6868). REML: F1,64 = 6.207, p=0.0153, with Sidak’s test for multiple comparisons; N = 33. (D) The pairing of medium-chain heptanoic acid (7C) and quinine (red) results in a significant reduction in PER compared to naïve flies. After training, PER to both 7C and medium-chain hexanoic acid (6C) was significantly lower in trained flies compared to naïve flies (7C: p<0.0001; 6C: p<0.0001). REML: F1,98 = 62.64, p<0.0001, with Sidak’s test for multiple comparisons; N = 49–51. (E) The pairing of medium-chain octanoic acid (8C) and quinine (red) results in a significant reduction in PER compared to naïve flies. After training, PER to both 8C and medium-chain hexanoic acid (6C) was significantly lower in trained flies compared to naïve flies (8C: p=0.0428; 6C: p=0.0214). REML: F1,43 = 7.642, p=0.0084, with Sidak’s test for multiple comparisons; N = 22–23. (F) The pairing of medium-chain octanoic acid (8C) and quinine (red) results in a significant reduction in PER compared to naïve flies. After training, PER to both 8C and medium-chain heptanoic acid (7C) was significantly lower in trained flies compared to naïve flies (8C: p=0.0017; 7C: p=0.0068). REML: F1,46 = 20.72, p<0.0001, with Sidak’s test for multiple comparisons; N = 24. Error bars indicate ± SEM. *p<0.05; **p<0.01; ****p<0.0001.

Figure 1—figure supplement 2. Tastant intensity does not affect the ability of Drosophila to discriminate between short-, medium-, and long-chain fatty acids.

Figure 1—figure supplement 2.

Aversive taste memory was measured as described in Figure 1A. (A) The pairing of medium-chain hexanoic acid (6C) and quinine (red) results in a significant reduction in proboscis extension response (PER) compared to naïve flies. After training, PER response to 1% 6C was significantly lower in trained flies compared to naïve flies (p=0.0090), but there was no difference in PER to 0.1% short-chain valeric acid (5C; p=0.9521). Restricted maximum likelihood (REML): F1,38 = 4.128, p=0.0492, with Sidak’s test for multiple comparisons; N = 20. (B) The pairing of medium-chain hexanoic acid (6C) and quinine (red) results in a significant reduction in PER compared to naïve flies. After training, PER response to 1% 6C was significantly lower in trained flies compared to naïve flies (p=0.0003), but there was no difference in PER to long-chain nonanoic acid (9C; p=0.8671). REML: F1,38 = 6.090, p=0.0158, with Sidak’s test for multiple comparisons; N = 20. (C) The pairing of medium-chain hexanoic acid (6C) and quinine (red) results in a significant reduction in PER compared to naïve flies. After training, PER to both 1% 6C and 0.1% medium-chain octanoic acid (8C) was significantly lower in trained flies compared to naïve flies (6C: p<0.0001; 8C: p<0.0001). REML: F1,38 = 35.94, p<0.0001, with Sidak’s test for multiple comparisons; N = 20. Error bars indicate ± SEM. **p<0.01; ***p<0.001; ****p<0.0001.

Figure 1—figure supplement 3. Drosophila of both sexes are responsive to short-, medium-, and long-chain fatty acids.

Figure 1—figure supplement 3.

N = 28–40.

Figure 1—figure supplement 4. Drosophila males can discriminate between short-, medium-, and long-chain fatty acids, but not among medium-chain fatty acids.

Figure 1—figure supplement 4.

Aversive taste memory was measured as described in Figure 1A, but in w1118 males. (A) The pairing of medium-chain hexanoic acid (6C) and quinine (red) results in a significant reduction in proboscis extension response (PER) compared to naïve flies. After training, PER response to 6C was significantly lower in trained flies compared to naïve flies (p=0.0085), but there was no difference in PER to short-chain butyric acid (4C; p=0. 9346). Restricted maximum likelihood (REML): F1,38 = 5.208, p=0.0282, with Sidak’s test for multiple comparisons; N = 20. (B) The pairing of medium-chain hexanoic acid (6C) and quinine (red) results in a significant reduction in PER compared to naïve flies. After training, PER response to 6C was significantly lower in trained flies compared to naïve flies (p=0.0193), but there was no difference in PER to long-chain nonanoic acid (9C; p=0.9999). REML: F1,38 = 4.143, p=0.0488, with Sidak’s test for multiple comparisons; N = 20. (C) The pairing of medium-chain hexanoic acid (6C) and quinine (red) results in a significant reduction in PER compared to naïve flies. After training, PER to both 6C and medium-chain octanoic acid (8C) was significantly lower in trained flies compared to naïve flies (6C: p=0.0056; 8C: p=0.0310). REML: F1,38 = 11.49, p=0.0017, with Sidak’s test for multiple comparisons; N = 20. Error bars indicate ± SEM. *p<0.05; **p<0.01.
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We first sought to determine whether flies are capable of differentiating between short- (3C–5C), medium- (6C–8C), and long-chain (>9C) fatty acids. We found that flies that were trained with pairing of quinine and medium-chain hexanoic acid (6C) exhibited proboscis extension response (PER) to subsequent application of short-chain valeric acid (5C; Figure 1B). Thus, aversive memory to 5C was not formed by training with 6C, suggesting that flies can discriminate between these short- and medium-chain fatty acids. Similarly, flies trained with 6C did not generalize aversive memory to 9C, consistent with the idea that flies can also discriminate between medium- and long-chain fatty acids (Figure 1C). To rule out the possibility that flies are unable to form aversive taste memories to short- and long-chain fatty acids, we trained with 5C and found robust aversive taste memory, which did not generalize to 9C (Figure 1D). Together, these results suggest that flies are capable of distinguishing between short-, medium-, and long-chain classes of fatty acids.

To determine whether flies can discriminate between compounds within each class of fatty acid, we tested the ability of flies to differentiate among each short-, medium-, and long-chain fatty acid class. We first trained flies to associate 4C, a short-chain fatty acid, with quinine, while a second short-chain fatty acid, 5C, was not reinforced. We found that the aversive memory formed with 4C was generalized to 5C, suggesting that flies cannot discriminate between different short-chain fatty acids (Figure 1E). Next, we trained flies to associate 6C, a medium-chain fatty acid, with quinine, while the medium-chain fatty acids 7C or 8C were not reinforced. In both cases, flies formed aversive memories to 6C, and these generalized to 7C and 8C, suggesting that flies cannot discriminate between different medium chain fatty acids (Figure 1F, G). To fortify these findings, we trained flies to 7C and measured the response to 8C. Again, flies formed aversive memory to 7C that was generalized to 8C (Figure 1H). Lastly, we trained flies to associate 10C, a long-chain fatty acid, with quinine, while a second long-chain fatty acid, 9C, was not reinforced. We again found that the aversive memory formed with 10C was generalized to 9C, suggesting that flies cannot discriminate among long-chain fatty acids (Figure 1I).

To verify that taste discrimination observed between different classes of fatty acids is not simply the result of prior experience, we performed reciprocal experiments to those in Figure 1, in which the tested tastant was used during training, and the trained tastant was used to assess taste discrimination. Consistent with the findings from the first series of experiments, we found that flies are able to discriminate between short-, medium-, and long-chain fatty acids (Figure 1—figure supplement 1A–C), but are unable to discriminate between fatty acids within a particular class (e.g., medium-chain fatty acids; Figure 1—figure supplement 1D–F). Further, it is possible that repeated presentation of fatty acid may alter the valence of the tastant over time. To address this, we asked whether there were any differences in PER between the pretest, training, and test applications among the naïve groups in each test of taste discrimination. We found that PER to consecutive applications of fatty acid shows no significant change in responsiveness over time, although in some cases PER to fatty acid trends downward (Figure 1—figure supplement 1). Taken together, these results reveal that flies cannot discriminate among different short-, medium-, or long-chain fatty acids, although they are able to discriminate medium-chain fatty acids from short- or long-chain fatty acids.

A potential confounding factor in the taste memory assay used to assess discrimination is that flies may discriminate based on perceived intensity rather than the class identity of fatty acids. To test this possibility, we tested whether flies trained to 1% 6C could discriminate a different fatty acid tested at a different concentration (0.1%). Even under these test conditions, flies remained able to discriminate between short- (5C) and medium-chain (6C) fatty acids as well as between long- (9C) and medium-chain (6C) fatty acids (Figure 1—figure supplement 2A, B). However, conditioning to a medium-chain fatty acid (6C) generalized to another medium-chain fatty acid (8C), suggesting that despite a 10× difference in concentration, flies remain unable to distinguish between medium-chain fatty acids (Figure 1—figure supplement 2C). These results fortify the notion that flies are able to distinguish between short-, medium-, and long chain fatty acids, but not within fatty acids of the same class.

We next sought to determine whether the discrimination observed in female flies is also found in males. PER analysis with a panel of short-, medium-, and long-chain fatty acids revealed that males respond to all fatty acids tested, though the overall responses were generally lower than those observed in females (Figure 1—figure supplement 3). To assess whether male flies are able to discriminate between different classes of fatty acids, we trained flies to a medium-chain fatty acid (6C) and then measured discrimination between 4C (short), 8C (medium), and 9C (long) fatty acids. Similar to results obtained in female flies, males were able to discriminate between 6C and 4C as well as between 6C and 9C, but not between 6C and 8C (Figure 1—figure supplement 4). Therefore, male flies are also able to discriminate different classes of fatty acids, but are not able to distinguish fatty acids within the same class.

The short-, medium-, and long-chain fatty acids that we tested have distinctly different olfactory activation profiles (Hallem and Carlson, 2006), raising the possibility that flies can discriminate between these compounds using a combination of olfactory and gustatory information. To exclude the effects of olfactory input, we surgically ablated the antennae, the maxillary palps, or both structures, and measured the ability of flies to discriminate between a representative tastant from each short-, medium-, or long-chain fatty acid (Figure 2A). All test groups were able to distinguish between sucrose and hexanoic acid, confirming that the ablation itself does not generally impact taste or memory formation (Figure 2B). Further, flies trained to a medium-chain fatty acid (6C) did not generalize aversion to short- (5C) or long-chain (9C) fatty acids, regardless of the absence of one or both olfactory organs (Figure 2C, D). Taken together, our findings reveal an ability of the taste system to encode the identity of different classes of fatty acids.

Figure 2. Ablation of olfactory organs has no effect on the ability of Drosophila to discriminate between short-, medium-, and long-chain fatty acids.

