title | author | date | theme | aspectratio | fontsize |
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Bioinformatic analysis of complex, high-throughput genomic and epigenomic data in the context of |
Ryan C. Thompson \
Su Lab \
The Scripps Research Institute
|
October 24, 2019 |
Boadilla |
169 |
14pt |
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36,528 transplants performed in the USA in 20181
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100 transplants every day!
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Over 113,000 people on the national transplant waiting list as of July 2019
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Organ donation statistics for the USA in 20181
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A graft is categorized based on the relationship between donor and recipient:
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Autograft: Donor and recipient are the same individual
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Allograft: Donor and recipient are different individuals of the same species
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Xenograft: Donor and recipient are different species
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TCR binds to both antigen and MHC surface \vspace{10pt}
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HLA genes encoding MHC proteins are highly polymorphic \vspace{10pt}
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Variants in donor MHC can trigger the same T-cell response as a foreign antigen
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\footnotetext[3]{\href{https://doi.org/10.1016/j.cell.2007.01.048}{Colf, Bankovich, et al. "How a Single T Cell Receptor Recognizes Both Self and Foreign MHC". In: Cell (2007)}}
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Graft recipient must take immune suppressive drugs indefinitely
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Graft is monitored for rejection and dosage adjusted over time
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Immune suppression is a delicate balance: too much and too little are both problematic.
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Compared to naïve cells, memory cells:
- respond to a lower antigen concentration
- respond more strongly at any given antigen concentration
- require less co-stimulation
- are somewhat independent of some types of co-stimulation required by naïve cells
- evolve over time to respond even more strongly to their antigen
Result:
- Memory cells require progressively higher doses of immune suppresive drugs
- Dosage cannot be increased indefinitely without compromising the immune system's ability to fight infection
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\onslide<2->{Genome-wide epigenetic analysis of H3K4 and H3K27 methylation in naïve
and memory
\onslide<3->{Improving array-based diagnostics for transplant rejection by optimizing data preprocessing}
\onslide<4->{Globin-blocking for more effective blood RNA-seq analysis in primate animal model for experimental graft rejection treatment}
\Large
Genome-wide epigenetic analysis of H3K4 and H3K27 methylation in naïve
and memory
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Cell surface markers fairly well-characterized
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But internal mechanisms poorly understood
. . .
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\large
Hypothesis: Epigenetic regulation of gene expression through
histone modification is involved in
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H3K4me3: "activating" mark associated with active transcription
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H3K4me2: Correlated with H3K4me3, hypothesized "poised" state
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H3K27me3: "repressive" mark associated with inactive genes
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. . .
\vfill
All involved in T-cell differentiation, but activation dynamics unexplored
ChIP-seq measures DNA bound to marked histones3
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\footnotesize
Data generated by Sarah Lamere, published in GEO as GSE73214
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- How do we define the "promoter region" for each gene? \vspace{10pt}
- How do these histone marks behave in promoter regions? \vspace{10pt}
- What can these histone marks tell us about T-cell activation and differentiation?
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\centering \LARGE
How do we define the "promoter region" for each gene?
\begin{figure} \centering \only<1>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/CCF-plots-A-SVG.png}} \only<2>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/CCF-plots-B-SVG.png}} \only<3>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/CCF-plots-C-SVG.png}} \caption{Strand cross-correlation plots show histone-sized wave pattern} \end{figure}
\begin{figure} \centering \only<1>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/FPKM-by-Peak-Violin-Plots-A-SVG.png}} \only<2>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/FPKM-by-Peak-Violin-Plots-B-SVG.png}} \only<3>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/FPKM-by-Peak-Violin-Plots-C-SVG.png}} \only<4>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/FPKM-by-Peak-Violin-Plots-D-SVG.png}} \only<5>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/FPKM-by-Peak-Violin-Plots-Z-SVG.png}} \caption{Expression distributions of genes with and without promoter peaks} \end{figure}
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How do we define the "promoter region" for each gene?
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- H3K4me2, H3K4me3, and H3K27me3 occur in broad regions across the genome
- Enriched regions occur more commonly near promoters
- Each histone mark has its own "effective promoter radius"
- Presence or absence of a peak within this radius is correlated with gene expression
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::: {.column width="50%"} \centering \only<1>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/CCF-plots-A-SVG.png}} \only<2>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/Promoter-Peak-Distance-Profile-SVG.pdf}} \only<3>{\includegraphics[width=\textwidth,height=0.7\textheight]{graphics/presentation/FPKM-by-Peak-Violin-Plots-A-SVG.png}} ::: ::::::::::
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How do these histone marks behave in promoter regions?
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Does the position of a histone modification within a gene promoter matter to that gene's expression, or is it merely the presence or absence anywhere within the promoter?
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How do these histone marks behave in promoter regions?
- Peak closer to promoter
$\Rightarrow$ higher gene expression - Slightly asymmetric in favor of peaks downstream of TSS
. . .
- Depletion of H3K27me3 at TSS
$\Rightarrow$ elevated gene expression - Enrichment of H3K27me3 upstream of TSS
$\Rightarrow$ more elevated expression - Other coverage profiles: no association
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What can these histone marks tell us about T-cell activation and differentiation?
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What can these histone marks tell us about T-cell activation and differentiation?
- Almost no differential histone modification observed between naïve and memory at Day 14, despite plenty of differential modification at earlier time points.
- Expression and 3 histone marks all show "convergence" between naïve and memory by Day 14 in the first 2 or 3 principal coordinates.
- MOFA captures this convergence pattern in a single latent factor, indicating that this is a shared pattern across all 4 data sets.
Define empirically using peak-to-promoter distances; validate by correlation with expression.
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Location matters! Specific coverage patterns correlated with elevated expression.
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Epigenetic & expression state of naïve and memory converges late after activation, consistent with naïve differentiation into memory.
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"Effective promoter region" is a useful concept but "radius" oversimplifies: seek a better definition
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Coverage profiles were only examined in naïve day 0 samples: further analysis could incorporate time and cell type
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Coverage profile normalization induces degeneracy: adapt a better normalization from peak callers like SICER
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Unimodal distribution of promoter coverage profiles is unexpected
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Experiment was not designed to directly test the epigenetic convergence hypothesis: future experiments could include cultured but un-activated controls
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High correlation between H3K4me3 and H3K4me2 is curious given they are mutually exclusive: design experiments to determine the degree of actual co-occurrence
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Epigenetic regulation through histone methylation is surely involved in immune memory
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Can we stop memory cells from forming by perturbing histone methylation?
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Can we disrupt memory cell function during rejection by perturbing histone methylation?
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Can we suggest druggable targets for better immune suppression by looking at epigenetically upregulated genes in memory cells?
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My mentors, past and present: Drs. Terry Gaasterland, Daniel Salomon, and Andrew Su
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My committee: Drs. Nicholas Schork, Ali Torkamani, Michael Petrascheck, and Luc Teyton.
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My many collaborators in the Salomon Lab
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The Scripps Genomics Core
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My parents, John & Chris Thompson
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Questions?
Footnotes
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Sarah LaMere. Ph.D. thesis (2015). ↩