Thesis/presentation.mkdn

---
title: Bioinformaticanalysis of complex, high-throughput genomic and epigenomic data in the context of $\mathsf{CD4}^{+}$ T-cell differentiation and diagnosis and treatment of transplant rejection
author: |
    Ryan C. Thompson \
    Su Lab \
    The Scripps Research Institute
date: October 24, 2019
theme: Boadilla
aspectratio: 169
fontsize: 14pt
---

## Organ transplants are a life-saving treatment

::: incremental

* 36,528 transplants performed in the USA in 2018[^1]

* 100 transplants every day!

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

:::

[^1]: [organdonor.gov](https://www.organdonor.gov/statistics-stories/statistics.html)

## Organ transplants are a life-saving treatment

![Organ donation statistics for the USA in 2018[^2]](graphics/presentation/transplants-organ-CROP-RASTER.png){ height=70% }

[^2]: [organdonor.gov](https://www.organdonor.gov/statistics-stories/statistics.html)

## Graft rejection is an adaptive immune response

<!-- TODO: Need a graphic for this -->

::: incremental

* The host's adaptive immune system identifies and attacks cells
  bearing non-self antigens

* An allograft contains differnet genetic variants from the host,
  resulting in protein-coding differences

* Left unchecked, the host immune system eventually notices these
  alloantigens and begins attacking (rejecting) the graft

* Rejection is the major long-term threat to organ allografts

:::

## Allograft rejection remains a major long-term problem

![Kidney allograft survival rates in children by transplant year[^3]](graphics/presentation/kidney-graft-survival.png){ height=65% }

[^3]:[ Kim & Marks. "Long-term outcomes of children after solid organ transplantation". In: Clinics (2014)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3884158/?report=classic)

## Rejection is treated with immune suppressive drugs

<!-- TODO: Need a graphic, or maybe a table of common drugs +
mechanisms, or a diagram for periodic checking. -->

::: incremental

* To prevent rejection, a graft recipient must take immune suppressive
  drugs for the rest of their life

* The graft is periodically checked for signs of rejection, and immune
  suppression dosage is adjusted accordingly

* Immune suppression is a delicate balance: too much leads to immune
  compromise; too little leads to rejection.
  
:::

## My thesis topics

### Topic 1: Immune memory
Genome-wide epigenetic analysis of H3K4 and H3K27 methylation in naïve
and memory $\mathsf{CD4}^{+}$ T-cell activation

### Topic 2: Diagnostics for rejection
Improving array-based diagnostics for transplant rejection by
optimizing data preprocessing

### Topic 3: Blood profiling during treatment
Globin-blocking for more effective blood RNA-seq analysis in primate
animal model for experimental graft rejection treatment

## Today's focus

### \Large Topic 1: Immune memory

\Large

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

## Memory cells: faster, stronger, and more independent

![Naïve and memory T-cell responses to activation](graphics/presentation/T-cells-A-SVG.png)

## Memory cells: faster, stronger, and more independent

![Naïve and memory T-cell responses to activation](graphics/presentation/T-cells-B-SVG.png)

## Memory cells: faster, stronger, and more independent

![Naïve and memory T-cell responses to activation](graphics/presentation/T-cells-C-SVG.png)

## Memory cells: faster, stronger, and more independent

![Naïve and memory T-cell responses to activation](graphics/presentation/T-cells-D-SVG.png)

## Memory cells are a problem for immune suppression

<!-- Need graphics -->

\large

Compared to naïve cells, memory cells:

\normalsize

* 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

## Memory cells are a problem for immune suppression

\large

Result:

\normalsize

* 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

## We need a better understanding of immune memory

* Cell surface markers of naïve and memory $\mathsf{CD4}^{+}$ T-cells
  are fairly well-characterized
* But internal mechanisms that allow memory cells to respond
  differently to the same stimulus (antigen presentation) are not
  well-understood
  
. . .

