Abstract

The relationship between phloem-feeding insects (PFIs) and plants offers an intriguing example of a highly specialized biotic interaction. These insects have evolved to survive on a nutritionally imbalanced diet of phloem sap, and to minimize wound responses in their host plants. As a consequence, plant perception of and responses to PFIs differ from plant interactions with other insect-feeding guilds. Transcriptome-wide analyses of gene expression are currently being applied to characterize plant responses to PFIs in crop plants with race-specific innate resistance, as well as in compatible interactions with susceptible hosts. Recent studies indicate that PFIs induce transcriptional reprogramming in their host plants, and that plant responses to PFIs appear to be quantitatively and qualitatively different from responses to other insects or pathogens. Transcript profiling studies also suggest that PFIs induce cell wall modifications, reduce photosynthetic activity, manipulate source–sink relations, and modify secondary metabolism in their hosts, and many of these responses appear to occur within the phloem tissue. Plant responses to these insects appear to be regulated in part by the salicylate, jasmonate, and ethylene signalling pathways. As additional transcript profiling data become available, forward and reverse genetic approaches will be necessary to determine which changes in gene expression influence resistance or susceptibility to PFIs.

Introduction

The majority of insects in the suborder Homoptera, such as aphids, whiteflies, psyllids, and planthoppers, are specialized to feed on phloem sap. Phloem-feeding insects (PFIs) are the most prevalent vectors of plant viruses and also damage crops by depleting photoassimilates, manipulating growth and nutrient partitioning, and, in some cases, injecting toxins into the plant (Nault, 1997; Madhusudhan and Miles, 1998; Macedo et al., 2003; Girousse et al., 2005). Because of the impact of PFIs on agriculture, it is important to identify the factors that regulate plant resistance or susceptibility to these insects.

The complexity of plant–insect interactions makes it difficult to determine which anatomical features, metabolites, and signalling pathways effectively limit PFI infestation. The field of genomics provides powerful tools to investigate these critical factors. Transcript profiling techniques allow the simultaneous examination of thousands of genes, and can be utilized to study changes in gene expression that are transcriptionally regulated. Microarray analysis is among the most common profiling tools, but requires the previous identification of a set of relevant transcripts. Other techniques such as cDNA amplified fragment length polymorphisms (cDNA-AFLPs) and suppression subtractive hybridization (SSH) are useful to identify previously unknown transcripts that are differentially regulated among treatment groups. These approaches can readily be combined to identify plant transcript profiles that correlate with PFI resistance or symptom development. Furthermore, genome-wide transcript analysis is currently being applied to identify putative avirulence genes, detoxification mechanisms, or virulence factors in PFIs. Beyond transcript profiling, genomics also facilitates the functional analysis of genes implicated in resistance or susceptibility. As signalling cascades and metabolic pathways are elucidated in model systems and crop plants, key regulatory genes can be targeted for silencing or over-expression to study the role of these pathways in plant–insect interactions.

To date, the majority of molecular and genomic studies on plant interactions with PFIs are limited to the analysis of plant gene expression in response to infestation. Therefore, this review will focus on plant transcriptomics, and will examine the effects of PFI infestation on known defensive pathways, oxidative stress responses, cell wall composition, and primary and secondary metabolism.

Determinants of plant interactions with PFIs

Herbivore damage is limited by a wide variety of constitutive or induced plant defences. Constitutive traits such as trichomes or preformed chemical defences are expressed prior to insect damage and frequently have deterrent effects on insect settling or feeding behaviours. Plants can display phenotypic plasticity even in preformed defences; trichome densities, for example, may increase in response to prior herbivory (Traw and Bergelson, 2003). Other forms of insect resistance depend upon far more rapid defence responses that are expressed only under herbivore pressure. In the case of race-specific innate resistance, plants that carry a particular resistance gene (R gene) recognize a corresponding avirulence gene product in the pest, resulting in an incompatible interaction (Flor, 1971). A rapid, local defence response occurs at the site of infection and blocks or dramatically reduces the initial establishment of the pest. The proximate mechanisms of race-specific insect resistance are not yet well understood, but possible defences include the generation of reactive oxygen species, a hypersensitive response at the feeding site, or the rapid generation of antibiotic compounds such as pathogenesis-related (PR) proteins (Kaloshian, 2004). Frequently, R-gene-mediated aphid resistance is characterized by reduced sap ingestion after the aphid contacts the phloem, suggesting that resistance might also involve phloem-limited feeding deterrents or phloem-sealing mechanisms (Caillaud and Niemeyer, 1996; Klingler et al., 1998; Kaloshian et al., 2000). Local disruption of assimilate transport triggered by PFI-induced intercellular calcium fluxes could limit the insect's food supply. Elegant experiments examining aphid-feeding behaviours in the sieve elements of Vicia faba suggest that calcium-mediated phloem sealing is modulated by calcium chelating components within aphid saliva, and this interaction could play an important role in determining susceptibility or resistance (Will and van Bel, 2006).

The majority of published studies have focused on transcript profiles of compatible interactions in plant species for which no genetic variation in resistance levels have been identified (Table 1). These studies seek to identify highly conserved, multigenic, induced defences that limit the severity of insect infestations, or mechanisms of tolerance that allow plants to sustain infestation while limiting symptom development. Plant traits that have been implicated in broad-spectrum induced resistance (IR) include cell wall modifications, proteins or secondary metabolites that have antixenotic or antibiotic properties, and plant volatiles that repel PFIs or attract their natural enemies (Kaloshian and Walling, 2005). In addition to broad-spectrum defences, many of the changes in gene expression observed in compatible interactions are involved in symptom development, such as chlorophyll loss. The primary challenge in plant transcriptomics is to discriminate among the complex array of changes that are induced by PFIs, to determine which of these changes have adaptive value to plants.

Table 1.

