Jasmonic acid signaling induced by caterpillar damage causes reduced resistance to target insect feeding by the brown planthopper, Nilaparvata lugens (Stål), in rice

doi:10.21203/rs.3.rs-3595183/v1

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Jasmonic acid signaling induced by caterpillar damage causes reduced resistance to target insect feeding by the brown planthopper, Nilaparvata lugens (Stål), in rice

https://doi.org/10.21203/rs.3.rs-3595183/v1

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A newly developed breeding variety of crops shows high resistance in laboratory conditions but exhibits low resistance to target insects in the field. This research aims to explain this phenomenon from inter-species interactions in insects as the herbivory by one insect species can induce physiological changes in plants that enhance attraction other insect species. The striped stem borer (SSB), Chilo suppressalis (Walker), and the brown planthopper (BPH), Nilaparvata lugens (Stål), are pests of rice (Oryza sativa L.) that cause major losses in grain production. This study examines how BPH feeding behavior on rice is affected by SSB herbivory. BPH performance and behavior were tested on the planthopper-resistant rice variety ‘Mudgo’ with pre-feeding of SSB. BPHs showed better growth and development, as well as feeding behavior, on SSB-damaged plants when compared to healthy/undamaged plants. Analysis of gene expression and phytohormone level revealed changes in the jasmonic acid (JA) biosynthesis in response to SSB feeding. The analysis of central metabolites and volatiles showed that SSB-damaged stem induced the volatiles emission, which benefits planthopper development or attracts BPH. In summary, we found that the JA biosynthesis was triggered by SSB and played a vital role in rice defense against BPH. This provides insight into the molecular and biochemical mechanisms of BPH preferences for SSB-damaged rice plants. Our study emphasizes the crucial role of inter-species interactions in enhancing host plant resistance to insect pests and evaluating germplasm resistance. These findings can serve as a basis for controlling BPH.

Oryza sativa

plant-insect interactions

phytohormones

volatiles

secondary metabolites

New breeds of agricultural seeds have high resistance to insects in the lab, however, sometimes have low resistance in the field. This research will use rice and two insect pest species as an example to explain this phenomenon. Rice, scientifically known as Oryza sativa L., serves as a primary food source for over fifty percent of the global population¹. The rice yield is frequently greatly reduced by various species of herbivorous insects belonging to different feeding guilds². Lepidopteran stem borers are major pests in all rice ecosystems, with the striped stem borer (SSB), Chilo suppressalis (Walker) (Lepidoptera: Crambidae) being one of the most destructive in temperate regions of Asia³. SSB larvae cause damage to plants such as withered sheaths, weak stems, "dead hearts," and "whiteheads" (empty panicles with a few filled grains). This pest is particularly harmful in China due to the widespread use of hybrid rice varieties⁴. Another important pest of rice is the brown planthopper (BPH) Nilaparvata lugens (Stål) (Hemiptera: Delphacidae), whose outbreaks in China have increased since the adoption of hybrid rice cultivars in the 1980s⁵. BPH is a phloem-feeding insect that sucks the phloem sap from the base of the tillers, causing a reduction in growth, vigor, and the number of productive tillers. BPH can infest rice plants at all stages of growth, and its feeding causes yellowing of the leaves and, in severe cases, can lead to plant death. Such damage results in significant losses for farmers every year^{6, 7}. Therefore, developing insect-resistant rice varieties and pest control strategies has become a crucial focus of rice breeding and production.

Recently, two genetically modified rice plants have been developed to control the populations of two pests: Bacillus thuringiensis (Bt) Cry proteins are overexpressed in one to control the SSB population⁸, while planthopper-resistance genes are utilized in the other to control BPH⁹. Studies have been conducted to understand the each mechanisms that host plants use to resist SSB and BPH^{6, 10, 11, 12, 13, 14, 15, 16}, but a new variety has shown mixed results when tested in both lab and field settings. This has raised the question of whether non-target insects affect a plant's resistance to targeted insects, making it essential to assess interspecific insect interactions when evaluating rice germplasm resistance

Previous research suggests that mutually beneficial interactions among species of herbivorous insects exist^{17, 18}. and BPH has a strong preference for transgenic Bt rice plants that are pre-infested by SSB¹⁹. BPH is insensitive to the Bt insecticidal Cry protein, but its population diminishes from a Bt rice field when it spreads to a neighboring non-Bt rice field due to the significantly reduced activity of the targeted SSB. This led to the concept of "ecological resistance" of Bt rice to non-target insects. This interaction mechanism involving two different insect species may be highly significant in evaluating germplasm resistance. The objectives of this study aimed to answer the following two questions: (1) Will newly-introduced resistance traits to BPH in rice varieties be reduced by SSB damage? and (2) how do rice plants mediate the physiological, biochemical, and molecular mechanisms of BPH preference for SSB-damaged rice plants?

For our study, we selected the rice type 'Mudgo' which has been identified as resistant to BPH^{20, 21, 22}. We aimed to uncover the physiological, biochemical, and molecular mechanisms that influence BPH's feeding habits. We began by conducting a series of preference tests to confirm that BPH prefers SSB-damaged rice plants over undamaged ones. We also checked if SSB damage caused any changes in the expression of the Bph1 gene. To gain further insights, we analyzed the transcriptome and phytohormone levels to identify any alterations in gene expression and JA, JA-Ile, SA, and ABA metabolite levels in response to SSB feeding. Additionally, we analyzed central volatiles to see if SSB-damaged stems induced volatile emissions. Our metabolomic analyses revealed changes in jasmonic acid signaling pathway metabolites, and we tested BPH's behavioral responses to rice volatiles. Overall, our study allowed us to understand how SSB damage reduces resistance to subsequent feeding by the brown planthopper in rice, by integrating transcriptome with metabolic profiles of phytohormones and volatiles.

BPH preferentially feeds on caterpillar-damaged rice plants

We investigated the feeding preferences of BPH by utilizing two-way choice tests that compared healthy and SSB-damaged Mudgo (BPH-resistant) and TN1 (BPH-susceptible) rice plants in four different scenarios. It was observed that the planthoppers showed a strong preference for the TN1 line when given a choice between healthy Mudgo and TN1 rice plants (P < 0.001) (Fig. 1a). When both Mudgo and TN1 plants were damaged by caterpillars, the planthoppers chose to feed on TN1 (P < 0.001) (Fig. 1b). However, When the Mudgo was damaged by SSB, the planthoppers preferred the damaged rice plants over the undamaged ones (P < 0.001) (Fig. 1c). In the case of SSB-damaged or undamaged TN1 line (Fig. 1d), no feeding preference was evident among planthoppers in the first 1–4 days (P > 0.05), but on the 5–7 days, they preferred the damaged rice plants over undamaged TN1 rice plants (P < 0.05). These findings indicate that planthoppers prefer to feed on damaged plants rather than on undamaged plants.

Effects of BPH feeding on damaged and healthy rice

Fitness of BPH feeding on SSB-damaged and healthy Mudgo and TN1 rice plants

Based on these findings, we then investigated the presence of different effects on BPH survival fitness when feeding on damaged plants (either the ‘Mundgo’ resistant or ‘TN1’ susceptible varieties) compared to feeding on undamaged plants (Fig. 2). The comparison showed that BPH had higher survival rates on damaged Mudgo rice plants (Log-rank test; χ² = 3.873; P = 0.049). To further confirm the effects of feeding on the damaged plants on BPH, we measured the duration of each BPH instar stage, including adulthood, as well as the length and weight of emerging adults (Table 1). The results showed that the number of instar days in BPH nymphs was markedly shorter on TN1 than on Mudgo regardless of whether plants were damaged or undamaged. However, the adult males emerged significantly earlier with markedly greater weight than those feeding on SSB-damaged versus undamaged Mudgo (P < 0.01). In conclusion, these results indicated that planthopper survival fitness was significantly greater when caterpillars fed on damaged Mudgo rice plants than on undamaged plants.

