Chlorophyll fluorescence imaging as tool for understanding the impact of fungal diseases on plant performance: a phenomics perspective

Chlorophyll fluorescence imaging as tool for understandingthe impact of fungal diseases on plant performance:a phenomics perspective

Julie D. ScholesA,B and Stephen A. RolfeA

ADepartment of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, UK.BCorresponding author. Email: [email protected]

This paper originates from a presentation at the 1st International Plant Phenomics Symposium,Canberra, Australia, April 2009.

Abstract. Chlorophyll fluorescence imaging is a non-invasive, non-destructive means with which to examine theimpact of fungal pathogens on the photosynthetic metabolism of host plants. As such, it has great potential for screeningpurposes in high-throughput phenomics environments. However, there is great diversity in the responses of plants todifferent plant-fungal pathogens and the choice of suitable experimental conditions and protocols and interpretation ofthe results requires both preliminary laboratory experiments and an understanding of the biology of the specific plant-pathogen interaction. In this review, we examine the interaction between biotrophic, hemi-biotrophic and necrotrophicfungal pathogens and their hosts to illustrate the extent to which chlorophyll fluorescence imaging can be used to detectthe presence of disease before the appearance of visible symptoms, distinguish between compatible and incompatiblefungal interactions, identify heterogeneity in photosynthetic performance within the infected leaf and provide insightsinto the underlying mechanisms. The limitations and challenges of using chlorophyll fluorescence imaging in highthroughput screens is discussed.

Additional keywords: biotroph, hemi-biotroph, necrotroph.

Introduction

The analysis of chlorophyll fluorescence quenching providesa sensitive, non-invasive method with which to probe theimpact of fungal pathogens on the photosynthetic metabolismof their hosts. With the development of chlorophyll fluorescenceimaging systems, this approach can be used readily in high-throughput ‘phenomics’ environments (Baker and Rosenqvist2004; Oxborough 2004a, 2004b; Baker 2008). However, toobtain robust and meaningful results in a high-throughputenvironment requires both a thorough understanding of theunderlying biology of the host-pathogen interaction and thedevelopment of suitable equipment and protocols.

Imaging systems are particularly appropriate for the study ofplant-pathogen interactions as the infection and effect on thehost is often highly heterogeneous. The impact of a fungalpathogen on its host will depend on many factors includingthe physiology of the host plant, the tissue that is infected andthe mode of nutrition of the pathogen (Fig. 1). At one extreme, anaggressive foliar necrotrophic pathogen, which produces cellwall degrading enzymes to digest host tissue, and in somecases phytotoxins, will have a rapid and extensive impact onphotosynthetic metabolism that will be readily discernibleusing imaging techniques. In contrast, a biotrophic pathogenthat obtains its nutrients from living tissue ‘reprograms’

metabolism to direct host nutrients to the fungal mycelium(Fig. 1). Such pathogens will have a much more subtle impacton photosynthetic metabolism which, in the early stages ofinfection, may be limited to small alterations in source/sinkrelationships (for reviews, see Biemelt and Sonnewald 2006;Berger et al. 2007a). As chlorophyll fluorescence analysisprobes the fundamental operation of the photosyntheticapparatus, even subtle alterations in photosynthetic functioncan, in theory, be detected, but remains technically challenging.

This review examines several plant–pathogen systems toillustrate the diversity of impacts that fungal pathogens haveon host physiology and identifies chlorophyll fluorescenceanalysis protocols that provide insight into these interactions.As the analysis of chlorophyll fluorescence quenching has beenwell reviewed (see Papageorgiou and Govindjee 2004; Baker2008) it is outlined only briefly below to introduce some of thekey concepts and parameters used in this manuscript. Manyother parameters can be calculated and a more extensivedescription of the terminology is described in (Maxwell andJohnson 2000; Baker 2008).

Chlorophyll fluorescence analysis

Incident light that is absorbed by the light harvestingcomplexes of the photosynthetic apparatus has three possible

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fates: it can be used to drive photosynthetic electron transportproducing chemical energy in the form of ATP and NADPH,it can be dissipated by non-radiative, non-photochemicalprocesses or it can be re-emitted as fluorescence. As theseprocesses compete with each other, changes in chlorophyllfluorescence yield can provide information on the operation ofthe photosynthetic apparatus. A wide range of fluorimeters(both imaging and non-imaging) are available to measurefluorescence yield. Some detect fluorescence arising fromincident illumination directly whilst others use a weak beam ofmodulated light (that does not significantly affect physiologicalprocesses) to measure fluorescence yield independently ofactinic irradiance. It is beyond the scope of this article todiscuss the principle and operation of fluorimeters but a detailedaccount can be found in Schreiber (2004) and Oxborough(2004a, 2004b). Figure 2 shows a typical fluorescence emissioncurve from a plant that has been placed in darkness and thenilluminated. In this example, chlorophyll fluorescence yieldhasbeen measured using a modulated beam.

