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Epigenomic landscape changes caused by generalist aphid feeding
Jessica Hohenstein, Iowa State University
I. Project Summary
Historically, epigenetics has been explained as “the interaction of genes with the environment
which brings the phenotype into being”. More recently, the study of epigenetics has been described as
changes in gene expression brought about not by genomic sequence changes, but rather by change in the
chromatin structure, genome packaging, and direct modifications to histones and DNA. Numerous
studies have found that covalent histone and DNA modifications can alter gene expression due to the
relative accessibility of RNA polymerase complexes to the target genomic sequence. These modifications
can also recruit chromatin remodeling complexes which also alter DNA accessibility. Interestingly,
epigenetic markings can be heritable and have the potential to alter offspring gene expression based on
the parental environment.
Intellectual Merit. Pathogen infection drastically changes the transcriptome profile of its host
plant. Various genes have been shown to be regulated epigenetically; however, the proportion of genes
regulated strictly by epigenetic mechanisms is currently unknown. Thus, it is important to determine the
total proportion of differential expression due to changes in the epigenomic landscape. Differential DNA
methylation patterns, histone modifications, and small RNA species will be quantified using bisulfite
sequencing, chromatin immunoprecipitation coupled with next-generation sequencing technologies, and
small RNA sequencing, respectively. This will allow the determination of which genes, if any are not
regulated by these epigenetic mechanisms. Furthermore, Gene Ontology analyses will be applied to
understand if certain epigenetic marks correspond with particular pathways or gene networks.
Broader Impacts. Several studies in Arabidopsis thaliana indicate that infection with virulent
strains of Pseudomonas syringae pv. tomato (DC3000) induces a wide range of changes in DNA
methylation, histone modifications, chromatin remodeling, and small RNA profiles. However, to date, no
studies have been conducted utilizing insect herbivores. Therefore, studying overall gene expression
changes coinciding with epigenetic modifications in the model interaction of Arabidopsis thaliana with
the phloem-feeding green peach aphid, Myzus persicae is critical. The generalist aphid is known to have
a wide range of host plants and is considered one of the most important pests worldwide. By using the
techniques described above, it can be tested if the majority of differential gene expression induced by
generalist aphid feeding is due to genome-wide epigenetic changes. Additionally, this work will begin to
decipher the “epigenetic code,” or the combinations of histone modifications and DNA methylation
patterns needed to occur to confer resistance or susceptibility to specific pathogens or herbivores.
II. Project description
A. Introduction
In eukaryotes, genes are expressed within the context of chromatin. Changes in chromatin
structure are modified by epigenetic mechanisms. Literally defined as “above genetics”, epigenetic
regulation denotes that changes in phenotype are not caused by a change in genotype. Rather, phenotypic
alterations are dependent upon the environment to which an organism is exposed, including biotic stress
such as aphid attack in plants. Thus, studying epigenetic modifications such as DNA and histone
methylation, histone acetylation, chromatin remodeling, as well as small RNA pathways which can have
both direct (RNA interference) and indirect (RNA-directed DNA methylation) effects on gene expression,
provides insight into the underlying mechanism of differential gene expression.
In plants, DNA methylation is the most studied epigenetic modification and mediates gene
imprinting, genome stability, regulation of transcription, and coordinating developmental pathways in a
variety of eukaryotic species (He et al., 2011). It entails the addition of a methyl group to the fifth
position in cytosine residues by DNA methyltransferases in symmetric (CG or CHG, where H can
represent C, A, or T), or asymmetric (CHH) contexts (Alvarez et al., 2010; He et al., 2011). In plants, CG
methylation is thought to participate in regulation of gene expression whereas CHG and CHH
methylation function to repress transposon activation and imprinting (Cao and Jacobsen, 2002).
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Accordingly, methylome profiling in several plant species reveal that heterochromatin, transposable
elements (TEs), and repetitive sequences contain all types of DNA methylation (Zhang et al., 2006;
Zilberman et al., 2007; Alvarez et al., 2010). Expressed non-TE genes have some levels of gene body
methylation (CG exclusively) concentrated in exons and have low levels of 5’ and 3’ methylation
(Zilberman et al., 2007; Feng et al., 2010). Tissue-specific genes tend to be methylated at their promoters
(Zhang et al., 2006). This indicates while levels of DNA methylation are important, location of the
epigenetic mark is critical for regulation of gene expression. However, in general, higher methylation
levels are negatively correlated to transcript levels (He et al., 2011). Methylation marks can be removed
by DNA glycosylases or perhaps more interestingly, stably propagated to progeny (Alvarez et al., 2010).
DNA is packaged around histone proteins that make up complexes known as nucleosomes.