Characterization of EPG waveform of BPH feeding rice

To characterize the differences in BPH feeding behavior, between SSB-damaged and undamaged plants, we recorded the DC-EPG waveform patterns produced by BPH feedings; as established in previous studies^{38, 39, 40}. We focused on six typical EPG waveforms, designated NP, N1, N2, N3, N4, and N5. The NP (non-penetration) waveform was correlated to zero feeding. The next feeding phase began with BPH inserting its stylet into the plant and generating irregular but relatively high amplitude EPG waveforms. The N1 waveforms were also generated by the stylet probing the rice epidermis and appeared for a very brief scale of a few seconds. Immediately after the N1 probing phase, the salivary stage began to create the N2 waveforms. The N2 waveforms consisted of regular cycles of small rapid spikes that gradually increased in overall amplitude but decreased in spike size, followed by an overall dip into a trough. The subsequent N3 waveform was characterized by higher amplitude and less regularity, indicating the stylet was moving outside the phloem cells. Next, the N4 waveform generated by sucking phloem sap was characterized by frequent, variable waves, while the N5 waveform, in which water was taken up from the xylem, appeared more stable with frequent, uniform waves (Fig. 3a).

To identify differences in feeding patterns that are potentially related to herbivory resistance, the total duration of probing times was examined in undamaged or damaged resistant (Mudgo) and susceptible (TN1) varieties. In accordance with the experiments above, the sum of the NP waveform’s total duration was significantly highest in undamaged Mudgo plants (Fig. 3b). N1, N2, and N3 waveforms showed no differences between healthy and damaged rice. N4, in which nutrients are acquired, showed a significantly longer duration in the damaged Mudgo rice than in undamaged Mudgo rice (Fig. 3b). Taken together, these results indicated that BPH consumed fewer phloem nutrients from undamaged rice than from damaged.

Bph1 gene expression in SSB-damaged rice

Bph1 gene expression levels were measured at different time points (0, 3, 6, 12, 24, 48, 72, and 96 h). qRT-PCR detected no significant change of Bph1 gene expression after SSB damage (Fig. 3a), and all data were normalized against the expression of the housekeeping gene ubiquitin 5. Bph1 gene expression was stable in Mudgo but not detected in TN1, whereas housekeeping gene ubiquitin 5 was stably expressed in both Mudgo and TN1 genes at any time point (Fig. 3b).

Transcriptomic changes in response to BPH feeding

Overview of the transcriptome in rice plants during SSB infestation

Global changes in the rice transcriptome were quantified by high throughput RNA sequencing analysis (RNA-seq) of damaged and undamaged Mudgo stem samples after 72 h feeding by SSB. In total, approximately 56 million clean reads were obtained with a GC content of 55–58% (Table S2). On average, > 77% of the reads could be mapped to the rice IRGSP-1.0 reference genome and > 95% of the mapped reads were distributed in exons (Table S3).

The differential expression patterns were confirmed by qRT-PCR of 17 genes related to phytohormone signaling, primary metabolism, and secondary metabolism using the stem samples from the same batch of rice plants as those used for RNA-seq. The qPCR expression profiles of these genes were largely consistent with those observed by RNA-seq (Figure S1).

Analyses of differentially expressed genes (DEGs)

In total, 32,322 genes were detected in all the samples (Table S4), among which 2,342 were differentially expressed genes (DEGs) (P < 0.05 corrected for False Discovery Rate and |fold-change| > 2 or < 0.05) (Table S5). Gene Ontology (GO) analysis showed that the 1220 up-regulated DEGs were mainly involved in cell wall, lipid, and secondary metabolism. Meanwhile, the 1122 down-regulated DEGs were mainly involved in cellular components. The Pearson correlation analysis showed high similarity between replicates in each treatment group (0.949–0.967), indicating good reproducibility between samples (Table S6). A Venn diagram illustrating the data revealed 1,077 genes distinctly expressed in damaged rice plants.

Analyses of transcriptomic changes focus on transcription factors (TFs)

The rice genome is known to contain at least 2,000 transcription factor genes, including 63 families⁴¹. TF-encoding genes were analyzed by searching the Plant Transcription Factor Database (PlnTFDB,V3.0) (http://plntfdb.bio.uni-potsdam.de/v3.0/)⁴². The search revealed 188 TFs, including 75 (39.9%) up- and 113 (60.1%) down-regulated, spanning 36 families among the 2,342 total DEGs (Table S7). We particularly focused on the MYB (myeloblastosis) TF family, which plays a key role in coping with biological stresses and adversity. A total of 23 MYB genes were regulated in this sequencing, among which 13 genes were up-regulated (OS12G0564100 gene was 6.02-fold up-regulated) and 10 genes were down-regulated. Moreover, the expressions of 15 WRKY TF family genes were affected by these treatments. Among them is OsWRKY7 with its level reported to be up-regulated upon SSB infestation rice growth and positively correlated with jasmonic acid (JA) and salicylic acid (SA) pathways⁴³. Genes from the AP2-EREBP TF family (apetala2-ethylene-responsive element binding proteins) that play an important role in stress response to biotic and abiotic factors in rice, were modified⁴⁴. Out of the 10 genes, OsERF3, which is involved in the induced reaction of chewing and piercing mouthpart insects in rice, was upregulated⁴⁵. In summary, SSB causes a remarkable enriching effect on rice for defense signaling pathways and upregulation of JA production.

SSB feeding induces plant hormone and related gene expression

To determine phytohormone changes, we analyzed the concentrations of JA, JA-isoleucine conjugate (JA-Ile), SA, and ABA in rice stems at the 0, 3, 6, 12, 24, 48, and 72 h of SSB and BPH infestation (Fig. 5). In Mudgo, JA and JA-Ile accumulated significantly after 3 and 48 hours, respectively, of SSB feeding. BPH feeding induced JA and JA-Ile accumulation peaks at 3 and 6 hours, respectively. SA and ABA levels were not increased except for 24 hours of BPH feeding. With the exception of LOX6 (OS03g0179900) and AOS2 (OS03g0225900), among all the genes involved in the JA pathway and all the other transcripts of lipoxygenases (LOX), allene oxide synthase (AOS), allene oxide cyclase (AOC), oxo-phytodienoate reductase (OPR), and jasmonate resistant (JAR) were significantly up-regulated following SSB herbivory (Fig. 6). GO, KEGG pathway enrichment, and hierarchical clustering analyses revealed that SSB feeding differentially affected rice phytohormone signaling pathways. Infestation showed a highly induced expression of JA signaling-dependent genes and moderately induced genes associated with other phytohormone signaling pathways. The genes involved in the JA synthesis pathway were up-regulated, with the exception of LOX (OS03G0179900) and AOS (OS03G0225900). In addition, four genes involved in JA signal transduction and JAZ homologs were also differentially upregulated (Fig. 6).

Table 1 Performance of Nilaparvata lugens feeding on 'Mudgo’ (BPH resistant) and TN1 (BPH susceptible) rice plants that were undamaged or damaged by a single 3^rd instar C.suppressalis caterpillar.