After a period of darkness, photochemical and non-radiativeenergy dissipation processes have relaxed and fluorescence islow (Fo). If the plant is then exposed to a brief pulse of saturatinglight, a maximum fluorescence value is obtained (Fm) as theacceptor side of photosystem II becomes transiently reduced (or‘closed’). These two measurements can be used to calculate themaximal quantum yield parameter, Fv/Fm ((Fm – Fo)/Fm). Thisparameter is typically around 0.83 in healthy leaves butdecreases if the photosynthetic apparatus is damaged; e.g. byphotoinhibition (Björkman and Demmig 1987; Demmig andBjörkman 1987). The leaf is then illuminated with an actiniclight (typically 50–400mmolm–2 s–1) causing a rapid increasein fluorescence (F ) that then declines as photochemical and

non-radiative energy dissipation processes become activateduntil a steady-state is achieved (Fs

0) after several minutes ofillumination. Irradiance can then be increased stepwise. Thecontribution of photochemical and non-radiative processes tofluorescence quenching canbe resolved using pulses of saturatinglight – under these conditions the PSII reaction centres becometransiently closed with only non-radiative processes (non-photochemical fluorescence quenching, NPQ) contributingsignificantly to fluorescence quenching (Fm

0). NPQ iscalculated as (Fm –Fm

0)/Fm0 (also known asDFm/Fm

0) (Demmig-Adams et al. 1990). This parameter encompasses severalquenching processes: qE, energy dependent quenchingresulting from the formation of a transthylakoid pH gradient(typically the largest component); qI, photoinhibitoryquenching; and qT, state transition quenching. The operatingefficiency of PSII (FPSII) at steady-state is calculated as(Fm

0 – Fs0)/Fm

0 and can be used to estimate the photosyntheticelectron transfer rate (ETR) by multiplying FPSII� absorbedirradiance� 0.5 (Genty et al. 1989).

Considerable care must be taken when using these parametersto probe the underlying physiology of the leaf. The calculationof ETR requires a measure of the proportion of incident lightthat is absorbed by the leaf and the proportion of energyabsorbed by the two photosystems. For healthy leaves, anabsorption value of 84% is quite typical, but this value will bereduced markedly in chlorotic tissue commonly seen inplant–pathogen interactions. Light absorption can be estimatedby comparing the reflection of red and far-red light from the leafbut several assumptions are still required. Likewise, theassumption that 50% of the absorbed light is utilised by PSIIis reasonable in many healthy plants but may not be true instressed plants.

Fig. 1. Schematic representation of the modes of nutrition of biotrophic, hemi-biotrophic andnecrotrophic fungal plant pathogens and their effects on host metabolism.

Chlorophyll fluorescence imaging of fungal diseases Functional Plant Biology 881

In healthy leaves, there is often a linear relationship betweenthe steady-state ETR and the rate of CO2 assimilation (Baker2008). However, as this relationship is affected byphotorespiration, the presence of alternative electron sinks(e.g. changes in nitrate reductase activity or the Mehler reaction)or alterations in the generation and maintenance of thetransthylakoid pH gradient (e.g. resulting from changes inmembrane integrity or the presence of toxins) it can be influencedstrongly by biotic and abiotic factors. Therefore, it ishighly beneficial to compare ETR determined by chlorophyllfluorescence analysis with rates of CO2 assimilation determinedindependently via gas exchange. Alterations in the relationshipbetween steady-state ETR and the rate of CO2 assimilation canbe highly informative in pathogen-infected plants and can beinvestigated using an appropriate experimental protocol. Forexample, the relaxation kinetics of NPQ in darkness can be usedto measure qE and assess membrane integrity and themaintenance of the transthylakoid pH gradient (Quick andStitt 1989).

In a high-throughput phenomics environment, the easiestparameter to measure is FPSII under steady-state conditions.A fluorescence image is captured under actinic illuminationand then a second image under saturating light. Fv/Fm is alsorelatively easy to record although a period of dark adaptation isrequired. NPQ, however, requires a comparison of imagestaken several minutes apart and under different illumination

conditions. This complicates the acquisition process and thetime constraints limit throughput. In addition, any leafmovement between measurements must be avoided orcorrected for which may be particularly challenging withpathogen-infected leaves that are often highly heterogeneous.Similar challenges are faced when measuring the kinetics ofchlorophyll fluorescence and associated parameters duringphotosynthetic induction. However, there are circumstanceswhere effects that would be difficult to detect under steady-state conditions are much more apparent during photosyntheticinduction. For example, Meng et al. (2001) showed that changesin source–sink relationships can be detected by examining thekinetics of FPSII induction in dark-adapted leaves which maybe of particular relevance to the study of the early stages ofbiotrophic infections.

What can chlorophyll fluorescence analysis tellus about plant–fungal interactions?

As chlorophyll fluorescence image analysis providesquantitative, spatially-resolved information aboutphotosynthetic function it has the potential to address manyquestions relevant to plant-fungal interactions. These include:

* When can symptoms of disease first be detected? Are impactson host metabolism detectable before the appearance ofvisible symptoms?

Fv

Fm

′

Fs′

Fo′

Fq′

ΔFm

Fm

Fo

Fig. 2. Example of a protocol for chlorophyll fluorescence quenching analysis duringphotosynthetic induction and steady-state photosynthesis at different irradiances (usingmodulated fluorescence). A leaf is dark-adapted (black bar) and then exposed to a weakmeasuring beam (<1mmol photonsm–2 s–1) allowing measurement of the minimal level offluorescence (Fo). A pulse of saturation irradiance (>6000mmol photonsm–2 s–1 for <1s)allows the maximal level of chlorophyll fluorescence (Fm) to be determined. Variablefluorescence (Fv) = (Fm – Fo). Actinic light that drives photosynthesis is then switch on.Chlorophyll fluorescence rises briefly and is then quenched to a steady-state level (Fs

0) as

photochemical and non-photochemical processes are initiated. In order to determine thecontribution of photochemical and non-photochemical processes to the quench in chlorophyllfluorescence, saturating pulses of light are applied at intervals to allow calculation of FPSII andNPQ. Fm

0 is the maximal level of chlorophyll fluorescence under actinic irradiance; Fo0 is

the minimal level of fluorescence under actinic irradiance. Fq0 is the difference in fluorescence

between Fm0 and Fs

0. Following photosynthetic induction, actinic irradiance can be increasedin a stepwise manner to allow generation of a light saturation curve.