Histone modifications represent important epigenetic modifications that can work in conjunction with or
opposition to DNA methylation. The most characterized histone modifications are the additions of
methyl groups or acetyl groups to lysine residues on histone H3 tails (He et al., 2011). These
modifications are catalyzed by histone lysine methyltransferases (HKMTs) and histone acetyltransferases
(HATs), respectively (Alvarez et al., 2010). Removal of these modifications is catalyzed by histone
demethylases (HDMs) and histone deacetylases (HDACs), respectively (He et al., 2011). These
modifications are thought to change gene expression by two mechanisms: (1) interactions between
nucleosomes are altered by the modification of electrostatic charges when acetyl- or methyl- groups are
added, and (2) chromatin remodeling proteins are recruited to modify chromatin structure based on the
histone modification (Alvarez et al., 2010). While acetylated residues are generally associated with
transcriptional activation, methylated residues have mixed effects depending on the residue and degree of
the modification (mono-, di-, or tri- methylated) (Alvarez et al., 2010). DNA methylation and H3K4me3
seem to be mutually exclusive, while the nucleic acid modification and H3K9me2 together modulate
chromatin condensation in heterochromatic regions (Alvarez et al., 2010). It is thought that gene
expression is dictated by the specific combination of both DNA and histone modifications present.
While small RNAs do not directly change chromatin structure like histone modifications, they
still function in regulation of gene expression. Sequence-specific DNA methylation occurs via the RNAdirected DNA methylation (RdDM) pathway (Kim, 2005). This phenomenon involves self-perpetuating
small interfering RNAs (siRNAs, originally generated by RNA Polymerase IV and processed to dsRNA
by RNA-dependent RNA Polymerase) that direct DNA methyltransferases to complementary sequences
and induce cytosine methylation at the CHH sequence motif (Hollick, 2010). Additional small RNA gene
regulation occurs by direct interaction of host mRNAs through the RNA interference pathway where
small dsRNA molecules are used to degrade host mRNAs by complementary sequence recognition and
subsequent cleavage of the transcript (Kim, 2005).
Epigenetic mechanisms modulate gene expression of a variety of pathways including
development to defense as well as transgenerational inheritance of various stresses. Throughout
development or when exposed to adverse conditions, DNA methylation patterns, histone modifications,
and small RNA profiles undergo major alterations. DNA methylation and small RNAs play central roles
during pollen and endosperm development in maize (He et al., 2011). Abiotic stressors induce
differential epigenetic markings in several plant species. For example, DNA methylation, histone
modifications, and gene expression were tightly correlated in progeny of salt-exposed Arabidopsis plants
(Bilichak et al., 2012). Additionally, histone modifications tightly corresponded to senescence-regulated
genes in Arabidopsis, however, some genes that were upregulated were not associated with the activating
H3K4me3 mark (Brusslan et al., 2012). In this study, only one activating histone mark was studied and
thus the possibility of another activating mark associated with the upregulation cannot be ruled out, nor
can it be ruled out that the upregulation of these genes is independent of epigenetic marks.
Various studies using biotic stressors have shown a correlation of gene expression and epigenetic
markings. At 24 hours post-inoculation with Pseudomonas syringae pv. tomato DC3000, Arabidopsis
thaliana showed massive hypomethylation and decondensation of heterochromatin (Pavet et al., 2006).
This could lead to increased homologous recombination and evolution of R-gene specificities, as R-genes
tend to cluster in TE and repetitive-sequence rich areas enriched with repressive epigenetic marks
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(Alvarez et al., 2010). Somatic recombination increased by the presence of the oomycete pathogen
Peronospora parasitica, while progeny of Tobacco Mosaic Virus (TMV)-exposed tobacco showed
enhanced recombination and increased resistance to several other pathogens (Lucht et al., 2002; Kathiria
et al., 2010). Inherited resistance was also found to require small RNA pathway components in
Arabidopsis (Rasmann et al., 2012). In contrast, another study of the Arabidopsis-Pseudomonas
interaction did not find this massive hypomethylation/heterochromatic decondensation after 5 days of P.
syringae infection (Dowen et al., 2012). Additional support for epigenetic control of defense pathways
can be seen in activating histone marks such as H3K4me3 deposition at salicylic acid-responsive genes,
which are generally effective against biotrophic pathogens (Alvarez et al., 2010). Although histone
deacetylation generally corresponds to repressed transcription, HDACs are involved in the activation of
jasmonate-induced defenses (Alvarez et al., 2010) that are effective against multiple necrotrophic
pathogens and herbivorous insects.
Following this, several studies have focused on the effect that plant pathogens have on host
epigenetic changes (Alvarez et al., 2010; Kathiria et al., 2010; De-La-Pena et al., 2012; Dowen et al.,
2012). However, many of these studies focus on only a subset of canonical defense genes while fewer
concentrate on genome-wide epigenetic modifications and the correlative gene expression. That is, the
proportion of differentially expressed genes regulated strictly by epigenetic mechanisms is currently
unknown. Furthermore, while epigenetic patterns have been extensively characterized for bacterialinfected plants, virtually no research has been conducted on the effect herbivores have on the epigenomic
landscape of their host plant and the overall gene expression changes attributable to epigenetic
modifications, in spite of the importance of the plant-aphid interaction to agriculture.
