An Atlas of Promoter Chromatin Modifications and HiChIP Regulatory Interactions in Human Subcutaneous Adipose-Derived Stem Cells

Abstract

The genome of human adipose-derived stem cells (ADSCs) from abdominal and gluteofemoral adipose tissue depots are maintained in depot-specific stable epigenetic conformations that influence cell-autonomous gene expression patterns and drive unique depot-specific functions. The traditional approach to explore tissue-specific transcriptional regulation has been to correlate differential gene expression to the nearest-neighbor linear-distance regulatory region defined by associated chromatin features including open chromatin status, histone modifications, and DNA methylation. This has provided important information; nonetheless, the approach is limited because of the known organization of eukaryotic chromatin into a topologically constrained three-dimensional network. This network positions distal regulatory elements in spatial proximity with gene promoters which are not predictable based on linear genomic distance. In this work, we capture long-range chromatin interactions using HiChIP to identify remote genomic regions that influence the differential regulation of depot-specific genes in ADSCs isolated from different adipose depots. By integrating these data with RNA-seq results and histone modifications identified by ChIP-seq, we uncovered distal regulatory elements that influence depot-specific gene expression in ADSCs. Interestingly, a subset of the HiChIP-defined chromatin loops also provide previously unknown connections between waist-to-hip ratio GWAS variants with genes that are known to significantly influence ADSC differentiation and adipocyte function.

1. Introduction

The distribution of adipose tissue throughout the body plays a significant role in predicting the health status of overweight and obese people independent of body mass index (BMI) [1,2]. Excess accumulation of fat in the upper body (apple-shaped) is positively correlated with higher HbA1c, circulating triglycerides (TG), and adverse serum lipid profiles [3]. In contrast, excess accumulation of fat in the lower body (pear-shaped), such as in the gluteofemoral (GF) depot, is negatively correlated with the same metabolic disease markers [4].

A recent theory to explain the effect of differential fat accumulation on metabolic health posits that the lower body adipose tissue serves as a sink for “healthy” lipid deposition and limits fat accumulation in the upper body, notably in visceral adipose tissue, the latter of which is associated with chronic inflammation and insulin resistance [5,6]. Although it is well established that growth hormone, cortisol, and sex steroids influence fat distribution [7,8,9], the underlying mechanism for why people develop the apple vs. pear body shape is still not completely understood. At the cellular level, besides the obvious role of mature adipocytes to sequester lipids, there is a likely contribution from the precursor cells or adipose-derived stem cells (ADSCs) which have the capacity to differentiate into mature adipocytes to store excess lipid. In fact, previous studies have demonstrated that human primary subcutaneous abdominal (ABD) vs. GF-ADSCs, have differential adipogenic capacity in vitro [10]. In addition, there are distinct transcriptional signatures, chromatin marks, and DNA methylation patterns in ABD vs. GF adipose tissue [11,12,13] that are partially maintained in isolated ADSCs following culture in vitro [14,15]. Taken together, these observations provide evidence for an underlying epigenetic memory that contributes to the different patterns of gene-expression-driven phenotypes. Our working hypothesis is that these cell-autonomous epigenetic programs maintain unique ABD- and GF-AT characteristics in the cultured ADSCs and contribute to unique ABD and GF adipose tissue characteristics. These earlier studies integrated gene expression and individual epigenetic marks to help explain the sustained patterns of differential gene expression in ADSCs that were isolated from different subcutaneous adipose depots and cultured over several rounds of cell division. In the current work, we aim to extend these preliminary results and interrogate the epigenomic landscape of ABD and GF-ADSCs around the TSS of the differentially expressed genes between the two adipose tissue depots by using an extensive ChIP-seq analysis for multiple histone marks (H3K4me3, H3K4me2, H3K27me3, H3K9me3), CTCF, and RNA polymerase II along with ATAC-seq to probe chromatin openness.

In the earlier studies mentioned above, gene annotation of the regulatory regions was performed using a nearest-neighbor linear-distance approach which only showed a modest connection between differential chromatin features and differential gene expression [14,15]. We recognized this was an overly simplistic approach because chromatin is highly organized and condensed with DNA packaged in a highly ordered fashion with histones and other proteins into a complex three-dimensional network [16,17]. The resulting highly condensed chromatin serves to position distally located regulatory elements close to proximal gene promoters that would otherwise be located far away from each other based on a linear (2D) view of the genome. High-resolution three-dimensional methods including Hi-C and ChiAPET were developed that capture these long-range interactions after partial digestion of the DNA followed by ligation of closely juxtaposed ends that are brought into close proximity by looping out of the intervening DNA [18,19,20].

In the second part of the current work, we aimed to determine how the differential gene expression patterns in ABD and GF-ADSCs were significantly influenced by chromatin modifications and long-range chromatin interactions using the related HiChIP method which, when focused on H3K27ac, will identify loops that are anchored through an active transcriptional enhancer region [21]. We next integrated the HiChIP data set with our differentially expressed RNA-seq data set that compares gene expression patterns in ABD vs. GF-derived ADSCs. These data were then compiled into an atlas that combines the different chromatin marks and active enhancer connectome related to gene expression patterns for ABD vs. GF-ADSCs isolated from apple and pear-shaped women.

This in-depth analysis of the chromatin structure and organization of ABD and GF-ADSCs provides both an initial in-depth understanding of the intrinsic genomic regulatory features that influence the functionally distinct cellular phenotypes of ABD and GF subcutaneous adipose tissue depots, and a resource for the adipose tissue research community. We also demonstrate the value of this data set as a resource by cross-referencing this information with a data set of WHR-related SNPs; together, these investigations provide significant new information for how different adipose depots contribute to differential adipose patterning and metabolic disease risk in humans.

2. Results

To evaluate the cell-autonomous differences and explore the molecular regulation of gene expression between ABD and GF adipose tissue depots, we isolated adipose-derived stem cells (ADSCs) from paired ABD and GF adipose tissue originating from five apple and five pear-shaped women. Principal component analysis using all the clinical parameters (the most relevant are listed in Supplementary Table S1), showed that individuals within each group are highly similar and that the two groups are well separated from each other (Figure 1A). For this reason, we analyzed the apple and pear samples separately. The ADSCs were all cultured the same way and passaged the same number of times (±1) (see Section 4 for details) prior to harvest. The overall workflow of the study is described in Figure 1B. In summary, we performed ChIP-seq for histone marks, CTCF, and RNAPII along with ATAC-seq on freshly isolated chromatin. An additional aliquot of cells was frozen and used for RNA-seq analysis. We also performed H3K27Ac-enriched HiChIP to evaluate the 3D organization of the active enhancer connectome.

Figure 1.

Sample acquisition and study design. (A) Principal component analysis (PCA) plot of the ten subjects used to isolate the ABD and GF-ADSCs based on clinical parameters. The first two principal components (PC) are plotted and colored according to body shape. PCA was performed using all clinical data collected during the clinical study. (B) Overview of the experimental workflow. Subcutaneous adipose tissue biopsies were performed on five apple-shaped and five pear-shaped subjects. From each biopsy, the stroma vascular fraction was isolated and the ADSCs were cultured in media supplemented with serum and growth factors. Cells were harvested, their chromatin was isolated and used for RNA isolation, ChIP, ATAC, and HiChIP assays, followed by sequencing.

2.1. Transcriptomic Signatures of ABD and GF-Adipose-Derived Stem Cells

We first identified the differentially expressed genes between ABD and GF-ADSCs using RNA-seq. Setting a cut-off of a 1.7-fold change and an FDR of 0.1, the RNA-seq analysis showed a total of 599 differentially expressed genes (DEGs) between the ABD and GF-ADSC samples, of which 364 exhibited GF-enriched features and 285 were ABD-enriched. This analysis revealed six clusters of DEGs (Figure 2A), stratified based on their level of expression in apple- and pear-shaped subjects.

Figure 2.

Depot-enriched gene expression and chromatin modification analysis of human ADSCs according to body shape. (A) Heat map showing the differentially expressed genes between ABD and GF-ADSCs in apple and pear-shaped subjects based on RNA-seq. The DEGs were grouped into clusters according to their level of expression in apple and pear samples. DESeq2 analysis, FDR < 0.01, FC > 0.75 Genes potentially involved in adipose tissue expansion are cited. (B) Dot plot showing the significant pathways of the DEGs in each cluster. Only the pathways with p < 0.05 are represented. (C) Legend showing the ChromHMM annotated states, with their emission values for individual chromatin marks. (D) Visualization of selected chromatin states (2, 3, 5, 6, 9, 10) around the TSS (±5 kbp) of DEGs per depot and body shape groups (rows) within gene clusters (columns). Arrows highlight when the states are visually different between ABD and GF. (E) Visualization of Genic enhancer chromatin state (state#1) around the TSS (±5 kbp) of DEGs per groups (colored) within gene clusters. Arrows highlight when the state is visually different between the ABD and GF samples.

Among the forty-two GF-enriched genes highly expressed in GF pear samples (Figure 2A, cluster 1), we found six pro-adipogenic marker genes (GPX3, PRDM1, FABP3, KLF5, WNT5B, and NRG1 [22]) which would be consistent with GF-ADSCs in pear-shaped women having the capacity to differentiate more robustly compared to GF-ADSCs from apple-shaped women. Importantly, further analysis revealed that the most significantly enriched pathway in cluster 1 contained genes involved in Wnt–β-catenin signaling (Figure 2B), which is known to play a significant role in adipocyte differentiation [23]. More recently, an extensive analysis combining scRNA-seq combined with a xenograft mouse model as validation, showed that Wnt signaling preserves progenitor cell multipotency during adipose tissue development [24], which would be predicted to ensure a healthy pool of progenitor cells capable of differentiation to mature adipocytes.

Cluster 2 includes 214 GF-enriched genes highly expressed in apple-shaped samples (Figure 2A), several of which are important for lipid droplet formation and others that increase in expression during adipogenesis, such as ALPL, CD44, CD36, CFD, MME, ENPP2, ELOVL2, and ABI3BP. Cluster 2 also contains fibroblastic or fibrotic marker genes (TGFBR3, LAMA3, TNFSF9, S100A4, VCAM1, CXCL12, ANPEP, COL5A) not found in cluster 1. These genes might participate in the formation of collagen, which can form a scaffold that constrains adipocyte expansion due to mechanical stress in GF apple samples [25,26].

