Aerobic exercise decreases the number and transcript expression of inflammatory M1 macrophages and CD8+ T cells in the epicardial adipose tissue of female pigs

Aerobic exercise decreases the number and transcript expression of inflammatory M1 macrophages and CD8+ T cells in the epicardial adipose tissue of female pigs | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Aerobic exercise decreases the number and transcript expression of inflammatory M1 macrophages and CD8+ T cells in the epicardial adipose tissue of female pigs Irshad Ahmad , Shreyan Gupta , Micah Thomas , View ORCID Profile James J. Cai , Cristine L. Heaps , View ORCID Profile Annie E. Newell-Fugate doi: https://doi.org/10.1101/2025.02.02.635562 Irshad Ahmad 1 Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station , Texas Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shreyan Gupta 2 Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station , Texas B.S. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Micah Thomas 1 Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station , Texas B.S. Find this author on Google Scholar Find this author on PubMed Search for this author on this site James J. Cai 2 Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station , Texas Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for James J. Cai Cristine L. Heaps 1 Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station , Texas Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Annie E. Newell-Fugate 1 Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station , Texas D.V.M., Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Annie E. Newell-Fugate For correspondence: anewell-fugate{at}cvm.tamu.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Background Epicardial adipose tissue (EAT) regulates coronary artery function via lipid metabolism and immune cell recruitment. Increased EAT is a risk factor for coronary artery disease (CAD), but aerobic exercise mitigates CAD. The effect of aerobic exercise on immune cells in EAT is unknown. We hypothesized that aerobic exercise creates an anti-inflammatory environment characterized by increased M2 macrophages and up-regulation of anti-inflammatory cytokine transcripts in EAT. Methods Female Yucatan pigs (n=7) were allocated to sedentary or exercised groups. To mimic CAD, a coronary artery was chronically occluded or remained non-occluded. EAT samples were processed for bulk and single nuclei transcriptomic sequencing. Results Sub-clustering identified immune, endothelial, smooth muscle, adipocytes, adipocyte progenitor cells (APSCs), and neuronal cells, with adipocytes and APSCs being dominant. Non-occluded sedentary EAT had the largest percentage of M1 macrophages and CD8+ T cells. Irrespective of occlusion, sedentary EAT had the largest fraction of cells expressing genes in the tumor necrosis factor (TNF) superfamily. Irrespective of occlusion, exercise upregulated peroxisome proliferator-activated receptor ( PPAR ) gamma ( G ) expression and enriched PPAR signaling pathways in adipocytes, macrophages, and T cells. However, PPARG expression was lowest in CD8+ T cells from non-occluded exercised EAT. The greatest number of significant cell-cell communications between adipocytes and immune cells via growth factors and adhesion molecules occurred in occluded sedentary EAT. Conclusion Aerobic exercise mitigates the proinflammatory nature of EAT in CAD via modulation of immune cell subpopulations, decreased TNF superfamily and increased PPARG gene expression, and decreased growth factor communication between adipocytes and immune cells. Introduction Epicardial adipose tissue (EAT) mechanically supports the coronary arteries, releases beneficial adipokines, such as adiponectin, and is an energy source for cardiomyocytes, endothelial cells, and smooth muscle cells. 1 Unlike other adipose tissue depots, EAT lacks distinct boundaries with neighboring tissues and shares its microcirculation with the myocardium. 2 Due to this close anatomical relationship with the heart, EAT modulates coronary artery function through paracrine secretion of anti- and pro-inflammatory adipokines. 3 Furthermore, EAT plays a critical role in the local regulation of heart function via lipid metabolism. 4 During periods of high energy demand, EAT releases free fatty acids to serve as energy substrates for the surrounding tissues. 5 Within EAT, released free fatty acids can activate the transcription of peroxisome proliferator-activated receptors (PPARs), which stimulates controlled adipogenesis and lipid metabolism. 3 , 6 Although EAT has many beneficial effects on the surrounding tissues, excessive expansion of EAT is a risk factor for coronary artery disease (CAD) due to its increased production of pro-inflammatory molecules, which subsequently invade the adjacent myocardium. 3 Moreover, as EAT expands and outgrows its vascular supply, hypoxia ensues, which down-regulates anti-inflammatory factors such as adiponectin and causes decreased transcription of antioxidant and thermoregulatory genes such as uncoupling protein 2 (UCP2) and PPAR gamma (PPARG). 7 PPARG is a master regulator of lipid metabolism in many cell types and is the predominant controller of adipogenesis. 7 Indeed, myocardial cells can undergo adipogenic differentiation when they express PPARG. 6 Although PPARG is predominantly expressed in adipocytes, it is also found in low levels in immune cells such as macrophages and T cells. 8 As EAT is a heterogeneous tissue containing adipocytes, adipocyte progenitor cells, endothelial cells, smooth muscle cells, and various resident and infiltrative immune cells, the role of PPARG in these cell types should be considered. In response to microenvironmental stimuli, macrophages in EAT exhibit plasticity, altering their polarization between M1 pro-inflammatory and M2 anti-inflammatory phenotypes. 9 This phenotypic shift occurs in response to local cytokines and inflammatory substances, with M1 polarization activated by interferon-γ (IFN-γ) or lipopolysaccharide (LPS) and M2 polarization activated by interleukin (IL)-13 or IL-4. 10 PPARG is responsible for the differentiation of monocytes to the M2 anti-inflammatory macrophage phenotype 11 and promotes the uptake of oxidized low-density lipoprotein (LDL), which contributes to foam cell formation. 12 Thus, PPARG in EAT may play a significant role as a potential mediator against CAD. 13 EAT volume contracts in response to aerobic exercise 14 due to an increased lipolytic rate of the tissue. 15 Moreover, most studies have demonstrated a correlation between decreased EAT volume and changes in circulating markers of cardiometabolic health, such as decreased C-reactive protein (CRP), LDL, total cholesterol 16 , and TNF-α, and increased adiponectin. 17 However, the effect of aerobic exercise on immune cells and genes related to metabolism in EAT is unknown. In this study, we examined the impact of aerobic exercise on the single nuclei transcriptome in EAT from sexually mature female Yucatan miniature swine with experimentally induced chronic ischemic heart disease. Materials and Methods The analytic methods and data are available by request to other researchers to reproduce the results or replicate the procedures. Experimental animal procedures Animal protocols were approved by the Texas A&M University Institutional Animal Care and Use Committee and conformed to the National Institutes of Health (NIH) Guide for Care and Use of Laboratory Animals, 8 th edition, revised 2011. Adult female Yucatan miniature swine (6-7 months of age) were surgically instrumented with an ameroid constrictor around the proximal left circumflex coronary artery as previously described. 18 – 20 The ameroid constrictor progressively narrows the artery and induces an inflammatory response resulting in gradual occlusion, with total obstruction occurring at approximately two to three weeks post-operatively. 21 Pigs recovered for eight weeks post-operatively before the sedentary or exercise training experimental regimen was initiated. Sedentary and exercise protocols Exercise-trained female pigs underwent a progressive treadmill exercise training program, five days/week for 14 weeks, as described previously. 20 , 22 Briefly, speed and duration of the exercise training sessions were progressively increased so that during the last three weeks of training, animals ran at 4-5.5 mph for 60 minutes and at 6 mph for 5-15 minutes. Grade of the treadmill was maintained at 0% throughout the exercise bouts. Exercise-trained and sedentary swine were fed once daily immediately after the exercise training session as positive reinforcement for the exercise bout, with water provided ad libitum . Sedentary pigs maintained normal activity in their pens throughout the experimental protocol. Effectiveness of the exercise training regimen for these animals was determined by comparing heart-to-body weight ratio and skeletal muscle citrate synthase activity. Data on these parameters for the study animals was presented in Ahmad et. al. 2024. 20 Euthanasia for tissue collection Pigs were anesthetized with intramuscular administration of xylazine (2.25 mg·kg -1 ) and ketamine (35 mg·kg -1 ), fitted with an anesthesia mask, induced with 3% isoflurane and then intubated and a surgical plane of anesthesia was maintained with 3% isoflurane and auxiliary O 2 . A left lateral thoracotomy was performed in the fourth intercostal space, followed by administration of heparin (500 U·kg -1 , i.v.). Hearts were removed, placed in Krebs bicarbonate buffer (0-4 °C) and weighed. Krebs bicarbonate buffer contained (in mM): 131.5 NaCl, 5 KCl, 1.2 NaH2PO4, 1.2 MgCl2, 2.5 CaCl2, 11.2 glucose, 13.5 NaHCO3 and 0.025 EDTA. Isolation of coronary epicardial adipose tissue Following humane euthanasia, epicardial perivascular fat was sectioned from both the non-occluded left anterior descending coronary artery and the collateral-dependent left circumflex coronary artery. Perivascular fat was snap-frozen in liquid nitrogen and stored at -80 °C for later analysis. Subsequent visual inspection of the ameroid constrictor during dissection of the left circumflex artery under a dissection microscope during dissection of the left circumflex artery indicated 100% occlusion in all pigs used in this study. Bulk RNA isolation and sequencing EAT (occluded and non-occluded tissue pooled) from exercise-trained (n=4) and sedentary (n=3) female pigs had total RNA extracted with TRIzol® (Thermofisher Scientific, Waltham, MA). Total RNA was quantified (Nanodrop 3300, Thermofisher, Wilmington, DE) followed by bioanalysis (Agilent 2100 Bioanalyzer, Agilent Technologies, Inc., Santa Clara, CA) for RNA quantity and quality. RNA integrity scores fell within the following range: 6.2–9.1. cDNA libraries were prepared, and sequencing was performed at the Molecular Genomics Core at Texas A&M University. Single-nucleus RNA isolation and sequencing Isolation of EAT nuclei (occluded and non-occluded isolated separately) was performed following the “Daughter of Frankenstein protocol for nuclei isolation from fresh and frozen tissues using OptiPrep continuous gradient V.2” 23 and as previously described. 