Paper - Review
10.1182/blood-2013-05-503201
DOI: 10.1182/blood-2013-05-503201
Key Points
⭐ Provide → a functional DNA methylation map of (human leukocyte subsets)
⭐ Identify cell-type-specific regulatory HMRs
⭐ Demonstrating → a potential link ← between 1⃣ gene polymorphisms 2⃣ DNA methylation 3⃣ Immune mediated disease
Abstract
DNA methylation
→ is an important mechanism
← by which gene transcription
∴ Cellular function → are regulated
Provide → detailed functional genome-wide methylome maps
← of 5 primary peripheral blood leukocyte subsets
← e.g. 1⃣ T cells 2⃣ B cells 3⃣ monocytes 4⃣ macrophages 5⃣ neutrophils
A comparison ← of these methylomes
→ revealed → 1⃣ highly specific cell-lineage 2⃣ cell-subset methylation profiles
DNA hypomethylation
→ is permissive → for gene expression
→ identified → cell-subset-specific hypomethylated regions (HMRs)
→ that strongly correlate ← with gene transcription levels → suggesting (these HRMs) may regulate (corresponding cell functions)
Single-nucleotide polymorphisms
→ associated ← with immune-mediated disease ← in genome-wide association studies
← localized to these cell-specific regulatory HMRs
Single-nucleotide polymorphisms
→ offer insight → into pathogenesis ← by highlighting cell subsets
← in which specific (epigenetic changes) → may drive disease
Our data
→ provide → a valuable reference tool
→ to investigate → the role of (DNA methylation) ← in regulating primary leukocyte functions
Introduction
Different cell types
→ display distinct (gene expression profiles)
→ which shape the (function & phenotype) of each tissue
A major role
→ for epigenetic mechanisms
← in regulating (1⃣ tissue-type-specific 2⃣ cell-type-specific) gene expression
DNA methylation
→ regulated → gene transcription
∴ Cellular function → through its effect ←on DNA accessibility state
Increased → methylation of CpGs
← when located around transcription start sites (TSSs)
← that contain high densities of CpGs
→ is associated ←with a condensed chromatin site
← leading to silencing of the respective gene
Hypomethylation
→ has the opposite effect
More complex mechanisms
← by which DAN methylation regulates gene expression
Its role ← in fundamental biological processes
← e.g. 1⃣ X-chromosome inactivation 2⃣ stem cell renewal 3⃣ genomic imprinting
Aberrant DAN methylation
→ is being increasingly implicated
← in causing disease
Epigenome
→ can be modified
← in response to environmental factors
∴ Complex diseases ← including several immune-mediated conditions
❓ The physioogical role ← of (DNA methylation)
← in regulating gene expression & function
→ is limited
∵ a lack of (genome-wide DNA methylation data)
→ combined with (gene expression analysis)
❗: investigate → the role of (DNA methylation)
← in regulating gene expression
← in primary purified subsets of leukocytes
Extracted → 1⃣ DNA 2⃣ RNA
← from unseparated peripheral blood mononuclear cells (PBMCs)
Generated
→ 1⃣ detailed genome-wide methylome maps 2⃣ exon-level gene expression profiles
Generated
→ methylation profiles ← using MeDIP-seq
→ for all cell subsets & PBMCs
Reveal → distinct (lineage & cell-subset-specific) DNA methylation profiles
Identify → cell-type-specific hypomethylated regions (HMRs)
∴ A role for methylation
← in driving cell-type-specific gene expression
Provide → a reference resource
→ which aiming to investigate → the role of (DNA methylation)
← in health & immune mediated diseases
Regions ← outside CGIs
→ play → a major role ← in regulating gene expression
SNPs
→ associated ← with a pre-disposition → to several immune-mediated diseases
Methods
Healthy volunteers
All individuals
→ had no known current & previous medical conditions
→ were non-smokers
→ were not taking any regular medication