Cluster 3 includes 108 DEGs enriched in both apple and pear GF samples (Figure 2A). Some of these were previously identified as GF-enriched markers for whole adipose tissue (TBX15, SHOX2, and SFRP2 [13,27]). Importantly, we also identified new depot-specific marker genes that are known to influence adipose tissue function (ZIC1, TWIST2, COL4A4, APOD [28,29,30,31]).

The 285 ABD-enriched genes were divided into three clusters of roughly equal size (clusters 4, 5, and 6—Figure 2A) based on their body shape expression pattern. Cluster 4 includes genes highly expressed in ABD apple samples but with low expression in GF pear samples. This cluster includes genes activated by hypoxia (TES, STC2, DDIT4, SLC2A1), cytokine/chemokine genes (IL33, IL11, CXCL5), and PDGFA, which is known to stimulate adipose progenitor proliferation and self-renewal but also is associated with increased adipose tissue fibrosis [32,33]. Cluster 4 also contains two other genes that may influence adipose tissue expansion: BAMBI, a gene known to regulate reactive oxygen levels [34] and PAWR, a suppressor of p53 [35].

Cluster 5 contains ABD-enriched genes highly expressed in both apples and pears, several of which have already been identified as ABD-enriched markers in whole adipose tissue (HOXA [27] and HOXD cluster genes, TBX5, and HOTAIRM1 [36]) and others that have been previously associated with type 2 diabetes pathogenesis (TBX5, PITX2, SKAP2 [37]) (Figure 2A). Genes known to limit adipose tissue expansion were also contained in cluster 5 (LIF, PLOD2, BDNF, and CDKN2B [38,39,40]).

Finally, cluster 6 includes genes highly expressed in ABD pear samples but with low expression in the other three samples, especially GF apple (Figure 2A). Interestingly, the pathway with the highest enrichment score in cluster 6 includes 80 genes related to epithelial–mesenchymal transition (EMT) (Figure 2B). Further analysis revealed these genes are also potential inhibitors of adipogenesis (INHBA, ITGA2, IL32, OXTR, CXCL8, MMP1, TPM1, TFPI2, MYLK, VCAN, COL7A1, PMEPA1, TGM2) [41] and this correlates well with our overall developmental/physiological hypothesis that there is reduced expansion of the ABD depot in pear subjects.

The MCODE analysis [42] revealed protein–protein interactions between the DEGs (Supplementary Figure S1). This analysis identified several proteins interacting with each other. Notably, FMN1 (up in GF depot) and EDN1 (up in ABD depot) were found at the center of two hubs, related to cell growth and inflammation, respectively. Several collagens were also interacting to a high degree. These proteins could be master regulators of the DEGs between ABD and GF-ADSCs and potential therapeutic targets to favorize lipid accumulation in the lower body adipose tissue rather than in the upper body depots.

2.2. Selective Epigenetic Hallmarks of Depot-Selective Transcription

The fact that several of the GF and ABD-ADSC-enriched genes were also enriched in the same samples from whole adipose tissue indicates the differential expression pattern is a stable feature that is likely maintained at least in part through depot-selective epigenetic regulation [14,15]. To evaluate genomic signatures that might contribute to the differential gene expression patterns highlighted above, we compared the chromatin features present in ABD vs. GF-ADSCs in the same samples used for the RNA-seq analysis. We performed ATAC-seq to evaluate chromatin openness and ChIP-seq targeting different histone marks (active chromatin: H3K27Ac—active enhancers/promoters, H3K4me2—active enhancers/promoters, H3K4me3—mainly active promoters; repressed chromatin: H3K27me3—facultative heterochromatin, H3K9me3—constitutive heterochromatin), CTCF (genome architectural protein), and the elongating form of RNAPII (phospho Ser 2 in CTD). Then, we integrated the chromatin modification data from all the samples to generate a combinatorial set of emission states using ChromHMM [43]. Emission parameters were learned de novo based on genome-wide recurrent combinations of the chromatin marks studies (see above) in ADSCs (ABD and GF combined). Importantly, each emission state was defined by a specific combination of chromatin features that may be associated with distinct biological expression patterns of their linked genes.

We distinguished 10 broad classes of chromatin emission states that are labeled according to their combined predicted influence on gene activity. These include “Genomic enhancers”, “Flanking Active TSS”, “Active TSS”, “CTCF-high”, “Bivalent/poised TSS”, “Repressed”, “Quiescent”, “Heterochromatin”, “Strong Transcription”, “Bivalent Enhancer” (Figure 2C), and they are independent of the tissue depot or body shape chromatin source. As a first step in understanding how these combined features influence gene activity, we displayed the average read density scores for the ten unique chromatin states around the transcription start site (TSS) of the DEGs in the two different depots (ABD and GF) and from the two different body shapes (apple and pear) in Figure 2D. Only three emission states were enriched at the TSS of differentially expressed genes. The first one “active TSS” (state 3, orange on the graphs in Figure 2D), contains all activate chromatin-associated histone marks along with CTCF, RNAPII, and the open chromatin signature (ATAC-seq peak) combined with very low levels of the repressive marks H3K27me3 and H3K9me3 (see legend Figure 2D). The active TSS state was enriched at the TSS of the GF-enriched genes belonging to clusters 1 and 3 in the GF samples (dark green arrows in Figure 2D) and at the TSS of the ABD-enriched genes belonging to cluster 5 in the ABD samples (dark orange arrows in Figure 2D).

The second enriched emission state corresponded to repressed regions (state 6, gray on the graphs in Figure 2D) which are characterized by an enrichment of the repressive H3K27me3 mark in the absence of the other features. The genes in clusters 5 and 6 are marked by a high level of the H3K27me3 repressive mark (grey arrows) at the TSS in the GF samples. This suggests that these genes are more highly expressed in ABD-ADSCs because their expression is repressed in the GF region. Taken together, these results support the concept that differential combinations of active and repressive chromatin marks at DEG’s TSSs contribute to depot-specific gene expression patterns in ABD and GF-ADSCs. We also found ‘bivalent domains’ of histone modifications (i.e., harboring both the repressive mark H3K27me3 and the activation-associated marks) near the TSS of genes with depot-specific expression (blue lane, Figure 2D).

To obtain a better assessment of the enhancer regions in the ABD vs. GF samples, we plotted only the “Genic enhancer” state (state 1 in Figure 2C) around the promoter of the DEGs. Figure 2E displays the average read density scores for this specific state around the TSS of the DEGs for the four different groups (ABD and GF, apple and pear subjects). The genes in clusters 2 and 3 (GF-enriched genes) are marked by an increase in enhancer marks in the GF samples whereas the genes in cluster 6 (ABD-enriched genes) are marked by an increase in enhancer marks in the ABD samples. These observations suggest an active role of enhancer genomic regions in depot-specific gene regulation in human ADSCs.

2.3. Alteration of Active and Repressive Epigenetic Marks Associated with Depot-Selective Gene Expression

To evaluate the individual contributions of histone modifications in depot-selective gene expression, we separately analyzed the ChIP-seq marks within the TSS of the DEGs and compared the data between the ABD and GF-ADSCs. We focused on the individual histone marks that define the active TSS state (H3K4me3, H3K27Ac, and H3K4me2) and repressed state (H3K27me3), and calculated the differences in their respective ChIP-seq signals in ABD and GF-ADSCs around the TSS (±2 kbp) of the ABD and GF-DEGs. Those reaching statistical significance (p < 0.05) are colored (orange for ABD-enriched histone mark and green for GF-enriched histone mark) in the volcano plots of Figure 3 (apple subjects) and Supplementary Figure S2 (pear subjects). The full list of DEGs with the fold change for each histone mark is listed in Supplementary Table S2 for the apple subjects and Supplementary Table S3 for the pear subjects. For example, HOXC11 and TBX15 (GF-enriched genes, cluster 3) showed an enrichment of the active histone marks and a decrease of the repressive mark H3K27me3 in the ABD samples compared to the GF samples. This provides a more detailed evaluation of the association of positive and negative histone modifications with differential gene expression patterns in ADSCs and extends what has been described in other cancer cell model systems [44,45].

Figure 3.

Association between depot-enriched expression and depot-enriched chromatin marks at the TSS (±2 kbp) in apple samples. Volcano plots show for each gene and each histone mark studied the average fold change of the ChIP-seq signal between ABD and GF-ADSCs at the TSS. Data are represented by cluster of DEGs (rows). Negative fold changes (green) indicate the ChIP-seq signal is significantly enriched in the GF samples, while positive fold changes (orange) indicate the ChIP-seq signal is significantly enriched in the ABD samples.

Our data also suggest that histone modifications affect expression of genes involved in adipogenesis such as PRDM1, ALPL, RUNX1T1, SFRP2, and GPX3. Importantly, the newly identified GF-enriched genes in our study, ZIC1, GREM2, and IL20RA, were also associated with GF-enriched differential patterns of histone modifications at their TSSs. Among the ABD-enriched genes within the highly active TSS emission state in ABD chromatin, we detected the key developmental genes HOXA5, HOXD1, HOXD3, HOXD8, and TBX5 in cluster 5 (ABD-enriched genes in both body shape types). The differential chromatin marks were also associated with DEGs that are involved in adipose tissue expansion, such as BAMBI, PAWR, and IL8 (clusters 4 and 6; ABD-enriched genes) along with other genes known to limit adipogenesis such as INHBA (cluster 6; ABD-enriched genes highly expressed in pear samples), TIMP1, and CDKN2B (cluster 5). Interestingly, two inflammatory genes (CXCL5 and IL33) showed higher H3K27me3 levels around their TSSs in GF samples compared to their TSSs in ABD samples (cluster 4; ABD-enriched genes highly expressed in apple samples), suggesting that the expression of these two genes may be selectively repressed by H3K27me3 in GF-ADSCs.

2.4. HiChIP Regulatory Interactions in ABD and GF-ADSCs

In an earlier study comparing open chromatin regions identified by ATAC-seq with differentially expressed genes in freshly isolated adipocytes, we showed that only a small fraction of the body-shape-specific open chromatin regions were annotated to DEGs [46]. In this earlier study, we used linear distance as a guide suggesting that long-range genomic interactions mediated by chromatin looping are likely involved in the differential gene expression patterns. To determine how the differential gene expression patterns in ADSCs from different adipose depots may be influenced by long-range chromatin interactions, especially by enhancer genomic regions as suggested by our data in Figure 2E, we performed H3K27ac-targeted HiChIP on chromatin from ADSCs across the ten subjects and two adipose tissue depots.