Parameters	Mudgo-H/TN1-H		Mudgo-D/TN1-D			Mudgo-D/ Mudgo-H				TN1-D/TN1-H
	Value	p		Value	p		Value	p	Value		p
1^st instar (d)¹	9.072	0.000		9.547	0.000		-0.508	0.612	-0.367		0.714
2^nd instar (d)¹	-2.142	0.035		-1.663	0.100		-0.195	0.846	-1.042		0.299
3^rd instar (d)¹	1.622	0.107		-2.983	0.004		-1.864	0.065	2.841		0.005
4^th instar (d)¹	5.971	0.000		4.602	0.000		-0.720	0.473	2.044		0.042
5^th instar (d)¹	4.044	0.000		3.841	0.000		0.209	0.835	0.556		0.579
Female adult emergence (d)¹	8.862	0.000		6.604	0.000		-0.869	0.387	0.550		0.583
Male adult emergence (d)¹	8.898	0.000		3.139	0.002		-3.324	.001	2.325		0.022
Female adults weight (mg)¹	-2.296	0.024		0.600	0.550		1.577	0.118	-1.321		0.189
Male adult weight (mm)¹	-3.654	0.000		2.750	0.007		4.054	0.000	-1.981		0.050
Female adults length (mm)¹	-5.348	0.000		-3.312	0.003		0.339	0.736	-1.046		0.298
Male adults length (mm)¹	-7.052	0.000		-4.971	0.000		1.271	0.208	-3.247		0.001
Sex ratios²	0.221	0.638		1.974	0.160		0.286	0.593	1.699		0.192
Brachypterous ratios²	3.136	0.077		1.198	0.274		1.741	0.187	0.295		0.587

¹ t-test

² Chi-Square Tests

Values are means ± SE, and numbers of specimens tested are indicated in parentheses. Color coding compare of up (pink) or down (blue). H as healthy and D as damaged.

To investigate the involvement of the JA pathway in response to SSB feeding, we conducted qRT-PCR analysis on 19 genes involved in JA biosynthesis and signaling in rice stems at different time points of SSB infestation; specifically at 0, 3, 6, 12, 24, 48, 72, and 96 hours of SSB infestation (Fig. 6). Our results indicated that the majority of these genes participated in JA and JA-Ile biosynthesis and signaling, with key genes such as LOC (OS12G0559934) induced within 3 h of SSB infestation (Fig. 6). These results suggest that JA pathway-associated genes were most strongly activated by SSB infestation and likely contributed to changes leading to preferential herbivory by BPH. Furthermore, other studies have linked herbivory-induced JA signaling with the production of fatty acid metabolites and other volatiles^{46, 47}.

Volatile compound production and metabolomic response to BPH

Differential production of plant volatiles following herbivory in rice caterpillar damage

In light of the RNA-seq evidence showing activation of JA-related pathways following herbivory, we next examined volatiles emission from Mudgo and TN1 rice using GC-MS. We detected a total of 115 compounds in the headspace of Mudgo and TN1 rice plants that were damaged or undamaged by SSB (Table S8). In Mudgo, 107 volatiles, including 34 significant, (3-hexenal, 2-heptanone, 1-hexanol, beta-myrcene, 2-nonanone, methyl salicylate, beta-caryophyllene, beta-sesquiphellandrene, and beta-bisabolene), were identified in the headspace of damaged plants, and 47 compounds (such as α-pinene, γ-terpinene, and 3-heptanone) that were not found in healthy Mudgo samples. Only one volatiles, 3,6,6-trimethyl-bicyclo(3.1.1)hept-2-ene, was decreased in Mudgo (Fig. 7). Overall, in response to caterpillar infestation the production of 31 compounds was significantly elevated and 1 was reduced in Mudgo plants.

Next, we conducted two-choice Y-tube assays to assess BPH's response to 20 volatile compounds at low doses (2 µL/mL) and high doses (200 µL/mL) of each compound. Eleven volatiles in this panel significantly attracted BPH. These are tetradecane, alpha-pinene, hexadecane, beta-caryophyllene, D-limonene, heptadecane, beta-myrcene, pentadecane, 2-heptanone, hexanal, and linalool. In contrast, five compounds were significantly repellent to BPH at high or both doses; these include methyl salicylate, benzeneacetaldehyde, 2-nonanone, tridecane, and naphthalene. Interestingly, only low doses of 1-hexanol, dibutyl phthalate, and gamma-terpinene, were attracted to female BPH, but were repelled by high doses. These results strongly suggest that the emission of volatiles following herbivory could attract BPH to feed on damaged Mudgo rice plants (Fig. 8).

Metabolome composition analyses following herbivory in caterpillar damaged rice

In addition to volatiles, the study also examined metabolome composition using LC-MS and GC-MS. A total of 114 known metabolites were detected as significantly different between damaged and undamaged Mudgo rice plants and were grouped into nine classes by mapping the general biochemical pathways based on KEGG and plant metabolic network (PMN). The greatest enrichment was found in carbohydrates (23), lipids (32), amino acids (14), nucleotides (6), phenolic acids (61), cofactors (2), peptides (2), secondary metabolites (16), and others (19) (Table S9). Overall, more metabolites increased than decreased in response to SSB infestation (Fig. 9).

To investigate the effect of metabolites on the development and survival of BPH, we selected 5 metabolites (9(S)-HOTrE, 9(S)-HpOTrE, 9(S)-HpODE, linoleate, jasmonic acid) for relative growth gain, and beta-sitosterol for survival rate fitness (Fig. 10). We also tested stigmasterol, similar in structure to beta-sitosterol, and reported to have negative effects on BPH⁴⁸. We found the following 3 trends: A) 9(S)-HpOTrE and linoleate can increase the relative weight gain of BPH. B) 9(S)-HpOTrE and jasmonic acid can inhibit BPH feeding. And C) 9(S)-HpODE had no effect on BPH feeding (Fig. 10a-e). In addition, increasing the dietary amount of beta-sitosterol reduced the number of surviving BPH nymphs. Pairwise correlation analysis further revealed that increasing concentrations of beta-sitosterol, at 0.063, 0.250, 0.500, or 1.000 mg/ml diet, led to a dose-dependent decrease in survival rates compared to that of the control (χ² = 6.294, df = 1, P = 0.012; χ² = 8.466, df = 1, P = 0.004; χ² = 15.924, df = 1, P ༜ 0.000 and χ² = 11.762, df = 1, P = 0.001, respectively) (Fig. 10f). Stigmasterol did not affect BPH survival compared to planthoppers on a holidic diet (Fig. 10g).

In summary, our transcriptome and metabolome analyses revealed that SSB activated the JA pathway, which in turn impacted BPH feeding (Fig. 11). We show transcriptional up-regulation of the genes was accompanied by the elevation of the main metabolites in the pathways. The verified volatiles or metabolites to BPH which are positive or negative are also showed in the sketch map. For example, linoleate, 9(S)-HpOTrE and jasmonic acid were increased after SSB infestation, which can increase the relative weight gain of BPH.

Our experiments showed a good example of breeding variety of crops (Mudgo, resistance to BPH) lose its resistance to target insects because of the non-target (SSB) infestation. BPH individuals prefer to feed on rice plants that had been previously damaged by SSB over healthy/undamaged rice plants. This aligns with previous research showing that non-Bt rice fields contain more BPH than Bt rice fields due to the significant caterpillar damage sustained by non-Bt rice plants^{19, 49}. Our research identifies key factors contributing to the loss of BPH resistance in the rice variety ‘Mudgo’ due to SSB damage, which may be evident in germplasm evaluation programs.

