Regulation of R‐gene Mediated Resistance via Light Dependent Pathways in Plants PLP 692 11‐8‐2010 Page | 1 Summery Recent research has shown evidence of extensive crosstalk between light dependent pathways and defense responses in plants. Of particular note are the recent observations that the expression of some R‐genes is regulated via photoreceptors in a light dependant manner [RD, J., et al. 2010, Wu & Yang 2010]. The researchers in this proposal set out to investigate the effects of light signaling on the expression of a wide range of R‐genes, and the potential impact this may have on the outcome of plant‐
pathogen interactions. By looking at a broad range of avr‐R‐gene interactions spanning multiple pathogen species in Arabidopsis thaliana, it is hoped that correlations may be drawn between the light dependent trends observed in expressions profiles and plant‐pathogen interactions. The researchers will make comparisons between different photoperiods, inoculation times, and photoreceptor mutants, to search for effects that light dependant pathways maybe having on R‐gene mediated resistance responses in plants. The over arching goal is to investigate the role of light dependant control mechanisms in modulating R‐protein mediated resistance, in the hopes of discovering common trends that will drive future studies. Introduction Light is the primary source of energy for all plant species. Plants utilize light via photosynthesis to fix carbon and form carbohydrates, which then serve as the main energy source for the all a plant’s cells. Since light is not constantly available to the plant under natural conditions, it makes sense that a plant cell would have to be able to control how and when its finite supply of energy is used. In fact this is what has been observed: when energy is plentiful, during daylight hours, plants turn on many metabolically costly processes that are later turned off at night, when energy is at a premium. Because of their reliance on light as an energy source, plants have developed a wide array of light dependent control mechanisms that govern how and when energy consuming processes are turned on. Plant defense machinery represents a heavy cost in terms of energy allocation, and is only needed when the plant is confronted by a pathogen [van Hulten, M., et al. 2006, MR, R., and ND, P. (2006), Heil 2002]. And in animals, for which light is not a direct source of energy, light has been shown to modulate immune responses [Lee & Ederyl 2008,Levi et. al. 2007]So it makes good sense that plants would rely on light to mediate the activity of their inducible defenses. Consistent with this line of reasoning, many parts of the plant immune response to infection have been shown to be dependent on the presence and quality of light. The accumulation of Salisylic Acid (SA) and the subsequent Hyper Sensitive (HS) response to avirulent Pseudomonas syringae pv tomato (DC3000 avrRpt2) as well as P. syringae pv maculicoa (ES4326 avrRpm1) was shown to be strongly dependent on the presence of light 6‐8 hours after infection of Arabidopsis thaliana [Griebel & Zeier 2008, Mateo et. al. 2004, Genoud et. al. 2002]. Additionally the development of Systemic Acquired Resistance (SAR) is completely suppressed in A. thaliana when the initial P. syringae infection takes place in the dark [Zeier et al 2004]. In contrast to this light dependency seen in the response to bacterial invaders, successful SA accumulation has been reported when plants are inoculated with turnip crinkle virus in the dark [Chandra‐Shekara, A.C., et al. 2006]. Other plant defense pathways, such as Jasmonic Page | 2 Acid (JA) production and camalexin biosynthesis, have been shown to be completely light independent [Zeier et al 2004]. These observations seem to indicate that the outcomes of plant‐pathogen interactions are in some instances modulated by light, and that this may vary in a pathogen dependent manner. The observed interaction between light and defense has initiated investigation into the underlying molecular mechanisms that may be responsible. Indeed cross talk between photosynthesis and defense pathways have been found. LSD1 is a protein that has been shown to negatively regulate Rpp2‐mediated HR in response to the redox state of the plastoquinone (PQ) pool, which is determined by the level of photosynthesis. It was also shown that LSD1 uses the same pathway to control programmed