We identified 52,489 and 52,615 loops in the apple and pear samples, respectively (hichipper, FDR < 0.01). Each sample had similar levels of high-quality uniquely mapped read pairs (Supplementary Figure S3A). Principal components analysis also showed that samples from each group clustered together, and their patterns were separated based on body shape and depot source (Figure 4A). Supplementary Figure S3B shows that the overall A/B compartment score distribution across all groups was identical for chromosome 7 and this was also evident on the whole genome level as shown by the saddle plots in Supplementary Figure S3C. The median loop length was 41 kb and as expected, the number of interactions decreased with increasing distance between loop anchors (Supplementary Figure S3D).

Figure 4.

Mapping epigenomic landscapes in ABD and GF-ADSCs. (A) Principal component analysis (PCA) plot of normalized in-loop H3K27ac HiChIP read counts. The first two principal components (PC) are plotted and colored according to body shape. (B) Dot plot showing the correlation of read densities between ABD and GF-ADSCs in apple subjects. Differential loops are colored in yellow (ABD-enriched) and green (GF-enriched). The non-significant loops are represented in gray. p-value < 0.05 logFC > 1.75. (C) Density plot showing the correlation between differential looping (x-axis) and differential H3K27ac (y-axis) at loop anchors. The H3K27ac signal was binned into 12 groups based on the magnitude of difference in H3K27ac. Data were plotted for the apple subjects. Similar observations were made for the pear subjects. (D) Genome browser visualization of SKAP2-HOX locus (left) and TBX15 locus (right) in ABD (yellow) and in GF (green) samples. Data were derived from apple subjects. Similar observations were made with data derived from pear subjects. From top to bottom: H3K27ac HiChIP interaction matrices, domainogram of insulation score, CTCF, ATAC-seq, H3K4me3, H3K27ac, H3K4me2, RNAPII, H3K9me3, H3K27me3, ChromHMM states, H3K27ac loops, and gene annotation. Color coding for ChromHMM plots is the same as Figure 2C.

We then overlapped the anchors of the HiChIP loops with gene promoters and enhancers and the resulting loop sets were binned into three different categories: enhancer–promoter loops (18,958 in apples and 19,011 in pears), enhancer–enhancer loops (28,075 in apples and 28,138 in pears), and promoter–promoter loops (5456 in apples and 5466 in pears).

To identify differential loops between the ABD and GF-ADSC samples, we ran diffloop analysis separately for the apple and pear groups. This revealed 852 ABD-enriched loops and 493 GF-enriched loops in the apple samples with a p < 0.05 and fold change of 1.75 between the groups (orange and green symbols in Figure 4B). The number of depot-enriched loops was lower for the pear groups (304 ABD-enriched and 238 GF-enriched, Supplementary Figure S3E). To validate the depot-specific regulatory loops identified by HiChIP, we overlapped the loop anchors with the read density from the independently performed H3K27ac ChIP-seq analysis on the same set of samples from Figure 2 and Figure 3. The fold change between the ABD and GF HiChIP reads at the loop anchors highly correlated with the fold change between the ABD and GF H3K27ac signal detected by ChIP-seq at the same loop anchors. This correlation is consistent with the HiChIP pipeline used in our study, accurately identifying authentic depot-enriched loops.

This comparison resulted in a list of high-confidence H3K27ac loops in the ABD and GF-ADSC samples, including promoter and enhancer interactions that we analyzed further below. At 2.5 kb resolution, the H3K27ac HiChIP maps revealed depot-specific promoter–enhancer interactions at the promoter of HOXA genes in the ABD sample which are known ABD-enriched genes [27] (Figure 4D, left) and at the promoter of the TBX15 gene in the GF samples which is a known GF-enriched gene (Figure 4D, right). Additionally, TBX15 expression correlates with WHR and there is some evidence that it is a master transcriptional regulator in adipose tissue [47]. The genomic regions that were enriched in loops (HiChIP results) also were highly enriched for the CTCF ChIP-seq peaks that were identified in the CTCF ChIP-seq data used in the ChromHMM analysis in Figure 2C. These sites co-mapped with genomic regions with a high insulation score, consistent with CTCF-associated looping organizing the 3D genomic architecture to regulate gene expression. There was also strong enrichment for H3K27ac binding along with higher RNAPII and other marks associated with gene activation in ABD chromatin at the HOXA locus (Figure 4D, left IGV snapshots and ChromHMM). Similarly, the GF-enriched TBX15 HiChIP loops were associated with more robust peaks for active histone marks and RNAPII in the GF sample (Figure 4D, right IGV snapshots and ChromHMM).

2.5. Loop Anchors Harbor DEGs and SNPs That Are Associated with Waist–Hip Ratio in Humans

To further define regulatory regions that might influence differential gene expression between ABD and GF-ADSCs, we first identified the HiChIP loop anchors that were linked to DEGs. In apples, this revealed 323 loops in the genomic regions of the DEGs between the ABD and GF samples, and these loops mapped to 64 unique DEGs. We found approximately the same results (325 loops mapping to 64 unique genes) in the pear group. From these lists we extracted the loops with at least one anchor found at the promoter region of the DEGs which corresponds to thirty-five unique DEGs (Table 1, in apples). The transcription of these genes is likely regulated by the enhancer region we identified by HiChIP (opposite loop anchor in Table 1), and some are potentially involved in fat distribution heterogeneity (HOXA, BDNF, IL33, EPHX2, IGF2BP1).

Table 1.

List of DEGs with at least one loop anchor located at their promoter.