Our initial experiments confirmed that BPH prefer SSB-infested rice plants regardless of their resistance to BPH (Fig. 1). Life parameters showed significant differences in planthopper survival, development time, body weight, and length between healthy 'TN1' and 'Mudgo' rice plants. BPH exhibited superior fitness on the sensitive rice variety TN1 compared to the resistant variety 'Mudgo' (Fig. 2), in line with previous studies^{50, 51, 52, 53}. A study of a ‘Mudgo’ variety that carries the gene Bph1 which provides moderate resistance to the brown planthopper, showed similar results of up-regulation of genes involved in the synthesis of amino sugars, nucleotide sugars, and carbohydrate metabolism⁵⁴. The EPG technique allows for detailed tracking of stylet activities during insect feeding⁴⁰. In line with our findings, Sogawa and Pathak (1970) found that the brown planthopper took longer to probe the susceptible rice variety TN1 than the resistant variety Mudgo⁵⁰. This phenomenon was also observed in aphids probing wheat. They probed more frequently and for longer periods on sensitive plants during feeding²⁴. In tea and melon plants, the duration of waveform E2 was negatively correlated with the resistance of different cultivars to aphids^{55, 56}.

In addition, the transcriptome analysis revealed that more DEGs were up-regulated than down-regulated in response to SSB larvae feedings. This is consistent with previous findings on MH63 rice²⁸ and maize^{57, 58, 59}. Transcription factors (TFs) play a crucial role in how plants respond to herbivory by regulating gene expression^{44, 60, 61}. More specifically, TFs regulate plant defenses both upstream and downstream of phytohormone signaling. During our research, we observed that the genes WRKY (TF family) exhibited the highest level of alteration in expression when subjected to SSB feeding. This suggests that WRKYs may play a crucial part in plant development and in responding to biotic stressors^{43, 44, 60, 62, 63}. It is known that WRKY are involved in JA and SA signaling pathways and herbivore-induced defense responses^{63, 64}. For example, it has been shown that OsWRKY70 mediates the prioritization of defense over growth by positively regulating cross-talk between JA and SA when rice is attacked by SSB ⁴³, while OsWRKY53 is induced upstream of the JA pathway but negatively regulates the ethylene pathway⁶³. Our study found that damage caused by SSB led to the activation of JA-associated genes, consistent with prior research⁶⁰(Fig. 7). In our study, SSB feeding was observed to activate genes related to the KEGG pathway "alpha-linolenic acid metabolism". The levels of both JA and JA-Ile increased significantly at all seven time points, with the most notable elevations occurring at 3 hours, 12 hours, and 48 hours after SSB damage to 'Mudgo' rice plants. Applying methyl JA externally to rice plants had a negative impact on the performance of two root herbivores - the cucumber beetle (Diabrotica balteata) and the rice water weevil (Lissorhoptrus)⁶⁵- and induced the release of volatiles that attract parasitoids⁶⁶.

We compared the volatiles emitted by healthy/damaged Mudgo and TN1 rice plants to understand the biochemical mechanisms that cause BPH to prefer caterpillar-damaged plants. Our hypothesis is that herbivory-induced plant volatiles (HIPVs) are responsible for the different behavioral responses of planthoppers toward damaged and undamaged rice plants. HIPVs are known to have a significant impact on insect community composition in the field⁶⁷, and changes in the composition of the volatile blends may significantly increase their appeal⁶⁸. Indeed, we found that when both BPH-resistant variety Mudgo and TN1 plants were damaged by SSB, Mudgo showed an increase in more volatile compounds (Fig. 8). For example, GC-MS analyses revealed that the concentrations of the rice volatiles α-pinene and β-caryophyllene were significantly increased by caterpillar damage in Mudgo plants. These volatiles have been previously shown to be attractive to planthoppers^{69, 70, 71}. Several other compounds, including tetradecane, hexadecane, D-limonene, heptadecane, beta-myrcene, pentadecane, 2-heptanone, hexanal, and linalool, have also been identified to have a considerable attraction effect on BPH. The release of these volatile compounds is under the control of the JA signaling pathway⁷². The JA signaling pathway is widely recognized as the mechanism that regulates the plant's defense response against herbivores⁷³. According to a recent study, the synthesis pathways of JA and esters are interconnected and overlap⁴⁶. In contrast, sterols, such as beta-sitosterol, stigmasterol, and campesterol have been reported to inhibit BPH feeding⁴⁸. Our analysis of the metabolome revealed that the consumption of SSB larvae led to a reduction in the beta-sitosterol levels in rice tissues. This can be attributed to the fact that fatty acids act as the primary building blocks for the production of jasmonic acid and esters, which then transform into 13(S)-hydroperoxylinolenic acid (13 S-HPOT) via the lipoxygenase pathway. ⁷⁴. 13 S-HPOT is a key intermediate in the JA synthesis pathway. We also found that as the concentrations of beta-sitosterol in the diet increased, progressively fewer BPH nymphs developed into adults (Fig. 11). This is similar to studies that demonstrate how BPH development is inhibited by decreasing levels of toxic beta-sitosterol^{19, 48}. We infer TFs are responsible for changing the levels of JA in response to SSB damage. This, in turn, triggers the JA signaling pathway, which regulates plant defense via a complex metabolic network. The network involves the release of HIPVs to attract BPH while decreasing the levels of sterols to promote BPH development.

In summary, our study delved into the differences in volatiles released by damaged and healthy Mudgo and TN1 rice plants. Our goal was to shed light on the biochemical mechanisms that make BPH favor caterpillar-damaged plants. By integrating transcriptomic analyses with profiles of metabolites, phytohormones, and plant volatiles, we gained more insight into the molecular and biochemical mechanisms that contribute to high resistance in laboratory settings but lower resistance in the field. We can infer that the JA signal pathway regulates plant defense through a complex metabolic network, as evidenced by the release of HIPVs to attract BPH and a decrease in the contents of metabolites that help improve BPH development. Our study shows that rice resistance to BPH feeding was reduced by SSB damage, which initiated the JA signaling pathway to counter rice plant defense responses. Our findings align with previous research showing that mealybugs (Phenacoccus solenopsis) thrive on cotton plants that were previously damaged by P. solenopsis but are negatively impacted when plants are induced with JA⁷⁵. Our research shows how the interactions between two insect species can impact the evaluation of germplasm resistance. Both species utilize the rice plant's defense responses, indicating a cooperative interaction that defies traditional interspecific competition theory. These findings highlight the intricate and complex dynamics present in the interplay between plants and insects and call into question traditional methods of evaluating germplasm resistance.

Plant Growth

For this study, we selected two types of rice: Mudgo, which has high resistance to BPH, and TN1, which is highly susceptible to BPH. We pre-germinated the seeds of both types of rice in water for 2–3 days, changing the water twice a day. After this, we planted the seeds in a mixture of peat and vermiculite (3:1) and waited until the seedlings were 15 days old. We then transplanted each seedling into an individual plastic pot (diameter 8 cm, height 10 cm). After 40–50 days of growth and at the jointing stage, we used the plants for experimentation. Throughout the process, we cultivated all plants in a greenhouse without pesticides, maintaining a temperature of 27 ± 3°C, relative humidity of 65 ± 10%, and a 16-hour light/8-hour dark cycle.

Insect Colonies

Specimens of BPH were maintained on conventional rice plant TN1 for > 20 generations. In addition, specimens of SSB were introduced and maintained on an artificial diet for > 70 generations²³. Both insect colonies were kept at 27 ± 2°C, 70–80% RH, and a photoperiod 16-hour light/8-hour dark cycle at the Anyang Institute of Technology, China.

Behavioral and biological characteristics studies of caterpillars and planthoppers

Insect bioassay

Forty-eight hours after the specimen plants were transferred to a greenhouse growth cabinet for experimentation (27 ± 1°C, 75 ± 5% RH, 16 L: 8 D photoperiod), the Mudgo or TN1 rice plants were individually infested with one 3rd instar larva of SSB that had been starved for at least 3 h. Following three days, SSB caterpillars were observed to check for drilling into the stems of the plants causing visible damage. These plants were classified as under ‘damaged’ treatment throughout the experiment. Plants that remained undamaged during the experiment are referred to as under the ‘healthy’ treatment. The caterpillars remained on the plants for the full duration of the experiments.

