https://orcid.org/0009-0005-2387-5373
Figures
Abstract
Myo-inositol, a sugar alcohol produced by most plants, serves as a nutrient and feeding stimulant for many phytophagous insects. Inositol-sensitive taste sensilla have been characterized in many Lepidoptera larvae, but their molecular bases remain unclear. In this study, we determined the gustatory receptors (GRs) for myo-inositol in larva of Helicoverpa armigera, a worldwide crop pest. First, electrophysiological analyses revealed that medial sensilla styloconica strongly responded to myo-inositol and ribose, with weaker responses to xylose, and one GRN inside sensillum may mediate the response to these three chemicals. Based on phylogenetic analysis of sugar GRs of Lepidoptera insects and previous results on Bombyx mori, we then selected two candidate GRs, HarmGR13 and HarmGR11. Using CRISPR-Cas9, we generated knockout mutants for the two GR genes. Knocking out HarmGR13 abolished the responses of the sensilla to myo-inositol, ribose, and xylose, while knocking out HarmGR11 showed no changes. Behavioral assays confirmed that larvae of HarmGR13 homozygous mutant lost the feeding preference to myo-inositol which the wild-type larvae had. Further functional analysis with Xenopus oocytes expressing system and two-electrode voltage-clamping demonstrated that myo-inositol and ribose specifically induced concentration-dependent currents in HarmGR13-expressing oocytes. Structural predictions and molecular docking of HarmGR13 revealed three amino acid residues might be involved in ligand binding. Mutation of these residues resulted in loss of oocyte responses to myo-inositol and ribose. We reveal that HarmGR13 is a receptor that mediates the activity of the cells sensitive to inositol and ribose in larvae, providing new molecular targets for the strategy of regulating the feeding behavior of pests by modifying taste.
Author summary
The larvae of lepidopteran insects primarily feed on plants, and their sense of taste is mediated by sensilla located on their mouthparts. Here we investigate cellular and molecular mechanisms by which the cotton bollworm larvae perceive inositol, a nutrient widely present in plants. First, we confirmed that a pair of sensilla on the mouthparts were very sensitive to inositol and ribose, and one of the gustatory sensory neurons inside might be responsible for detecting them. Next, we selected two candidate gustatory receptors, HarmGR11 and HarmGR13 to analyze their function through gene editing. We found that the larvae with HarmGR13 knocked out lost physiological responses to inositol and ribose, while the larvae with HarmGR11 knocked out responded to inositol in the same way as the wild-type. Ectopic expression further confirmed that the Xenopus oocytes expressing HarmGR13 responded to inositol and ribose. Structure prediction and molecular docking suggested three amino acid residues were involved in ligand-receptor binding, and then the subsequent point mutations showed that changing the three residues to nonpolar residues respectively eliminated the tuning activity of HarmGR13. In conclusion, HarmGR13 is an inositol receptor that mediates the physiological and behavioral responses of the larvae to inositol.
Citation: Tan Y-S, Mo B-T, Li G-C, Guo Y-R, Zhang J-Z, Wang C-Z (2025) HarmGR13 mediates myo-inositol taste perception in Helicoverpa armigera larvae. PLoS Genet 21(6): e1011744. https://doi.org/10.1371/journal.pgen.1011744
Editor: Thomas B. Friedman,, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, UNITED STATES OF AMERICA
Received: February 4, 2025; Accepted: May 25, 2025; Published: June 3, 2025
Copyright: © 2025 Tan 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: All relevant data are within the manuscript and its Supporting Information files.
Funding: This work was supported by the National Natural Science Foundation of China (Grant No. 32130090 and 32372541 (To CZW); URL: www.nsfc.gov.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
Myo-inositol is a ubiquitous sugar alcohol in green plants. It is a key structural component of phospholipids, and it is involved in plant growth and development in many ways, such as cell wall formation, osmoregulation, cell signaling and phosphate storage. It is also a nutrient and energy substance for insects [1,2]. The level of inositol in plants may reflect some important characteristics and nutritional value of plants, and thus become an important compound for insects to recognize host plants and evaluate plant nutritional value.
When an insect comes into contact with a potential host plant, whether to accept or reject it depends largely on its contact chemoreception. Taste sensilla in insects occur outside the mouthparts, and are also often present on the tarsi and other parts of the body [3]. Commonly there are four or six gustatory sensory neurons (GSNs) associated with a single sensilla and their dendrites run through the dendrite sheath, ending just inside the pore. The sensory properties of GSNs are determined by gustatory receptors (GRs) expressed in the dendrite membranes [4]. GRs are seven-transmembrane-domain membrane proteins that bind to tastants and trigger action potentials of neurons. Research on insect GRs began with Drosophila melanogaster [5]. Phylogenetic analyses have categorized GRs into four clades, the DmGr43a, sugar receptors, carbon dioxide receptors, and bitter receptors. Except for the carbon dioxide receptors, which are associated with olfactory sensing, the receptors in the other three clades are involved in gustatory sensing. In Drosophila, DmGr43a is a fructose receptor only expressed in the gut and certain parts of the brain in adults but primarily expressed in the periphery of larvae [6,7]; eight receptors (DmGr5a, DmGr61a-f and DmGr64f) in the sugar receptor clade are required for adult responses to multiple sugars [8–12]; 33 receptors co-expressed with Gr66a in the bitter receptor clade mediate the detection of bitter substances.
Taste perception of insects for inositol is mostly studied on Lepidopteran insects. Behavioral and electrophysiological studies have shown that caterpillars of many species respond to inositol [13]. Most electrophysiological works have focused on two sensilla on the maxillary galea, the medial sensillum styloconicum and the lateral sensillum styloconicum, which play an important role in host plant recognition [14]. Of the two sensilla, at least one is sensitive to inositol [15]. However, the molecular mechanisms underlying inositol detection by these sensilla remain poorly understood, with only two conflicting reports on Bombyx mori available. One study reported that the Sf9 cells expressing the sugar receptor BmGr8 showed concentration-dependent responses to inositol and epi-inositol [16]. In contrast, using the Xenopus laevis oocyte expression system and the HEK293T cell expression system, another study found that the cells expressing BmGr10 responded to inositol and epi-inositol, while the cells expressing BmGr8 did not [17]. In either study, the function of the relevant Grs was not validated in vivo.
Helicoverpa armigera (Lepidoptera: Noctuidae) is a globally significant agricultural pest, causing annual economic losses exceeding $3 billion [18,19]. With a broad host range of over 300 plant species from 68 families, the larvae primarily feed on plant reproductive organs. Previous studies showed that the lateral sensilla styloconica of H. armigera larvae were sensitive to sucrose, while the medial sensilla styloconica of H. armigera larvae were sensitive to inositol [20]. In a recent study, we showed that the Xenopus oocytes expressing HarmGR10 highly expressed in the maxillary galeas of the larvae had specific responses to sucrose, and knockout of HarmGR10 resulted in the larvae losing the behavioral and electrophysiological response to sucrose, indicating that HarmGR10 mediates the response of lateral sensilla styloconica to sucrose in larvae [21]. However, the GR that mediates the responses of medial sensilla styloconica to inositol in H. armigera larvae remains unknown.
