Skip to content

Latest commit

 

History

History
632 lines (394 loc) · 17.9 KB

presentation.mkdn

File metadata and controls

632 lines (394 loc) · 17.9 KB
title author date theme aspectratio fontsize
Bioinformatic analysis of complex, high-throughput genomic and epigenomic data in the context of $\mathsf{CD4}^{+}$ T-cell differentiation and diagnosis and treatment of transplant rejection
Ryan C. Thompson \ Su Lab \ The Scripps Research Institute
October 24, 2019
Boadilla
169
14pt

Organ transplants are a life-saving treatment

::: incremental

  • 36,528 transplants performed in the USA in 20181

  • 100 transplants every day!

  • Over 113,000 people on the national transplant waiting list as of July 2019

:::

Organ donation statistics for the USA in 20181

\centering

Types of grafts

A graft is categorized based on the relationship between donor and recipient:

. . .

::: incremental

  • Autograft: Donor and recipient are the same individual

  • Allograft: Donor and recipient are different individuals of the same species

  • Xenograft: Donor and recipient are different species

:::

Recipient T-cells reject allogenic MHCs

:::::::::: {.columns}

::: {.column width="55%"}

:::: incremental

  • TCR binds to both antigen and MHC surface \vspace{10pt}

  • HLA genes encoding MHC proteins are highly polymorphic \vspace{10pt}

  • Variants in donor MHC can trigger the same T-cell response as a foreign antigen

::::

:::

::: {.column width="40%"}

TCR binding to self (right) and allogenic (left) MHC\footnotemark{ height=70% } :::

::::::::::

\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)}}

Allograft rejection is a major long-term problem

Kidney allograft survival rates in children by transplant year{ height=65% }

Rejection is treated with immune suppressive drugs

::: incremental

  • Graft recipient must take immune suppressive drugs indefinitely

  • Graft is monitored for rejection and dosage adjusted over time

  • Immune suppression is a delicate balance: too much and too little are both problematic.

:::

Memory cells: faster, stronger, and more independent

Naïve T-cell activated by APC

Memory cells: faster, stronger, and more independent

Naïve T-cell differentiates and proliferates into effector T-cells

Memory cells: faster, stronger, and more independent

Post-infection, some effectors cells remain as memory cells

Memory cells: faster, stronger, and more independent

Memory T-cells respond more strongly to activation

::: notes

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

:::

3 problems relating to transplant rejection

1. How are memory cells different from naïve?

\onslide<2->{Genome-wide epigenetic analysis of H3K4 and H3K27 methylation in naïve and memory $\mathsf{CD4}^{+}$ T-cell activation}

2. How can we diagnose rejection noninvasively?

\onslide<3->{Improving array-based diagnostics for transplant rejection by optimizing data preprocessing}

3. How can we evaluate effects of a rejection treatment?

\onslide<4->{Globin-blocking for more effective blood RNA-seq analysis in primate animal model for experimental graft rejection treatment}

Today's focus

\Large 1. How are memory cells different from naïve?

\Large

Genome-wide epigenetic analysis of H3K4 and H3K27 methylation in naïve and memory $\mathsf{CD4}^{+}$ T-cell activation

We need a better understanding of immune memory

  • Cell surface markers fairly well-characterized

  • But internal mechanisms poorly understood

. . .

\vfill

\large

Hypothesis: Epigenetic regulation of gene expression through histone modification is involved in $\mathsf{CD4}^{+}$ T-cell activation and memory.

Which histone marks are we looking at?

. . .

::: incremental

  • H3K4me3: "activating" mark associated with active transcription

  • H3K4me2: Correlated with H3K4me3, hypothesized "poised" state

  • H3K27me3: "repressive" mark associated with inactive genes

:::

. . .

\vfill

All involved in T-cell differentiation, but activation dynamics unexplored

ChIP-seq measures DNA bound to marked histones3

\centering

{ height=70% }

Experimental design

\centering

{ height=70% }

\footnotesize

Data generated by Sarah Lamere, published in GEO as GSE73214

Time points capture phases of immune response

\centering

A few intermediate analysis steps are required

\centering

Questions to focus on

::: incremental

  1. How do we define the "promoter region" for each gene? \vspace{10pt}
  2. How do these histone marks behave in promoter regions? \vspace{10pt}
  3. What can these histone marks tell us about T-cell activation and differentiation?

:::

First question

\centering \LARGE

How do we define the "promoter region" for each gene?

Histone modifications occur on consecutive histones

ChIP-seq coverage in IL2 gene{ height=65% }

Histone modifications occur on consecutive histones

\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}

SICER identifies enriched regions across the genome

Finding "islands" of coverage with SICER

IDR identifies reproducible enriched regions

Example irreproducible discovery rate score consistency plot{ height=65% }

Finding enriched regions across the genome

Peak-calling summary statistics

Each histone mark has an "effective promoter radius"

Enrichment of peaks near promoters

Peaks in promoters correlate with gene expression

\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}

First question

\centering \LARGE

How do we define the "promoter region" for each gene?

Answer: Define the promoter region empirically!

:::::::::: {.columns} ::: {.column width="50%"}

  • 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

:::

::: {.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}} ::: ::::::::::

Next question

\centering \LARGE

How do these histone marks behave in promoter regions?

::: notes

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?