Host preference of planthoppers

To study the BPH preference of choice in host plants, we compared four pairs of rice hosts infested with BPH using ‘H’ equipment as described by Wang et al., 2018¹⁹. The four plant pairs included: (i) healthy Mudgo rice plants versus healthy TN1 rice plants; (ii) damaged Mudgo rice plants versus healthy Mudgo rice plants; (iii) damaged TN1 rice plants versus healthy TN1 rice plants; (iv) damaged Mudgo rice plants versus damaged TN1 rice plants. For each test, the main stems of the two rice plants were contained in a cylindrical plastic tube (diameter 8.0 cm, length 19.0 cm).Then twenty 3rd instars of BPH were released in the middle of the ‘H’ equipment and the number of planthoppers per plant was recorded for seven consecutive days. Each paired choice test was repeated 20–23 times (replicates).

Bioassays of planthoppers

To study BPH development in healthy or damaged TN1 or Mudgo rice, we conducted two bioassays. The first bioassay was carried out to determine the survival rate of BPH. The second bioassay was performed to establish other BPH life parameters such as rate of development, weight and body length, sex ratio, and brachypterous ratios. In this experiment, damaged and healthy rice plants were treated as described above in the choice study. After the 48 h wait following plant transfer to the greenhouse, yellow leaves were removed and the main stem of a single living plant from each treatment was enclosed in a plastic cylindrical tube (diameter 8.0 cm, height 9.5 cm). Lastly, a single first-instar BPH nymph was introduced into the cylindrical tube. Thereafter, we checked the BPH every day, removing the molting, and recording life parameters until to the adult. The living adult nymphs were then weighed on an electronic balance (CPA2250, Sartorius AG, Germany, readability = 0.01 mg), and photographed with a digital camera (DP73, Olympus, Japan) mounted on a microscope (SZX7, Olympus, Japan) to determine their body length. The procedures for both bioassays were identical. Each bioassay test was repeated 55–140 times (in replicates).

Monitoring of BPH Feeding Behavior

We selected adult brachypterous females for this experiment. The electrical penetration graph (EPG) technique was used to monitor the feeding behavior recorded using a GIGA-8 DC EPG (Wageningen University) amplifier system^{24, 25}. To improve monitoring, we first slowed insect activity by cooling BPH to -20℃ for 60 s and then quickly and carefully connected a 3 cm length of 18.5 mm diameter gold wire with conductive silver glue to their dorsum. Following a 2 h starvation period, the BPH with gold wire were attached to a DC-EPG amplifier. To complete the electronic circuit, they were also connected to a stem area of each rice plant 5–8 cm above the soil. Throughout the experiment, the devices were placed around a Faraday cage to encourage clear electrical signals. BPH behavior was recorded for six hours continuously with 26–33 replicates per treatment. Data gathering, data analysis, and calculated total time of each waveform were attained using the Stylet + d Software (W.F. Tjallingii, Wageningen, the Netherlands).

Bph1 gene expression in SSB-damaged rice

Specific primers designed by Beacon Designer software (Premier Biosoft, version 7.0) and synthesized by Sangon Biotech were used to check the change of Bph1 gene ²² expression in Mudgo healthy or SSB-damaged rice. For negative control, the same method above was used on TN1. Information on primer sequences is shown in Table S1. Housekeeping gene ubiquitin 5²⁶ was used for quantitative real-time PCR (qRT-PCR) as an internal gene analyses. The plant stem samples were collected from SSB-damaged rice at different time points (0, 3, 6, 12, 24, 48, 72, and 96 h). Total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). The first-strand cDNA synthesis kit (Promega, Madison, WI, USA) was used to reverse transcribed 500 ng total RNA and DNase I (Thermo Fisher Scientific, Waltham, MA, USA) was used to digest. Then the cDNA 50X was diluted for qPCR. The qRT-PCR reaction was performed using SYBR Premix Ex Taq Ready Mix with ROX reference dye (Takara Biotech, Kyoto, Japan) and an ABI life Real-time PCR Q6 instrument (Thermo Fisher Scientific). PCR amplification program reactions were all carried out under the following conditions: one cycle at 30 s at 95°C, followed by 40 cycles of 5 s at 95°C and 34 s at 60°C. Cycle threshold (Ct) values for the ubiquitin 5 gene were used to normalize the expression patterns of the analyzed genes from 0 h and other time points. The relative fold changes of gene expression were calculated using the comparative 2^−ΔΔCT method²⁷.

Transcriptome Analyses

RNA Extraction, Library Preparation RNA-Sequencing, and Quantitative Real-Time PCR

Potted Mudgo rice plants were housed in a cylindrical plastic tube and infested with one 3rd instar larva of SSB per plant. The larvae were starved for 3 h before they were caged with the rice plants. After 72 h, the main rice stems 8–10 cm around the area damaged by the larvae were cut off and split in the middle to take out SSB. The control healthy rice was also housed in a cylindrical plastic tube but without SSB and harvested at the same time. All plant samples were immediately frozen in liquid nitrogen and stored at −80°C for later analyses. Three replicates samples were collected and used for transcriptome analyses.

Total RNA was extracted from the rice stem samples using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. RNA degradation and contamination were validated using electrophoresis on 1% agarose gel with 180V voltage for 16 min and quantified concentration using the NanoPhotometer® spectrophotometer (IMPLEN, CA, USA). An RNA library was constructed with a 3 µg RNA per sample using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (New England Biolabs, Ipswich, MA USA) following the manufacturer’s recommendations. Index codes were added to attribute sequences to each sample. The library preparations were sequenced on an Illumina Hiseq platform (Illumina Hiseq 4000; Illumina, San Diego, CA, USA), and 125 bp-150 bp paired-end reads were generated.

To validate the results of RNA-seq, 17 genes were selected based on the signaling of phytohormones, primary metabolism, and secondary metabolism for quantitative real-time PCR (qRT-PCR) analyses. The plant stem samples for qRT-PCR were collected from the same split stem which was sampled for RNA-seq experiments and by the same method.

RNA-Sequencing Data Analysis

The transcriptome data analysis was conducted according to the discription as Liu et al., 2016²⁸. Differential expression analysis of damaged and healthy groups was performed using the DESeqR package (1.18.0)²⁹ and filtered with the following thresholds: P-value < 0.05 and |log2 (fold change)| ≥ 2 using the Benjamini and Hochberg’s approach for controlling the false discovery rate³⁰.

Gene ontology (GO) enrichment analysis was carried out on the GOseq R package, in which gene length bias was corrected³¹. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the DEGs was conducted using the clusterProfiler package in R and KOBAS software to test the statistical enrichment of differential expression genes in KEGG pathways³².

Quantification of phytohormones and related gene expression

To measure the amount of JA, JA-Ile, SA, and ABA phytohormones, tissue was collected from the rice stems at different time points (0, 3, 6, 12, 24 48, and 72 h). The phytohormones were extracted and analyzed using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS, LCMS-8040 system, Shimadzu, Kyoto, Japan) following the method of Wu et al., 2007³³. Each phytohormone was quantified by comparing its peak area with the peak area of its respective internal standard. Four replicates stem samples for each time point were analyzed.

To further verify the genes were in response to SSB feeding, we conducted a qRT-PCR analysis to quantify the expression of 19 genes involved in JA biosynthesis and signaling in rice stems at 0, 3, 6, 12, 24, 48, 72, and 96 hours of SSB infestation. The qRT-PCR method was as described above and the primers are shown in Table S10.