In order to elucidate the molecular mechanisms underlying inositol detection in H. armigera larvae, we first characterized electrophysiological responses of the sensilla styloconica on the maxillary galea to sugars and sugar alcohols. Based on analysis of the phylogenetic tree of GRs in Lepidopteran insects, two candidate GRs for inositol detection were selected. Subsequently, we used CRISPR-Cas9 to knock out the two GR genes separately, and then examined the electrophysiological and behavioral responses of the larvae of two mutants and the wild-type to inositol. Moreover, we employed the Xenopus oocyte expression system and two-electrode voltage-clamping to validate necessity and adequacy of the receptor for tuning inositol. In addition, through structural modeling prediction, molecular docking, and site-specific mutagenesis, we further identified key amino acid residues for receptor-inositol interactions. All these results comprehensively reveal the cellular and molecular basis of inositol sensing in bollworm larvae.
Results
Electrophysiological responses of sensilla styloconica on maxillary galea of H. armigera larvae to sugars and sugar alcohols
We first examined the electrophysiological responses of the lateral and medial sensilla styloconica on maxillary galea in 5th instar larvae of H. armigera to 11 sugars and sugar alcohols, which were selected based on their abundance in plants, structural diversity, and biological relevance as primary energy sources for insects. The medial sensilla styloconica responded strongly to inositol and ribose, weakly to xylose, and had no response to other compounds (Fig 1A). Dose-response curves showed that the threshold concentrations of inositol, ribose, and xylose were 0.1 mM, 0.1 mM, and 10 mM for the medial sensilla styloconica, respectively (Fig 1B). In contrast, the lateral sensilla styloconica exhibited a strong response to sucrose and a weak response to fucose, and no responses to other compounds, consistent with the previous results (S1 Fig) [20].
(A) Left: Quantifications of firing rates of medial sensilla styloconica to representative sugars and sugar alcohols at 10 mM (n = 10; each dot represents one larva). Right: Representative spike traces of responses. Data are mean ± SEM; *p < 0.05; **p < 0.01; ***p < 0.001. Data were analyzed by independent-samples t-test (compared with control). (B) Dose-response curves of medial sensilla styloconica to inositol, ribose, and xylose (inositol: n = 6–9; ribose: n = 9; Xylose: n = 8). Data are mean ± SEM. Data were analyzed by one-way ANOVA with Tukey’s HSD test (p < 0.05). (C) Representative spike traces and waveforms of 0.1 mM inositol, 0.1 mM ribose, and 10 mM xylose, independently and binary mixtures.
To estimate whether the responses of the medial sensilla styloconica to inositol, ribose, and xylose were mediated by the same or different GSNs, we compared spiking patterns elicited by single compounds (0.1 mM inositol, 0.1 mM ribose, and 10 mM xylose) and their binary mixtures (0.1 mM inositol + 0.1 mM ribose, 0.1 mM inositol + 10 mM xylose, and 0.1 mM ribose + 10 mM xylose). Compared to the responses induced by single compounds, the mixtures induced additive responses (S2A-C Fig) with only one amplitude spikes (Fig 1C), suggesting that the same GSN in the medial sensillum styloconicum responded to inositol, ribose and xylose.
Phylogenetic analysis
To explore the molecular basis of the medial sensilla styloconica responses to inositol, ribose, and xylose, we conducted a phylogenetic analysis of GRs from H. armigera, D. melanogaster, B. mori, Plutella xylostella, and Manduca sexta. The phylogenetic tree showed that HarmGR11 and HarmGR13 are orthologs of BmGR8 and BmGR10 in B. mori previously reported, respectively [16,17] (Fig 2). We further analyzed transcriptomic data of the maxillary galea of H. armigera, and the transcripts per million mapped reads (TPM) showed that only HarmGR10 exhibited the highest expression level among the sugar receptors and the DmGr43a-like receptor in the tissue, but HarmGR13 has the third highest TPM value among selected GRs and the TPM value of HarmGR11 is much lower (S3 Fig).
Dm: Drosophila melanogaster. Lepidoptera: Bm: Bombyx mori; Px: Plutella xylostella; Msex: Manduca sexta; Harm: Helicoverpa armigera. Receptors with bold black are inositol receptors of B. mori and their homologous receptors of H. armigera. Numbers on branches indicate ultrafast bootstrap approximation (UFBoot).
Establishment of HarmGR13 and HarmGR11 homozygous mutants
Using CRISPR-Cas9, we successfully generated homozygous knockout mutants for HarmGR13 (GR13-/-) and HarmGR11 (GR11-/-). For GR13-/-, a 13-bp deletion in exon 2 truncated HarmGR13 from 436 to 106 amino acids (Fig 3A). For GR11-/-, a 19-bp deletion and a 9-bp insertion in exon 5 resulted in the truncation of HarmGR11 from 435 to 249 amino acids (Fig 3B). Potential off-target effects were evaluated using the CRISPOR online tool. Comparison of target sequences with the H. armigera genome revealed no potential off-target sites with three or fewer mismatches. The two mutants showed no difference in larval weight, larval stage, pupal weight, pupal stage, and number of eggs laid compared with wild-type larvae (S4A-J Fig). It is worth noting that the larval stage of GR13-/- was significantly longer than that of the wild-type (S4G Fig).
(A) Gene structure of HarmGR13, target region of sgRNA, and sequencing chromatogram for homozygous mutants. (B) Gene structure of HarmGR11, target region of sgRNA, and sequencing chromatogram for homozygous mutants. (C) Mating process to obtain homozygous mutants. (D) Representative spike traces of medial sensilla styloconica of GR13-/- compared to WT. (E) Quantifications of firing rates of WT and GR13-/- to (n = 10). (F) Representative spike traces of medial sensilla styloconica of GR11-/- compared to WT. (G) Response intensity of WT and GR11-/- (n = 8–10). In (E) and (G), each dot represents one larva; Data are mean ± SEM; *p < 0.05; **p < 0.01; ***p < 0.001. Data were analyzed by independent-samples t-test.
Effects of HarmGR13 or HarmGR11 knockout on the electrophysiological responses of H. armigera larvae
We compared the electrophysiological responses of the medial sensilla styloconica on maxillary galea in larvae of GR13-/-, GR11-/- and the wild-type to inositol, ribose, and xylose, and sinigrin at a concentration of 10 mM, and sinigrin serves as positive control. The average firing rates of GR13-/- larvae to inositol, ribose and xylose were only 7.1 spikes/s, 7.4 spikes/s, 6.9 spikes/s, significantly lower than those of wild-type larvae (Fig 3D and 3E), while the responses of GR11-/- larvae to the three compounds were not significantly different from those of wild-type larvae (Fig 3F and 3G). However, the responses of the two mutants to the bitter compound sinigrin were not significantly different from those of the wild-type larvae (Fig 3E and 3G). These results indicate that HarmGR13 is involved in the taste perception of inositol, ribose and xylose by the medial sensilla styloconica of H. armigera larvae, while HarmGR11 is not; None of them are involved in the taste perception of sinigrin by the medial sensilla styloconica of H. armigera larvae.
