ABSTRACT
Neutrophils and monocytes provide a first line of defense against infections as part of the innate immune system. Here we report the integrated analysis of transcriptomic and epigenetic landscapes for circulating monocytes and neutrophils with the aim to enable downstream interpretation and functional validation of key regulatory elements in health and disease. We collected RNA-seq data, ChIP-seq of six histone modifications and of DNA methylation by bisulfite sequencing at base pair resolution from up to 6 individuals per cell type. Chromatin segmentation analyses suggested that monocytes have a higher number of cell-specific enhancer regions (4-fold) compared to neutrophils. This highly plastic epigenome is likely indicative of the greater differentiation potential of monocytes into macrophages, dendritic cells and osteoclasts. In contrast, most of the neutrophil-specific features tend to be characterized by repressed chromatin, reflective of their status as terminally differentiated cells. Enhancers were the regions where most of differences in DNA methylation between cells were observed, with monocyte-specific enhancers being generally hypomethylated. Monocytes show a substantially higher gene expression levels than neutrophils, in line with epigenomic analysis revealing that gene more active elements in monocytes. Our analyses suggest that the overexpression of c-Myc in monocytes and its binding to monocyte-specific enhancers could be an important contributor to these differences. Altogether, our study provides a comprehensive epigenetic chart of chromatin states in primary human neutrophils and monocytes, thus providing a valuable resource for studying the regulation of the human innate immune system.
INTRODUCTION
Human neutrophils and monocytes are the most abundant nucleated myeloid cells in peripheral blood and are essential elements of the innate immune system that provide the first line of defense against infection (Dale et al., 2008). Neutrophils represent approximately 50-60% of leukocytes in blood, while approximately 1-5% are monocytes. During hematopoiesis both neutrophils and monocytes are derived from the same progenitor, the myelomonocytic progenitor (CFU-GM). Despite this and the fact that both have similar functions as phagocytes in their clearance of microbial pathogens and cytotoxic activity, these cells differ in many of their specific biological functions.
Neutrophils are mature terminally differentiated cells with a very short survival time that are key in the innate immune response to acute inflammation. They are recruited to the site of infection and kill bacterial and fungal pathogens by phagocytosis and by releasing reactive oxygen species and antibacterial proteins in order to destroy pathogens in surrounding tissues that the neutrophils have infiltrated. Under certain conditions, they also release the so-called neutrophil extracellular traps (NETs) that originate from their chromatin (Kolaczkowska et al., 2013; Mantovani et al., 2011).
Monocytes have multiple roles in innate and adaptive immunity. They are also recruited into the extravascular tissues where they rapidly differentiate into macrophages or dendritic cells that then remain for weeks to months within the local tissue environment and fulfill highly specialized cellular functions (Galli et al., 2011). Monocytes themselves perform phagocytosis; on activation they elicit a proinflammatory cytokine response (Fairfax et al., 2014) and present antigen, thus may also initiate the adaptive immune response in T cells (Randolph et al., 2008).
Neutrophils and monocytes are involved in human diseases including bone marrow failure or chonic neutropenia syndromes, as well as myeloproliferative disorders or myeloid leukemias (Dale et al., 2008). Moreover, monocytes and derived cells have been implicated in multiple autoimmune diseases including systemic lupus erythematosus (Marshak-Rothstein et al., 2006) and multiple sclerosis, where monocytes may be key in the response to IFN-β treatment (Zula et al., 2011).
As both monocytes and neutrophils circulate in the blood, their epigenome is directly influenced by the presence of factors such as inflammatory agents, nutrients and metabolites. The rapid turnover of this class of blood cells makes them suitable targets for epigenetic drugs. Indeed, HDAC and SIRT inhibitors are suggested to have a marked effect on monocyte function (and possibly neutrophil function) including migration, autoimmune responses and attenuation of inflammatory responses (Orecchia et al., 2011; Adcock, 2007). However, to refine these epigenetic modulation strategies, a detailed definition of the epigenetic state of these cell types is essential (Ostuni et al, 2016).