882 Functional Plant Biology J. D. Scholes and S. A. Rolfe

* How heterogeneous is photosynthesis in infected leaves? Isthe impact of the pathogen on the host restricted to areasinvaded by the fungal mycelium or are they more widespread?

* What are the mechanisms underlying changes in hostmetabolism? How do they relate to the mode of nutrition ofthe fungus (biotroph, hemi-biotroph, necrotroph), the virulenceof the strain or whether the interaction is compatible orincompatible.

* How do abiotic factors influence the severity of the disease andthe impact on photosynthesis?

* Can chlorophyll fluorescence analysis allow us to makepredictions about plant performance?

The use of chlorophyll fluorescence imaging to addressmany of these questions will be illustrated by consideringseveral case studies in detail including infection ofArabidopsis thaliana (L.) by Albugo candida (white blisterrust) (Chou et al. 2000); barley (Hordeum vulgare L.) leavesbyBlumeria graminis (powderymildew) (Swarbrick et al. 2006);tobacco (Nicotiana tabacum L.) leaves by Phytophthoranicotiana (Scharte et al. 2005), bean (Phaseolus vulgaris L.)leaves by Colletotricum lindemuthianum (Meyer et al. 2001)and wheat (Triticum aestivum L.) leaves by Mycosphaerellagraminicola.

How does the biotrophic pathogen Albugo candida(white blister rust) alter photosynthesis of its hostArabidopsis thaliana as the disease progresses?

Albugo candida (Pers.) Kuntze (Peronosporales,Oomycota) is abiotrophic pathogen that can cause severe damage to brassicasincluding agricultural crops (e.g. cabbage, cauliflower, rape(Brassica napus L.)) and weeds (e.g. shepherd’s purse(Capsella bursa-pastoris L. Medik)) and A. thaliana (Ellis andEllis 1997). Following infection of leaves by A. candida, themycelium spreads intercellularly with one or more smallspherical haustoria in each cell. Visible symptoms of thedisease include chlorosis on the upper leaf surface and theappearance of concentric rings of white blisters on the lowerleaf surface as sporulation begins. The metabolic symptoms ofthis disease are characteristic of biotrophic pathogens andinclude an increase in invertase activity, accumulation ofsoluble carbohydrates, downregulation of photosynthetic geneexpression and a decline in the rate of photosynthesis (Tanget al. 1996). As part of a larger study into the way in whichthis pathogen reprogrammed host metabolism Chou et al.(2000) used chlorophyll fluorescence imaging to examine howphotosynthesis was altered in the different regions of the infectedleaf (in areas directly associated with the fungal mycelium andin areas remote from the site of infection) and whethermetabolic symptoms of disease preceded the appearance ofvisible symptoms.

The chlorophyll fluorescence imaging protocol was similarto that illustrated in Fig. 2 and used the system described inRolfe and Scholes (1995). In addition, the rate of CO2

assimilation was measured simultaneously using Infra RedGas Analysis (IRGA). Leaves were dark-adapted for 5min,Fv/Fm determined and then exposed to low actinic irradiance(~90 umolm–2 s–1) for 15min to allow the leaf to go through

photosynthetic induction and reach steady-state. Irradiancewas then increased stepwise and photosynthesis was allowedto reach steady-state each time (after ~10min). Pulses ofsaturating light were given at 60 s intervals throughout theexperiment allowing FPSII and NPQ to be calculated (Chouet al. 2000).

In a high-throughput screen of the effect of fungal pathogenson photosynthesis, it would be highly advantageous to usethe parameter ETR (calculated from chlorophyll fluorescencemeasurements) to estimate CO2 assimilation although, asmentioned previously, the relationship between theseparameters may differ between healthy and infected plants.The validity of this approach can be tested by measuring gasexchange by IRGA and simultaneous chlorophyll fluorescenceimaging. Figure 3 shows the relationship betweenETR (averagedacross the whole leaf) and CO2 assimilation in healthy andA. candida-infected A. thaliana leaves at different irradiancesand stages of disease development. In this case, the samerelationship between ETR and CO2 assimilation was observedin both healthy and diseased leaves indicating that additionalelectron sinks had not been induced in the diseased comparedwith the healthy leaves even though rates of photosynthesiswere markedly reduced at later stages of infection; thus ETRcould be used as an estimate of CO2 assimilation in this host-pathogen system (Chou et al. 2000). The relationship betweenETR and CO2 assimilation was also similar in healthy and rust-infected oat (Avena sativa L.) leaves (Scholes and Rolfe1996) and powdery mildew-infected wheat leaves (Swarbricket al. 2006) but differed markedly between uninfectedand Cladosporium fulvum-infected tomato (Lycopersiconesculentum L.) leaves (J. Scholes and S. Rolfe, unpubl. data).