Figure 2.

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Aversive taste memory was measured as described in Figure 1A. Flies were trained by pairing 1% medium-chain hexanoic acid (6C) with quinine (Training; see Figure 1—figure supplement 3) and then proboscis extension response (PER) in response to either 10 mM sucrose, 1% short-chain valeric acid (5C), or 1% long-chain nonanoic acid (9C) was measured in the absence of quinine (Test). (A) Aversive taste memory was measured in unmanipulated control flies (first panel), in flies without antennae (second panel), maxillary palps (third panel), or both antennae and maxillary palps (fourth panel). (B) For all ablation treatments, taste memory to medium-chain hexanoic acid (6C) was significantly lower in trained flies compared to naïve flies, but there was no difference in PER to sucrose. Restricted maximum likelihood (REML): F1,86 = 42.41, p<0.0001, with Sidak’s test for multiple comparisons; N = 13–26. (C) For all ablation treatments, taste memory to 6C was significantly lower in trained flies compared to naïve flies, but there was no difference in PER to short-chain valeric acid (5C). REML: F1,103 = 51.87, p<0.0001, with Sidak’s test for multiple comparisons; N = 19–31. (D) For all ablation treatments, taste memory to 6C was significantly lower in trained flies compared to naïve flies, but there was no difference in PER to long-chain nonanoic acid (9C). REML: F1,97 = 11.47, p=0.0010, with Sidak’s test for multiple comparisons; N = 22–27. Error bars indicate ± SEM. **p<0.01; ***p<0.001; ****p<0.0001.

Figure 2—source data 1. Raw taste discrimination data after ablation of olfactory organs.

In previous work, we reported that IR56d neurons are required for 6C taste (Tauber et al., 2017). The finding that flies cannot discriminate between medium-chain fatty acids raises the possibility that IR56d neurons mediate taste of medium-chain fatty acids, but not short- and long-chain fatty acids. To test this possibility, we silenced IR56d-expressing neurons using the synaptobrevin cleavage peptide tetanus toxin light chain (TNT; Sweeney et al., 1995) and measured PER to multiple classes of fatty acids, including short-, medium-, and long-chain fatty acids (Figure 3A). To control for any non-specific effects of TNT, we compared PER in flies with silenced IR56d-expressing neurons (IR56d-GAL4 > UAS TNT) to flies expressing the inactive variant of TNT in IR56d-expressing neurons (IR56d-GAL4 > UAS impTNT). Consistent with previous findings (Tauber et al., 2017), we observed no effect of silencing IR56d-expressing neurons on PER to sucrose (Figure 3B). Next, we measured PER to a panel of saturated fatty acids ranging from 4C (butanoic acid) to 10C (decanoic acid) in length (Figure 3C). Control flies exhibited a robust PER to all seven fatty acids, revealing that at least at a 1% concentration many diverse classes of fatty acids can trigger this behavioral response. To determine whether IR56d neurons are generally required for detection of fatty acids or selectively required for sensing hexanoic acid, we next measured PER in flies with IR56d-expressing neurons silenced. Silencing IR56d-expressing neurons significantly reduced PER to the three medium-chain fatty acids (6C, 7C, and 8C). Conversely, there was no difference in PER between control and IR56d-silenced flies in response to short-chain (4C and 5C) and long-chain (9C and 10C) fatty acids. Therefore, IR56d-expressing neurons are required for medium-chain fatty acid taste perception, but are dispensable for PER to both short- and long-chain fatty acids.

Figure 3. Silencing both IR56D- and GR64f-expressing neurons reduces taste perception to medium-chain fatty acids.

(A) Proboscis extension response (PER) was measured in female flies after 24 hr of starvation. Either 10 mM sucrose or 1% fatty acid was applied to the fly’s labellum for a maximum of 2 s and then removed to observe proboscis extension reflex. (B) Blocking synaptic release by genetic expression of light-chain tetanus toxin (UAS-TNT) in IR56D-expressing neurons has no effect on PER to sucrose compared to control flies expressing an inactive form of tetanus toxin (UAS-impTNT). Mann–Whitney test: U = 595, p=0.8410; N = 35. (C) Silencing IR56D-expressing neurons significantly reduces PER to medium-chain fatty acids (6C–8C), but has no effect on PER to either short- (4C, 5C) or long-chain fatty acids (9C, 10C). Restricted maximum likelihood (REML): F1,406 = 25.03, p<0.0001, with Sidak’s test for multiple comparisons; N = 24–45. (D) Blocking synaptic release by genetic expression of light-chain tetanus toxin (UAS-TNT) in GR64f-expressing neurons significantly reduces PER to sucrose compared to control flies expressing an inactive form of tetanus toxin (UAS-impTNT). Mann–Whitney test: U = 177, p<0.0001; N = 30. (E) Silencing GR64f-expressing neurons significantly reduces PER to medium-chain fatty acids (6C–8C), but has no effect on PER to either short- (4C, 5C) or long-chain fatty acids (9C, 10C). REML: F1,58 = 22.68, p<0.0001, with Sidak’s test for multiple comparisons; N = 30. Error bars indicate ± SEM. **p<0.01; ***p<0.001; ****p<0.0001.

Figure 3—source data 1. Raw data from proboscis extension response experiments to short-, medium-, and long-chain fatty acids.

Figure 3.

Figure 3—figure supplement 1. IR76b and IR25a are required for taste perception to short-, medium-, and long-chain fatty acids.

Figure 3—figure supplement 1.

Proboscis extension response (PER) was measured as described in Figure 3A. (A) Both control and IR76b2 mutant flies are responsive to sucrose. Mann–Whitney test: U = 740, p=0.5502; N = 40. (B) IR76b2 mutant flies significantly reduce PER to fatty acids compared to control flies. Restricted maximum likelihood (REML): F1,88 = 82.53, p<0.0001, with Sidak’s test for multiple comparisons; N = 40–50. (C) Both control and IR25a1 mutant flies are responsive to sucrose. Mann–Whitney test: U = 565, p=0.2799; N = 33–40. (D) IR25a1 mutant flies significantly reduce PER to fatty acids compared to control flies. REML: F1,71 = 43.40, p<0.0001, with Sidak’s test for multiple comparisons; N = 17–40. Error bars indicate ± SEM. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001.
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Since IR56d is required for taste response to medium-chain, but not short- or long-chain fatty acids, it is possible that other IRs mediate this response. As a first step in identifying which additional receptor(s) may be involved, we measured PER in both IR76b and IR25a mutants to short-, medium-, and long-chain fatty acids as these broadly expressed receptors have been previously found to mediate taste response to medium-chain fatty acids (Ahn et al., 2017). In agreement with these findings, PERs to medium-chain fatty acids were significantly reduced for both the IR76b and IR25a mutants, while PER to sucrose was normal (Figure 3—figure supplement 1). Additionally, we found that PERs to both short- and long-chain fatty acids were also significantly reduced in both mutants, suggesting that both IR76b and IR25a are required for taste response to all three classes tested (Figure 3—figure supplement 1).

The finding that IR56d-expressing neurons are required selectively for PER to medium-chain fatty acids raises the possibility that other subsets of sweet-sensing GR64f-expressing neurons are required for PER to short- (4C, 5C) and long-chain (9C–10C) fatty acids. Alternatively, it is possible that these classes of fatty acids elicit PER through gustatory neurons that are not labeled by GR64f. To differentiate between these possibilities, we silenced GR64f-expressing neurons and measured PER to short-, medium-, and long-chain fatty acids. In agreement with previous findings, silencing of GR64f-expressing neurons significantly reduced PER to sucrose (10 mM) as well as to medium-chain fatty acids (Figure 3D, E). However, we found no significant differences in responses to both short- and long-chain fatty acids between GR64f-silenced (GR64f-GAL4 > UAS TNT) and control (GR64f-GAL4 > UAS impTNT) flies. Therefore, PER elicited by short- and long-chain fatty acids is conferred by neurons that express neither IR56d nor GR64f.

To directly assess whether the IR56d receptor mediates responses to medium-chain fatty acids, we used the CRISPR/Cas9 system to generate an IR56d allele in which a GAL4 element is inserted into the IR56d locus (IR56dGAL4; Figure 4A), thereby allowing expression of UAS-transgenes under the control of the IR56d promoter. To confirm that the GAL4 knock-in element is indeed expressed in IR56d-expressing neurons, we generated flies carrying both UAS-mCD8:GFP and the IR56dGAL4 allele (IR56dGAL4>UAS-mCD8:GFP) and mapped the expression of GFP. Consistent with previous findings, we found GFP expression in labellar neurons that projected axons to both the taste peg and sweet taste regions of the SEZ (Figure 4B–E; Koh et al., 2014; Tauber et al., 2017). In agreement with previous findings from genetic silencing of IR56d-expressing neurons, PER to sucrose did not differ between IR56dGAL4 and control flies (Figure 4F), suggesting that IR56dGAL4 is dispensable for response to sucrose. To examine the role of IR56d in fatty acid taste, we measured PER to fatty acids ranging from 4C to 10C in length (Figure 4G). We found that PER to medium-chain fatty acids was disrupted in IR56dGAL4 flies (6C–8C), whereas PER to short- (4C and 5C) and long-chain fatty acids (9C and 10C) was not affected. Although both the IR56dGAL4 mutants and the IR56d-silenced flies have reduced response to medium-chain fatty acids, the response of the IR56d mutant flies was relatively lower, likely caused by the complete deletion of the IR56d gene. Flies that were heterozygous for the IR56d deletion (IR56dGAL4/+) exhibited similar responses to those of control flies for all tastants measured. The observed decrease in PER to medium-chain fatty acids was rescued by transgenic expression of IR56d in the IR56dGAL4 mutant background (IR56dGAL4;UAS-IR56d/+), confirming that the behavioral deficit of IR56dGAL4 flies is in fact due to loss of IR56d function. Similar results were observed in males. Although taste responses were generally lower, PER to sucrose did not differ between IR56dGAL4 and control flies, but PER to medium-chain hexanoic acid (6C) was significantly lower (Figure 4—figure supplement 1). Therefore, IR56d appears to be selectively required for taste sensing of medium-chain fatty acids.

Figure 4. IR56D mediates taste perception to medium-chain fatty acids.