Although the morphological and functional differences between these common immune cells have been extensively studied during the last century, our current knowledge on the molecular determinants that drive these different phenotypes is very limited. To understand how these different cells arise and to characterize their role in different pathological conditions, it is important to investigate differences in their gene expression programs in the broader context of their common and cell-specific epigenetic characteristics.
Previous studies have set out to investigate specific epigenetic features in monocytes and neutrophils (Ostuni et al, 2016). In neutrophils, DNA methylation has been studied using whole-genome bisulfite sequencing (WGBS) at a 10x resolution in a pool of 6 healthy individuals (Hodges et al., 2011) and by 450K microarrays Ronnerblad et al., 2014). In monocytes, subsets of histone modifications have been profiled (Schmidl et al., 2014; Pham et al., 2012; Pham et al., 2013) as well as the DNA methylome using enrichment procedures such as methylated DNA immunoprecipitation (MeDIP) (Salpea et al., 2012; Shen et al., 2013). More recently, the International Human Epigenome Consortium (IHEC) has generated released >7000 reference epigenomic datasets (Stunnenberg et al, 2016) including these cell types.
Here, as part of the BLUEPRINT consortium (Abbott, 2011; Adams et al., 2012; Martens and Stunnenberg, 2013; www.blueprint-epigenome.eu) we present a comparative analysis of IHEC reference epigenome maps for human adult and cord blood monocytes and neutrophils which are already available to the scientific community (Tables S1-4). The data produced by BLUEPRINT for peripheral blood and cord blood monocytes and neutrophils includes:
* ChIP-seq data for H3K4me1 (H3 lysine 4 monomethylation), H3K4me3 (H3 lysine 4 trimethylation), H3K9me3, H3K27me3, H3K36me3 and H3K27ac (H3 lysine 27 acetylation).
* High-resolution DNA methylation maps of the neutrophil and monocytes by whole genome bisulfite sequencing (WGBS) at >50x coverage
* Transcriptome characterization through strand specific RNA-seq on ribo-depleted RNA.
Together, these data represented the first complete reference epigenomes for these two cell types as defined by the International Human Epigenome Consortium (IHEC) (www.ihec-epigenomes.org).
Bioinformatic analysis of this comprehensive dataset (Table S5) revealed that any single epigenetic feature, be it DNA methylation, a histone modification or the transcriptional profile could distinguish monocytes from neutrophils. The combinatorial analysis of chromatin marks showed that monocytes generally have more promoters with active marks and have a higher number of non-promoter regulatory active regions (enhancers) compared to neutrophils. Neutrophils, on the contrary were found to have a larger proportions of heterochromatin as well as regions with no marks. Although the DNA methylomes of these cell types were overall very similar, most differences in DNA methylation were found to overlap enhancer regions, suggesting DNA methylation could be used to modulate their activity. Transcriptome profiling by RNA-seq showed higher transcriptional activity in monocytes compared to neutrophils, with more genes overexpressed in monocytes. These results suggest that monocytes, as precursor cells with diverse differentiation potential, have a more plastic and active epigenome than the terminally differentiated neutrophils.
RESULTS
Monocytes display more active promoters and enhancers than neutrophils
We generated genome-wide maps for histone modifications associated with diverse regulatory and epigenetic functions. These include histone H3 lysine 4 mono- and trimethylation (H3K4me1 and −me3), H3K9me3, H3K27me3, H3K36me3 and H3K27ac. We profiled these histone marks in neutrophils and monocytes from the peripheral blood of four adults (AB) and from the umbilical cord blood of two newborns (CB) (Fig. 1A-C, Fig. S1-2). Principal component analysis (PCA) analysis revealed that histone modification patterns within a given cell type cluster together. Similar results were obtained with DNA methylation or gene expression data (Fig. 1D). Given the similarities between peripheral AB and CB for monocytes and neutrophils we have focused our analyses on comparing peripheral adult blood monocytes and neutrophils.