* A reasonable hypothesis is that some of these mechanisms are
  epigenetic: using histone marks or DNA methylation to regulate the
  expression of certain genes
* We can test this hypothesis by measuring gene expression (using
  RNA-seq) and histone methylation (using ChIP-seq) in naïve and
  memory T-cells before and after activation
  
## Experimental design

* Separately isolate naïve and memory $\mathsf{CD4}^{+}$ T-cells from
  4 donors
* Activate with CD3/CD28 beads
* Take samples at 4 time points: Day 0 (pre-activation), Day 1 (early
  activation), Day 5 (peak activation), and Day 14 (post-activation)
* Do RNA-seq + ChIP-seq for 3 histone marks (H3K4me2, H3K4me3, &
  H3K27me3) for each sample.

Data generated by Sarah Lamere, published in GEO as
[GSE73214](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73214)

## A few intermediate analysis steps are required

![Flowchart of workflow for data analysis](graphics/CD4-csaw/rulegraphs/rulegraph-all-RASTER300.png)

## Histone modifications occur on consecutive histones

![ChIP-seq coverage in IL2 gene[^5]](graphics/presentation/LaMere-thesis-fig3.9-SVG-CROP.png){ height=70% }

[^5]: Sarah LaMere. "Dynamic epigenetic regulation of CD4 T cell activation and memory formation". PhD thesis. TSRI, 2015.

## Histone modifications occur on consecutive histones

![Strand cross-correlation plots](graphics/CD4-csaw/csaw/CCF-plots-PAGE2-CROP-RASTER.png)

## Finding enriched regions across the genome

<!-- Graphic from SICER paper: Fig1 -->
<!-- Use one of my IDR example plots for IDR -->

* Scan across the genome looking for regions with read coverage above
  background level in each donor using SICER peak caller[^6]
* Use Irreducible Discovery Rate framework to identify peaks that are
  called consistently across multiple donors[^7]

[^6]: [Chongzhi Zang et al. “A clustering approach for identification of enriched domains from histone modification ChIP-Seq data”. In: Bioinformatics 25.15 (2009) doi: 10.1093/bioinformatics/btp340.](https://doi.org/10.1093/bioinformatics/btp340)
[^7]: [Qunhua Li et al. “Measuring reproducibility of high-throughput experiments”. In: AOAS (2011)](https://doi.org/10.1214/11-AOAS466)

## Finding enriched regions across the genome

![Peak-calling summary statistics](graphics/presentation/RCT-thesis-table2.2-SVG-CROP.png)


## Each histone mark has an "effective promoter radius"

![Enrichment of peaks near promoters](graphics/CD4-csaw/Promoter-Peak-Distance-Profile-PAGE1-CROP-RASTER.png)

## Peaks in promoters correlate with gene expression

<!-- TODO: Pull out one column and rotate it -->

![Expression distributions of genes with and without promoter peaks](graphics/CD4-csaw/FPKM-by-Peak-Violin-Plots-CROP-RASTER.png)

## The story so far

* H3K4me2, H3K4me3, and H3K27me3 occur on many consecutive histones in
  broad regions across the genome
* These enriched regions occur more commonly within a certain radius
  of gene promoters
* This "effective promoter radius" is consistent across all samples
  for a given histone mark, but differs between histone marks
* Presence or absence of a peak within this radius is correlated with
  gene expression
  
. . .

Next: 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

![(Insert figure legend)](graphics/CD4-csaw/ChIP-seq/H3K4me2-neighborhood-clusters-CROP.png)

## H3K4me2 promoter neighborhood cluster PCA

![(Insert figure legend)](graphics/CD4-csaw/ChIP-seq/H3K4me2-neighborhood-PCA-CROP.png)

## H3K4me2 promoter neighborhood cluster expression

![(Insert figure legend)](graphics/CD4-csaw/ChIP-seq/H3K4me2-neighborhood-expression-CROP.png)

## H3K4me3 promoter neighborhood K-means clusters

![(Insert figure legend)](graphics/CD4-csaw/ChIP-seq/H3K4me3-neighborhood-clusters-CROP.png)

## H3K4me3 promoter neighborhood cluster PCA

![(Insert figure legend)](graphics/CD4-csaw/ChIP-seq/H3K4me3-neighborhood-PCA-CROP.png)

## H3K4me3 promoter neighborhood cluster expression

![(Insert figure legend)](graphics/CD4-csaw/ChIP-seq/H3K4me3-neighborhood-expression-CROP.png)