Transcript profiling studies of plant responses to phloem-feeding insects


Plant
 

Insect
 

Number of infestationsa
 

Durationb
 

Analysis
 

Array type
 

Reference
 
Arabidopsis thaliana (S)c Green peach aphid (Myzus persicae10 72–96 h Array Selected ESTs (105) Moran et al., 2002 
Arabidopsis thaliana (S) Green peach aphid (Myzus persicae40 72 h Array, QPCR, RNA blot Arabidopsis GeneChip (23 750) De Vos et al., 2005 
Sorghum (S) (Sorghum bicolorGreenbug aphid (Schizaphis graminum20 6–48 h Array and RNA blot Subtracted cDNA (672) Zhu-Salzman et al., 2004 
Sorghum (S/R) (Sorghum bicolorGreenbug aphid (Schizaphis graminum30 72 h Array and RNA blot Subtracted cDNA (3508) Park et al., 2005 
Native tobacco (S) (Nicotiana attenuataMyzus nicotianae 72 h Array Selected Oligo (789) Heidel and Baldwin, 2004 
Native tobacco (S) (Nicotiana attenuataMyzus nicotianae 48 h Array Selected cDNA (240) Voelckel et al., 2004 
Rice (S/R) (Oryza sativaBrown plant hopper (Nilaparvata lugens10 72 h Array and RNA blot Selected cDNA (108) Zhang et al., 2004 
Rice (S) (Oryza minutaBrown plant hopper (Nilaparvata lugens10 6–72 h Array and RNA blot Subtracted cDNA (960) Cho et al., 2005 
Celery (S) (Apium graveolensGreen peach aphid (Myzus persicae10–20 72–168 h Array and RNA blot Subtracted cDNA ESTs (1278) Divol et al., 2005 
Apple (S/R) (Malus domesticaRosy apple aphid (Dysaphis plantaginae20 72 h cDNA-AFLP and RNA blot  Qubbaj et al., 2005 
Tomato (S) (Lycopersicon esculentum)
 
Silverleaf whitefly (Bemisia argentifolii)
 
25
 
25 d
 
Array
 
CGEP tomato arrays
 
McKenzie et al., 2005
 

Plant
 

Insect
 

Number of infestationsa
 

Durationb
 

Analysis
 

Array type
 

Reference
 
Arabidopsis thaliana (S)c Green peach aphid (Myzus persicae10 72–96 h Array Selected ESTs (105) Moran et al., 2002 
Arabidopsis thaliana (S) Green peach aphid (Myzus persicae40 72 h Array, QPCR, RNA blot Arabidopsis GeneChip (23 750) De Vos et al., 2005 
Sorghum (S) (Sorghum bicolorGreenbug aphid (Schizaphis graminum20 6–48 h Array and RNA blot Subtracted cDNA (672) Zhu-Salzman et al., 2004 
Sorghum (S/R) (Sorghum bicolorGreenbug aphid (Schizaphis graminum30 72 h Array and RNA blot Subtracted cDNA (3508) Park et al., 2005 
Native tobacco (S) (Nicotiana attenuataMyzus nicotianae 72 h Array Selected Oligo (789) Heidel and Baldwin, 2004 
Native tobacco (S) (Nicotiana attenuataMyzus nicotianae 48 h Array Selected cDNA (240) Voelckel et al., 2004 
Rice (S/R) (Oryza sativaBrown plant hopper (Nilaparvata lugens10 72 h Array and RNA blot Selected cDNA (108) Zhang et al., 2004 
Rice (S) (Oryza minutaBrown plant hopper (Nilaparvata lugens10 6–72 h Array and RNA blot Subtracted cDNA (960) Cho et al., 2005 
Celery (S) (Apium graveolensGreen peach aphid (Myzus persicae10–20 72–168 h Array and RNA blot Subtracted cDNA ESTs (1278) Divol et al., 2005 
Apple (S/R) (Malus domesticaRosy apple aphid (Dysaphis plantaginae20 72 h cDNA-AFLP and RNA blot  Qubbaj et al., 2005 
Tomato (S) (Lycopersicon esculentum)
 
Silverleaf whitefly (Bemisia argentifolii)
 
25
 
25 d
 
Array
 
CGEP tomato arrays
 
McKenzie et al., 2005
 
a

Number of insects at the initial infestation.

b

Duration of the infestation.

c

S, susceptible; R, resistant.

Plant perception of PFIs

PFIs minimize wounding and plant wound responses

Plant responses to herbivore attack can be correlated with the mode of feeding and the amount of tissue damage occurring at the feeding site (Walling, 2000). Chewing insects such as caterpillars cause extensive cellular disruption, which plays an important role in plant perception of these herbivores. Transcript profiles induced by caterpillar feeding show significant overlap with profiles involved in wound responses (Reymond et al., 2000). By contrast, most PFIs probe plant tissue intercellularly to establish feeding sites in the phloem sieve elements that can be maintained for hours or even weeks (reviewed in Tjallingii, 2006). This mode of feeding minimizes wounding and limits the local induction of defence responses to a minimal number of cells and tissue types. Probing does, however, result in cell wall disturbance, disruption of plasma membranes, and penetration of epidermal, mesophyll, and parenchyma cells (Pollard, 1973; Tjallingii and Hogen Esch, 1993). As a result, limited local induction of proteinase inhibitors and other wound-responsive transcripts have been observed in response to PFI infestation (Martinez de Ilarduya et al., 2003; Zhu-Salzman et al., 2004; Park et al., 2005). The degree of injury that occurs during probing varies considerably among PFI species.

