Explorations of DNA methylation and transgenerational effector-triggered immunity in Arabidopsis thaliana against its intimate parasitic oomycete,
Hyaloperonospora arabidopsidis
4
November 12, 2012
Project Summary
Plants have evolved their own immune systems consisting of two lines of defense, pathogen-associated molecular-pattern (PAMP)-triggered immunity (PTI) and effectortriggered immunity (ETI). PTI is a plant’s general defense against broad pathogens. Upon
PAMP recognition, PTI induces production of callose at the cell wall and antimicrobials.
Following local PTI events, signals diffuse to systemic tissues to prime PTI-signaling for future pathogen exposures; this is known as systemic acquired resistance (SAR).
Evidence suggests that priming of PTI-signaling induced by SAR is passed on to progeny making them more resistant to pathogens and is mediated by DNA methylation.
However, the epigenetic mechanisms and changes to the host methylome remain obscure.
Furthermore, it remains to be investigated whether ETI, the specialized line of defense, is transgenerational and whether DNA methylation is associated.
ETI effects a hypersensitive cell death response (HR) mediated by nucleotide binding-site leucine-rich repeat receptors (NBS-LRRs) that are encoded from resistance
( R ) genes. NBS-LRRs induce ETI upon recognition of cognate pathogen effectors, which are produced to suppress PTI and enhance virulence. ETI has evolved as a consequence of PTI-suppression induced by pathogen effectors. This specialized molecular battle is an idiosyncrasy of intimate obligate parasites that have co-evolved with their plant hosts.
Throughout evolutionary history, circuitous evolution has shaped effector and R gene repertoires of obligate parasites and their host plants.
Among the most highly studied pathosystems of obligate plant parasites is the interaction between Arabidopsis thaliana (Arabidopsis) and the oomycete downy mildew, Hyaloperonospora arabidopsidis ( Hpa ). Arabidopsis and Hpa have co-evolved and are continuously found together in nature. RPP13 is an Arabidopsis R gene essential for eliciting an effective ETI-response against Hpa upon recognition of the effector,
ATR13. Specific allelic forms found in different Arabidopsis accessions and Hpa isolates are required for both RPP13 and ATR13 in order to elicit an effective ETI-response. In our proposed research, we will employ the ArabidopsisHpa pathosystem as a model emphasizing the RPP13 -ATR13 allelic-specific interactions to investigate whether transgenerational priming of ETI exists by testing for transgenerational resistance phenotypes in Arabidopsis progeny following Hpa -infection and whether increased resistance is a consequence of primed ETI. Furthermore, we will investigate whether differential DNA methylation occurs at the RPP13 locus conditioning enhanced transcription mediating transgenerational priming of ETI. Our proposed research will contribute to the breadth of knowledge for plant resistance to obligate parasites and pathogens mediated by epigenetic mechanisms and present a novel discovery of transgenerational priming of ETI.

2
Introduction
Molecular plant-pathology is an ongoing and marketable area of research. In seek of a common objective, molecular plant-pathologists effort to advance biotechnology for producing disease resistant transgenic plants. Candidate genes are discovered and characterized for potential engineering of plant resistance. Typically, these genes are categorized according to their functions in discrete networks of the plant immune system.
With numerous connections and distinctions with animals, plants have evolved their own immune systems. As a general surveillance for pathogen-associated molecular patterns (PAMPs) such as peptidoglycans, lipopolysaccharides and bacterial flagellin, the plant immune system employs pattern-recognition receptor proteins (PRRs) (Jones and
Dangl 2006). This basal line of defense, also known as PAMP-triggered immunity (PTI), leads to downstream PRR signaling cascades resulting in callose deposition at the pathogen-host cell interface and production of antimicrobials for inhibiting the pathogen from breaching the cell wall (Jones and Dangl 2006). As a counterattack, plant-pathogens have evolved a supply of parasitism genes encoding secreted proteins termed effectors that suppress PTI in their cognate host plant species. Furthermore, an intimate hostpathogen relationship has lead to the evolution of a more specific line of plant defense, effector-triggered immunity (ETI). Mediating ETI are the rapidly evolving resistance ( R ) genes encoding for nucleotide binding site leucine-rich repeat receptor proteins (NBS-
LRRs) (Jones and Dangl 2006). Pathogen-secreted effectors are either directly or indirectly recognized by NBS-LRRs leading to the hypersensitive cell death response
(HR). In an effort to avoid HR, selection leads to the evolution of unrecognizable alleles of effectors or recruitment of new and jettison of old, recognized effectors increasing the pathogen’s fitness and thus making it more successful (Jones and Dangl 2006). The pathogen may also evolve effectors of novel function suppressing ETI or enhancing virulence through some other means (i.e., metabolic or nuclear reprogramming) (Jones and Dangl 2006). Lastly, the cognate host plant species may evolve new resistance genes alike the pathogen in recognition of newly evolved effectors for increasing its overall fitness and thus survival (Jones and Dangl 2006). This circuitous evolution between pathogen parasitism genes and host R genes has been recognized as a great model for studying the coevolution between two species (Jones and Dangl 2006).
Similar to the animal’s mobile, adaptive immune system, plants are able to signal to remote tissues after a localized infection leading to the activation or priming of resistance against future pathogen attacks, this is termed Systemic Acquired Resistance
(SAR) (Spoel and Dong 2012). Various signaling molecules have been identified such as methylsalicylic acid (MeSA), azelaic acid, and glycerol-3-phosphate (Spoel and Dong
2012). These SAR signals travel through the vasculature from infected to uninfected tissues leading the accumulation of salicylic acid (SA) and activation of basal defense conditioning antimicrobial activities induced by pathogenesis-related (PR) proteins
(Spoel and Dong 2012). This is a successful strategy for plants to cope with environments of high pathogen-pressure (Spoel and Dong 2012).
Evidence suggests that SAR leads to life-long protection against future pathogen attacks through priming of resistance signaling mechanisms (Spoel and Dong 2012).