Therefore, we propose to study overall gene expression changes coinciding with epigenetic
modifications in the model interaction of Arabidopsis thaliana with the phloem-feeding green peach
aphid, Myzus persicae. This generalist aphid is known to have a wide host range and is considered to be
one of the most important pests worldwide (Margaritopoulos et al., 2009). Various microarrays have
been conducted on plants exposed to M. persicae, and results indicate that aphid feeding induces various
plant defenses, such as salicylic acid (SA)-mediated defense responsive genes PR1 and PR2, yet seems to
inactivate effectual jasmonate (JA)-mediated defenses, purportedly via an antagonistic crosstalk between
SA and JA (Moran and Thompson, 2001; De Vos et al., 2005; Walling, 2008). Additionally, aphid
feeding increases transcripts associated with oxidative stress and cell wall modification (Couldridge et al.,
2007). We wish to understand the genome-wide effect of epigenetic modifications underlying this
differential gene expression. Our expertise in gene expression analysis and background with aphids
benefit this area of research.
B. Hypothesis and Objectives
We hypothesize that the majority of differential gene expression cause by Myzus persicae
infestation and feeding is due to epigenetic changes. We will test this hypothesis using the following
experiments:
Objective 1: Correlate DNA methylation and gene expression changes caused by infestation of the
generalist aphid, Myzus persicae
Hypothesis: Differentially expressed genes will have a higher incidence of differential methylation,
relative to that of those genes that are not differentially expressed due to aphid infestation.
Objective 2: Correlate histone modification and gene expression changes induced by M. persicae
feeding
Hypothesis: Genes with differential expression will exhibit a higher incidence of histone modifications
than those genes that are not differentially expressed due to aphid infestation.
Objective 3: Correlate M. persicae-induced small RNA profiles with gene expression changes
Hypothesis: Differentially expressed genes due to aphid infestation will have a higher incidence of
differentially expressed complementary small RNA species than those genes that are not differentially
expressed.
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C. Rationale and Significance
Since the generalist phloem-feeding aphid Myzus persicae is a worldwide problem, we believe it
is critical to understand the underlying mechanisms controlling gene expression changes within its host
plant. Here, we use the model plant Arabidopsis thaliana because of numerous resources associated with
it, including a sequenced genome and the high availability of mutants that could be used in future studies.
By conducting this research, we will understand what effect Myzus persicae has on its host plant’s
epigenomic landscape and learn if certain gene networks or pathways are regulated by specific epigenetic
mechanisms. Furthermore, this research can be expanded to other plant-aphid systems, or perhaps other
plant-phloem feeder systems that are economically important, such as the potato aphid interaction with
numerous host plants, or the specialized soybean-soybean aphid interaction.
D. Experimental Approach
Objective 1: Correlate DNA methylation and gene expression changes caused by infestation of the
generalist aphid, Myzus persicae
Hypothesis: Differentially expressed genes will have a higher incidence of differential methylation,
relative to that of those genes that are not differentially expressed due to aphid infestation.
To investigate the amount of differential gene expression in Myzus persicae-infested plants
associated with DNA methylation, we will map the methylome of Arabidopsis thaliana aphid-infested
and uninfested plants to previously published microarray results obtained by De Vos et al. (2005), which
will allow us to examine the relationship between expression and methylation in response to aphid
infestation. This dataset includes approximately 2100 genes that are differentially regulated after 72
hours of aphid infestation (832 upregulated and 1349 down-regulated); such a large data set will give our
assays higher resolution. We will transfer a mixture of 40 wingless aphid nymphs and adults with a soft
paint brush to Arabidopsis thaliana ecotype Col-0 plants. To simulate any mechanical stimulus from the
paintbrush, we will imitate applying aphids to control plants with a clean paintbrush. After 72 hours of
infestation, we will collect control and aphid-infested rosette leaves by immediate immersion into liquid
nitrogen. Three replicates of each treatment will be collected. To verify that data from the microarray is
consistent in our experiment, we will conduct quantitative RT-PCR on thirty genes from the previous
study: 10 upregulated, 10 downregulated, and 10 non-differentially expressed. We will use the Qiagen
RNeasy Plant Mini kit to extract total RNA and perform DNase digestion using Turbo DNA-free
(Ambion). Synthesis of cDNA will be conducted using the Bio-Rad iScript select cDNA synthesis kit
using the supplied Oligo(dT)20 primer. Quantitative PCR will be done using gene-specific primers in an
MX4000 thermocycler (Stratagene) using ROX as a reference dye with the ABsolute SYBR green mix
(Thermo Scientific), according to the manufacturer’s instructions. Normalization will be conducted using
AtUBI10.