PFI saliva may regulate plant–insect interactions

While wounding plays a major role in plant response to chewing insects, insect oral secretions are also important in modulating plant transcript profiles (Korth and Dixon, 1997; Lawrence and Novak, 2004). Volicitin and other fatty acid conjugates in caterpillar oral secretions trigger the release of plant volatiles that attract the natural enemies of insect herbivores (Alborn et al., 1997). Conversely, caterpillars also produce glucose oxidase, that is postulated to benefit herbivores by suppressing plant defence responses (Eichenseer et al., 1999; Musser et al., 2002). Given the significance of oral secretions in plant interactions with chewing insects, it is highly probable that insect secretions also mediate plant interactions with PFIs. PFI salivas contain complex mixtures of lipoproteins, phospholipids, and carbohydrates, as well as numerous enzymes with proteolytic, hydrolytic, oxidative, or cell wall-degrading activities (Miles, 1999; Cherqui and Tjallingii, 2000; Tjallingii, 2006). In compatible interactions, these factors probably aid in stylet penetration and could detoxify defensive compounds in the host plant (Jiang, 1996; Miles, 1999). In some cases, transport of salivary components within the plant contributes to the development of symptoms such as veinal chlorosis (Madhusudhan and Miles, 1998). In incompatible interactions, PFI oral secretions are also a potential source of avirulence (avr) factors. To date, no avr genes have been cloned in an insect; however, N Lapitan and coworkers (personal communication) have identified a protein fraction isolated from Russian wheat aphids (RWA; Diuraphis noxia) that could have a key role in determining plant compatibility. When injected into susceptible wheat genotypes, this protein fraction induced the leaf-rolling symptom typical of RWA feeding in compatible interactions. Injecting the protein fraction into RWA-resistant genotypes did not induce leaf rolling, but increased the expression of defensive peroxidases and catalases compared with the RWA-susceptible genotypes. In the future, construction of cDNA libraries derived from insect salivary glands should assist in identifying other determinants of virulence in PFIs. High-throughput transient expression systems can also be used to express aphid gene products in plant tissue to test the effects of putative a/virulence factors on plant defence and symptom expression. This approach has proven effective in the study of bacterial effector proteins (Kamoun et al., 2003; Van der Hoorn et al., 2000).

Transcriptional reprogramming by PFIs

Transcriptomics reveals a high degree of overlap between plant transcript profiles in compatible and incompatible interactions with PFIs

Whether elicited by mechanical damage or oral secretions, herbivores induce numerous changes in their host plants, many of which are transcriptionally regulated. Relatively few studies, however, have examined plant transcript profiles in incompatible interactions with PFIs (Zhang et al., 2004; Park et al., 2005; Qubbaj et al., 2005). Studies that compare insect-responsive gene expression in incompatible (resistant) and compatible (susceptible) interactions are designed to identify a subset of genes that are directly related to innate resistance. Qubbaj et al. (2005) utilized cDNA-AFLP and RNA blot analyses to investigate the interaction between apple and the rosy apple aphid 72 h after infestation, but identified only six transcriptional responses that were unique to the incompatible interaction. In resistant, but not in susceptible plants, aphid feeding up-regulated expression of genes encoding a pectin acetyl esterase, RNase L inhibitor-like protein, ADP-ribosylation factor, vacuolar H+-ATPase subunit-like protein, and inositol phosphatase-like protein, and down-regulated the large-chain precursor of Rubisco. In addition, He and coworkers utilized a 108-element selected cDNA array to examine gene expression in resistant and susceptible rice cultivars 72 h after infestation with the brown planthopper (Zhang et al., 2004). In the resistant cultivar, planthopper feeding up-regulated expression of glutamine synthase and S-adenosylmethionine synthase 2, whereas these transcripts were down-regulated in susceptible rice plants. A BTF3 transcription factor was also down-regulated in resistant plants and up-regulated in the compatible interaction. Aside from these three genes, insect-responsive transcripts identified in resistant plants showed similar expression patterns in the susceptible cultivar. Transcriptional differences between resistant and susceptible sorghum lines in response to greenbug feeding are difficult to evaluate because of the extreme damage caused by aphid feeding on susceptible lines (Park et al., 2005). After 72 h of aphid feeding on a susceptible sorghum line the plants were severely wilted with widespread necrotic spots, whereas the resistant line had only a few necrotic spots and otherwise appeared healthy. Greenbug feeding on the resistant sorghum line for a similar period of time resulted in differential expression of 84 genes (72 up-regulated, 12 down-regulated), which included 13 genes (11 up-regulated, 2 down-regulated) that were co-regulated in the susceptible line. Potentially, innate resistance in these systems is due to differences in the timing and magnitude of plant defences, rather than to qualitatively different responses. Furthermore, the initial recognition and signalling events are likely to be rapid, fleeting, and highly localized to the feeding site. For example, electronic monitoring of aphid feeding behaviour on resistant tomato and melon cultivars suggests that the resistance response in these plants occurs within 2 h of the onset of probing, and involves defences that are localized to the phloem (Klingler et al., 1998; Kaloshian et al., 2000). Future transcriptional studies should examine earlier time points in the plant–insect interaction, and consider the temporal and spatial components of plant responses to PFIs.

The experimental design of transcriptomics experiments may influence their outcome

The widely accepted MIAME (Minimum Information About a Microarray Experiment) guidelines dictate how the experimental methods for microarray experiments should be documented for publication (Brazma et al., 2001). When it comes to setting standards for the methods themselves, however, researchers have yet to reach a consensus on many basic issues, such as minimum requirements for replication or preferred statistical analyses. Researchers who study plant interactions with PFIs must also decide what bioassay design for tissue collection is best-suited to their particular biological system. As mentioned earlier, the time scale over which transcript profiles are monitored is critical, and should be chosen based upon observations of the plant–insect interaction. Other aspects of experimental design that could influence the outcome of genomic studies include the developmental stages of the plants and insects and the inoculum level used. For example, Van de Ven et al. (2000) demonstrated that whitefly larvae induced different transcriptional responses than adults.