3
Such priming includes the accumulation of the mitogen-activated protein kinases MPK3 and MPK6, which are maintained in an inactive state until additional pathogen exposure
(Beckers et al. 2009), and the transcription cofactor NONEXPRESSOR OF PR GENES 1
(NPR1), which has been shown to prime target genes for enhanced transcription (Mou,
Fan and Dong 2003). Involvement of DNA repair and homologous recombination has been proposed in the priming of NPR1 target genes associated with resistance signaling through promoter recruitment of RAD51 (Wang et al. 2010), BREAST CANCER
SUSCEPTIBILITY 2 (BRCA2) (Wang et al. 2010) and SUPPRESSOR OF SNI1 2
(SSN2), though the priming mechanism remains obscure (Spoel and Dong 2012).
Furthermore, epigenetic changes such as DNA methylation and histone modifications have been evidenced to assist the long-lasting immune memory in plants and have been shown through genetic analyses to persist for at least two generations after parental pathogen-exposure conditioning more resistant progeny (Luna et al. 2012). Differential
DNA methylation was suggested to be the signal persisting the chromosomal changes associated with the priming of NPR1 target gene expression for transgenerational resistance (Luna et al. 2012). Lastly, it has been suggested that epigenetic modifications leading to chromatin remodeling induced by pathogen exposure occur at highly clustered
R gene loci (Jaskiewicz, Conrath and Peterhansel 2011) resulting in destabilization and thus higher frequency of recombination leading to the formation of new resistance genes through duplication and unequal recombination potentially making future generations less susceptible to disease (Meyers et al. 2003, Baumgarten et al. 2003, Lucht et al.
2002).
Though differential DNA methylation is clearly an underlying mechanism for enhancing transgenerational basal immunity, there are many questions to be addressed regarding the changes that occur in plant methylomes during pathogen stress. Are only loci associated with resistance changing in their methylation patterns or does this occur at non-resistance loci as well? Do the changes in the methylome persist past the third generation when parental plants are not exposed to pathogen stress, or does the plant’s fitness deprivation prevent a permanent immune memory? Furthermore, it remains to be investigated whether ETI against obligate biotrophic pathogens, which are able to breach the host’s basal immune system and are thus more effective in overcoming SAR, is efficiently retained in future generations, if differential DNA methylation plays a role, and whether there is a correlation between DNA methylation density and the enhanced rate of evolution observed at R gene loci. Answering these questions would mark a significant advance not only for molecular plant pathology, but also for understanding the general epigenetic mechanisms in response to environmental stresses. For instance, understanding differential DNA methylation in response to biotic stress such as pathogen exposure will be insightful for understanding the forces at hand for coping with other biotic and abiotic stresses such as drought, flooding, temperature, mineral deficiency, etc.
In addition, epigenetics is a new and expanding field with publications in both biological and biomedical research. Hence, investigating these questions will also contribute to the breadth of knowledge for the field of epigenetics and likewise lead to new discoveries for animal and human diseases and disorders. Lastly, understanding the evolutionary forces at hand for establishing a more efficient immune system in plants, such as the generation of new R genes, will be insightful and possibly applicable for all immunological research

4 leading to more durable resistance for crops and generation of new vaccines and antimicrobial treatments for animal and human diseases.
Central Hypothesis : Differential DNA methylation facilitates the spawning of RPP13 epialleles mediating transgenerational priming of Arabidopsis thaliana effector-triggered immunity against Hyaloperonospora arabidopsidis
Objectives and Experimental Summaries
1.
Test for transgenerational resistance
Arabidopsis thaliana accessions Columbia (Col) and Niederzenze (Nd) plants will be infected with the obligate parasitic oomycete Hyaloperonospora arabidopsidis isolates Maks9 and Emoy2. Mock- and pathogen-inoculated plants will be allowed to set seed for establishing three generations of progeny. Plants from each mock- and pathogen-inoculated generation will be analyzed for resistance. These methods will be tested on plants derived from DNA methylation mutants and transgenic lines.
2.
Test for transgenerational priming of ETI
The allelic variants Maks9 and Emoy2 of the Hpa effector ATR13 will be delivered into leaves of P
0
and P
1
generations from both accessions of Arabidopsis including all genetic backgrounds with the use of the Effector Detector Vector (EDV) and the type-III secretion system (TTSS) of Pseudomonas syringae pv. tomato DC3000 ( Pst
DC3000). Scoring of ETI will consist of two separate experiments following procedures exploited by Fabro et al. 2011 and Kawai-Yamada et al. 2009. First, we will employ bacterial growth assays of Pst DC3000 expressing the operon of
Photorhabdus luminescens ( Pst -LUX) by measuring emitted bioluminescence.
Second, we will quantitatively test for HR with the weakly virulent Pst DC3000
ΔCEL (
Pst -ΔCEL) strain by quantifying electrolyte leakage measured over time in inoculated leaves.
3.
Map DNA methylation changes at the RPP13 locus and quantify resulting expression
Infected leaf genomic DNA (gDNA) will be isolated from all Arabidopsis specimens.
Bisulfite conversion will be carried out on gDNA with a method derived from
Frommer et al. 1992. RPP13 promoters and genic regions will be cloned and sequenced. Comparisons will be made between all Arabidopsis specimens for mapping changes in methylation status at the RPP13 locus. We will then carry out quantitative real-time PCR (qRT-PCR) to profile RRP13 expression levels for all
Arabidopsis specimens. Finally, we will compare DNA methylation status and potential changes in transcription occurring simultaneously at the RPP13 locus.
Rationale and Significance
Virulent strains or isolates of an intimately obligate parasitic species not only suppress PTI but also are effective in suppressing ETI in the corresponding strain or accession of host plant species. Two questions can be developed from these observations.

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First, when these intimately obligate parasites of minimal virulence or avirulence infect their host plants, are future generations derived from the infected hosts less susceptible and thus more resistant to infection? It could be that the optimum expression of R genes and thus NBS-LRRs is passed on to future generations through differential DNA methylation for spawning epialleles that are more primed for enhanced expression thus making descendant plants more resistant to the pathogen eliciting more efficient ETIresponses. Second, when a virulent, obligate parasite infects its susceptible host plant leading to severely deprived fitness, are future generations likely to gain a new form of resistance through generation of new R genes mediated by differential DNA methylation in the parent conditioning elevated recombination frequencies at R gene loci? In our proposed research we will thoroughly investigate the first question using the intimately obligate relationship evolved between the parasitic oomycete downy mildew,
Hyaloperonospora arabidopsidis ( Hpa ), and its host plant Arabidopsis thaliana
(Arabidopsis) as a model pathosystem. Focus will be on the Arabidopsis R gene RPP13 and cognate Hpa effector, ATR13, which have been shown to form an essential interaction for eliciting an effective ETI in Arabidopsis against Hpa (Leonelli et al.
2011).
Hpa was selected based on its observed abilities to abate host resistance and impair cell death mechanisms thus indicating its highly intimate relationship and coevolution with its host, which has a vast generation time enhancing our establishment of mock- and pathogen-inoculated descendant generations (Coates and Beynon 2010).
Furthermore, sequencing of the Arabidopsis (ecotype Col-0) methylome and associated transcriptome has been completed from wild type floral tissue, and DNA methylation mutants have been shown to perturb transgenerational resistance thus making this system ideal for our investigation (Lister et al. 2008, Luna et al. 2012). The second question will remain elusive until future studies investigate correlations between DNA methylation status and rates of evolution at R gene loci.
This study may result in a number of revelations for molecular plant pathology such as the epigenetic mechanisms underlying host plant survival against obligate parasites (i.e., Hpa ), the first DNA methylome data for an R gene locus (i.e., RPP13 ) in response to a specific pathogen stress, the epigenetic forces leading to the rapid evolution of R gene loci, and insights for the development of crops with broad-spectrum resistance requiring fewer chemicals to control disease. As a more broad revelation, discovery of the epigenetic and evolutionary forces underlying the host plant immune system may provide insights for animal and human immunological research and thus innovative approaches for constructing vaccines and antimicrobial treatments.
Experimental Approach
Test for transgenerational resistance
Our first objective is to test for transgenerational resistance against Hpa.
We hypothesize that parental exposure of Arabidopsis plants to Hpa infection makes offspring more resistant, but only in accessions that carry the R gene allele corresponding to the allele of the cognate effector that can be recognized from a specific Hpa isolate. In addition, trangenerational resistance observed between corresponding Arabidopsis accessions and Hpa isolates is lost in de novo DNA methylation mutants.