(A) IR56dGAL4 was generated using the CRISPR/Cas9 system. In IR56dGAL4 flies, the IR56d gene was replaced by GAL4 and RFP elements (green boxes). The relative location and orientation of genes in the region are represented as gray arrows. (B–E) Expression pattern of IR56dGAL4 is visualized with GFP. IR56d-expressing neurons are located on the (B) labellum and project to the (C) subesophageal zone of the brain. Distinct regions of projection include the (D) posterior and (E) anterior subesophageal zones. Background staining is NC82 antibody (magenta). Scale bar = 50 μm. (F) Sucrose taste perception is similar in control and IR56dGAL4 mutant flies. Kruskal–Wallis test: H = 0.1758, p=0.9814, with Dunn’s test for multiple comparisons; N = 33–40. (G) The IR56dGAL4 flies have reduced proboscis extension response to medium-chain fatty acids (6C–8C) relative to control, IR56dGAL4 heterozygotes, and IR56dGAL4 rescue flies. However, all genotypes respond similarly to both short- and long-chain fatty acids (4C, 5C; 9C, 10C). Restricted maximum likelihood: F3,850 = 17.80, p<0.0001, with Sidak’s test for multiple comparisons; N = 28–40. Error bars indicate ± SEM. ****p<0.0001.

Figure 4—source data 1. Raw data from IR56dGAL4proboscis extension response experiments.

Figure 4.

Figure 4—figure supplement 1. IR56d mediates taste perception to medium-chain hexanoic acid (6C) in male flies.

Figure 4—figure supplement 1.

Although sucrose taste perception is similar in control and IR56dGAL4 mutant flies, IR56dGAL4 flies have reduced proboscis extension response to medium-chain hexanoic acid (6C) relative to control, IR56dGAL4 heterozygotes, and IR56dGAL4 rescue flies. Restricted maximum likelihood: F3,150 = 5.394, p=0.0015, with Sidak’s test for multiple comparisons; N = 37–40. Error bars indicate ± SEM. ****p<0.0001.
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In previous work, we found that IR56d-expressing neurons are responsive to both sucrose and hexanoic acid (Tauber et al., 2017). To determine whether other classes of fatty acids can also evoke activity in these neurons and whether their activity is dependent on IR56d, we measured Ca2+ responses to a panel of tastants. We expressed the Ca2+ sensor GCaMP6.0 under the control of IR56dGAL4 and measured tastant-evoked activity in the posterior projections (Figure 5A–D, Figure 5—figure supplement 1), which emanate from labellar taste neurons and are both necessary and sufficient for taste perception to medium-chain hexanoic acid (6C; Koh et al., 2014; Tauber et al., 2017). In flies heterozygous for IR56dGAL4, the labeled neurons were responsive to sucrose and all fatty acids tested, which ranged from 4C to 10C (Figure 5E). Thus, IR56d neurons respond to diverse appetitive stimuli. Flies with a deletion of IR56d (IR56dGAL4; UAS-GCaMP6.0) lacked responses exclusively to medium-chain fatty acids (6C–8C), while responses to short- (4C and 5C) and long-chain fatty acids (9C and 10C) remained intact (Figure 5F). Consistent with the rescue of behavioral defects, inclusion of an IR56d rescue transgene (IR56dGAL4; UAS-GCaMP6.0/UAS-IR56d) restored the physiological response to medium-chain fatty acids (Figure 5G). Quantification of the responses to all tastants confirmed that Ca2+ responses to 6C–8C fatty acids are disrupted in IR56dGAL4 flies and restored to levels observed in control flies by expression of UAS-IR56d (Figure 5H). Overall, these results demonstrate that at both behavioral and physiological levels IR56dGAL4 is required for taste responses to medium-chain fatty acids.

Figure 5. Neuronal responsiveness in posterior labellar taste neurons of IR56dGAL4 mutant flies is reduced in response to medium-chain fatty acids.

(A) Diagram of live-imaging experimental protocol. A tastant is applied to the proboscis while florescence is recorded simultaneously. (B–D) Representative pseudocolor images of calcium activity in the posterior projections of IR56D neurons in response to water (B), 10 mM sucrose (C), or 1% hexanoic acid (D). Shown is the change in UAS-GCaMP6 fluorescence (ΔF). Scale bar = 50 μm. (E–G) Activity traces of the posterior projections of IR56D neurons in response to each tastant in the (E) IR56dGAL4 heterozygote controls, (F) IR56dGAL4 mutants, and (G) IR56dGAL4 rescue flies. The shaded region of each trace indicates ± SEM. (H) Average peak change in fluorescence for data shown in (E–G). Neuronal responses to medium-chain fatty acids (6C–8C) are significantly reduced in IR56dGAL4 mutants compared to IR56dGAL4 heterozygote controls and IR56dGAL4 rescue flies. All genotypes respond similarly to both short- and long-chain fatty acids (4C, 5C; 9C, 10C), as well as to water and sucrose. Two-way ANOVA: F2,256 = 23.67, p<0.0001, with Sidak’s test for multiple comparisons; N = 8–14. Error bars indicate ± SEM. ***p<0.001.

Figure 5—source data 1. Raw imaging data from the posterior labellar region in IR56dGAL4 flies.

Figure 5.

Figure 5—figure supplement 1. Representative pseudocolor images of calcium activity in the posterior projections of IR56d neurons in response to fatty acid presentation.

Figure 5—figure supplement 1.

The change in UAS-GCaMP6 fluorescence (ΔF) is shown for (A) IR56dGAL4 heterozygote controls, (B) IR56dGAL4 mutants, and (C) IR56dGAL4 rescue flies. Error bars indicate ± SEM. ****p<0.0001.
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The neurons expressing IR56d project to two distinct regions within the SEZ: a posterior set of projections that overlap with GR64f-expressing neurons and a set of anterior projections that originate from the taste pegs (Koh et al., 2014; Tauber et al., 2017). The two populations appear to differ in function as the posterior population is responsive to both sucrose and hexanoic acid, while the anterior population is responsive to hexanoic acid, but not sucrose (Tauber et al., 2017). It is possible that the anterior neurons are selectively responsive to medium-chain fatty acids, providing a mechanism for taste discrimination. We generated flies labeling IR56d- and GR64f-expressing neurons with different genetically encoded fluorescent reporters (IR56dGAL4; UAS-RFP; GR64f-LexA > LexAop GFP) and confirmed that IR56dGAL4 labels both the anterior and posterior projections (Figure 6A–C). To selectively measure fatty acid responses in anterior taste peg neurons, we expressed UAS-GCaMP6 with a genetic intersection strategy (IR56dGAL4/+; UAS-GCaMP6; GR64f-LexA > LexAop-GAL80) and measured activity to a panel of tastants. In agreement with our previous findings, these neurons were robustly responsive to hexanoic acid (6C), but not by sucrose (Figure 6D–F; Tauber et al., 2017). These neurons also responded to other classes of fatty acids including short- (4C and 5C), medium- (7C and 8C) and long-chain (9C and 10C) (Figure 6D–F). Unlike the posterior projections, which respond similarly to all three classes of fatty acids, the response to short-chain fatty acids was lower in the anterior population. Therefore, it is possible that differential response between the anterior and posterior populations provides a mechanism of discrimination between different classes of fatty acids. However, this alone is insufficient to fully explain discrimination between medium- and long-chain fatty acids. Taken together, these findings support the notion that medium-chain fatty acids are detected through a shared sensory channel, allowing flies to distinguish medium-chain from short- or long-chain, but not between different medium-chain fatty acids.

Figure 6. Anterior, non-GR64f-IR56d-expressing neurons are response to short-, medium-, and long-chain fatty acids.

Figure 6.

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(A–C) Colocalization of IR56dGAL4 and GR64f-expressing neurons occurs in the posterior subesophageal zone. (A) Expression pattern of IR56dGAL4 and GR64f is visualized with IR56dGAL4 driving UAS-RFP and GR64f-LexA driving LexAop-GFP. Colocalization (yellow) is detected in the posterior projections (B), but not in the anterior projections (C). Scale bar = 50 μm. (D–F) Restricting UAS-GCaMP6 expression to the non-overlapping anterior projection neurons does not significantly impact neuronal activity in response to short-, medium-, or long-chain fatty acid presentation. Live imaging was performed as described in Figure 5A. (D) Representative pseudocolor images of calcium activity in the anterior projections of non-GR64f-IR56d-expressing neurons in response to tastant presentation. Shown is the change in UAS-GCaMP6 fluorescence (ΔF). Scale bar = 50 μm. (E) Activity traces of the anterior projections of non-GR64f-IR56d-expressing neurons in response to each tastant in the IR56dGAL4 rescue flies. The shaded region of each trace indicates ± SEM. (F) Average peak change in fluorescence for data shown in (E). Neuronal activity in response to water and sucrose presentation is significantly reduced compared to fatty acid presentation, while responses to short-chain fatty acids (4C, 5C) are intermediate compared to medium- and long-chain fatty acids (6C–10C). No difference in neural activity between medium- and long-chain fatty acid presentation was observed. One-way ANOVA: F8,76 = 45.22, p<0.0001, with Sidak’s test for multiple comparisons; N = 7–12. Error bars indicate ± SEM. ***p<0.001.

Figure 6—source data 1. Raw imaging data from the anterior, non-GR64f-IR56d-expressing region in IR56dGAL4 flies.

Discussion

Receptors for sweet and bitter taste have been well defined in both flies and mammals (Carleton et al., 2010; Hallem et al., 2006; Scott, 2018), but less is known about detection of fats. Previous studies identified IR56d as a receptor for hexanoic acid and carbonation (Ahn et al., 2017; Sánchez-Alcañiz et al., 2018). Our findings suggest that IR56d is selectively involved in responses to medium-chain fatty acids, including 6C, 7C, and 8C fatty acids, and dispensable for responses to shorter and longer-chain fatty acids. Such receptor specificity for different classes of fatty acids based on chain length has not been documented in other systems. In flies, both sugars and fatty acids evoke activity in neurons that co-express the receptors GR64f and IR56d. The finding that short- and long-chain fatty acids also evoke activity in IR56d-expressing neurons posits that additional fatty acid receptors are present in these neurons. Previously, we and others have found that deletion of Phospholipase C (PLC) signaling selectively impairs fatty acid response while leaving sweet taste intact, raising the possibility that activation of distinct intracellular signaling pathways could serve as a mechanism for discrimination between sucrose and fatty acid (Masek and Keene, 2013; Ahn et al., 2017; Tauber et al., 2017), while another suggests TRPA1 and GR64e are targets of PLC and are generally required for fatty acid sensing (Kim et al., 2018). Determining whether or not short- and long-chain fatty acids also signal through PLC may provide insight into whether signaling mechanisms are shared between different fatty acid receptors expressed in IR56d-expressing neurons.