cell death in the presence of Excess Excitation Energy (EEE), which results from the overflow of photosynthetic energy and the subsequent formation of reactive oxygen species [Muhlebock et. al. 2008, Kaminaka et al. 2006]. The fact that this pathway is shared between both oxidative and biotic stress responses illustrates that defense pathways are relying on the same mechanisms that control light dependent processes. LSD1 is not the only example of such crosstalk, it was also recently shown that both intracellular hydrogen peroxide(produced via photorespiration) and functional SA biosynthesis are needed to induce HR in response to a biotic stress in A. thaliana , neither pathway can induce necrosis alone [Chaouch et. al. 2010]. This interdependency of the ROS and SA signaling shows another example of defense responses coupled to light dependant functions. These studies seem to indicate that in a plant cell, light is a necessity for the induction of programmed cell death in response to a pathogen invader. In addition to gating the induction of cell death, recent evidence also points to light modulating a plant’s sensitivity to certain pathogen invaders by controlling resistance (R)‐genes. The photoreceptors cryptochrome (CRY)2 and phototropin (PHOT) 2 have been shown to interact with an E3 ubiquitin ligase and suppress the degradation of the R‐protein HRT, which confers resistance to Turnip Crinkle Virus (TCV). In the absence of light HRT is degraded, and the plant becomes susceptible to TCV [RD, J., et al. 2010]. PHOT2 has also been shown to coimmuno precipitate with the R‐protein RPS2, perhaps pointing to a functional interaction [Qi & Katagiri 2009]. And the over expression of another photoreceptor, CRY1, has been shown to enhance avrRpt2 induced resistance in A. thaliana, through the increased expression of PR genes [Wu & Yang 2010]. Furthermore, plants with double mutations in phytochromes(PHY) A and B were unable to establish SAR[Griebel & Zeier 2008]. Though a ubiquitous control mechanism has not been shown between photoreceptors and R‐gene induction of defenses, it is clear that it is present in at least in some interactions, and the possibilities of other such mechanisms have not been thoroughly explored. Mechanisms to control the expression of R‐genes are of great importance to plants, due not only to the potential costs in terms of energy allocation, but also because of the potential toxicity of R‐
protein mediated defenses[Heil 2002]. For these reasons it is important that R‐proteins are present only when they are likely to be needed and can be utilized. As stated above, light is needed to mount a successful HR response to a pathogen, and so even if a plant is confronted by an invader, the absence of light may render an R‐protein a useless and dispensable commodity. It makes intrinsic sense that light, as integral component of a successful R‐gene mediated response, would act as a natural switch to turn Page | 3 costly R‐genes on only when they are useful, and it has recently been shown to do so in certain instances [RD, J., et al. 2010, Wu & Yang 2010]. With this in mind, we hypothesize that modulation of R‐
gene expression in a light dependant manner is a broadly conserved regulatory mechanism, that has effects on the outcome of plant‐pathogen interactions. Providing evidence of a widely conserved control mechanism for R‐gene expression would give valuable insight into how plants balance the costs and benefits associated with this type of resistance. This understanding could help to successfully design and implement R‐genes in crop plants, in a manner that would minimize costs to yield, while at the same time maximizing overall resistance to pathogens. At the same time it could also improve our overall understanding of how plant defenses fluctuate during the day, and perhaps help to develop a treatment strategy to apply when a plant’s immunity is at its weakest. In the end, this proposed study will certainly improve our understanding of the mechanisms that mediate plant defenses. Hypothesis and Objectives In view of the recent evidence of light’s direct effect on R‐gene expression as well as defense mechanisms in general, we hypothesis that modulation of R‐gene expression in a light dependant manner is a broadly conserved regulatory mechanism, that has effects on the outcome of plant‐