Venepuncture
→ was performed ← at a similar time
→ to minimize (gene expression differences)
→ arising from circadian variation
Cell separation
Cell separations ← of (1⃣ PBMCs 2⃣ individual primary leukocyte cell subsets)
→ were performed
DNA and RNA extraction
DNA & RNA
→ were extracted
←using the QIAGEN
Affymetrix Human Gene 1.1 ST array
Aliquots of total RNA
→ were labeled
→ were hybridized
DNA methylation analysis
Genome-wide DNA methylation analysis
→ was performed
← using the Human Methylation 450 chip
MeDIP-seq → was performed
← on a subset of samples
Genomic DNA
→ was randomly sheared
→ to a median fragment size of 200 base pairs
DNA library sequencing
→ was performed
← on an Illumina Genome Analyzer II
Statistical analysis
The resulting data
→ were imported → into the R statistical programming language
→ was used for normalized & further analysis
Differential methylation analysis
Methylation status → for each probe
→ was calculated ← using the M value
→ 1⃣ methylated probe intensity 2⃣ unmethylated probe intensity
Differential methylation
← at individual probe loci
→ was assessed ← using the limma Bioconductor package
→ to analyze the M-value data
The "gammaFitEM" function ← of the lumi package
→ was used → to fit a 2-compoennt γ mixture model
The output of this function
→ calls → the methylation status
→ as 1⃣ unmethylated 2⃣ marginal 3⃣ methylated
HMRs
→ were defined
→ as regions spanning over > 20 bp ← 1⃣ a minimum of 2 unmethylated 2⃣ partially methylated CpG sites
Cell-type-speicific differentially HMRs (dHMRs)
→ were identified ← by comparison of each given cell
Affymetrix expression array analysis
→ was performed
Data → were normalized
← using the variance-stabilizing normalization algorithm
The expression signal
← from probe sets representing exons
→ summarized ← using robust multi-array analysis
Transcripts → were called
→ as differentially expressed ← where 80%
← of their mapped exon probe set loci → displayed differential expression
Cell-subset-specific HMRs
→ were described
← as having a positive correlation ←with gene transcript expression
GWAS-identified SNPs ← with differential HMRs
→ was analyzed
Linkage disequilibrium (LD) blocks
→ were calculated → for each SNP
← using the 1000 genomes phase 1 data
→ to identify highly correlated nearby SNPs
Overlap of (these LD blocks)
← with 1⃣ HMRs 2⃣ dHRMs 3⃣ rHMRs
→ was determined ← by assessing the equivalent HMR overlap
MeDIP-seq analysis → was performed
MEDIPS package
→ was used
→ to generate absolute methylation status values
→ as a normalized value ← [0, 1000]
Values ← below 200
→ were treated ← as fully unmethylated
Receiver operating characteristic (ROC) analysis
→ was performed
→ to compare methylation calls ← at each Illumina probe locus
Results
Primary human leukocyte subsets have distinct DNA methylomes
Generated → genome-wide methylation profiles
→ for each of 5 purified cell subsets
← including 1⃣ B cells 2⃣ T cell 3⃣ monocytes 4⃣ macropahges 5⃣ neutrophils
→ using the Infinium HumanMethylation 450 BeadChip
← which interrogates the methylation status of 480K CpG sites
CpG sites
→ were classified
← as 1⃣ unmethylated 2⃣ partially methylated 3⃣ methylated
The total number of (fully methylated CpG sites)
→ did NOT vary → greatly ←between cell types
Partial methylation
← in cells of the lymphoid lineage ← than in cell of the myeloid lineage
Unmethylated & partially methylated sites
→ were likely to be located ← upstream of or within TSSs
→ are associated ← with 1⃣ CGIs 2⃣ shores ← regardless of (cell type)
∵ the genomic context of the CpG sites
Performed → 1⃣ PCA 2⃣ hierarchical clustering
→ to analyzed → the relationship ← between (the different methylation profiles)
Distinct clustering ← of individual cell subsets