Our aversive taste memory assay confirmed previous findings that flies can discriminate between sugars and fatty acids (Tauber et al., 2017), and led to the surprising observation that flies can distinguish between different classes of fatty acids, even though the baseline responsiveness to short-, medium-, and long-chain fatty acids was similar in innate preference assays. Fatty acids are natural by-product of yeast fermentation (Diwan and Gupta, 2018; Nyanga et al., 2013; Oliveira et al., 2011), and their abundance in peaches, for example, declines after ripening (Duan et al., 2013). Further, fatty acids have antifungal activity, which scales with chain length (i.e., the greater the chain length, the greater the antifungal efficiency; Pohl et al., 2011). Thus, the ability to discriminate between different classes of fatty acids is likely to be important in determining the stage of fruit ripeness, degree of fermentation, and the general palatability of a potential food source/oviposition site.

The finding that flies can distinguish between different classes of fatty acids contrasts with the results of a previous study that applied a similar assay and found that flies were unable to discriminate between different sugars or bitter compounds (Masek and Scott, 2010). One possibility is that this is due to differences in fatty acid detection, which is dependent on IRs, and sweet and bitter tastant detection, which relies on GRs (Chen and Dahanukar, 2020). Our findings highlight the complexity of taste discrimination, which extends beyond simple PER as a readout for taste. For example, all types of fatty acids tested increase GR64f neural responsiveness; however, only GR64f neurons are required for PER to medium-chain fatty acids, thereby raising the possibility that short-, medium-, and long-chain fatty acid taste discrimination occurs through different neural channels. These findings stress the need to define the fatty acid receptors and neural circuits that govern responses to short- and long-chain fatty acid taste. Furthermore, the ability of the Drosophila the taste system to discriminate suggests it may be more like the olfactory system than previously appreciated. Flies are able to distinguish between many different odorants, likely due to the complexity of olfactory coding at the level of the receptor as well as in the antennal lobe (Amin and Lin, 2019; Cognigni et al., 2018; Guven-Ozkan and Davis, 2014). However, flies can also discriminate between odorants sensed by a single olfactory receptor, suggesting that temporal coding also plays a role in discrimination (DasGupta and Waddell, 2008). It is possible that similar mechanisms underlie discrimination between different classes of fatty acid tastants.

The Drosophila genome encodes 66 IRs, which comprise a recently identified family of receptors implicated in taste, olfaction, and temperature sensation (Benton et al., 2009; Rytz et al., 2013). IRs are involved in the detection of many different tastants and function as heteromers that confer sensory specificity (Rytz et al., 2013; van Giesen and Garrity, 2017). While IR56d expression is restricted to a subset of sweet taste neurons, it likely functions in a complex with IR25a and IR76b, all three of which are required fatty acid taste (Ahn et al., 2017; Sánchez-Alcañiz et al., 2018). Other tastants whose responses are mediated by IRs are also likely to be detected by IR complexes. For example, roles for IR25a, IR62a, and IR76b have been described for Ca2+ taste (Lee et al., 2018). The broad degree of co-expression of IRs in the brain and periphery can provide candidates for those involved in detecting short- and long-chain fatty acids.

The identification of taste discrimination between different classes of fatty acids provides the opportunity to identify how different tastants are encoded in the brain and how these circuits are modified with experience. Although projections of primary taste neurons to the SEZ have been mapped in some detail, little is known about connectivity with downstream neurons and whether sensory neurons responsive to different appetitive tastants can activate different downstream circuits. Recent studies have identified a number of interneurons that modify feeding, including IN1, a cholinergic interneuron responsive to sucrose (Yapici et al., 2016), E564 neurons that inhibit feeding (Mann et al., 2013), and Fdg neurons that are required for sucrose-induced feeding (Flood et al., 2013). Future work can investigate whether these and other downstream neurons are shared for fatty acid taste. Previous studies have found that incoming sensory information is selectively modulated within the SEZ in accordance with feeding state (Chu et al., 2014; LeDue et al., 2016). It will be interesting to determine if similar modulation promotes differentiation of sugars and fatty acids, which are sensed by shared GRNs. Large-scale brain imaging has now been applied in flies to measure responsiveness to different tastants (Harris et al., 2015), and a comparison of brain activity patterns elicited by different classes of fatty acids may provide insight into differences in their sensory input and processing.

All experiments in this study tested flies under starved conditions, which is necessary to elicit the PER that is used as a behavioral readout of taste acceptance. However, responses to many tastants and odorants are altered in accordance with feeding state (LeDue et al., 2016; Root et al., 2008). For example, the taste of acetic acid is aversive to fed flies but attractive to starved flies, revealing a hunger-dependent switch (Devineni et al., 2019). Similarly, hexanoic acid evokes activity in both sweet and bitter-sensing taste neurons, and the activity of bitter taste neurons is dependent on different receptors from those involved in the appetitive response (Ahn et al., 2017). Further, hunger enhances activity in sweet taste circuits and suppresses that of bitter taste circuits, providing a mechanism for complex state-dependent modulation of taste response that increase activity of both appetitive and deterrent neurons (Inagaki et al., 2014; LeDue et al., 2016).

The neural circuits that are required for aversive taste memory have been well defined for sugar, yet little is known about how fatty acid taste is conditioned. The pairing of sugar with bitter quinine results in aversive memory to sugar. Optogenetic activation of bitter taste neurons that are activated by quinine, in combination with the presentation of sugar, is sufficient to induce sugar avoidance (Keene and Masek, 2012). Further studies have elucidated that aversive taste memories are dependent on mushroom body neurons that form the gamma and alpha lobes, the PPL1 cluster of dopamine neurons, and alpha lobe output neurons, revealing a circuit regulating taste memory that differs from that controlling appetitive olfactory memory (Kirkhart and Scott, 2015; Masek et al., 2015). It will be interesting to determine whether shared components regulate conditioning to fatty acids or whether distinct mushroom body circuits regulate sweet and fatty acid taste conditioning. Further, examination of the central brain circuits that regulate aversive taste conditioning to different classes of fatty acids will provide insight into how taste discrimination is processed within the brain.

Materials and methods

Drosophila stocks and maintenance

Flies were grown and maintained on standard food media (Bloomington Recipe, Genesee Scientific, San Diego, CA). Flies were housed in incubators (Powers Scientific, Warminster, PA) on a 12:12 LD cycle at 25°C with humidity of 55–65%. The following fly strains were ordered from the Bloomington Stock Center: w1118 (#5905; Levis et al., 1985), IR56d-GAL4 (#60708; Koh et al., 2014), UAS-impTNT (#28840; Sweeney et al., 1995), UAS-TNT (#28838; Sweeney et al., 1995), IR25a1 (#41376; Benton et al., 2009); IR76b2 (#51310; Zhang et al., 2013), UAS-GFP (#32186; Pfeiffer et al., 2010), UAS-GCaMP5 (#42037; Akerboom et al., 2012), UAS-RFP;GFP-LexAop (#32229; Pfeiffer et al., 2010), and LexAop-GAL80 (#32213). GR64f-LexA was kindly provided by H. Tanimoto and was previously described in Thoma et al., 2016. UAS-IR56d was generated using IR56d cDNA, amplified with primers that generated a NotI-KpnI fragment that was cloned in the pUAS vector. The IR56dGAL4 line was generated by WellGenetics (Taipei City, Taiwan) using the CRISPR/Cas9 system to induce homology-dependent repair. At the gRNA target site, a dsDNA donor plasmid was inserted containing a GAL4::VP16 and RFP cassette. This line was generated in the w1118 genetic background and was validated by PCR and sequencing. All lines were backcrossed to the w1118 fly strain for 10 generations. Unless stated otherwise, mated female flies aged 7–9 days were used. For ablation experiments, the antenna and/or maxillary palp were removed 2 days post eclosion.

Reagents

The following fatty acids were obtained from Sigma Aldrich (St. Louis, MO): butyric acid (4C; #B103500), valeric acid (5C; #240370), hexanoic acid (6C; #21530), heptanoic acid (7C; #75190), octanoic acid (8C; #O3907), nonanoic acid (9C; #N5502), and decanoic acid (10C; #C1875). All fatty acids were dissolved in water, although both nonanoic and decanoic acids required heating to fully go into solution. Quinine hydrochloride was also obtained from Sigma Aldrich (#Q1125), while sucrose was purchased from Fisher Scientific (#FS S5-500; Hampton, New Hampshire).

Immunohistochemistry

Brains were prepared as previously described (Kubrak et al., 2016). Briefly, brains of 7–9-day-old female flies were dissected in ice-cold phosphate buffered saline (PBS) and fixed in 4% formaldehyde, PBS, and 0.5% Triton-X for 30 min at room temperature. Brains were rinsed 3× with PBS and 0.5% Triton-X (PBST) for 10 min at room temperature and then incubated overnight at 4°C. The next day brains were incubated in primary antibody (1:20 mouse nc82; Iowa Hybridoma Bank; The Developmental Studies Hybridoma Bank, Iowa City, IA) diluted in 0.5% PBST at 4°C for 48 hr. Next, the brains were rinsed 3× in 0.5% PBST 3 × 10 min at room temperature and placed in secondary antibody (1:400 donkey anti-mouse Alexa Fluor 647; #A-31571; ThermoFisher Scientific, Waltham, MA) for 90 min at room temperature. The brains were again rinsed 3× in PBST for 10 min at room temperature and then mounted in Vectashield (VECTOR Laboratories, Burlingame, CA). Brains were imaged in 2 μm sections on a Nikon A1R confocal microscope (Nikon, Tokyo, Japan) using a 20× oil immersion objective. Images presented as the Z-stack projection through the entire brain and processes using ImageJ2 (Tauber et al., 2017).