We used a Hidden Markov model approach (ChromHMM) for unsupervised segmentation of the genome into chromatin states (Ernst and Kellis, 2010). Several models searching for the optimal number of different states were generated, and a model with 11 different chromatin states was eventually selected as it offered the maximum number of states with biological interpretability (Fig. 2A, S4-5 and Table S6). Note that state 2 is characterized by the absence of marks tested here. As some of these states were functionally related, the 11 states were collapsed into five biological groups: (1) Repressive heterochromatin (RHet), characterized by the enrichment of H3K9me3 (state 1), the enrichment of H3K27me3 (state 3) or the absence of any signal (state 2). RHet regions tend to be devoid of gene expression (~0.5 fold compared to the genome average, Table S6) and DNase I signal (~0.2 fold) in monocytes. (2) Active promoters (Apro) marked with H3K4me3, either alone (state 6), in combination with H3K4me1 (states 5) or with H3H27ac (state 7). Apro regions frequently overlap with a TSS (~48 fold enrichment). (3) Repressed promoters (Rpro, state 4) with H3K4me3, H3K4me1 and the H3K27me3 - also called bivalent or poised - that show lower accessibility by DNaseI (~9 fold) than Apro regions. (4) Regulatory elements (RegE) with H3K4me1 alone (state 9) or in combination with H3K27ac (state 8). RegE regions are enriched in DNase I (~22 fold) but show a lower enrichment in TSS (~2 fold) than in Apro (48 fold). These elements likely function as latent or active enhancers. (5) Transcribed regions (TranR, states 10 and 11) marked with H3K36me3 that show a clear enrichment in gene expression measured by RNA-seq (4 fold compared to the genome average).
To first characterize cell-specific and shared putative regulatory elements, we compared the chromatin landscapes of adult monocytes and neutrophils by identifying regions that showed a consistent state in all four biological replicates for each cell type, while having a different state in the other cell type. We focused on the five functional groups described above (Fig 2B). Five times more repressive chromatin (RHet) segments were inferred in adult blood neutrophils compared to monocytes (Fig. 2B). In contrast, a greater number of regulatory elements (RegE, 5-fold) and active promoters (Apro, 4-fold) were inferred in monocytes. This difference in enhancer-like RegE segments matched the higher number of loci occupied by H3K27ac and H3K4me1 in monocytes observed in the differential analysis of single modifications between the two cell types (Fig. S6). Monocytes were enriched for transcribed regions (TranR) and regulatory elements (RegE), while the same genomic regions tended to encompass heterochromatic (RHet) regions in neutrophils (Fig. 2C). Interestingly, CB monocytes or neutrophils had a higher number of RegE regions than their AB counterparts (Fig. S7). Together, these results show that there is a marked difference in usage of specific epigenomic segments in neutrophils and monocytes. Neutrophils have far less marks associated with active transcription, as it can be expected for terminally differentiated cells. Based on their chromatin signature, monocytes can be considered lineage-committed multipotent cells, as they can further differentiate into macrophages, dendritic cells or osteoclasts.
We further sought to explore whether cell type-specific gene expression was associated to specific enhancer activity (Hnisz et al., 2013; Andersson et al., 2014). We partitioned chromatin segments labeled as enhancers (RegE) in monocytes and neutrophils into three groups: monocyte-specific (18,679), neutrophil-specific (3,749) and common enhancers (12,027). We found that enhancers were enriched both upstream and downstream annotated TSSs, with a prevalence in intronic regions (Fig. S8). We also searched for transcription factor binding sites (TFBS) sequences that were enriched in the neutrophil-specific and monocyte-specific enhancers. While no significantly enriched motifs were found in the neutrophil-specific enhancers, we identified many factors that potentially could bind to the monocyte-specific enhancers (False Discovery Rate [FDR] < 0.05, Table S7). Among them were TF motifs known to be important for monocyte and macrophage biology, such as PPARG, PU.1 and IRF (Fig. 2D) and interestingly also for pluripotency factors, including MYC.