DEG		LOOP					ANCHOR1					ANCHOR2
DEG	Cluster	ID	Overlap	Width	Type	Sig	Annotation	Dist. from TSS	ENS_ID	Gene Name	Gene Type	Annotation	Dist. from TSS	ENS_ID	Gene Name	Gene Type
HOXA11	3	19124	anchor1	8628	e-p	down	promoter-TSS (NM_019102)	−94	ENSG00000106004	HOXA5	protein-coding	non-coding (NR_038832, exon 3 of 3)	4272	ENSG00000122592	HOXA7	protein-coding
HOXA11	3	19125	anchor1	23,154	e-p	down	promoter-TSS (NM_019102)	−94	ENSG00000106004	HOXA5	protein-coding	intron (NR_037940, intron 1 of 2)	−1392	ENSG00000078399	HOXA9	protein-coding
HOXA11	3	19127	anchor1	35,064	e-p	down	promoter-TSS (NM_019102)	−94	ENSG00000106004	HOXA5	protein-coding	intron (NR_037939, intron 1 of 1)	1021	ENSG00000253293	HOXA10	protein-coding
HOXA11	3	19126	anchor1	30,023	e-p	down	promoter-TSS (NM_019102)	−94	ENSG00000106004	HOXA5	protein-coding	exon (NM_018951, exon 1 of 2)	522	ENSG00000253293	HOXA10	protein-coding
GALNT16	3	35883	anchor1	12,524	e-p	ns	promoter-TSS (NM_004926)	−870	ENSG00000185650	ZFP36L1	protein-coding	Intergenic	−11,065	ENSG00000185650	ZFP36L1	protein-coding
GALNT16	3	35884	anchor1	20,626	e-p	ns	promoter-TSS (NM_004926)	−870	ENSG00000185650	ZFP36L1	protein-coding	Intergenic	−19,168	ENSG00000185650	ZFP36L1	protein-coding
GALNT16	3	35885	anchor1	47,834	e-p	ns	promoter-TSS (NM_004926)	−870	ENSG00000185650	ZFP36L1	protein-coding	Intergenic	−46,375	ENSG00000185650	ZFP36L1	protein-coding
ABHD14A-ACY1	3	10292	anchor1	12,344	e-p	ns	promoter-TSS (NM_004704)	150	ENSG00000041880	PARP3	protein-coding	promoter-TSS (NM_080865)	−523	ENSG00000180929	GPR62	protein-coding
ABHD14A-ACY1	3	10293	anchor1	24,976	e-p	ns	promoter-TSS (NM_004704)	150	ENSG00000041880	PARP3	protein-coding	promoter-TSS (NM_020418)	−24	ENSG00000090097	PCBP4	protein-coding
ABHD14A-ACY1	3	10294	anchor1	32,056	e-p	ns	promoter-TSS (NM_004704)	150	ENSG00000041880	PARP3	protein-coding	promoter-TSS (NM_032750)	61	ENSG00000114779	ABHD14B	protein-coding
ABHD14A-ACY1	3	10295	anchor1	42,148	e-p	ns	promoter-TSS (NM_004704)	150	ENSG00000041880	PARP3	protein-coding	intron (NM_001198898, intron 2 of 13)	1127	ENSG00000243989	ACY1	protein-coding
ABHD14A-ACY1	3	10296	anchor1	53,012	e-p	ns	promoter-TSS (NM_004704)	150	ENSG00000041880	PARP3	protein-coding	intron (NM_000992, intron 1 of 3)	369	ENSG00000162244	RPL29	protein-coding
ABHD14A-ACY1	3	10297	anchor1	112,696	e-p	ns	promoter-TSS (NM_004704)	150	ENSG00000041880	PARP3	protein-coding	intron (NM_001947, intron 1 of 2)	1362	ENSG00000164086	DUSP7	protein-coding
ABHD14A-ACY1	3	10298	anchor1	146,827	e-p	ns	promoter-TSS (NM_004704)	150	ENSG00000041880	PARP3	protein-coding	intron (NM_001161580, intron 9 of 9)	−24,228	NA	LINC00696	ncRNA
CFD	2	45670	anchor1	123,402	e-p	ns	intron (NM_001317335, intron 1 of 4)	760	ENSG00000197766	CFD	protein-coding	promoter-TSS (NM_024100)	−501	ENSG00000065268	WDR18	protein-coding
RIN1	2	29943	anchor1	70,293	e-p	ns	exon (NM_003793, exon 1 of 13)	193	ENSG00000174080	CTSF	protein-coding	promoter-TSS (NM_001198843)	4	ENSG00000173933	RBM4	protein-coding
PLEKHA4	2	48434	anchor1	26,441	e-p	ns	promoter-TSS (NR_130317)	−39	ENSG00000105467	SYNGR4	protein-coding	intron (NM_006801, intron 1 of 4)	782	ENSG00000105438	KDELR1	protein-coding
GORAB-AS1	2	3998	anchor1	99,172	e-p	ns	Intergenic	−31,865	NA	GORAB-AS1	ncRNA	promoter-TSS (NM_022716)	−485	ENSG00000116132	PRRX1	protein-coding
RAI14	5	13716	anchor1	28,634	e-p	ns	intron (NM_001145520, intron 1 of 17)	1670	ENSG00000039560	RAI14	protein-coding	promoter-TSS (NM_001145523)	−764	ENSG00000039560	RAI14	protein-coding
RAI14	5	13724	anchor1	257,285	e-p	ns	intron (NM_001145520, intron 1 of 17)	1670	ENSG00000039560	RAI14	protein-coding	promoter-TSS (NM_002853)	58	ENSG00000113456	RAD1	protein-coding
BDNF	5	28813	anchor1	409,349	e-p	ns	promoter-TSS (NM_170734)	−18	ENSG00000176697	BDNF	protein-coding	promoter-TSS (NM_031217)	646	ENSG00000169519	METTL15	protein-coding
HOXA9	5	19120	anchor1	9859	e-p	down	Intergenic	−3159	ENSG00000197576	HOXA4	protein-coding	promoter-TSS (NM_019102)	−94	ENSG00000106004	HOXA5	protein-coding
IL33	4	22820	anchor1	15,077	e-p	ns	promoter-TSS (NM_001314046)	29	ENSG00000137033	IL33	protein-coding	intron (NM_001199640, intron 1 of 6)	−6168	ENSG00000137033	IL33	protein-coding
CHMP1B-AS1	4	45102	anchor1	7276	e-p	ns	promoter-TSS (NM_020412)	−40	ENSG00000255112	CHMP1B	protein-coding	intron (NM_001261444, intron 1 of 7)	896	ENSG00000141404	GNAL	protein-coding
CHMP1B-AS1	4	45103	anchor1	96,022	e-p	ns	promoter-TSS (NM_020412)	−40	ENSG00000255112	CHMP1B	protein-coding	Intergenic	−34,112	ENSG00000141401	IMPA2	protein-coding
MIR210HG	4	28219	anchor1	7749	e-p	ns	promoter-TSS (NR_038262)	−231	ENSG00000247095	MIR210HG	ncRNA	promoter-TSS (NM_001286583)	−33	ENSG00000070047	PHRF1	protein-coding
HSD17B6	4	32706	anchor1	89,094	e-p	ns	promoter-TSS (NM_005419)	−96	ENSG00000170581	STAT2	protein-coding	TTS (NM_012064)	129	ENSG00000111602	TIMELESS	protein-coding
HSD17B6	4	32707	anchor1	102,550	e-p	ns	promoter-TSS (NM_005419)	−96	ENSG00000170581	STAT2	protein-coding	Intergenic	−5825	ENSG00000176422	SPRYD4	protein-coding
HSD17B6	4	32708	anchor1	108,572	e-p	ns	promoter-TSS (NM_005419)	−96	ENSG00000170581	STAT2	protein-coding	intron (NM_207344, intron 1 of 1)	197	ENSG00000176422	SPRYD4	protein-coding
COL7A1	6	10135	anchor1	6365	e-p	ns	promoter-TSS (NM_001317138)	−734	ENSG00000114268	PFKFB4	protein-coding	promoter-TSS (NM_033199)	−121	ENSG00000145040	UCN2	protein-coding
COL7A1	6	10136	anchor1	39,463	e-p	ns	promoter-TSS (NM_001317138)	−734	ENSG00000114268	PFKFB4	protein-coding	Intergenic	−1840	ENSG00000114270	COL7A1	protein-coding
COL7A1	6	10137	anchor1	77,961	e-p	ns	promoter-TSS (NM_001317138)	−734	ENSG00000114268	PFKFB4	protein-coding	promoter-TSS (NM_022911)	−37	ENSG00000225697	SLC26A6	protein-coding
PODNL1	6	47001	anchor1	21,746	e-p	ns	non-coding (NR_036515, exon 1 of 1)	2808	NA	LOC284454	ncRNA	promoter-TSS (NR_146095)	−180	ENSG00000187556	NANOS3	protein-coding
CPNE7	6	40745	anchor1	52,925	e-p	ns	Intergenic	−1189	ENSG00000197912	SPG7	protein-coding	promoter-TSS (NM_000977)	−533	ENSG00000167526	RPL13	protein-coding
HSD17B14	6	48432	anchor1	111,879	e-p	ns	non-coding (NR_130317, exon 6 of 6)	7398	ENSG00000142227	EMP3	protein-coding	promoter-TSS (NM_031485)	−706	ENSG00000105447	GRWD1	protein-coding
HSD17B14	6	48433	anchor1	135,056	e-p	ns	non-coding (NR_130317, exon 6 of 6)	7398	ENSG00000142227	EMP3	protein-coding	promoter-TSS (NM_004228)	−754	ENSG00000105443	CYTH2	protein-coding
HOXA11	3	19120	anchor2	9859	e-p	down	Intergenic	−3159	ENSG00000197576	HOXA4	protein-coding	promoter-TSS (NM_019102)	−94	ENSG00000106004	HOXA5	protein-coding
HOXA11	3	19110	anchor2	44,790	e-p	down	intron (NR_038367, intron 1 of 1)	2891	ENSG00000233429	HOTAIRM1	ncRNA	promoter-TSS (NM_019102)	−94	ENSG00000106004	HOXA5	protein-coding
HOXA11	3	19115	anchor2	32,933	e-p	down	intron (NM_153631, intron 2 of 3)	−8159	ENSG00000105996	HOXA2	protein-coding	promoter-TSS (NM_019102)	−94	ENSG00000106004	HOXA5	protein-coding
HOXA11	3	19099	anchor2	287,607	e-p	down	intron (NM_003930, intron 1 of 12)	1555	ENSG00000005020	SKAP2	protein-coding	promoter-TSS (NM_019102)	−94	ENSG00000106004	HOXA5	protein-coding
HOXA11	3	19092	anchor2	293,364	e-p	down	intron (NM_001303468, intron 3 of 12)	7312	ENSG00000005020	SKAP2	protein-coding	promoter-TSS (NM_019102)	−94	ENSG00000106004	HOXA5	protein-coding
GALNT16	3	35722	anchor2	852,295	e-p	ns	intron (NM_001321817, intron 8 of 11)	118,897	ENSG00000182185	RAD51B	protein-coding	promoter-TSS (NM_004926)	−870	ENSG00000185650	ZFP36L1	protein-coding
GALNT16	3	35737	anchor2	653,186	e-p	ns	intron (NM_001321817, intron 8 of 11)	318,006	ENSG00000182185	RAD51B	protein-coding	promoter-TSS (NM_004926)	−870	ENSG00000185650	ZFP36L1	protein-coding
GALNT16	3	35749	anchor2	546,374	e-p	ns	intron (NM_001321817, intron 8 of 11)	380,377	NA	LOC100996664	ncRNA	promoter-TSS (NM_004926)	−870	ENSG00000185650	ZFP36L1	protein-coding
GALNT16	3	35779	anchor2	329,101	e-p	ns	intron (NM_001321817, intron 10 of 11)	163,104	NA	LOC100996664	ncRNA	promoter-TSS (NM_004926)	−870	ENSG00000185650	ZFP36L1	protein-coding
GALNT16	3	35797	anchor2	285,117	e-p	ns	intron (NM_001321818, intron 10 of 10)	119,120	NA	LOC100996664	ncRNA	promoter-TSS (NM_004926)	−870	ENSG00000185650	ZFP36L1	protein-coding
GALNT16	3	35821	anchor2	248,544	e-p	ns	intron (NM_001321818, intron 10 of 10)	82,547	NA	LOC100996664	ncRNA	promoter-TSS (NM_004926)	−870	ENSG00000185650	ZFP36L1	protein-coding
GALNT16	3	35841	anchor2	156,840	e-p	ns	intron (NM_001321818, intron 10 of 10)	−9157	NA	LOC100996664	ncRNA	promoter-TSS (NM_004926)	−870	ENSG00000185650	ZFP36L1	protein-coding
GALNT16	3	35850	anchor2	125,930	e-p	ns	intron (NM_001321818, intron 10 of 10)	−40,067	NA	LOC100996664	ncRNA	promoter-TSS (NM_004926)	−870	ENSG00000185650	ZFP36L1	protein-coding
GALNT16	3	35855	anchor2	111,357	e-p	ns	TTS (NM_001321818)	−54,640	NA	LOC100996664	ncRNA	promoter-TSS (NM_004926)	−870	ENSG00000185650	ZFP36L1	protein-coding
GALNT16	3	35861	anchor2	99,498	e-p	ns	Intergenic	−66,499	NA	LOC100996664	ncRNA	promoter-TSS (NM_004926)	−870	ENSG00000185650	ZFP36L1	protein-coding
GALNT16	3	35868	anchor2	90,796	e-p	ns	Intergenic	−75,201	NA	LOC100996664	ncRNA	promoter-TSS (NM_004926)	−870	ENSG00000185650	ZFP36L1	protein-coding
GALNT16	3	35874	anchor2	77,461	e-p	ns	Intergenic	76,591	ENSG00000185650	ZFP36L1	protein-coding	promoter-TSS (NM_004926)	−870	ENSG00000185650	ZFP36L1	protein-coding
GALNT16	3	35878	anchor2	39,487	e-p	ns	Intergenic	38,617	ENSG00000185650	ZFP36L1	protein-coding	promoter-TSS (NM_004926)	−870	ENSG00000185650	ZFP36L1	protein-coding
GALNT16	3	35881	anchor2	8795	e-p	ns	Intergenic	7925	ENSG00000185650	ZFP36L1	protein-coding	promoter-TSS (NM_004926)	−870	ENSG00000185650	ZFP36L1	protein-coding
EGFL8	3	16744	anchor2	82,077	e-p	ns	Intergenic	−4749	ENSG00000168477	TNXB	protein-coding	promoter-TSS (NM_022107).6	−641	ENSG00000213654	GPSM3	protein-coding
EGFL8	3	16752	anchor2	67,171	e-p	ns	promoter-TSS (NM_001136153).2	−747	ENSG00000213676	ATF6B	protein-coding	promoter-TSS (NM_022107).6	−641	ENSG00000213654	GPSM3	protein-coding
EGFL8	3	16756	anchor2	43,230	e-p	ns	promoter-TSS (NM_030651)	149	ENSG00000204314	PRRT1	protein-coding	promoter-TSS (NM_022107).6	−641	ENSG00000213654	GPSM3	protein-coding
EGFL8	3	16759	anchor2	18,816	e-p	ns	promoter-TSS (NM_001371437)	−2	NA	NA	NA	promoter-TSS (NM_022107).6	−641	ENSG00000213654	GPSM3	protein-coding
EGFL8	3	16760	anchor2	6385	e-p	ns	TTS (NM_022107).6	423	ENSG00000204304	PBX2	protein-coding	promoter-TSS (NM_022107).6	−641	ENSG00000213654	GPSM3	protein-coding
SELENBP1	3	3317	anchor2	72,559	e-p	ns	promoter-TSS (NM_001330721)	−25	ENSG00000143393	PI4KB	protein-coding	exon (NM_002796, exon 2 of 7)	427	ENSG00000159377	PSMB4	protein-coding
RAP1GAP	2	960	anchor2	63,962	e-p	ns	promoter-TSS (NM_001113347)	−389	ENSG00000117298	ECE1	protein-coding	intron (NM_001113348, intron 1 of 18)	1538	ENSG00000117298	ECE1	protein-coding
EPHX2	2	21394	anchor2	21,110	e-p	ns	promoter-TSS (NM_001831)	−968	ENSG00000120885	CLU	protein-coding	intron (NM_182826, intron 1 of 5)	2718	ENSG00000168077	SCARA3	protein-coding
PLEKHG4	2	39878	anchor2	83,771	e-p	ns	promoter-TSS (NM_003789)	297	ENSG00000135722	FBXL8	protein-coding	intron (NM_001318202, intron 1 of 23)	3455	ENSG00000135723	FHOD1	protein-coding
TBXA2R	2	46172	anchor2	33,942	e-p	ns	intron (NM_006339, intron 1 of 9)	159	ENSG00000064961	HMG20B	protein-coding	promoter-TSS (NR_038865)	−171	ENSG00000006638	TBXA2R	protein-coding
TBXA2R	2	46175	anchor2	27,033	e-p	ns	TTS (NM_006339)	−5466	ENSG00000179855	GIPC3	protein-coding	promoter-TSS (NR_038865)	−171	ENSG00000006638	TBXA2R	protein-coding
SULT1A3	2	39468	anchor2	64,396	e-p	ns	promoter-TSS (NM_001040056)	7	ENSG00000102882	MAPK3	protein-coding	intron (NM_001193333, intron 7 of 11)	−1075	NA	LOC606724	pseudo
CA12	2	37448	anchor2	39,130	e-e	ns	promoter-TSS (NR_147233)	−956	NA	TPM1-AS	ncRNA	Intergenic	−32,387	ENSG00000103642	LACTB	protein-coding
GORAB-AS1	2	3978	anchor2	32,309	e-p	ns	promoter-TSS (NM_001320252)	−1	ENSG00000120370	GORAB	protein-coding	Intergenic	−31,865	NA	GORAB-AS1	ncRNA
TES	4	20235	anchor2	44,811	e-p	ns	promoter-TSS (NM_001172897)	−615	ENSG00000105974	CAV1	protein-coding	intron (NR_120506, intron 4 of 4)	44,196	ENSG00000105974	CAV1	protein-coding
MIR210HG	4	28217	anchor2	32,639	e-p	ns	promoter-TSS (NM_176795)	−473	ENSG00000174775	HRAS	protein-coding	promoter-TSS (NR_038262)	−231	ENSG00000247095	MIR210HG	ncRNA
HSD17B6	4	32695	anchor2	137,481	e-p	ns	promoter-TSS (NR_040053)	−752	ENSG00000181852	RNF41	protein-coding	promoter-TSS (NM_005419)	−96	ENSG00000170581	STAT2	protein-coding
HSD17B6	4	32702	anchor2	60,102	e-p	ns	intron (NM_004077, intron 1 of 10)	229	ENSG00000062485	CS	protein-coding	promoter-TSS (NM_005419)	−96	ENSG00000170581	STAT2	protein-coding
HSD17B6	4	32704	anchor2	44,090	e-p	ns	promoter-TSS (NM_014255)	−125	ENSG00000257727	CNPY2	protein-coding	promoter-TSS (NM_005419)	−96	ENSG00000170581	STAT2	protein-coding
COL7A1	6	10130	anchor2	87,150	e-p	ns	TTS (NM_001271022)	190	ENSG00000213689	TREX1	protein-coding	promoter-TSS (NM_001317138)	−734	ENSG00000114268	PFKFB4	protein-coding
PODNL1	6	46954	anchor2	109,067	e-p	ns	promoter-TSS (NM_001320561)	−709	ENSG00000104957	CCDC130	protein-coding	non-coding (NR_036515, exon 1 of 1)	2808	NA	LOC284454	ncRNA
PODNL1	6	46970	anchor2	65,696	e-p	ns	promoter-TSS (NM_014047)	−42	ENSG00000104979	C19orf53	protein-coding	non-coding (NR_036515, exon 1 of 1)	2808	NA	LOC284454	ncRNA
CPNE7	6	40657	anchor2	186,088	e-e	ns	promoter-TSS (NM_001242885)	−22	NA	LOC100287036	protein-coding	Intergenic	−1189	ENSG00000197912	SPG7	protein-coding
CPNE7	6	40720	anchor2	76,223	e-e	ns	promoter-TSS (NR_110931)	57	NA	LOC101927817	ncRNA	Intergenic	−1189	ENSG00000197912	SPG7	protein-coding
CPNE7	6	40622	anchor2	290,001	e-p	ns	promoter-TSS (NM_182531)	−506	ENSG00000170100	ZNF778	protein-coding	Intergenic	−1189	ENSG00000197912	SPG7	protein-coding
SPAAR	6	23267	anchor2	79,338	e-p	ns	promoter-TSS (NM_016446)	468	ENSG00000204930	FAM221B	protein-coding	TTS (NM_001039792)	1400	ENSG00000196196	HRCT1	protein-coding
IGF2BP1	6	43400	anchor2	51,386	e-p	ns	promoter-TSS (NR_038458)	−99	ENSG00000229980	TOB1-AS1	ncRNA	Intergenic	−49,988	ENSG00000141232	TOB1	protein-coding
HSD17B14	6	48426	anchor2	62,105	e-e	ns	promoter-TSS (NM_001331098)	5	ENSG00000178150	ZNF114	protein-coding	non-coding (NR_130317, exon 6 of 6)	7398	ENSG00000142227	EMP3	protein-coding
TSPAN13	6	19005	anchor2	64,574	e-p	ns	promoter-TSS (NM_020319)	285	ENSG00000136261	BZW2	protein-coding	Intergenic	−42,715	ENSG00000106537	TSPAN13	protein-coding