Effects of HarmGR13 or HarmGR11 knockout on the behavioral responses of H. armigera larvae
In order to further clarify the behavioral responses of H. armigera larvae to inositol, ribose and xylose and their relationship with HarmGR11 and HarmGR13 expression, we used two-choice tests to determine the feeding time within 5 min of the fifth-instar larvae of the wild-type, GR11-/-, and GR13-/- on agar containing each compound and control agar (Fig 4A). Wild-type larvae fed longer on the agar containing 10 mM inositol than on the control agar, showing feeding preference of larvae for inositol (Fig 4B). However, this preference disappeared when inositol concentration was reduced to 0.01 mM (S5 Fig). The wild-type larvae had no feeding preference for ribose and xylose at 10 mM (S5 Fig). GR11-/- larvae showed feeding preference for 10 mM inositol as wild-type larvae (Fig 4C). GR13-/- larvae showed no feeding preference for 10 mM inositol and even a slight bias towards control agar (Fig 4D). In addition, both GR13-/- and GR11-/- larvae showed feeding preference for sucrose and feeding deterrence to sinigrin (Fig 4C and 4D). These results indicate that inositol has a feeding stimulating effect on H. armigera larvae, and HarmGR13 mediates to this behavioral response.
(A) Schematic of the two-choice feeding test. Left represents what to do with the larvae before test. Middle represents test process. Right represents the agar arrangement on petri dish. (B) Feeding responses of WT larvae to 10 mM inositol, sucrose, and sinigrin (n = 20–21). (C) Feeding responses of GR11-/- larvae to the same compounds (n = 16). (D) Feeding responses of GR13-/- larvae to the same compounds (n = 15–17). In (B, C, D): *p < 0.05; **p < 0.01; ***p < 0.001. Data were analyzed by paired t-test.
Functional analysis of HarmGR13 by ectopic expression
To further validate the function of HarmGR13 ectopically, we reverse-transcribed the gene into cRNA and injected it into Xenopus oocytes. After incubating the oocytes at 18°C for two days, inward currents elicited by stimulation with 11 sugars and sugar alcohols were recorded by using two-electrode voltage-clamp. The results demonstrated that the oocytes expressing HarmGR13 showed strong responses to 10 mM inositol and 100 mM ribose (Fig 5A), with inward current amplitudes of 1.98 ± 0.6 μA and 2.58 ± 0.52 μA, respectively (Fig 5A). The dose-response curves showed that the threshold concentrations of inositol and ribose evoked responses were 1 mM and 10 mM, respectively (Fig 5B).
(A) Top: The representative traces of HarmGR13-expressing oocytes to 11 sugars and 3 sugar alcohols at 100 mM and 10 mM concentrations. Bottom: Quantifications of the responses of GR13-expressing oocytes to each sugar and sugar alcohol (n = 5). (B) Top: The representative traces of HarmGR13-expressing oocytes to ribose following concentration gradient; Middle: The representative traces of HarmGR13-expressing oocytes to inositol following concentration gradient; Bottom: Dose-response curves for inositol and ribose (n = 5). In A and B Data are mean ± SEM. Data were analyzed by one-way ANOVA with Tukey’s HSD test (p < 0.05). Ara, arabinose; Fru, fructose; Fuc, fucose; Glu, glucose; Ino, inositol; Mal, maltose; Man, mannitol; Suc, sucrose; Sor, sorbitol; Tre, trehalose; Xyl, xylose.
According to the phylogenetic tree of GRs, HarmGR13 belongs to the same clade as BmGr9 from B. mori, MsexGR10.1 from M. sexta, and DmGr43a from D. melanogaster (Fig 2). Based on the homotetrameric structure of BmGr9 recently resolved [22], we predicted the structure of HarmGR13 using AlphaFold3. The resulting model had an ipTM score of 0.78 and a pTM score of 0.8, indicating the predicted structure is highly reliable. Using the AutoDock Vina algorithm, we further performed molecular docking simulations of the structure with the two ligands, and the estimated binding energies of HarmGR13 to inositol and ribose are -6.2 kcal/mol and -5.2 kcal/mol, respectively. Polar interaction analysis indicated that HarmGR13 forms polar contacts with inositol at Asp92, Asp159, and Gln339, and with ribose at Asp92 and Gln339 (Fig 6A and 6B). These residues are conserved across HarmGR13, BmGR10, and MsexGR10.1 (S6 Fig).
(A, B) Predicted 3D protein structure of HarmGR13 and molecular docking with inositol and ribose. (C) Representative traces of oocytes expressing GR13 and its point mutants (D92A, D159A, and Q339A) to 100 mM inositol and 500 mM ribose. (D) Quantifications of the responses of oocytes expressing HarmGR13 and its mutants to 100 mM inositol and 500 mM ribose (n = 5). Data are mean ± SEM and were analyzed by one-way ANOVA with Tukey’s HSD test (p < 0.05).
To test the functional significance of Asp92, Asp159, and Gln339 in HarmGR13, we performed site-specific mutagenesis at each site and substituted them with the non-polar amino acid alanine. The unmutated HarmGR13 and three single-site mutated genes were transcribed into cRNA and injected into Xenopus oocytes for functional analysis. The oocytes expressing the unmutated HarmGR13 exhibited inward currents in response to 100 mM inositol and 500 mM ribose, but the oocytes expressing each of three mutated HarmGR13 did not show any inward currents in response to the same stimuli (Fig 6C and 6D), indicating that Asp92, Asp159, and Gln339 are essential for HarmGR13 binding to inositol and ribose.
Discussion
In this study, we demonstrate that HarmGR13 functions as a receptor tuned to inositol and ribose in the medial sensilla styloconica of H. armigera larvae. Additionally, we identified three amino acid residues critical for HarmGR13 binding to inositol as a feeding stimulant for larvae of H. armigera. These results may have general implications for understanding why larvae of many lepidoptera species have the ability to detect myo-inositol.
Myo-inositol is a ubiquitous metabolite of plants. It plays a crucial role in physiological processes of plant growth and development, including phosphate storage, cell wall biosynthesis, production of stress-related molecules, intercellular communication, and storage and transport of plant hormones. With a sweetness similar to sugar, inositol is an indispensable nutrient and energy source and feeding stimulus for herbivorous insects [23].
Lepidoptera is an important model for studying the taste perception of inositol because caterpillars of many species are sensitive to inositol [24]. Behavioral studies have shown that myo-inositol induces feeding and extends feeding duration in B. mori larvae [25,26]. Similarly, myo-inositol has been observed to stimulate feeding and suppress bitter taste perception in M. sexta larvae [27,28]. Most of the electrophysiological studies focused on two styloconic sensilla on the maxillary galea, which play a critical role in host plant selection of caterpillars. In B. mori, Operophtera brumata, and Adoxophyes orana, the lateral sensilla styloconica are sensitive to inositol. In Heliothis virescens, H. armigera, Helicoverpa assulta, Mamestra configurata, Mamestra brassicae, Spodoptera exempta, and Spodoptera litura, the medial sensilla styloconica are sensitive to inositol. In M. sexta, Euproctis phaeorrhoea, and Philosamia cynthia, both the medial and lateral sensilla styloconica are sensitive to inositol [14].
Given the general sensitivity of caterpillars to inositol, elucidating the molecular mechanisms underlying taste perception of inositol is pivotal for understanding host-plant recognition in Lepidopteran insects. However, until now the GRs responsible for inositol detection in caterpillars remains unclear. There are only three related studies on B. mori. Zhang et al. (2011) [16] reported that BmGr8 functions independently in Sf9 cells and responds in a concentration-dependent manner to myo-inositol and epi-inositol, which is the first chemoreceptor shown to respond specifically to inositol. Employing Xenopus oocyte expression system with two-electrode voltage-clamp and HEK293T cell expression systems with the fluorescent Ca2+ indicator GCaMP3, Kikuta et al. (2016) [17] found that the cells expressing BmGr10 specifically respond to myo-inositol and epi-inositol, but the cells expressing BmGR8 did not. It is worth noting that the latter study used a BmGR8 variant lacking 38 amino acids compared to the former study. Recently, it has been further proved that BmGr10 is a putative inositol receptor in the epipharyngeal sensillum using HEK293T cells with the fluorescent Ca2+ indicator Flur-4 [29]. Endo et al. (2024) [30] used the HEK293T cell expression system and found that BmGR9 and BmGR6 were highly sensitive to mulberry compounds such as chlorogenic acid and isoquercetin, and also to inositol at concentrations as low as 10 pM and 1 nM, respectively. However, inositol might not be the primary ligand for these receptors, as the threshold concentrations for inositol differed significantly from those for other ligands. In the above studies, the function of related GRs was not verified in vivo.