Primarily separating cell subsets
← derived from 1⃣ the myeloid 2⃣ the lymphoid lineages
Each individual cell type
→ has its unique methylome
∵ these data
Functionally related cell types
→ exhibit → similar DNA methylation profiles
∵ the proximity of the 1⃣ CD4+ 2⃣ CD8+ T cell clusters
The importance of (cell separation)
→ prior → to performing (DNA methylation) analyses
→ for gene transcription analyses
Compared → DNA methylomes
← from the individual cell subsets
→ to identify → both 1⃣ cell-type 2⃣ lineage specific methylation differences
The number of (differentially methylated loci)
→ varied greatly
→ to whether the comparison was ← between cel types of 1⃣ the same lineages 2⃣ different lineages
Observed → distinct clustering ← of 1⃣ hypomethylated 2⃣ partially methylated CpG sites
Identified → cell-subset-specific HMRs
→ that 1⃣ were present ← in only 1 cell types 2⃣ where there was a cell-subset-specific expansion
Analysis of (the distribution of dHMRs)
← across the genome of the leukocyte subsets
→ showed that → dHMRs were more numerous
← in myeloid compared with lymphoid cell subsets
A significant number of dHMRs
→ were located ← in "open sea" regions
→ were NOT associated ← with CGIs
∵ Analysis of the distribution of dHMRs
dHMRs
←that were associated ← with CGIs
→ were rarely found entirely ← within the CGI itself
→ were usually located ← in 1⃣ CpG shores 2⃣ shelves
Cell-type-specific methylation differences
→ occur → most frequently outside CGIs
← in 1⃣ adjacent shelves 2⃣ shores
The majority overlap 1⃣ 5' promoter 2⃣ intragenic regions
← with only a small fraction found ← within promoter regions
Gene ontology analysis
→ performed ← on gene ← associated with cell-type-specific dHMRS
∴ Enrichment ← for relevant cellular immune functions
∴ These dHMRs → is involved
← in regulating specific cellular phenotypes
Cell-type-specific HMRs are associated with cell-type-specific gene expression
❗: to investigate → the possible effect of cell-type-specific dHMRs
mRNA levels
→ were measured ← by microarray analysis
The location of (the differentially expressed genes)
→ compared ←with the position of the dHMRs
Specific dHMRs
→ were found
→ to be associated ← with DEG
∴ Cell-type-specific transcription → may be regulated ← by methylation
Potentially regulatory dHMRS (rHMR)
→ varied substantially ← between cell subsets
CD16+ neutrophils
→ were associated ← with dHMRs
A major role → for hypomethylation outside CGIs
← e.g. 1⃣ CpG shelves 2⃣ CpG shores
← in the regulation of (gene transcription)
A significant proportion ← of rHMRs
→ were located
← either 1⃣ partially 2⃣ fully ← within gene bodies
∴ The importance of (intragenic methylation) ← in regulating gene transcription
Verification of K450 array data using MeDIP-seq
MeDIP-seq → was performed
← on 1⃣ all cell subsets 2⃣ PBMCs ← from 1 individual
The K450 array
→ interrogates → DNA methylation ← of (individual CpG sites)
← based on (bisulfite conversion)
∴ High-resolution coverage of the human genome
MeDIP-seq
→ uses → antibody-based affinity enrichment of (methylated DNA)
→ to generate (a detailed map) ← of methylated regions → throughout the entire genome
Observed → a strong correlation
← between 1⃣ the methylation status of (CpG sites) ← which interrogated by the K450 array 2⃣ Corresponding MeDIP-seq data
Analysis of (the K450 array data)
→ identified 3 cell-specific HMRs
← in the CD8+ T-cell subset ← located both 1⃣ upstream of 2⃣ within the gene
MeDIP-seq
→ confirming → their existence
→ revealing → an expansion ← in their size
Location of immune-mediate GWAS SNPs within leukocyte-specific potential rHMRs
The genetic contributions
← which made by common SNPs → to complex immune-mediate diseases