ns = not significant; NA = not applicable.

In the last decade, large population studies have used GWAS to explore the genetic influences on WHR [48]. Historically, human GWAS studies have been performed without regard to chromatin structure. We next asked if the location of genes important to clinical phenotypes like WHR, or genes involved in adipocyte function, might overlap with our atlas of chromatin structure in ADSCs. We identified known SNPs associated with WHR (48 studies reporting 4797 unique SNPs annotated to genes listed in Supplementary Table S4) located inside chromatin loops: all loops, differential loops in apples (described in Figure 4B), and differential loops in pears (described in Supplementary Figure S3E). This is a conservative analysis as SNPs that were nearby, but not exactly inside the loop anchors, were not included. We found 417 WHR-associated SNPs in loop anchors identified in our study (in apple and in pear samples).

To ascertain if this could be a random finding, we performed a random permutation test and found that the number of SNPs that overlap with loop anchors was significantly higher than the number expected by random (Figure 5A).

Figure 5.

Integration of loop anchors and GWAS-SNPs associated with WHR. (A) Permutation test showing the overlap between loop anchors and SNPs. Green lane shows the observed overlap (n = 417) and the gray histogram shows the expected distribution of overlaps by shuffling the SNP positions 2500 times. Dotted line indicates the mean expected overlap, which was used to calculate significance at p-value < 0.05 (red lane). (B) Venn diagram showing number of overlapping SNPs with loop anchors, grouped by enriched loops in the ABD (yellow) or GF (green) samples and common loops between the ABD and GF samples (grey). (C) Boxplots showing the level of expression of genes annotated to loop anchors overlapping with WHR-SNP in ABD and GF-ADSCs. Paired Wilcoxon test * p < 0.05 ** p < 0.01 (D,E) Genome browser visualization of SKAP2-HOXA locus (D) and PPARG locus (E) in ABD (yellow) and in GF (green) samples. From top to bottom: H3K27ac loops, ChromHMM states, gene annotation. The zoom in windows of HOXA locus (D) and PPARG last exons (E) show H3K27ac in the ABD (yellow) and GF (green) samples, the loop anchors (colored in yellow when belonging to the ABD-enriched loop), and WHR-SNPs. Color coding for ChromHMM plots is the same as Figure 2C.

From these 417 SNPs, 39 were found in ABD-enriched loops and 7 were found in GF-enriched loops (Figure 5B and Table 2). Some of the genes annotated to the depot-enriched loops, in other words having a WHR-SNP in their anchor, were also differentially expressed in one of the adipose tissue depots as depicted in the plots in Figure 5C that emphasize the differences in expression from each individual in both groups (HOXA3, MLXIP, SBF2, PPL, KCNJ6, HOXA11). Interestingly, MLXIP is a bHLH transcription factor that dimerizes with CHREB to regulate the expression of genes involved in glucose metabolism, glycogen synthesis, triglyceride synthesis, and insulin signaling [49]. MLXIP expression has also been implicated in the development of metabolic diseases such as obesity, insulin resistance, and T2DM [50,51]. Additionally, MLXIP has also been recently identified as a marker of a sub-population of human adipocytes that are highly responsive to insulin [52].

Table 2.

List of WHR-SNPs overlapping with loop anchors found in both subcutaneous adipose tissue depots.