6
In a recent study of SAR, researchers found that when parental Arabidopsis Col-0 plants (P
0
) were infected with the generalist bacterium Pseudomonas syringae pv tomato
DC3000 ( Pst DC3000), which causes bacterial speck disease in a wide range of plant species, offspring (P
1
) were more resistant to both Pst DC3000 and Hpa when compared to uninfected, control offspring (C
1
) (fig. 1C) (Luna et al. 2012). This “priming” for SAR could be further detected in a third generation of offspring of infected (P
1
P
2
) and uninfected (P
1
C
2
) P
1
parents against both Pst DC3000 and Hpa when compared to uninfected, control offspring (C
1
C
2
) (fig. 1C). Furthermore, it was concluded that this priming was dependent on NPR1 as the null mutant npr1 inhibited transgenerational priming supporting the notion that SAR is the underlying phenomenon responsible for the observed resistance (fig. 1C).
Luna et al. 2012, Plant Physiology
Luna et al. went on to show that the apparent transgerational SAR was associated with priming of SA-dependent genes with cross-effects on JA-dependent resistance and that chromatin modifications were occurring at defense gene promoters. Their concluding investigation was whether or not DNA methylation plays a role in transgenerational SAR contributing to the observed changes in defense hormone signaling and chromatin modifications. Transgenerational resistance was compared between wild-type plants and the drm1drm2cmt3 triple mutant ( ddc ), which exhibits a reduction in non-CpG aka de novo DNA methylation. As a result, the ddc mutant lacked enhanced resistance in P
1 as

7 compared to C
1
but was more resistant as compared to C
1
wild-type lines (fig. 5A) revealing that relaxed DNA methylation in the ddc mutant mimics the transgenerational resistance phenotype of P
1
wild-type plants. Quantification of PR-1 gene induction levels compared between C
1
and P
1
plants of wild-type and ddc mutants showed enhanced PR-1 induction in ddc compared to wild-type, but no significant change was observed between
C
1
and P
1 ddc mutants (fig. 5B). These results were consistent with the mimicking of transgenerational priming of SA-dependent defense in the ddc mutant via relaxed DNA methylation. They concluded that the apparent hypomethylation was responsible for the transmission of transgenerational resistance from Pst DC3000-infected plants.
Luna et al. 2012, Plant Physiology
As a means for testing our hypothesis, we plan to utilize two approaches in parallel with that from the above study (Luna et al. 2012). In the first approach, we will complement the experimental design illustrated in figure 1A with Hpa isolates in place of
Pst DC3000 tested on both Arabidopsis-Col and -Nd. Fitness-reduction and resistance measurements, conidiospore inoculations, leaf staining and microscopy, and statistical analyses will be carried out alike that illustrated in figure 1B&C. In the second approach, we will utilize ddc mutants for both Arabidopsis-Col and -Nd alike that shown in figure
5A to investigate whether or not de novo aka non-CpG DNA methylation facilitates trangenerational resistance. In addition, we will generate DDC overexpression lines under the control of the constitutive 35S promoter. Analyses will be carried out in parallel with that illustrated in figure 5A (Luna et al. 2012). Since, to the best of our knowledge, no marker genes are established with conviction for quantifying ETI-induction from RPP13 activation, we cannot employ the method used from Luna et al. for quantifying SARinduction illustrated in figure 5B; a different approach will be described in the next section.
From our primary infections, we expect that the seed weight or fitness of P
0
will be lower from Arabidopsis-Col infected with both Hpa -Maks9 and –Emoy2 as compared

8 to Arabidopsis-Nd infected with Hpa -Maks9, but that there will be minimal difference in fitness compared between Arabidopsis-Col infected with either isolate. Furthermore, we expect that Arabidopsis-Nd infected with Hpa -Emoy2 will show minimal difference in fitness compared with both Arabidopsis-Col isolate infections, but that Hpa -Maks9 infection of Arabidopsis-Nd will show the highest fitness of all. The logic behind these expectations is that Arabidopsis-Nd is more resistant to Hpa -Maks9 granting it higher fitness because it contains an allele of RPP13 , an Arabidopsis R gene, that recognizes the
Hpa -Maks9 allele of the effector, ATR13 (Leonelli et al. 2011). The RPP13 allele from
Arabidopsis-Col does not recognize ATR13 alleles from either Hpa -Maks9 or Emoy2 making it more susceptible and thus decreasing its fitness (Leonelli et al. 2011).
Expectations can be fashioned from the observation of enhanced resistance of
Arabidopsis-Nd to Hpa -Maks9 through the proficient RPP13 -ATR13 allelic interaction.
We anticipate that Arabidopsis-Nd Hpa -Maks9-infected P
1
will show lower disease phenotype compared to C
1
than that of Arabidopsis-Col Hpa -Maks9 and –Emoy2infected P
1
. These expectations are based on the belief that parental Arabidopsis exposure spawns new RPP13 epialleles (alleles with changes in DNA methylation status) that are primed for enhanced transcription in the progeny thus fashioning a more efficient immune system. However, since RPP13 -Col does not recognize either ATR13-Maks9 or
–Emoy2 (Leonelli et al. 2011), enhanced transcription via spawning of new
RPP13 -Col epialleles is ineffective for enhancing resistance because recognition is still nonexistent.
Furthermore, we expect that the greater resistance in Arabidopsis-Nd progeny will continue through the third generation for P
1
P
2
and P
1
C
1, as documented for transgenerational SAR (Luna et al. 2012).
From our final approach for testing transgenerational resistance, we presume that
Arabidopsis-Nd Hpa -Maks9-infected ddc mutants will lack the enhanced resistance compared between P
1
and C
1
, but P
1 and C
1
will both show enhanced resistance as compared to wild-type Arabidopsis-Nd Hpa -Maks9-infected P
0
, but not P
1
, and
Arabidopsis-Col Hpa -Maks9 and –Emoy2-infected P
0
and P
1
generations. The logic behind these expectations is that ddc mutants possess reductions in de novo aka non-CpG
DNA methylation statuses at all de novo methylated loci (Lister et al. 2008). Since R gene loci are associated with de novo methylated euchromatic regions in Arabidopsis
(Lister et al. 2008, Baumgarten et al. 2003), it is likely that in ddc mutants RPP13 is hypomethylated resulting in enhanced transcription explaining the lack of enhanced resistance compared between Arabidopsis-Nd HpaMaks9-infected P
1
and C
1 ddc mutants and the existence of enhanced resistance compared between Arabidopsis-Nd
HpaMaks9-infected P
0 ddc mutants with wild-type P
0
. Since RPP13 is already hypomethylated, a “more” primed immune system cannot be passed on to progeny, but they will likely remain primed for defense signaling. Our expected results for
Arabidopsis-Col Hpa -Maks9 and –Emoy2-infected wild-type plants remains consistent with our previous expected results described above. Consistent with our reasoning,
Arabidopsis-Col ddc mutants will most likely show disease phenotypes consistent with wild-type, since RPP13 -Col seizes recognition of either ATR13 alleles (Leonelli et al.
2011), as previously mentioned. Lastly, since DDC overexpression would result in hypermethylation at de novo DNA methylated loci (Lister et al. 2008), we expect that the resultant hypermethylation at the RPP13 locus will completely abolish resistance to Hpa in every combination of our Arabidopsis accessions and Hpa isolates.