:::

H3K4me2 promoter neighborhood K-means clusters

Cluster means for H3K4me2{ height=70% }

H3K4me2 promoter neighborhood K-means clusters

:::::::::: {.columns} ::: {.column width="50%"} Cluster means for H3K4me2{ height=70% } ::: ::: {.column width="50%"}

::: ::::::::::

H3K4me2 cluster PCA shows a semicircular "fan"

:::::::::: {.columns} ::: {.column width="50%"} Cluster means for H3K4me2{ height=70% } ::: ::: {.column width="50%"} PCA plot of promoters{ height=70% } ::: ::::::::::

H3K4me2 near TSS correlates with expression

:::::::::: {.columns} ::: {.column width="50%"} Cluster means for H3K4me2{ height=70% } ::: ::: {.column width="50%"} Cluster expression distributions{ height=70% } ::: ::::::::::

H3K4me3 pattern is similar to H3K4me2

:::::::::: {.columns} ::: {.column width="50%"} Cluster means for H3K4me3{ height=70% } ::: ::: {.column width="50%"} PCA plot of promoters{ height=70% } ::: ::::::::::

H3K4me3 pattern is similar to H3K4me2

:::::::::: {.columns} ::: {.column width="50%"} Cluster means for H3K4me3{ height=70% } ::: ::: {.column width="50%"} Cluster expression distributions{ height=70% } ::: ::::::::::

H3K27me3 clusters organize into 3 opposed pairs

:::::::::: {.columns} ::: {.column width="50%"} Cluster means for H3K27me3{ height=70% } ::: ::: {.column width="50%"} PCA plot of promoters{ height=70% } ::: ::::::::::

Specific H3K27me3 profiles show elevated expression

:::::::::: {.columns} ::: {.column width="50%"} Cluster means for H3K27me3{ height=70% } ::: ::: {.column width="50%"} Cluster expression distributions{ height=70% } ::: ::::::::::

Current question

\centering \LARGE

How do these histone marks behave in promoter regions?

Answer: Presence and position both matter!

H3K4me2 & H3K4me3

  • Peak closer to promoter $\Rightarrow$ higher gene expression
  • Slightly asymmetric in favor of peaks downstream of TSS

. . .

H3K27me3

  • Depletion of H3K27me3 at TSS $\Rightarrow$ elevated gene expression
  • Enrichment of H3K27me3 upstream of TSS $\Rightarrow$ more elevated expression
  • Other coverage profiles: no association

Last question

\centering \LARGE

What can these histone marks tell us about T-cell activation and differentiation?

Differential modification disappears by Day 14

Differential modification between naïve and memory samples at each time point

Differential modification disappears by Day 14

Differential modification between naïve and memory samples at each time point

Promoter H3K4me2 levels converge at Day 14

\centering

Promoter H3K4me3 levels converge at Day 14

\centering

Promoter H3K27me3 levels converge at Day 14?

\centering

Expression converges at Day 14 (in PC 2 & 3)

\centering

But the data isn't really that clean...

:::::::::: {.columns} ::: {.column width="50%"} H3K4me2 ::: ::: {.column width="50%"} H3K4me3 ::: ::::::::::

But the data isn't really that clean...

:::::::::: {.columns} ::: {.column width="50%"} H3K27me3 ::: ::: {.column width="50%"} RNA-seq ::: ::::::::::

MOFA: cross-dataset factor analysis

MOFA factor analysis schematic{ height=70% }

Some factors are shared while others are not

\centering

Variance explained in each data set by each LF{ height=70% }

3 factors are shared across all 4 data sets

\centering

LFs 1, 4, and 5 explain variation in all 4 data sets{ height=70% }

MOFA LF5 captures convergence pattern

LF1 & LF4: time point effect; LF5: convergence

Last question

\centering \LARGE

What can these histone marks tell us about T-cell activation and differentiation?

Answer: Epigenetic convergence between naïve and memory!

  • 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.

Answers to key questions

How do we define the "promoter region" for each gene?

Define empirically using peak-to-promoter distances; validate by correlation with expression.

. . .

How do these histone marks behave in promoter regions?

Location matters! Specific coverage patterns correlated with elevated expression.

. . .

What can we learn about T-cell activation and differentiation?

Epigenetic & expression state of naïve and memory converges late after activation, consistent with naïve differentiation into memory.

Further conclusions & future directions

  • "Effective promoter region" is a useful concept but "radius" oversimplifies: seek a better definition

  • Coverage profiles were only examined in naïve day 0 samples: further analysis could incorporate time and cell type

  • Coverage profile normalization induces degeneracy: adapt a better normalization from peak callers like SICER

  • Unimodal distribution of promoter coverage profiles is unexpected

Further conclusions & future directions

  • Experiment was not designed to directly test the epigenetic convergence hypothesis: future experiments could include cultured but un-activated controls

  • High correlation between H3K4me3 and H3K4me2 is curious given they are mutually exclusive: design experiments to determine the degree of actual co-occurrence

Implications for transplant biology

::: incremental

  • Epigenetic regulation through histone methylation is surely involved in immune memory

  • Can we stop memory cells from forming by perturbing histone methylation?

  • Can we disrupt memory cell function during rejection by perturbing histone methylation?

  • Can we suggest druggable targets for better immune suppression by looking at epigenetically upregulated genes in memory cells?

:::

Acknowledgements

  • My mentors, past and present: Drs. Terry Gaasterland, Daniel Salomon, and Andrew Su

  • My committee: Drs. Nicholas Schork, Ali Torkamani, Michael Petrascheck, and Luc Teyton.

  • My many collaborators in the Salomon Lab

  • The Scripps Genomics Core

  • My parents, John & Chris Thompson

{.plain}

\centering

\huge

Questions?

Footnotes

  1. organdonor.gov 2

  2. Kim & Marks (2014)

  3. Furey (2012)

  4. Sarah LaMere. Ph.D. thesis (2015).

  5. Zang et al. (2009)

  6. Li et al. (2011)

  7. Argelaguet, Velten, et. al. (2018)