Proboscis extension response

Female flies were starved for 24 hr prior to each experiment and then PER was measured as previously described (Masek and Keene, 2013; Tauber et al., 2017). In experiments using males, flies were starved for 18 hr prior to each experiment. Briefly, flies were anesthetized on CO2 and then restrained inside of a cut 200 µL pipette tip (#02-404-423; Fisher Scientific) so that their head and proboscis were exposed while their body and tarsi remain restrained. After a 60 min acclimation period in a humidified box, flies were presented with water and allowed to drink freely until satiated. Flies that did not stop responding to water within 5 min were discarded. A wick made of Kimwipe (#06-666; Fisher Scientific) was placed partially inside a capillary tube (#1B120F-4; World Precision Instruments, Sarasota, FL) and then saturated with tastant, thereby enabling flies to taste, but not ingest tastant. The saturated wick was then manually applied to the tip of the proboscis for 1–2 s and proboscis extension reflex was monitored. Only full extensions were counted as a positive response. Each tastant was presented a total of three times, with 1 min between each presentation. Unless otherwise stated, fatty acids were dissolved in water and tested at a concentration of 1%, while sucrose was tested at a concentration of 10 mM. PER was calculated as the percentage of proboscis extensions divided by the total number of tastant presentations. For example, a fly that extends its proboscis twice out of the three presentations will have a PER response of 66%. Experiments were run blinded, ~3 times per week until completion. Additionally, genotype and tastant presentation was randomized to ensure data reproducibility.

In vivo calcium imaging

Female flies were starved for 24 hr prior to imaging, as described (Tauber et al., 2017). Flies were anesthetized on ice and then restrained inside of a cut 200 µL pipette tip so that their head and proboscis were accessible, while their body and tarsi remain restrained. The proboscis was manually extended and then a small amount of dental glue (#595953WW; Ivoclar Vivadent Inc, Amherst, NY) was applied between the labium and the side of the pipette tip, ensuring the same position throughout the experiment. Next, both antennae were removed. A small hole was cut into a 1 cm2 piece of aluminum foil and then fixed to the fly using dental glue, creating a sealed window of cuticle exposed. Artificial hemolymph (140 mM NaCl, 2 mM KCl, 4.5 mM MgCl2, 1.5 mM CaCl2, and 5 mM HEPES-NaOH with pH = 7.1) was applied to the window and then the cuticle and connective tissue were dissected to expose the SEZ. Mounted flies were placed on a Nikon A1R confocal microscope and then imaged using a 20× water-dipping objective lens. The pinhole was opened to allow a thicker optical section to be monitored. All recordings were taken at 4 Hz with 256 resolution. Similar to PER, tastants were applied to the proboscis for 1–2 s with a wick, which was operated using a micromanipulator (Narishige International USA, Inc, Amityville, NY). Experiments were run ~3 times per week until completion. For analysis, regions of interest were drawn manually around posterior IR56D projections. Baseline fluorescence was calculated as the average fluorescence of the first five frames, beginning 10 s prior to tastant application. For each frame, the % change in fluorescence (%ΔF/F) was calculated as: (peak fluorescence – baseline fluorescence)/baseline fluorescence * 100. Average fluorescence traces were created by taking the average and standard error of %ΔF/F for each recording of a specific tastant.

Aversive taste memory

Taste discrimination was assessed by measuring aversive taste memory, as described previously (Tauber et al., 2017). Female flies were starved for 24 hr prior to each experiment. Flies were then anesthetized on CO2 and the thorax of each fly was glued to a microscope slide using clear nail polish (#451D; Wet n Wild, Los Angeles, CA). Flies were acclimated to these conditions in a humidified box for 60 min. For each experiment, the microscope slide was mounted vertically under a dissecting microscope (#SM-1BSZ-144S; AmScope, Irvine, CA). Flies were water satiated prior to each experiment and in between each test/training session. For tastant presentation, we used a 200 µL pipette tip attached to a 3 mL syringe (#14-955-457; Fisher Scientific). For the pretest, 1% fatty acid was presented to the proboscis three times with a 30 s interval between applications, and the number of full proboscis extensions was recorded. During training, a similar protocol was used except that each tastant presentation was immediately followed by 50 mM quinine presentation, in which flies were allowed to drink for up to 2 s or until an extended proboscis was retracted. A total of three training sessions were performed. In between each session, the proboscis was washed with water and flies were allowed to drink to satiation, lasting ~2 min. To assess taste discrimination, flies were tested either with that same tastant without quinine or with an untrained tastant. Another group of flies were tested as described above but quinine was never presented (naïve). At the end of each experiment, flies were given 1 M sucrose to check for retained ability to extend proboscis and all non-responders were excluded.

Statistical analysis

All measurements are presented as bar graphs showing mean ± standard error. Measurements of PER and aversive taste memory were not normally distributed and so the non-parametric Kruskal–Wallis test was used to compare two or more genotypes. To compare two or more genotypes and two treatments, a restricted maximum likelihood (REML) estimation was used. For data that was normally distributed (calcium imaging data), a one-way or two-way analysis of variance (ANOVA) was used for comparisons between two or more genotypes and one treatment or two or more genotypes and multiple treatments, respectively. All post hoc analyses were performed using Sidak’s multiple comparisons test. Statistical analyses and data presentation were performed using InStat software (GraphPad Software 8.0, San Diego, CA). Sample sizes for behavioral and functional imaging experiments are consistent with previous studies (Kirkhart and Scott, 2015; Tauber et al., 2017). Generally, ~30 flies were used for each experimental or control group for behavioral experiments and ~10–12 flies per group for imaging experiments.

Acknowledgements

We would like to thank members of the Keene and Dahanukar labs for technical assistance and helpful discussion. This work was supported by NIH grants R01 NS085252 to ACK and R01DC017390 to ACK and AD, as well as support from FAU’s Jupiter Life Science Initiative.

Funding Statement

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

Contributor Information

Anupama Dahanukar, Email: anupama.dahanukar@ucr.edu.

Alex C Keene, Email: keenea@fau.edu.

Ilona C Grunwald Kadow, Technical University of Munich, Germany.

Piali Sengupta, Brandeis University, United States.

Funding Information

This paper was supported by the following grant:

  • National Institutes of Health NIH R01DC017390 to Elizabeth B Brown, Kreesha D Shah, Justin Palermo, Manali Dey, Anupama Dahanukar.

Additional information

Competing interests

No competing interests declared.

Author contributions

Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing.

Formal analysis, Validation, Investigation, Writing - original draft.

Formal analysis, Validation, Investigation, Visualization, Writing - review and editing.

Resources, Formal analysis, Investigation, Writing - original draft, Writing - review and editing.

Conceptualization, Formal analysis, Investigation, Visualization, Writing - original draft, Project administration, Writing - review and editing.

Conceptualization, Resources, Supervision, Writing - original draft, Project administration, Writing - review and editing.

Additional files

Transparent reporting form

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 all figures.

References

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Decision letter

Editor: Ilona C Grunwald Kadow1

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

In this manuscript, the authors build on their previous work on fatty acid detection by the taste system of Drosophila melanogaster. They show, by developing a novel behavioral assay, that flies can distinguish medium-length fatty acids from other fatty acids by using different taste sensilla. These findings suggest that flies have the capacity to discriminate molecules of the same chemical class.

Decision letter after peer review:

[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]

Thank you for submitting your work entitled "Ir56d-dependent fatty acid responses in Drosophila uncover taste discrimination between different classes of fatty acids" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by Kristin Scott as Reviewing Editor and a Senior Editor. The reviewers have opted to remain anonymous.

Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife in its current form.

The reviewers find fatty acid taste discrimination potentially interesting and agree that the experiments are performed to a high standard. One major concern is whether discrimination is based on intensity rather than quality. A second limitation is that the mechanism of FA detection is not greatly advanced beyond the authors' previous work: the cellular mechanisms for long and short chain FA detection remain unclear. The reviewers agreed that if the major concerns of Reviewer 1 were addressed, this manuscript would provide a broader understanding of fatty acid discrimination.

Reviewer #1:

This paper investigates fatty acid taste in flies and asks the broad question of whether flies can discriminate different compounds within a single taste modality. The authors' main finding is that flies can discriminate between long, medium, and short chain fatty acids using a previously established aversive memory taste paradigm. When they delve into the cellular and molecular basis of fatty acid detection they find that IR56d neurons respond to all three classes of fatty acids, but are required only for the behavioural responses to medium chain molecules. Similarly, CRISPR/Cas9 deletion of the IR56d receptor reveals that it too is required only for medium-chain fatty acid responses. Thus, different fatty acid classes presumably activate distinct, but partially overlapping subsets of appetitive taste neurons. In general I think the paper is potentially interesting (see comment 1 below) and the data mostly supports the conclusions. However, there is some lack of attention to details that make some of the data hard to interpret.

1. The ability of flies to discriminate between different fatty acid classes is presented as the interesting finding, since, as the authors point out, discrimination between compounds within a taste modality is generally not thought to occur. On the surface I agree that this is interesting. However, in the authors' set up of the main question (line 101), they raise an important issue: "Is it possible that flies are capable of differentiating between tastants of the same modality, or is discrimination within a modality exclusively dependent on concentration?" This should be rephrased to replace "concentration" with "intensity" since not all tastants at the same concentration have the same intensity, and from a behavioural perspective it is intensity that matters. Given that, the authors don't do anything to demonstrate that their discrimination task does not depend on intensity, aside from the fact that 1% solutions of all the FA seem to give similar PER. They need to show more explicitly that this task is truly showing identity-based discrimination.

2. The second broad concern I have is over the nature of short and long chain fatty acid detection. Interpreting the discrimination results would be greatly aided if we knew what other neurons mediate the PER to these molecules. Is it the non-IR56d population of Gr64f neurons? Two experiments would go a long way to addressing this question: (1) silence Gr64f neurons to test whether the broader population is required for short and long chain FA PER and (2) do calcium imaging of Gr64f neurons and see whether non-IR56d projections (which according to the authors' previous work are spatially segregated in a more dorsal area of the SEZ) respond to short and long chain FA.

Reviewer #2:

In the present paper Brown et al., study the ability of Drosophila melanogaster to discriminate between Fatty Acids (FAs) of different lengths. Using a combination of behavioral experiments, molecular biology and in vivo calcium imaging, the authors show that a subset of Ir56d expressing neurons are able to differentiate FAs. However, the Ir56d receptor is only necessary for the detection of medium-length FAs but not short- or long-. The paper explores in detail the role of the Ir56d receptor as FA detector, a role previously described by the authors in a previous paper Tauber et al. 2017.