pathogen interactions. To confirm my hypothesis I have set forth several objectives to drive this study forward: 1) Assess the expression patterns of previously characterized plant R‐gene under a regular light cycle, at the transcript as well protein level, and determine whether they affect the outcome of plant‐pathogen interactions. 2) Assess the effects of differential photoperiods across a broad range of characterized R‐
protein mediated responses. 3) Assess the effects that photoreceptor and circadian rhythm deficient mutants have on a broad range of characterized R‐protein mediated responses Rational and Significance The development of crops that are resistant to pathogen infection has been a goal of plant breeders for nearly as long as crops have been domesticated. Recent years have seen large strides forward in technologies such as precision breeding and genetic modification, which has led many people in both industry and academia to explore these as routes for the development of durable resistance in plants. In order to realize the full potential of these technologies to improve crop resistance, it is vital to develop a clear understanding of the molecular mechanisms that govern a plant’s response to a pathogen invasion. Understanding how R‐genes as well as basal defense pathways are regulated will allow us to design resistance pathways in a way that maximizes benefits and minimizes costs, in the same manner that we see them occurring naturally within plants [MR, R., and ND, P. 2006]. An Page | 4 accumulating body of evidence points to light acting as an integral regulator of plant defenses, and of particular note is the observation that some R‐proteins are directly regulated by photoreceptors in a light dependent manner [Roden & Ingle 2009, RD, J., et al. 2010, Wu & Yang 2010]. If we can understand how light is mediating these expressional changes, we may be better able to successfully transfer those defenses to other systems where such control mechanism are not endogenously present. Additionally, an understanding of the natural cycles of plant defenses, which occur in response to light, could help to develop pest management strategies that complement these natural cycles. If nothing else, the studies we propose should lead to a more complete understanding of the complex mechanisms governing R‐gene mediated resistance in plants. Experimental Approach Objective 1: Assess the expression patterns of previously characterized plant R‐gene under a regular light cycle, at the transcript as well protein level, and determine whether they affect the outcome of plant‐pathogen interactions. If light regulation is a broadly conserved mechanism for regulating R‐gene expression, we would predict that there are variations in transcript and/or protein levels over the course of an regular light cycle (12h light/12h dark). As mentioned in the introduction this type of variation has been previously reported in several R‐genes [Roden & Ingle 2009, Weyman 2006]. we plan to expand on previous observations to include 14 previously characterized R‐genes in A. thaliana (Table 1), and determine if such temporal expression changes are wide spread and consistent amongst the R‐genes in A. thaliana. To do this we will grow A. thaliana in growth chambers set to an regular light cycle (12h light/12h dark). We will then sample both mRNA and Protein from leaves every 4 hours, over a 24 hour period. We will quantify mRNA levels using Real Time RT‐PCR, and protein levels for each time point will be assessed using western blots in combination with R‐protein specific antibodies. By reanalyzing and confirming previously reported R‐gene expression changes in parallel with those that haven’t yet been investigated, we will be able to draw conclusions about a broad spectrum of different R‐genes. If we observe a consistent expression pattern amongst the 14 R‐genes, we can conclude that they most likely share a common control mechanism. On the other hand, should we observe differences between the expression levels; we will be able to conclude that different R‐genes are unlikely to share a common control mechanism. At the same time we may be able to observe correlations between different groups of R‐genes that will allow us to form hypothesize for future investigations. Should we observe changes in R‐gene expression over the course of a day, we will go on to assess whether these differences result in changes in plant‐pathogen interaction. To do