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Cholecystokinin-like peptide mediates satiety by inhibiting sugar attraction

['Di Guo', 'College Of Plant Protection', 'Nanjing Agricultural University', 'Nanjing', 'China State', 'Local Joint Engineering Research Center Of Green Pesticide Invention', 'Application', 'Jiangsu', 'Yi-Jie Zhang', 'Su Zhang']

Date: 2021-11

Abstract Feeding is essential for animal survival and reproduction and is regulated by both internal states and external stimuli. However, little is known about how internal states influence the perception of external sensory cues that regulate feeding behavior. Here, we investigated the neuronal and molecular mechanisms behind nutritional state-mediated regulation of gustatory perception in control of feeding behavior in the brown planthopper and Drosophila. We found that feeding increases the expression of the cholecystokinin-like peptide, sulfakinin (SK), and the activity of a set of SK-expressing neurons. Starvation elevates the transcription of the sugar receptor Gr64f and SK negatively regulates the expression of Gr64f in both insects. Interestingly, we found that one of the two known SK receptors, CCKLR-17D3, is expressed by some of Gr64f-expressing neurons in the proboscis and proleg tarsi. Thus, we have identified SK as a neuropeptide signal in a neuronal circuitry that responds to food intake, and regulates feeding behavior by diminishing gustatory receptor gene expression and activity of sweet sensing GRNs. Our findings demonstrate one nutritional state-dependent pathway that modulates sweet perception and thereby feeding behavior, but our experiments cannot exclude further parallel pathways. Importantly, we show that the underlying mechanisms are conserved in the two distantly related insect species.

Author summary Food intake is critical for animal survival and reproduction and is regulated both by internal states that signal appetite or satiety, and by external sensory stimuli. It is well known that the internal nutritional state influences the strength of the chemosensory perception of food signals. Thus, both gustatory and olfactory signals of preferred food are strengthened in hungry animals. However, the molecular mechanisms behind satiety-mediated modulation of taste are still not known. We show here that cholecystokinin-like (SK) peptide in brown planthopper and Drosophila signals satiety and inhibits sugar attraction by lowering the activity of sweet-sensing gustatory neurons and transcription of a sugar receptor gene, Gr64f. We show that SK peptide signaling reflects the nutritional state and inhibits feeding behavior. Re-feeding after starvation increases SK peptide expression and spontaneous activity of SK producing neurons. Interestingly, we found that SK peptide negatively regulates the expression of the sweet gustatory receptor and that activation of SK producing neurons inhibits the activity of sweet-sensing gustatory neurons (GRNs). Furthermore, we found that one of the two known SK peptide receptors is expressed in some sweet-sensing GRNs in the proboscis and proleg tarsi. In summary, our findings provide a mechanism that is conserved in distantly related insects and which explains how feeding state modulates sweet perception to regulate feeding behavior. Thus, we have identified a neuropeptide signal and its neuronal circuitry that respond to satiety, and that regulate feeding behavior by inhibiting gustatory receptor gene expression and activity of sweet sensing GRNs.

Citation: Guo D, Zhang Y-J, Zhang S, Li J, Guo C, Pan Y-F, et al. (2021) Cholecystokinin-like peptide mediates satiety by inhibiting sugar attraction. PLoS Genet 17(8): e1009724. https://doi.org/10.1371/journal.pgen.1009724 Editor: Liliane Schoofs, Katholieke Universiteit Leuven, BELGIUM Received: January 6, 2021; Accepted: July 17, 2021; Published: August 16, 2021 Copyright: © 2021 Guo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: Raw sequence Data of the RNA-seq analysis in this study are available in the Short Read Archive (SRA) database of NCBI with the accession numbers and NCBI URLs as below: dsNlsk_1, (SRR12460889, https://www.ncbi.nlm.nih.gov/sra/?term=SRR12460889), dsNlsk_2, (SRR12460895, https://www.ncbi.nlm.nih.gov/sra/?term=SRR12460895), dsNlsk_3, (SRR12460894, https://www.ncbi.nlm.nih.gov/sra/?term=SRR12460894), dsNlsk_4, (SRR12460893, https://www.ncbi.nlm.nih.gov/sra/?term=SRR12460893), dsgfp_1 (SRR12460896, https://www.ncbi.nlm.nih.gov/sra/?term=SRR12460896), dsgfp_2 (SRR12460892, https://www.ncbi.nlm.nih.gov/sra/?term=SRR12460892), dsgfp_3 (SRR12460891, https://www.ncbi.nlm.nih.gov/sra/?term=SRR12460891) and dsgfp_4 (SRR12460890, https://www.ncbi.nlm.nih.gov/sra/?term=SRR12460890). Funding: This work was supported by the National Natural Science Foundation of China to SFW (No. 32022011 & 31772205) (https://isisn.nsfc.gov.cn/egrantindex/funcindex/prjsearch-list?locale=zh_CN). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Introduction Neuronal control of feeding is interesting for at least two reasons: in the human population there is a growing problem with excess food consumption causing obesity and associated severe health problems, and secondly, pest insects consume large amounts of our crops worldwide. Both problems are very costly to society. Therefore, understanding mechanisms behind regulation of food search, feeding and satiety is of great interest. In general, animal behavior is guided by internal states and external stimuli [1–5]. Hence, behavioral decisions depend on the integration of signals from the internal and external environment by circuits of the brain. For instance, feeding behavior, which is essential for the survival and reproduction of animals, is regulated by the nutritional state of the organism and depends on the efficacy of chemosensory organs in localizing food in the environment [3,6–12]. Thus, internal nutrient sensors monitor the energy homeostasis and signal hunger or satiety to the nervous system. Hunger signals received by brain circuits are relayed to sense organs to increase their sensitivity. Hence, in a hungry animal the sensory threshold is lowered in olfactory and gustatory receptors that respond to food cues and thereby increases appetitive behavior and food seeking [6,10–13]. Concomitantly, the hunger signals increase the detection threshold for aversive stimuli, such as bitter tastants [11,14]. After food ingestion, satiety signals lower the attractive sensory thresholds and also act on neuronal circuits that regulate feeding behavior, thereby stopping further food intake [12,15]. In Drosophila and other insects, the modulation of sensory gain in appetitive behavior is largely dependent on neuropeptides and peptide hormones [4,12,15–17]. These signaling systems also orchestrate animal behavior and link internal and external sensory cues [see [17–19]]. Thus, several neuropeptides are known to trigger appetitive behavior, foraging, and mobilize energy stores, at the same time as they suppress other conflicting behaviors such as sleep and reproductive behavior [see [17–20]]. At the sensory level neuropeptides such as short neuropeptide F (sNPF), myoinhibitory peptide (MIP), CCH2amide and tachykinin (TK) regulate the sensitivity of subpopulations of olfactory receptor neurons (ORNs) to promote food seeking in hungry flies [6,10,21–24]. Also gustatory neurons in Drosophila are modulated by neuropeptides to regulate sugar and bitter sensitivities [11]. Hence, in hungry flies neuropeptide F (NPF), via dopamine cells, increases sweet sensitivity in Gr5a expressing cells and sNPF decreases bitter sensitivity [11]. NPF and sNPF are also known to regulate aspects of feeding, metabolism and sleep [25–32], suggesting action at several levels of the organism. Another neuropeptide known to regulate food intake in Drosophila is drosulfakinin, DSK, related to the mammalian peptide cholecystokinin (CCK) [33]. CCK in mammals and sulfakinins (SKs) in insects, including Drosophila, signal satiety and decrease food ingestion [34–38]. DSK has furthermore been reported to modulate aggression and courtship behavior in Drosophila [39–41]. The first insect SK was identified from head extracts of the cockroach, Rhyparobia maderae [42]. Characteristic of SKs is that it contains a sulfated tyrosine residue (DY(SO3)GHM/LRFamide) [43]. In a variety of insects, such as the desert locust, the German cockroach, and the red flour beetle, SK significantly reduced food intake [44–46]. SK is also known to inhibit the activity of digestive enzymes in the migratory locust and brown planthopper [35,47]. In Drosophila, upon feeding, DSK is released and the meal is terminated [12,15,33]. However, it is not known how DSK acts to decrease feeding. The mechanisms controlling sweet gustation and feeding in insects are remarkably similar to those in vertebrates. Sweet-sensing neurons are housed in taste sensilla in the labellum on the proboscis (the insect equivalent of the vertebrate tongue), the tarsal segments of the legs, and the pharynx [48–50]. In Drosophila, nine types of gustatory receptors (Gr) participate in sweet sensation. One of them, Gr64f, is a co-receptor that is required in combination with other gustatory receptors for sugar detection in Drosophila [49–51]. However, the mechanisms behind how the feeding state modulates sugar receptor gene expression and sweet-sensing neurons activity are poorly known. We chose to study SK signaling in regulation of gustatory input and feeding behavior in two insect species, the brown planthopper Nilaparvata lugens and the genetic model insect Drosophila melanogaster. The brown planthopper is a serious pest on rice in Asia and causes damage costing more than 300 million US dollars annually [52]. N. lugens is a monophagous pest that pierce the rice stem and sucks sap, thereby transferring a virus [53] that destroys the plant [54]. With the magnificent genetic toolbox available, Drosophila has been extensively used as a model to study regulation of feeding, metabolism and sensory mechanisms underlying food seeking [see [4,12,13,15,17,55]]. Thus, we combine two insect species in our quest to understand how a neuropeptide regulates gustatory perception of food-related taste and ensuing initiation of food ingestion. Using N. lugens in an RNA-seq transcriptome screen for altered gene expression after downregulation of SK, we found among others an upregulation of sweet-sensing gustatory receptors and the takeout (to) gene. Analysis of manipulations of SK and its receptor SKR, as well as gustatory receptors