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The significance of these expected results is that we will demonstrate that transgenerational resistance occurs in Arabidopsis plants exposed to Hpa , consistent with previous studies of Pst DC3000-induced priming of Arabidopsis SAR (Luna et al. 2012), but also be the first to provide evidence that supports transgenerational priming of ETI.
These results will also help set the tone for our second and third objectives, but will not be the determining factor. Though we are optimistic in observing enhanced resistance to
Hpa -Maks9 in Arabidopsis-Nd and that the enhanced resistance will be absent from our
DNA methylation mutants and overexpression lines, it is possible that we will observe comparable results from HpaEmoy2 infections and even in Arabidopsis-Col Hpa -Maks9 and –Emoy2 infected plants. This is because transgenerational SAR, which primes PTI in progeny (Luna et al. 2012), is effective against a broad-spectrum of pathogens and could likewise be detrimental to both Hpa isolates on both Arabidopsis accessions (Jones and
Dangl 2006). However, specialized obligate biotrophs like Hpa have evolved effectors for suppressing PTI, termed effector-triggered susceptibility (ETS), and thus the host’s
ETI defense system must take over. Consequently, since Arabidopsis depends on the action of RPP13 for ETI against Hpa , without an effective allele of RPP13 Hpa is free to wreak havoc on Arabidopsis (Leonelli et al. 2011) making our devised plan of action highly probable for producing results in line with our expectations.
Tentative Timetable
Generation of DDC overexpression lines and Niederzenze ddc mutants 4 months
Establishment of third-generation progeny lines (i.e., P
1
P
2
, P
1
C
2
and C
1
C
2
) 6 months
Conidiospore inoculations and resistance scoring 2 months
Total estimated time ~ 1 year
Test for transgenerational priming of ETI
For our second objective, we will test for transgenerational priming of ETI by adopting the Effector Detection Vector (EDV) system used for delivering exogenous pathogen effectors into host leaf tissue with the hijacking of Pst
DC3000’s TTSS. Two approaches adapted from Fabro et al. 2011 and Kawai-Yamada et al. 2009 will be employed for this approach. First, ATR13-Maks9 and –Emoy2 alleles will be subcloned into pEDV and introduced into Pst -LUX for bacterial growth assays on leaves from P
0 and P
1
generations from both Arabidopsis accessions and all genotypes measuring emitted bioluminescence (Fabro et al. 2011). Second, pEDV-ATR13-Maks9 and –Emoy2 will be introduced into weakly virulent Pst -ΔCEL and inoculations will be carried out on leaves from P
0
and P
1 generations from both Arabidopsis accessions and all genotypes to quantitatively test for HR by measuring electrolyte leakage over time (Kawai-Yamada et al. 2009). We hypothesize that Pst -LUX-Maks9 will show the least emitted bioluminescence and thus growth when challenged against Arabidopsis-Maks9 wild-type
P
1
and ddc mutant P
0
and P
1
versus all of the remaining combinations. Furthermore, we hypothesize that HR-like cell death will be most severe in Arabidopsis-Maks9 wild-type
P
1
and ddc mutant P
0
and P
1
versus all of the remaining combinations exhibiting the most rigorous electrolyte leakages.

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In need of a semi-high-throughput approach, we will employ an adaptation of the bacterial growth assay explored by Fabro et al.
2011 (fig. 2). Hpa -ATR13-Maks9 and –
Emoy2 will be subcloned into pEDV and separately introduced by conjugation into Pst -
LUX thus providing us with the two specimens of transgenic Pst -LUX that will be utilized for our growth experiments. New seeds will be planted from all of our specimens for P
0
generations while seeds from
P
0
seed stocks generated from our earlier investigations will be planted for P
1 generations. This will consist of 20 seeds per generation. High-throughput inoculations will be performed with an airbrush system attached to a compressor. Three days post inoculation
(DPI), bioluminescence will be measured using an ultra low light CCD camera by scoring photon counts per second (CPS) per gram of fresh weight (FW) per plant from
Fabro et al. 2011, PLoS Pathogens each generation from each specimen and averaged ( x
). The mean ( x
) for P
1
will be divided by the mean ( x
) for P
0
to provide a final value indicative of changes in bacterial growth between P
1
and P
0
for each generation from each specimen (fig. 2). Values less than
1 will indicate decreases in bacterial growth in
P
1
compared to P
0
generations and thus increased resistance, which in these experiments is a specific function of the recognition of a specific allele(s) of Hpa -
ATR13 by the corresponding Arabidopsis-
RPP13 allele(s).
To test for HR, we will employ an adaptation of the quantitative procedure
Figure 2. Functional screening method.
Hpa effector candidates (HaRxLs) were delivered on 12 Arabidopsis accessions through the bacterial TTSS of the Pst-LUX strain.
Levels of bacterial growth were measured quantifying bioluminescence (photon counts) emitted by the bacteria present on whole plants. The ratio of the average photon counts per second (CPS) per gram of fresh weight (FW) emitted by the bacteria delivering a given HaRxL versus the bacteria delivering control proteins was determined per accession.
Experiments were repeated at least three times and statistical tests applied. exploited by Kawai-Yamada et al. 2009 (fig. 6E) measuring electrolyte leakage over time in leaves from P
0
and P
1
generations from both Arabidopsis accessions including all genetic backgrounds inoculated with the weakly virulent Pst -ΔCEL strain containing either Hpa -ATR13-Maks9 or –Emoy2. Similar to that mentioned above, new seeds will be planted from all of our specimens for P
0
generations while seeds from P
0
seed stocks generated from our earliest investigations will be planted for P
1
generations. Hpa -
ATR13-Maks9 and –Emoy2 will be separately introduced by conjugation into Pst -ΔCEL.
Leaves will then be inoculated with 5 X 10
8
colony-forming units of transgenic Pst -
ΔCEL. Three leaf discs inoculated with Pst -ΔCEL will be obtained per plant and