this, we will grow plants under the regular light cycle, and inoculate them with the avr‐gene containing pathogens at different times during the observed R‐gene expression cycle. The plant response as well as the spread of the pathogen will be measured at 24, 48, and 72 hours after infection. If the fluctuations in R‐gene expression are functionally relevant, then we would expect to observe changes in the plant response to infection depending on the time of day it was inoculated. So if the R‐gene expression is low at the time of inoculation, then we would expect the resulting plant response to be low, and the pathogen to show a relatively high rate of infection, and vice versa. This type of correlation will lend support to the idea that natural fluctuation in R‐gene levels result in fluctuation in resistance levels in the plant. Conversely Page | 5 if the R‐gene fluctuations don’t correlate with a change in plant resistance, than we would infer that other defense pathways may be involved. In either case it will be necessary to change the expression patterns of the R‐gene to conclusively draw a causal relationship with plant resistance response, which depending on time and money availability may prove to be outside the scope of this study. Nevertheless a strong correlation seen between fluctuations in level of multiple R‐genes and there corresponding response to pathogens would be compelling evidence, and would open the door for future studies. Objective 2: Assess the effects of differential photoperiods across a broad range of characterized R‐
protein mediated responses. It has been previously put forth that changes in photoperiod could affect the outcome of a plant‐pathogen interaction [RD, J., et al. 2010]. If this light mediated modulation of plant defense takes place at the level of R‐gene expression, we would expect to see shifts in the expression patterns of R‐genes as well as different magnitudes of defense responses between plants exposed to different photoperiods. To test this, A. thaliana plants will be grown in regular light cycle (12h light/?h dark) conditions, and will then be switched to one of four different photoperiods; short day (SD) (6h light/18h dark), long day (LD) (18h light/6h dark), constant light (CL) (24h light/0h dark), or no light (NL) (0h light/24h dark). To observe the R‐gene expression changes, we will sample both mRNA and Protein from leaves every 4 hours, over a 24 hour period. We will quantify mRNA levels using Real Time RT‐PCR, and protein levels for each time point will be assessed using western blots in combination with R‐protein specific antibodies. To assess the effects that changes in photoperiod have on plant‐
pathogen interactions, we will also use the avirulent pathogens to elicit a HR response in the R‐gene containing plants exposed to the four photoperiods (Table 1). We will quantify the level of infection as well as the plant response by looking at the size of the lesions produced by the HR response, and the level of Pathogen growth on the leaves at 24, 48, and 72 hours post inoculation. If we observe no difference in the spread of the pathogen or the plant response to infection between treatments, we can conclude that photoperiods have no effect on the R‐gene mediated defense response. If we do see differences, we can draw conclusions about what photoperiods favor the plant versus the pathogen. By looking at the relative expression of the R‐genes under different photoperiods, we will be able to see if they correlate with shifts in the plant‐pathogen interaction. Such a correlation would point to a causal relationship between R‐protein expression changes and fluctuations in the response to an invading pathogen. By looking at multiple avr‐R‐gene interactions, we can also draw conclusions about whether or not R‐genes react to changes in photoperiod in a consistent manner. For instance it is possible that certain R‐gene responses will fluctuate to different degrees, or not fluctuate at all in response to photoperiod shifts. Differences seen in responses could be attributed to multiple factors, including differences in the defense pathways activated, differences in the ways pathogens are affected by plant defenses, as well as potential differences in the mechanisms of each R‐gene’s response. As stated earlier, delving into the actual molecular mechanisms that underlie our observations will most likely surpass