and to, suggests that feeding-induced SK signaling downregulates sweet sensing receptors and that to is a mediator of SK signaling in the planthopper. Further experiments in Drosophila unravel mechanisms behind the satiety signaling that modulates gustatory neurons. Feeding upregulates Dsk transcription and increases spontaneous activity and calcium signaling in DSK expressing median protocerebrum (MP) neurons. Furthermore, feeding downregulates Gr64f expression and starvation increases Gr64f. Optogenetic activation of Gr64f neurons increases the motivation to feed. In addition to this, knockdown of dsk leads to an upregulation of Gr64f transcription and activation DSK expressing MP neurons inhibit the sensitivity of gustatory neurons. Remarkably, we found expression of the DSK receptor CCKLR-17D3 in a subpopulation of the Gr64f GRNs in proleg tarsi, proboscis and maxillary palps and this receptor is downregulated in the appendages after feeding. Finally, knockdown of 17D3 in sweet sensing GRNs decreases the PER. Thus, in summary DSK signaling modulates the sensitivity of sweet-sensing GRNs in a nutrient-dependent fashion in both the planthopper and Drosophila, suggesting a conserved peptidergic signal pathway in these distantly related insects. It should be emphasized that our experiments cannot exclude further components in the state-dependent regulation of gustatory sensitivity, including contributions of other neuropeptides or neuromodulators, such as those indicated earlier in this introduction.

Discussion Food seeking and feeding are under complex control by neuronal circuits as well as by neuropeptides and peptide hormones [3,12,15,40,55,79–81]. Thus, sensory systems, central circuits and interorgan communication, in a nutrient-dependent fashion, contribute to feeding decisions and regulation of food ingestion. In this study, we have analyzed mechanisms of nutrient state-dependent peptidergic regulation of gustatory inputs and feeding. We show herein that in both the planthopper N. lugens and the fly Drosophila, SK signaling mediates satiety and decreases sensitivity of gustatory neurons (GRNs) expressing the gustatory sugar receptor Gr64f. Thus, SK release not only decreases food intake [33,34,82], but also downregulates attraction to sugar. We find that food ingestion diminishes and starvation increases Gr64f expression under control of SK signaling. In the brown planthopper, the gene takeout is upregulated by SK, and we showed that knockdown of takeout upregulates Gr64f and increases feeding in both insects. We performed additional experiments in Drosophila to reveal further SK signaling mechanisms. Calcium and membrane activity in DSK expressing MP neurons in the brain increase after feeding, suggesting that these neurons receive nutrient signals. We could also show that optogenetic activation of DSK neurons in the brain decreases the PER, whereas activation of Gr64f expressing GRNs increases the PER. DSK neurons were found to make functional contacts with Gr64f expressing GRNs in the SEZ, and one of the two DSK receptors, CCKLR-17D3, is expressed in Gr64f expressing GRNs in proleg tarsi, labellum and maxillary palps. Furthermore, the ccklr-17D3 levels are downregulated in these appendages after feeding. Our data suggest that the CCKLR-17D3 inhibits activity in Gr64f expressing GRNs and hence knockdown of this receptor leads to a decrease in sugar sensitivity and reduced PER. In Drosophila, takeout knockdown not only increases feeding, but also expression of Gr64f, further suggesting a role of takeout in DSK-mediated satiety signaling. Hence, we find that food ingestion activates SK signaling in the two insects and that SK acts to decrease expression of a sweet receptor and thereby diminish food attraction and feeding (Fig 9G). However, our data also suggest that SK in not the only satiety-induced signal that contributes to nutritional state modulation of sugar gustation (see Fig 9G). As mentioned in the introduction, neuropeptides such as NPF and sNPF, as well as dopamine, are also known to regulate gustatory perception [11]. Furthermore, food search also depends on olfaction, and neuropeptides like MIP, CCH2amide and TK have been found to modulate the sensitivity of ORNs [6,10,21–24]. We can also not rule out the possibility that manipulating DSK signaling could affect food intake and dietary status and thus indirectly influence sweet sensing via other signaling pathways. Invertebrate SKs and the vertebrate CCKs are known as satiety inducing peptides that regulate food ingestion [34,36,38,44–46,81,83,84]. However, these peptides also act in a multitude of other regulatory functions both centrally and in the periphery [37,38,85]. In insects SKs play additional roles in gut motility [42], digestive enzyme production and release [35,47,86], regulation of sexual arousal [41], as well as hyperactivity and aggression [39,67]. Similarly, in mammals CCK stimulates pancreatic enzyme secretion and release of insulin and glucagon, gallbladder contraction and gut motility, and is implicated in fear, anxiety, and aggression [see [36,38,85,87]]. In addition CCK has diverse roles in the brain as a neuromodulator regulating other neurotransmitter systems [see [85]]. Thus, in planthoppers and flies, one can expect that