Chromatin Structure in Water-Deficit Stress in Arabidopsis Yong Ding, Karin van Dijk, Sridhar Malkaram, Rong Liu, J.J.M. Riethoven, Jingi Yang, Han Chen, Yuannan Xia, Dong Wang, S. Ladunga, Zoya Avramova, & M. Fromm NSF EPSCoR Chromatin Biology Grant This work was supported by NSF grant EPS

What we want to learn: how chromatin modifications affect the water-deficit mRNA response –How do mRNA levels correlate with chromatin modifications when comparing many different genes? –How do mRNA levels correlate with chromatin modifications in the same gene when it changes expression during water deficit stress? –How does atx1 mutation in H3K4 methyltransferase affect chromatin and gene expression –How does chromatin affect drought sensitivity of atx1

ChIP-Seq: Chromatin Immunoprecipitation (ChIP) followed by High Throughput DNA Sequencing Specific histone modification or Bound Protein of interest Crosslink protein to DNA and fragment DNA Immunoprecipitate with antibodies to target modification or bound protein Specific Antibody Enriched chromatin after immunoprecipitation High throughput DNA Sequencing

Experimental Design 4 week old Arabidopsis plants in soil at vegetative stage Watered Watered Deprived to 65% RWC (Wilted leaves) Isolate mRNA for Microarray measurements of gene expression Isolate chromatin for immunoprecipitation with H3K4 methylation specific antibodies Analyze gene expression levels and chromatin modification for H3K4me1, H3K4me2 and H3K4me3 across Arabidopsis genome Solexa sequence analysis Affymetrix microarray analysis

Table I. Number of sequencing reads from each chromatin immunoprecipitation experiment Number of sequencing reads TreatmentH3K4me1H3K4me2H3K4me3 Watered17,451,83727,354,17912,285,745 Water deficit16,972,74939,299,90318,012,924 a Number of sequences that are unique in the Arabidopsis genome and contain 2 or less mismatches

RD29A and RD29B are an adjacent ancient gene duplication RD29B inducedRD29A induced AT5G52290 No Change

Phosphate responsive protein is repressed by water deficit stress

GAPDH is constitutively expressed

Gene Solexa Watered Solexa DryFoldQ-PCRSTD RAB CBF LTP GAPC eEF1b XERO ATHB ATHB SAG RD29B RD29A LR4, LTP GLP Comparison of H3K4me3 levels by Solexa and Q-PCR measurements

Average profiles by expression levels

Table II. Percentage of H3K4 methylation peaks mapping to genes Treatment and type of H3K4 methylation Number of H3K4 methylation regions Number of regions mapping to genes* Percentage of regions mapping to genes Water: H3K4me Dry: H3K4me Water: H3K4me Dry: H3K4me Water: H3K4me Dry: H3K4me Intersection of regions containing H3K4 me1, me2, and me *Includes 200 bp upstream and downstream of transcribed regions of annotated genes

Table III. Percentage of genes with H3K4 methylation regions ____________________________________________________________________________ Treatment and type of H3K4 methylation Number of genes with H3K4 methylation Percentage of genes with H3K4 methylation Water: H3K4me Dry: H3K4me Water: H3 K4me Dry: H3K4me Water: H3K4me Dry: H3K4me Genes with one or more types of H3K4 methylation

Table V. Expressed Genes without H3K4 methylation comprise only 1% of all expressed genes Gene Expression Percentile WateredDry Number of genes a Percent of 542 genes b Calculated percent of 31,762 genes Number of genes Percent of 542 genes Calculated a percent of 31,762 genes %5.8% %5.6% %1.6% %1.7% %0.3%193.5%0.3% %0.3%254.6%0.4% %0.2%91.7%0.1% %0.2%163.0%0.2% Total genes5428.3%5428.3%

Focus on the induced or repressed genes Many induced genes are ABA inducible What happens to the H3K4 methylation status of individual genes when induced or repressed Are there unique chromatin profiles of inducible genes?

Median and +/- 1 standard deviation range for changes in H3K4 methylation when gene expression changes Trends Induced Me3 up Me2 up Me1 down Repressed Me3 down Me2 up Me1 up

The broad h3K4me3 profile exists before gene induction and is not dependent on expression level (RD29B has almost undetectable expression before induction) RD29B inducedRD29A induced No Change

Inducible genes have broader H3K4me3 profiles along the length of the gene All expressed genes

Conclusions 92% of genes are marked by one or more types of H3K4 methylation No simple correlation of H3K4 methylation levels with transcription levels for different genes A change in the transcription of the same gene shows a strong correlation with a change in H3K4me3 levels Reduced nucleosome density or modification level upstream of TSS

What we want to learn: how atx1 mutant affects the water-deficit mRNA response –ATX1 is a H3K4 methyltransferase (Avramova). –Atx1 mutants have pleiotropic phenotypes. –How does atx1 mutation in H3K4 methyltransferase affect chromatin and gene expression –How does chromatin affect drought sensitivity of atx1

Arabidopsis ATX1 (Arabidopsis thaliana TRITHORAX ) protein is complex with multiple domains SET peptide [for Su(var)3-9, E(z), Trithorax], encoded by the Drosophila melanogaster Su(var)39-, E(z)-, and Trithorax-related genes, carries histone lysine methyltransferase Conserved Trithorax domains: H3K4 methylases

Soil drought assay – Yong Ding Drought treat 9 days Re-water 3 days WT atx1 WT atx1 45/61 20/66 WT atx1 Water

Plant Survival ratio (%) * Soil drought assay – Yong Ding

The drought response gene expression level W.t. atx1

gene expression level W.t. atx1

H3K4 Tri-methylation level changes in atx1

New Genomics Statistics How to tell the False Discover Rates of differences in peaks in chromatin studies 1. Variation in replicates to determine frequency of random peaks: two wild type and two atx1 mutant samples 2. Signal: avg wild type – avg atx1 3. FDR = # peaks replicates/signal peaks

ATX1: A H3K4 methyltransferase that affects drought sensitivity and chromatin structure Zoya Avramova Small percent of genome shows significant changes in H3K4me3 New statistical methods for determining False Discover Rate (FDR) Physiological – drought sensitivity of atx1 Basis for drought sensitivity – low ABA biosynthesis in Nced3 gene (Nine-cis- epoxycarotenoid Dioxygenase 3);

Acknowledgements NSF EPSCoR Molecular Biology Yong Ding, Karin van Dijk, Han Chen, M. Fromm Zhen Wang, Amit Mehra, Heriberto Cerutti, Zoya Avramova Computational Sridhar Malkaram, Rong Liu, J.J.M. Riethoven, Jingi Yang, Steve Ladunga, Jamie Davila Statistics Dong Wang Microarrays Yuannan Xia