20 Nuclei were resuspended in 0.1% BSA in PBS and immediately processed to generate single-nuclei RNA libraries using the microdroplet-based RNA. Nuclei samples were diluted, if necessary, to a target concentration of between 500 – 1,500 nuclei/µL and used for single nuclei RNA sequencing library preparation. 24 Single nuclei 3’ sequencing libraries were generated following the 10x Genomics dual index manual preparation v3.1 reagent kit with a target nuclei recovery of approximately 5,000 – 10,000 nuclei for each reaction. GEM partitioning was carried out in the 10x Genomics Chromium X system and following library construction following the manufacturer’s protocol. Sequencing libraries were quantified with the Qubit 4.0 Fluorometer high sensitivity dsDNA detection kit (Thermofisher). Library sizes were assessed using the Agilent TapeStation 4200, D1000 DNA tape system (Agilent Technologies). All sequencing library concentrations were normalized to 4 nM and pooled at equimolar ratios for sequencing on an Illumina NovaSeq 6000 2x150 sequencing run to generate a minimum of 200 million, paired-end sequencing reads for each sample. Sequencing reads were loaded into the 10x Genomics Cell Ranger software for sequence alignment, filtering, barcode and unique molecular identifier (UMI) counting. Bioinformatic Analysis Bulk RNA Analysis Raw bulk RNA sequencing data underwent quality control using FastQC (v0.11.9) ( http://www.bioinformatics.babraham.ac.uk/projects/fastqc ). Read alignment to the reference genome was performed with Bowtie2 (v2.3.5.1) 25 using default parameters. Gene-level counts were obtained with htseq-count (v2.0.2). 26 Data normalization was carried out using the upper-quartile method to account for library size differences. Differential expression analysis was performed in female pigs, comparing the exercised and sedentary groups using R (v4.1.0). The edgeR package (v3.34.1) 27 was used for count filtering and dispersion estimation, and limma (v3.48.3) 28 was used with the voom transformation for linear modeling. A total of 13,663 genes were included in the analysis, identifying 495 genes (275 upregulated and 220 downregulated) as differentially expressed genes (DEGs) (P-value 1.5). DEGs were plotted as a volcano plot. Log2FC values for all genes were imported into the ‘fgsea’ R package 29 for gene set enrichment analysis (GSEA) against the KEGG Human 2023 gene sets. Pathways with an absolute normalized enrichment score (NES) > 1.645 and adjusted P-value < 0.05 were significantly enriched. A total of 19 pathways met these criteria. Single nuclei RNA analysis UMI count matrices for each single-cell sample were generated by aligning reads to the genome (Landrace_pig_v1 (GCA_001700215.1) using 10X Genomics Cell Ranger software. Data was analyzed by exercise status alone (Exercise (Ex) versus Sedentary (Sed)) or the combination of exercise with occlusion status (N_Ex: non-occluded-exercised, O_Ex: occluded-exercised, N_Sed: non-occluded sedentary, O_Sed: occluded-sedentary). The count matrices for all four treatment groups were merged into one Seurat object without batch correction using the Seurat v4 R package. 30 Cells with less than 500 feature counts were filtered out. Genes expressed in less than 15 cells also were filtered out. The ‘ NormalizeData’ function was used for normalization. This function divided the gene expression counts for each cell by the total counts for that cell and multiplied the value by 10,000. Then, the counts were Log1p transformed and scaled using the ‘ ScaleData ’ function. ‘ FindVariableFeatures’ function was used to identify the top 2,000 highly variable genes. These 2,000 genes were then used for Principal Component Analysis (PCA). Uniform Manifold Approximation and Projection (UMAP) was performed using the first 50 principal components to project the data onto a three-dimensional (3D) space for visualization. Clustering was performed using the K-means algorithm on the 3D UMAP projection to identify 50 cell clusters. The K-means algorithm assigns data points (cells) to the nearest centroids (cluster center), and recomputes the centroids based on the predefined number of clusters. 31 After identifying cell clusters, immune cells were separated from the other cell clusters, and their UMAP projections were recalculated. After parsing out the immune cell cluster, its cell constituents were clustered into four groups using the K-means algorithm and the UMAP projections. Significant marker genes for each cluster were identified by performing differential analysis using the Wilcoxon rank sum test. Genes with minimum percent expressed in cluster > 25%, |log2(FC)| > 0.25, and false discovery rate (FDR) < 0.05 were considered marker genes. Each cluster was annotated using the top 10 computed marker genes and canonical cell type markers from PanglaoDB 32 and the pig atlas ( https://dreamapp.biomed.au.dk/pigatlas/ ). Differential expression analysis was performed using the Wilcoxon rank-sum test for each cell type and across all four treatment groups. Significantly differentially expressed genes (DEG) were selected by a FDR cut-off of 0.05 and |log2(FC)| >2. DEG for each cell type were used for gene set enrichment analysis with the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways database using fgsea R package. 33 Cell scores for gene sets were calculated using the AUCell 34 for gene sets. Gene sets were obtained from the KEGG Human Pathway Database. Visualization was carried out using Seurat’s inbuilt plotting function and the ggplot R library. The CellChat (v1) R package 35 was used to infer cell-cell communication within and across cell types in the single nuclei dsata set. To quantitatively analyze the cell-cell interaction, all cells from the four treatment groups (N_Ex, N_Sed, O_Ex, O_Sed) were pooled and grouped by cell types for CellChat analysis. Default parameters were used. The human ligand-receptor database, without any selection, was used. For each cell type, significantly differentially overexpressed ligand-receptor pairs were identified. Next, the communication likelihood was computed using the average expression values of a ligand in one cell type compared to that of a receptor or cofactor in another cell type. Significant interactions were pruned by permutation tests (P value < 0.05). An intercellular communication network was generated along with communication probabilities that measure the strength of relationships between each ligand-receptor pair. The communication network for each signaling pathway was generated by summing up all corresponding ligand-receptor interactions for that pathway. The CellChatDB was subdivided to identify paracrine, endocrine, and juxtacrine signaling pathways. The visualization was carried out using CellchatR’s inbuilt plotting functions. Results Aerobic exercise enriches pathways related to immune function and cytoskeletal organization and negatively enriches pathways related to nutrient utilization and metabolism in EAT There were 13,663 DEG, of which 338 were significantly up-regulated, and 293 were significantly down-regulated in the exercise-trained group ( Figure 1A ). The most significantly upregulated genes in EAT from exercise-trained pigs were: shisa family member 2 ( SHISA2 ), cadherin 7( CDH7 ), heparin-binding EGF-like growth factor ( HB-EGF ), family with sequence similarity 71 member E1 ( FAM71E1 ), and pleckstrin homology-like domain family A member 2 ( PHLDA2 ) ( Figure 1A ; Supplemental Dataset 1 ). Genes that were most significantly down-regulated in EAT from exercise-trained pigs were: cytochrome P450 family 2 subfamily B member 6 ( CYP2B6 ), acetylcholinesterase ( ACHE ), polypeptide N-acetyl-galactos-aminyl-transferase like 5 ( GALNTL5 ), Metallothionein 2A (MT2A ), claudin-4 ( CLDN4 ), estrogen-related receptor gamma ( ESRRG ), keratin 14 ( KRT14 ), galactose-3-O-sulfotransferase 3 ( GAL3ST3 ) ( Figure 1A ; Supplemental Dataset 1 ) Download figure Open in new tab Figure 1. Analysis of differentially expressed genes and enrichment of KEGG pathways in the EAT from exercised-trained female pigs. (A) Volcano plot of up-regulated and down-regulated genes in exercise-trained versus sedentary pigs. (B) Identification of KEGG pathways significantly enriched (upregulated genes) or negatively enriched (downregulated genes) in exercise-trained pigs. The threshold set points for DEG were fold change > 2 and adjusted P value < 0.05. NS: not significant. Log 2 FC: log base 2-fold change. Log 10 : log base 10 Significant enrichment based on KEGG pathway analysis was found for asthma, chemokine signaling pathway, leishmania infection, regulation of actin cytoskeleton, autoimmune thyroid disease, and FcγR-mediated phagocytosis in exercise-trained pigs ( Figure 1B ). In contrast, pathways with negative enrichment scores in exercise-trained pigs were: TCA cycle, butanoate metabolism, oxidative phosphorylation, propanoate metabolism, pyruvate metabolism, peroxisome, retinol metabolism, PPAR signaling, valine-leucine-isoleucine degradation, and fatty acid metabolism ( Figure 1B ). Non-occluded EAT from sedentary pigs has large numbers of macrophages and T cells The total number of nuclei derived from single nuclei processing in occluded and nonoccluded exercised and sedentary EAT was 28,473. After removing nuclei with less than 500 feature counts, 27,828 single nuclei were available for downstream analysis. K-clustering analysis and marker gene annotation revealed seven cell populations in the four treatment groups ( Figure 2A-B , Figure S1, Supplemental Dataset 2 ). Each cell type showed a specific set of enriched gene expression: adipocytes - perilipin 1 ( PLIN1 ) and adiponectin, C1Q, and