SK acts at several levels and that these actions are coordinated to generate a relevant behavioral and physiological outcome. In Drosophila and planthopper there are cell bodies of SK-producing neurons only in the brain, and to our knowledge no SK peptide is produced in the intestine or other tissues [41,58]; see also FlyAtlas2 http://flyatlas.gla.ac.uk, [88]. We show here that four pairs of posterior DSK interneurons, MP1 and MP3, with wide arborizations in the brain are likely to underlie the regulation of Gr64f-expressing GRNs. These MP1 neurons display increased calcium and spontaneous electric activity after feeding, and we demonstrate here that the MP1 neurons have processes that superimpose GRN axon terminations in the SEZ [see also [41]]. Furthermore, optogenetic activation of DSK neurons rapidly inhibits the PER suggesting direct neuronal connections. A subset of the brain insulin-producing cells (IPCs) is also known to co-express DSK [33,39,41], but our experiments exclude these cells in modulation of GRNs. The IPCs may instead release DSK as a circulating hormone to act on the intestine in regulation of digestive enzymes as demonstrated in the planthopper and other insects [see [35,47,86]], but not yet shown in Drosophila. We found that knockdown of takeout increases feeding, and also that this upregulates expression of Gr64f in planthopper and fly. Interestingly, it has been shown earlier that takeout is expressed in GRNs of the labellum in Drosophila and that mutant flies are deficient in sugar sensing and regulation of food ingestion [62]. Takeout mutants are also aberrant in their starvation-induced locomotor activity and display increased mortality during starvation [61,62]. Furthermore, the gene takeout is expressed also in the intestine and fat body, is under control by the circadian clock, and was proposed to link circadian rhythms and feeding behavior [61]. Since takeout encodes a putative juvenile hormone (JH) binding protein, it was suggested that it regulates levels of circulating JH and that this may impart the effects on locomotor activity and food intake, as well as effects on metabolism [62]. The role of takeout in the GRNs in modulation of sugar sensitivity by regulating Gr64f in the same cells requires further investigation. As mentioned, SK acts at several levels of the CNS and periphery and modulates conflicting behaviors such as food search, feeding, sexual arousal and aggression [33,39,41,67]. These studies show that Dsk neurons increase aggression and decrease feeding and mating. The DSK-expressing MP neurons are central in suppression of both feeding (this study), mating [41] and aggression [66], but aggression was also found dependent on DSK in the IPCs [39,67]. Modulation of feeding and mating relies on different downstream circuits. For suppression of male sexual behavior DSK (from MP neurons) acts on male-specific fruitless-expressing P1 neurons that coordinate arousal-related behaviors such as sex, sleep and locomotion [41]. In parallel, as we show here, the same DSK neurons target GRNs to suppress sweet attraction and inhibit feeding. It is not clear at present how DSK released from IPCs suppresses feeding and what the hormonal DSK targets are [33]. Our data herein suggest that SK is not the only signal that modulates sweet attraction and feeding behavior in response to food ingestion. Other systems, such as the four widely arborizing SIFamide expressing neurons of the pars Intercerebralis, are also known to coordinate hunger and satiety signals to stimulate appetitive behavior and suppress mating behavior and sleep [18,89]. Also this SIFamide system acts at different levels, such as olfactory and gustatory circuits, as well as sleep and activity circuits and fruitless expressing neurons [18,90]. Several studies have also shown peptidergic modulation of ORNs and GRNs to promote state-dependent food seeking in Drosophila. Thus, nutrient dependent insulin signaling modulates sNPF and TK signaling to alter sensitivity of ORNs [6,10,21], whereas NPF and sNPF regulate sweet and bitter sensitivity, respectively [11]. NPF and sNPF are also known to act in other circuits to modulate feeding, metabolism and sleep [see e. g. [25–28,30–32]]. Previous studies found that short-term starvation changes dopamine signaling, which leads to sensitization of sweet sensing neurons [11]. However, in our experiments the starvation time is 24 hours, which is longer than in the earlier experiments. Thus, we assume that in our experiments the effect on the sugar response is not mediated by dopamine. In summary, we show that DSK secreted from brain neurons regulates sugar sensitivity of DSK receptor-expressing GRNs in response to food ingestion and thereby diminishes food attraction (Fig 9G). Mechanistically this state-dependent desensitization of specific GRNs is by SK receptor mediated downregulation of Gr64f expression in GRNs, possibly involving action of takeout. Importantly, the mechanisms described herein are conserved in Drosophila and the brown planthopper, although these insects are only distantly related. It is likely that our findings have revealed only a portion of the mechanisms involved in mediating nutritional state dependent regulation of sensory perception and subsequent feeding behavior.