Unspecific Anchor		ABD-Specific Anchor		GF-Specific Anchor
SNP_ID	Gene Name	SNP_ID	Gene Name	SNP_ID	Gene Name
rs12138127	ZMIZ1-AS1	rs1037925	ARNTL2	rs7907173	LASTR
rs7530102	REEP2	rs61955587	B3GNT4	rs2734741	PPL
rs3119837	NA-BARX1	rs1139653	DNAJA3	rs2957674	SBF2
rs3747636	MIR3188-RPL39P38	rs2074553	DOT1L	rs2853986	RNU6-283P-FGFR3P1
rs12078075	GDF5	rs2240128	DOT1L	rs273507	MAST3
rs762705	DYRK1A-KCNJ6	rs2835810	DYRK1A-KCNJ6	rs7350438	LASTR
rs758801	PPL	rs4722669	GORAB-PRRX1	rs2923125	AMPD3
rs12495674	RAI1	rs114709597	H6PD-SPSB1
rs11724804	RAI1	rs564101206	H6PD-SPSB1
rs55962025	KANSL1	rs10248288	HOTTIP-EVX1-AS
rs2137234	GATA4	rs7801581	HOXA11
rs9742	SLC44A4	rs17501111	HOXA11
rs77881454	C2	rs9770544	HOXA11-AS-HOXA13
rs6546164	RNU6-682P-RPL10P19	rs1725074	HOXA2-HOXA3
rs34312154	SMIM20-LINC02357	rs61384251	HOXA3
rs3782086	PSORS1C1	rs10827226	NRP1
rs117737783	DNM2	rs875896	HOXA7-HOXA9
rs12580347	LOXL1	rs34957925	HOXA9, HOXA10
rs2277339	RFLNA	rs368928402	HOXA-AS3, HOXA3
rs771846	PHGR1	rs8043060	IQCH-AS1, IQCH
rs10827226	NRP1	rs28768122	KMT5A
rs6480914	HLA-DMB	rs10514889	MAPT
rs12419064	LIN52	rs9896485	MAPT
rs982085		rs885683	MAST3
rs34000	PBRM1	rs2048498	MLXIP-LRRC43
rs3904600	MLXIP	rs925460	MLXIP-LRRC43
rs4722669	GORAB-PRRX1	rs711082	NAV3
rs56285369	LY75, CD302	rs2474724	NRP1
rs9402211	FLRT1, MACROD1	rs4646342	PEMT
rs7823561	RPL35P2-NUDT3	rs771846	PHGR1
rs71511927	MICB-DT	rs750619494	ABHD2
rs6994124	MRPS18A-VEGFA	rs747616512	ABHD2
rs112875651	RPS10-NUDT3	rs4135300	PPARG-TSEN2
rs2725308	MIR9-1HG	rs2655268	PPARG-TSEN2
rs2166365	LINC01142, LINC01681	rs1699347	PPARG-TSEN2
rs7256111	MICB-DT	rs778984966	SMAD3
rs143384	GDF5	rs12140013	THEMIS2
rs11664106	SMCHD1-EMILIN2	rs1583969	ZFAT
rs2058914	USP3	rs55650389	ZFAT
rs876476	CLEC16A
rs2925979	CMIP
rs12435046	RAD51B
rs12042959	SDCCAG8
rs780159	ZMIZ1
rs7907173	LASTR
rs793456	COL8A1-CMSS1
rs797486	DLEU1, DLEU7
rs8043060	IQCH-AS1, IQCH
rs8071778	CDK5RAP3-COPZ2
rs1139653	DNAJA3
rs12575252	TRIM66
rs12828016	WNK1
rs3736485	DMXL2
rs4646342	PEMT
rs4660808	PPIEL
rs6021889	LINC01524
rs1122080
rs459193	RPL26P19-C5orf67
rs605203	EHMT2-AS1
rs2276824	PBRM1
rs2845885	FLRT1, MACROD1
rs3810068	SMCHD1-EMILIN2
rs7801581	HOXA11
rs3741378	SIPA1
rs3747577	CORO7-PAM16, CORO7
rs1051921	MLXIPL
rs544668	TSPAN9
rs11893688	ADAM17
rs2595004	ATG7
rs807067	PAQR7
rs380654	COL24A1
rs7783857	KLF14-H4P1
rs12868881	NA-LINC02337
rs2957658	AMPD3
rs6694768	TRIM33
rs7025089	MED27
rs11694173	THADA
rs2747399	TSHZ2
rs4871958	EBF2
rs2835810	DYRK1A-KCNJ6
rs2734741	PPL
rs7166081	SMAD3-AAGAB
rs4575098	ADAMTS4
rs465002	C5orf67
rs75766425	NID2
rs9379082	RREB1
rs79823890	NID2
rs740746	NHLRC2-ADRB1
rs750619494	PLIN1
rs2284178	HCP5
rs2921097	PRAG1-RN7SL178P
rs2921036	PRAG1-RN7SL178P
rs35190619	NA-RN7SL178P
rs56367294	MFHAS1
rs10098531	RNU6-682P-RPL10P19
rs2797963	KRT18P9-CYCSP55
rs10248288	HOTTIP-EVX1-AS
rs57340203	RREB1
rs3857546	H1-4-H2BC5
rs11435482	H3C9P-BTN3A2
rs9379850	H3C9P-BTN3A2
rs4282054	NT5DC2
rs7695004	RBPJ
rs11697492	LINC01524
rs532086	C2
rs4646404	PEMT
rs7224739	RAI1
rs11649804	RAI1
rs10514889	MAPT
rs11653367	KANSL1
rs377346776	EYA1
rs7928917	PNPLA2
rs4841580	LINC00208-GATA4
rs10503426	GATA4
rs2957674	SBF2
rs12419342	RAPSN
rs778984966	SMAD3
rs76748772	PEPD
rs1264376	HCG20-LINC00243
rs2535324	HCG20
rs2853986	RNU6-283P-FGFR3P1
rs7629072	WDR82
rs885910	DDR1
rs1265158	POU5F1
rs2263314	MICA
rs28436034	MICA
rs730213	LINC02875-TBX4
rs494620	SLC44A4
rs521977	SLC44A4
rs2844452	C2
rs2734313	TNXB
rs2856451	TNXB
rs1150754	TNXB
rs448231	RNU6-682P-RPL10P19
rs6917995	H3C9P-BTN3A2
rs17643342	RNU6-682P-RPL10P19
rs313736	COL24A1
rs804281	GATA4
rs7826055	GATA4
rs1228024	NUP160-PTPRJ
rs6501784	GRB2
rs11386443	FNDC3B
rs3773842	DLG1
rs4690196	DGKQ
rs11724232	NA-LINC01365
rs1567651	SMIM20-LINC02357
rs5025813	SMIM20-LINC02357
rs14323	H1-10-AS1, H1-10
rs6764238	H1-10-AS1-RPL32P3
rs3131014	CCHCR1
rs254431	SPRY4-AS1
rs3095304	PSORS1C1
rs77169818	GALR1
rs2074553	DOT1L
rs55818584	DNMT1, S1PR2
rs55660036	DNM2
rs273507	MAST3
rs7246274	PDE4C
rs11130362	TKT
rs6068216	LINC01524
rs28710106	TSHZ2
rs62206548	TSHZ2
rs6494407	USP3
rs12441130	LOXL1
rs7191812	CORO7-PAM16, CORO7
rs1291695	CORO7, CORO7-PAM16, VASN
rs4785960	CORO7-PAM16, CORO7
rs116734066	DNAJC27-AS1-EFR3B
rs79761284	LINC01381-DNMT3A
rs17745484	DNMT3A
rs7954697	SCARB1
rs61953572	DNAH10, CCDC92, DNAH10OS
rs752843328	RFLNA
rs825508	RFLNA
rs1906937	RFLNA
rs35777573	PHGR1
rs1473781	RPAP1
rs201612157	OR7E159P-GNG2
rs117311385	GNG2
rs28469812	RILPL2
rs117209788	RILPL1
rs137963709	RILPL1-MIR3908
rs148118721	ATP6V0A2
rs6488898	ATP6V0A2
rs2271049	HIP1R
rs940904	PITPNM2
rs139192229	DNAH10OS, DNAH10, CCDC92
rs59364353	RFLNA
rs17753769	PPP1R14BP5-CENPW
rs1725074	HOXA2-HOXA3
rs368928402	HOXA-AS3, HOXA3
rs875896	HOXA7-HOXA9
rs34957925	HOXA9, HOXA10
rs17501111	HOXA11
rs9770544	HOXA11-AS-HOXA13
rs28576490	JAZF1
rs57291069	NKX2-6-NA
rs144362803	TRIB1-NA
rs1583969	ZFAT
rs7834111	ZFAT
rs2474724	NRP1
rs35727416	EYA1
rs35416759	RILPL2
rs181981038	BAZ1B
rs7487608	MLXIP
rs11057291	MLXIP
rs2048498	MLXIP-LRRC43
rs61955587	B3GNT4
rs117269855	KNTC1-HCAR2
rs2277346	KNTC1
rs3121911	LINC01681
rs1332952	LINC01681, LINC01142
rs12131969	HAUS4P1-GORAB-AS1
rs11808978	GORAB-PRRX1
rs2641431	SMG6
rs8081548	POLR2A-Y_RNA
rs11641142	CMIP
rs114709597	H6PD-SPSB1
rs564101206	H6PD-SPSB1
rs2999140	ASAP3-E2F2
rs140681455	FUBP1
rs2927327	CMIP
rs62064595	RNA5SP443-ARHGAP27
rs9303523	MAPT
rs8080903	MAPT
rs720856	RAI1
rs3818717	RAI1
rs36058389	ALKBH5-LLGL1
rs2240128	DOT1L
rs8191979	SHC1
rs147847496	DPM3-HMGN2P18
rs756916254
rs35444446
rs201632637	KLF14-H4P1
rs2309651	AFF3
rs56186131	LY75, LY75-CD302
rs145272880	PLA2R1
rs7594266	GRB14-COBLL1
rs148358468	TTLL4
rs4135300	PPARG-TSEN2
rs11213979	SIK2
rs60906625	SSPN
rs61914547	SSPN-ITPR2
rs1037925	ARNTL2
rs144737537	SP7-SP1
rs12426763	CISTR-RN7SKP289
rs4332564	HOXC13-HOXC12
rs2366149	HOXC13-HOXC12
rs75493807	HOXC6, HOXC9, HOXC-AS2
rs10778496	RFX4
rs1922432	RFX4
rs157512	C5orf67
rs10070929	FGF1, SPRY4-AS1
rs9379081	RREB1
rs1620540	GNG2
rs730566	TMA7-ATRIP
rs34365302	DNAH1
rs2655268	PPARG-TSEN2
rs1699347	PPARG-TSEN2
rs67409736	STAB1
rs11176017	RPL21P18-RNA5SP362
rs716446	RFX4
rs925460	MLXIP-LRRC43
rs7316114	CLIP1-ZCCHC8
rs140323250	NA-MIR148A
rs287621	KLF14-H4P1
rs854793	MYO15A
rs9896485	MAPT
rs4135268	PPARG
rs12358916	ARID5B-RTKN2
rs4290124	ARID5B-RTKN2
rs7917772	SFXN2
rs2244524	SFXN2
rs11199755	NA-RPL19P16
rs61876729	GATD1-CEND1
rs7107271	GATD1-CEND1
rs12799550	MACROD1, FLRT1
rs1006207	MACROD1, FLRT1
rs2186643	MACROD1, FLRT1
rs17158803	FLRT1, MACROD1
rs73502335	PRDX5-CCDC88B
rs1662185	PRDX5-CCDC88B
rs55869750	AHNAK
rs67308910	EML3
rs1893458	INTS5-C11orf98
rs7978072	RASSF8-BHLHE41
rs77757339	BHLHE41, SSPN
rs7955859	SSPN
rs7134738	SSPN
rs9668178	SSPN
rs3094014	HCP5
rs2596473	LINC01149-HCP5
rs9380180	SUCLA2P1-RANP1
rs2797964	KRT18P9-CYCSP55
rs1759637	RPL35P2-NUDT3
rs12195665	MICB-DT
rs10661543	MICB-DT
rs2534681	MICB
rs62395355	MICB
rs12204413	MRPS18A-VEGFA
rs145416558	FAM13A
rs2905757	HCG22
rs116594542	RPS10-NUDT3
rs2763977	HSPA1A
rs2607015	VARS1
rs10223666	VEGFA-LINC02537
rs35208023	MIR9-1HG
rs34469991	PC
rs55650389	ZFAT
rs144831544	NCR3-UQCRHP1
rs2857694	AIF1-PRRC2A
rs2763981	SLC44A4,
rs644774	SLC44A4
rs9267653	SLC44A4
rs7301643	NA-HOXC13-AS
rs67330701	MYEOV
rs10750786	BRD9P1
rs313734	COL24A1
rs12734458	COL24A1
rs2990657	LINC01142, LINC01681
rs71455259	HOXC13-AS
rs10784510	LINC02425
rs711082	NAV3
rs7139153	NA-HOXC13-AS
rs7307887	KNTC1-HCAR2
rs7896335	NA-RPL19P16
rs2509985	AHNAK
rs34341044	PBRM1
rs6772089	IL17RD
rs111593386	GLYCTK-AS1-DNAH1
rs62265318	EFCC1
rs41264253	PBXIP1
rs60925903	EFR3B
rs11124930	THADA
rs12466434	LINC01937-TWIST2
rs852425	ACTB
rs17145717	BAZ1B
rs143214539	PPP1R14BP5-CENPW
rs9381248	MRPS18A-VEGFA
rs28768122	KMT5A
rs4759364	KNTC1-HCAR2
rs80024005	VPS37B-ABCB9
rs111854458	CCDC92
rs2378280	ZC3H11B-SLC30A10
rs73078824	PBRM1
rs4786485	VASN, CORO7, PAM16
rs73507245	PAM16, CORO7-PAM16
rs60570301	ELL
rs1363120	PGPEP1-GDF15
rs885683	MAST3
rs72832896	RNA5SP443-ARHGAP27
rs112881773	EMILIN2
rs4378729	MIR3188-RPL39P38
rs11670016	RPL39P38-LSM4
rs61876744	PNPLA2
rs2008019	EBPL
rs13412	P3H4
rs854788	MYO15A
rs7219992	ZBTB4, SLC35G6
rs7218457	LINC02210-CRHR1
rs55762977	SLC25A19-GRB2
rs550600266	TRMT11
rs73243890	LINC02357
rs421215	LINC01948
rs61384251	HOXA3
rs2108864	FGF1-LINC01844
rs73005768	ESR1
rs811458	ASTN2
rs7350438	LASTR
rs144100226	KRT18P9-CYCSP55
rs2923125	AMPD3
rs60521849	KANSL1
rs650180	TSPAN9
rs57561811	SLC38A6-PRKCH
rs28378811	LINC00316-MTCO1P3
rs4371408	LINC01524
rs10992447	BICD2
rs2246618	MICB-PPIAP9
rs2904597	MICB-DT
rs2844498	MICB
rs3130277	FKBPL-PRRT1
rs77318243	HLA-DMB
rs3132584	TUBB
rs1264375	HCG20-LINC00243
rs1076829	DHX16
rs2857595	NCR3-UQCRHP1
rs3132450	PRRC2A
rs28752890	LINC02571-HLA-B
rs2844495	MICB-PPIAP9
rs11057401	CCDC92
rs6931262	RREB1
rs150999300	LINC02775-LINC01348
rs12140013	THEMIS2
rs190930640	THSD4
rs769422497	FAM168A
rs565732042	LIN52
rs199913532	KIDINS220
rs1982963	NID2
rs17223632	SPRY4-AS1, FGF1
rs747616512	PLIN1
rs370499275	PLIN1
rs12549058	EYA1
rs11989744	NKX2-6-NA
rs16996700	LINC01524
rs532552327	RSPRY1
rs222487	COX7A2L
rs139516594
rs7975017	SSPN
rs2590838	PBRM1
rs1894633	DNM3
rs10783615	HOXC12
rs12489828	NT5DC2
rs1872992	SSPN-ITPR2
rs13241538	KLF14-H4P1
rs10743579	SSPN-ITPR2
rs12443634	CMIP
rs6088552	PIGU