Volatile analysis

Collection and analysis of rice plant volatiles

Damaged Mudgo or TN1 rice plants were individually infested with one 3rd instar larva of SSB for 3 days. Healthy plants were the same batch without infested. Volatile compounds were collected using a dynamic headspace collection system as described by Jiao et al., 2018³⁴. In brief, five plants as a treatment group were transferred into a glass bottle. Next, the air was filtered utilizing a combination of activated charcoal, molecular sieves (5 Å, beads, 8–12 mesh, Sigma-Aldrich, St Louis, Missouri, USA), and silica gel Rubin (cobalt-free drying agent, Sigma-Aldrich, St Louis, Missouri, USA). Lastly, the air was inserted into the glass bottle and collected via a glass tube (5 mm diameter, 8 cm height) filled with 30 mg Super Q adsorbent traps (80/100 mesh, Alltech Associates, Deerfield, IL, USA) lasted for 4 h at 400 mL/min.

The gas chromatography-mass spectrometry (GC-MS) analysis method was described by Hu et al., 2020³⁵. A quantity of 500 ng of nonyl acetate was added to the samples as an internal standard for relative quantifications of compounds based on areas³⁶. The collection of plant volatiles was repeated seven or eight times for each treatment.

Choice bioassay using a Y-tube tube olfactometer

Based on the GC-MS results, 16 volatiles, including heptadecane, benzeneacetaldehyde, β-myrcene, naphthalene, dibutyl phthalate, pentadecane, tridecane, hexanal, linalool, hexadecane, 1-hexanol, 2-heptanone, 2-nonanone, dibutyl phthalate, tetradecane, methyl salicylate, were selected for further experiments to determine their effects on BPH behavior. These compounds had shown significant induction or were newly produced in response to SSB damage. The compounds with analytical grade purity were purchased from Shanghai Aladdin Bio-Chem Technology Co., Ltd (Shanghai, China).

To investigate the behavioral responses of BPH females to the selected compounds, a Y-tube (10 cm stem; 10 cm arms at 75° angle; 1.5 cm internal diameter) was used, one side was the testing odor and the other side was the control odor hexane. The testing volatile compounds were individually dissolved in pure hexane. Each compound was tested at a high dose of 200 µL per 1 ml pure hexane and a low dose of 2 µL/mL. Then, the 10 µL of the volatile solution or 10 µL pure hexane (control) were loaded on the filter papers (1 × 2 cm) and were, respectively, put into two glass jars (diameter 10.5 cm; height 25 cm) as a pair of odor sources. The procedure for the Y-tube assays was the same as the dynamic headspace collection system described above. Sixty insects were tested for each compound.

Metabolic Analyses

Collection and analysis of rice metabolic

The treatment of rice was identical to the transcriptome analyses. Metabolic samples were analyzed using liquid chromatography-mass spectroscopy (LC-MS) and gas chromatography-mass spectrometry (GC-MS) platforms by Metabolon and performed by the automated Microlab STAR^® system (Hamilton Company, Bonaduz, Switzerland). The method was described by Liu et al., 2016 ²⁸. In brief, stem samples were divided into four portions for the analysis used: i) LC-MS with positive ion mode electrospray ionization, ii) LC-MS with negative ion mode electrospray ionization, iii) GC-MS, and iv) reserved for backup, respectively. The quantitative values of the metabolites were derived from integrated raw detector counts of the mass spectrometers. Those with great variations due to instrument-integrated tuning differences were normalized on a similar graphical scale. The relative abundances of each metabolite were log-transformed before analysis to meet normality. Student T-test was used to compare the abundance of each metabolite between damaged and healthy rice plants. Ten samples were collected and used for each treatment.

Identification of metabolite toxicity to BPH using artificial diet

To examine whether the artificial diet contained metabolite that affects normal development and survival rate of BPH, two tests were performed. To study development, we conducted a mean relative growth gain for newly brachypterism females. For evaluating the survival rate, a fitness bioassay was carried out on newly hatched nymphs of BPH. Both were fed the artificial diet by Fu et al. (2001)³⁷. A primary stock solution of linoleate, linoleate 9(S)-HOTrE, jasmonic acid, 9(S)-HpOTrE, and 9(S)-HpODE was respectively prepared with acetone. The gradient dilution by the artificial diet reached concentrations of 0.1, 1.0, and 10.0 mmol/L. β-Sitosterol and stigmasterol were prepared with distilled absolute ethyl alcohol. The gradient dilution by the artificial diet reached the concentrations of 0, 0.031, 0.063, 0.125, 0.25, 0.5, and 1 mg/mL diet of β-Sitosterol, and 0, 0.0008, 0.004, 0.02, 0.1, 0.5, 2.5 mg/mL diet of stigmasterol. Glass tubes (2.5 cm diameter, 15 cm length) with smooth openings at both ends were used as feeding devices. One side held feed packaging covered with double-deck stretched paraffin film (Parafilm M, USA), while the other side ended with an air terminal secured with 80-mesh nylon nets. 10 µL of the artificial diet was placed on the sandwiched food bag film. (1) For relative growth gain calculation: a newly brachypterous female of BPH was starved for 2 hours and weighed as weight 1. Then they were randomly selected and introduced into the tube for 48 hours of feeding and subsequently weighed as weight 2. Relative growth gain = weight 2 - weight 1. (2) For survival fitness rate calculation: a newly hatched BPH nymph was introduced into the tube and checked daily, the ecdysis removed, and the artificial diet renewed. The feeding container was covered by a black wet cotton cloth, except at the ends where the food bag opened to a light source. Their development and mortality were recorded daily. Twenty insects were tested in each treatment for relative growth gain and fifty insects for survival rate fitness.

Statistical analyses

Before conducting any statistical analysis, we ensured to check the normality and equality of variances for all data. Paired t-tests were used to compare planthopper feeding choices. We used independent student’s t-tests to analyze body weight, body length, development time, EPG activities, volatiles, and metabolome composition. To compare the survival rates of planthoppers, sex ratio, and brachypterous ratios, we carried out a Chi-square test. We also used Dunnett’s tests to compare genes in the JA pathway and the total amount of phytohormone in rice stems that had been damaged by SSB larvae for 3, 6, 12, 24, 48, or 72 hours relative to the undamaged control (0 hours). Additionally, we conducted Dunnett's tests to compare the BPH relative growth gain with different concentrations of metabolome composition. All statistical analyses were performed using the IBM SPSS 22.0 software package.

Acknowledgments

Funding: The study was supported by China Post-Doctoral Program (Grant No. 2020M672204), China Scholarship Council (Grant No.202108410242), National Natural Science Foundation of China (Grant No. 31802007), Henan Province Post-Doctoral Research Program (Grant No. 201902042). Anyang Institute of Technology Post-Doctoral Program (Grant No. BHJ2019002); Youth Backbone Training Program of Colleges and Universities of Henan Province (Grant No. 2020GGJS232); New Agricultural Research and Practice Reform Project of Henan Province (Grant No. 2020JGLX144); Key Research Project of Colleges and Universities of Henan Province (Grant No.19A210007).

Author contributions: X.W. conceived the idea. X.W. and J.W. designed the research; X.W., X.Z., Y.L. and K.Z. performed research; V.T., L.H., and X.W. analyzed the data; and all authors were involved in writing the manuscript.

Competing interests. The authors declare no competing financial interests.

Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors

Khush GS. What it will take to feed 5.0 billion rice consumers in 2030. Plant Mol Biol 59, 1-6 (2005).
Lou YG, Zhang GR, Zhang WQ, Hu Y, Zhang J. Biological control of rice insect pests in China. Biol Control 67, 8-20 (2013).
Cohen MB, Chen M, Bentur J, Heong K, Ye GY. Bt rice in Asia: potential benefits, impact, and sustainability. In: Integration of insect-resistant genetically modified crops within IPM programs. Romeis J, Shelton AM, and Kennedy GG (eds). Springer Netherlands, Dordrecht, 223-248 (2008).
Sogawa K, Liu GJ, Shen JH. A review on the hyper-susceptibility of chinese hybrid rice to insect pests. Chinese J Rice Sci 17, 23-30 (2003).
Chen M, Shelton A, Ye GY. Insect-resistant genetically modified rice in China: from research to commercialization. Annu Rev Entomol 56, 81-101 (2011).
Liu Y, et al. A gene cluster encoding lectin receptor kinases confers broad-spectrum and durable insect resistance in rice. Nat Biotech 33, 301-305 (2015).
Kumar K, et al. High-resolution genetic mapping of a novel brown planthopper resistance locus, Bph34 in Oryza sativa L. X Oryza nivara (Sharma & Shastry) derived interspecific F2 population. Theor Appl Genet 131, 1163-1171 (2018).
Li Y, Hallerman EM, Liu Q, Wu K, Peng Y. The development and status of Bt rice in China. Plant Biotechnol J 14, 839-848 (2016).
Cheng X, Zhu L, He G. Towards understanding of molecular interactions between rice and the brown planthopper. Molecular Plant 6, 621-634 (2013).
Guo J, et al. Bph6 encodes an exocyst-localized protein and confers broad resistance to planthoppers in rice. Nat Genet 50, 297-306 (2018).
Murata K, Fujiwara M, Murai H, Takumi S, Mori N, Nakamura C. Bph9, a dominant brown planthopper resistance gene, is located on the long arm of chromosome 12. 17, 84-86 (2000).
Kumar K, Kaur P, Kishore A, Vikal Y, Singh K, Neelam K. Recent advances in genomics-assisted breeding of brown planthopper (Nilaparvata lugens) resistance in rice (Oryza sativa). Plant Breeding 139, 1052-1066 (2020).
Ye M, et al. Silencing a simple extracellular leucine-rich repeat gene OsI-BAK1 enhances the resistance of rice to brown planthopper Nilaparvata lugens. International Journal of Molecular Sciences 22, 12182 (2021).
Wang Y, et al. Map-based cloning and characterization of BPH29, a B3 domain-containing recessive gene conferring brown planthopper resistance in rice. J Exp Bot 66, 6035-6045 (2015).
Zhao Y, et al. Allelic diversity in an NLR gene BPH9 enables rice to combat planthopper variation. Proc Natl Acad Sci USA 113, 12850-12855 (2016).
Ren J, et al. Bph32, a novel gene encoding an unknown SCR domain-containing protein, confers resistance against the brown planthopper in rice. Sci Rep 6, 37645 (2016).
Soler R, et al. Plant-mediated facilitation between a leaf-feeding and a phloem-feeding insect in a brassicaceous plant: from insect performance to gene transcription. Funct Ecol 26, 156-166 (2012).
Liu Q, Turlings TCJ, Li Y. Can herbivores sharing the same host plant be mutualists? Trends Ecol Evol 38, 509-511 (2023).
Wang X, et al. Bt rice could provide ecological resistance against nontarget planthoppers. Plant Biotechnol J 16, 1748-1755 (2018).
Ling Y, Weilin Z. Genetic and biochemical mechanisms of rice resistance to planthopper. Plant Cell Rep 35, 1559-1572 (2016).
Zhang W, et al. Small brown planthopper resistance loci in wild rice (Oryza officinalis). Mol Genet Genomics 289, 373-382 (2014).
Suk-Man K, Jae-Keun S. Identification of a rice gene (Bph 1) conferring resistance to brown planthopper (Nilaparvata lugens Stål) using STS markers. Mol Cells 20, 30-34 (2005).
Han LZ, Li SB, Liu PL, Peng YF, Hou ML. New artificial diet for continuous rearing of Chilo suppressalis (Lepidoptera: Crambidae). Ann Entomol Soc Am 105, 253-258 (2012).
Liu FH, Kang ZW Tan XL, Fan YL, Tian HG, Liu TX. Physiology and defense responses of wheat to the infestation of different cereal aphids. J. Integr. Agric. 19, 1464-1474 (2020).
Kobayashi T. Evolving ideas about genetics underlying insect virulence to plant resistance in rice-brown planthopper interactions. J Insect Physiol 84, 32-39 (2016).
Jain M, Nijhawan A, Tyagi AK, Khurana JP. Validation of housekeeping genes as internal control for studying gene expression in rice by quantitative real-time PCR. Biochem Biophys Res Commun 345, 646-651 (2006).
Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2−^ΔΔCT method. Methods 25, 402-408 (2001).
Liu Q, Wang X, Tzin V, Romeis J, Peng Y, Li Y. Combined transcriptome and metabolome analyses to understand the dynamic responses of rice plants to attack by the rice stem borer Chilo suppressalis (Lepidoptera: Crambidae). BMC Plant Biol 16, 259 (2016).
Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biology 11, R106 (2010).
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statist. Soc. B 57, 289-300 (1995).
Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biology 11, R14 (2010).
Mao X, Cai T, Olyarchuk JG, Wei L. Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics 21, 3787-3793 (2005).
Wu J, Hettenhausen C, Meldau S, Baldwin IT. Herbivory rapidly activates MAPK signaling in attacked and unattacked leaf regions but not between leaves of Nicotiana attenuata. Plant Cell 19, 1096-1122 (2007).
Jiao Y, Hu X, Peng Y, Wu K, Romeis J, Li Y. Bt rice plants may protect neighbouring non-Bt rice plants against the striped stem borer, Chilo suppressalis. Proc Biol Sci 285, 20181283 (2018).
Hu X, et al. Caterpillar-induced rice volatiles provide enemy-free space for the offspring of the brown planthopper. eLife 9, e55421 (2020).
De Lange ES, et al. Spodoptera frugiperda caterpillars suppress herbivore-induced volatile Emissions in Maize. J Chem Ecol 46, 344-360 (2020).
Fu Q, Zhang Z, Hu C, Lai F, Sun Z. A chemically defined diet enables continuous rearing of the brownplanthopper, Nilaparvata lugens (Stål) (Homoptera : Delphacidae). Applied Entomology & Zoology 36, 111-116 (2001).
Seo BY, Kwon YH, Jung JK, Kim GH. Electrical penetration graphic waveforms in relation to the actual positions of the stylet tips of Nilaparvata lugens in rice tissue. J Asia-Pacif Entomol 12, 89-95 (2009).
Losel PM, Goodman LJ. Effects on the feeding behaviour of Nilaparvata lugens (Stål) of sublethal concentrations of the foliarly applied nitromethylene heterocycle 2-nitromethylene-1, 3-thiazinan-3-yl-carbamaldehyde. Physiol Entomol 18, 67-74 (1993).
Ghaffar MBA, Pritchard J, Ford-Lloyd B. Brown planthopper (N. lugens Stål) feeding behaviour on rice germplasm as an indicator of resistance. PloS one 6, e22137 (2011).
Gao G, et al. DRTF: a database of rice transcription factors. Bioinformatics (Oxford, England) 22, 1286-1287 (2006).
Pérez-Rodríguez P, Riaño-Pachón DM, Corrêa LGG, Rensing SA, Kersten B, Mueller-Roeber B. PlnTFDB: updated content and new features of the plant transcription factor database. Nucleic Acids Res 38, D822-827 (2010).
Li R, et al. Prioritizing plant defence over growth through WRKY regulation facilitates infestation by non-target herbivores. eLife 4, e04805 (2015).
Giri MK, et al. The Arabidopsis thaliana At4g13040 gene, a unique member of the AP2/EREBP family, is a positive regulator for salicylic acid accumulation and basal defense against bacterial pathogens. J Plant Physiol 171, 860-867 (2014).
Lu J, et al. An EAR‐motif‐containing ERF transcription factor affects herbivore‐induced signaling, defense and resistance in rice. The Plant Journal 68, 583-596 (2011).
Zhang J, et al. Lipid profiles reveal different responses to brown planthopper infestation for pest susceptible and resistant rice plants. Metabolomics 14, 120 (2018).
Liu Q, et al. Cooperative herbivory between two important pests of rice. Nat Commun 12, 6772 (2021).
Shigematsu Y, Murofushi N, Ito K, Kaneda C, Kawabe S, Takahashi N. Sterols and asparagine in the rice plant, endogenous factors related to resistance against the brown planthopper (Nilaparvata lugens). Agric Biol Chem 46, 2877-2879 (1982).
Chen M, Ye G, Hu C, Tu J, Datta S. Effect of transgenic Bt rice on dispersal of planthoppers and leafhoppers as well as their egg parasitic wasps. J Zhejiang Univ (Agric Life Sci), 5 (2003).
Sogawa K, Pathak MD. Mechanisms of brown planthopper resistance in mudgo variety of rice (Hemiptera : Delphacidae). Appl Entomol Zool 5, 145-158 (1970).
Zhou YH, Han ZJ.Individual virulence index of Nilaparvata lugens on a resistant variety of rice Mudgo. Acta Entomol Sin 46, 305-310 (2003).
Xu HX, et al. Changes in endosymbiotic bacteria of brown planthoppers during the process of adaptation to different resistant rice varieties. Environ Entomol, nvv054 (2015).
Song XM, et al. Functional analysis of rice OsLecRK1 in resistance to rice brown planthopper. J Environ Entomol 43, 1210-1219 (2021).
Ji R, et al. Comparative transcriptome analysis of salivary glands of two populations of rice brown planthopper, Nilaparvata lugens, that differ in virulence. PloS one 8, e79612 (2013).
Garzo E, Soria C, Gómez-Guillamón ML, Fereres A. Feeding behavior of Aphis gossypii on resistant accessions of different melon genotypes (Cucumis melo). Phytoparasitica 30, 129-140 (2002).
Liang LY, Liu LF, Yu XP, Han BY. Evaluation of the resistance of different tea cultivars to tea aphids by EPG technique. Journal of Integrative Agriculture 11, 2028-2034 (2012).
Tzin V, et al. Dynamic maize responses to aphid feeding are revealed by a time series of transcriptomic and metabolomic assays. Plant Physiol 169, 1727-1743 (2015).
Qi J, et al. Oral secretions from Mythimna separata insects specifically induce defense responses in maize as revealed by high-dimensional biological data. Plant Cell Environ 39: 1749-1766 (2016).
Guo J, et al. The Asian corn borer Ostrinia furnacalis feeding increases the direct and indirect defence of mid-whorl stage commercial maize in the field. Plant Biotechnol J 17, 88-102 (2019).
Zhou G, Wang X, Yan F, Li R, Cheng J, Lou Y. Genome‐wide transcriptional changes and defence‐related chemical profiling of rice in response to infestation by the rice striped stem borer Chilo suppressalis. Physiol Plant 143, 21-40 (2011).
Li R, et al. Prioritizing plant defence over growth through WRKY regulation facilitates infestation by non-target herbivores. elife 4, e04805 (2015).
Huang XZ, et al. Dynamic transcriptome analysis and volatile profiling of Gossypium hirsutum in response to the cotton bollworm Helicoverpa armigera. Sci Rep 5, 11867 (2015).
Hu L, et al. The rice transcription factor WRKY53 suppresses herbivore-induced defenses by acting as a negative feedback modulator of mitogen-activated protein kinase activity. Plant Physiol 169, 2907-2921 (2015).
Yoo SJ, et al. Involvement of the OsMKK4-OsMPK1 cascade and its downstream transcription factor OsWRKY53 in the wounding response in rice. Plant Pathol J 30, 168-177 (2014).
Lu J, et al. Induced jasmonate signaling leads to contrasting effects on root damage and herbivore performance. Plant Physiol 167, 1100-1116 (2015).
Lou YG, Du MH, Turlings TC, Cheng JA, Shan WF. Exogenous application of jasmonic acid induces volatile emissions in rice and enhances parasitism of Nilaparvata lugens eggs by the Parasitoid Anagrus nilaparvatae. J Chem Ecol 31, 1985-2002 (2005).
Xiao Y, et al. Specific herbivore-induced volatiles defend plants and determine insect community composition in the field. Ecol Lett 15, 1130-1139 (2012).
Blubaugh CK, et al. Dual-guild herbivory disrupts predator-prey interactions in the field. Ecology 99, 1089-1098 (2018).
Wang Q, Xin Z, Li J, Hu L, Lou Y, Lu J. (E)-β-caryophyllene functions as a host location signal for the rice white-backed planthopper Sogatella furcifera. Physiol Mol Plant Pathol 91, 106-112 (2015).
Xiao Y, et al. Specific herbivore-induced volatiles defend plants and determine insect community composition in the field. Ecol Lett 15, 1130-1139 (2012).
Waris MI, et al. The role of chemosensory protein 10 in the detection of behaviorally active compounds in brown planthopper, Nilaparvata lugens. Insect Sci 27, 531-544 (2020).
Hagenbucher S, Olson D, Ruberson J, Wäckers F, Romeis J. Resistance mechanisms against arthropod herbivores in cotton and their interactions with natural enemies. Crit Rev Plant Sci 32, 458-482 (2013).
Erb M, Reymond P. Molecular interactions between plants and insect herbivores. Annu Rev Plant Biol 70, 527-557 (2019).
Pare PW, Tumlinson JH. Plant volatiles as a defense against insect herbivores. Plant Physiol 121, 325-332 (1999).
Zhang P, et al. Suppression of jasmonic acid-dependent defense in cotton plant by the mealybug Phenacoccus solenopsis. PloS one 6, e22378 (2011).