For detection of differentially methylated cytosine residues in all sequence contexts, we will use
bisulfite sequencing technologies. Bisulfite treatment converts unmethylated cytosine residues to uracil
while methylated cytosine residues remain unaltered (He et al., 2011). Plant gDNA extraction will be
carried out using the Qiagen DNeasy Plant Mini kit, and quality will be verified throughout the procedure
using the Bioanalyzer 2100 (Agilent Technologies). For bisulfite conversion of unmethylated cytosine
residues and library preparation, we will use Illumina’s whole‐genome bisulfite sequencing protocol
according to the instructions, including unmethylated lambda gDNA as a control for bisulfite conversion
rate. Briefly, gDNA will be fragmented through sonication with subsequent end repair to blunt the
sheared DNA. We will adenylate the 3’ end, perform adaptor ligation, ligation product purification,
bisulfite conversion, and finally, enrichment of bisulfite-converted ligation products. These samples will
be submitted for sequencing utilizing the Illumina TruSeq platform through the Iowa State University
DNA facility utilizing the Illumina HiSeq 2000.
Following sequencing and primary analysis, we will filter and normalize the data and map unique
bisulfite-treated reads to the Arabidopsis thaliana Col-0 genome reference sequence (TAIR9) and the
lambda genome using the Bowtie algorithm v0.11.3 (Langmead et al., 2009). Total methylated cytosines
for each replicate will be identified as previously described (Lister et al., 2009). Differentially methylated
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cytosine residues and regions (DMRs) will be determined as previously described (Dowen et al., 2012).
All DMRs will be assigned to a nearby protein-encoding gene including 5 kb upstream of the translational
start site using the alignment with the TAIR9 Arabidopsis thaliana reference genome. This analysis will
allow us to determine differentially methylated promoters and gene bodies, including the within-gene
body DMR distribution. From this analysis, we will generate a list of all genes with DMRs.
Additionally, we will pair this data with genes that are differentially expressed as described by De Vos et
al. (2005) and their relative transcript levels, conducting statistics with Wilcoxon test in the R statistical
software package. We will verify our sequencing data using a subset of genes known found by bisulfite
sequencing to be differentially methylated caused by aphid feeding using Methyl-DNA
immunoprecipitation followed by qRT-PCR. Using the lists described above, we will categorize genes as
differentially methylated and differentially expressed, differentially methylated but not differentially
expressed, and lastly, non-differentially methylated but differentially expressed, taking into account
magnitude and direction of expression and methylation changes.
Expression status/methylation status
Abbreviation
High gene expression, high methylation
HgHm
High gene expression, unchanging methylation
Hg-m
High gene expression, low methylation
HgLm
Unchanging gene expression, high methylation
-gHm
Unchanging gene expression, unchanging methylation -g-m
Unchanging gene expression, low methylation
-gLm
Low gene expression, high methylation
LgHm
Low gene expression, unchanging methylation
Lg-m
Low gene expression, low methylation
LgLm
code
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Table 1: Categorization of differential gene expression and methylation status taking into account relative magnitude and direction of changes.
Using these lists, we will apply a Gene Ontology (GO) analysis to further understand if certain
pathways or responses are over- or under-represented in any of the categories. Doing so will allow us to
detect underlying mechanisms of regulation in gene networks. For example, in progeny of salt-stressed
Arabidopsis plants, genes encoding chromatin structure-modifying proteins were highly over-represented
(Bilichak et al., 2012).
Additionally in the progeny of these salt-stressed plants, only 3-4% of gene promoters and about
7% of transcribed regions tested were differentially methylated (Bilichak et al., 2012). However, this
study does not represent the entire genome and while some epigenetic modifications are heritable, it is
probable that several are lost before meiotic divisions. Additionally, this study of the partial methylome
was low-resolution and promoters or genes showing 50% or less differential methylation were not
counted as differentially methylated. Therefore, in our experiments that examine high resolution, samegeneration DNA methylation changes, we predict that an increased amount of differentially expressed
genes exhibit differential methylation. However, the progeny of salt exposed plants showed that
promoters of JA biosynthetic or signaling genes ALLENE OXIDE CYCLASE 2 (AOC2) and
JASMONATE-REGULATED GENE 21 (JRG21), respectively are hypermethylated (Bilichak et al., 2012),
indicating that these genes are regulated by methylation status. We expect that the purported suppression
of JA-mediated defenses by Myzus persicae is partially mediated by deposition of DNA methylation
marks throughout gene promoters and gene bodies. To see if this is true, we plan to use our GO analysis
to choose defense genes shown to be inducible by jasmonate and correlate the methylation status with
gene expression data.
Potential problems and solutions: Because the aphid-infestation experiments carried out by De Vos et al.
(2005) and our group may not have the exact same conditions, our qRT-PCR validation experiments may
show dissimilar gene expression patterns for the genes tested. Should this be the case, we will conduct a
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separate microarray analysis on the original collected tissue using the Affymetrix GeneChip®
Arabidopsis ATH1 Genome Array. Although bisulfite sequencing has become chief in determining
genome-wide methylation status, alternative methods such as MethylC-seq using restriction based
distinction of methylated cytosines and subsequent sequencing or tiling arrays, such as the Affymetrix
GeneChip® Arabidopsis Tiling 1.0R Array can be used. However, tiling arrays have inherently has lower
resolution than sequencing methods.
Objective 2: Correlate histone modification and gene expression changes induced by M. persicae
feeding
Hypothesis: Genes with differential expression will exhibit a higher incidence of histone modifications
than those genes that are not differentially expressed due to aphid infestation.