The overlap between WHR-SNPs and loop anchors identified as enriched in the ABD samples was more revealing than the seven WHR-SNPs found in the anchors of the GF-enriched loop library (Figure 5B). Indeed, nine SNPs were found in loop anchors in the HOXA cluster on chromosome 7 (Figure 5D) and three SNPs were found in loop anchors in the PPARG gene (Figure 5E). The HOXA genes are differentially regulated in ABD vs. GF adipose tissue, preadipocytes, and adipocytes [15,27,46], whereas PPAR gamma is a master transcription factor enriched in preadipocytes and adipocytes, necessary for adipogenesis and also regulates fat and glucose metabolism [53].

Taken together, these results suggest that these SNPs may affect WHR by the regulation of ABD but not GF adipose tissue function and that this effect is driven by differential looping in the ADSCs and potentially in mature adipocytes.

3. Discussion

Chromatin loops can link enhancers physically close to their target genes and help to better understand the alterations of gene transcription that affect disease. Our work, described here for the first time in primary human ADSCs, provides an extensive atlas of 3D-associated regulatory interactions. To gain insight into the potential function of these long-range chromatin interactions, we integrated the HiChIP interactome with the genes differentially expressed between the ABD and GF samples, with genes known to influence adiposity and cardiometabolic traits, and with GWAS-SNPs that are associated with WHR. We also established a list of loops that describe differential 3D genomic interactions in two groups of women (apple and pear-shaped). Importantly, some of these interactions were associated with ABD and/or GF-ADSC gene expression profiles that we highlighted by RNAPII ChIP-seq analysis performed in parallel.

In our earlier study where we were limited to using linear annotation, we showed that only a small fraction of body-shape-specific open chromatin regions were annotated to DEGs [46]. We proposed that long-range genomic interactions mediated by chromatin looping were likely involved in the differential gene expression patterns. Thus, in the present study, we used H3K27ac HiChIP to interrogate active enhancer-associated looping in regulating depot-enriched gene expression and this resulted in the identification of 35 unique DEGs with associated loop anchors (Table 1). The transcription of these genes is potentially regulated through the enhancer loop interaction revealed in our HiChIP data set (opposite loop anchor in Table 1) and some likely influence differential fat distribution (HOXA, BDNF, IL33, EPHX2, IGF2BP1). Importantly, our work discovered a potential new key transcription factor such as ZFP36L1, an inhibitor of adipogenesis [54,55], as a master regulator of depot-specific gene expression. We described 14 loops at its promoter (Table 1), reflecting its high potential of interaction with other distally located genomic regions. Other genes related to obesity and/or adipogenesis were identified by our HiChIP analysis as regulators of ABD vs. GF gene transcription, such as METTL15 [56] and RBM4 [57]. Overall, our study supports the idea that long-range chromatin loops may affect the development or differentiation of ADSCs and could explain in part subcutaneous adipose tissue dysfunction in diseases such as T2D or PCOS.

Previously reported large population studies have relied on GWAS to link genes to WHR [48]. Importantly, the gene connections have been performed relying largely on linear annotation and have not typically considered the importance of longer range chromatin interactions that are defined using more involved chromatin looping methods. Using our HiChIP data set, we connected SNPs known to be associated with WHR (48 studies reporting 4797 unique SNPs annotated to genes) with key chromatin loops: all loops (Figure 4B and Supplementary Figure S3E). It should be noted that this is a conservative estimate because we narrowly defined the SNPs to be located within the loop anchors and did not consider closely associated anchors in this analysis. Importantly, this revealed genes that were also differentially expressed in one of the adipose tissue depots (Figure 5C) that are known to influence adipose tissue function including HOXA3, MLXIP, SBF2, and PPL. Taken together, these findings demonstrate that genomic interactions play an important role in adipose depot-specific gene regulation in human ADSCs. In addition, by comparing the loops identified between the two adipose tissue depots studied (ABD vs. GF), we highlighted depot-enriched chromatin interactions that likely contribute to depot-selective 3D chromatin organization; this organization influences gene transcription and therefore the distinct functional phenotypes in ABD vs. GF-ADSCs.

We also report here for the first time in human primary ADSCs, that differential histone modifications at gene promoters influence patterns of depot-selective gene expression in ABD vs. GF depots. By studying the correlation between histone marks and differential gene expression between ABD and GF-ADSCs, our work revealed that combinations of histone marks are associated with transcriptional activity in ABD and GF-ADSCs. When the individual marks were combined to generate a combinatorial set of ChromHMM emission patterns, the data are even more supportive of the model.