Gene editing techniques have been widely used in model systems such as Drosophila for in vivo analysis of gene function, but their application in Lepidoptera insects remains limited. CRISPR-Cas9 can completely eliminate the expression of a given GR gene in insects, and this is usually not lethal to insects, so it is a highly effective tool to verify the function of a certain GR in vivo. In this study, we used CRISPR-Cas9 to knock out HarmGR11 and HarmGR13 in H. armigera larvae to verify the function of these two genes in vivo. We found that knockout of HarmGR13 resulted in loss of the electrophysiological responses to inositol, ribose and xylose in the medial sensilla styloconica of the larvae, and the larvae no longer had a feeding preference for inositol, while knockout of HarmGR11 did not cause any changes in the electrophysiology and behavior of the larvae, indicating that HarmGR13 is indispensable for inositol detection. Unexpectedly, in the behavior test, GR13-/- larvae even showed a slight bias to control agar, which might be caused by the compensatory changes in peripheral taste after the knockout of HarmGR13. Furthermore, GR13-/- larvae grows much slower than WT larvae. This is very likely due to the loss of inositol perception ability, which affects the feeding efficiency and nutrient intake of larvae. Using the Xenopus oocyte expression system and two-electrode voltage clamp, we further reveal that expression of HarmGR13 alone is sufficient for the cell responding to inositol and ribose. However, the function of HarmGR11 remains unclear. A previous study indicated that BmGr8, the homolog of HarmGR11, was highly expressed in the testis of B. mori [16]. In this study, we find that HarmGR11 is not functional in the medial sensilla styloconica of H. armigera larvae. Moreover, knocking out HarmGR11 has no effect on the reproductive capacity of H. armigera adults.
There are only two contact chemosensilla on the maxillary galea of H. armigera larvae: the lateral sensillum styloconicum is sensitive to sucrose, and the medial sensillum styloconicaum is sensitive to inositol. Our recent work found that HarmGR10 mediates sucrose sensing in the lateral sensillum styloconicum, and this study reveals that HarmGR13 mediates inositol sensing in the medial sensillum styloconicum. The threshold concentrations of evoking responses of the two sensilla were all 0.1 mM, but the expression level of HarmGR10 is significantly higher than that of HarmGR13. This can be explained by the fact that HarmGR10 is less sensitive to sucrose than HarmGR13 to inositol. Ectopic expression experiments showed that the threshold concentration of sucrose for HarmGR10 was 100 mM, while that of inositol for HarmGR13 was 1 mM. Therefore, the response intensity of a given GRN to a ligand mainly depends on the expression level of the GR, the binding affinity of the GR to the ligand, and the concentration of the ligand.
The results of our behavioral, electrophysiological and molecular experiments are well matched for inositol, but less so for ribose and xylose. This discrepancy may be due to the different sensitivities of the inositol GSN to the three compounds, or the involvement of other GSNs sensitive to ribose or xylose. Ribose is a vital component of nucleic acids and participates in the pentose phosphate pathway and ATP synthesis, serving as both a substrate for energy production (ATP) and a signaling molecule in bacterial quorum sensing [31]. Studies on insect ribose perception are limited. In Drosophila larvae, ribose attracts feeding behavior and is detected by the Gr28 cluster genes, while in adults, ribose induces aversion, mediated by GSNs in s-type sensilla and bitter receptors [32,33]. Research on xylose perception in insects is also scarce. In nature, xylose is a major component of plant cell walls and a primary sugar in some plant nectars [34,35]. However, pollinating insects, such as Cape honeybees (Apis mellifera capensis), exhibit aversion to xylose [36]. The mechanism of sensing ribose and xylose in H. armigera larvae needs further study.
We used AlphaFold3 to predict the HarmGR13 structure in reference to the recently resolved structure of the fructose receptor BmGR9 in B. mori [22]. Since HarmGR13 and BmGR9 belong to the same subfamily of DmGr43a homologues, the predicted structure is expected to be relatively accurate. Gaining insight into how HarmGR13 interacts with ligands such as inositol, we confirmed three key amino acid residues involved in ligand binding by site-directed mutagenesis. However, during molecular docking, the predicted docking amino acid residues may not be exactly consistent with actual binding interactions. Although the mutation of the predicted residue led to the loss of HarmGR13 function, other residues may also be involved in the binding process. Understanding in detail how these residues interact with ligands and open ion channels requires resolving the actual three-dimensional structure of the receptor using the cryo-electron microscopy.
In this work, by using behavior assays, electrophysiology, CRISPR-Cas9, ecotopic expression, molecular docking, and site-specific mutagenesis, we convincingly reveal that HarmGR13 is indispensable and sufficient for tasting inositol and ribose in the medial sensilla styloconica of H. armigera larvae and three amino acid residues are critical for HarmGR13 functioning, and confirm that inositol has a stimulating effect on the feeding of larvae. Understanding the cellular and molecular mechanisms of inositol detection in H. armigera larvae is of great significance for further understanding host-plant selection of this serious crop pest and other Lepidoptera insects. It also offers new pathways for designing effective and environmentally friendly control strategies. In addition to larvae, the F5a sensilla in the foreleg tarsus of H. armigera adults also have electrophysiological responses to inositol, which can trigger proboscis extension reflex and prolongs proboscis extension duration [37]. Whether inositol detection in H. armigera adults is also mediated by HarmGR13 needs further study.
Materials and methods
Ethics statement
All experiments were approved by the Animal Care and Use Committee of the Institute of Zoology, Chinese Academy of Sciences, in accordance with laboratory animal care guidelines (IOZ17090-A).
Insects and Xenopus laevis
H. armigera used in the study is a laboratory-maintained wild-type strain reared under controlled conditions (28 ± 1 °C, 30%-50% relative humidity). Larvae are fed an artificial diet (nutrients include wheat bran, yeast, and tomato paste) in glass tubes. Once the larvae reach the third instar, they are transferred to a 24-well plate for individual rearing. After pupation, they are placed in a cage covered with gauze until they reach sexual maturity, at which point mating occurs, and eggs are laid on the gauze. The egg gauze is then collected and disinfected with 10% formaldehyde for 5 minutes before being dried and stored for future larval hatching and rearing. Xenopus laevis is reared in the indoor breeding facility at Shanxi University, with a temperature of 20 ± 1°C and approximately 50% humidity. They are fed fish feed, with water changes every two days and feeding every four days.
Chemicals
Total 13 chemicals, including representative sugar and sugar alcohols (pentose, hexose, disaccharide and sugar alcohol), sinigrin and potassium chloride were used in tip-recoding and two-electrode voltage-clamping. Each chemical was of the highest purity available commercially. The detailed information of each compound was compiled in S1 Table.