300

250

200

150

100

ET

R A

bs. i

rrad

ianc

e ×

ΦP

SII

(µm

ol m

–2 s

–1)

50

00 2 4

Assimilation rate (µmol m–2 s–1)

6 8 10 12

Fig. 3. The relationship between the rate of CO2 assimilation determinedby infrared gas analysis and FPSII� absorbed irradiance determined byanalysis of chlorophyll fluorescence images. The two measurements werelinearly related for healthy (O) and Albugo candida-infected (&) leaves.The solid line represents a linear regression (r2 = 0.926, gradient 20.4).From Chou et al. (2000).


In A. thaliana leaves infected with A. candida, visiblesymptoms of disease appeared ~8 days after inoculation (dai)of the leaves with a zoospore suspension and consisted of smallwhite pustules on the lower leaf surface. Symptoms were not

visible on the upper leaf surface at this stage. As the diseasedeveloped concentric rings of pustules appeared from 11 to14 dai (Fig. 4a) and chlorosis was visible on the upper leafsurface. In this host-pathogen system the visible symptoms

1.0

(a)

(c)

(b)

0.80.60.40.20.0

Fv/

Fm

ET

R(µ

mol

m–2

s–1

) 20

15

10

5

0

0 1.0 2.0

0

Distance along transect (cm)1.0 2.0


of disease correlated with the position of fungal myceliumwithinthe leaf (Chou et al. 2000). Images of FPSII and NPQ taken atsteady-state photosynthesis at a relatively high irradiance of380mmolm–2 s–1 revealed that photosynthesis washeterogeneous in the infected leaf (Fig. 4a); it declinedprogressively in the areas invaded by the fungal mycelium butthere was little effect in regions remote from the site of infectionuntil late during the disease lifecycle when photosynthesiswas also lower than in comparable regions of the healthy leaf.As photosynthesis declined in areas directly invaded by fungalmycelium, NPQ increased indicating that light energy thatwas not used for photosynthetic electron transport wasdissipated by non-photochemical processes (Fig. 4a). In thisbiotrophic host–pathogen interaction, chlorophyll fluorescenceparameters (including raw images of chlorophyll fluorescence(Fm) and calculated parameters Fv/Fm, FPSII and NPQ acquiredat different irradiances) did not reveal the presence of thepathogen before the appearance of visible symptoms. This wasalso true of oat leaves infected with the biotrophic fungusPuccinia coronata where the onset of visible symptomscorrelated with the first detectable changes in FPSII and NPQ.This is not always the case however and may depend uponthe specific plant pathogen interaction. Pre-symptomaticdetection of the necrotrophic fungal pathogen Cercosporabeticola in sugar beet (Beta vulgaris L.) leaves was possibleby imaging raw chlorophyll fluorescence (Chaerle et al.2007). In tobacco leaves infected with pepper mild mottletobamovirus, pre-symptomatic leaves revealed a characteristictime-varying alteration in NPQ (but not FPSII) when comparedwith uninfected leaves (Pérez-Bueno et al. 2006). Finally, inbean leaves infected with the biotrophic bacterialpathogen Pseudomonas syringae pv. Phaseolicola, images ofboth FPSII and NPQ revealed the presence of the pathogenbefore the onset of visible symptoms (Rodríguez-Morenoet al. 2008).

Chlorophyll fluorescence images captured duringphotosynthetic induction often reveal complex metabolicheterogeneity that is not always apparent at steady-statephotosynthesis as demonstrated by the A. thaliana–A. candidainteraction. In healthy A. thaliana leaves, the induction of FPSII

and NPQ, at an irradiance of 90mmolm–2 s–1, was relativelysimple; FPSII increased upon illumination reaching a maximumvalue after ~3min (Fig. 4b). This correlated closely with anincrease in transpiration as stomata opened (Chou et al. 2000).NPQ increased transiently and then declined as the Calvincycle was activated using ATP hence reducing thetransthylakoid pH gradient. In contrast, a more complex andheterogeneous pattern was observed in infected leaves. Of

particular interest was a ring of cells, 2–3mm wide, at theleading edge of the fungal colony where FPSII was inducedmore rapidly than in the control leaf (Fig. 4b). This rapidinduction of FPSII was not due to altered rates of stomatalconductance (Chou et al. 2000) but more likely reflected highmetabolic/sink activity in this area. Meng et al. (2001)demonstrated that metabolically active regions of leaveswhere carbon export is low and respiration high (i.e. in sinktissue) often exhibit more rapid induction of FPSII. Inaddition, images of NPQ and FPSII acquired at steady-stateand low irradiance (90mmolm–2 s–1) revealed that NPQwas lower in the infected region of the infected leaf whencompared with the control leaf even though values of FPSII

were similar in both tissue types (Fig. 4b). The lower ratioof NPQ :FPSII suggests a high demand for ATP (i.e. highmetabolic activity) resulting in a lower trans-thylakoid pHgradient and hence NPQ. Biochemical analyses of this regionof the infected leaf revealed high invertase activity and anaccumulation of soluble carbohydrates (Chou et al. 2000)supporting the conclusions drawn from the chlorophyllfluorescence analysis. Low values of NPQ relative to FPSII

have also observed in virus-infected tobacco leaves(Balachandran et al. 1994) and rust-infected oat leaves(Scholes and Rolfe 1996).