However, we cannot formally conclude whether differential gene expression is the cause or consequence of differential histone modifications. Henikoff et al. showed that histone modifications were more likely the consequences than the causes of transcription, especially for H3K4me3 [58]. Regardless of the direction, these histone marks provide a stable memory of recent transcriptional activity and provide a template for a robust mechanism to sustain the observed differential pattern of transcription between depots.

A limitation of our work is that we used H3K27Ac HiChIP to identify the depot-specific connectome. However, a depot-enriched loop might be identified as specific due to the fact that those regions exhibit a high depot-enriched H3K27ac signal. We cannot conclude if it is this the result of an actual architectural change or simply a difference in H3K27ac at these anchors.

We focused here on loops associated with genes that were differentially expressed in ADSCs across different depots in apple vs. pear-shaped women. It should be noted that all other key genes involved in adipose function were not differentially expressed in our study. Taken together, these and prior experiments in human ADSCs reveal a potential epigenomic mechanism by which the differential growth and function of adipose tissue depots lead to common metabolic diseases.

4. Materials and Methods

4.1. Participants, Tissue Collection, and Isolation of Human Adipose-Derived Stem Cells

The method of recruitment, clinical, and biochemical parameters of subjects were previously published by Divoux A. et al. [46]. All procedures were performed under a research protocol approved by the AdventHealth Institutional Review Board. A subgroup of 10 healthy premenopausal, weight-stable women were used for this study. Five women displayed lower body adiposity characterized by a waist-to-hip ratio (WHR) < 0.78 (pear group; age = 34 ± 9.6 years; BMI = 29.2 ± 2.26 kg/m²) and five women displayed upper body adiposity, characterized by a WHR > 0.85 (apple group; age = 38 ± 8.1 years; BMI = 28.6 ± 3.54 kg/m²). Briefly, paired abdominal and gluteofemoral subcutaneous white adipose tissue samples were obtained from each participant and the stromal–vascular fractions (SVFs) were isolated by 45 min collagenase digestion (collagenase type I, Worthington). SVFs were plated and grown in proliferation medium containing 2.5% FBS, FGF, and EGF. Human adipose-derived stem cell (ADSC) populations were enriched as previously described [14]. The cells presenting at their surface the endothelial marker CD31 (MAB2148-C, MilliporeSigma, Burlington, MA, USA) were removed by magnetic beads.

4.2. Chromatin Immunoprecipitations

Chromatin immunoprecipitations (ChIPs) were performed on confluent ADSCs and analyzed as described [59]. ChIP grade Diagenode (Denville, NJ, USA) rabbit anti-H3K4me3 (C15410003), rabbit anti-H3K4me2 (pAb-035-050), rabbit anti-H3K27me3 (C15410069), and Abcam (Waltham, MA, USA) rabbit anti-H3K27Ac (ab4729) were used to study the histone marks. The CTCF antibody from Active Motif (Carlsbad, CA, USA) (61311) was used to study CTCF boundary sites. Rabbit anti-RNAPII (abcam, ab5095) was used to study the binding of the elongating form of RNA polymerase II (Serine 2 phospho form).

4.3. Assay for Transposase-Accessible Chromatin (ATAC)

ATAC was performed as previously described by Divoux A. et al. [15].

4.4. HiChIP Assay

Approximately 5 × 10⁶ cells were crosslinked in 1% formaldehyde (methanol-free, dissolved in phosphate-buffered saline—PBS) for 10 min at room temperature in a 10 mL final volume. Formaldehyde was quenched with the addition of 1.5 mL 1M glycine for 5 min at room temperature. Cells were scraped and lysed in lysis buffer (1% Triton x−100, 0.1% SDS, 150 mM NaCl, 1 mM EDTA, and 20 mM Tris, pH 8.0) for 1 h in rotation in a cold room. Isolated nuclei were pelleted by centrifugation, resuspended in 100 μL 0.5% SDS, and incubated for 10 min at 65 °C. SDS was quenched by the addition of Triton-X for 15 min at 37 °C. Nuclei were incubated overnight at 37 °C in a vigorous shaker (speed—850 rpm) in the presence of MboI (375U). The following day, the samples were incubated at 65 °C for 20 min to heat the inactivate MboI. Samples were left at room temperature for 20 min to cool down. Biotin fill-in of sticky ends was performed for 1 h at 37 °C in a vigorous shaker (speed—850 rpm) followed by ligation of blunt ends at room temperature for 6 h while rotating. Nuclei were spun, resuspended in lysis buffer in the presence of 5 μg H3K27ac antibody (ab4729, Abcam, Waltham, MA, USA), and incubated overnight on a rotator at 4 °C. The next day, antibody chromatin complexes were pulled down with protein A paramagnetic beads and sequentially washed: once in wash buffer 1 (1% Triton, 0.1% SDS, 150 mM NaCl, 1 mM EDTA, 20 mM Tris, pH 8.0, and 0.1% NaDOC), twice in wash buffer 2 (1% Triton, 0.1% SDS, 500 mM NaCl, 1 mM EDTA, 20 mM Tris, pH 8.0, and 0.1% NaDOC), once in wash buffer 3 (0.25 M LiCl, 0.5% NP−40, 1 mM EDTA, 20 mM Tris, pH 8.0, 0.5% NaDOC), and once in TE-buffer. After the removal of TE-buffer, DNA was eluted from the beads in an elution buffer. DNA was quantified with the Qubit dsDNA HS kit. Approximately, 40–50 ng DNA was used for biotin pull-down with streptavidin paramagnetic beads. Sequencing libraries were constructed with the Nugen Ovation Ultralow V2 kit (Tecan, Mannedorf, Switzerland) according to the manufacturer’s recommendations. Libraries were quantified with the Quibit dsDNA HS kit and subjected to bioanalyzer fragment analysis before paired-end sequencing.

4.5. HiChIP Data Processing

HiChIP data were analyzed using the default parameters of nf-core/hic (https://zenodo.org/records/2669513, accessed on 20 December 2023; version 1.3.0). In summary, the following steps were followed: (1) mapping to the hg19 reference genome using a two-step strategy to rescue reads spanning the ligation sites (bowtie2) [60]; (2) detection for valid interaction products; (3) duplicates removal; and (4) generating raw and normalized contact maps using the ICE algorithm at various resolutions using a cooler. The quality control of the sample was included in the pipeline (HiC-Pro [61]). A/B compartments, saddle plots, and insulation scores were calculated using GENOVA [62]. Representative interaction heat maps were generated using cloops2 [63] with the -corr option. We used hichipper [64] to identify chromatin loops, using the consensus H3K27ac ChIP-seq peaks per group. Significant differential looping was calculated using diffloop [65] with -nreplicates set to 3 and -nsamples set to 8.

Loop anchor positions were overlapped with BMI-adjusted waist–hip ratio SNPs (EFO:0007788) and the permutation test was performed to test the significance of overlap compared to permutated (n = 2500) locations using the regioneR package [66].

4.6. Sequencing Library Preparation

RNA-seq, ATAC-seq, ChIP-seq, and HiChIP libraries were prepared and sequenced using standard Illumina protocols for a HiSeq 2500 instrument (Illumina, San Diego, CA, USA).

4.7. RNA Sequencing and Analysis

RNA sequencing was performed as described by Divoux [15]. The raw RNA-seq reads’ sequencing quality was evaluated by FastQC and the reads were aligned to the hg19 reference genome using STAR (version 2.7.7a) [67]. Genes were quantified using featureCounts from Rsubread (version 2.4.0) [68]. The R package, edgeR with paired analysis, was used for differential gene expression analysis with cutoffs CPM > 3 in more than 4 samples. p-value < 0.05 was used to determine statistical significance for differentially expressed genes. DEGs were used for k-means clustering to create modules and visualized as a heat map.

4.8. ChIP-seq and ATAC-seq Analysis

Sequencing quality was evaluated by the FastQC software (v0.12.0). Reads were mapped to the human reference genome (hg19) using the default parameters of BWA MEM aligner [69]. Low mapping quality reads (MAPQ < 10), reads mapping to ENCODE human blacklisted regions [70], and duplicated reads were discarded from the downstream analyses, using bedtools intersectBed [71] and samtools rmdup [72]. Coverage profiles represent reads per kilobase million (RPKM) values, calculated using deeptools2 bamCoverage [73] and visualized in IGV.

4.9. Gene Set Enrichment and Visualization

EnrichR was used for gene set enrichment and visualization [74]. The enrichment was calculated to the hallmark gene set of the Molecular Signature Database (MSigDb). Pathways with p-values < 0.05 were selected as significant.

4.10. Chromatin State Discovery with ChromHMM

Tissue-specific chromatin states were identified using the ChromHMM (version 1.21) hidden Markov model (HMM) [43]. Bam files from RNAPII, CTCF, H3K27ac, H3K27me3, H3K4me2, H3K4me3 ChIP-seq, and ATAC-seq were binarized into default 200 bp bins using the function BinarizeBam from each of the 5 ABD-ADSC and 5 GF-ADSC samples in each group (apple and pear), as previously described [75]. We ran ChromHMM with a range of possible states and settled on a 10 states model as it accurately captured information from higher state models and provided sufficient resolution to identify biologically meaningful patterns in a reproducible way [43].

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms25010437/s1.

Author Contributions

L.H.: formal analysis, visualization, writing—review and editing. A.D.: conceptualization, formal analysis, investigation, visualization, writing—original draft. K.S.: conceptualization, methodology, investigation, review and editing. E.E.: formal analysis, visualization, review and editing. B.D.: methodology, review and editing. S.R.S.: conceptualization, supervision, funding acquisition, writing—review and editing. T.F.O.: Conceptualization, methodology, supervision, funding acquisition, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

This study was approved by the AdventHealth Institutional Review Board. All participants provided written and informed consent before screening and participation in the study. Trial registration: clinicaltrials.gov, NCT02728635. Registered 24 March 2016, retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT02728635 (accessed on 5 April 2016).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

All sequencing data have been deposited to the NCBI GEO database (http://www.ncbi.nlm.nih.gov/geo/, accessed on 20 December 2023) under accession number GSE#176603, GSE#224770, GSE#224777.

Conflicts of Interest

Authors Adeline Divoux and Steven R. Smith are employed by the company AdventHealth. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding Statement

This research was funded by the NIH, grant number R01DK123456.