→ are being progressively revealed ← by data from large GWAS
❓: The mechanisms
← which increase → an individual's risk of (developing disease)
→ remain largely unknown
❓: Such SNPs
→ may lead → to altered transcription → through DNA methylation changes
→ may fall ← within HMRs
Overlap
← between 1⃣ GWAS-identified SNPs 2⃣ the HMRs 3⃣ dHMRs 4⃣ potential rHMRs ← which identified ← by the methylation array analysis
The most significant enrichment
→ was found ← in SNPs associated ← with primary biliary cirrhosis
Disease-associated SNPs
→ may function ← by modulating gene expression
A significant number ← of the SNPs
→ fell → into cell-type-specific HMRs
The SNPs function
← by modulating gene expression
← in a cell-type-specific manner
The finding → that a small number of these SNPs
← which overlap cell-type-specific HMRs
→ that are associated ← with the cell-type-specific expression of an adjacent gene
This SNP
→ overlaps → a HMR
← that is only found ← in CD4 T-cells
← which correlates ← with the elevated expression of CTLA4
These SNPs
→ may function ← by altering CpG content
∴ Changing chromatin accessibility
A number of the SNPs
→ mapping to these rHMRs potentially lead → to 1⃣ gain 2⃣ loss of CpG sites
These SNPs
→ may alter transcription factor (binding sites)
← within these region of (open chromatin)
∴ These SNPs → function as expression quantitative trait loci
Discussion
Changes ← in DNA methylation
→ are established ← as the major (epigenetic mechanisms)
❓: How DNA methylation
→ may be implicated
← in regulating different functional pathways
Provide → detailed methylomes
→ for 1⃣ unseparated PBMCs 2⃣ purified primary leukocyte subsets
Each cell subset
→ displays → a unique methylation profile
Observe → substantial differences
← between cell subsets ← derived from either 1⃣ the myeloid 2⃣ lymphoid lineage
PBMC samples
→ have a composite profile
∴ PBMC → are sensitive to changes
← in cellular composition
These are performed
← on separate cells
→ to avoid observed differences ← merely being driven by alterations
← in cellular composition between individuals
Correlated → DNA methylation
← with gene expression
→ to identify regions → that are likely to be involved ← in regulating gene expression
Correlated → cell-subset-specific HMRs
← with genes ← that were over-expressed ← in each cell type
A large proportion ← of these potential HMRs
→ were confirmed ← using MeDIP-seq data
The majority were found
→ to be located outside of CGIs
→ were not associated ← with CGIs
∵ Analysis of these potential rHMRs
These regions of the genome
→ are crucial
← in regulating gene expression
The impact of (DNA methylation)
→ on cellular differentiation ← during hematopoiesis
The lymphoid lineage
→ is associated ← with higher levels of (DNA methylation)
Fully differentiated cell types
← with stable transcriptional programs
← e.g. neutrophils
→ show more extensive hypo-methylation ← compared with lymphoid cells
Lymphoid lineages
→ contain → more heterogeneous populations
→ comprise → many naive cells ← that require (an ongoing capacity) for differentiations
Large-scale GWAS
→ have been conducted ← on many complex diseases
→ have led → to the identification of (genetic variants)
❓: gene sequence
← which contribute → to disease pathogenesis
A large proportion of these SNPs
→ are located → outside of coding regions
∴ These SNPs → alter (gene expression)
❗: Another potential role → for epigenetic mechanisms
Provide → a potential link
← between 1⃣ genetic pre-disposition 2⃣ epigenetic mechanisms
→ for complex immune-mediated diseases
Genetic modification ← altering DNA methylation ← within potential rHMRs
→ may alter → 1⃣ transcription factor binding 2⃣ the overall chromatin structure
→ leading to changes in gene expression