In conclusion, in this study chlorophyll fluorescence imagingshowed that A. candida caused a progressive decline inphotosynthesis as the disease developed which was largelyconfined to areas directly invaded by fungal mycelium andthat the appearance of visible symptoms coincided withalterations in photosynthetic metabolism. Images of FPSII andNPQ acquired during photosynthetic induction revealedadditional heterogeneity in photosynthesis in the infected leafthat was not apparent at steady-state photosynthesis andidentified regions of high and low metabolic activity. As wellas providing an insight into the mechanisms underlyingthe effects of A. candida on the photosynthetic metabolismof its host, the experimental protocol used allowed theidentification of the most informative parameters/protocolfor a high throughput screen. For example, calibration ofthe relationship between FPSII and CO2 assimilationdemonstrated that ETR could be used in a quantitativemanner to compare rates of photosynthesis in healthy anddiseased leaves. The maximum information about theeffects of this pathogen on photosynthesis would be obtainedby imaging chlorophyll fluorescence during photosyntheticinduction at low irradiance followed by an increase inirradiance to least 400mmolm–2 s–2 for acquisition of imagesat steady-state photosynthesis.

Fig. 4. (a) Images of photochemical (FPSII) and non-photochemical quenching (NPQ) for healthy and Albugo candida-infected leaves of Arabidopsisthaliana. Images ofFPSII and NPQwere calculated frommeasurements of chlorophyll fluorescencemade after photosynthesis had reached steady-state (1260 s)at an actinic irradiance of 380mmolm–2 s–1. First two columns: photographs of the lower and upper surfaces of control and infected leaves (8, 11 and 14 daysafter inoculation; dai) used for imaging. Second two columns: Images of FPSII and NPQ. The scales show the relationship between colour and the relevantchlorophyll fluorescence measurement. (b) Images of FPSII and NPQ at selected time points throughout the induction of photosynthesis for control andA. candida-infected leaves 11 dai. Times are indicated in seconds after the actinic light was first switched on. Induction was performed at low irradiance(93mmolm–2 s–1). The first two columns show images of FPSII and NPQ for a control leaf and the next two columns for an infected leaf. Figure adapted fromChou et al. (2000). (c) Images of dark-adapted Fv/Fm and steady-state electron transport rate (ETR) (at 400mmol photonsm–2 s–1) of healthy wheat leavesand leaves infected with Mycosphaerella graminicola during the necrotrophic phase of growth 17 dai. Inset figures show values of Fv/Fm and ETR extractedfrom a transect across control (black line) and infected (grey line) leaves.


How do hemi-biotrophic/necrotrophic pathogens alterphotosynthetic metabolism in their hosts?Many studies have investigated the mechanisms underlyingchanges in photosynthesis in response to biotrophic fungaldiseases but less is known about the hemi-biotrophs andnecrotrophs despite the fact that they are responsible for someof the most important diseases worldwide. Two recent studieshave utilised chlorophyll fluorescence imaging to examinehow the hemi-biotrophs Mycosphaerella graminicola andColletotrichum lindemuthianum alter photosynthesis indifferent areas of wheat and bean leaves, respectively, duringthe biotrophic and necrotrophic phases of the life cycle.These studies also illustrate how such knowledge is essentialfor assessing the loss in photosynthetic yield of infectedcrops from visible symptoms, as performed in epidemiologicalmodelling studies.

M. graminicola, the causal agent of Septoria leaf blotch, iseconomically significant in all wheat growing areas and mayresult in up to 40% loss of yield (Eyal 1999). The pathogenpenetrates the host via stoma and once inside the leaf hyphae

grows intercellularly in the apoplast. This asymptomatic,biotrophic phase of growth can last anywhere between2–6 weeks and is known as the latent period (Kema et al.1996). During the necrotrophic phase of growth the pathogenproduces cell wall degrading enzymes leading to the rapidappearance of necrotic lesions but, unlike many necrotrophicpathogens, M. graminicola does not appear to produce aphytotoxin (Palmer and Skinner 2002). In order to quantifychanges in photosynthesis in different areas of wheat leavesinfected with M. graminicola, we acquired chlorophyllfluorescence images during steady-state photosynthesis at anirradiance of 400mmolm–2 s–1 during the biotrophic (7 dai)and necrotrophic (17 dai) phases of infection. Whole leaf CO2

assimilation was measured using IRGA. During the biotrophicstage of growth before the appearance of visible symptoms,values of Fv/Fm, FPSII and NPQ were similar in infected andcontrol leaves (Figs 4c, 5) indicating that fungal growth duringthe latent period did not damage the photosynthetic apparatusor influence the photosynthetic capacity of the host cells eitherinside or outside the area of fungal infection. However, once

Fv/

Fm

/ΦP

SII/

NP

Q

(a)

(b)

CO

2 as

sim

ilatio

n(µ

mol

m–2

s–1

)

Fig. 5. The effect of Mycosphaerella graminicola on the rate of CO2 assimilation and thechlorophyll fluorescence parameters Fv/Fm, FPSII and NPQ in healthy wheat leaves andinfected with M. graminicola during (a) the biotrophic and (b) the necrotrophic phases ofthe life cycle.


the pathogen began to produce cell wall degrading enzymesnecrotic lesions developed rapidly and by 17 dai covered~20% of the leaf (Fig. 4c). Images of Fv/Fm and FPSII

revealed that both parameters declined dramatically within thefungal lesion (Fig. 4c) indicating severe damage to thephotosynthetic apparatus. However, this effect was highlylocalised to the lesions; in surrounding green areas of the leafphotosynthesis was actually higher than in a control leaf asrevealed by images of ETR (Fig. 4c). This stimulation ofphotosynthesis may have been due to a compensation for lossof green leaf area or to an increase in sink demand in the leaf andmay certainly explain why measurements of whole leaf CO2

assimilation showed little difference between control and infectedleaves at this time (Fig. 5). Under these experimental conditions,the decline in photosynthesis in necrotic lesions was balanced bythe enhanced rate of photosynthesis in surrounding areas of theleaf. Previous work on M. graminicola infections have alsoshown that the residual green leaf area in infected leaves iscapable of fixing CO2 at a higher rate than uninfected leaves(Zuckerman et al. 1997) but the precise effect on photosynthesisis likely to depend on the developmental stages of the lesions,and abiotic factors such as irradiance and nitrogen supply.