Materials and methods Experimental insects and husbandry The brown planthopper N. lugens was reared on ‘Taichung Native 1’ (TN1) rice (Oryza sativa L.) seedlings in the laboratory and maintained at 27 ± 1 ∘C, with 70 ± 10% relative humidity, under a 16 h: 8 h light dark photoperiod [91]. Flies were maintained on standard molasses/cornmeal/yeast/agar food at 25°C on a 12:12 LD cycle with humidity set to 60 ± 5% unless otherwise indicated. The following fly strains were ordered from Bloomington Stock Center: w1118 (Bloomington Stock number: #5905); Canton-S (#64349); Gr64f-GAL4 (#57668; [92]); Actin5c-GAL4 (#4414); UAS-ArcLight (#51056; [68]); UAS-RedStinger (#8547; [93]); UAS-CaLexA (#66542; [69]); UAS-Stinger-GFP (#84277); UAS-Chrimson, attp18 (#55134; [70]); UAS-Chrimson, attp40 (#55135; [70]); UAS-dTrpA1 [94]; UAS-NaChBac (#9467; [95]); LexAop-rCD2::RFP, UAS-mCD8::GFP (#67093); UAS-nSyb-spGFP1-10, LexAOP-CD4-spGFP11 (#64314; [96]); LexAOP-nSyb-spGFP1-10, UAS-CD4-spGFP11 (#64315; [96]); Δ17D1[Df(1)Exel9051] (#7762;17D1KO [78]). UAS-DskRNAi (THU2073), UAS-17D1RNAi (THU2656), UAS-17D3RNAi (THU2970), and UAS-Gr64fRNAi (TH04838.N) were purchased from Tsinghua Fly Center at the Tsinghua University [97,98]. UAS-Kir2.1 [99], LexAop2-IVS-nlstdTomato [93], Dsk-GAL4, Dsk-LexA, 17D3KO, 17D3GAL4, Dsk1, and UAS-mCD8::GFP have been described previously [41]. Dilp2-GAL4 (#37516; [100]) was kindly provided by Dr. Wei Song. Gr64fLexA and sugar blind flies (R1,Gr5aLexA;+; Δ61a, Δ64a-f) were a gift from Dr. Hubert Amrein [72,73]. DskattP, DskLexA(RY), 17D1attP, and 17D3attP were kind gifts from Dr. Yi Rao [101]. Peptide synthesis Sulfakinin peptides were synthesized by Genscript (Nanjing, China) Co., Ltd. Peptides mass was confirmed by MS and the amount of peptide was quantified by amino acid analysis. The amino acid sequence of the peptides used in this study are: N. lugens sulfakinin 1: (NlSK1): SDDYGHMRFamide; sulfakinin 2: (NlSK2): GEADDKFDDYGHMRFamide; sulfated sulfakinin1 (sNlSK): SDDY(SO 3 H)GHMRFamide; sulfated sulfakinin2 (sNlSK2): GEADDKFDDY(SO 3 H)GHMRFamide. Gene cloning and sequence analysis We used the NCBI database with BLAST programs to carry out sequence alignment and analysis. Then we predicted Open Reading Frames (ORFs) with EditSeq. The primers were designed by Primer designing tool–NCBI. Total RNA Extraction was using the TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. The cDNA template used for cloning was synthesized using the Biotech M-MLV reverse transcription kit and the synthesized cDNA template was stored at -20°C. The transmembrane segments and topology of proteins were predicted by TMHMM v2.0 (http://www.cbs.dtu.dk/services/TMHMM-2.0/) [102]. Multiple alignments of the complete amino acid sequences were performed with Clustal Omega (http://www.ebi.ac.uk/Tools/msa/clustalo). Phylogenetic tree was constructed using MEGA 7.0 software with the Maximum Likelihood Method and bootstrapped with 1000 replications [103]. Quantitative RT-PCR The first-strand cDNA was synthesized with HiScript II Q RT SuperMix for qPCR (+gDNA wiper) kit (Vazyme, Nanjing, China) using an oligo(dT)18 primer and 500 ng total RNA template in a 10 μl reaction, following the instructions. Real-time qPCRs in the various samples used the UltraSYBR Mixture (with ROX) Kit (CWBIO, Beijing, China). The PCR was performed in 20 μl reaction including 4 μl of 10-fold diluted cDNA, 1μl of each primer (10 μM), 10 μl 2 × UltraSYBR Mixture, and 6 μl RNase-free water. The PCR conditions used were as follows: initial incubation at 95°C for 10 min, followed by 40 cycles of 95°C for 10 s and 60°C for 45 s. N. lugens 18S rRNA or Drosophila rp49 were used as an internal control (S1 Table). Relative quantification was performed via the comparative 2−△△CT method [104]. We used Drosophila heads to analyze the expression of Dsk and GAL4 in starved or refed condition. RNA interference in N. lugens For lab-synthesized dsRNA, gfp, Nlsk, Nlto and NlGr64f fragments were amplified by PCR using specific primers conjugated with the T7 RNA polymerase promoter (primers listed in S1 Table). dsRNA was synthesized by the MEGAscript T7 transcription kit (Ambion, Austin, TX, USA) according to the manufacturer’s instructions. Finally, the quality and size of the dsRNA products were verified by 1% agarose gel electrophoresis and the Nanodrop 1000 spectrophotometer and kept at -70°C until