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Jasmonic acid signaling induced by caterpillar damage causes reduced resistance to target insect feeding by the brown planthopper, Nilaparvata lugens (Stål), in rice

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Abstract

Figures

Background

Results

BPH preferentially feeds on caterpillar-damaged rice plants

Effects of BPH feeding on damaged and healthy rice

Fitness of BPH feeding on SSB-damaged and healthy Mudgo and TN1 rice plants

Characterization of EPG waveform of BPH feeding rice

Transcriptomic changes in response to BPH feeding

Overview of the transcriptome in rice plants during SSB infestation

Analyses of differentially expressed genes (DEGs)

Analyses of transcriptomic changes focus on transcription factors (TFs)

SSB feeding induces plant hormone and related gene expression

Volatile compound production and metabolomic response to BPH

Differential production of plant volatiles following herbivory in rice caterpillar damage

Metabolome composition analyses following herbivory in caterpillar damaged rice

Discussion

Methods

Plant Growth

Insect Colonies

Behavioral and biological characteristics studies of caterpillars and planthoppers

Insect bioassay

Host preference of planthoppers

Bioassays of planthoppers

Monitoring of BPH Feeding Behavior

Transcriptome Analyses

RNA Extraction, Library Preparation RNA-Sequencing, and Quantitative Real-Time PCR

RNA-Sequencing Data Analysis

Quantification of phytohormones and related gene expression

Volatile analysis

Collection and analysis of rice plant volatiles

Choice bioassay using a Y-tube tube olfactometer

Metabolic Analyses

Collection and analysis of rice metabolic

Identification of metabolite toxicity to BPH using artificial diet

Statistical analyses

Declarations

References

Supplementary Tables

Additional Declarations

Supplementary Files

Status:

Version 1