To understand the amount of gene expression coupled with histone modifications, we will apply
chromatin immunoprecipitation (ChIP)-sequencing analysis to our aphid-infested and control samples.
To detect sequences associated with modified histones, we will utilize antibodies specific for various
histone modifications to pull down the associated genomic DNA and sequence these regions. Using these
technologies, we can accurately show the sequence distribution of histone modifications induced by
Myzus persicae feeding. We plan to study several histone modifications including trimethylation of
histone H3 on lysine residues 4 and 27 (H3K4me3 and H3K27me3), which are generally associated with
transcriptional activation and repression, respectively. Additionally, we will target acetylation of lysine
residue 9 on histone H3 (H3K9ac), which is associated with transcriptional activation. Using antibodies
specific to these modifications (Millipore), we will first verify optimal antibody conditions. Following
optimization, we will prepare nuclei, crosslink and fragment chromatin, and perform chromatin
immunoprecipitation and purification from the control and Myzus persicae-infested Arabidopsis leaf
samples according to Brusslan et el. (2012) but with our optimized antibody conditions. Additionally, we
will have a “no antibody” control sample and anti-H3 (Millipore) antibody that will be treated as all other
samples. Subsequently, we will use Illumina's TruSeq ChIP sample preparation kit according to the
instructions. Briefly, we will adenylate 3’ ends, perform adaptor ligation, perform ligation product
purification by selecting 200±25 bp products, and finally, enrichment of ligation products. These samples
will be submitted for sequencing utilizing the Illumina TruSeq platform through the Iowa State University
DNA facility utilizing the Illumina HiSeq 2000.
Following sequencing and primary analysis, we will filter the data and map unique reads to the
Arabidopsis thaliana Col-0 genome reference sequence (TAIR9) using the Bowtie algorithm v0.11.3
(Langmead et al., 2009). We will normalize the data using the input control and subsequently calculate a
fold-ratio between the sequences with modified histones and control input. Sequence data will be mapped
to a nearby protein-encoding gene including 5 kb upstream of the translation start site using the alignment
with the TAIR9 Arabidopsis thaliana reference genome, and comparisons between sequences found for
the different histone modifications used for pull down between infested and uninfested samples will be
made. This analysis will allow us to generate a list of sequences associated with differentially modified
histones. Additionally, we will pair this data with genes that are differentially expressed as described by
De Vos et al. (2005) and their relative transcript levels, conducting statistics with Wilcoxon test in the R
statistical software package. After determining sequences of genes or gene families associated with
repressive or activating histone modifications, we can validate our ChIP-seq experiments using chromatin
immunoprecipitation coupled with qRT-PCR.
Using these lists, we can group genes based on the magnitude and direction of their expression
and extent of activating or repressive histone marks. Subsequently, we will conduct a Gene Ontology
analysis to determine if certain gene networks are differentially represented; this will allow us to detect
underlying mechanisms of gene regulation of groups of genes. Additionally, by using high-resolution
detection of histone modifications in the Arabidopsis thaliana-Myzus persicae interaction, we can begin
to see the combination of histone modifications (known as the “histone code”) that are required to
regulate gene expression in this specific plant-insect interaction.
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In Arabidopsis, the antagonistic nature between salicylic-mediated signaling and jasmonatemediated signaling is well characterized. Studies have identified proteins that function in this crosstalk.
Among this list of genes, the transcription factor WRKY70 has been characterized as a gene that activates
SA signaling while repressing JA signaling (Alvarez-Venegas et al., 2007). This study showed that, in
plants infected with P. syringae pv. tomato DC3000, histones associated with WRKY70 were modified by
the histone methyltransferase ARABIDOPSIS HOMOLOG OF TRITHORAX 1 (ATX1) which
trimethylates histone H3K4 on a subset of genes, many of which are associated with disease responses
(Alvarez-Venegas et al., 2007). However, in atx1 plants, nearly 1600 genes were misexpressed,
indicating that epigenetic mechanisms may work primarily on transcription factors, which in turn regulate
several other genes (Alvarez-Venegas et al., 2007). Following this, we predict that aphid-induced
salicylic-responsive genes are upregulated through the addition of activating histone marks such as
H3K4me3 and H3K9ac at SA-responsive transcription factor promoters. That is, using our Gene
Ontology analyses, we expect SA-responsive transcription factors to be overrepresented in the
immunoprecipitated H3K4me3 and H3K9ac Arabidopsis-aphid datasets.
Potential problems and solutions: Bias in conducting the cDNA library (Zhang et al., 2011) which may
exaggerate differences between lowly- and highly-expressed small RNAs. Therefore, careful
consideration must be taken during normalization and interpretation of results. Tiling arrays, such as the
Affymetrix GeneChip® Arabidopsis Tiling 1.0R Array can be used, yet inherently has lower resolution
than sequencing methods.
Objective 3: Correlate M. persicae-induced small RNA profiles with gene expression changes
Hypothesis: Differentially expressed genes due to aphid infestation will have a higher incidence of
differentially expressed complementary small RNA species than those genes that are not differentially
expressed.