The outcome of transcript profiling studies will also vary depending upon the tissue types examined. PFIs have both local and systemic effects on plant gene expression, and these effects may differ among cell types. Because PFIs have an intimate and sustained interaction with the phloem sieve elements and inject watery saliva into the phloem, many important plant responses to PFIs may be localized to the vascular tissue. This tissue makes up a small proportion of the leaf, however, and whole leaf transcriptional assays could miss rare transcripts that are expressed within sieve elements. Divol and co-workers (2005) have exploited the unique anatomy of celery petioles to examine transcriptional changes that occur specifically in the phloem tissue in a compatible interaction with green peach aphid. This study identified 126 genes that were systemically induced by aphid feeding, including transcripts involved in oxidative stress, signalling, cell wall modification, water transport, metal homeostasis, vitamin biosynthesis, carbon assimilation, and nitrogen and carbon mobilization. Conspicuously absent from this analysis was the induction of high levels of pathogenesis-related (PR) proteins that are characteristic of the local response to aphid feeding in whole leaf tissues. This study illustrates that localizing gene expression is critical to understanding the role of specific genes involved in the plant–PFI interaction.

The nature of the gene sets used in microarray experiments, as well as the manner in which the experiments are replicated and analysed can also influence the conclusions that are drawn from the data. For example, the current literature presents conflicting views of the extent and magnitude of PFI-induced transcriptional reprogramming in plants. Several studies of selected stress-responsive genes have suggested that PFIs have less impact on plant gene expression than chewing insects (Fidansef et al., 1999; Heidel and Baldwin, 2004; Kaloshian and Walling, 2005). By contrast, De Vos et al. (2005) identified 2181 genes (832 up-regulated and 1349 down-regulated) in an Arabidopsis thaliana full-genome array that were differentially expressed in response to the green peach aphid (Myzus persicae). The number of genes differentially expressed in response to aphids was much greater than observed for chewing insects (186 genes: cabbage white butterfly larvae, Pieris rapae) or cell-content feeders (199 genes: thrips, Frankliniella occidentalis), and was comparable to the number of genes that were responsive to the bacterial pathogen Pseudomonas syringae (2034 genes). Potentially, the use of a whole-genome array rather than a preselected gene set allowed the identification of a larger set of aphid-responsive genes. However, it is also important to note that this study utilized only one biological replicate, pooled from 20 plants, and identified differentially regulated genes on the basis of fold-changes, rather than statistical analysis (De Vos et al., 2005). More recently, the green peach aphid–Arabidopsis interaction was studied using six biological replicates to probe the Arabidopsis full-genome array, and this study found only 27 genes (2 at 2 h and 25 at 36 h) with altered expression (J Pritchard, personal communication). These conflicting data sets demonstrate the importance of standardizing the experimental design in both performing and interpreting global transcription studies.

Although relatively few published studies have examined plant transcript profiles in response to PFIs, these studies have used a wide range of experimental systems and methodologies (Table 1), and this limits our ability to compare the outcome of these studies. Based on the analysis of both compatible and incompatible interactions, however, it is evident that PFIs influence known defensive pathways, oxidative stress responses, and plant structure and metabolism.

Influence of PFIs on known defence signalling pathways

PFI-induced gene expression reveals mixed signals

PFI infestation has been shown to modulate salicylic acid, jasmonic acid, and ethylene, three signalling compounds that play key roles in regulating plant defence. Salicylic acid (SA) is required for systemic acquired resistance (SAR) to many viruses, bacteria, and fungi, as well as for certain forms of race-specific disease resistance (Rairdan and Delaney, 2002; Durrant and Dong, 2004). Jasmonic acid (JA) and related oxylipins mediate induced resistance to chewing insects and cell-content feeders, as well as to certain fungal pathogens (Halitschke and Baldwin, 2004; Pozo et al., 2004). JA and ethylene (ET) are both wound-responsive, and are required for the elicitation of systemic induced disease resistance (ISR) by rhizobacteria (Pozo et al., 2004). Plant transcriptional responses to pathogens and herbivores are determined in part by the co-ordinate regulation of the SA, JA, and ET signalling pathways that can have both synergistic and antagonistic interactions. ET, for example, enhances certain JA- and SA-dependent responses, but inhibits others (Rojo et al., 2003). Numerous studies have also demonstrated negative cross-talk between SA and JA (Rojo et al., 2003; Felton and Korth, 2000), but recent evidence indicates that these hormones can also have overlapping or even synergistic effects (Schenk et al., 2000; van Wees et al., 2000; Salzman et al., 2005). The interaction between SA and JA varies depending upon hormone concentration and the relative timing of induction (Devadas et al., 2002; Thaler et al., 2002). Further studies are needed to determine how SA, JA, and ET contribute to the outcome of plant interactions with PFIs.

PFIs elicit the SA signalling pathway

Aphid feeding induces expression of PR genes and other transcripts associated with SA-mediated signalling in several plant species, including Arabidopsis, tomato, sorghum, and Nicotiana attenuata (Moran and Thompson, 2001; Moran et al., 2002; Martinez de Ilarduya et al., 2003; Heidel and Baldwin, 2004; Zhu-Salzman et al., 2004; De Vos et al., 2005; Park et al., 2005). Direct quantification has also demonstrated that aphids induce salicylate accumulation in wheat, barley, soybean, and tomato (Mohase and van der Westhuizen, 2002; Chaman et al., 2003; Zhu and Park, 2005; DA Navarre and FL Goggin, unpublished data), although no changes in SA levels were detected in aphid-infested Arabidopsis or N. attenuata (Heidel and Baldwin, 2004; De Vos et al., 2005). In wheat, SA induction was observed in incompatible but not compatible interactions with the Russian wheat aphid (Mohase and van der Westhuizen, 2002). In tomato, accumulation of the SA-responsive PR-1 transcript was stronger and more rapid in incompatible than compatible interactions (Martinez de Ilarduya et al., 2003). Furthermore, Kaloshian (2004) stated the tomato plants carrying the aphid resistance gene Mi lost resistance when transformed with NahG, a gene encoding a bacterial enzyme that degrades SA. These results suggest that SA plays a role in certain forms of innate aphid resistance.