use. The 4th instar nymph of brown planthoppers was used for injection of 60 nl of 5 μg/μl dsRNA per insect. Injection of an equal volume of dsgfp was used as negative control. RNAi efficiency was examined by qPCR using a pool of ten individuals on the 3rd day after dsRNA injections. The insects of the third day after dsRNA injection were used for feeding assay and gene relative expression analysis. Feeding assay of N. lugens The animals were food deprived for 5 h before onset of the experiment to ensure that all experimental animals were in the same nutritional state prior to the experiment. This was based on several periods of starvation (2, 5, 12, and 24 h) we tested, from which a starvation for 5 h gave the best results in terms of intraexperiment variation. The animals were starved in the morning and the experiments were done in the afternoon. The feeding assay method and artificial diet was adopted as previously reported with modification [105]. Briefly, the antifeedant potency of sulfakinins was measured in fourth instar nymphs of N. lugens. Prior to injection, the peptides were dissolved in PBS. Individual brown planthoppers were then injected with 40 nl of peptide solution (2.25 pmol/insect) or 40 nl of PBS in the lateral side of the abdomen using a FemtoJet system (Eppendorf-Nethler-Hinz, Hamburg, Germany). Immediately after injection, ten animals were placed in separate plastic containers (9 cm long and 2 cm in diameter), provided with 200 μl of artificial diet and allowed to feed ad libitum for 24 hours. For studying the effect of gene silencing on the feeding behavior of N. lugens, the dsRNA-injected 4th instar nymphs were reared on rice seedlings in the laboratory and maintained at 27 ± 1 ∘C, with 70 ± 10% relative humidity, under a 16 h: 8 h light dark photoperiod to recover for 2 d. After 5 hours starvation, ten nymphs were transferred into separate plastic containers (9 cm long and 2 cm in diameter), provided with 200 μl of artificial diet and allowed to feed ad libitum for 24 hours as mentioned above. The feeding amount of brown planthopper in each feeding chamber was recorded after 24h. The experiment was repeated at least four times. RNA-seq analysis Total RNA of thirty 4th instar nymphs was isolated at day three after dsNlsk or dsgfp injections in the 4th instar nymphs using a TRIzol reagent (Invitrogen) according to the manufacturer’s protocol. Library construction and sequencing was performed by Novogene with Illumina HiSeq2000 platform (Novogene Bioinformatics Technology Co.Ltd, Beijing, China). Raw sequence data were submitted to the Short Read Archive (SRA) database of NCBI under the accession numbers SRR12460889 (dsNlsk-1), SRR12460895 (dsNlsk-2), SRR12460894 (dsNlsk-3), SRR12460893 (dsNlsk-4) and SRR12460896 (dsgfp-1), SRR12460892 (dsgfp-2), SRR12460891 (dsgfp-3), and SRR12460890 (dsgfp4). The raw data were analyzed after filtering the low-quality sequences. Sequences were aligned to the Nilaparvata lugens genome (https://www.ncbi.nlm.nih.gov/genome/?term=Nilaparvata+lugens) using Hisat2 v2.0.5. The expression level of genes from the RNA sequencing was normalized by the FPKM method (Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced). This method considers the effect of sequencing depth and gene length for the reads count at the same time and is currently the most commonly used method for estimating gene expression levels. Differential expression analysis was performed using the DESeq2 R package (1.16.1). The clusterProfiler R package was used for Gene Ontology (GO) enrichment analysis and KEGG pathway analysis [106]. FDR-adjusted multiple tests were added to the hypergeometric test. Generation of 17D1GAL4 knock-in line To prepare the 17D1GAL4 line, we used CRISPR-HDR (clustered regularly interspaced short palindromic repeats–homology directed repair) method based on previous methods [41]. We chose the upstream and a downstream guide RNAs targeting the part of first exon using the CRISPR Optimal Target Finder: http://tools.flycrispr.molbio.wisc.edu/targetFinder/. In brief, the part of first CCKLR-17D1 coding exon was replaced by GAL4::p65 (S6A Fig). Firstly, two gRNAs (gRNA1: 5′-GATTTATAAACTCGGGTCGCA-3′; gRNA2: 5′-TCACCGACAGCGGAGATCTC-3′) against CCKLR-17D1 were inserted into pCFD4 as previously described [107]. We then fused GAL4::p65 into pHD-DsRed (Addgene #51434) between the EcoRI and the NdeI