To understand the amount of gene expression altered by small RNAs, we will conduct small RNA
sequencing on our Myzus persicae-infested and control samples. To detect changes in small RNA
profiles, we will use the Qiagen miRNeasy Plant Mini kit to extract total RNA (which includes small
RNAs) and perform DNase digestion using Turbo DNA-free (Ambion). After quality analysis using a
Bioanalyzer 2100 (Agilent Technologies), we plan to use Illumina’s TruSeq Small RNA Sample
Preparation Kit according to the instructions. Briefly, we will ligate 3’ and 5’ end adaptors, and perform
RT-PCR with adaptor-specific primers to generate amplified stable cDNAs. After purification of the
enriched cDNAs, we will concentrate the library and verify the quality using a Bioanalyzer 2100 (Agilent
technologies). The samples will be submitted for sequencing utilizing the Illumina TruSeq platform
through the Iowa State University DNA facility utilizing the Illumina HiSeq 2000. Additionally, using
Northern blots and qRT-PCR, we will verify the results from our small RNA sequencing experiment.
Following sequencing and primary analysis, we will apply filters, normalize our data, and apply
shrortran (Gupta et al., 2012). This analysis tool is freely available online and conducts data preprocessing, mapping to the TAIR9 Arabidopsis thaliana reference genome using Bowtie (Langmead et
al., 2009) and prediction of miRNAs and their gene targets. We will create a list of differentially
expressed small RNAs and correlate these with the gene expression data (De Vos et al., 2005), taking into
account relative magnitude and direction of changes. Subsequently, we will conduct a Gene Ontology
analysis to determine if certain gene networks are differentially represented; this will allow us to
determine underlying regulatory mechanisms genes that function in similar pathways.
There are 340,000 unique small RNA sequences in Arabidopsis and thus several sequences have
the potential to be regulated by these small RNAs (Rajagopalan et al., 2006). Viruses have been found to
suppress the host small RNA silencing process to enhance pathogen infection (Ruiz-Ferrer and Voinnet,
2009). P. syringae modifies plant small RNA profiles that are important for controlling several hormone
biosynthesis and signaling pathways including abscisic acid, auxins, and jasmonate (Zhang et al., 2011).
In particular, a miRNA regulating the biosynthesis of JA, miR319, was highly induced by infection with
avirulent and mutant P. syringae, further indicating that SA-mediated defenses are antagonistic to JA-
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mediated defenses (Zhang et al., 2011). Thus, we expect that aphid feeding will induce miR319 and
miRNAs that target other JA biosynthetic and signaling-related genes.
Potential problems and solutions: Although generally correlative, inconsistencies between small RNA
sequencing and validation experiments have been found due to bias in conducting the cDNA library
(Zhang et al., 2011) which may exaggerate differences between lowly- and highly-expressed small RNAs.
Therefore, careful consideration must be taken during normalization and interpretation of results.
E. Future Directions
We propose to determine entire dynamic epigenomic landscape changes due to feeding by the
generalist aphid, Myzus persicae. In the future, we can use the DNA methylation mutants (or using the
demethylation agent 5-azacytidine), histone modifier mutants, or mutants lacking small RNA pathway
components to fully understand the effect of each separate epigenetic modification. Studies have been
conducted with various pathogens using DNA methyltransferase mutants met1 (CG), drm1/2 (CHG),
cmt3 (CHH), and to a lesser extent, the DNA glycosylase mutant ros1. By studying these mutants in our
Arabidopsis-aphid system, we will be able to understand what effect sequence-specific methylation has
on aphid performance. For example, CHH methylation patterns, which are generally associated with
heterochromatin, have been suggested to change based on distinct stresses (Dowen et al., 2012).
Additionally, because NBS-LRR genes tend to cluster in regions rich with TEs and repetitive elements,
which also have enriched CHH methylation, different stresses may have differing impacts on homologous
recombination between repetitive sequences within NBS-LRR gene clusters, thereby contributing to the
evolution of resistance genes (Alvarez et al., 2010). The rate of somatic homologous recombination could
be measured in wild type and mutant plants to understand the effect aphids have on their host plants using
an in planta recombination assay using a reporter gene that, when recombined, produces a functional
marker (Lucht et al., 2002). It is interesting to note that there is no known resistance gene against aphids
such as M. persicae in commonly used accessions of Arabidopsis.
Nucleosome positioning, chromatin remodelers, and other uncharacterized histone modifications
could be a focus of future studies. Experiments can be conducted to determine nucleosome positioning or
histone variant deposition in aphid-infested plants. Surprisingly, other histone modifications such
phosphorylation and ubiquitination marks or modifications on other histones have not been characterized
to date. Conducting these experiments and comparing various pathogen epigenomic landscapes would
allow us to more fully understand the epigenomic code required for responses to specific pathogens.
Additionally, experiments could be used to examine wild type plants and mutant plants deficient in
specific chromatin remodelers, as they have been shown to be direct targets of pathogen effector
molecules (Alvarez et al., 2010).