the time and money allotted for this study. But by making these comparisons we will draw attention to the effects that photoperiods have on a broad range of avr‐R‐gene interactions, and promote future studies into the causal mechanisms of these observations. Page | 6 Objective 3: Assess the effects that photoreceptor and circadian rhythm deficient mutants have on a broad range of characterized R‐protein mediated responses. Recent evidence points to a role of photoreceptors exerting control over R‐gene expression [Roden & Ingle 2009, RD, J., et al. 2010, Wu & Yang 2010]. At the same time the plant Circadian rhythm has been shown to be partially controlled by light cycles via certain photoreceptors [Hotta et. al. 2007]. And circadian rhythms have also been shown to play a role in the immune responses of animals [Lee & Ederyl 2008,Levi et. al. 2007]. If control of R‐
gene expression via photoreceptors and/or circadian rhythms is a broadly conserved control mechanism in Plants, then we would expect to see differential expression of R‐genes, as well as differences in defense responses in plants with mutations in photoreceptors or circadian regulation. To explore these possibilities, we will look at defense responses and R‐gene expression in several mutants. The mutants will include CRY1/2 knock outs, PHOT1/2 knock outs, PHYA/B knockouts, as well as LUX knock outs, which are arrhythmic in their circadian cycles [Hotta et. al. 2007]. Mutant plants will be grown under CL conditions and inoculated with pathogens containing the avr‐genes known to induce an incompatible interaction under normal conditions. We will quantify the level of infection as well as the plant response by looking at the size of the lesions produced by the HR response, and the level of Pathogen growth on the leaves. Both mRNA and Protein from leaves will also be sampled every 4 hours, over a 24 hour period, in both inoculated and noninoculated plants. We will quantify mRNA levels using Real Time RT‐
PCR, and protein levels for each time point will be assessed using western blots in combination with R‐
protein specific antibodies. If we see differences in R‐gene expression levels between mutant and wild type plants, we can conclude that the mutated photoreceptor circadian rhythm protein is likely involved in controlling the temporal expression patterns of the R‐gene. At the same time, by looking at the level of pathogen infection in these mutant plants compared to wild type, we can determine whether or not disruptions in R‐gene expression result in concordant changes in pathogen infectivity and plant responses. If we observe these type of correlations we can conclude that R‐gene expression changes, influenced by mutations in light perception pathways, affect the outcomes of plant‐pathogen interactions. Since we have interrogated the same sets of avr‐R‐gene pairs throughout all of the experiments in this study, it is possible that we may be able to use data gained in the first two objectives to help draw more extensive conclusions. Should the same avr‐R‐gene pair show light modulation in either the photoperiod or the time of day inoculation experiments (see objectives 2 and 1 respectively) as well as objective 3 experiments, we can postulate that these mutated proteins are what initiated the shifts in R‐
gene expression in the first two experiments. In this way we may be able to guide the direction of future studies into the plant mechanisms responsible for the observed R‐gene expression changes. Difficulties and limitations: Limitations on time, money, and understanding of the pathosystems utilized in this study lead to several inherent difficulties in conducting our experiments. One problem of particular note involves comparing the results of experiments conducted using different accessions of A. thaliana and different pathogens. Because R‐genes may not all be present in the same genetic background, there may be natural differences between accessions that could result in differences in the resistance response that are not due to R‐gene levels. This is something that must be kept in mind when trying to quantify and Page | 7 draw conclusions about a plant’s response to a pathogen. For instance, it is possible that one avr‐R‐gene pair produces a HR that is naturally less vigorous or less apparent than another interacting pair. This could result in pathogenesis shifts that are less apparent in one