Tip-recording
The tip-recording technique is used to record the taste sensilla responses of H. armigera larvae based on previously published methods [38,39]. The test compounds are introduced into a glass capillary in contact with the sensilla, with a silver wire inserted as a recording electrode. The capillary opening has a diameter of approximately 50 μm, encompassing the entire sensillum. Action potentials (spikes) generated within the first second of stimulation are amplified by a preamplifier and sampled using a computer equipped with a Metrabyte DAS16 A/D conversion board. The first stage of amplification utilizes an AD 515-K (Analog Devices) integrated circuit, producing an input bias current of <1 pA, with an input impedance of 1015 ohms and 0.8 pF. A GO-box interface is used for signal conditioning, incorporating a second-order band-pass filter (-3 dB frequencies: 180 and 1700 Hz). All tested compounds are dissolved in 2 mM potassium chloride, with 2 mM potassium chloride serving as a negative control. All solution concentrations for testing reactions are set at 10 mM. Sugars that elicit significant electrophysiological responses from the taste sensilla are tested in a dose-response from low to high concentrations (0.01 mM, 0.1 mM, 1 mM, 10 mM, and 100 mM). To prevent adaptation of the sensilla to the stimulating compounds, the time interval between different compound stimulations on the same sensillum is at least three minutes. Digital signals are stored using SAPID 16.0 software, and the data is analyzed with AutoSpike 3.7 software (Syntech, Buchenbach, Germany). Spikes are classified based on typical biphasic waveforms and peak amplitudes, with frequency counts of the same type of spikes starting from the first second after stimulation. electrophysiological recordings from the larvae are conducted using first-day fifth-instar larvae. To ensure that all experimental larvae are at the same developmental stage, fourth-instar larvae near molting are selected and transferred to glass tubes, where they are fed green peppers. Prior to electrophysiological recordings, the test larvae are starved for approximately two hours. The head of the test insect is cut and fixed onto a circular silver wire support, which is connected to the amplifier via a copper microconnector. The sensilla styloconica on the maxillary of the larvae are exposed to assess electrophysiological responses.
Phylogenetic analysis of sugar GRs
The amino acid sequences of fructose and sugar receptors annotated of H.armigera and those annotated by Drosophila melanogaster, B. mori, P. xylostella and M. sexta were used to construct phylogenetic trees [40–43]. MAFFT v7.455 [44] was used to make sequence alignment of amino acid sequences. Phylogenetic trees were reconstructed using a maximum-likelihood method implemented in IQ-TREE v2.2.0 [45] under the Jones-Taylor-Thornton (JTT)+F + G4 model for 5000 ultrafast bootstraps. FigTree v1.4.4 was used to visualize and edit the constructed phylogenetic tree.
In vitro synthesis of single-guide RNA
Target sites for sgRNA of HarmGR11 and HarmGR13 were selected using CRISPOR online website (http://crispor.tefor.net/). For HarmGR11, the sgRNA target site was in the fifth exon, and for HarmGR13, it was in the second exon. The design principles of sgRNA primers are detailed in S2 Table, according to the requirements of the GeneArt Precision gRNA Synthesis Kit (Invitrogen, Vilnius, Lithuania), and synthesized following the GeneArt gRNA Prep Kit (Invitrogen, Vilnius, Lithuania) protocol. Purification was carried out with the GeneArt gRNA Clean Up kit (Invitrogen, Vilnius, Lithuania), and the final sgRNA products were dissolved in RNase-free water and stored at -80 °C until use.
Embryo microinjection
The synthesized sgRNA was diluted to 200 ng/μL, and TrueCut Cas9 protein (Invitrogen, Vilnius, Lithuania) was diluted to 500 ng/μL sgRNA and Cas9 protein were mixed at a 4:1 ratio, resulting in final concentrations of 180 ng/μL sgRNA and 100 ng/μL Cas9 protein. Freshly fertilized H. armigera eggs (within 1hour post-laying) were aligned on a slide with double-sided tape. The sgRNA-Cas9 mix was injected into prepared eggs using a PLI-100A PICO-INJECTOR (Warner Instruments, Hamden, Connecticut, USA). Injected eggs were placed in a 10 cm Petri dish with moist filter paper, maintained at 26 °C for larval hatching, and hatched larvae were individually reared in glass tubes with artificial diet.
DNA extraction and mutagenesis detection
To screen for homozygous mutants of HarmGR13 and HarmGR11, the tarsus of the hind legs from adults on their first day post-emergence was used to extract genomic DNA for sequence amplification of sgRNA target sites. Genomic DNA was extracted using the TIANcombi DNA Lyse&Det PCR Kit (TIANGEN Biotech, Beijing, China). For PCR, a 25 μL reaction system was prepared with 1.5 μL DNA template, 1 μL of each primer, 12.5 μL of 2 × Rapid Taq Master Mix (Vazyme, China), and 9 μL of ddH2O. The PCR program was 95 °C for 3 min (initial denaturation), followed by 35 cycles (95 °C for 15 s, 57 °C for 30 s for HarmGR11 or 55 °C for 15 s for HarmGR13, and 72 °C for 10 s), and a final extension at 72 °C for 5 min. PCR products were sent to SinoGenoMax for Sanger sequencing, and results were analyzed with SnapGene software (from Insightful Science; available at snapgene.com). Primier for mutagenesis detection is in S2 Table.
Screening homozygous mutants
Homozygous mutant lines GR11-/- and GR13-/- were established respectively. To obtain GR11-/-, G0 chimeras were backcrossed with wild-type individuals to produce G1 offspring; heterozygous G1 individuals with a 19 bp deletion and 9 bp insertion on one chromosome were then self-crossed to yield homozygous HarmGR11 mutants in the G2 generation, which were self-crossed to generate G3 homozygotes. The same protocol was used for GR13-/-, with a 13 bp deletion on one chromosome in the G1 heterozygotes. Mutation rates for each generation are provided in the S3 Table.
Off-target effects detection
To verify the absence of off-target effects in homozygous mutants GR11-/- and GR13-/-, potential off-target sites were predicted as described using CRISPOR (http://crispor.tefor.net/) to match sgRNA targeting sites with the H. armigera genome [40]. Results showed no potential off-target sites with less than three mismatches. Further validation followed published methods [21,46] included comparing developmental time, weight, pupal weight and duration, and oviposition rates between wild-type and mutant strains. Randomly selected wild-type, GR11-/- and GR13-/- larvae were tested. Developmental time was recorded from hatching to the fifth instar and to pupation; larval weight was measured on the first day of the fifth instar, pupal weight on 3–4 days after pupation, and the number of eggs laid by a female when paired with two males was determined.
Effects of HarmGR13 or HarmGR11 knockout on contact chemoreception in larvae
For wild-type, GR11-/- and GR13-/- larvae, tip recording methodology was the same as describe above and slightly adapted for testing compounds (e.g., inositol, ribose, xylose, and sinigrin). Sinigrin triggered responses in the bitter GRN of the medial sensilla styloconica [46] and served as a positive control, while 2 mM potassium chloride served as a negative control for all sensilla.
Effects of HarmGR13 or HarmGR13 knockout on feeding behaviors of larvae
We adapted a previously reported two-choice assay [21]. Fourth-instar H. armigera larvae at the pre-molting stage were transferred to fresh glass tubes and starved overnight to ensure they remained unfed until the experiment. Inositol, ribose, and xylose were dissolved to the required concentrations and mixed with 30 ML of 1% agar, while water agar served as the control. Two opposing sectors of compound agar and water agar were placed in a 10 cm Petri dish, filling the dish entirely. Wild-type and mutant larvae were introduced to the dish and allowed to feed. The duration of continuous feeding on each agar block was recorded over 5 minutes. Initial test concentrations were set at 10 mM, with concentration gradient experiments conducted at 0.01 mM, 0.1 mM, 1 mM, and 10 mM.