C. lindemuthianum causes anthracnose disease of beansand is characterised by the appearance of dark brown lesionsadjacent to the leaf veins. The vein necrosis is often followedby wilting and bleaching which begins at the leaf tip and thenspreads to the margins and finally to the centre of the leafblade (Meyer et al. 2001). The fungus is known to decreasephotosynthesis in infected leaves to a greater extent than can beexplained by a simple loss of green leaf area (Bassanezi et al.2002). Meyer et al. (2001) used chlorophyll fluorescenceimaging together with simultaneous measurements of CO2

assimilation under different CO2 concentrations to examinethe effect of the fungus on both stomatal conductance andphotosynthesis with high spatial resolution. Images ofphotosynthetic electron transport revealed that during thenecrotrophic phase of infection photosynthesis was mostseverely inhibited within the lesions but it was also reduced ingreen areas between lesions thus leading to a greater decline inCO2 assimilation than predicted from the loss of green leaf areaalone. By acquiring chlorophyll fluorescence images in air orunder photorespiratory or saturating CO2 concentrationsMeyer et al. (2001) were able to show that the decrease inphotosynthesis was due partly to stomatal inhibition as thenecrotic lesions disrupted water transport within the leaf,but also to metabolic inhibition at the level of ribulose1,5-bisphosphate regeneration.

In the studies described above, chlorophyll fluorescenceimaging allowed quantification of the effects ofM. graminicola and C. lindemuthianum on changes inphotosynthesis in different regions of the infected leaf andshed light on the mechanisms involved. In both cases, thechanges in photosynthesis were heterogeneous and not simplythe result of a loss of green leaf area in the necrotic lesions.In the M. graminicola–wheat interaction, the effect onphotosynthesis was less, and in the C. lindemuthianum–beaninteraction more, than predicted by the loss of green leafarea. Such information is invaluable for crop simulationmodels (e.g. Bastiaans 1991) that seek to predict crop losses

based on measurements of leaf damage (visible symptoms) andunderlying physiological processes; i.e. rates of photosynthesisand respiration. Meyer et al. (2001) demonstrated that the twodifferent types of anthracnose symptoms, necrosis and wiltingwith their different effects on photosynthesis have to be takeninto account differently in simulation models in order toaccurately predict the effect of the disease on photosynthesis.

Can chlorophyll fluorescence imaging differentiatebetween the effects of compatible and incompatibleinteractions on leaf photosynthetic metabolism?

Fungal pathogens are responsible for large losses in crop yieldeach year (Chrispeels and Sadava 2003) and plant breeders arein a continuous race to produce cultivars with improvedresistance. A great deal of time is invested in screeningdifferent genotypes and visually scoring plants forsusceptibility or resistance to different fungal pathogens. Plantbreeders are also interested in being able to identify and predictthe consequences of different types of resistance interactions.For example, potato (Solanum tuberosum L.) cultivars exhibitboth race-specific and non-race-specific resistances toPhytophthora infestans. These different types of resistancesresult in different epidemiological profiles. Thus, theirpresence singly or in combination in different genotypes, isoften inferred from the analysis of disease progress curvesobtained from field experiments in comparison to thoseobtained from standard reference cultivars (Andrivon et al.2006). Such experiments are very time consuming and a rapid,automated, high throughput screen, such as chlorophyllfluorescence imaging, that could distinguish betweencompatible and incompatible interactions and quantifysymptoms would be highly advantageous.

Swarbrick et al. (2006) examined whether, using chlorophyllfluorescence imaging, it was possible to (i) detect resistanceresponses in barley leaves inoculated with B. graminis beforevisual symptoms became apparent, and (ii) differentiatebetween a compatible interaction and between race-specificand broad spectrum resistance responses. Race-specificresistance in barley to B. graminis is controlled by alleles attheMla locus, e.g.Mla 12 (Shen et al. 2003).Mla 12 race-specificresistance is characterised initially by death of the penetratedepidermal cell and then by a cell death response in subtendingmesophyll cells (Fig. 6a (ii) inset). This leads to the appearanceof spreading necrotic lesions on the leaf that are clearly visibleby 5 dai (Fig. 6a (ii)). Broad spectrum resistance to all racesof B. graminis is controlled by recessive mutations in the Mlogene. In contrast to race-specific resistance, mlo-mediatedresistance is much more rapid and localised and ischaracterised by the formation of a cell wall apposition aroundthe infection peg which prevents further penetration of theepidermal cell (Fig. 6a (iii) inset). Although there is no celldeath response in the epidermal cell a very small number ofsubtending mesophyll cells undergo cell death at a laterstage (Shirasu and Schulze-Lefert 2000) giving rise totiny necrotic flecks that are barely visible to the naked eye(Fig. 6a (iii)).