interaction compared to another. Conversely the same problem occurs when trying to compare the changes in infectivity of different pathogens. One pathogens response to plant defenses may be very different from that of another, which could lead to difficulties in trying to quantify the effects of shifts in R‐gene expression. To try and overcome this problem and make meaningful comparison we will be limited to looking at trends in pathogenisicity, and try to avoid comparing the amount of infection change between pathogens as well as between accessions. Another limitation that arises is the lack of data on individual avr‐R‐gene pairs. Because we are looking at a large number of R‐genes, instead of just concentrating on one or two, we are limited in so for as how thoroughly we can look at each individual interaction and its underlying mechanisms. By looking at a large number of R‐genes, we hope to gather data on how widely conserved light based control mechanisms maybe. If our study shows a wide distribution of such mechanisms, we hope that future studies will be undertaken to elucidate the finer details. Finally, it should be noted again that the scope of this study has limits, and our experiments fall far short of addressing all the factors that could affect the outcome of avr‐R‐gene interactions. Factors that are worth mentioning include, but are not limited to, plant age, temperature, light intensity, and humidity. All of which could prove to be variables worthy of future study. R‐gene Effector/avr‐gene Pathogen HRT Coat Protein Turnip Crinkle Virus RPM1 AvrRpm1, AvrB P. syringae RPS2 AvrRpt2 P. syringae RPS5 AvrPphB P. syringae RPS4 AvrRps4 P. syringae RPP8 NK P. parasitica RPP13 NK P. parasitica RPP1 NK P. parasitica RPP4 NK P. parasitica RPP5 NK P. parasitica RPW8 NK Erisyphe chicoracearum RRS1‐R NK Ralstonia solanacearum RTM1 NK Tobacco Etch Virus RTM2 NK Tobacco Etch Virus Table 1. 14 resistance proteins present in A. thaliana and there corresponding avr‐proteins from various pathogens. ‘NK’ stands for Not Known. Table contents taken from Martin et al. 2003 Future Directions Page | 8 Future Directions Though our proposed study would be the broadest conducted to date, it is by no means exhaustive. Depending on the final results of our experiments, there are several avenues of study that may prove to be of interest. One study of particular interest could look at how light mediates R‐gene expression in different plant species. A. thaliana is an easy and convenient model to observe fluctuations in R‐genes, but in order for these results to be applicable, they must first of course be translated to crop plants. Yet there are still plenty of other questions that could still utilize A. thaliana as a model system. Investigations into the effects of light intensity on R‐gene mediated resistance have not been undertaken, but could prove meaningful in view of the fact that light intensity is not constant under natural conditions. Limits on time and money may prevent us from looking in depth at the molecular interactions that cause fluctuations in R‐gene expression, which are of great importance. To later expand our investigation to include all known R‐genes in A. thaliana could allow us to draw more concrete conclusions about R‐genes that share a common expression profile or control mechanism. In any case, the results from this study will certainly suggest routes for new inquiries into the nature of R‐
gene mediated resistance in plants. TimeLine Year 1: Objective 1 experiments involving natural variations in R‐gene expression as well as effects of inoculation time on plant‐pathogen interactions. Year2: Objective 2 experiments involving the effects of photoperiod variations on R‐gene expression and plant‐pathogen interactions. And crosses of Objective 3 mutant plants into appropriate backgrounds will be undertaken. Year3:Objective 3 experiments involving effects of mutations in photoreceptors and circadian rhythm on R‐gene expression and plant‐pathogen interactions. Page | 9 Budget justification Total budget: $497,078 Salaries: Total: $274,046 (Principal investigator) $4,000/month: Assure the timely completion as well as integrity of all work done. Guide the lab members through the methods and intellectual rigors of each experiment. Assist in writing papers as well as wet lab work when needed. Prop P. Doc, (Post Doctorial Associate) $3,125/month: Responsible for the majority of the wet lab work, and also the writing of published works. Additionally he will be there to help guide the Graduate Assistant through the process of learning methods, accumulating the data necessary for and writing publications his dissertation. Rando G. Student, (Graduate Assistant) $1,400/month: Will be put in charge of progressing several avr‐