collagen domain containing ( ADIPOQ ); adipose progenitor and stem cells (APSC) – platelet-derived growth factor receptor alpha ( PDGFRA ) and collagen type VI alpha 1 chain ( COL6A1 ); endothelial cells - von Willebrand factor ( VWF ) and fatty acid binding protein 5 (FABP5 ); macrophages - CD163 molecule ( CD163 ) and stabilin 1 (STAB1); neurons - synuclein alpha ( SNCA ), neurexin 1 (NRXN1 ); smooth muscle cells - actin alpha 2, smooth muscle ( ACTA2 ) and gap junction protein gamma ( GJC1 ); T cells - Neurocalcin delta ( NCALD) and Src kinase-associated phosphoprotein 1( SKAP1) ( Figure 2C ; Supplemental Dataset 2; Table S1 ). Although APSCs are characterized by upregulated PDGFRA and COL6A1 gene expression, these genes have increased expression in only 20-30% of APSCs. Nearly all other marker genes are expressed in > 50% of each cell type except for ACTA2, which is only expressed in 30% of smooth muscle cells ( Figure 2C ). Download figure Open in new tab Figure 2. Distribution by treatment group and cell type of nuclei from occluded and non-occluded EAT of exercise-trained and sedentary female pigs. (A) UMAP plot of single nuclei from EAT of exercise-trained and sedentary female pigs showing the origins of nuclei in different treatment groups. (B) UMAP plot of single nuclei from EAT of female pigs showing seven major cell types from sedentary (occluded and nonoccluded) and exercised (occluded and non-occluded) tissue samples. (C) Bubble heatmap demonstrating expression levels of marker genes for each cell (nuclei) type. N: non-occluded; O: occluded; Ex: exercise trained; Sed: sedentary. APSC: adipocyte progenitor cells. Irrespective of exercise status, non-occluded EAT had a large number of adipocytes (3437 cells in N_Ex, 2985 cells in N_Sed) ( Figure 3A-B ). By contrast, APSCs were most abundant in N_Sed and O_Ex EAT. Endothelial and smooth muscle cell numbers in EAT were similar across treatment groups except for the N_Ex, which had lower numbers of these cell types ( Table S2 ). Neuron abundance in EAT was the same across all treatment groups. N_Sed had a higher number of T cells (254 cells) and macrophages (1199 cells) compared to any other treatment group ( Figure 3A-B , Table S2 ). Cell numbers showed batchwise distribution and abundance differences in adipocytes and APSCs in N_Ex and O_Ex EAT. N_Sed showed higher numbers but no difference in batchwise distribution of immune cells and adipocytes ( Figure 3C , Table S2 ). Download figure Open in new tab Figure 3. Quantity and distribution of cell types across all treatment groups. (A) Barplot of the number of cell types in each treatment group. (B) Barplot of the percentage of cell types in each treatment group. (C) Batchwise distribution of each cell type. N: non-occluded; O: occluded; Ex: exercise trained; Sed: sedentary. APSC: adipocyte progenitor cells. Irrespective of coronary artery occlusion status, aerobic exercise upregulates genes in most cell types in EAT related to unsaturated fatty acid metabolism and PPAR signaling The differentially expressed genes (DEGs) for adipocytes, macrophages, T cells, endothelial cells, and smooth muscle cells were identified in occluded and non-occluded EAT from exercise-trained and sedentary pigs ( Supplemental Datasets 3 & 4 ). Irrespective of treatment group, adipocytes had the greatest number and percentage of DEGs in response to exercise (non-occluded: 677/8445; 8% of genes; occluded: 732/8803; 8.3% of genes; Figure S2 & S5 ). Malic enzyme 1 ( ME1 ) and stearoyl-CoA desaturase ( SCD ) were significantly upregulated in response to exercise in adipocytes from both occluded and non-occluded EAT (Figure S2 & S5 ). In N_Ex EAT, genes such as pyruvate dehydrogenase kinase 4 ( PDK4 ), fibronectin 1 ( FN1 ), and glutathione peroxidase ( GPX3 ) were significantly downregulated in adipocytes ( Figure S2 ). In contrast, in O_Ex genes such as cysteine-rich transmembrane BMP regulator 1 ( CRIM1 ), glycoprotein M6A ( GPM6A ), and PKHD1 like 1 ( PKHD1L1 ) were significantly down-regulated in adipocytes ( Figure S5 ). In adipocytes from occluded and non-occluded EAT PPAR signaling, retinol metabolism, biosynthesis of unsaturated fatty acids, and pyruvate metabolism were significantly enriched in response to exercise training. In exercised pigs, non-occluded EAT also had significant enrichment of the insulin signaling and adipocytokine and glycolysis-gluconeogenesis pathways. In contrast, occluded EAT from exercised pigs had an enrichment of steroid and terpenoid biosynthetic pathways. Exercise training enriched PPAR signaling in all cell types examined except smooth muscle cells. With respect to immune cells in EAT, irrespective of occlusion status, acetyl-CoA carboxylase alpha ( ACACA ), ELOVL fatty acid elongase 6 ( ELOVL6 ), and ME1 were significantly upregulated in macrophages, and pyruvate metabolism and biosynthesis of unsaturated fatty acids pathways were enriched in response to exercise training (Figure S3 & S6 ). Similar to adipocytes from non-occluded EAT, macrophages from non-occluded EAT had enrichment of the insulin signaling pathway in response to exercise training ( Figure S3 ). On the other hand, in T cells, the occlusion status of EAT significantly affects the types of genes and pathways impacted by exercise training. In T cells from N_Ex EAT, the top genes significantly upregulated were ATP citrate lyase ( ACLY ), SCD , and Rho GTPase activating protein 28 ( ARHGAP28) ( Figure S3 ). In T cells from O_Ex EAT, the top genes that were significantly upregulated were ELOVL6 , ACACA, and ME1 ( Figure S6 ). The only significantly enriched pathway in T cells from N_Ex EAT was PPAR signaling; this finding is likely due, in part, to the fact that only 13 genes were significantly upregulated in T cells from N_Ex EAT ( Figure S3 ). The enriched pathways in T cells from O_Ex EAT were like those for adipocytes: PPAR signaling, pyruvate metabolism, and biosynthesis of unsaturated fatty acids. Interestingly, pathways significantly negatively enriched in T cells from O_Ex included those related to T cell signaling and metabolism: T cell receptor signaling pathway, Jak-Stat signaling pathway, and natural killer cell-mediated cytotoxicity ( Figure S6 ). In cells that likely comprise small capillaries in EAT – endothelial and smooth muscle cells – genes related to metabolism ELOVL6 , ME1 , ACLY , and ACACA were upregulated ( Figures S4 & S7 ). Like adipocytes from O_Ex EAT, endothelial and smooth muscle cells from O_Ex EAT, had downregulation of GPM6A and PKHD1L1 ( Figure S7 ). Irrespective of occlusion status, exercise training did not significantly enrich pathways from smooth muscle cells. Similar to adipocytes and irrespective of occlusion status, exercise training significantly enriched PPAR signaling, pyruvate metabolism and biosynthesis of unsaturated fatty acids in endothelial cells (Figures S4 & S7) . Furthermore, like adipocytes from N_Ex EAT, endothelial cells from N_Ex EAT had significant enrichment of the insulin signaling pathway ( Figure S4 ). In endothelial cells from O_Ex EAT, insulin signaling was also enriched, as was the ribosome pathway and glycerolipid metabolism ( Figure S7 ). Sedentary EAT has increased numbers of CD8+ T cells expressing TNF superfamily genes The pro-inflammatory factors synthesized by immune cells act as key mediators in CAD 36 . Therefore, we performed a detailed bioinformatic analysis of the macrophages and T cells in our samples to ascertain the relationship between chronic coronary artery ischemia, exercise training, and immune cells in EAT. N_Sed EAT had the largest number of macrophages ( Figure 4A ). On the other hand, T cells were abundant in EAT from both the N_Sed and O_Ex groups ( Figure 4A-B ). Macrophages had a higher expression of CD163 and STAB1 than T cells did. On the other hand, T cells had a higher expression of SKAP, NCALD, and TOX ( Figure 4C ). T cell and macrophage clustering analysis demonstrated a separation of both the T cell and macrophage populations from O_Ex EAT from these cell populations from the rest of the treatment groups ( Figure 4D-E ). Based on the degree of separation, the O_Ex EAT macrophages differed more from all other macrophages than the O_Ex EAT T cells differed from all other T cells. Macrophages from sedentary EAT were similar irrespective of occlusion status. Download figure Open in new tab Figure 4. Characterization of the cell number and gene signatures of macrophages and T cells in EAT in response to exercise and coronary artery occlusion. (A) Barplot the number of cells in each treatment group. (B) Barplot the percentage of cells in each treatment group. (C) Violin plot of the expression of selected marker genes in each cell type from EAT. (D) UMAP plot of T cell and macrophage clusters identified by unsupervised clustering. (E) UMAP plot of single nuclei from T cells and macrophages showing the origins of nuclei in different treatment groups. N: non-occluded; O: occluded; Ex: exercise trained; Sed: sedentary. STAB1: stabilin 1. CD163: CD163 molecule. SKAP1: Src kinase-associated phosphoprotein 1. NCALD: neurocalcin delta. TOX: thymocyte selection associated high mobility group box. Further analysis of the T cell population allowed partitioning of the CD8+ T cells, the most abundant T cell type in the EAT samples ( Figure 5 ; Figure S8 ). Irrespective of occlusion status, EAT from sedentary pigs had more T cells than EAT from exercised pigs ( Figure 5A ). However, O_Ex EAT had double the number of T cells as N_Ex EAT. The CD8+ cell population was classified by increased expression of the following gene markers: CD8 subunit alpha ( CD8A), C-C motif chemokine ligand 5 ( CCL5), CD2 molecule (CD2), granzyme H (GZMH), and transforming growth factor beta receptor 2 ( TGFBR2 ) ( Figure 5B ). CD8+ T cells from O_Sed EAT had the highest expression levels of all CD8+ T cell gene markers compared to EAT from all other treatment groups. CD8A and TGFBR2 were highly expressed in CD8+ T cells from all treatment groups. CD8+ T cells from N_Ex EAT also had high expression of CD2. Irrespective of occlusion status, sedentary EAT had the highest expression of tumor necrosis factor (TNF) superfamily gene expression ( Figure 5C ). Within exercise treatment group, non-occluded EAT had higher expression of TNF superfamily genes than occluded EAT; however, this finding only reached significance for the occluded EAT ( Figure 5C ). The PPARG expression