Images of ETR at steady-state photosynthesis(200mmolm–2 s–1) 3 days after inoculation of near isogenic


(a)

(b)

(i) Susceptible (ii) Race-specific (iii) Broad-spectrum

Fig. 6. (a) Development of visible symptoms and fungal hyphae on barley leaves infected with Blumeria graminis during (i) a susceptibleinteraction, (ii) a race-specific resistant interaction (Mla12) and (iii) a broad-spectrum resistant interaction (mlo5), 3, 5 and 7 dai. Images of the extent offungalmyceliumon the leaf are for 7dai. Insetfigure in (ii) demonstratesUVautofluorescenceof an epidermal cell undergoinghypersensitive cell deathduring race-specific resistance, 36 h after inoculation and in (iii) shows UV autofluorescence of a cell wall apposition deposited beneath a fungalappressorium on a barley leaf undergoing a broad-spectrum resistance response, 24 h after inoculation. (b) Images of the rate of photosyntheticelectron transport (ETR) and non-photochemical quenching (NPQ) of a control leaf and B. graminis-challenged leaves 3 dai. Images were capturedduring steady-state photosynthesis at an irradiance of 200mmolm–2 s–1. Images are adapted from Swarbrick et al. (2006).


backcross lines of barley cultivar Ingrid (susceptible or exhibitingMla12 or mlo-mediated resistance) with the same race ofB. graminis are shown in Fig. 6b. In the susceptibleinteraction, ETR was reduced across the whole of the infectedleaf when compared with the control leaf despite the fact thatdiscrete fungal colonies developed on the leaf surface. In leavesundergoing broad spectrum, mlo-mediated, resistancephotosynthesis was reduced in small highly localised areas ofthe leaf coincident with cell death whilst inMla-based resistancea more extensive depression in photosynthesis was observedwhich was correlated with the spreading necrotic lesions.This study demonstrates that it is possible to visualise bothcompatible and incompatible interactions before visualsymptoms are apparent and to distinguish between thedifferent interactions based on the pattern and extent ofchanges in leaf photosynthesis (Fig. 6b). Chaerle et al. (2007)were also able to distinguish between a compatible and anincompatible interaction to C. beticola in sugar beet leavesbased on differential patterns of chlorophyll fluorescence.

An additional advantage of a chlorophyll fluorescencescreen is that it has the potential to provide information aboutthe effect of both susceptible and resistant reactions onphotosynthetic performance in a quantitative manner. Inrecent years, there has been growing interest in the potentialcosts of activating resistance pathways on crop yield (e.g. Brown2002; Walters and Boyle 2005). For example, Smedegaard-Petersen and Tolstrup (1985) found that infection of barleycultivars with incompatible races of B. graminis resulted in ayield reduction when compared with uninoculated controls.Although the mechanism underlying this yield depressionwas unclear, it was hypothesised that it was due to the ‘cost’or energy requirements associated with activating resistancepathways such as cell wall reinforcement, the production ofreactive oxygen species, phytoalexins and pathogenesis-relatedproteins. In the barley–B. graminis incompatible interactions(both race-specific and broad spectrum) ETR was mostseverely depressed in cells undergoing programmed cell deathbut it was also depressed in cells adjacent to the dying cells(Fig. 6b). In these cells, the induction of FPSII was very rapidsuggesting areas of high metabolic activity (Swarbrick et al.2006). In addition, the steady-state images of NPQ revealedlower values of NPQ (for a given rate of ETR) in these areaswhen compared with control values (Fig. 6b). Thesecharacteristics are consistent with an induction of sinkmetabolism and a greater metabolic demand for ATPassociated with the activation of defence pathways. Verysimilar results were obtained by Scharte et al. (2005) whoinvestigated changes in photosynthetic and carbon metabolismin tobacco leaves following an incompatible interaction withPhytophthora nicotianae. In this incompatible interaction,hypersensitive cell death occurred within hours of inoculationof the leaf with an incompatible race of the pathogen.Chlorophyll fluorescence imaging revealed a severe inhibitionof photosynthesis in areas where cell death occurred and asmaller depression in photosynthesis in adjacent cells.Induction of photosynthetic electron transport was also fasterin areas undergoing the resistance response which was visiblefrom 1 h following inoculation. In both studies, the inductionof sink metabolism and the activation of defence pathways in

the localised cells undergoing resistance reactions were verifiedby detailed microscopy and biochemical analyses (Scharte et al.2005; Swarbrick et al. 2006).

The extraction of quantitative information from images ofFPSII or ETR in different areas of leaves undergoing a rangeof resistance reactions has shown that photosynthesis isheterogeneous; it is severely inhibited in localised areas

100(a)

(b)

80

60

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20%

tota

l lea

f are

a oc

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edIn

tegr

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)

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af

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Fig. 7. (a) The percentage of total leaf area occupied and by differenttypes of disease symptoms and (b) Integrated ETR (nmol 10 cm–2 s–1) for asusceptible barley leaf or a leaf undergoing race-specific (Mla12) or broad-spectrum resistance (mlo) after infection with Blumeria graminis for 7 days.Photosynthesis was measured at 200mmolm2 s–1; values are means� s.e. ofsix leaves.


associated with cell death and is depressed to a lesser extent inadjacent cells where high metabolic activity is associated withthe generation of ATP to fuel the defence pathways. Althoughmany assumptions must be made, it is possible to usequantitative information obtained from chlorophyllfluorescence images to ‘scale up’ to estimate the relativeimpact of different types of defence responses on whole leafphotosynthesis. Swarbrick et al. (2006) used image analysissoftware to determine for a 10 cm2 barley leaf, the total areaoccupied by the centre of resistance lesions (dead cells), cellsimmediately adjacent to the lesion and the remainder of theleaf for the race-specific and broad spectrum resistancereactions (Fig. 7a). The mean value of FPSII (weighted forarea) for each cell type was then extracted from the imagesallowing an integrated, whole leaf ETR to be calculated(Fig. 7b). This analysis showed suggested that photosynthesiswas most severely depressed (30%) in susceptible leaveswhilst in leaves exhibiting either Mla or mlo-mediatedresistance photosynthesis was ~10% lower when comparedwith control leaves.