11
Kawai-Yamada et al. 2009, JOBC monitored for electrolyte leakage at several time points relative to total ion leakage from the autoclaved bacterial samples (fig. 6E).
In line with the expected results from our first objective, we anticipate Pst -
LUX-pEDVHpa -ATR13-Maks9 will result in the smallest value for emitted bioluminescence when inoculated on
Arabidopsis-Nd wild-type P
1
measured over
P
0
compared to all other combinations of transgenic Pst -LUX and generations and genetic backgrounds of Arabidopsis specimens, including inoculations of Pst -
LUX-pEDVHpa -ATR13-Maks9 on the ddc
FIGURE 6. AtBI-1 expression associated with HR caused by P. syringae pv. tomato DC3000 carrying avr-
Rpt2.
E, electrolyte leakage induced by HR cell death. The electrical conductivity was measured in the Pst(avrRPT2)inoculated control (GFP), AtBI-1 KD, and atbi1-1 (CM) plants as described under “Experimental Procedures.” Data are mean ± S.E. (error bars) of five plants each. mutant of Arabidopsis-Nd. Furthermore, we can posit that comparable results will be discerned for our assays of electrolyte leakage employed for testing for variations of HR-like symptoms. To elaborate,
Arabidopsis-Nd wild-type P
1
and ddc mutant P
0
and P
1
inoculated with Pst -ΔCELpEDVHpa -ATR13-Maks9 can be expected to show the most efficient electrolyte leakages (i.e., greatest and fastest relative ion leakage over time, fig. 6E) compared to all other combinations of transgenic Pst -ΔCEL and generations and genetic backgrounds of
Arabidopsis specimens.
The logic underlying these expectations are based on the rationale described above for objective one. For instance, with the recognition of Hpa -ATR13-Maks9 by
ArabidopsisRPP13 -Nd, a successful ETI response can be reached (Leonelli et al. 2011).
However, with the incompatibility between all of the following: Hpa -ATR13-
Maks9/ArabidopsisRPP13 -Col, Hpa -ATR13-Emoy2/ArabidopsisRPP13 -Nd and Hpa -
ATR13-Emoy2/ArabidopsisRPP13 -Col, the ETI response is either limiting or nonexistent (Leonelli et al. 2011). Naturally, it can be postulated that within these apparently unharmonious pathosystems other effector-NBS-LRR-interactions take place, irrefutable evidence shows that without an effective Hpa -ATR13 recognition by
ArabidopsisRPP13 , ETI is insufficient thus allowing Hpa to wreak havoc (Leonelli et al.
2011), as mentioned above. Furthermore, in expecting that progeny of Arabidopsis P
0
are primed for more efficient transcription of RPP13 , it is plausible that P
1
generations elicit more efficient ETI-responses. In light of this evidence and rationale, Pst -LUX-pEDV-
Hpa -ATR13-Maks9 produces an effective ETI-response only in Arabidopsis-Nd conditioning less pathogen growth and thus emitted bioluminescence as well as more efficient electrolyte leakage and thus HR-like symptoms, which is primed in P
1
leading to extreme observations with the same trend as those found in P
0
. However, insignificant differences would be observed between P
0
and P
1
generations in all other combinations of
Hpa isolates and Arabidopsis accessions, including overexpression DDC lines from the expected constant hypermethylation at the RPP13 locus. Finally, with the already primed
ETI-response in P
0 ddc mutant Arabidopsis-Nd plants due to an expected RPP13 hypomethylation, no significant changes would be observed between P
0
and P
1
in either

12 of our assays for bacterial growth and electrolyte leakage, though both generations would show similar results to Pst Hpa -ATR13-Maks9-infected Arabidopsis-Nd P
1
plants.
With our expected results, we will be the first to present convincing evidence that transgenerational ETI is effective in producing progeny that are primed for future encounters with one of their intimate and co-evolving biotrophic pathogens while also providing results suggesting a role for DNA methylation during the process. Although we believe our approaches are thorough for investigating our hypothesis, more genetics could be suggested. For instance, Arabidopsisrpp13 -Nd knockout mutants or knockdown lines would most likely abolish all significant results we expect to see by eliminating recognition of Hpa -ATR13-Maks9 and thus the effective ETI-response
(Leonelli et al. 2011). We do not plan to explore this avenue, but suggest that it be investigated in future studies. Another obvious alternative could be a macroscopic and microscopic investigation of HR-like symptoms in addition to our quantitative approach measuring electrolyte leakage in testing for variations in ETI efficiencies. We chose to avoid this approach based on the need for a more objective and quantitative method for detecting the slightest changes in ETI efficiencies (Kawai-Yamada et al. 2009).
Furthermore, it is very likely that our methods would produce results in parallel with these alternatives, as their correlations have been documented (Fabro et al. 2011).
Tentative timetable (simultaneously carried out with objective 3)
Generation of Pst -LUX and –ΔCEL constructs 4 months
Plant production
Assays for bacterial growth and electrolyte leakage
4 months
10 months
Total estimated time 1 year, 6 months
Map DNA methylation changes at the RPP13 locus and quantify resulting expression
Our final objective will be to map any potential changes in DNA methylation occurring at the RPP13 locus and quantify simultaneous changes in gene expression.
Bisulfite sequencing of RPP13 will be carried out on Arabidopsis gDNA extracted from
Hpa -inoculated leaf tissue obtained from our earlier investigations of transgenerational resistance. We hypothesize that the P
0
generation from all Arabidopsis wild-type specimens will show decreases in methylation at the RPP13 locus compared to uninfected C
0
and that the hypomethylation remains in P
1
. Furthermore, ddc mutants will show the equivalent hypomethylation to infected wild-type specimens at RPP13 regardless of Hpa -inoculation and the opposite will be seen in DDC overexpression lines.
As for changes in transcription at RPP13 , we hypothesize that there is a negative correlation between changes in DNA methylation and gene expression (i.e., hypomethylation leads to higher rates of transcription).
To begin this investigation, leaf gDNA inoculated with Hpa -Maks9 or –Emoy2 will be isolated from 10 plants from each P
0
and P
1
generation of all genotypes from both
Arabidopsis accessions as well as inclusion of a MOCK-inoculated control (C
0
) from each accession. Isolated gDNA samples will be treated with sodium bisulfite for conversion of unmethylated cytosines to uracil (Frommer et al. 1992). The promoter and genic region of RPP13 will be PCR amplified from all bisulfite-converted gDNA samples using multiple primer sets for full coverage and cloned into Lucigen’s highly stable pEZ