RNA extraction and cDNA synthesis
The galea of larval of H. armigera were dissected and placed in 1 mL of QIAzol Lysis Reagent (QIAGEN, Hilden, Germany). Total RNA was extracted following the kit’s protocol. The quality and concentration of RNA were measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, USA). For cDNA synthesis, 0.1 μg of total RNA was used, following the steps in the reverse transcription kit (Thermo Fisher Scientific, Waltham, USA). The cDNA was dissolved in ddH2O and stored at -20 °C for further use.
Gene cloning of sugar GRs
Specific primers were designed based on transcriptome data from maxillary galea of H. armigera larval and previously reported sugar receptor gene sequences in the NCBI database [21,47,48]. GenBank accession numbers and specific primers for cloning full-length sequences are provided in S2 Table. Gene cloning was performed with Q5 High-Fidelity DNA Polymerase (New England Biolabs, Beverly, MA, USA) in a 25 μL PCR reaction system: 5 μL 5 × Q5 Reaction Buffer, 0.5 μL 10 μM dNTP, 1.25 μL of each primer, 15.75 μL H2O, 1 μL cDNA, and 0.25 μL Q5 High-Fidelity DNA polymerase. The PCR program was 98 °C for 10 s, 35 cycles (98 °C for 10 s, 60 °C for 30 s, 72 °C for 1 min), with a final extension at 72°C for 2 min. PCR products were separated by agarose gel electrophoresis, and target bands were excised and recovered using a gel extraction kit (TransGen Biotech, Beijing, China). The extracted products were ligated into the pEASY-Blunt3 vector (TransGen Biotech, Beijing, China), propagated in E. coli, and plasmids were sequenced at SinoGenoMax for Sanger sequencing. Sequence integrity and similarity to the reported sequence were confirmed, and no alternative splicing forms were detected during sequence analysis or cloning.
Ectopic expression and functional analysis of GRs
Full-length sugar receptor sequences were PCR-amplified, introducing restriction enzyme sites and a Kozak sequence, and cloned into the pGEM-T vector. After digestion with suitable enzymes, sequences were subcloned into the pCS2 + vector, linearized, and cRNA synthesized using the mMESSAGE mMACHINE SP6 kit (Ambion, Austin, TX, USA), diluted to 2000 ng/μL in ddH2O, and stored at -80 °C. Xenopus oocytes dissection followed established methods [49], with anesthetized frogs on crushed ice. Oocytes were isolated and treated with 2 mg/mL collagenase type I (Sigma-Aldrich, St Louis, MO, USA) for 30 min at room temperature. HarmGR13 was expressed in oocytes via cRNA injection using a NANOJECT III (Drummond Scientific Company, Broomall, PA, USA) with a total injection volume of 27.6 nL (approx. 80 ng at 10 nL/s). Negative controls included oocytes injected with ddH2O. Oocytes were incubated for 2 days at 18 °C in Ringer’s solution (96 mM NaCl, 2 mM KCl, 1 mM MgCl2, 1.8 mM CaCl2, 5 mM HEPES, pH 7.6). Whole-cell currents were recorded using a two-electrode voltage clamp, with glass electrodes filled with 3 M KCl and resistance set at 0.2-2.0 MΩ. Signals were amplified with an OC-725C amplifier (Warner Instruments,Hamden, CT, USA) and maintained at -80 mV, low-pass filtered at 50 Hz and digitized at 1kHz. A total of 11 representative sugars and sugar alcohols were tested. All compounds were dissolved in 1 × Ringer solution at 100 mM or 10 mM as the initial test concentration. The compounds were made to stimulate clawed frog oocytes by perfusion for about 20 s per stimulation in random order and 1 × Ringer rinses. Dose-response tests were performed for inositol and ribose in order of low to high concentration (0.01 mM, 0.1 mM, 1 mM, 10 mM, 100 mM, 1000 mM, with myo-inositol supersaturating at a concentration of 1000 mM). Data were acquired and analyzed using the instrument Digidata 1322A and the software pCLAMP 10.4.2.0 (Axon Instruments Inc., Foster City, CA, USA).
Molecular docking
We utilized AlphaFold3 [50] to predict the 3D structure of the HarmGR13. HarmGR13 was modeled using its amino acid sequence. The 3D structures of ligand compounds were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and converted into docking-compatible formats using Open Babel version 3.1.1 [51] and AutoDock [52]. Next, the predicted receptor 3D structures were processed using PyMOL version 3.1 (The PyMOL Molecular Graphics System, Version 3.1 Schrödinger, LLC.) and AutoDock. For molecular docking, the receptor and ligands were imported into AutoDock, and the docking regions were defined. Docking was performed using the AutoDock Vina algorithm [53,54]. Docking visualizations, graphical editing and indentification of polar-interaction amino acid residues were conducted in PyMOL version 3.1.
Site-directed mutagenesis
Site-directed mutagenesis was performed on amino acid residues identified through molecular docking analysis. Primers were designed to cover the mutation site, with the codon at the target amino acid position changed to GCC (alanine codon) and approximately 20 bp overlaps. Using 2 × Phanta Max Master Mix (Vazyme, China) and high-fidelity polymerase, plasmids were linearized via PCR, the primer sequence is provided in S2 Table. The amplification program consisted of the following steps: 95 °C for 3 minutes, 35 cycles of 98 °C for 15 seconds, 60 °C for 15 seconds, and 72 °C for 3 minutes, followed by a final extension at 72 °C for 5 minutes. The PCR products were purified and transformed into competent cells, and single bacterial colonies were screened to obtain site-directed mutagenesis plasmids. The plasmids were sent to Beijing Tsingke Biotech Co., Ltd. for Fast NGS sequencing to confirm plasmid integrity and the single amino acid mutation. The mutagenesis plasmids were then ectopically expressed in Xenopus oocytes, and inward currents of inositol and ribose under perfusion were recorded.
Data analysis and graph generation
Statistical analyses were performed using GraphPad Prism 9.0.0 (Dotmatics, San Diego, CA, USA). For tip recordings, data comparisons between test compounds and controls were conducted using independent-samples t-tests. Data from two-choice assays were analyzed using paired-samples t-tests. Concentration gradient recordings and two-electrode voltage-clamp data were analyzed via one-way ANOVA followed by Tukey’s HSD test for multiple comparisons. All graphs were generated using GraphPad Prism 9.0.0, with subsequent graph layout made in Adobe Illustrator 2020 (Adobe Systems, San Jose, CA, USA).
Supporting information
S1 Fig. Electrophysiological responses of lateral sensilla styloconica to sugars, sugar alcohols.
(A) Left: Quantifications of firing rates of lateral sensilla styloconica to representative sugars and sugar alcohols at 10 mM (n = 10; each dot represents one larva). Right: Representative spike traces of responses. Data are presented as mean ± SEM; *p < 0.05; **p < 0.01; ***p < 0.001. Data were analyzed by independent-samples t-test (compared with control).
https://doi.org/10.1371/journal.pgen.1011744.s001
(TIF)
S2 Fig. Quantifications of firing rates of medial sensilla styloconica for mixtures of 0.1 mM inositol, 0.1 mM ribose, and 10 mM xylose.