It has been suggested that clusters of enhancers drive expression of genes that define cell identity (Hnisz et al., 2013). To identify such genes in monocytes and neutrophils, we calculated the number of putative enhancers for each gene and computed the difference between the two cell types. Of the top 20 genes with highest absolute difference, 19 showed more enhancers in monocytes, including NCOR2, IRF8 and MYC (Fig 2E, S10). Remarkably, not only MYC binding motifs were enriched in monocyte-specific enhancers (Fig. 2D), but also the MYC gene itself was associated with 39 enhancers in monocytes, while none of these enhancers were associated with MYC in neutrophils (Fig. 2E). Finally, we investigated functional clustering of genes corresponding to the inferred neutrophil-specific and monocyte-specific enhancers, using functional enrichment analysis implemented in GREAT (McLean et al., 2010). Neutrophil-specific enhancers were enriched in pathways related with p53, IL4, IL8, TNF and IFN signaling (Fig S9). Among the signaling pathways enriched in monocyte-specific enhancers were NFAT, IL2, IL12, CD40 and FAS.
Overall, these analyses suggest a greater plasticity of monocyte epigenomes compared to neutrophils. The data generated represents a large catalog of putative cell-specific and shared regulatory domains for further investigation, highlighting a host of known and novel pathways and transcriptional regulators for further study.
Whole Genome DNA methylation maps and chromatin states
To establish comprehensive, high-resolution DNA methylation maps of the neutrophils and monocytes, we performed whole genome bisulfite sequencing with a median sequencing coverage of 63x per sample in a total of 12 samples (Fig. 1B). DNA methylation levels were inferred from the aligned reads using an algorithm that distinguishes between bisulfite-induced C-to-T conversion and genetic C-to-T differences (Kulis et al., 2012). Across all samples, we obtained DNA methylation estimates for an average of 23.68 million CpG sites. Details of the sequencing and calling statistics by sample are shown in Table S8. DNA methylation levels for a common set of 18.9 million CpG sites could be estimated in all samples, which forms the basis for the in-depth analysis reported below. In contrast, we did not observe any sites of consistent non-CpG methylation (Ziller et al., 2011) across replicates, and therefore our analyses focus exclusively on CpG methylation.
Consistent with previous observations in purified mouse hematopoietic cell populations (Ji et al., 2010; Bock et al., 2012), global DNA methylation levels were highly similar between neutrophils and monocytes (Fig. S11). Nevertheless, DNA methylation differences at the CpG level are highly informative, as PCA analysis of the DNA methylation profiles allowed the clear separation not only between neutrophils and monocytes, but also between cord blood and adult blood (Fig. 1D). In our segmentation analysis we showed that the DNA methylation status is very different in the chromatin state segments defined above (Fig. 2A). Our analysis revealed DNA hypermethylation at heterochromatin states 1 and 2 as well as at transcribed gene bodies (states 10 and 11), in line with previous reports (Lister et al., 2009, Brinkman et al, 2012). In contrast, hypomethylation was detected at active promoter segments, while regulatory elements showed intermediate to high levels of DNA methylation (Fig. 2A). To assess if the differences in DNA methylation at enhancer elements are linked to accessibility of the enhancers, we examined DNA methylation in enhancer regions overlapping with DHSs (available only for monocytes), compared to those that do not overlap. Interestingly, DNA methylation at accessible enhancer regions (identified with DNase I data) in monocytes was lower than at non-accessible regions (FDR < 10−16, T-test, Fig. S12), corroborating previous observations (Stadler et al., 2011) and suggesting that chromatin accessibility and DNA methylation can further refine subclasses of enhancers.