Epigenetics inherently indicates that modifications to the genome in other ways than sequence
change can be passed to the next generation. Studies have shown that progeny of plants exposed to
specific stresses induce defenses more quickly and are effective against a wide range of plant pathogens
(Rasmann et al., 2012). These enhanced induced defenses may be a direct result of chromatin structure
modification that allows easy access of RNA Polymerase II to defense gene promoters.
Transgenerational inheritance of induced defenses is a recent area of research that will gain attention
quickly, and it is evident that the study of epigenetics and epigenomic landscapes is still in its infancy.
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F. Timeline
Objective 1
correlation of DNA
methylation with
gene expression
Objective 2
correlation of
histone
modifications with
gene expression
Objective 3
correlation of small
RNA profile with
gene expression
Conduct aphidfeeding
experiments
Year 1
Validate our
experiments against
De Vos et al. 2005
microarray
Conduct antibody optimization assays for chromatin
immunoprecipitation
Conduct small
RNA extraction and
sequencing
Analyze small RNA data and validate
datasets using Northern blots or
qPCR
Year 2
Objective 1
correlation of DNA
methylation with
gene expression
Objective 2
correlation of histone
modifications with
gene expression
Objective 3
correlation of small
RNA profile with
gene expression
Conduct chromatin
immunoprecipitation
and sequencing
Analyze ChIP-seq data
Verify ChIP-seq
data using qPCR
Prepare manuscript(s) for submission to peer-reviewed journal
Year 3
Objective 1
correlation of DNA
methylation with
gene expression
Objective 2
correlation of histone
modifications with
gene expression
Objective 3
correlation of small
RNA profile with
gene expression
Conduct bisulfite
conversion and
sequencing
Conduct data analysis on bisulfitetreated DNA
Validate bisulfite
data set using
MeDIP-qPCR
Prepare manuscript(s) for submission to peer-reviewed journal
Prepare manuscript(s) for submission to peer-reviewed journal
10
III. References
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11
Moran, P.J., and Thompson, G.A. 2001. Molecular responses to aphid feeding in Arabidopsis in relation
to plant defense pathways. Plant Physiology 125:1074-1085.
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microRNAs in Arabidopsis thaliana. Genes & Development 20:3407-3425.
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IV. Budget and Justification
Please see the attached budget Excel file. Materials and supplies required for our experiments
include general lab chemicals and reagents in addition to kits for RNA extraction, DNase treatment,
cDNA synthesis, and quantitative RT-PCR. We require histone modification antibodies anti-H3K4me3,
anti-H3K27me3, anti-H3K9ac as well as anti-H3 for chromatin immunoprecipitation experiments.
Additional kits are required for preparation of aphid-infested and uninfested samples for small RNA
extraction and chromatin immunoprecipitation, which are available through Illumina.
Subsequent to sample preparation, we require funding for sample submission to the Iowa
University DNA facility for next-generation sequencing using the Illumina platform. High resolution
sequencing is required for all experiments to fully understand how the epigenomic landscape changes due
to generalist aphid infestation. Most analysis programs are available free of charge, however we require
funding for using Iowa State University computers or the Iowa State University Genome Informatics
facility. Additional funding is required for validation of next-generation sequencing experiments
including reagents for quantitative RT-PCR and Northern blot analysis.
Our graduate research assistant will conduct Myzus persicae-Arabidopsis thaliana experiments
and will conduct quantitative RT-PCR to verify that De Vos et al. (2005) microarray results are
compatible for comparing gene expression to our epigenetic assays. The postdoctoral researcher will
optimize antibody conditions for ChIP-seq. Both the graduate student and postdoctoral researcher will
prepare samples for bisulfite sequencing, ChIP-seq, and small RNA sequencing. Additionally, the
graduate student will verify the results of all sequencing experiments and the postdoc will analyze the
results of the sequencing data. Both will perform Gene Ontology analyses and write manuscripts to be
submitted to peer-reviewed journals.
Additional funding is requested for annual registration and travel for the Plant and Animal
Genomics Conference where both the postdoctoral researcher and graduate students will present posters
unless requested to give a talk. The principle investigator does not request any salary funding through
this grant.