The effects of SA induction on aphid performance in compatible interactions, however, are not yet clear. Exogenous application of benzothiadiazole (BTH), a synthetic analogue of SA, reduced aphid population growth on the foliage of both resistant and susceptible tomato cultivars (Cooper et al., 2004), but the range of defences induced by BTH was recently reported to differ from those induced by SA (Heidel and Baldwin, 2004). In Arabidopsis, analysis of mutant lines deficient in SA-signalling suggested that SA does not play a direct role in limiting aphid infestation on this species (Table 2). Shah and coworkers observed a decrease in aphid numbers on two mutant lines that have elevated SA levels (cpr5 and ssi2) and an increase in aphid populations on a mutant that has reduced SA accumulation (pad4); however, they attributed this variation in aphid performance to differences in leaf senescence, rather than to direct effects of SA-dependent defences (Pegadaraju et al., 2005). Aphid resistance was maintained in the ssi2-NahG double mutant despite its dramatically reduced SA levels, and several other mutations that affected SA signalling but not senescence (snc1, npr1, sid2-2) failed to influence aphid populations (Pegadaraju et al., 2005). Similarly, Moran and Thompson (2001) observed no difference in green peach aphid reproduction between wild-type plants and the eds5 and eds9 mutants, which are compromised in SA signalling. Furthermore, the results of Schultz and coworkers suggest that certain mutations in SA signalling can enhance basal aphid resistance in Arabidopsis (Mewis et al., 2005). They observed reduced aphid performance on npr1 and NahG mutants compared with the wild-type plants, which suggests that SA accumulation and signalling may enhance host suitability in compatible interactions. These results are consistent with the ‘decoy’ hypothesis, that proposes aphids manipulate plant defence responses through pathway cross-talk, amplifying the SA-signalling pathway to repress a potentially more biologically effective JA-signalling pathway (Zhu-Salzman et al., 2004, 2005). Some caution is required in interpreting these results, however, because the effects of npr1 and NahG are not limited to salicylate signalling. A functional Npr1 gene was required for JA- and ET-dependent ISR (Dong, 2004), and NahG had pleiotropic effects on gene expression that also reduced JA and ET signalling (Heck et al., 2003).

Table 2.

Functional genomics of Arabidopsis–aphid interactions: jasmonic acid (JA), ethylene (ET), and salicylic acid (SA) signalling


Mutant
 

Effect on signalling
 

Reference
 

Aphid species
 

Populationa
 

Stage
 

Reference
 
coi1 JA-insensitive Ellis and Turner, 2002 M. persicae Increase (7) Bolting Ellis et al., 2002 
  Feys et al., 1994  Increase (10) Rosette Mewis et al., 2005 
   B. brassicaceae Increase (10) Rosette Mewis et al., 2005 
etr1 ET-insensitive Gamble et al., 1998 M. persicae No effect (10) Rosette Mewis et al., 2005 
   B. brassicaceae No effect (10) Rosette Mewis et al., 2005 
cev1 Constitutive JA and ET signalling Ellis and Turner, 2001 M. persicae Decrease (7) Bolting Ellis et al., 2002 
hrl1 Elevated SA and constitutive SA- and JA-dependent responses Devadas et al., 2002 M. persicae Increase (10) Rosette Mewis et al., 2005 
   B. brassicaceae No effect (10) Rosette Mewis et al., 2005 
cpr5 Elevated SA levels and constitutive SA-dependent responses; increased leaf senescence Bowling et al., 1997 M. persicae Decrease (2) Not stated Pegadaraju et al., 2005 
snc1 Elevated SA levels and constitutive SA-dependent responses Zhang et al., 2003 M. persicae No effect (2) Not stated Pegadaraju et al., 2005 
ssi2 Elevated SA levels and constitutive SA-dependent responses; increased leaf senescence Shah et al., 2001 M. persicae Decrease (2) Not stated Pegadaraju et al., 2005 
eds5 Reduced SA accumulation Rogers and Ausubel, 1997 M. persicae No effect (7) Bolting Moran and Thompson, 2001 
  Nawrath et al., 2002     
eds9 Reduced SA-mediated pathogen resistance Rogers and Ausubel, 1997 M. persicae No effect (7) Bolting Moran and Thompson, 2001 
NahG Reduced SA accumulation: degradation by salicylate hydroxylase Delaney et al., 1994 M. persicae Decrease (10) Rosette Mewis et al., 2005 
    No effect (2) Not stated Pegadaraju et al., 2005 
   B. brassicaceae Decrease (10) Rosette Mewis et al., 2005 
npr1 Reduced SA-dependent responses and enhanced JA signalling Cao et al., 1994 M. persicae Decrease (10) Rosette Mewis et al., 2005 
  Spoel et al., 2003  Inconsistent (7) Bolting Moran and Thompson, 2001 
    No effect (2) Not stated Pegadaraju et al., 2005 
   B. brassicaceae Decrease (10) Rosette Mewis et al., 2005 
pad4-1 Reduced SA accumulation; delayed leaf senescence Glazebrook et al., 1997 M. persicae Increase (2) Not stated Pegadaraju et al., 2005 
sid2-2
 
Reduced SA synthesis
 
Wildermuth et al., 2001
 
M. persicae
 
No effect (2)
 