To further understand the relationship between DNA methylation and chromatin states, we tested differential methylation in DNA segments defined by the chromatin state patterns between samples. For this analysis, we used RnBeads software to test for differential methylation in each segment (Fig. S13) (Assenov et al., 2014). This approach has the advantage of increasing the statistical power in DMR detection, as it combines statistical evidence from neighboring CpGs in each DNA segment previously defined (Bock et al., 2012), as well as facilitating the biological interpretation of the significant DMRs. We compared neutrophils and monocytes using RnBeads and detected 17,129 DMRs (FDR < 0.01, beta difference > 0.1) comprising 4,62 Mb of the human genome, with approximately two thirds of the DMRs (11,429) showing higher methylation levels in monocytes. About half of the DMRs (53%) fall into introns, with only 34% of the DMRs located outside genes (Fig. S8).
Inspection of the DMRs in the context of chromatin states revealed that these are most enriched in regions that are enhancer/regulatory regions (RegE) in monocytes and neutrophils Fig. 3A-F) (FDR = 2 × 10−46, Chi-squared test), or enhancers in monocytes and heterochromatic (Rhet) in neutrophils (FDR = 2 × 10−20, Chi-squared test) (Fig. 3E-H). We observed 3.5 fold more hypermethylated DMR elements at regulatory regions (RegE) in monocytes compared to neutrophils, likely correlating with presence of these elements in introns of actively transcribed (and high in DNA methylation) genes (Lister et al., 2009). In contrast, the majority of RegE regions in monocytes that are marked as RHet in neutrophils, tend to be hypomethylated in monocytes (16 fold, Fig. 3E-G Fig S14). TSS methylation did not differ substantially between monocytes and neutrophils even when separating promoters in different configurations (Fig. S15).
These results suggests that the cell-type specific differences observed in methylation of transcribed and regulatory regions were larger than those observed at the promoter regions. The hypermethylated heterochromatic regions in neutrophils that are marked as hypomethylated regulatory elements in monocytes are associated with genes involved in various signaling pathways (GREAT analysis, FDR < 0.01). Interestingly, gene-sets corresponding to targets of c-Myc were enriched in monocyte-specific RegE regions that show differential methylation (Fig. 3H). Taken together, the observed differences in chromatin states between monocytes and neutrophils would suggest a higher transcriptional activity in monocyte, mediated both in cis- and in trans-by more regulatory elements and more active promoters.
Connecting the epigenome with gene expression regulation
Our results so far suggest that monocytes may have higher transcriptional activity than neutrophils, given that they show higher number of active enhancers, fewer heterochromatic regions and more promoters that have cell-type specific active marks. We performed RNA-Seq on ribo-depleted RNA and mapped, on average, 150 million, 100 nucleotide paired-end reads per sample (Table S9) and used them to quantify different transcriptional elements (Djebali et al., 2012) as annotated in Gencode (Harrow et al., 2012) (version v15; Table S3). We observed >35% of reads mapped to intronic regions both for neutrophils and monocytes (Table S9), an expected result from the used RNA-seq protocol that preserves immature RNAs. PCA based on gene expression separated the samples according to the different biological classes (Fig 1D).
We observed that monocytes exhibit higher overall transcriptional activity than neutrophils. They express more genes (Table S10 and Fig. 4B) and at higher levels (Fig. 4A). We detected 4,334 genes that were differentially expressed between neutrophils and monocytes in adult peripheral blood (FDR < 0.01, and a fold change |logFC| > 2). Consistent with a higher transcriptional activity in monocytes, more than twice as many protein-coding genes and pseudogenes were up-regulated in monocytes compared to neutrophils (Table S11). To try to control for the effect of sequencing coverage when performing RNAseq, we compared the number of detected genes in each sample by serial ‘in silico dilutions’. We randomly selected subsets of reads and counted the number of genes using an arbitrary threshold of RPKM (reads per kilobase per million mapped reads) > 0.1. We found a read depth independent difference between the number of genes expressed in monocytes and neutrophils, with the latter consistently showing a lower number in cord blood and adult peripheral blood (Fig. 4B). To corroborate if the higher number of genes in monocytes is reflected in higher levels of RNA, we compared the yields of RNA extraction for monocytes and neutrophils from peripheral blood of 48 individuals (reference). We observed that the average RNA quantity extracted from monocytes is 10 times higher than in neutrophils (p < 10−16, T-test, Fig. S16).