13
BAC vector, which enables blue/white screening and cloning of large amplicons, and sequenced using 454-pyrosequencing technology.
In our second approach for this investigation, total RNA from leaf tissue inoculated with Hpa -Maks9 or –Emoy2 will be isolated from plants of the same stocks used for our gDNA isolations and reverse transcribed using BIO-RAD’s iScript
TM
Reverse Transcription Supermix. Gene expression analyses will be carried out with the use of quantitative real-time PCR (qRT-PCR). Gene-specific primers will be designed to unique 150-200 bp stretches of the RPP13 open reading frame. We will use ubiquitin as the internal reference gene. Four biological replicates will be used for RPP13 qRT-PCR experiments for each specimen, each biological replicate consisting of 5 plants. Three technical replicates will be included for each biological replicate. Data will be analyzed using the delta-delta Ct method.
Overwhelming evidence indicates that hypomethylation at gene promoters and within some genic regions is correlated with increases in gene expression. It has been proposed that the removal of methyl-groups from cytosines in promoter regions allows access for chromatin remodeling and histone modifying enzymes for efficient localization of transcriptional machinery for accelerating transcription. Though less definitive, further evidence suggests that removal of methyl-groups from cytosines within genic regions further accelerates transcription (Rangani et al. 2012) and can be pictured as a kind of chromatin “breathing” mechanism for passage of RNA polymerase and other transcriptional machinery. In light of this evidence, we expect to observe hypomethylation at the RPP13 locus, especially in the promoter, in P
0
and P
1
for all specimens other than DDC overexpression lines. Expectations can then be drawn for subsequent increases in RPP13 gene expression in all specimens other DDC overexpression lines. Rationale for these expected results are that Hpa infection leads to hypomethylation at the RPP13 locus in all wild-type Arabidopsis plants in P
0
and is maintained in P
1
for priming of RPP13 transcription for a more efficient ETI-response to future Hpa encounters, although it is only effective for plants containing RPP13 alleles that recognize the cognate ATR13 allele (i.e., Arabidopsis-NdHpa -Maks9) (Leonelli et al. 2011). Lastly, the obvious expectation will be hypomethylation at RPP13 in all generations for both accessions of ddc mutants and hypermethylation for DDC overexpression lines (Lister et al. 2008).
The significance of these expected results is that we will be the first to provide irrefutable evidence that at least one R gene locus (i.e., ArabidopsisRPP13 ) undergoes differential methylation in response to pathogen stress (i.e., Hpa ), which is maintained in the next generation as a means for priming a more effective ETI-response thus making the transmission of new epallelic forms of R genes appear to be an important mechanism for increasing a plant’s fitness in pathogen-rich environments.
We trust that our approaches for mapping DNA methylation and subsequent gene expression changes at the RPP13 locus are thorough for testing our hypothesis. We chose not to include P
1
P
2
generations in our investigation due to the ease of drawing conclusions from our results observed for the P
1
generation in combination with results generated from our previous investigations of objectives one and two. It could be suggested to perform reverse transcriptase PCR (RT-PCR) or northern blot analysis for measuring RPP13 gene expression changes, but we believe the more sensitive qRT-PCR approach is needed for detecting even the slightest changes in expression.

14
Tentative timetable (simultaneously carried out with objective 2) gDNA and RNA preps
Bisulfite sequencing and qRT-PCR analyses
4 months
1 year, 2 months
Total estimated time
Future Directions
1 year, 6 months
With our expected results from this study, we will have demonstrated that upon
Hpa infection, Arabidopsis initiates the removal of 5-methylcytosines of non-CpG dinucleotides at the RPP13 locus for activation of transcription. Consequently, this hypomethylation is maintained in future generations for transcriptional priming of RPP13 conditioning more efficient ETI-responses to future Hpa encounters. However, transcriptional priming of RPP13 and thus transgenerational priming of ETI is only effective in corresponding allelic forms of RPP13 and its cognate effector, ART13.
Together, these evidences will contribute to the breadth of knowledge established for understanding the epigenetic associations with plant resistance to obligate biotrophic pathogens and will likely lead to the uncovering of intimate epigenetic forces at work that enable plant populations to cope with obligate biotrophic pathogen stress. Furthermore, this work could be extended to animal and human disease research leading to novel discoveries for the generation of vaccines and antimicrobial treatments.
Future studies shall extend this work to other intimate plant host-pathogen relations for oomycete and fungal as well as nematode, bacterial and viral plant pathogens. With the mass of R gene loci found in most, if not all, plant genomes it is likely that the results established from this study will be conserved in many plant species for coping with the stresses caused by obligate biotrophic pathogens. Extending this work to animal-pathogen interactions, NLR (nucleotide-binding oligomerization domain and
LRR-containing protein)-producing loci, which possess vast similarities with R genes, are intracellular immune receptors and thus priming of transcription at these loci via hypomethylation may be a conserved mechanism for coping with pathogen stress similar to plants, and it could be postulated as being transgenerational (Spoel and Dong 2012).
As previously mentioned, a characteristic of R genes is their rapid rates of evolution (Jones and Dangl 2006). It has been suggested that this permits elevated recombination frequencies at R gene loci conditioning the production of new alleles or loci effective in recognizing pathogen effectors thus eliciting ETI (Meyers et al. 2003,
Baumgarten et al. 2003, Lucht et al. 2002). Evidence suggests that high frequencies of recombination are associated with relaxed DNA methylation (Boyko et al. 2007). In light of this evidence, it can be posited that transgenerational hypomethylation at R gene loci not only primes for transcription in eliciting more efficient ETI-responses, but also leads to elevated recombination frequencies for producing new R gene loci for further advancing their progenies’ immune systems against the pathogen(s) for which they were exposed. Following this study, we may investigate whether this myth is actual using genetics, genomics and statistical approaches for analyzing whether recombination frequencies at R gene loci are elevated in Arabidopsis ddc mutants when subjected to

hypomethylation, and if this can similarly be detected when exposed to obligate biotrophic pathogens at the corresponding R gene loci.
Timeline (flowchart diagram)
15
References
Baumgarten, A., S. Cannon, R. Spangler & G. May (2003) Genome-level evolution of resistance genes in Arabidopsis thaliana. Genetics, 165 , 309-319.
Beckers, G. J. M., M. Jaskiewicz, Y. D. Liu, W. R. Underwood, S. Y. He, S. Q. Zhang & U.
Conrath (2009) Mitogen-activated protein kinases 3 and 6 are required for full priming of stress responses in Arabidopsis thaliana. Plant Cell, 21 , 944-
953.
Boyko, A., P. Kathiria, F. J. Zemp, Y. L. Yao, I. Pogribny & I. Kovalchuk (2007)
Transgenerational changes in the genome stability and methylation in pathogen-infected plants (Virus-induced plant genome instability). Nucleic
Acids Research, 35 , 1714-1725.