level was similar across treatment groups when analyzed with all T cells batched together ( Figure S9 ). However, when analyzing CD8+ T cells only, the PPARG expression level was very low in the N_Ex treatment group as compared to all other treatment groups ( Figure S9 ). Download figure Open in new tab Figure 5. Number and gene signature of CD8+ T cells in EAT by exercise and coronary artery occlusion treatment group. (A) Barplot of the CD8+ T cell number in EAT from each treatment group. (B) Violin plot of the expression of selected marker genes for identification of the CD8+ T cell population in EAT from each treatment group. (C) Box plot of AUCell scores of T cells from EAT of each treatment group expressing genes in the tumor necrosis factor (TNF) superfamily. N: non-occluded; O: occluded; Ex: exercise trained; Sed: sedentary. CD8A: CD8A molecule. CCL5: C-C motif chemokine ligand 5. CD2: CD2 molecule. GZMH: granzyme H. TGFBR2: transforming growth factor receptor 2. AUCell uses the Area Under the Curve to determine if a subset of the input gene set is enriched in the expressed genes for a given cell type. NS: not significant. * P < 0.05. *** P < 0.001. Sedentary EAT contains an increased number of pro-inflammatory macrophages M1 expressing TNF superfamily genes Based on their polarization, macrophages can be broadly categorized into M0, M1, and M2 phenotypes. M0 macrophages are naïve, inactivated cells. M1 macrophages participate in pro-inflammatory responses, while M2 macrophages exhibit an anti-inflammatory profile 37 . M0 macrophages are phenotypically different from M1 and M2 macrophages ( Figure 6A ). A subpopulation of M2 macrophages separated from the rest of the M2 macrophages was more similar to M0 macrophages than other M2 macrophages ( Figure 6A ) . N_Sed EAT had the most macrophages of all the treatment groups, with M0 and M1 macrophages being the most abundant ( Figure 6B ) . Irrespective of occlusion status, EAT from sedentary pigs had a higher percentage of M1 macrophages than EAT from exercised pigs ( Figure 6B ). EAT from exercised pigs had a higher percentage of M0 macrophages than EAT from sedentary pigs. The percentage of macrophages that were M2 polarization was similar between the four treatment groups ( Figure 6B ) . M1 macrophages had the highest expression level of CD80 molecule ( CD80 ) and interferon regulatory factor 5 ( IRF5 ). In contrast, M2 macrophages had the highest expression level of CD209 molecule (CD209), and transforming growth factor beta 1 (TGFB1) ( Figure 6C ). Irrespective of occlusion status, sedentary EAT had the highest expression of TNF superfamily genes ( Figure 6D ). Download figure Open in new tab Figure 6. Functional analysis of macrophage populations in EAT in response to exercise and coronary artery occlusion status. (A) UMAP plot of macrophages classified by respective marker genes. (B) Barplot of the number and percentage of M0, M1, and M2 macrophages in each treatment group. (C) Violin plot showing the expression level of selected marker genes in each macrophage class. (D) Box plot of AUCell scores of macrophages from EAT of each treatment group expressing genes in the tumor necrosis factor (TNF) superfamily. N: non-occluded; O: occluded; Ex: exercise trained; Sed: sedentary. CD209: CD209 molecule. TGFB1: transforming growth factor beta 1. CD80: CD80 molecule. IRF5: interferon regulatory factor 5. PPARG: peroxisome proliferator-activated receptor gamma PPARG expression was similar across all macrophage subpopulations ( Figure 6C ). Genes related to PPARG signaling were upregulated in macrophages in EAT from O_Ex pigs ( Figure S10 ). In contrast, occlusion status did not affect PPARG or lipoprotein lipase ( LPL ) expression in adipocytes from EAT, but exercise status did ( Figure S10 ). On the other hand, exercise did not affect the expression of peroxisome proliferator-activated receptor delta ( PPARD) , SCD, or ME1 in adipocytes from EAT, but occlusion status did ( Figure S10 ). Adipocytes, adipocyte stem cells, and immune cells from occluded sedentary EAT communicate via growth factors and cell adhesion molecules CellChat found 393 ligand-receptor pairs within the macrophages, T cells, adipocytes, and APSC from EAT. A weighted communication-directed plot based on the significant connections between cell types found that adipocytes and APSCs engaged in the most substantial interactions with the immune cells ( Figure 7A ). Adipocytes had the greatest number of communications, most of which were with APSCs, followed by macrophages. O_Ex EAT had the greatest number of incoming and outgoing interactions across all cell types ( Figure S11 ). However, N_Sed EAT had the greatest number of incoming interactions in adipocytes ( Figure S11). Download figure Open in new tab Figure 7. Cell-to-cell communications between immune and non-immune cells in epicardial adipose tissue. (A) Dot plot of the number of significant interactions between cell types. Circle color correlates to the probability of cell-cell interaction. Circle size correlates to the number of cell-cell interactions. (B) Heat map of the crosstalk between immune cells, adipocytes, and APSCs in EAT. Communication strength for each interaction, as well as the sender and receiver, are shown for each cell-to-cell interaction. N_Ex: occluded exercise; N_Sed: occluded sedentary O_Ex: occluded exercise; O_Sed: occluded sedentary. ADI: adipocytes. APSC: adipocyte stem cells. MAC: macrophages. TC: T cells. See the abbreviation list for gene names. Differential communication analysis found significant alterations in the expression of numerous ligands and receptors as determined by calculated communication scores ( Figure 7B ). O_Sed EAT had the strongest cell-cell interactions (14) of any treatment group, which occurred between all four cell types examined. Adipocytes in O_Sed EAT signaled to macrophages via fibroblast growth factor 7 ( FGF7 ), growth arrest-specific 6 ( GAS6 ), interleukin 34 ( IL34 ), and to T cells via nectin cell adhesion molecule 2 ( NECTIN2 ) and transforming growth factor beta 2 ( TGFB2 ). Adipocytes modulated immune cell adhesion and migration of both immune cell types via thrombospondin 2 ( THB2 ) ( Figure 7B ). On the other hand, macrophages signaled to adipocytes via insulin-like growth factor 1 ( IGF1 ), semaphorin 3C ( SEMA3C ), and semaphorin 6A ( SEMA6A ), which suggests invasion and adhesion of macrophages into O_Sed EAT. T cells in O_Sed EAT adhered and migrated to adipocytes and APSCs via CD226 molecule ( CD226 ) to NECTIN1 communication ( Figure 7B ). In N_Sed EAT, immune cells had nine strong signals to adipocytes without any signaling involvement of the APSCs. While macrophages in N_Sed EAT had strong signaling to adipocytes via SEMA3C as macrophages in O_Sed EAT did, they also had strong signals to adipocytes via FN1 , laminin gamma subunit 1 ( LAMC1 ), laminin alpha subunit 2 ( LAMA2 ), and ectonucleoside triphosphate diphosphohydrolase 1 ( ENTPD1 ) ( Figure 7B ). T cells in N_Sed EAT also had strong signals to adipocytes by these same factors except for SEMA3C . In contrast to O_Sed EAT, O_Ex EAT did not involve APSCs in strong cell-cell signaling interactions. EAT from exercised pigs also contained many less strong cell-cell signaling interactions than EAT from sedentary pigs (10 versus 23 interactions; Figure 7B ). Adipocytes signaled to macrophages via adiponectin, C1Q and collagen domain containing ( ADIPOQ ), and IGF1 but signaled to T cells vis ADIPOQ only ( Figure 7B ). Macrophages and T cells also signaled back to adipocytes via ADIPOQ. In N_Ex EAT, macrophages and T cells had strong signaling interactions with adipocytes via SEMA3C . Discussion Given the critical role of inflammation in the development of CAD 38 , this study was designed to build upon our initial exploration of the effects of exercise training on the cellular composition and transcript expression of EAT in female pigs with chronic ischemic heart disease. 20 Previously, we found that exercise training increased interactions amongst immune, mesenchymal, and endothelial cells in female EAT. 20 We applied both RNA-seq and snRNA-seq to gain additional insights into the diversity of cell types in EAT and their responses to exercise and coronary artery occlusion. Using snRNA-Seq, we clustered cell types by gene signatures. We then analyzed cell-to-cell communication between immune cells, adipocytes, and adipocyte stem cells in EAT from sedentary and exercised-trained female pigs with and without coronary artery occlusion. Bulk RNA-seq of EAT found that exercise training increased the transcript expression of genes related to cellular growth factors and cell adhesion molecules and decreased the transcript expression of genes related to metal ion binding and transferase actions, tight junctions, and cytoskeletal structure. These results are from bulk RNA-seq, so they cannot be attributed to a particular cell type. However, genes like CDH7, a type II cadherin significantly upregulated in EAT from exercised pigs, are known to be responsible for slow and weak cell-to-cell adhesions 39 , as might be seen with the migration of immune cells into EAT from the periphery. Another highly upregulated growth factor in EAT from exercise-trained pigs, HB-EGF, is highly correlated with the movement of monocytes and dendric cells into tissues. 40 Indeed, based on the types of highly significantly upregulated genes in EAT of exercise-trained pigs, it is not surprising that pathways enriched in this treatment group are predominantly related to the enhancement of immune cell function (i.e., chemokine signaling, phagocytosis, asthma). Interestingly, MT2A, which modulates intracellular zinc levels, was downregulated in EAT from exercise-trained pigs, and could be related to immune cell function in EAT. Zinc is critical for cell survival and controls apoptosis in many cell types. 41 Moreover, zinc has been demonstrated to control T cell signal transduction 42 , 43 and regulate monocyte function 44 , 45 , possibly via nuclear factor kappa beta (NF-κβ). 46 CYP2B6 and GALNTL5, which were downregulated in EAT from exercised pigs, control metal ion binding with CYP2B6, specifically binding iron. Iron is increased in tissue from obese individuals, and iron overload in adipocytes has been linked to insulin resistance. 