Inferring master regulators in monocytes and neutrophils
Having observed a substantial consistency between functional characterization of monocytes and neutrophils at the epigenomic and gene expression level, we proceeded to infer the main regulators in the two cell types using ISMARA (Integrated System for Motif Activity Response Analysis; Piotr at el., 2014). ISMARA identifies the key transcription factors and miRNAs that could drive the observed expression changes between different conditions, in this case our two cell types. ISMARA analysis is focused on known transcription start sites defined with CAGE (Cap Analysis of Gene Expression, Piotr at el., 2014). When we applied it to our RNA-seq data, it identified a total of 31 motifs with a Z-score higher than 2). Of these, 12 are bound by TFs that are predicted to be more active in monocytes and 19 motifs in neutrophils. We integrated the putative regulators identified by ISMARA into networks with their regulatory relationships as annotated in TFactS (Essaghir et al., 2010) and within the context of known functional interactions from REACTOME (Croft et al., 2013). This leads to monocyte- and neutrophil-specific interaction networks (Fig. 4C-D). As shown, many of the master regulators identified in one specific cell-type show higher expression in that cell type.
The most significant motifs predicted by ISMARA as enriched in monocytes are predicted to be bound by the family of basic-helix-loop-helix (bHLH) proteins. Amongst these, based on differential expression, AHR and ARNT are prime candidate regulators given their suggested roles in regulating immunological responses and hematopoietic differentiation (Boitano et al., 2010, Gasiewicz et al., 2010). Another bHLH protein expressed to higher levels in monocytes as compared to neutrophils is MYC that we have shown that is enriched in monocyte-specific enhancers. Finally, E2F motifs are enriched in regulatory regions of monocyte genes, possibly reflecting that the fact that only monocytes retain some proliferative capacity (Dale et al., 2008).
The transcriptional subnetwork that is predicted to be more active in monocytes (1173 genes) is enriched for functions related to multi-cellular organismal processes, regulation of transcription and RNA metabolsim, defense and inflammatory responses. Most of these functions are mediated by MYC targets (553 genes), which dominate the subnetwork. The neutrophil specific transcriptional subnetwork is much smaller than the monocyte one (248 genes) and is not enriched for any specific function.
In neutrophils, ISMARA finds specific enrichment of motifs of the immediate early response gene family (EGR1, 2 and 3) thought to underlie the ability of neutrophils to respond rapidly to inflammatory stimuli (Cullen et al., 2010). In addition, we detect enrichment for motifs bound by factors such as HBP1, HMGA1 and 2, which are architectural elements of chromatin and are involved in the regulation of multiple DNA-dependent processes. In this case, these factors might be potential contributors to the unique spatial organization of neutrophil DNA in a segmented nucleus. Finally, we identified enrichment for motifs bound by factors involved in Interferon and Interleukin response such as the STAT (STAT2,4,6) and the IRF (IRF1, 2 and 7) family. RNA-seq analysis (Fig. S17) revealed a larger number of genes of the type I interferon (IFN) signaling pathway expressed in neutrophils suggesting this to be a specific activated function in neutrophils compared to monocytes. Comparing adult with cord blood neutrophils revealed increased expression of IFN genes in adult blood, potentially reflecting a poised IFN state. Interestingly, despite higher expression of IFN pathway genes in neutrophils, most TSSs in both cell types are marked with active chromatin segments (Fig. S17). These results suggest that, compared to the naive state of cord blood neutrophils, adult circulating neutrophils are already in a more active state, potentially due to previous environmentally induced triggers, the presence of a normal gut flora (which is absent in newborns at the moment of delivery), colonization of the skin and/or ageing.