16
Coates, M. E. & J. L. Beynon (2010) Hyaloperonospora arabidopsidis as a pathogen model. Annual Review of Phytopathology, 48 , 329-345.
Fabro, G., J. Steinbrenner, M. Coates, N. Ishaque, L. Baxter, D. J. Studholme, E.
Koerner, R. L. Allen, S. J. M. Piquerez, A. Rougon-Cardoso, D. Greenshields, R.
Lei, J. L. Badel, M.-C. Caillaud, K.-H. Sohn, G. Van den Ackerveken, J. E. Parker,
J. Beynon & J. D. G. Jones (2011) Multiple Candidate Effectors from the
Oomycete Pathogen Hyaloperonospora arabidopsidis Suppress Host Plant
Immunity. Plos Pathogens, 7.
Frommer, M., L. E. McDonald, D. S. Millar, C. M. Collis, F. Watt, G. W. Grigg, P. L.
Molloy & C. L. Paul (1992) A GENOMIC SEQUENCING PROTOCOL THAT
YIELDS A POSITIVE DISPLAY OF 5-METHYLCYTOSINE RESIDUES IN
INDIVIDUAL DNA STRANDS. Proceedings of the National Academy of Sciences of the United States of America, 89 , 1827-1831.
Jaskiewicz, M., U. Conrath & C. Peterhansel (2011) Chromatin modification acts as a memory for systemic acquired resistance in the plant stress response. Embo
Reports, 12 , 50-55.
Jones, J. D. G. & J. L. Dangl (2006) The plant immune system. Nature, 444.
Kawai-Yamada, M., Z. Hori, T. Ogawa, Y. Ihara-Ohori, K. Tamura, M. Nagano, T.
Ishikawa & H. Uchimiya (2009) Loss of calmodulin binding to bax inhibitor-1 affects Pseudomonas-mediated hypersensitive response-associated cell death in Arabidopsis thaliana. Journal of Biological Chemistry, 284 , 27998-
28003.
Leonelli, L., J. Pelton, A. Schoeffler, D. Dahlbeck, J. Berger, D. E. Wemmer & B.
Staskawicz (2011) Structural elucidation and functional characterization of the Hyaloperonospora arabidopsidis effector protein ATR13. PLoS Pathogens , e1002428.
Lister, R., R. C. O'Malley, J. Tonti-Filippini, B. D. Gregory, C. C. Berry, A. H. Millar & J. R.
Ecker (2008) Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell (Cambridge), 133 , 523-536.
Lucht, J. M., B. Mauch-Mani, H. Y. Steiner, J. P. Metraux, J. Ryals & B. Hohn (2002)
Pathogen stress increases somatic recombination frequency in Arabidopsis.
Nature Genetics, 30 , 311-314.
Luna, E., T. J. A. Bruce, M. R. Roberts, V. Flors & J. Ton (2012) Next-generation systemic acquired resistance. Plant Physiology, 158 , 844-853.
Meyers, B. C., A. Kozik, A. Griego, H. H. Kuang & R. W. Michelmore (2003) Genomewide analysis of NBS-LRR-encoding genes in Arabidopsis. Plant Cell, 15 , 809-
834.
Mou, Z., W. H. Fan & X. N. Dong (2003) Inducers of plant systemic acquired resistance regulate NPR1 function through redox changes. Cell, 113 , 935-944.
Rangani, G., M. Khodakovskaya, M. Alimohammadi, U. Hoecker & V. Srivastava
(2012) Site-specific methylation in gene coding region underlies transcriptional silencing of the Phytochrome A epiallele in Arabidopsis thaliana. Plant Molecular Biology, 79 , 191-202.
Spoel, S. H. & X. N. Dong (2012) How do plants achieve immunity? Defence without specialized immune cells. Nature Reviews Immunology, 12 , 89-100.

Wang, S., W. E. Durrant, J. Q. Song, N. W. Spivey & X. N. Dong (2010) Arabidopsis
BRCA2 and RAD51 proteins are specifically involved in defense gene transcription during plant immune responses. Proceedings of the National
Academy of Sciences of the United States of America, 107 , 22716-22721.
17

Total funding distributed over 3 years = $481,379
Researchers
For our proposed research project, we plan to employ two postdocs, preferably recent graduates.
Salaries will be allotted to the recipients in agreement with their research experience. With benefits,
$46,848 will be allotted per recipient in their first year ($3,200 monthly) with incremental increases over the next two years. Recipients will be expected to complete all project relevant tasks. To elaborate, both will work together for completing objective one – establishing transgenic lines, P
0
-
P /C
2 1
P
2
generations, resistance scoring, etc. After completion of the first objective, one researcher will carry out objective two (i.e., generation of Pst -LUX and –ΔCEL constructs, plant production, bacterial growth assays and HR scoring) while the other will simultaneously carry out objective three
(i.e., gDNA and RNA preps, bisulfite sequencing and qRT-PCR analyses).
Equipment
Two kinds of equipment will be essential for our investigations: a real-time PCR detection system and an ultra low light CCD camera. For qRT-PCR analyses quantifying gene expression levels for
RPP13 we will need BIO-RAD’s CFX96TOUCH real-time PCR detection system, which runs around
$25,000. For our bacterial growth assays we will need an ultra low light CCD camera provided by
Photek for quantifying photon counts emitted from Pst -LUX inoculated on Arabidopsis leaves, which runs around $5,000. Previously, ultra low light CCD cameras were very costly running around
$15,000-$25,000 until Photek’s recent advancement for producing much more affordable cameras.
Materials and Supplies
Year Total
1 $4,479
General description of materials and supplies, venders and estimated costs
Columbia & Niederzenze wt and ddc knockouts, NASC, $88 ; E. coli Dh5-Alpha,
MCLAB, 100X50uL tubes, $440 ; Agrobacterium tumefaciens, Science Stuff, 1 item,
$30 ; pBI121 empty plant transformation vector, ABRC, $200 ; pGEMT-easy cloning vector system, Promega, 100 reactions, $608 ; lactophenol-blue (staining for disease), SIGMA-ALDRICH, 100mL, $30 ; General Purpose Chemicals (i.e., antibiotics, fertilizers, media, buffers, etc.), $320 ; AcuuPrime TM Taq DNA
Polymerase System, Invitrogen, 1000 reactions, $1,136 ; QIAquick PCR
Purification System, QIAGEN, 250 reactions, $500 ; QIAquick Gel Extraction Kit,
QIAGEN, $210 ; QIAprep Spin Miniprep Kit, QIAGEN, 200 reactions, $350 ;
Restriction Enzymes, Invitrogen, $500 ; Primers, IDT, $67
2 $2,352 E. coli HB101, MCLAB, 30X100uL tubes, $330 ; Pst -LUX, ATCC, $400 ; Pst -ΔCEL,
ATCC, $400 ; Vectors for pEDV3 (effector detect vector) creation: pBBR broadhost range vector, Mo Bi Tech, 5ug, $200 and pV316-1A for AvrRPS4 promoter subcloning into pBBR, $200 ; sodium bisulfite for bisulfite conversion, SIGMA-
ALDRICH, 100g, $25 ; Primers, IDT, $67 ; iScript TM Reverse Transcription
Supermix, BIO-RAD, 25X20uL reactions, $150 ; iTaq TM SYBR ® Green Supermix,
BIO-RAD, 500X50uL reactions, $780
3 $1,000 In case we run low on stocks of materials and supplies purchased in the first two years of our project, $1,000 shall be provided in case of emergencies
Instrumentation facilities
Facilities we will be using include: ISU sequencing facility, microscopy facility and a facility with a conductivity meter. We will need $2,000 each year to cover the costs for these services.
Publication Costs
We have used the publication costs from PNAS as an estimate. We expect to be finished with this project in our third year and plan to publish our manuscript as follows: page charges =
$700 (10 pages, $70 per page), SI = $250 (up to five pages), color charges = $600 (3 figures,
$200 per figure), total publication cost = $1,550.