Not stated
 
Pegadaraju et al., 2005
 

Mutant
 

Effect on signalling
 

Reference
 

Aphid species
 

Populationa
 

Stage
 

Reference
 
coi1 JA-insensitive Ellis and Turner, 2002 M. persicae Increase (7) Bolting Ellis et al., 2002 
  Feys et al., 1994  Increase (10) Rosette Mewis et al., 2005 
   B. brassicaceae Increase (10) Rosette Mewis et al., 2005 
etr1 ET-insensitive Gamble et al., 1998 M. persicae No effect (10) Rosette Mewis et al., 2005 
   B. brassicaceae No effect (10) Rosette Mewis et al., 2005 
cev1 Constitutive JA and ET signalling Ellis and Turner, 2001 M. persicae Decrease (7) Bolting Ellis et al., 2002 
hrl1 Elevated SA and constitutive SA- and JA-dependent responses Devadas et al., 2002 M. persicae Increase (10) Rosette Mewis et al., 2005 
   B. brassicaceae No effect (10) Rosette Mewis et al., 2005 
cpr5 Elevated SA levels and constitutive SA-dependent responses; increased leaf senescence Bowling et al., 1997 M. persicae Decrease (2) Not stated Pegadaraju et al., 2005 
snc1 Elevated SA levels and constitutive SA-dependent responses Zhang et al., 2003 M. persicae No effect (2) Not stated Pegadaraju et al., 2005 
ssi2 Elevated SA levels and constitutive SA-dependent responses; increased leaf senescence Shah et al., 2001 M. persicae Decrease (2) Not stated Pegadaraju et al., 2005 
eds5 Reduced SA accumulation Rogers and Ausubel, 1997 M. persicae No effect (7) Bolting Moran and Thompson, 2001 
  Nawrath et al., 2002     
eds9 Reduced SA-mediated pathogen resistance Rogers and Ausubel, 1997 M. persicae No effect (7) Bolting Moran and Thompson, 2001 
NahG Reduced SA accumulation: degradation by salicylate hydroxylase Delaney et al., 1994 M. persicae Decrease (10) Rosette Mewis et al., 2005 
    No effect (2) Not stated Pegadaraju et al., 2005 
   B. brassicaceae Decrease (10) Rosette Mewis et al., 2005 
npr1 Reduced SA-dependent responses and enhanced JA signalling Cao et al., 1994 M. persicae Decrease (10) Rosette Mewis et al., 2005 
  Spoel et al., 2003  Inconsistent (7) Bolting Moran and Thompson, 2001 
    No effect (2) Not stated Pegadaraju et al., 2005 
   B. brassicaceae Decrease (10) Rosette Mewis et al., 2005 
pad4-1 Reduced SA accumulation; delayed leaf senescence Glazebrook et al., 1997 M. persicae Increase (2) Not stated Pegadaraju et al., 2005 
sid2-2
 
Reduced SA synthesis
 
Wildermuth et al., 2001
 
M. persicae
 
No effect (2)
 
Not stated
 
Pegadaraju et al., 2005
 
a

Population development compared with the wild type (number of days population development was monitored).

PFIs trigger modest induction of JA/ET-dependent responses

Exogenous application of jasmonates to cotton, wheat, sorghum, and tomato reduce aphid host preference, survival, and fecundity (Omer et al., 2001; Bruce et al., 2003; Cooper et al., 2004; Zhu-Salzman et al., 2004; Cooper and Goggin, 2005). Furthermore, population growth of the green peach aphid was significantly reduced on the cev1 mutant in Arabidopsis, in which JA and ET signalling is constitutively activated (Ellis et al., 2002). These results suggest that defences regulated by JA and/or ET can effectively reduce aphid infestations, although the extent to which aphids induce these defences is unclear. In barley, ET generation was positively correlated with greenbug (Schizaphis graminum) resistance, but was associated with susceptibility to the Russian wheat aphid (Miller et al., 1994; Argandoña et al., 2001). In Medicago truncatula, the blue green aphid (Acyrthosiphon kondoi) induced marker genes associated with JA signalling in a resistant but not a susceptible cultivar (LL Gao, KB Singh, OR Edwards, personal communication). In tomato, however, transcripts of the JA- and wound-responsive proteinase inhibitors (PinI and PinII) and MeJA/ET-responsive basic β-1,3-glucanase (GluB) showed only a weak, local, and transient accumulation in response to aphids, and patterns of induction did not differ between compatible and incompatible interactions (Martinez de Ilarduya et al., 2003). These data suggest that JA and ET signalling may be involved in some, but not all, forms of innate aphid resistance.

In compatible interactions, studies of marker genes associated with SA and JA/ET signalling suggested that aphids elicited local induction of all three pathways, but that induction of SA signalling was more pronounced (Moran and Thompson, 2001; Zhu-Salzman et al., 2004). Cluster analysis of microarray data showed that several genes encoding enzymes required for JA and ET synthesis were co-ordinately up-regulated at low levels by Myzus nicotianae feeding on Nicotiana attenuata (Heidel and Baldwin, 2004; Voelckel et al., 2004). Some, but not all, isozymes of lipoxygenase that are involved in JA synthesis were also induced by aphid feeding in tomato, Arabidopsis, and sorghum (Fidantsef et al., 1999; Moran and Thompson, 2001; Zhu-Salzman et al., 2004). Compared with chewing insects or artificial wounding, however, aphid feeding had a far weaker influence on genes encoding JA and ET biosynthetic enzymes (Fidantsef et al., 1999; Heidel and Baldwin, 2004). To date, few studies have directly quantified JA or ET production in response to PFIs. Argandoña et al. (2001) and Miller et al. (1994) demonstrated that greenbug infestation induces ET production in barley, but no significant changes in ET levels were observed in Arabidopsis in response to the green peach aphid (De Vos et al., 2005). Aphid infestation also did not modify JA levels in N. attenuata or Arabidopsis (Heidel and Baldwin, 2004; De Vos et al., 2005).