47 Given that iron is required for adipogenesis 48 and decreased iron in adipose tissue is related to decreased lipid uptake 49 , it makes sense that CYP2B6 is downregulated in EAT in response to exercise. Interestingly, CLDN4, a tight junction regulator downregulated in the EAT from exercised pigs, is increased in visceral adipose tissue in obese individuals 50 and has been demonstrated to be involved in T cell communication. 51 Unlike the linear relationship between upregulated genes and enriched pathways, pathways not enriched in EAT in response to exercise don’t correlate mechanistically with the most significantly downregulated genes. Pathways that are not enriched are all related to energy production (i.e., TCA cycle, oxidative phosphorylation, pyruvate metabolism) and adipogenesis (i.e., fatty acid metabolism and PPAR signaling). The significance of this disconnect is not clear and requires further investigation. snRNA-seq provided a more nuanced assessment of the cellular landscape of EAT, revealing unique cellular distribution patterns across each treatment group. Non-occluded EAT in both activity groups had many adipocytes, which suggests that growth factors and lipids required to support adipogenesis are likely sourced from the systemic circulation. Interestingly, APSCs were highly abundant in N_Sed EAT but not N_Ex EAT, which suggests that the N_Sed EAT has a large capacity for additional adipogenesis. Moreover, N_Sed EAT had the most T cells and macrophages of any treatment group. This finding suggests these immune cells may migrate into EAT from other locations in the body in sedentary as opposed to exercised pigs. Thus, similar to what is known to occur in visceral adipose tissue 52 , inflammation and recruitment of immune cells in EAT may occur in response to a sedentary lifestyle. Moreover, contrary to what we expected, there are more immune cells in N_Sed than O_Sed EAT, which indicates that coronary artery occlusion may prevent peripheral immune cells from trafficking to EAT. Opposite of what was found in the analysis of the bulk RNA-seq data set, DGE analysis of individual cell types in the snRNA-seq dataset found significant upregulation in metabolic pathways in response to exercise. Irrespective of coronary artery occlusion status, ME1 and SCD were upregulated in adipocytes in response to exercise. These genes are essential for the TCA cycle and fatty oxidation, indicating that exercise training increases energy production by the adipocyte pool in EAT. Adipocytes from N_Ex EAT were enriched for insulin signaling, glucose turnover pathways, and adipokine signaling. Thus, adipocytes from EAT with adequate localized blood flow likely have increased insulin sensitivity in response to exercise. Our results in EAT correspond with findings that high-intensity exercise increases visceral, but not subcutaneous, adipose tissue sensitivity. 53 Indeed, O_Ex EAT did not have these same enriched pathways, except for PPAR signaling. Surprisingly, PPAR signaling, a master regulator of adipogenesis 54 , is enriched in adipocytes in response to exercise, irrespective of coronary artery occlusion status. This finding is at odds with human studies, which have found a reduction in EAT volume in response to endurance exercise. 55 , 56 However, PPARs in adipocytes have also been shown to inhibit inflammation-related gene transcription by blocking transcription factors such as NF𝜅𝛽, signal transducer and activator of transcription (STAT), and nuclear factor of activated T cells (NFAT). 57 Therefore, it seems more likely that the enrichment of PPAR signaling in adipocytes in response to exercise may play an anti-inflammatory, as opposed to adipogenic, role in these cells. In response to exercise, immune cells also had upregulation of genes related to fatty acid biosynthesis ( ACACA, ELOVL6 ) and energy production ( ME1 ) and enrichment of pathways such as biosynthesis of unsaturated fatty acids, pyruvate metabolism, and insulin signaling. PPAR signaling was enriched in response to exercise in all immune cells, irrespective of the occlusion status of the coronary arteries. PPARG activation in macrophages of mice causes suppression of immunoreactive cytokines such as tumor necrosis factor-alpha (TNF𝛼), interleukin 6 (IL6), and interleukin 1 beta (IL1𝛽) 58 and promotion of immunotolerant factors such as interleukin 4 (IL4), CD36 molecule (CD36), and interleukin 10 (IL10). 59 Therefore, PPARG activation in macrophages favors the transition from an M1 inflammatory to an M2 anti-inflammatory phenotype. Indeed, PPAR signaling was enriched in macrophages from exercised EAT, and fewer M1 inflammatory macrophages were found in exercised EAT. By contrast, N_Sed EAT had many M1 inflammatory macrophages compared to the other treatment groups. In contrast, O_Sed EAT had about a third of the number of M1 inflammatory macrophages that N_Sed EAT had. However, irrespective of the number of macrophages in the sedentary EAT or the coronary artery occlusion status, a significant fraction of macrophages in sedentary EAT expressed genes in the tumor necrosis factor (TNF) superfamily. TNF ligands and receptors are proinflammatory modulators of programmed cell death. 60 Thus, from a macrophage gene expression perspective, sedentary EAT is more pro-inflammatory than exercised EAT. However, as N_Sed EAT had the largest number of M1 macrophages and increased gene expression of pro-inflammatory markers on macrophages, it is the unhealthiest EAT of all the groups. The leukocyte transendothelial migration pathway is negatively enriched in O_Ex EAT, which supports the theory that coronary artery occlusion decreases the number of peripheral immune cells able to migrate into EAT. M0 or unpolarized macrophages are monocytes that have moved into tissue from the bloodstream and await cytokine cues to polarize into M1 or M2 macrophages. 61 Therefore, the exceptionally high numbers of M0 macrophages in N_Sed EAT further indicate that migration of peripheral monocytes into EAT was highest in sedentary states and required intact coronary artery circulation. However, EAT from exercise-trained environments had a higher percentage of M0 macrophages than EAT from sedentary environments. Thus, exercise may dampen the release of cytokines from other immune cells that could program M0 macrophages to polarize. Similar to our findings in macrophages, exercise training enriched PPAR signaling pathway in T cells. Most studies regarding the role of PPARG activation in T cells have focused on CD4+ T cells, such as T helper cells. Indeed, PPARG signals with mechanistic target of rapamycin kinase (mTOR) in CD4+ T cells to directly program fatty acid uptake to provide energy for the activation and proliferation of this T cell subpopulation. 62 Similar to the findings in macrophages, N_Sed EAT had the highest number of T cells of any treatment group. However, unlike the findings in macrophages, O_Ex EAT also had a very high number of T cells. Indeed, UMAP analysis of O_Ex T cells demonstrated the separation of this T cell group from all other T cells in EAT. Examination of the KEGG pathway analysis provides clues regarding the possible functional differences in the T cell population from O_Ex EAT. T cells from O_Ex EAT, but not N_Ex EAT, have enrichment of pathways related to energy production and fatty acid biosynthesis, which would be required for differentiation and activation. 63 However, T cells from O_Ex EAT have negative enrichment of T cell receptor signaling, Jak-Stat signaling, and natural killer (NK) cell mediated cytotoxicity. Jak-stat is a major regulator of T cell metabolism 64 and cell homeostasis. 65 T cell receptor signaling is essential for T cell activation, differentiation, and immune response. 66 Interestingly, one of the roles of NK cells in adipose tissue is to work with T cells to secrete cytokines to polarize macrophages. 67 Based on the functional analysis of these negatively enriched pathways, T cells from O_Ex EAT may be in an inactive, quiescent state. Many inactive T cells could be a reason for the large number of unpolarized macrophages found in O_Ex EAT. Interestingly, although CD8+ T cells were the most abundant T cell subpopulations in EAT, O_Ex and N_Ex EAT had the lowest number of CD8+ T cells. Given that O_Ex had the second largest number of T cells of any treatment group, the low number of CD8+ T cells in O_Ex EAT suggests many T cells in O_Ex EAT may be from a different T cell subpopulation, likely CD4+ T cells. In contrast, N_Sed EAT had the most T cells of any treatment group and the most CD8+ T cells of any treatment group. Therefore, most of T cells in N_Sed EAT were CD8+ T cells. The N_Sed EAT had the largest fraction of T cells expressing genes in the TNF superfamily. O_Sed EAT had the second largest fraction of T cells expressing genes in the TNF superfamily and had a significantly higher fraction of T cells expressing TNF superfamily genes than T cells from O_Ex EAT. Therefore, T cells from N_Sed EAT were CD8+ T cells with high cytokine expression levels. Meanwhile, T cells from O_Sed EAT were likely CD8+ cells with moderate cytokine expression levels. Inflamed visceral adipose tissue, as found in obese individuals, contains activated CD8+ T cells, which secrete tumor necrosis factor-alpha (TNF𝛼), interferon gamma (INF𝛾), and tumor necrosis factor beta (TNF𝛽). 68 Similar to what we proposed for macrophages, T cells from N_Sed EAT were likely activated CD8+ T cells from the peripheral circulation that secreted pro-inflammatory cytokines. T cells from O_Sed EAT also were likely activated CD8+ T cells with lower cytokine activity. However, further research should be done to determine the sources of these CD8+ T cells, particularly in O_Sed EAT. Interestingly, PPARG was highly expressed in T cells from all treatment groups. However, when CD8+ T cells were examined, PPARG expression was extremely low for CD8+ T cells from N_Ex EAT. Activation of PPARG in CD8+ T cells causes a pro-inflammatory phenotype with increased production of inflammatory cytokines. 69 Thus, not only are there very few CD8+ T cells in N_Ex EAT, their activation via PPARG is nearly nonexistent. Indeed, most T cells from N_Ex EAT could have been CD4+ T cells with moderate cytokine expression levels. By contrast, T cells from O_Ex could have been CD4+ T cells with low cytokine expression levels. T helper 1 (Th1) and Th17 cells are CD4+ T cells that play a role in tissue inflammation in obesity. 70 , 71 In contrast, T regulatory cells (Tregs) are CD4+ T cells that have an anti-inflammatory role in adipose tissue. 