Enhancer activity could account for higher gene expression levels in monocytes
We proceeded to investigate which of the epigenomic features mapped contributes most in determining the gene expression in a specific cell-type. Considering factors that are likely to affect gene expression, we focused on DNA methylation and chromatin state at the TSS and on the presence of neighboring active enhancers associated to a specific gene. We divided genes into 100 classes of increasing expression to examine the dependence between the three epigenetic variables and expression. We observe a clear negative correlation in both monocytes and neutrophils between TSS methylation and gene expression but only in the low range of expression (Fig. 5A). This is consistent with our previous observation that DNA methylation at the TSS does not seem to differ substantially between monocytes and neutrophils (Fig. S15).
On the contrary, we find a strong positive correlation, persisting into higher expression ranges, between the expression and the promoter activity state (related to the number of samples with active or repressed chromatin state at the promoter in a specific cell-type, see Supp. methods) (Fig. 5B). This result suggest that the histone marks summarised by the chromatin states may have a stronger connection to gene expression than the TSS DNA methylation levels. Finally, we investigated to what extent the cell-type specific gene expression programmes could be influenced by the presence of non-promoter regulatory elements. We defined an enhancer score as the number of RegE regions that were associated to each gene by GREAT (McLean et al., 2010), either specifically in one cell type or in both. We observe a strong positive correlation between expression values and the enhancer score, especially in more highly expressed genes (Fig. 5C). Moreover, for the almost 2,000 genes that have enhancer regions associated in both cell types, monocytes show clearly higher enhancer scores (data not shown), suggesting that the presence of more distal regulatory elements in monocytes is an important contribution to the higher transcriptional activity observed in this cell-type.
Our results have shown that the correlation of these three epigenomics features with gene expression varies depending on the range of gene expression levels that we investigate. To assay the statistical significance of the contribution of each epigenomic feature in determining gene expression, we fitted linear regression models using these variables in different combinations (Table S12). As similar results were obtained using the data from neutrophils or from monocytes, we will focus here in discussing the models for monocytes. The most informative factor appears to be the promoter activity state, explaining by itself 19% of the variance in expression. (Table S12). Adding the enhancer score to the model further improves the results (21.6% of variance explained) while it remains unchanged by the addition of the TSS methylation in the full model (21.7% of variance explained).
We then built three different multivariate models with the three epigenomic features: one model for all genes, a second model for low expressed genes, and a third model for high expressed genes (Fig. 5D). TSS DNA methylation only has a significant contribution for those genes that are expressed at low levels (p = 0.05) but it is not a relevant feature for highly expressed genes (p = 0.76). Promoter marking as the main predictor of gene activity of low expressed genes (p < 10−16), while enhancer activity is most predictive for higher expressed genes (p < 10−16). These results indicate that the number of enhancers per gene can be an important factor regulating the expression levels of many genes.
DISCUSSION
Knowledge of chromatin organization is fundamental to our understanding of how different cell types arise from a single hematopoietic stem cell. Although in recent years many epigenomic datasets have become available, most notably through the Encyclopedia of DNA Elements (ENCODE) project (http://www.genome.gov/10005107), these datasets have been largely restricted to immortalized cell lines. In contrast, comprehensive epigenomic analysis of primary human cell types has only been initiated in recent years (Satterlee et al., 2010; Bernstein et al., 2010). In this manuscript, BLUEPRINT generated and analyzed the first reference epigenomic data sets of primary human neutrophils and monocytes. For a total of 12 samples we describe the detailed and integrated analysis of RNA-seq data, ChIP-seq of six histone modifications and DNA methylation by bisulfite sequencing at base pair resolution for neutrophils and monocytes isolated from peripheral or cord blood, thereby providing a first insight into the epigenetic programming of two different types of myeloid cells as well as providing a reference for ongoing efforts to examine epigenetic variation within monocytes and neutrophils obtained from 200 healthy individuals.