Several studies on plant–aphid interactions have utilized mutant plant lines deficient in JA or ET signalling to investigate the role of these pathways in compatible interactions with aphids (Table 2). The contribution of ET remains unclear, as Mewis et al. (2005) did not observe a significant effect of ET insensitivity (conferred by the etr1 mutation) on aphid population growth on Arabidopsis. Jasmonate-insensitive Arabidopsis mutants (coi1) showed a modest increase in aphid population growth, suggesting that JA-dependent responses can limit aphid performance on wild-type plants (Ellis et al., 2002; Mewis et al., 2005). By contrast, suppression of JA signalling in tomato had neutral or, in some cases, negative effects on aphid performance. The jasmonate-insensitive jai-1 mutant in tomato had no detectable effect on population growth of the potato aphid (Macrosiphum euporbiae) (FL Goggin, unpublished data). Furthermore, aphid survival and fecundity on tomato was dramatically reduced by the spr2 mutation (FL Goggin, unpublished data), which blocks the conversion of linoleic to linolenic acid and the subsequent synthesis of JA (Li et al., 2003). Aphid resistance in spr2 might be due to enhanced SA-dependent responses, or to the impact of modified fatty acid profiles on other biosynthetic pathways. Data from potato suggest that volatile aldehydes derived from linoleic acid and other oxylipins play a role in limiting aphid infestation (Vancanneyt et al., 2001).

Taken together, these results suggest that the roles of SA and JA in plant defence vary among plant species, and between compatible and incompatible interactions. Further work is also needed to explore the potential roles of other hormones, including auxin and gibberellins, in plant responses to PFIs (Park et al., 2005).

Oxidative stress responses to PFIs

PFIs induce enzymes involved in both generating and detoxifying reactive oxygen species

Components of aphid salivary secretions generate local and systemic production of reactive oxygen species (ROS) (reviewed in Tjallingii, 2006). In addition, plants respond to many forms of biotic stress by generating ROS that participate in defensive signalling and potentiate a hypersensitive response (HR) at the infection site (Lamb and Dixon, 1997). Induction of isolate-specific aphid resistance in certain plants has been correlated with localized cell death that resembles HR, although this phenomenon is not observed in all cases of innate aphid resistance (Belefant-Miller et al., 1994; Moran et al., 2002; Sauge et al., 2002; Martinez de Ilarduya et al., 2003; Klingler et al., 2005). In general, plants appear to strike a balance between generating ROS as a defensive mechanism and producing ROS-detoxifying enzymes to cope with their own oxidative damage (Zhu-Salzman et al., 2004). Systemic responses within the phloem appear to favour detoxification (Divol et al., 2005), but oxidative stress-related genes are not uniformly regulated in whole leaf tissues in response to PFIs. Within a single plant species, aphid feeding can induce expression of certain antioxidant enzymes while suppressing others. In Arabidopsis, for example, green peach aphid infestation up-regulated expression of one superoxide dismutase gene (Cu/ZnSOD or CSD1) but down-regulated another (FeSOD) (Moran et al., 2002). Greenbug infestation of susceptible sorghum induced transcripts of two glutathione-S-transferase genes, but down-regulated one isoform of catalase (Zhu-Salzman et al., 2004). Similarly, greenbug feeding on resistant sorghum induced the expression of peroxidase, glutathione-S-transferase, and quinone oxidoreductase genes, but both up- and down-regulated different catalase genes (Park et al., 2005).

PFI-induced changes in structure and metabolism

Do PFIs elicit plant cell wall remodelling?

A common feature among the transcript profiling studies was the identification of genes encoding proteins that alter cell wall structure. Genes encoding cell wall-modifying enzymes such as xyloglucan endotransglycosylase (XTH) and pectin methyl esterases were typically up-regulated in infested plants (Moran et al., 2002; Heidel and Baldwin, 2004; Voelckel et al., 2004; Divol et al., 2005; Qubbaj et al., 2005). XTH modifies hemicelluloses by removing and re-attaching oligosaccharides to strengthen cell walls, while pectin esterases can also contribute to cell wall stiffening by controlling both the assembly and disassembly of the pectin network (Willats et al., 2001). Divol et al. (2005) also found strong systemic up-regulation of genes encoding cellulose synthase and expansin in celery petiole phloem in response to green peach aphid feeding on the leaves. Modification of the cell wall structure could play a role in aphid resistance in plants. Genes encoding pectin methyl esterase and inositol phosphatase-like protein were specifically up-regulated in response to rosy apple aphid feeding on a resistant cultivar of apple (Qubbaj et al., 2005), and five cell wall-related genes (2 glucosyl transferases, caffeic acid O-methyl transferase, proline-rich protein, delta pyrroline-5-carboxylate dehydrogenase) were up-regulated in response to greenbug feeding on resistant sorghum (Park et al., 2005). These structural modifications are thought to deter PFI herbivory both locally and systemically by strengthening barriers to insect probing within the host tissues.

PFIs influence primary metabolism in their hosts

Even at low populations, PFIs can significantly reduce photosynthetic rates in their host plants (Macedo et al., 2003). Transcript profiling has revealed that PFI infestation down-regulates expression of photosynthesis-related genes, such as those required for Rubisco synthesis (Heidel and Baldwin, 2004; Zhu-Salzman et al., 2004; Voelckel et al., 2004; Qubbaj et al., 2005; Yuan et al., 2005). Similar responses that are induced across multiple insect feeding guilds could represent a shift in resource allocation from growth to defence (Heidel and Baldwin, 2004). PFIs also modify source–sink relationships and water relations within the plant, because they must extract large volumes of phloem sap to attain adequate nitrogen (Douglas, 2006). Sugar depletion at PFI feeding sites created localized metabolic sinks by inducing genes involved in carbon assimilation and mobilization (Moran and Thompson, 2001; Moran et al., 2002; Zhu-Salzman et al., 2004). Within the phloem of celery petioles, for example, green peach aphid feeding up-regulated genes implicated in remobilizing mannitol reserves (Divol et al., 2005). Mannitol remobilization might also represent a response to osmotic stress caused by aphid feeding. Aphid infestation reduced foliar water potential (Cabrera et al., 1994) and up-regulated genes encoding aquaporins, membrane-intrinsic proteins, and other transcripts associated with water stress (Zhu-Salzman et al., 2004; Divol et al., 2005). PFIs also modified nitrogen allocation in their hosts by competing with plant sinks and altering the amino acid composition of the phloem sap (Sandstrom et al., 2000; Heidel and Baldwin, 2004; Voelckel et al., 2004; Girousse et al., 2005). Unlike other herbivores, PFIs up-regulated genes involved in nitrogen assimilation (Heidel and Baldwin, 2004; Zhu-Salzman et al., 2004). In particular, aphids induced genes encoding enzymes required for synthesis of tryptophan and certain other amino acids (Moran et al., 2002; Zhang et al., 2004; Divol et al., 2005). These responses can benefit PFIs by enhancing the content of essential amino acids in the phloem sap (Sandstrom et al., 2000). Overall, further work is needed to determine if PFI-induced changes in carbon and nitrogen assimilation, allocation, and water balance contribute to host susceptibility, or are a means for the plant to compensate for insect damage.