sites. Next, each homologous arm was subcloned into the pHD-DsRed vector too. We injected the modified pCFD4 and pHD-DsRed plasmids into the embryo of vas-Cas9 flies (# 51324). The correct insertion was confirmed by 3xP3-DsRed screening and recombination accuracy was confirmed by sequencing. NSK/DSK antibody Rabbit anti-DSK antibody was generated by using the peptide N′-FDDYGHMRFC-C′ that corresponds to the predicted DSK-1 and DSK-2 peptides as antigen as previously reported [41]. We used the DSK antibody to recognize both SK peptides of N. lugens and Drosophila since the antigen share the same sequence in two species. Immunohistochemistry Unless otherwise stated, fourth instar nymph of brown planthopper and 3-5-day old mated female flies were dissected under phosphate-buffered saline (PBS; pH 7.4) or Schneider’s insect medium (S2) as previously described [41,108]. The tissues were fixed in 4% paraformaldehyde in PBS for 30 min at room temperature. After extensive washing with PAT (0.5% Triton X-100, 0.5% bovine serum albumin in PBS), the tissues were incubated in primary antibody for 24 h at 4°C and in secondary antibody for 24 h at 4°C. Primary antibodies used: mouse anti-GFP (Sigma-Aldrich Cat# G6539, 1:1000), rabbit anti-RFP (Abcam Cat#ab62341, 1:1000), mouse anti-Bruchpilot (Developmental Studies Hybridoma Bank nc82, 1:30), rabbit anti-DSK (see antibody generation section, 1:100). Secondary antibodies used: donkey anti-mouse IgG conjugated to Alexa 488 (1:500) or Alexa 555 (1:500) and donkey anti-rabbit IgG conjugated to Alexa 488 (1:500) or Alexa 555 (1:500) (Molecular Probes). The samples were mounted in Vectorshield (Vector Laboratory). Images were acquired with Zeiss LSM 700 confocal microscopes, and were processed with Image J software [109]. To quantify NlSK level in the brain, we stained the brains with DSK antibody (Fig 1E and 1F). The dsgfp- or dsNlsk-injected samples were processed in parallel and using the same solution and imaged with the same laser power and scanning settings. With the imaged data, we got “Sum Slices” Z-projection of the sub-stacks encompassing whole brain to measure fluorescence intensity (F dsgfp and F dsNlsk ), then select a small region without signal as the background fluorescence (B dsgfp and B dsgfp ) using ImageJ. Then we obtained the relative fluorescence of NlSK as the ratio of NlSK signal to the control signal ((F dsNlsk —B dsNlsk )/(F dsgfp − B dsgfp )). Arclight imaging Imaging of freshly dissected brain explants of starved (24 hours) or refed (1.5 hours refed after 24 hours starvation) was performed on a Zeiss 710 NLO Axio Examiner confocal microscope using a water immersion objective (Zeiss, Germany). ArcLight was excited with the 488 nm laser. The objective C-mount image was projected onto the 256 × 256 pixel chip controlled by Zen2010 software (Zeiss Germany). Images were recorded at a frame rate of roughly 80 Hz, and depicted optical traces were spatial averages of intensity of all pixels within the region of interest (ROI, in this study, cell bodies of MP1 neurons), with signals processed as previously reported [68]. Statistical analysis and plotting of the data were performed using Excel and Prism GraphPad. CaLexA measurements Calcium activity of DSK neurons following starvation and refed was measured using the calcium-dependent nuclear import of LexA (CaLexA) reporter system [69]. Female flies (4–7-days old) carrying Dsk-GAL4 and UAS-CaLexA system were collected. They were then divided in two groups: one group was starved for 24 hours in presence of water and another group was refed 1.5 hours after 24 hours starvation. Following this period, the fly brains were quickly dissected and were processed for immunohistochemistry. The GFP fluorescence were quantified as described above. CsChrimson activation For optogenetic stimulation, tester flies were collected within twelve hours after eclosion and transferred into a vial with regular food containing 200 μM all-trans retinal (116–31–4, Sigma-Aldrich). The vials were covered by aluminum foil to protect from light for 3–5 days before PER test. We immobilized flies on a glass slide with back down so that the proboscis was exposed to the upside and stimulated the animal with red (620 nm, 0.03 mW/mm2, Vanch Technology, Shanghai, China) light. Unless otherwise noted, light stimulation was presented continuously throughout the observation