Genome segmentation based on histone modifications allowed the identification of genomic elements with different chromatin states in the two cell types. The chromatin states identified with the combination of these histone marks are similar to previously reported chromatin segments in cell lines and other primary cells (Hoffman et al., 2013). Remarkably, in comparison to neutrophils, monocytes have more regulatory regions that are active, probably reflecting a more plastic epigenome that can still undergo further differentiation into several types of macrophages (Saeed et al., submitted), dendritic cells and osteoclasts. In contrast, most of the neutrophil-specific features tend to be in a silent chromatin state. However, it should be noted that the majority of the heterochromatin is characterized by the absence of signal (state 2). Therefore, we cannot rule out the possibility that part of these neutrophil-specific ‘heterochromatin’ segments may be decondensed chromatin (Martinod et al., 2013).
Our analysis of differential DNA methylation in the context of chromatin states allowed us to identify regulatory regions (enhancers) as the most frequent chromatin regions were differential methylation occurs. Most monocyte-specific regulatory regions were hypomethylated compared with the heterochromatic-like and high methylation status of these regions in neutrophils. TSS methylation does not seem to differ substantially between the two cell types and is unlikely to be mediating the regulation of the main gene expression programs that drive the different functions of monocytes and neutrophils. Indeed, modeling of epigenetic features related to gene expression correlates mostly with the presence of putative enhancers and marks of active promoters in the TSS of the corresponding genes.
Although a subset of genes is more highly expressed in neutrophils (such as many of the IFN pathway genes), our analysis revealed generally higher transcriptional activity in monocytes. More genes with high RPKM values were detected in monocytes, while in neutrophils relatively more genes had lower RPKM values. Accordingly, monocytes yield higher levels of RNA, further strengthening the notion that in monocytes genes are generally higher expressed. In monocytes more genes are associated with multiple regulatory elements, which likely represent super-enhancers (Hnisz et al., 2013) that reflect the cells identity. Interestingly, one of these enhancer clusters is associated with the transcription factor MYC which is higher expressed in monocytes than neutrophils and has been described as a transcriptional amplifier (Nie, 2012). We found that MYC binding motif is enriched at monocyte-specific enhancers and that DMRs are enriched in known targets of MYC. In an independent analysis where epigenomic information was not used, ISMARA predicted that the gene expression of MYC targets could be associated to a higher activity of MYC in monocytes. It is thus tempting to speculate that the higher MYC levels in monocytes might have an important role for the higher transcription in monocytes compared to neutrophils. Finally, we examined the relative contribution of each epigenetic mark to gene expression. Interestingly, the number of enhancers associated per gene is the most informative feature to predict gene expression levels of highly expressed genes. These simple models suggest that the highest number of enhancers in monocytes could indeed play a role in their higher gene expression levels.
Altogether, our study provides comprehensive epigenetic charts of chromatin states in primary human phagocytes as well as new insights into the regulatory program of neutrophils and monocytes, thus providing a comprehensive resource that can be used for studying the regulation of the human innate immune system.
ACKNOWLEDGEMENTS
The work described in the manuscript was supported by the European Union’s Seventh Framework Program through the BLUEPRINT grant with code HEALTH-F5-2011-282510. NS’s research was further supported by the Wellcome Trust (Grant Codes WT098051 and WT091310), the EU FP7 (EPIGENESYS Grant Code 257082 and BLUEPRINT Grant Code HEALTH-F5-2011-282510), research in the Ouwehand laboratory is further supported by program grants from the National Institute for Health Research (to WHO, NF; http://www.nihr.ac.uk) and the British Heart Foundation (to AR; http://www.bhf.org.uk) under numbers RP-PG-0310-1002 and RG/09/12/28096. AV was also funded by Spanish Ministry of Economy and Competitivity (MINECO, BIO2012-40205). Both the Cambridge BioResource and FACS cell sorting facility were supported by a NIHR grant to the Cambridge NIHR Biomedical Research Centre. VP was supported by a FEBS long-term fellowship. DR’s research is supported by a Wellcome Trust Seed Award in Science (206103/Z/17/Z).