PFIs modify secondary metabolite production

Many secondary metabolites have been implicated in plant defences against PFIs, including phenylpropanoids, terpenoids, alkaloids, hydroxamic acids, and glucosinolates. Transcript profiling studies suggest that PFI feeding modulates expression of enzymes required for secondary metabolite synthesis. In rice, for example, brown planthopper feeding down-regulated several genes involved in phenylpropanoid biosynthesis and up-regulated a gene required for sesquiterpene synthesis (Zhang et al., 2004; Cho et al., 2005). Functional genomic tools can be applied to investigate the impact of secondary metabolites on PFIs. Aharoni et al. (2003) dramatically increased terpenoid volatile production in Arabidopsis by transforming plants with a terpene synthase gene from strawberry and determined that transgenic plants significantly repelled the green peach aphid. For most secondary metabolites, however, further work is needed to identify key biosynthetic genes before we can manipulate their synthesis in a targeted manner or understand how PFIs modulate their accumulation. To date, the majority of molecular and genomic studies on the role of secondary metabolites in plant–aphid interactions have focused on glucosinolates.

Glucosinolates in brassicaceous plants

Glucosinolates (GSs) are sulphonated thioglycosides derived from methionine, tryptophan or phenylalanine. Upon cellular damage, these compounds are hydrolysed by myrosinases to form defensive products such as isothiocyanates, nitriles, and epithionitriles. Levy et al. (2005) recently demonstrated that green peach aphid feeding on Arabidopsis induced a modest increase in transcript abundance of IQD1, which encodes a calmodulin-binding protein that stimulates GS synthesis. Aphid host preference was reduced on a mutant line that overexpressed IQD1, whereas preference was enhanced on a loss-of-function mutant (Levy et al., 2005). High-performance liquid chromatography also revealed that feeding by the green peach aphid and the cabbage aphid enhanced the content of aliphatic GSs in Arabidopsis (Mewis et al., 2005). When aphid population growth was compared on hormone-signalling mutants that varied in GS content, numbers for both aphid species were negatively correlated with constitutive and induced GS levels (Mewis et al., 2005). These results suggest that GSs play a role in plant defences against both generalist and specialist aphid species. Further studies, however, are needed to understand the effects of specific GSs in plant–insect interactions. In Brassica species, certain GSs were negatively correlated with aphids' intrinsic rate of increase while others showed a positive correlation, and the impact of specific GSs varied between the green peach aphid and the cabbage aphid (Cole, 1997). This variability may be related to the fact that certain GSs act as feeding stimulants for the cabbage aphid and other insects that specialize on brassicaceous hosts (Gabrys and Tjallingi, 2002). Further genomic, proteomic, and metabolomic studies are also needed to determine how glucosinolates and their derivatives are regulated in response to herbivory. Pontoppidan et al. (2003) demonstrated that cabbage aphid feeding on Brassica napus up-regulated transcripts encoding a myrosinase-binding protein and a myrosinase-associated protein, but the impact on myrosinase activity, hydrolysis of glucosinolates, or aphid performance have yet to be determined.

Emerging areas of research

To achieve a detailed understanding of plant interactions with PFIs, it will ultimately be necessary to combine transcriptomic approaches with proteomic, metabolomic, and mutational analyses. While plant responses have been the focus of most transcriptomic studies, additional levels of complexity can also be analysed with genomic tools. Investigating changes that occur concurrently within the insect is essential to understand the basis of an effective plant defence. As an example of this approach, He and coworkers performed companion studies to examine gene expression profiles in the brown planthopper as well as in its host plant (Zhang et al., 2004; Yang et al., 2005; Yuan et al., 2005). In addition, the ecological context of plant interactions with PFIs must be considered. Bacterial endosymbionts as well as plant pathogens vectored by PFIs can influence plant responses to these insects (Douglas, 2006). Likewise, plant responses to PFIs can attract natural enemies or influence the success of competing herbivores and pathogens on a shared host plant (Mayer et al., 2002). These multitrophic interactions must be investigated in order to develop a realistic view of the complex biology that occurs under field conditions.

It will also be necessary to conduct future genomic studies in a manner that facilitates the comparison of data sets from different laboratories and different species. It is critical for studies to provide cross-validation of genomic data, as well as clear and detailed explanations of experimental design (Brazma et al., 2001). The data generated from these experiments should serve as a community resource, which requires a level of standardization and consistent access to the primary data (Sherlock and Ball, 2005). In short, the complexity and diversity of plant interactions with PFIs is beyond the scope of any one laboratory, but emerging bioinformatic approaches will make it possible to study these interactions in a far more integrative, multidisciplinary manner than ever before.

This project was supported by the National Research Initiative of the USDA Cooperative State Research, Education and Extension Service (CSREES), grant number 2005-03384. GAT was supported by an Independent Research Plan while working at the National Science Foundation. Any opinions, findings, and conclusions or recommendations contained in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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