period. Light intensity was measured by placing an optical power meter (PS-310 V2, Gentec, Canada) nearby the location of glass slide. dTrpA1 activation Experimental flies were maintained at 22°C, cold anesthetized and loaded into CAFE vial, and allowed to recover for at least 30 min at 22°C. Vials containing flies were then placed at experimental temperatures (30°C) for 24 hours to recording the feeding amount. Extracellular tip recording 7–10 days old females were used for electrophysiological recordings. All flies were kept in dark after eclosion and fed with 200 μM all-trans-retinal for 3 to 5 days. Before the assays, flies were transferred to a tube contained a filter paper with 2ml of all-trams-retinal solution (200 μM all-trans-retinal diluted in 2 ml ultrapure water) for 24 hours, and then some groups were refed for 1.5 hour. Electrophysiological recording from Drosophila labellar taste sensilla were implemented as previously reported [110]. A reference electrode was inserted into dorsal thorax of fly, and proboscis was fully extended. The recording electrode was approached to the tip of a single L-sensilla on labellum and covered ~ 50% of the total shaft length. Beadle-Ephrussi Ringer solution (B&E) was used as the reference electrode electrolyte, which contained 7.5 g NaCl, 0.35g KCl, and 0.279 g CaCl2∙2H2O in one liter of ultrapure water. Using 30 mM tricholine citrate solution (TCC) solved with 100 mM sucrose as the recording electrode electrolyte. Both reference and recording electrodes were capillary glass (borosilicate glass with filament, BF120-69-15; O.D.: 1.2mm, I.D.: 0.69mm) that was pulled on a P-97 puller (Sutter Instrument Corp). The recording electrode was connected to an amplifier (TastePROBE DTP-02, SYNTECH) and a data acquisition device (Axon Digidata 1550B, Molecular Device) under the control of Axon pCLAMP 10.6 software (Molecular Devices). Data was analyzed with Clampfit 10.7 software. Proboscis extension response (PER) assays We performed the PER assays at about 2:00–5:00 pm (ZT6–ZT10) at room temperature. 3-5-day-old females were used for the assays. Before the assays, flies were starved for 24 hours, and refed for 1.5 hour. We first anesthetized flies with carbon dioxide and stuck them on microscope slides. After one hour recovery, we tested flies with water. Proboscis were touched by a water drop, and if the fly did not extend its proboscis in three seconds, we performed the assays with different concentrations of sucrose. Two values were used in the PER assays. A score of 1 means a fly that extended its proboscis and ingested after being fed the sucrose water drop. If not, the score of that fly is 0. We averaged the scores of 5–10 flies as one replicate. Capillary feeding (CAFE) assay This method was modified from Ja et al. [71]. A vial (9 cm height × 2 cm diameter), filled with 5 ml of 1% agarose to provide water for the flies, was used for this assay. Capillaries (5 μl, VWR International) were exchanged daily with new ones containing fresh food solution (5% sucrose and 5% yeast extract dissolved in water). Typically, 24-h feeding of every fly is shown after 1 or 2 d of habituation in this assay. The amount of consumed food minus evaporation was quantified. Statistics We used the GraphPad Prism 7 software package to generate graphs and statistically analyze data. Data presented in this study were first verified for normal distribution by D’Agostino–Pearson normality test. If normally distributed, Student’s t test was used for pairwise comparisons, and one-way ANOVA was used for comparisons among multiple groups, followed by Tukey’s multiple comparisons. If not normally distributed, Mann–Whitney test was used for pairwise comparisons, and Kruskal–Wallis test was used for comparisons among multiple groups, followed by Dunn’s multiple comparisons. All data are presented as mean ± s.e.m. The sample sizes and statistical tests used for each experiment are stated in the figures or figure legends. The raw data of this project are shown in the S7 Table.

Acknowledgments We thank Yi Rao (Peking University), Wei Zhang (Tsinghua University) and Wei Song (Wuhan University) for sharing fly strains. Other members of the Wu and Gao laboratory are thanked for helpful discussions.

[END]

[1] Url: https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1009724

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