Project Budget Worksheet - Iowa State University of Science and Technology
Form updated 7-27-12
Eff. 7-1-12
Program Sponsor: National Science Foundation
Title: Explorations of DNA methylation and transgenerational effector-triggered immunity in Arabidopsis thaliana against its intimate parasitic oomycete, Hyaloperonospora arabidopsidis
PI: 4
Period of Performance: 1/1/2013-12/31/2015
Deadline: 11/12/2012
Year 1 Year 2 Year 3 Year 4 Year 5 Total
A
6
7
8
9
4
5
1
2
3
Key Personnel
B Other Personnel
1 Post Doc
2 Post Doc
3 Research Asst-Halftime
4 Research Asst-Halftime
5 Hourly Undergraduate student
6 Hourly Undergraduate student
7 P&S
8 P&S
9 Secretarial/Clerical
10 Secretarial/Clerical
11 Non-Student Hourly
12 Non-Student Hourly
Subtotal: Salaries and Wages
Monthly
$3,200
$3,200
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
Salary
Monthly
$0
$0
$0
$0
$0
$0
$0
$0
$0
C Fringe Benefits
0
0
0
0
0
0
0
0
0
Post Doc
Post Doc
Research Asst-Halftime
Research Asst-Halftime
Hourly Undergraduate student
Hourly Undergraduate student
P&S
P&S
Secretarial/Clerical
Secretarial/Clerical
Non-Student Hourly
Non-Student Hourly
30.5%
30.5%
30.5%
22.0%
22.0%
12.9%
12.9%
4.6%
4.6%
Rate
30.5%
30.5%
30.5%
30.5%
30.5%
30.5%
37.0%
37.0%
49.7%
49.7%
12.0%
12.0%
12.00
12.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Calendar
Months
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Calendar
Months
Academic
Months
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
1.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Summer
Months
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Number of persons $0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$81,477
$40,739
$40,739
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$81,477
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$17,925
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$8,962
$8,962
$0
$0
$0
$0
$0
$79,104
$39,552
$39,552
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$79,104
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$17,403
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$8,701
$8,701
$0
$0
$0
$0
$0
$76,800
$38,400
$38,400
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$76,800
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$16,896
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$8,448
$8,448
$0
$0
$0
$0
$0
$237,381
$52,224
$0
$0
$0
$0
$26,112
$26,112
$0
$0
$0
$0
$0
$0
$0
$0
$0
$237,381
$118,691
$118,691
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0 Check
$0.00
$0
$0
$0
$0
$0.00
$0.00
$52,223.85
$96,507
$0
$99,402
$0
$0
$0
$0
$0
$289,605
$30,000 D
E
F
Subtotal: Salaries, Wages, and Benefits
Equipment (List Item and $ amount for each item > $5k)
1. real-time PCR detection system
2. ultra lowlight CCD camera
Travel
1. Domestic Travel
2. Foreign Travel
Participant Support Cost
1. Stipend
2. Travel
3. Subsistence
4. Other
See notes below
G Other Direct Costs
1 Materials and Supplies
2 Publication cost
3 Computing support
4 Instrumentation facility
5 Subcontractor1 - Subject to IDC (first $25,000) See notes below
NOT subject to IDC (Amount over $25,000)
6 Subcontractor2 - Subject to IDC (first $25,000) See notes below
NOT subject to IDC (Amount over $25,000)
7 Tuition - Non-Engineering
8 Tuition - Engineering
(Click on "Tuition" sheet)
(Click on "Tuition" sheet)
9 Other
10 Other
Subtotal: Total Direct Costs (TDC)
Subtotal: Modified Total Direct Costs
[ MTDC = TDC - Tuition - Equipment - Participant Support Cost ]
H Indirect Costs
IDC on MTDC
Rate
48.0%
[ IDC = MTDC * Indirect Rate ]
I Total Project Costs
[ Total = TDC + IDC ]
$93,696
$30,000
$25,000
$5,000
$0
$0
$0
$0
$0
$0
$0
$0
$6,479
$4,479
$0
$0
$2,000
$0
$0
$0
$0
$0
$0
$0
$0
$130,175
$100,175
$48,084
$48,084
$178,259
$4,352
$2,352
$0
$0
$2,000
$0
$0
$0
$0
$0
$0
$0
$0
$100,859
$100,859
$0
$0
$0
$0
$0
$0
$0
$0
$48,412
$48,412
$149,271
$4,550
$1,000
$1,550
$0
$2,000
$0
$0
$0
$0
$0
$0
$0
$0
$103,952
$103,952
$0
$0
$0
$0
$0
$0
$0
$0
$49,897
$49,897
$153,849
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$15,381
$7,831
$1,550
$0
$6,000
$0
$0
$0
$0
$0
$0
$0
$0
$334,986 $334,985.97
$304,986
$146,393
$481,379 $481,379.23