I consider that the experiments are properly done, and so the statistical analysis, however gain in knowledge is very limited. So far, the authors can prove that flies can discriminate FAs of different lengths, being Ir56d the receptor detecting medium-length FAs, a result that expands the knowledge gained in Tauber et al. 2017. In figure 3, the authors show that silencing Ir56d neurons using tetanus toxin expression, reduces dramatically PER to medium-length fatty acids, but not to short or long, pointing to a different set of neurons involved in their detection. However, the in vivo calcium imaging experiments show that Ir56d neurons also respond to short- and long- FAs. In this regard, I disagree with the statement at the abstract: Characterization of hexanoic acid-sensitive Ionotropic receptor 56d (Ir56d) neurons reveals broad responsive to short-, medium-, and long- chain fatty acids, suggesting selectivity is unlikely to occur through activation of distinct sensory neuron populations. In fact, I consider that selectivity would come from the activation of different subsets of gustatory neurons. It seems that Ir56d neurons could be a subset of the neurons that generally respond to FAs, providing the specificity for medium-length FAs. Other neurons, in addition to the Ir56d ones might be responding to short- and long- FAs in an Ir56d independent manner.

I consider the authors should explore in deep how short- and long- FAs are actually detected, whether it depends on other Ionotropic Receptors (probably Ir25a and Ir76b might be involved (Ahn et al. 2017)) and which subset of gustatory neurons are actually responding to these compounds, considering they do not require Ir56d nor Ir56d neurons.

Reviewer #3:

In this manuscript Brown et al. characterized fatty acid taste discrimination in Drosophila melanogaster. Fat taste is relatively poorly understood, but has critical implications for feeding and obesity research; thus, studies that advance our understanding of the molecular and physiological underpinning of this modality are important. The finding that Ir56d neurons enable organisms to discriminate between short, medium and long chain fatty acids but not to differentiate between types of medium chain fatty acids is certainly novel and interesting. It is also surprising but fascinating that this receptor is only required for the detection of medium fatty acids. The manuscript is well written and the figures presented in a clear and thoughtful manner. These findings lay out ground for future exciting work to investigate how sweet taste and fatty acid taste perception are selectively modulated by the brain since these gustatory neurons overlap and whether such discrimination is altered depending on the state of hunger.

1. Despite the overlapping nature of taste neurons in this case, i.e., Ir56d neurons being co-expressed with Gr64f – those that broadly label the sweet GRNs and the fact that Ir56d neurons are responsive to both sucrose and fatty acids; mutation in Ir56d results in loss of taste for hexanoic acid, but not sucrose. Authors use this taste discrimination to their advantage in combination with a robust aversive taste memory assay to address the question of differential fatty acid taste perception.

2. Authors rule out the potential involvement of olfaction in modulating taste perception.

3. Use of CRISPR-Cas9 to generate Ir56dGAL4 flies, implying accurate and targeted genome editing, provide validation to the results obtained when Ir56d expressing neurons are silenced. Additionally, use of the fly gustatory system for in-vivo Ca2+ imaging strengthens and corroborates the results at the physiological level, especially the rescue experiments.

Overall comments and questions:

1. Are the differences in taste discrimination between male and female flies?

2. Individual data points should be shown whenever possible for all figures (except PER because that would make it impossible to interpret).

3. Can the authors discuss how discriminating between different fatty acids types may be adaptive? Are they found in different food sources, some of which are "good" and some "bad"? Is there evidence from other organisms about this type of molecular discrimination in fatty acid taste?

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Ir56d-dependent fatty acid responses in Drosophila uncovers taste discrimination between different fatty acid classes" for consideration by eLife.

Your revised article has been evaluated by a Senior Editor, a Reviewing Editor and three reviewers who wish to remain anonymous.

Essentially, the reviewers find your observation that flies can distinguish medium-length fatty acids from other fatty acids very interesting. They also praise the behavioral assay that you have developed to test this ability. Although the reviewers appreciate your revisions, unfortunately, the earlier concerns regarding a lack of concrete mechanism beyond what you have previously reported remain. Specifically, the reviewers felt that the molecular and circuit mechanisms underpinning the ability to discern medium chain fatty acids are still unresolved. Moreover, they would have liked to see some data elucidating how the animals recognise short and long-chain fatty acids.

Reviewer #1:

The revision have improved the quality and impact of the manuscript and opened new questions about the molecular nature of detection of different FA classes and of the circuitry underlying this sensory modality.

There are some typos and run on sentences in the manuscript (i.e line 100, line 352).

An image of the Ir56D GAL4/Gr64f-Lexa; UAS-GCaMP6/LexAop-GAL80 would be useful.

Reviewer #2:

The authors set out to address the question as whether the fly's gustatory system can discriminate tastants within the same modality. To some extent, this issue has been addressed already. For example, when flies are offered different sugars at the same concentration, they show different propensities to extend their proboscis (Slone et al. Curr Biol 2007). When flies are given a choice between two carboxylic acids at the same concentration (e.g. acetic acid and lactic acid), they prefer lactic acid (Rimal et al. Cell Reports 2019). Even the responses to octanoic acid (medium chain) and oleic acid (long chain) appear to be different (Kim et al. PLOS Genetics 2018), and this is also documented in an earlier paper from the senior author's lab (Tauber et al. PLOS Genetics 2017). So, the overarching question as to whether flies can discriminate different tastants within the same modality is not completely unexplored. Nevertheless, the authors use a very nice aversive memory assay to show that the flies can discriminate short, medium and long chain fatty acids (FAs), but not different medium chain FAs from each other. They knocked out Ir56d and show that is required for the sensation of medium FAs, consistent with their previous study reporting that Ir56d neurons are required for FA taste. In addition, the Ca2+ responses of the Ir56d-expressing neurons were reduced in response in medium chain fatty acids. Unfortunately, the work does not provide a molecular or cellular explanation as to how the flies discern different medium channel FAs. Without such an explanation, the contribution of this work is somewhat incremental over what is already known.

1. Can flies discriminate between different short-chain FAs, and can they discriminate between different long-chain FAs?

2. Lines 156-159: Silencing Ir56d-expressing neurons is not a test of whether Ir56d is required. Nevertheless, the authors have already reported that Ir56d neurons are needed for the response to medium chain FAs so I cannot discern what is new in Figure 3.

3. The authors state that they backcrossed the Ir56dGAL4 mutant to w1118. But they do not say for how many generations. 5 generations is typical to eliminate background mutations. At the very least, since they have only one allele, they should confirm the phenotype over a deficiency. The rescue is helpful, but it also changes the genetic background.

4. The authors use GCaMP to examine Ca2+ responses to FAs. While GCaMP provides a very good proxy for neuronal activation, the authors should be mindful that rises in Ca2+ do not always lead to neuronal activation, and the GCaMP is not the same as measuring action potentials. They should not say that they are measuring neuronal activation. GCaMP allows them to measure neuronal responsiveness, not activation.

Reviewer #3:

In this paper, Brown et al. expand earlier observations that fatty acids (FA) at low concentration are detected by neurons expressing Gr64f and IR56d receptor genes. The authors show here that when quinine is associated with FAs of middle range length, flies "learn" not to extend their proboscis to FAs of close length, while they keep extending their proboscis in response to longer or shorter chains. The authors further show that inactivating neurons expressing Gr64f or IR56d prevents proboscis extension to middle range FAs but not longer of shorter FAs. They further create a genetic construction by inserting a Gal4 into the IR56d gene and confirm through calcium imaging experiments, that labellar responses to FAs 6C-8C is mediated by neuron expressing IR56d. From these observations, the authors conclude that flies are able to discriminate between FAs belonging to different categories, ie short-long FAs versus middle length FAs. These data clearly indicate that with the experimental protocol used here, FAs of different length are detected by different populations of gustatory receptor neurons.

However, I am not fully convinced that we have here a clear case of categorical perception. In Masek and Scott 2010 as well as in Kirkhart and Scott 2015, the aversive stimulus was independent of the appetitive stimulus. Here, the situation is more complicated because FAs are mixed with quinine during the training phase.

The conclusions proposed by the authors here rely on the untested assumption that quinine and FAs have no interactions. This might not be the case. Actually, quinine is known to interact with sugar perception (see for ex Meunier et al. 2003), where an exposure to 10 mM quinine (as here) induces an irregular activity in the taste neurons and actually reversibly prevents sugar-sensitive neurons to respond to 50 mM sucrose. Mixing quinine with FAs might thus have a differential effect on gustatory neurons – a repetitive exposure to the mixture might silence the receptors depending on FAs chain length.

Furthermore, hexanoic acid and octanoic acid are known to have a toxic effect on flies. These chemicals are considered as one of the main reasons why noni (the fruit) is toxic to flies except to D sechellia. Earlier observations show that while low doses of these FAs are appetitive, higher doses are deterrent. This means that in the experiments shown here, an additional assumption is that repeated presentations of FAs are not changing the valence of the stimulus. While the toxicity of 6C and 8C FAs is documented, nothing is known about the effects of FAs of a different chain length. Furthermore, to my knowledge, it is not known yet if the deterrent effects of these FAs is due to a disturbance of the responses of neurons responding to sugar or if it activates other populations of gustatory neurons, for example bitter-sensitive.

These two objections could be alleviated if the authors could show data supporting the assumption that gustatory neurons are not changing their responses when in response to consecutive stimulations of FAs and if responses to FAs +quinine mixtures are equivalent irrespective of FAs length.

eLife. 2021 May 5;10:e67878. doi: 10.7554/eLife.67878.sa2

Author response

[Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]

The reviewers find fatty acid taste discrimination potentially interesting and agree that the experiments are performed to a high standard. One major concern is whether discrimination is based on intensity rather than quality. A second limitation is that the mechanism of FA detection is not greatly advanced beyond the authors' previous work: the cellular mechanisms for long and short chain FA detection remain unclear. The reviewers agreed that if the major concerns of Reviewer 1 were addressed, this manuscript would provide a broader understanding of fatty acid discrimination.

We are thankful for this thoughtful and critical feedback of our manuscript. We have addressed questions related to intensity through additional behavioral and physiological experiments. In addition, we have expanded on the discussion to highlight the significance of this work over our previous work. In short, we believe the that impact of these studies on discrimination advances our understanding of the general principles of taste coding, extending beyond the neural basis of fatty acid taste. As noted previously, these are some of the most insightful reviews we have received, and we believe that as a consequence, the revised version of this manuscript is substantially improved.