Summary and future perspectives

As chlorophyll fluorescence imaging provides a direct measureof primary plant function it is likely to play a central role inmany high-throughput phenomics facilities. In this review,several parameters and protocols have been described thatprovide information about the impacts of fungal pathogens onhost metabolism. Whilst these relatively simple protocols arealways likely to be informative, a more rigorous approachto selecting the best parameters has been proposed by Matoušet al. (2006) and Berger et al. (2007b) in which many alternativeparameters are calculated and those which discriminate betweenselected conditions identified by advanced statistical analysisof sequences of fluorescence images. In addition, kineticmeasurements can be made under fluctuating actinic irradiancesthat generate fluorescence transients that can improvediscrimination between infected and uninfected tissue (Matoušet al. 2006). Such strategies are likely to be useful in studyingplant-fungal interactions due to the diversity of responsesobserved in different pathosystems. It is clear that identificationof appropriate screening protocols and interpretation ofchlorophyll fluorescence parameters requires calibrationmeasurements for each plant–pathogen interaction to be madeunder tightly controlled laboratory conditions before larger scalescreensareattempted.

Chlorophyll fluorescence imaging, unlike IRGA techniques,is a non-contact measurement which greatly facilitates its use inhigh-throughput screening but it cannot provide a directmeasure of CO2 assimilation rates. As some plant–fungalinteractions lead to a deviation from the relationship betweenCO2 assimilation and ETR seen in healthy plants, othertechniques are required to fully interpret chlorophyllfluorescence parameters. These may include the use ofpreliminary laboratory studies in which gas exchange ismeasured by IRGA or the use of isotopes of CO2 to directlymeasure assimilation rates (although such assays aredestructive). Thermal imaging can also be used to measureleaf temperature and calculate transpiration rates due to

evaporative cooling (Chaerle et al. 2004, 2007; Leinonenet al. 2006). Similarly, correlating measurements ofphotosynthesis with growth analysis will provide a moreintegrated view of plant performance. However, whilst it isrelatively straightforward to measure the growth of aerialportions of the plant (generally sources of carbon), any growthanalysis must consider the impact of carbon sinks (e.g. the roots)that are often below ground and difficult to measure. Otherwavelengths of light (e.g. multispectral imaging) may alsoprovide additional information about plant responses to stress(e.g. Lenk et al. 2007).

One of the main considerations of phenomics is throughput.Current systems tend to rely on a single imaging stationthat is either moved to measure a plant (or group of plants) orthe plants are transported to a fixed station. This inherentlimitation to throughput may be overcome by the use ofmultiple, inexpensive imaging systems that can be used toimage many plants at the same time. Developments in cameratechnology have already led to massive reductions in costsbut the sophisticated lighting regimes are still expensive thuslimiting this strategy. However, as LED illumination systemscontinue to develop in sophistication and fall in cost, the useof multiple imaging systems in a single facility is likely tobecome widespread.

In the examples considered in this review, images havebeen taken of leaves that are either inherently orientedhorizontally or have been held horizontal to facilitatemeasurements. Whilst the leaves of some plants are naturallyoriented close to the horizontal (e.g. rosette plants such asArabidopsis or the leaves of many dicot species), manymonocot leaves are much more upright which makes imagingin their natural orientationmuchmore challenging. Also physicalconstraints will tend to limit access of imaging systems toolder leaves particularly if high resolution images are requiredto visualise early events or highly-localised responses to fungalinfection. Therefore adopting techniques developed formeasuring and modelling 3D plant architecture are essential ifmeasurements of photosynthesis made on a limited number ofleaves are to be applied to the whole plant and to incorporatemodels of irradiance and disease development within theplant canopy – the ultimate aim if overall plant performanceunder field conditions is to be estimated. This will also help todeal with diverse plant architectures so that investment inexpensive infrastructure can be used to measure diverseplant morphotypes without extensive reconfiguration of thefacility.

In this review, we have examined how chlorophyllfluorescence imaging can be used to study plant–pathogeninteractions. It is clear that chlorophyll fluorescence imagingcan provide a useful means with which to detect infection indiverse plant-pathogen interactions sometimes, but notalways, before visible symptoms appear. It providesquantitative information about the extent and nature ofthe impact of the pathogen on host metabolism and has thepotential to predict future plant productivity. Many of thechallenges which remain in implementing this approach arecommon to many measurements of plant physiology in a high-throughput phenomics environment and are the subject ofintense research as described elsewhere in this issue.


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Manuscript received 9 June 2009, accepted 24 August 2009


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Chlorophyll fluorescence imaging as tool for understanding the impact of fungal diseases on plant performance: a phenomics perspective