(A-C) Quantifications of firing rates of medial sensilla styloconica for 0.1 mM inositol, 0.1 mM ribose, and 10 mM xylose and their binary mixture (n = 9). Data are mean ± SEM. Data were analyzed by one-way ANOVA with Tukey’s HSD test (p < 0.05).
https://doi.org/10.1371/journal.pgen.1011744.s002
(TIF)
S3 Fig. Expression patterns of taste receptors in maxillary galea of larvae of Helicoverpa armigera.
TPM values of 10 annotated sugar and fructose receptors obtained via transcriptome sequencing (n = 3). Data are mean ± SEM and analyzed by one-way ANOVA with Tukey’s HSD test (p < 0.05).
https://doi.org/10.1371/journal.pgen.1011744.s003
(TIF)
S4 Fig. Off-target effect assessment in GR13-/- and GR11-/-.
(A-E) Comparisons between GR13-/- and WT in larval weight, larval stage, pupal weight, pupal stage, and number of eggs laid. (F-J) Comparisons between GR11-/- and WT for the same parameters. A, C, D n = 17; B n = 27,14; E, J n = 5; G, I n = 15; G n = 12; H n = 20; (A–J) Data are mean ± SEM; ns, p > 0.05; *p < 0.05; **p < 0.01; ***p < 0.001. Data were analyzed by independent-samples t-tests.
https://doi.org/10.1371/journal.pgen.1011744.s004
(TIF)
S5 Fig. Additional two-choice feeding test in WT larvae.
Feeding responses to inositol at 1 mM, 0.1 mM, and 0.01 mM (n = 19–21); ribose at 10 mM (n = 24); and xylose at 10 mM (n = 23). Each line represents one larva. *p < 0.05; **p < 0.01; ***p < 0.001. Data were analyzed by paired t-test.
https://doi.org/10.1371/journal.pgen.1011744.s005
(TIF)
S6 Fig. Amino acid sequence alignment of three homologous receptors: BmGR10, HarmGR13, and MsexGR10.1.
Arrows indicate key residues identified via molecular docking: D92, D159, and Q339. Red arrows denote residues involved in polar interactions with both inositol and ribose; green arrows denote residues interacting only with inositol.
https://doi.org/10.1371/journal.pgen.1011744.s006
(TIF)
S2 Table. Primer sequences used for the experiment.
https://doi.org/10.1371/journal.pgen.1011744.s008
(XLSX)
S3 Table. Summary of the CRISPR-Cas9 mutation rates.
https://doi.org/10.1371/journal.pgen.1011744.s009
(XLSX)
S4 Table. Raw data used in the figures and statistical analyses.
https://doi.org/10.1371/journal.pgen.1011744.s010
(XLSX)
Acknowledgments
We are grateful to our lab member Ling-Qiao Huang for her technical support, Xin-Lin Gong, Zi-Lin Li, Pei-Chao Wang, Xiao Xu for their helps doing the gene knock-out. We are indebted to Shuai-Shuai Zhang, Yan Chen, Hao Guo, Ke Yang and Jun Yang for their useful advice on doing experiments. The authors dedicate this paper in memory of the late Professors of University of Arizona, Dr. E.A. Bernays and Dr. R.F. Chapman, who introduced the senior author CZW into the field of insect taste.
References
- 1. Loewus FA, Loewus MW. Myo-Inositol: its biosynthesis and metabolism. Annu Rev Plant Physiol. 1983;34(1):137–61.
- 2. Loewus FA, Murthy PPN. myo-Inositol metabolism in plants. Plant Science. 2000;150(1):1–19.
- 3.
Bernays EA, Chapman RF. Host-plant selection by phytophagous insects. New York: Chapman and Hall. 1994.
- 4. Hallem EA, Dahanukar A, Carlson JR. Insect odor and taste receptors. Annu Rev Entomol. 2006;51:113–35. pmid:16332206
- 5. Clyne PJ, Warr CG, Carlson JR. Candidate taste receptors in Drosophila. Science. 2000;287(5459):1830–4. pmid:10710312
- 6. Morinaga S, Nagata K, Ihara S, Yumita T, Niimura Y, Sato K, et al. Structural model for ligand binding and channel opening of an insect gustatory receptor. J Biol Chem. 2022;298(11):102573. pmid:36209821
- 7. Miyamoto T, Slone J, Song X, Amrein H. A fructose receptor functions as a nutrient sensor in the Drosophila brain. Cell. 2012;151(5):1113–25. pmid:23178127
- 8. Jiao Y, Moon SJ, Wang X, Ren Q, Montell C. Gr64f is required in combination with other gustatory receptors for sugar detection in Drosophila. Curr Biol. 2008;18(22):1797–801. pmid:19026541
- 9. Slone J, Daniels J, Amrein H. Sugar receptors in Drosophila. Curr Biol. 2007;17(20):1809–16. pmid:17919910
- 10. Jiao Y, Moon SJ, Montell C. A Drosophila gustatory receptor required for the responses to sucrose, glucose, and maltose identified by mRNA tagging. Proc Natl Acad Sci U S A. 2007;104(35):14110–5. pmid:17715294
- 11. Dahanukar A, Lei Y-T, Kwon JY, Carlson JR. Two Gr genes underlie sugar reception in Drosophila. Neuron. 2007;56(3):503–16. pmid:17988633
- 12. Dahanukar A, Foster K, van der Goes van Naters WM, Carlson JR. A Gr receptor is required for response to the sugar trehalose in taste neurons of Drosophila. Nat Neurosci. 2001;4(12):1182–6. pmid:11704765
- 13.
Bernays EA, Simpson SJ. Control of food intake. In: Berridge MJ, Treherne JE, Wigglesworth VB, editors. Advances in Insect Physiology. Vol. 16. London: Academic Press; 1982. p. 59–118.
- 14. Schoonhoven L, Van Loon J. An inventory of taste in caterpillars: each species its own key. Acta Zoologica Academiae Scientiarum Hungaricae. 2002;40(Suppl. 1):215–63.
- 15. Glendinning JI, Davis A, Rai M. Temporal coding mediates discrimination of “bitter” taste stimuli by an insect. J Neurosci. 2006;26(35):8900–8. pmid:16943545
- 16. Zhang H-J, Anderson AR, Trowell SC, Luo A-R, Xiang Z-H, Xia Q-Y. Topological and functional characterization of an insect gustatory receptor. PLoS One. 2011;6(8):e24111. pmid:21912618
- 17. Kikuta S, Endo H, Tomita N, Takada T, Morita C, Asaoka K, et al. Characterization of a ligand-gated cation channel based on an inositol receptor in the silkworm, Bombyx mori. Insect Biochem Mol Biol. 2016;74:12–20. pmid:27132146
- 18. Riaz S, Johnson JB, Ahmad M, Fitt GP, Naiker M. A review on biological interactions and management of the cotton bollworm,Helicoverpa armigera(Lepidoptera: Noctuidae). J Applied Entomology. 2021;145(6):467–98.