Reviewer #1:

This paper investigates fatty acid taste in flies and asks the broad question of whether flies can discriminate different compounds within a single taste modality. The authors' main finding is that flies can discriminate between long, medium, and short chain fatty acids using a previously established aversive memory taste paradigm. When they delve into the cellular and molecular basis of fatty acid detection they find that IR56d neurons respond to all three classes of fatty acids, but are required only for the behavioural responses to medium chain molecules. Similarly, CRISPR/Cas9 deletion of the IR56d receptor reveals that it too is required only for medium-chain fatty acid responses. Thus, different fatty acid classes presumably activate distinct, but partially overlapping subsets of appetitive taste neurons. In general I think the paper is potentially interesting (see comment 1 below) and the data mostly supports the conclusions. However, there is some lack of attention to details that make some of the data hard to interpret.

1. The ability of flies to discriminate between different fatty acid classes is presented as the interesting finding, since, as the authors point out, discrimination between compounds within a taste modality is generally not thought to occur. On the surface I agree that this is interesting. However, in the authors' set up of the main question (line 101), they raise an important issue: "Is it possible that flies are capable of differentiating between tastants of the same modality, or is discrimination within a modality exclusively dependent on concentration?" This should be rephrased to replace "concentration" with "intensity" since not all tastants at the same concentration have the same intensity, and from a behavioural perspective it is intensity that matters. Given that, the authors don't do anything to demonstrate that their discrimination task does not depend on intensity, aside from the fact that 1% solutions of all the FA seem to give similar PER. They need to show more explicitly that this task is truly showing identity-based discrimination.

Thank you for these suggestions. We have now replaced the word concentration with intensity. We also include results of new experiments to examine whether discrimination is based on the intensity or the identity of the fatty acid tastants. We find that training flies at a concentration of 1% fatty acid and then testing PER to a different fatty acid at a concentration of 0.1% does not change their ability to discriminate between short-, medium-, and long-chain fatty acids. These data are now shown in Figure 1—figure supplement 2. We now state in the results: “A potential confounding factor in the taste memory assay used to assess discrimination is that flies may discriminate based on perceived intensity, rather than the class identity of fatty acids. To test this possibility, we tested whether flies trained to 1% 6C could discriminate a different fatty acid tested at a different concentration (0.1%). Even under these test conditions, flies remained able to discriminate between short- (5C) and medium-chain (6C) fatty acids as well as between long- (9C) and medium-chain (6C) fatty acids (Figure 1—figure supplement 2A, B). However, conditioning to a medium-chain fatty acid (6C) generalized to another medium chain fatty acid (8C), suggesting that despite a 10x difference in concentration, flies remain unable to distinguish between medium chain fatty acids (Figure 1—figure supplement 2C). These results fortify the notion that flies are able to distinguish between short-, medium-, and long chain fatty acids, but not within fatty acids of the same class” (Line 160).

2. The second broad concern I have is over the nature of short and long chain fatty acid detection. Interpreting the discrimination results would be greatly aided if we knew what other neurons mediate the PER to these molecules. Is it the non-IR56d population of Gr64f neurons? Two experiments would go a long way to addressing this question: (1) silence Gr64f neurons to test whether the broader population is required for short and long chain FA PER and (2) do calcium imaging of Gr64f neurons and see whether non-IR56d projections (which according to the authors' previous work are spatially segregated in a more dorsal area of the SEZ) respond to short and long chain FA.

Thank you for this suggestion. We have now examined PER in flies with silenced GR64f neurons. In agreement with previous data, we find that GR64f-GAL4>UAS-TNT flies have reduced PER to medium-chain (6C-8C) fatty acids. We also find that short- (4C-5C) and long-chain (9C-10C) fatty acid response remains intact. Therefore, while IR56d neurons, and therefore a subset of GR64f neurons, are responsive to short- and long-chain fatty acids, these neurons are dispensable for the behavioral response. These findings suggest that neurons outside the canonical sweet-sensing neurons that detect both sugars and fatty acids are responsible for PER to short- and long-chain fatty acids. These data are now shown in Figure 3D-E. Further, imaging of neural activity in non-Gr64f-expressing IR56d projections revealed that these neurons are responsive to short-, medium-, and long-chain fatty acids. These data are now shown in Figure 6.

Reviewer #2:

In the present paper Brown et al., study the ability of Drosophila melanogaster to discriminate between Fatty Acids (FAs) of different lengths. Using a combination of behavioral experiments, molecular biology and in vivo calcium imaging, the authors show that a subset of Ir56d expressing neurons are able to differentiate FAs. However, the Ir56d receptor is only necessary for the detection of medium-length FAs but not short- or long-. The paper explores in detail the role of the Ir56d receptor as FA detector, a role previously described by the authors in a previous paper Tauber et al. 2017.

I consider that the experiments are properly done, and so the statistical analysis, however gain in knowledge is very limited. So far, the authors can prove that flies can discriminate FAs of different lengths, being Ir56d the receptor detecting medium-length FAs, a result that expands the knowledge gained in Tauber et al. 2017. In figure 3, the authors show that silencing Ir56d neurons using tetanus toxin expression, reduces dramatically PER to medium-length fatty acids, but not to short or long, pointing to a different set of neurons involved in their detection.

We have now included data silencing all GR64f-expressing neurons (Figure 3D-E). We find that silencing these neurons reduces PER to medium-chain fatty acids, but not to short- or long-chain fatty acids, bolstering support for the conclusion that these classes of fatty acids are also detected by other populations of neurons.

However, the in vivo calcium imaging experiments show that Ir56d neurons also respond to short- and long- FAs. In this regard, I disagree with the statement at the abstract: Characterization of hexanoic acid-sensitive Ionotropic receptor 56d (Ir56d) neurons reveals broad responsive to short-, medium-, and long- chain fatty acids, suggesting selectivity is unlikely to occur through activation of distinct sensory neuron populations. In fact, I consider that selectivity would come from the activation of different subsets of gustatory neurons. It seems that Ir56d neurons could be a subset of the neurons that generally respond to FAs, providing the specificity for medium-length FAs. Other neurons, in addition to the Ir56d ones might be responding to short- and long- FAs in an Ir56d independent manner.

Thank you for these comments. Indeed, we agree with this notion. In the initial statement we meant to convey that selectivity is unlikely to occur through different subsets of IR56d neurons rather than other subsets of gustatory neurons. We have made this correction. We now state: “While IR56d neurons are broadly activated by short-, medium-, and long-chain fatty acids, genetic deletion of IR56d selectively disrupts response to medium-chain fatty acids. Further, Ir56d+Gr64f+ neurons are necessary for PER to medium-chain fatty acids, but both IR56d and GR64f neurons are dispensable for PER to short- and long-chain fatty acids, indicating the involvement of one or more other classes of neurons.” (line 32). In addition, we note in the revised manuscript that the anterior and posterior projecting populations of IR56d neurons have different responses to short-, medium-, and long-chain FAs, providing an additional mechanism of selectivity. Overall, we understand that our results do not fully address the question of selectivity. However, our observations of differences in taste representation of fatty acid groups provide clear paths to investigate the underlying mechanisms.

I consider the authors should explore in deep how short- and long- FAs are actually detected, whether it depends on other Ionotropic Receptors (probably Ir25a and Ir76b might be involved (Ahn et al. 2017)) and which subset of gustatory neurons are actually responding to these compounds, considering they do not require Ir56d nor Ir56d neurons.

We have now included data measuring taste response to short-, medium-, and long-chain fatty acids in both IR76b and IR25a mutant flies (Figure 3 – Supplemental Figure 1). We find that taste responses to all fatty acids tested are reduced in both mutants, suggesting that these receptors, in addition to IR56d, are required for response to medium-chain fatty acids, but that additional, yet unidentified, receptors are required for response to both short- and long-chain fatty acids. We now state in the results: “Since IR56d is required for taste response to medium-chain, but not short- or long-chain fatty acids, it is possible that other IRs mediate this response. As a first step in identifying which additional receptor(s) may be involved, we measured PER in both IR76b and IR25a mutants to short-, medium, and long-chain fatty acids, as these broadly expressed receptors have been previously found to mediate taste response to medium-chain fatty acids (Ahn et al., 2017). In agreement with these findings, PER to medium-chain fatty acids were significantly reduced for both the IR76b and IR25a mutants, while PER to sucrose was normal (Figure 3—figure supplement 1). Additionally, we found that PER to both short- and long-chain fatty acids were also significantly reduced in both mutants, suggesting that both IR76b and IR25a are required for taste response to all three classes tested (Figure 3—figure supplement 1).” (line 219). As noted above, we also include results showing that PER to short- and long-chain fatty acids remain unaffected in GR64f-GAL4>UAS-TNT flies, and imaging data to show that GRNs expressing IR56d but not GR64f respond to short-, medium- and long-chain fatty acids.

Reviewer #3:

[…]

Overall comments and questions:

1. Are the differences in taste discrimination between male and female flies?

We now include data for PER to short-, medium-, and long-chain fatty acids in male flies. In comparison to females, we find that PER is generally lower in males. We also measured taste discrimination in male flies and found that, similar to females, males are also able to differentiate between short-, medium-, and long-chain fatty acids. We now state in the results: “We next sought to determine whether the discrimination observed in female flies is also found in males. PER analysis with a panel of short-, medium-, and long-chain fatty acids revealed that males respond to all fatty acids tested, though the overall responses were lower than those observed in females (Figure 1—figure supplement 3A). To assess whether male flies are able to discriminate between different classes of fatty acids, we trained flies to a medium-chain fatty acid (6C) and then measured discrimination between 4C (short), 8C (medium), and 9C (long) fatty acids. Similar to results obtained in female flies, males were able to discriminate between 6C and 4C as well as between 6C and 9C, but not between 6C and 8C (Figure 1—figure supplement 3B-D). Therefore, male flies are also able to discriminate different classes of fatty acids, but are not able to distinguish fatty acids within the same class” (line 172).

2. Individual data points should be shown whenever possible for all figures (except PER because that would make it impossible to interpret).

We have added individual data points to the appropriate figures.

3. Can the authors discuss how discriminating between different fatty acids types may be adaptive? Are they found in different food sources, some of which are "good" and some "bad"? Is there evidence from other organisms about this type of molecular discrimination in fatty acid taste?

Thank you, this is an excellent suggestion and an interesting topic for us to explore. We now include a paragraph in the discussion addressing this matter, stating: “Fatty acids are natural by product of yeast fermentation (Diwan and Gupta, 2018; Nyanga, et al., 2013; Oliveira et al., 2011), and their abundance in fruit declines after ripening (Duan et al., 2013). Further, fatty acids have antifungal activity, which scales with chain length (i.e the greater the chain length, the greater the antifungal efficiency; (Pohl et al., 2011)). (Pohl et al. 2011). Thus, the ability to discriminate between different classes of fatty acids is likely be important in determining the stage of fruit ripeness, degree of fermentation, and the general palatability of a potential food source/oviposition site.” (line 336).