## H3K27me3 promoter neighborhood K-means clusters

![(Insert figure legend)](graphics/CD4-csaw/ChIP-seq/H3K27me3-neighborhood-clusters-CROP.png)

## H3K27me3 promoter neighborhood cluster PCA

![(Insert figure legend)](graphics/CD4-csaw/ChIP-seq/H3K27me3-neighborhood-PCA-CROP.png)

## H3K27me3 promoter neighborhood cluster expression

![(Insert figure legend)](graphics/CD4-csaw/ChIP-seq/H3K27me3-neighborhood-expression-CROP.png)

## What have we learned?

### H3K4me2 & H3K4me3

* Peak closer to promoter $\Rightarrow$ more likely gene is highly
  expressed
* Slightly asymmetric in favor of peaks downstream of TSS

. . .

### H3K27me3

* Depletion of H3K27me3 at TSS associated with elevated gene
  expression
* Enrichment of H3K27me3 upstream of TSS even more strongly associated
  with elevated expression
* Other coverage profiles not associated with elevated expression

## Differential modification disappears by Day 14

![Differential modification between naïve and memory samples at each time point](graphics/presentation/RCT-thesis-table2.4-SVG-CROP.png)

## Convergence at Day 14 H3K4me2

![(Insert figure legend)](graphics/CD4-csaw/ChIP-seq/H3K4me2-promoter-PCA-group-CROP.png)

## Convergence at Day 14 H3K4me3

![(Insert figure legend)](graphics/CD4-csaw/ChIP-seq/H3K4me3-promoter-PCA-group-CROP.png)

## Convergence at Day 14 H3K27me3

![(Insert figure legend)](graphics/CD4-csaw/ChIP-seq/H3K27me3-promoter-PCA-group-CROP.png)

## Convergence at Day 14 RNA-seq (PC 2 & 3)

![(Insert figure legend)](graphics/CD4-csaw/RNA-seq/PCA-final-23-CROP.png)

## MOFA identifies shared variation across all 4 data sets

![(Insert figure legend)](graphics/CD4-csaw/MOFA-varExplaiend-matrix-CROP.png)

## MOFA shared variation captures convergence pattern

![(Insert figure legend)](graphics/CD4-csaw/MOFA-LF-scatter-small.png)

## What have we learned?

* Almost no differential modification observed between naïve and
  memory at Day 14, despite plenty of differential modification at
  earlier time points.
* RNA-seq data and all 3 histone marks' ChIP-seq data all show
  "convergence" between naïve and memory by Day 14 in the first 2 or 3
  principal coordinates.
* MOFA captures this convergence pattern in one of the latent factors,
  indicating that this is a shared pattern across all 4 data sets.

<!-- ## Slide -->

<!-- ![(Insert figure legend)](graphics/CD4-csaw/LaMere2016_fig8-RASTER.png) -->


## Takeaway 1: Each histone mark has an "effective promoter radius"

* H3K4me2, H3K4me3, and H3K27me3 ChIP-seq reads are enriched in broad
  regions across the genome, representing areas where the histone
  modification is present
* These enriched regions occur more commonly within a certain radius
  of gene promoters
* This "effective promoter radius" is specific to each histone mark
* Presence or absence of a peak within this radius is correlated with
  gene expression
  
## Takeaway 2: Peak position within the promoter is important

* H3K4me2 and H3K4me3 peaks are more strongly associated with elevated
  gene expression the closer they are to the TSS, with a slight bias
  toward downstream peaks.
* H3K27me3 depletion at the TSS and enrichement upstream are both
  associated with elevated expression, while other patterns are not.
* In all histone marks, position of modification within promoter
  appears to be an important factor in association with gene
  expression

## Takeaway 3: Expression & epigenetic state both converge at Day 14

* At Day 14, almost no differential modification observed between
  naïve and memory cells
* Naïve and memory converge visually in PCoA plots
* Convergence is a shared pattern of variation across all 3 histone
  marks and gene expression
* This is consistent with the hypothesis that the naïve cells have
  differentiated into a more memory-like phenotype by day 14.