R‐gene pairs through the experiments described in the three objectives. In addition he will also help the Post Doctorial Assistant with the labor intensive processes involved in other experiments. In doing so he should gain knowledge of laboratory methods and significantly contribute to the work load of the lab. Materials and supplies: Total: $61,818 PCR supplies(~ $15,000): Includes the cost of consumables such as reagents, tips, and plates used in each RT‐PCR run. Western blot supplies (~ $25,000): Includes the cost of consumables such as gels, blots, and imaging as well as most significantly the R‐gene specific antibodies utilized in the approaches to all three objectives. Plant culture(~ $5,000): Includes thecost of growth chambers as well as consumables such as soil, water, and containers. Pathogen culture(~ $16,818): Includes the cost of appropriate licensing as well as consumable costs of growth media, sterile transfer, containment, and infiltration supplies. Indirect costs: Total: $161,215 Page | 10 References CH, J. (2004). Precise circadian clocks in prokaryotic cyanobacteria. In: CURRENT ISSUES IN MOLECULAR BIOLOGY: HORIZON SCIENTIFIC PRESS, PO BOX 1, NORFOLK, WYMONDHAM NR18 0JA, ENGLAND. 103‐110. Chandra‐Shekara, A.C., et al. (2006). Light‐dependent hypersensitive response and resistance signaling against Turnip Crinkle Virus in Arabidopsis. Plant Journal 45:320‐334. Chaouch, S., et al. (2010). Peroxisomal Hydrogen Peroxide Is Coupled to Biotic Defense Responses by ISOCHORISMATE SYNTHASE1 in a Daylength‐Related Manner. Plant Physiology 153:1692‐1705. Genoud, T., Buchala, A.J., Chua, N.H., and Metraux, J.P. (2002). Phytochrome signalling modulates the SA‐perceptive pathway in Arabidopsis. Plant Journal 31:87‐95. Griebel, T., and Zeier, J. (2008). Light regulation and daytime dependency of inducible plant defenses in arabidopsis: Phytochrome signaling controls systemic acquired resistance rather than local defense. Plant Physiology 147:790‐801. Heil, M. (2002). Ecological costs of induced resistance. Current Opinion in Plant Biology 5:345‐350. Hotta, C.T., et al. (2007). Modulation of environmental responses of plants by circadian clocks. Plant Cell and Environment 30:333‐349. Kaminaka, H., et al. (2006). bZIP10‐LSD1 antagonism modulates basal defense and cell death in Arabidopsis following infection. Embo Journal 25:4400‐4411. Lee, J.E., and Ederyl, I. (2008). Circadian regulation in the ability of Drosophila to combat pathogenic infections. Current Biology 18:195‐199. Levi, F., Filipski, E., Iurisci, I., Li, X.M., and Innominato, P. (2007). Cross‐talks between circadian timing system and cell division cycle determine cancer biology and therapeutics. Cold Spring Harbor Symposia on Quantitative Biology 72:465‐475. Martin, G.B., Bogdanove, A.J., and Sessa, G. (2003). Understanding the functions of plant disease resistance proteins. Annual Review of Plant Biology 54:23‐61. Mateo, A., Funck, D., Muhlenbock, P., Kular, B., Mullineaux, P.M., and Karpinski, S. (2006). Controlled levels of salicylic acid are required for optimal photosynthesis and redox homeostasis. Journal of Experimental Botany 57:1795‐1807. Mateo, A., et al. (2004). Lesion simulating disease 1 ‐ Is required for acclimation to conditions that promote excess excitation energy. Plant Physiology 136:2818‐2830. MR, R., and ND, P. (2006). Seduced by the dark side: integrating molecular and ecological perspectives on the inflence of light on plant defence against pests and pathogens. In: NEW PHYTOLOGIST: BLACKWELL PUBLISHING, 9600 GARSINGTON RD, OXFORD OX4 2DQ, OXON, ENGLAND. 677‐699. Muhlenbock, P., et al. (2008). Chloroplast Signaling and LESION SIMULATING DISEASE1 Regulate Crosstalk between Light Acclimation and Immunity in Arabidopsis. (vol 20, pg 2339, 2008). Plant Cell 20:3480‐3480. Qi, Y.P., and Katagiri, F. (2009). Purification of low‐abundance Arabidopsis plasma‐membrane protein complexes and identification of candidate components. Plant Journal 57:932‐944. RD, J., et al. (2010). Cryptochrome 2 and phototropin 2 regulate resistance protein‐mediated viral defense by negatively regulating an E3 ubiquitin ligase. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 107:13538‐13543. Roden, L.C., and Ingle, R.A. (2009). Lights, Rhythms, Infection: The Role of Light and the Circadian Clock in Determining the Outcome of Plant‐Pathogen Interactions. Plant Cell 21:2546‐2552. van Hulten, M., Pelser, M., van Loon, L.C., Pieterse, C.M.J., and Ton, J. (2006). Costs and benefits of priming for defense in Arabidopsis. Proceedings of the National Academy of Sciences of the United States of America 103:5602‐5607. Page | 11 Weyman, P.D., Pan, Z.Q., Feng, Q., Gilchrist, D.G., and Bostock, R.M. (2006). A circadian rhythm‐
regulated tomato gene is induced by arachidonic acid and Phythophthora infestans infection. Plant Physiology 140:235‐248. Wu, L., and Yang, H.Q. (2010). CRYPTOCHROME 1 Is Implicated in Promoting R Protein‐Mediated Plant Resistance to Pseudomonas syringae in Arabidopsis. Molecular Plant 3:539‐548. Zeier, J., Pink, B., Mueller, M.J., and Berger, S. (2004). Light conditions influence specific defence responses in incompatible plant‐pathogen interactions: uncoupling systemic resistance from salicylic acid and PR‐1 accumulation. Planta 219:673‐683. Plant Resistance to Pseudomonas syringae in Arabidopsis. Molecular Plant 3:539‐548. Page | 12 Worksheet for Project Budget - Cumulative Budget
Year 1
Year 1
Year 2
Year 2
Year 3
Year 3
Year 4
Year 4
Year 5
Year 5
Total
Total
Total
Cost of Project (all
sources)
Funds Requested
Cost-Sharing /
Matching Funds
Funds Requested
Cost-Sharing /
Matching Funds
Funds Requested
Cost-Sharing /
Matching Funds
Funds Requested
Cost-Sharing /
Matching Funds
Funds Requested
Cost-Sharing /
Matching Funds
Funds Requested
Cost-Sharing /
Matching Funds
A
Senior/Key Personnel
$61,920
$0
$49,440
$0
$50,923
$0
$0
$0
$0
$0
$162,283
$0
$162,283
B
Other Personnel
$64,109
$0
$23,474
$0
$24,179
$0
$0
$0
$0
$0
$111,762
$0
$111,762
Total Number other Personnel
2.00
Total Salary, Wages and Fringe Benefits
2.00
2.00
0.00
0.00
$126,029
$0
$72,914
$0
$75,102
$0
$0
$0
$0
$0
$274,046
$0
$274,046
C
Equipment
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
D
Travel
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
Domestic
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
Foreign
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
E
Participant/Trainee Support Costs
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
1
Tuition/Fees/Health Insurance
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
2
Stipends
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
3
Travel
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
4
Subsistence
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
5
Other
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
6
Number of Participants/Trainees
F
Other Direct Costs
$20,000
$0
$20,600
$0
$21,218
$0
$0
$0
$0
$0
$61,818
$0
$61,818
1
Materials and Supplies
$20,000
$0
$20,600
$0
$21,218
$0
$0
$0
$0
$0
$61,818
$0
$61,818
2
Publication Costs
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
3
Consultant Services
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
4
ADP/Computer Services
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
5
Subawards/Consortium/Contractual Costs
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
6
Equipment or Facility Rental/User Fees
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
7
Alterations and Renovations
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
8
Graduate Student Tuition
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
9
other
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
10
other
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$146,029
$0
$93,514
$0
$96,320
$0
$0
$0
$0
$0
$335,864
$0
$335,864
$70,094
$0
$44,887
$0
$46,234
$0
$0
$0
$0
$0
$161,215
$0
$161,215
$216,124
$0
$138,401
$0
$142,553
$0
$0
$0
$0
$0
$497,078
$0
$497,078
Percent
Requested
Percent CostShared
100.00%
0.00%
G
Direct Costs
H
Indirect Costs
I
Total Direct and Indirect Costs
J
Fee
0.00
Rate
48.00%
Base Amount > >
$
146,029
0.00
$
-
0.00
$
93,514
0.00
$
-
0.00
$
96,320
0.00
$
-
0.00
$
-
0.00
$
-
0.00
$
-
0.00
$
-
If this is an NIH Modular application, this is your modular
amount each year (must be in multiple of $25,000) >>
$146,029
$93,514
$96,320
$0
$0
$335,864
Salary
Fringe
$102,300
$23,729
$69,963
$2,952
$72,062
$3,040
$0
$0
$0
$0
$244,324
$29,721
100.00%