[Editors’ note: what follows is the authors’ response to the second round of review.]

The observation that short- and long-chain PER responses are independent of Gr64f neurons suggests that the discrimination between medium vs. short- or long-chain FA may actually be no more surprising (in retrospect) than the discrimination between medium-chain FA and sugars (reported previously). In both cases, the "other" compound can elicit PER through GRNs that are not activated by MC-FA. Therefore, one could speculate that in both cases, the flies learn that IR56d+ GRN activation is "bad" but still perform PER because LC-FA, SC-FA, or sucrose all elicit PER without IR56+ GRNs (even though they all also activate those GRNs).

Indeed, we agree with these comments, however, we believe they highlight the novelty of this system. As reviewers stated, previous work on sugars concluded that flies are unable to discriminate within an individual modality (Masek and Scott, 2010). The differences described suggest neural circuits involved in discrimination are dissimilar from those typically studied as being required for PER. Therefore, we hope this manuscript (and Tauber et al., 2017) position flies as a model to study taste discrimination in addition to innate feeding response. Towards this end, we now state in the discussion: “Our findings highlight the complexity of taste discrimination, which extends beyond simple PER as a readout for taste. For example, all types of fatty acids tested increase GR64f neural responsiveness, however, only GR64f neurons are required for PER to medium-chain fatty acids, thereby raising the possibility that short-, medium-, and long-chain fatty acid taste discrimination occurs through different neural channels. These findings stress the need to define the fatty acid receptors and neural circuits that govern responses to short- and long-chain fatty acid taste.” (line 375).

Reviewer #1:

The revision have improved the quality and impact of the manuscript and opened new questions about the molecular nature of detection of different FA classes and of the circuitry underlying this sensory modality.

There are some typos and run on sentences in the manuscript (i.e line 100, line 352).

We have proofread for typos and run on sentences and have modified the manuscript accordingly.

Reviewer #2:

The authors set out to address the question as whether the fly's gustatory system can discriminate tastants within the same modality. To some extent, this issue has been addressed already. For example, when flies are offered different sugars at the same concentration, they show different propensities to extend their proboscis (Slone et al. Curr Biol 2007). When flies are given a choice between two carboxylic acids at the same concentration (e.g. acetic acid and lactic acid), they prefer lactic acid (Rimal et al. Cell Reports 2019). Even the responses to octanoic acid (medium chain) and oleic acid (long chain) appear to be different (Kim et al. PLOS Genetics 2018), and this is also documented in an earlier paper from the senior author's lab (Tauber et al. PLOS Genetics 2017). So, the overarching question as to whether flies can discriminate different tastants within the same modality is not completely unexplored. Nevertheless, the authors use a very nice aversive memory assay to show that the flies can discriminate short, medium and long chain fatty acids (FAs), but not different medium chain FAs from each other. They knocked out Ir56d and show that is required for the sensation of medium FAs, consistent with their previous study reporting that Ir56d neurons are required for FA taste. In addition, the Ca2+ responses of the Ir56d-expressing neurons were reduced in response in medium chain fatty acids. Unfortunately, the work does not provide a molecular or cellular explanation as to how the flies discern different medium channel FAs. Without such an explanation, the contribution of this work is somewhat incremental over what is already known.

We hope our comment above, that “our findings highlight the complexity of taste discrimination, which extends beyond simple PER as a readout for taste” sufficiently emphasizes the difference between taste discrimination and innate preference assays. We also now state in the discussion: “Our aversive taste memory assay confirmed previous findings that flies can discriminate between sugars and fatty acids (Tauber et al., 2017), and led to the surprising observation that flies can distinguish between different classes of fatty acids, even though the baseline responsiveness to short- medium-, and long-chain fatty acids were similar in innate preference assays.” (line 359).

1. Can flies discriminate between different short-chain FAs, and can they discriminate between different long-chain FAs?

2. Lines 156-159: Silencing Ir56d-expressing neurons is not a test of whether Ir56d is required. Nevertheless, the authors have already reported that Ir56d neurons are needed for the response to medium chain FAs so I cannot discern what is new in Figure 3.

3. The authors state that they backcrossed the Ir56dGAL4 mutant to w1118. But they do not say for how many generations. 5 generations is typical to eliminate background mutations. At the very least, since they have only one allele, they should confirm the phenotype over a deficiency. The rescue is helpful, but it also changes the genetic background.

The IR56dGAL4 mutant line was backcrossed for 10 generations. We now include in the methods: “All lines were backcrossed to the w1118 fly strain for 10 generations.” (line 463).

4. The authors use GCaMP to examine Ca2+ responses to FAs. While GCaMP provides a very good proxy for neuronal activation, the authors should be mindful that rises in Ca2+ do not always lead to neuronal activation, and the GCaMP is not the same as measuring action potentials. They should not say that they are measuring neuronal activation. GCaMP allows them to measure neuronal responsiveness, not activation.

We understand this comment. We did not intend to imply that GCaMP was reflective of action potentials. We have modified the term ‘neuronal activation’ to ‘neuronal responsiveness’ or ‘taste-evoked changes in Ca2+’ where appropriate.

Reviewer #3:

In this paper, Brown et al. expand earlier observations that fatty acids (FA) at low concentration are detected by neurons expressing Gr64f and IR56d receptor genes. The authors show here that when quinine is associated with FAs of middle range length, flies "learn" not to extend their proboscis to FAs of close length, while they keep extending their proboscis in response to longer or shorter chains. The authors further show that inactivating neurons expressing Gr64f or IR56d prevents proboscis extension to middle range FAs but not longer of shorter FAs. They further create a genetic construction by inserting a Gal4 into the IR56d gene and confirm through calcium imaging experiments, that labellar responses to FAs 6C-8C is mediated by neuron expressing IR56d. From these observations, the authors conclude that flies are able to discriminate between FAs belonging to different categories, ie short-long FAs versus middle length FAs. These data clearly indicate that with the experimental protocol used here, FAs of different length are detected by different populations of gustatory receptor neurons.

However, I am not fully convinced that we have here a clear case of categorical perception. In Masek and Scott 2010 as well as in Kirkhart and Scott 2015, the aversive stimulus was independent of the appetitive stimulus. Here, the situation is more complicated because FAs are mixed with quinine during the training phase.

The FAs are not mixed with quinine during the training phase. They are each applied independently to the proboscis. We have clarified this in the manuscript. In the results we now state: “We used an aversive taste memory assay in which an appetitive tastant applied to the proboscis is paired with application of bitter quinine immediately afterwards, resulting in an associative memory that inhibits responses to the appetitive tastant (Masek et al., 2015).” (line 115). We have also clarified this in Figure 1A. We now state: “Next, flies were trained by pairing this fatty acid with quinine presentation immediately following tastant application (Training).” (line 780).

The conclusions proposed by the authors here rely on the untested assumption that quinine and FAs have no interactions. This might not be the case. Actually, quinine is known to interact with sugar perception (see for ex Meunier et al. 2003), where an exposure to 10 mM quinine (as here) induces an irregular activity in the taste neurons and actually reversibly prevents sugar-sensitive neurons to respond to 50 mM sucrose. Mixing quinine with FAs might thus have a differential effect on gustatory neurons – a repetitive exposure to the mixture might silence the receptors depending on FAs chain length.

Furthermore, hexanoic acid and octanoic acid are known to have a toxic effect on flies. These chemicals are considered as one of the main reasons why noni (the fruit) is toxic to flies except to D sechellia. Earlier observations show that while low doses of these FAs are appetitive, higher doses are deterrent. This means that in the experiments shown here, an additional assumption is that repeated presentations of FAs are not changing the valence of the stimulus. While the toxicity of 6C and 8C FAs is documented, nothing is known about the effects of FAs of a different chain length. Furthermore, to my knowledge, it is not known yet if the deterrent effects of these FAs is due to a disturbance of the responses of neurons responding to sugar or if it activates other populations of gustatory neurons, for example bitter-sensitive.

In the experiments performed here, toxicity is highly unlikely because flies were not allowed to ingest the FAs with the assay system we utilized, in which a wick saturated with tastant contacts the proboscis to stimulate proboscis extension. We have clarified this in the methods. We now state: “A wick made of Kimwipe (#06-666; Fisher Scientific) was placed partially inside a capillary tube (#1B120F-4; World Precision Instruments; Sarasota, FL) and then saturated with tastant, thereby enabling flies to taste, but not ingest tastant.” (line 501). In response to repeated presentations of FAs not changing the valence of the stimulus, we find that behaviorally, PER to consecutive applications of FA show no significant change in responsiveness over time. We now state in the results: “Further, it is possible that repeated presentation of fatty acid may alter the valence of the tastant over time. To address this, we asked whether there were any differences in PER between the pretest, training, and test applications among the naïve groups in each test of taste discrimination. We found that PER to consecutive applications of fatty acid show no significant change in responsiveness over time, although in some cases PER to fatty acid trends downward (Figure 1—figure supplement 1).” (line 154).

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Figure 1—source data 1. Raw taste discrimination data between short-, medium-, and long-chain fatty acids.
    elife-67878-fig1-data1.xlsx (70.3KB, xlsx)
    Figure 2—source data 1. Raw taste discrimination data after ablation of olfactory organs.
    elife-67878-fig2-data1.xlsx (15.5KB, xlsx)
    Figure 3—source data 1. Raw data from proboscis extension response experiments to short-, medium-, and long-chain fatty acids.
    elife-67878-fig3-data1.xlsx (30KB, xlsx)
    Figure 4—source data 1. Raw data from IR56dGAL4proboscis extension response experiments.
    elife-67878-fig4-data1.xlsx (21.8KB, xlsx)
    Figure 5—source data 1. Raw imaging data from the posterior labellar region in IR56dGAL4 flies.
    elife-67878-fig5-data1.xlsx (16.2KB, xlsx)
    Figure 6—source data 1. Raw imaging data from the anterior, non-GR64f-IR56d-expressing region in IR56dGAL4 flies.
    elife-67878-fig6-data1.xlsx (13.8KB, xlsx)
    Transparent reporting form
    elife-67878-transrepform.docx (249.2KB, docx)

    Data Availability Statement

    All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for all figures.

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