- 19. Tay WT, Soria MF, Walsh T, Thomazoni D, Silvie P, Behere GT, et al. A brave new world for an old world pest: Helicoverpa armigera (Lepidoptera: Noctuidae) in Brazil. PLoS One. 2013;8(11):e80134. pmid:24260345
- 20. Zhang H-J, Faucher CP, Anderson A, Berna AZ, Trowell S, Chen Q-M, et al. Comparisons of contact chemoreception and food acceptance by larvae of polyphagous Helicoverpa armigera and oligophagous Bombyx mori. J Chem Ecol. 2013;39(8):1070–80. pmid:23779267
- 21. Zhang S-S, Wang P-C, Ning C, Yang K, Li G-C, Cao L-L, et al. The larva and adult of Helicoverpa armigera use differential gustatory receptors to sense sucrose. eLife. 2024;12.
- 22. Gomes JV, Singh-Bhagania S, Cenci M, Chacon Cordon C, Singh M, Butterwick JA. The molecular basis of sugar detection by an insect taste receptor. Nature. 2024;629(8010):228–34. pmid:38447670
- 23.
Loewus FA, Tanner W. Plant Carbohydrates I: Intracellular Carbohydrates. Encyclopedia of Plant Physiology. New York: Springer Science & Business Media; 2012.
- 24.
Schoonhoven LM, van Loon JJA, Dicke M. Insect-Plant Biology. 2 ed. Oxford: Oxford University Press; 2005.
- 25. Ito T. Effect of sugars on feeding of larvae of the silkworm, Bombyx mori. Journal of Insect Physiology. 1960;5(2):95–107.
- 26. Hamamura Y, Hayashiya K, Naito K-I, Matsuura K, Nishida J. Food selection by silkworm larvæ. Nature. 1962;194(4830):754–5.
- 27. Yamamoto RT, Fraenkel G. Assay of the Principal Gustatory Stimulant for the Tobacco Hornworm, Protoparce Sexta, from Solanaceous Plants1. Annals of the Entomological Society of America. 1960;53(4):499–503.
- 28. Glendinning JI, Nelson NM, Bernays EA. How do inositol and glucose modulate feeding in Manduca sexta caterpillars?. J Exp Biol. 2000;203(Pt 8):1299–315. pmid:10729279
- 29. Tsuneto K, Takada T, Kasubuchi M, Yamagishi T, Adegawa S, Sato R. BmGr10 is a putative functional gustatory receptor in the myo-inositol neuron in the epipharyngeal sensillum. Journal of Insect Biotechnology and Sericology. 2019;88(1):1_007-1_015.
- 30. Endo H, Tsuneto K, Mang D, Zhang W, Yamagishi T, Ito K, et al. Molecular basis of host plant recognition by silkworm larvae. J Insect Physiol. 2024;154:104628. pmid:38387524
- 31. Kim E-K, Lee K-A, Hyeon DY, Kyung M, Jun K-Y, Seo SH, et al. Bacterial nucleoside catabolism controls quorum sensing and commensal-to-pathogen transition in the drosophila gut. Cell Host Microbe. 2020;27(3):345-357.e6. pmid:32078802
- 32. Sang J, Lee Y. Age-dependent switched taste behavior to ribose. Insect Biochem Mol Biol. 2024;174:104194. pmid:39406300
- 33. Mishra D, Thorne N, Miyamoto C, Jagge C, Amrein H. The taste of ribonucleosides: novel macronutrients essential for larval growth are sensed by Drosophila gustatory receptor proteins. PLoS Biol. 2018;16(8):e2005570. pmid:30086130
- 34. Nicolson SW, Van Wyk B-E. Nectar sugars in proteaceae: patterns and processes. Aust J Bot. 1998;46(4):489.
- 35. Wyk BV, Nicolson SW. Xylose is a major nectar sugar in Protea and Faurea. South African Journal of Science. 1995;91(3):151–3.
- 36. Allsopp M, Nicolson SW, Jackson S. Xylose as a nectar sugar: the response of cape honeybees, Apis mellifera capensis Eschscholtz (Hymenoptera: Apidae). African Entomology. 1998;6(2):317–23. https://hdl.handle.net/10520/AJA10213589_237
- 37. Zhang Y-F, van Loon JJA, Wang C-Z. Tarsal taste neuron activity and proboscis extension reflex in response to sugars and amino acids in Helicoverpa armigera (Hubner). J Exp Biol. 2010;213(Pt 16):2889–95. pmid:20675558
- 38. Pradhan RN, Shrestha B, Lee Y. Avoiding cantharidin through ionotropic receptors. J Hazard Mater. 2024;466:133497. pmid:38278077
- 39. van Loon JJ. Chemoreception of phenolic acids and flavonoids in larvae of two species of Pieris. Journal of Comparative Physiology A. 1990;166:889–99.
- 40. Pearce SL, Clarke DF, East PD, Elfekih S, Gordon KHJ, Jermiin LS, et al. Genomic innovations, transcriptional plasticity and gene loss underlying the evolution and divergence of two highly polyphagous and invasive Helicoverpa pest species. BMC Biol. 2017;15(1):63. pmid:28756777
- 41. Howlett N, Dauber KL, Shukla A, Morton B, Glendinning JI, Brent E, et al. Identification of chemosensory receptor genes in Manduca sexta and knockdown by RNA interference. BMC Genomics. 2012;13:211. pmid:22646846
- 42. Engsontia P, Sangket U, Chotigeat W, Satasook C. Molecular evolution of the odorant and gustatory receptor genes in lepidopteran insects: implications for their adaptation and speciation. J Mol Evol. 2014;79(1–2):21–39. pmid:25038840
- 43. Guo H, Cheng T, Chen Z, Jiang L, Guo Y, Liu J, et al. Expression map of a complete set of gustatory receptor genes in chemosensory organs of Bombyx mori. Insect Biochem Mol Biol. 2017;82:74–82. pmid:28185941
- 44. Rozewicki J, Li S, Amada KM, Standley DM, Katoh K. MAFFT-DASH: integrated protein sequence and structural alignment. Nucleic Acids Res. 2019;47(W1):W5–10. pmid:31062021
- 45. Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol. 2020;37(5):1530–4. pmid:32011700
- 46. Chen Y, Wang P-C, Zhang S-S, Yang J, Li G-C, Huang L-Q, et al. Functional analysis of a bitter gustatory receptor highly expressed in the larval maxillary galea of Helicoverpa armigera. PLoS Genet. 2022;18(10):e1010455. pmid:36206313
- 47. Xu W, Liu N, Liao Y, Anderson A. Molecular characterization of sugar taste receptors in the cotton bollworm Helicoverpa armigera. Genome. 2017;60(12):1037–44. pmid:28825966
- 48. Jin M, North HL, Peng Y, Liu H, Liu B, Pan R, et al. Adaptive evolution to the natural and anthropogenic environment in a global invasive crop pest, the cotton bollworm. Innovation (Camb). 2023;4(4):100454. pmid:37388193
- 49.
Nakagawa T, Touhara K. Functional assays for insect olfactory receptors in Xenopus oocytes. In: Touhara K, editor. Touhara. Totowa, NJ: Humana Press; 2013. p. 107–19.
- 50. Abramson J, Adler J, Dunger J, Evans R, Green T, Pritzel A, et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature. 2024;630(8016):493–500. pmid:38718835
- 51. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. J Cheminform. 2011;3:33. pmid:21982300
- 52. Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ. Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc. 2016;11(5):905–19.
- 53. Eberhardt J, Santos-Martins D, Tillack AF, Forli S. AutoDock Vina 1.2.0: new docking methods, expanded force field, and python bindings. J Chem Inf Model. 2021;61(8):3891–8. pmid:34278794
- 54. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455–61. pmid:19499576