72 , 73 We postulate that exercise may increase the number of CD4+ T cells in the tissue and favor the Treg cell type. The next steps in this research should be to collect the occluded and non-occluded tissue from exercised and sedentary pigs to examine the concentration of cytokines in the tissue and perform flow cytometry for specific T cell subpopulations. As there were too few T cells from other subpopulations than CD8+ in our samples, future approaches will require more tissue to determine all the T cell populations in EAT. Analysis of cell-cell communication with CellChat demonstrated that sedentary EAT had the strongest cell-cell communications. In support of our theory that N_Sed EAT had increased numbers of immune cells due to migration into the tissue from the coronary arteries, all outgoing significant strong communications in N_Sed EAT originated from macrophages or T cells to adipocytes and utilized genes related to cell adhesion and migration. Thus, immune cells could likely migrate into EAT via connections with adipocytes. O_Sed EAT had stronger cell-cell communications than N_Sed EAT. However, unlike N_Sed EAT, there was incoming and outgoing communication between immune cells and adipocytes in O_Sed EAT. Furthermore, while communication from adipocytes to immune cells utilized cell adhesion and migration genes, adipocytes also communicated to immune cells via cytokines and growth factors in O_Sed EAT. This suggests cell migration, possibly through capillaries in EAT instead of coronary arteries, and local proliferation of immune cell populations may have contributed to increased immune cells in O_Sed EAT. Interestingly, while the overall number of macrophages and T cells in O_Sed EAT was not more than the number of these cells from exercised EAT, there were more pro-inflammatory M1 macrophages and CD8+ T cells in O_Sed EAT than exercised EAT. Given that the coronary artery adjacent to the EAT was occluded, localized cell proliferation and polarization of resident cells likely occurred in O_Sed EAT. There were very few strong cell-cell communications in exercised EAT. O_Ex EAT, but not N_Ex EAT, had communication via growth factors from adipocytes to immune cells and immune cells to adipocytes. This suggests that the larger pool of CD8+ T cells and M1 macrophages found in O_Ex EAT as opposed to N_Ex EAT resulted from local tissue proliferation. However, the number of CD8+ T cells and M1 macrophages in O_Ex EAT is about half that of O_Sed EAT, suggesting the exercise mitigates the proliferation of these two pro-inflammatory immune cell classes. Under sedentary conditions, EAT releases excess low-density lipoproteins (LDL) and adipokines, such as interleukin 6 (IL6), that can elicit a pro-inflammatory response. 74 , 75 However, exercise training has been previously found to suppress visceral adipose tissue inflammation and inflammatory macrophage accumulation in obese male mice by reducing the number of M1 macrophages and CD8+ T cells. 76 Our findings build upon what is known about the effects of exercise on visceral adipose tissue. This data underscores the importance of exercise training in the modulation of cellular communication dynamics in EAT, potentially mitigating an inflammatory status brought on by coronary artery occlusion. In summary, our research demonstrates that a sedentary lifestyle increases the number of inflammatory M1 macrophages and CD8+ T cells, their expression of TNF superfamily genes, and the number of strong communications with adipocytes in EAT. Interestingly, the number of inflammatory M1 macrophages and CD8+ T cells in sedentary EAT is decreased when the adjacent coronary artery is occluded. However, irrespective of coronary artery occlusion status, exercise training mitigates EAT inflammation by decreasing the number of inflammatory M1 macrophages and CD8+ T cells and their expression of TNF superfamily genes. Exercise also increases the expression of the anti-inflammatory factor PPARG by adipocytes and macrophages which likely mitigates the number of M1 macrophages in exercised EAT. These findings emphasize that exercise training, even in the presence of coronary artery occlusion, creates an anti-inflammatory milieu in EAT. Future studies will focus on how the anti-inflammatory milieu of EAT in the exercised female communicates with and modulates the function of the surrounding cardiac structures. Sources of Funding Clinical and Translational Science Award Pilot Grant (NIH 1ULTR003163-01A1, Subaward GMO: 220103 PO: 0000002553 between UT Southwestern Medical Center and Texas A&M AgriLife (ANF and CLH); NIH HL139903 (CLH); Cancer Prevention and Research Institute of Texas RP230204 (JJC). Disclosures None Supplemental Material Supplemental Datasets (Excel format) Supplemental Figures and Figure Legends (PDF file format) Supplemental Tables and supporting information (PDF file format) Abbreviations and Acronyms ACACA acetyl coA carboxylase alpha ACLY ATP citrate lyase ACTA2 actin alpha 2, smooth muscle ADIPOQ adiponectin, C1Q and collagen domain containing ARHGAP28 Rho GTPase activating protein 28 CAD coronary artery disease CCL5 C-C motif chemokine ligand 5 CD163 CD163 molecule CD2 CD2 molecule CD8A CD8A molecule CD80 CD80 molecule CD209 CD209 molecule CDH7 cadherin 7 CDH13 cadherin 13 COL1A2 collagen type alpha 2 chain CRIM1 cysteine rich transmembrane BMP regulator 1 CSF1R colony stimulating factor 1 receptor DAB2 DAB adaptor protein 2 DEG differentially expressed genes DGAT2 diacylglycerol O-acyltransferase 2 EAT epicardial adipose tissue EGFR epithelial growth factor receptor ELOVL6 ELOVL fatty acid elongase 6 FABP5 fatty acid binding protein 5 FASN fatty acid synthase FGF7 fibroblast growth factor 7 FN1 fibronectin 1 GALNT16 polypeptide N-acetyl galatosaminyltransferase 16 GAS6 growth arrest-specific 6 GJC1 gap junction protein gamma 1 GMZH granzyme H GPM6A glycoprotein M6A GPX3 glutathione peroxidase 3 GZMH granzyme H IGF1 insulin-like growth factor 1 INF𝛾 interferon gamma IKZF3 IKAROS family zinc finger 3 IL34 interleukin 34 IL6 interleukin 6 IRF5 interferon regulatory factor 5 KEGG Kyoto encyclopedia of genes and genomes LAMA2 laminin subunit alpha 2 LDB2 LIM domain binding 2 LDL low density lipoprotein LPL lipoprotein lipase ME1 malic enzyme 1 MRVI1 inositol 1,4,5-triphosphate receptor associated 1 mTOR mechanistic target of rapamycin kinase NCALD neurocalcin delta NECTIN1 nectin cell adhesion molecule 2 NFAT nuclear factor of activated T cells NK natural killer NF𝜅𝛽 nuclear factor kappa beta NRXN1 neurexin 1 PCA principal component analysis PDGFRA platelet-derived growth factor receptor alpha PDK4 pyruvate dehydrogenase kinase 4 PICK1 protein interacting with PRKCA 1 PKHD1L1 PKHD1 Like 1 PLIN perilipin 1 POSTN periostin PPAR peroxisome proliferator-activated receptor PPARD peroxisome proliferator-activated receptor delta PPARG peroxisome proliferator-activated receptor gamma RBPJ recombination signaling binding protein for immunoglobulin kappa J region SCARA5 scavenger receptor class A member 5 SCD stearoyl-coA desaturase SEMA3C semaphorin 3C SEMA6A semaphorin 6A SKAP1 Src kinase-associated phosphoprotein 1 SNCA synuclein alpha STAB1 stabilin 1 STAT signal transducer and activator of transcription SYNPO2 synaptopodin 2 TGFB1 transforming growth factor beta 1 TGFB2 transforming growth factor beta 2 TGFBR2 transforming growth factor receptor 2 THB2 thrombospondin 2 TNF tumor necrosis factor TNF𝛼 tumor necrosis factor alpha TNF𝛽 tumor necrosis factor beta TOX thymocyte selection associated high mobility group box UMAP uniform manifold approximation and projection UMI unique molecular identifier VWF von Willebrand factor Acknowledgments We thank Jeff Bray and Andrew Hillhouse for their assistance with this work. Footnotes ↵ # current affiliation: Faculty of Fisheries, Sher-e-Kashmir University of Agricultural Sciences and Technology, Kashmir, India References (1). ↵ Iacobellis , G. ; di Gioia , C. R. ; Cotesta , D. ; Petramala , L. ; Travaglini , C. ; De Santis , V. ; Vitale , D. ; Tritapepe , L. ; Letizia , C . Epicardial adipose tissue adiponectin expression is related to intracoronary adiponectin levels . Horm Metab Res 2009 , 41 ( 3 ), 227 – 231 . DOI: 10.1055/s-0028-1100412 From NLM. OpenUrl CrossRef PubMed (2). ↵ Mahmoud , I. ; Dykun , I. ; Kärner , L. ; Hendricks , S. ; Totzeck , M. ; Al-Rashid , F. ; Rassaf , T. ; Mahabadi , A. A . Epicardial adipose tissue differentiates in patients with and without coronary microvascular dysfunction . Int J Obes (Lond ) 2021 , 45 ( 9 ), 2058 – 2063 . DOI: 10.1038/s41366-021-00875-6 From NLM. OpenUrl CrossRef PubMed (3). ↵ Iacobellis , G . Epicardial adipose tissue in contemporary cardiology . Nature Reviews Cardiology 2022 , 19 ( 9 ), 593 – 606 . DOI: 10.1038/s41569-022-00679-9 . OpenUrl CrossRef PubMed (4). ↵ Leandro , A. C. ; Michael , L. F. ; Almeida , M. ; Kuokkanen , M. ; Huynh , K. ; Giles , C. ; Duong , T. ; Diego , V. P. ; Duggirala , R. ; Clarke , G. D . Influence of the Human Lipidome on Epicardial Fat Volume in Mexican American Individuals . Frontiers in Cardiovascular Medicine 2022 , 9 , 889985 . OpenUrl CrossRef PubMed (5). ↵ Li , C. ; Liu , X. ; Adhikari , B. K. ; Chen , L. ; Liu , W. ; Wang , Y. ; Zhang , H . The role of epicardial adipose tissue dysfunction in cardiovascular diseases: an overview of pathophysiology, evaluation, and management . Frontiers in Endocrinology 2023 , 14 , 1167952 . OpenUrl CrossRef PubMed (6). ↵ Yamaguchi , Y. ; Cavallero , S. ; Patterson , M. ; Shen , H. ; Xu , J. ; Kumar , S. R. ; Sucov , H. M . Adipogenesis and epicardial adipose tissue: a novel fate of the epicardium induced by mesenchymal transformation and PPARγ activation . Proceedings of the National Academy of Sciences 2015 , 112 ( 7 ), 2070 – 2075 . OpenUrl Abstract / FREE Full Text (7). ↵ Mazzatti , D. ; Lim , F. L. ; O’Hara , A. ; Wood , I. S. ; Trayhurn , P . A microarray analysis of the hypoxia-induced modulation of gene expression in human adipocytes . Arch Physiol Biochem 2012 , 118 ( 3 ), 112 – 120 . DOI: 10.3109/13813455.2012.654611 From NLM. OpenUrl CrossRef PubMed (8). ↵ Clark , R. B. ; Bishop-Bailey , D. ; Estrada-Hernandez , T. ; Hla , T. ; Puddington , L. ; Padula , S. J . The Nuclear Receptor PPARγ and Immunoregulation: PPARγ Mediates Inhibition of Helper T Cell Responses . The Journal of Immunology 2000 , 164 ( 3 ), 1364 – 1371 . DOI: 10.4049/jimmunol.164.3.1364 . OpenUrl Abstract / FREE Full Text (9). ↵ Strizova , Z. ; Benesova , I. ; Bartolini , R. ; Novysedlak , R. ; Cecrdlova , E. ; Foley , L. K. ; Striz , I . M1/M2 macrophages and their overlaps–myth or reality? Clinical Science 2023 , 137 ( 15 ), 1067 – 1093 . OpenUrl CrossRef PubMed (10). ↵ Arora , S. ; Dev , K. ; Agarwal , B. ; Das , P. ; Syed , M. A . Macrophages: Their role, activation and polarization in pulmonary diseases . Immunobiology 2018 , 223 ( 4-5 ), 383 – 396 . OpenUrl CrossRef PubMed (11). ↵ Tontonoz , P. ; Nagy , L. ; Alvarez , J. G. A. ; Thomazy , V. A. ; Evans , R. M . PPARgamma Promotes Monocyte/Macrophage Differentiation and Uptake of Oxidized LDL . Cell 1998 , 93 ( 2 ), 241 – 252 . DOI: 10.1016/S0092-8674(00)81575-5 (acccessed 2024/12/20). OpenUrl CrossRef PubMed Web of Science (12). ↵ Nagy , L. ; Tontonoz , P. ; Alvarez , J. G. A. ; Chen , H. ; Evans , R. M . Oxidized LDL Regulates Macrophage Gene Expression through Ligand Activation of PPARγ . Cell 1998 , 93 ( 2 ), 229 – 240 . DOI: 10.1016/S0092-8674(00)81574-3 (acccessed 2024/12/20). OpenUrl CrossRef PubMed Web of Science (13). ↵ Bermúdez , V. ; Durán , P. ; Rojas , E. ; Díaz , M. P. ; Rivas , J. ; Nava , M. ; Chacín , M. ; Cabrera de Bravo , M. ; Carrasquero , R. ; Ponce , C. C. The sick adipose tissue: new insights into defective signaling and crosstalk with the myocardium . Frontiers in Endocrinology 2021 , 12 , 735070 . OpenUrl CrossRef PubMed (14). ↵ Monti , M. ; Di Renzi , P. ; Pirro , M. R. ; Borgognoni , F. ; Vincentelli , G. M . New evidences about the strict relationship between the epicardial fat and the aerobic exercise . IJC Metabolic & Endocrine 2015 , 6 , 55 – 58 . OpenUrl CrossRef (15). ↵ Kim , M. K. ; Tomita , T. ; Kim , M. J. ; Sasai , H. ; Maeda , S. ; Tanaka , K . Aerobic exercise training reduces epicardial fat in obese men . J Appl Physiol (1985) 2009 , 106 ( 1 ), 5 – 11 . DOI: 10.1152/japplphysiol.90756.2008 From NLM. OpenUrl CrossRef PubMed Web of Science (16). ↵ Bairapareddy , K. C. ; Maiya , A. G. ; Kumar , P. ; Nayak , K. ; Guddattu , V. ; Nayak , V . Effect of aerobic exercise on echocardiographic epicardial adipose tissue thickness in overweight individuals . Diabetes Metab Syndr Obes 2018 , 11 , 303 – 312 . DOI: 10.2147/dmso.S145862 From NLM. OpenUrl CrossRef PubMed (17). ↵ Serrano-Ferrer , J. ; Crendal , E. ; Walther , G. ; Vinet , A. ; Dutheil , F. ; Naughton , G. ; Lesourd , B. ; Chapier , R. ; Courteix , D. ; Obert , P . Effects of lifestyle intervention on left ventricular regional myocardial function in metabolic syndrome patients from the RESOLVE randomized trial . Metabolism 2016 , 65 ( 9 ), 1350 – 1360 . DOI: 10.1016/j.metabol.2016.05.006 From NLM. OpenUrl CrossRef PubMed (18). ↵ Heaps , C. L. ; Bray , J. F. ; Parker , J. L . Enhanced KCl-mediated contractility and Ca(2+) sensitization in porcine collateral-dependent coronary arteries persist after exercise training . American journal of physiology. Heart and circulatory physiology 2020 , 319 ( 4 ), H915 – h926 . DOI: 10.1152/ajpheart.00384.2020 From NLM. OpenUrl CrossRef PubMed (19). Johnson , K. A. ; Jeffery , E. ; Bray , J. F. ; Murphy , M. M. ; Heaps , C. L . Exercise training rescues impaired H(2)O(2)-mediated vasodilation in porcine collateral-dependent coronary arterioles through enhanced K(+) channel activation . American journal of physiology. Heart and circulatory physiology 2023 , 324 ( 5 ), H637 – h653 . DOI: 10.1152/ajpheart.00710.2022 From NLM. OpenUrl CrossRef PubMed (20). ↵ Ahmad , I. ; Gupta , S. ; Faulkner , P. ; Mullens , D. ; Thomas , M. ; Sytha , S. P. ; Ivanov , I. ; Cai , J. J. ; Heaps , C. L. ; Newell-Fugate , A. E . Single-nucleus transcriptomics of epicardial adipose tissue from female pigs reveals effects of exercise training on resident innate and adaptive immune cells . Cell Commun Signal 2024 , 22 ( 1 ), 243 . DOI: 10.1186/s12964-024-01587-w From NLM. OpenUrl CrossRef PubMed (21). ↵ White , F. C. ; Carroll , S. M. ; Magnet , A. ; Bloor , C. M . Coronary collateral development in swine after coronary artery occlusion . Circulation Research 1992 , 71 ( 6 ), 1490 – 1500 . DOI: 10.1161/01.res.71.6.1490 . OpenUrl Abstract / FREE Full Text (22). ↵ Heaps , C. L. ; Robles , J. C. ; Sarin , V. ; Mattox , M. L. ; Parker , J. L . Exercise training-induced adaptations in mediators of sustained endothelium-dependent coronary artery relaxation in a porcine model of ischemic heart disease . Microcirculation 2014 , 21 ( 5 ), 388 – 400 . OpenUrl CrossRef PubMed (23). ↵ Martelotto , L. G. Daughter of Frankenstein protocol for nuclei isolation from fresh and frozen tissues using OptiPrep® conti . 2021 . (24). ↵ Ahmad , I. ; Gupta , S. ; Faulkner , P. ; Mullens , D. ; Thomas , M. ; Sytha , S. P. ; Ivanov , I. ; Cai , J. J. ; Heaps , C. L. ; Newell-Fugate , A. E . Single-nucleus transcriptomics of epicardial adipose tissue from females reveals exercise control of innate and adaptive immune cells . bioRxiv 2023 , 2023.2011. 2002.565385. (25). ↵ Langmead , B. ; Trapnell , C. ; Pop , M. ; Salzberg , S. L . Ultrafast and memory-efficient alignment of short DNA sequences to the human genome . Genome Biology 2009 , 10 ( 3 ), R25 . DOI: 10.1186/gb-2009-10-3-r25 . OpenUrl CrossRef PubMed (26). ↵ Putri , G. H. ; Anders , S. ; Pyl , P. T. ; Pimanda , J. E. ; Zanini , F . Analysing high-throughput sequencing data in Python with HTSeq 2.0 . Bioinformatics 2022 , 38 ( 10 ), 2943 – 2945 . DOI: 10.1093/bioinformatics/btac166 (acccessed 2/22/2024). OpenUrl CrossRef PubMed (27). ↵ Chen , Y. ; Lun , A. T. ; Smyth , G. K . From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline . F1000Res 2016 , 5 , 1438 . DOI: 10.12688/f1000research.8987.2 From NLM. OpenUrl CrossRef PubMed (28). ↵ Ritchie , M. E. ; Phipson , B. ; Wu , D. ; Hu , Y. ; Law , C. W. ; Shi , W. ; Smyth , G . K. limma powers differential expression analyses for RNA-sequencing and microarray studies . Nucleic Acids Res 2015 , 43 ( 7 ), e47 . DOI: 10.1093/nar/gkv007 From NLM. OpenUrl CrossRef PubMed (29). ↵ Korotkevich , G. ; Sukhov , V. ; Budin , N. ; Shpak , B. ; Artyomov , M. N. ; Sergushichev , A . Fast gene set enrichment analysis . bioRxiv 2021 , 060012 . DOI: 10.1101/060012 . OpenUrl Abstract / FREE Full Text (30). ↵ Hao , Y. ; Hao , S. ; Andersen-Nissen , E. ; Mauck , W. M. ; Zheng , S. ; Butler , A. ; Lee , M. J. ; Wilk , A. J. ; Darby , C. ; Zager , M . Integrated analysis of multimodal single-cell data . Cell 2021 , 184 ( 13 ), 3573 – 3587. e3529 . OpenUrl CrossRef PubMed (31). ↵ Hartigan , J. A. ; Wong , M. A . Algorithm AS 136: A k-means clustering algorithm . Journal of the royal statistical society. series c (applied statistics ) 1979 , 28 ( 1 ), 100 – 108 . OpenUrl CrossRef (32). ↵ Franzén , O. ; Gan , L.-M. ; Björkegren , J. L . PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data . Database 2019 , 2019 , baz046 . OpenUrl CrossRef PubMed (33). ↵ Korotkevich , G. ; Sukhov , V. ; Budin , N. ; Shpak , B. ; Artyomov , M. N. ; Sergushichev , A . Fast gene set enrichment analysis . BioRxiv 2016 , 060012 . (34). ↵ Aibar , S. ; González-Blas , C. B. ; Moerman , T. ; Huynh-Thu , V. A. ; Imrichova , H. ; Hulselmans , G. ; Rambow , F. ; Marine , J.-C. ; Geurts , P. ; Aerts , J . SCENIC: single-cell regulatory network inference and clustering . Nature methods 2017 , 14 ( 11 ), 1083 – 1086 . OpenUrl CrossRef PubMed (35). ↵ Jin , S. ; Guerrero-Juarez , C. F. ; Zhang , L. ; Chang , I. ; Ramos , R. ; Kuan , C.-H. ; Myung , P. ; Plikus , M. V. ; Nie , Q . Inference and analysis of cell-cell communication using CellChat . Nature communications 2021 , 12 ( 1 ), 1088 . OpenUrl CrossRef PubMed (36). ↵ Fernández-Ruiz , I . Immune system and cardiovascular disease . Nature Reviews Cardiology 2016 , 13 ( 9 ), 503 – 503 . DOI: 10.1038/nrcardio.2016.127 . OpenUrl CrossRef PubMed (37). ↵ Wu , J. ; He , S. ; Song , Z. ; Chen , S. ; Lin , X. ; Sun , H. ; Zhou , P. ; Peng , Q. ; Du , S. ; Zheng , S. ;, et al. Macrophage polarization states in atherosclerosis . Front Immunol 2023 , 14 , 1185587 . DOI: 10.3389/fimmu.2023.1185587 From NLM. OpenUrl CrossRef (38). ↵ Henein , M. Y. ; Vancheri , S. ; Longo , G. ; Vancheri , F . The Role of Inflammation in Cardiovascular Disease . Int J Mol Sci 2022 , 23 ( 21 ). DOI: 10.3390/ijms232112906 From NLM. OpenUrl CrossRef (39). ↵ Chu , Y.-S. ; Eder , O. ; Thomas , W. A. ; Simcha , I. ; Pincet , F. ; Ben-Ze’ev , A. ; Perez , E. ; Thiery , J. P. ; Dufour , S . Prototypical Type I E-cadherin and Type II Cadherin-7 Mediate Very Distinct Adhesiveness through Their Extracellular Domains* . Journal of Biological Chemistry 2006 , 281 ( 5 ), 2901 – 2910 . DOI: 10.1074/jbc.M506185200 . OpenUrl Abstract / FREE Full Text (40). ↵ Van Hiep , N. ; Sun , W.-L. ; Feng , P.-H. ; Lin , C.-W. ; Chen , K.-Y. ; Luo , C.-S. ; Dung , L. N. ; Van Quyet , H. ; Wu , S.-M. ; Lee , K.-Y . Heparin binding epidermal growth factor–like growth factor is a prognostic marker correlated with levels of macrophages infiltrated in lung adenocarcinoma . Frontiers in Oncology 2022 , 12 , Original Research. DOI: 10.3389/fonc.2022.963896 . OpenUrl CrossRef (41). ↵ Hojyo , S. ; Fukada , T . Roles of Zinc Signaling in the Immune System . J Immunol Res 2016 , 2016 , 6762343 . DOI: 10.1155/2016/6762343 From NLM. OpenUrl CrossRef (42). ↵ Romir , J. ; Lilie , H. ; Egerer-Sieber , C. ; Bauer , F. ; Sticht , H. ; Muller , Y. A . Crystal structure analysis and solution studies of human Lck-SH3; zinc-induced homodimerization competes with the binding of proline-rich motifs . J Mol Biol 2007 , 365 ( 5 ), 1417 – 1428 . DOI: 10.1016/j.jmb.2006.10.058 From NLM. OpenUrl CrossRef PubMed (43). ↵ Csermely , P. ; Somogyi , J . Zinc as a possible mediator of signal transduction in T lymphocytes . Acta Physiol Hung 1989 , 74 ( 2 ), 195 – 199 . From NLM. OpenUrl PubMed Web of Science (44). ↵ Haase , H. ; Ober-Blöbaum , J. L. ; Engelhardt , G. ; Hebel , S. ; Heit , A. ; Heine , H. ; Rink , L . Zinc Signals Are Essential for Lipopolysaccharide-Induced Signal Transduction in Monocytes1 . The Journal of Immunology 2008 , 181 ( 9 ), 6491 – 6502 . DOI: 10.4049/jimmunol.181.9.6491 (acccessed 1/24/2025). OpenUrl Abstract / FREE Full Text (45). ↵ von Bülow , V. ; Dubben , S. ; Engelhardt , G. ; Hebel , S. ; Plümäkers , B. ; Heine , H. ; Rink , L. ; Haase , H . Zinc-dependent suppression of TNF-alpha production is mediated by protein kinase A-induced inhibition of Raf-1, I kappa B kinase beta, and NF-kappa B . J Immunol 2007 , 179 ( 6 ), 4180 – 4186 . DOI: 10.4049/jimmunol.179.6.4180 From NLM. OpenUrl Abstract / FREE Full Text (46). ↵ Bao , S. ; Liu , M. J. ; Lee , B. ; Besecker , B. ; Lai , J. P. ; Guttridge , D. C. ; Knoell , D. L . Zinc modulates the innate immune response in vivo to polymicrobial sepsis through regulation of NF-kappaB . Am J Physiol Lung Cell Mol Physiol 2010 , 298 ( 6 ), L744 – 754 . DOI: 10.1152/ajplung.00368.2009 From NLM. OpenUrl CrossRef PubMed (47). ↵ Basuli , D. ; Stevens , R. G. ; Torti , F. M. ; Torti , S. V . Epidemiological associations between iron and cardiovascular disease and diabetes . Front Pharmacol 2014 , 5 , 117 . DOI: 10.3389/fphar.2014.00117 From NLM. OpenUrl CrossRef PubMed (48). ↵ Moreno-Navarrete , J. M. ; Ortega , F. ; Moreno , M. ; Ricart , W. ; Fernández-Real , J. M . Fine-tuned iron availability is essential to achieve optimal adipocyte differentiation and mitochondrial biogenesis . Diabetologia 2014 , 57 ( 9 ), 1957 – 1967 . DOI: 10.1007/s00125-014-3298-5 From NLM. OpenUrl CrossRef PubMed (49). ↵ Green , A. ; Basile , R. ; Rumberger , J. M . Transferrin and iron induce insulin resistance of glucose transport in adipocytes . Metabolism 2006 , 55 ( 8 ), 1042 – 1045 . DOI: 10.1016/j.metabol.2006.03.015 From NLM. OpenUrl CrossRef PubMed Web of Science (50). ↵ Fernández-García , P. ; Taxerås , S. D. ; Reyes-Farias , M. ; González , L. ; Soria-Gondek , A. ; Pellitero , S. ; Tarascó , J. ; Moreno , P. ; Sumoy , L. ; Stephens , J. M. ;, et al. Claudin-1 as a novel target gene induced in obesity and associated to inflammation, fibrosis, and cell differentiation . European Journal of Endocrinology 2024 , 190 ( 3 ), 201 – 210 . DOI: 10.1093/ejendo/lvae018 (acccessed 1/24/2025). OpenUrl CrossRef PubMed (51). ↵ Kawai , Y. ; Hamazaki , Y. ; Fujita , H. ; Fujita , A. ; Sato , T. ; Furuse , M. ; Fujimoto , T. ; Jetten , A. M. ; Agata , Y. ; Minato , N . Claudin-4 induction by E-protein activity in later stages of CD4/8 double-positive thymocytes to increase positive selection efficiency . Proc Natl Acad Sci U S A 2011 , 108 ( 10 ), 4075 – 4080 . DOI: 10.1073/pnas.1014178108 From NLM. OpenUrl Abstract / FREE Full Text (52). ↵ Burini , R. C. ; Anderson , E. ; Durstine , J. L. ; Carson , J. A . Inflammation, physical activity, and chronic disease: An evolutionary perspective . Sports Medicine and Health Science 2020 , 2 ( 1 ), 1 – 6 . DOI: 10.1016/j.smhs.2020.03.004 . OpenUrl CrossRef (53). ↵ Engin , B. ; Willis , S. A. ; Malaikah , S. ; Sargeant , J. A. ; Yates , T. ; Gray , L. J. ; Aithal , G. P. ; Stensel , D. J. ; King , J. A . The effect of exercise training on adipose tissue insulin sensitivity: A systematic review and meta-analysis . Obesity Reviews 2022 , 23 ( 7 ), e13445 . DOI: 10.1111/obr.13445 . OpenUrl CrossRef (54). ↵ Tontonoz , P. ; Spiegelman , B. M . Fat and beyond: the diverse biology of PPARgamma . Annu Rev Biochem 2008 , 77 , 289 – 312 . DOI: 10.1146/annurev.biochem.77.061307.091829 From NLM. OpenUrl CrossRef PubMed Web of Science (55). ↵ Christensen , R. H. ; Wedell-Neergaard , A.-S. ; Lehrskov , L. L. ; Legaard , G. E. ; Dorph , E. ; Larsen , M. K. ; Launbo , N. ; Fagerlind , S. R. ; Seide , S. K. ; Nymand , S. ;, et al. Effect of Aerobic and Resistance Exercise on Cardiac Adipose Tissues: Secondary Analyses From a Randomized Clinical Trial . JAMA Cardiology 2019 , 4 ( 8 ), 778 – 787 . DOI: 10.1001/jamacardio.2019.2074 (acccessed 1/25/2025). OpenUrl CrossRef PubMed (56). ↵ Launbo , N. ; Zobel , E. H. ; von Scholten , B. J. ; Faerch , K. ; Jørgensen , P. G. ; Christensen , R. H . Targeting epicardial adipose tissue with exercise, diet, bariatric surgery or pharmaceutical interventions: A systematic review and meta-analysis . Obes Rev 2021 , 22 ( 1 ), e13136 . DOI: 10.1111/obr.13136 From NLM. OpenUrl CrossRef PubMed (57). ↵ Kim , I. S. ; Silwal , P. ; Jo , E.-K . Peroxisome Proliferator-Activated Receptor-Targeted Therapies: Challenges upon Infectious Diseases . Cells 2023 , 12 ( 4 ), 650 . OpenUrl CrossRef (58). ↵ Su , M. ; Cao , J. ; Huang , J. ; Liu , S. ; Im , D. S. ; Yoo , J. W. ; Jung , J. H . The In Vitro and In Vivo Anti-Inflammatory Effects of a Phthalimide PPAR-γ Agonist . Mar Drugs 2017 , 15 ( 1 ). DOI: 10.3390/md15010007 From NLM. OpenUrl CrossRef (59). ↵ Bouhlel , M. A. ; Derudas , B. ; Rigamonti , E. ; Dièvart , R. ; Brozek , J. ; Haulon , S. ; Zawadzki , C. ; Jude , B. ; Torpier , G. ; Marx , N. ;, et al. PPARgamma activation primes human monocytes into alternative M2 macrophages with anti-inflammatory properties . Cell Metab 2007 , 6 ( 2 ), 137 – 143 . DOI: 10.1016/j.cmet.2007.06.010 From NLM. OpenUrl CrossRef PubMed Web of Science (60). ↵ Dostert , C. ; Grusdat , M. ; Letellier , E. ; Brenner , D . The TNF Family of Ligands and Receptors: Communication Modules in the Immune System and Beyond . Physiological Reviews 2019 , 99 ( 1 ), 115 – 160 . DOI: 10.1152/physrev.00045.2017 . OpenUrl CrossRef PubMed (61). ↵ Beyer , M. ; Mallmann , M. R. ; Xue , J. ; Staratschek-Jox , A. ; Vorholt , D. ; Krebs , W. ; Sommer , D. ; Sander , J. ; Mertens , C. ; Nino-Castro , A .;, et al. High-resolution transcriptome of human macrophages . PLoS One 2012 , 7 ( 9 ), e45466 . DOI: 10.1371/journal.pone.0045466 From NLM. OpenUrl CrossRef PubMed (62). ↵ Angela , M. ; Endo , Y. ; Asou , H. K. ; Yamamoto , T. ; Tumes , D. J. ; Tokuyama , H. ; Yokote , K. ; Nakayama , T . Fatty acid metabolic reprogramming via mTOR-mediated inductions of PPARγ directs early activation of T cells . Nature Communications 2016 , 7 ( 1 ), 13683 . DOI: 10.1038/ncomms13683 . OpenUrl CrossRef PubMed (63). ↵ Lochner , M. ; Berod , L. ; Sparwasser , T . Fatty acid metabolism in the regulation of T cell function . Trends Immunol 2015 , 36 ( 2 ), 81 – 91 . DOI: 10.1016/j.it.2014.12.005 From NLM. OpenUrl CrossRef PubMed (64). ↵ Villarino , A. V . Transcriptional programing of T cell metabolism by STAT family transcription factors . European Journal of Immunology 2023 , 53 ( 6 ), 2048825 . DOI: 10.1002/eji.202048825 . OpenUrl CrossRef (65). ↵ Fortelny , N. ; Farlik , M. ; Fife , V. ; Gorki , A. D. ; Lassnig , C. ; Maurer , B. ; Meissl , K. ; Dolezal , M. ; Boccuni , L .; Ravi Sundar Jose Geetha , A. ; et al. JAK-STAT signaling maintains homeostasis in T cells and macrophages . Nat Immunol 2024 , 25 ( 5 ), 847 – 859 . DOI: 10.1038/s41590-024-01804-1 From NLM. OpenUrl CrossRef PubMed (66). ↵ Hwang , J.-R. ; Byeon , Y. ; Kim , D. ; Park , S.-G . Recent insights of T cell receptor-mediated signaling pathways for T cell activation and development . Experimental & Molecular Medicine 2020 , 52 ( 5 ), 750 – 761 . DOI: 10.1038/s12276-020-0435-8 . OpenUrl CrossRef PubMed (67). ↵ De Barra , C. ; O’Shea , D. ; Hogan , A. E . NK cells vs. obesity: A tale of dysfunction & redemption . Clin Immunol 2023 , 255 , 109744 . DOI: 10.1016/j.clim.2023.109744 From NLM. OpenUrl CrossRef PubMed (68). ↵ Wang , L. ; Sun , P. ; Wu , Y. ; Wang , L . Metabolic tissue-resident CD8(+) T cells: A key player in obesity-related diseases . Obes Rev 2021 , 22 ( 3 ), e13133 . DOI: 10.1111/obr.13133 From NLM. OpenUrl CrossRef (69). ↵ Park , H. J. ; Kim , D. H. ; Choi , J. Y. ; Kim , W. J. ; Kim , J. Y. ; Senejani , A. G. ; Hwang , S. S. ; Kim , L. K. ; Tobiasova , Z. ; Lee , G. R. ;, et al. PPARγ negatively regulates T cell activation to prevent follicular helper T cells and germinal center formation . PLoS One 2014 , 9 ( 6 ), e99127 . DOI: 10.1371/journal.pone.0099127 From NLM. OpenUrl CrossRef PubMed (70). ↵ Zou , J. ; Lai , B. ; Zheng , M. ; Chen , Q. ; Jiang , S. ; Song , A. ; Huang , Z. ; Shi , P. ; Tu , X. ; Wang , D. ;, et al. CD4+ T cells memorize obesity and promote weight regain . Cellular & Molecular Immunology 2018 , 15 ( 6 ), 630 – 639 . DOI: 10.1038/cmi.2017.36 . OpenUrl CrossRef PubMed (71). ↵ Wanjalla , C. N. ; McDonnell , W. J. ; Ram , R. ; Chopra , A. ; Gangula , R. ; Leary , S. ; Mashayekhi , M. ; Simmons , J. D. ; Warren , C. M. ; Bailin , S .;, et al. Single-cell analysis shows that adipose tissue of persons with both HIV and diabetes is enriched for clonal, cytotoxic, and CMV-specific CD4+ T cells . Cell Reports Medicine 2021 , 2 ( 2 ), 100205 . DOI: 10.1016/j.xcrm.2021.100205 . OpenUrl CrossRef PubMed (72). ↵ Torres , S. V. ; Man , K. ; Elmzzahi , T. ; Malko , D. ; Chisanga , D. ; Liao , Y. ; Prout , M. ; Abbott , C. A. ; Tang , A. ; Wu , J. ;, et al. Two regulatory T cell populations in the visceral adipose tissue shape systemic metabolism . Nat Immunol 2024 , 25 ( 3 ), 496 – 511 . DOI: 10.1038/s41590-024-01753-9 From NLM. OpenUrl CrossRef PubMed (73). ↵ Soedono , S. ; Sharlene , S. ; Vo , D. H. N. ; Averia , M. ; Rosalie , E. E. ; Lee , Y. K. ; Cho , K. W . Obese visceral adipose dendritic cells downregulate regulatory T cell development through IL-33 . Frontiers in Immunology 2024 , 15 , Original Research. DOI: 10.3389/fimmu.2024.1335651 . OpenUrl CrossRef (74). ↵ Hartman , J. ; Frishman , W. H . Inflammation and atherosclerosis: a review of the role of interleukin-6 in the development of atherosclerosis and the potential for targeted drug therapy . Cardiology in review 2014 , 22 ( 3 ), 147 – 151 . OpenUrl CrossRef PubMed (75). ↵ Mazurek , T. ; Zhang , L. ; Zalewski , A. ; Mannion , J. D. ; Diehl , J. T. ; Arafat , H. ; Sarov-Blat , L. ; O’Brien , S. ; Keiper , E. A. ; Johnson , A. G . Human epicardial adipose tissue is a source of inflammatory mediators . Circulation 2003 , 108 ( 20 ), 2460 – 2466 . OpenUrl Abstract / FREE Full Text (76). ↵ Kawanishi , N. ; Mizokami , T. ; Yano , H. ; Suzuki , K . Exercise attenuates M1 macrophages and CD8+ T cells in the adipose tissue of obese mice . Med Sci Sports Exerc 2013 , 45 ( 9 ), 1684 – 1693 . OpenUrl CrossRef PubMed Web of Science View the discussion thread. 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Share Aerobic exercise decreases the number and transcript expression of inflammatory M1 macrophages and CD8+ T cells in the epicardial adipose tissue of female pigs Irshad Ahmad , Shreyan Gupta , Micah Thomas , James J. Cai , Cristine L. Heaps , Annie E. Newell-Fugate bioRxiv 2025.02.02.635562; doi: https://doi.org/10.1101/2025.02.02.635562 Share This Article: Copy Citation Tools Aerobic exercise decreases the number and transcript expression of inflammatory M1 macrophages and CD8+ T cells in the epicardial adipose tissue of female pigs Irshad Ahmad , Shreyan Gupta , Micah Thomas , James J. Cai , Cristine L. Heaps , Annie E. 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