Project Budget Worksheet - Iowa State University of Science and Technology
Eff. 7-1-12
Program Sponsor
Title
PI
Epigenomic landscape changes due to generalist aphid feeding
2
Period of Performance
1/1/2013-12/31/2015
Deadline
Year 1
Year 2
Year 3
Total
Salary
Monthly
Calendar
Months
Academic
Months
Summer
Months
$0
$0
$0
$0
1
$0
0.00
0.00
0.00
$0
$0
$0
$0
2
$0
0.00
0.00
0.00
$0
$0
$0
$0
3
$0
0.00
0.00
0.00
$0
$0
$0
$0
4
$0
0.00
0.00
0.00
$0
$0
$0
$0
5
$0
0.00
0.00
0.00
$0
$0
$0
$0
6
$0
0.00
0.00
0.00
$0
$0
$0
$0
7
$0
0.00
0.00
0.00
$0
$0
$0
$0
8
9
$0
$0
0.00
0.00
0.00
0.00
Number of
persons
$0
$0
$0
$0
$0
$0
$0
$0
Monthly
0.00
0.00
Calendar
Months
$64,500
$66,435
$68,428
$199,363
$3,500
$42,000
$43,260
$44,558
$129,818
A
Key Personnel
B
Other Personnel
1 Post Doc
12.00
1.00
$0
0.00
0.00
$0
$0
$0
$0
3 Research Asst-Halftime
$1,875
12.00
1.00
$22,500
$23,175
$23,870
$69,545
4 Research Asst-Halftime
$0
0.00
0.00
$0
$0
$0
$0
5 Hourly Undergraduate student
$0
0.00
0.00
$0
$0
$0
$0
6 Hourly Undergraduate student
$0
0.00
0.00
$0
$0
$0
$0
7 P&S
$0
0.00
0.00
$0
$0
$0
$0
8 P&S
$0
0.00
0.00
$0
$0
$0
$0
9 Secretarial/Clerical
$0
0.00
0.00
$0
$0
$0
$0
10 Secretarial/Clerical
$0
0.00
0.00
$0
$0
$0
11 Non-Student Hourly
$0
0.00
0.00
$0
$0
$0
12 Non-Student Hourly
$0
0.00
0.00
$0
$0
$0
$64,500
$66,435
$68,428
$199,363
Rate
$12,143
$12,507
$12,882
$37,531
0
30.5%
$0
$0
$0
$0
0
30.5%
$0
$0
$0
$0
0
30.5%
$0
$0
$0
$0
0
30.5%
$0
$0
$0
$0
0
30.5%
$0
$0
$0
$0
0
30.5%
$0
$0
$0
$0
0
30.5%
$0
$0
$0
$0
0
30.5%
$0
$0
$0
0
30.5%
$0
$0
$0
$0
Post Doc
22.0%
$9,240
$9,517
$9,803
$28,560
Post Doc
22.0%
$0
$0
$0
$0
Research Asst-Halftime
12.9%
$2,903
$2,990
$3,079
$8,971
Research Asst-Halftime
12.9%
$0
$0
$0
$0
Hourly Undergraduate student
4.6%
$0
$0
$0
$0
Hourly Undergraduate student
4.6%
$0
$0
$0
$0
P&S
37.0%
$0
$0
$0
P&S
37.0%
$0
$0
$0
Secretarial/Clerical
49.7%
$0
$0
$0
$0
Secretarial/Clerical
49.7%
$0
$0
$0
$0
Non-Student Hourly
12.0%
$0
$0
$0
$0
Non-Student Hourly
12.0%
$0
$0
$0
$0
$76,643
$78,942
$81,310
$236,894
$0
$0
$0
$0
Travel
$0
$0
$0
$0
1. Domestic Travel
2. Foreign Travel
$0
$0
$0
$0
$0
$0
$0
$0
$2,800
$2,800
$2,800
$1,000
$1,500
$200
$100
$1,000
$1,500
$200
$100
$1,000
$1,500
$200
$100
$8,400
$3,000
$4,500
$600
$300
$31,271
$51,664
$26,591
$12,000
$500
$2,000
$7,000
$0
$0
$0
$0
$9,771
$0
$0
$0
$7,000
$500
$2,000
$32,000
$0
$0
$0
$0
$10,164
$0
$0
$0
$6,000
$1,000
$2,000
$7,000
$0
$0
$0
$0
$10,591
$0
$0
$0
$109,527
$25,000
$2,000
$6,000
$46,000
$0
$0
$0
$0
$30,527
$0
$0
$0
Subtotal: Total Direct Costs (TDC)
$110,714
$133,406
$110,701
$354,821
Subtotal: Modified Total Direct Costs
$100,943
$123,242
$100,110
$324,294
$48,452
$59,156
$48,053
$155,661
$48,452
$59,156
$48,053
$159,166
$192,562
$158,754
2 Post Doc
Check
$0.00
$69,545.25
Subtotal: Salaries and Wages
C
Fringe Benefits
Subtotal: Salaries, Wages, and Benefits
Equipment (List Item and $ amount for each item > $5k)
D
$69,545.25
$0
$37,531.25
1
2
E
F
Participant Support Cost
See notes below
1. Stipend
2. Travel
3. Subsistence
4. Other
G
Other Direct Costs
1
2
3
4
5
6
7
8
9
10
Materials and Supplies
Publication cost
Computing support
Instrumentation facility
Subcontractor1 - Subject to IDC (first $25,000) See notes below
NOT subject to IDC (Amount over $25,000)
Subcontractor2 - Subject to IDC (first $25,000) See notes below
NOT subject to IDC (Amount over $25,000)
Tuition - Non-Engineering
(Click on "Tuition" sheet)
Tuition - Engineering
(Click on "Tuition" sheet)
Other
Other
$354,820.98
[ 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 ]
$510,482
$510,482.25