IMR Press / RCM / Volume 23 / Issue 3 / DOI: 10.31083/j.rcm2303109
Cite this article
Nanoparticles: Properties, applications and toxicities
168
Downloads
2
Citations
595
Views
Submit to RCM
Review for RCM
Apply for Special Issue
Chapters
Figures
References
Abstract
Keywords
1. Introduction
2. Methods
3. Results
4. Discussion
5. Conclusions
Abbreviations
Author Contributions
Ethics Approval and Consent to Participate
Acknowledgment
Funding
Conflict of Interest
Supplementary Material
References
Open Access Original Research
Macrophage-Related Genes Biomarkers in Left Ventricular Remodeling Induced by Heart Failure
Yue Zheng1,2,3,4,5,†, Yuheng Lang3,4,5,†, Zhenchang Qi3,4,5,6, Bingcai Qi3,4,5,6, Wenqing Gao2,4,5, Xiaomin Hu2,3,4,5,6, Tong Li1,2,3,4,5,6,*
Show Less
1 School of Medicine, Nankai University, 300071 Tianjin, China
2 Department of Heart Center, Nankai University Affiliated Third Center Hospital, 300170 Tianjin, China
3 Department of Heart Center, The Third Central Clinical College of Tianjin Medical University, 300170 Tianjin, China
4 Department of Heart Center, The Third Central Hospital of Tianjin, 300170 Tianjin, China
5 Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, 300170 Tianjin, China
6 Institute of Hepatobiliary Disease, 300170 Tianjin, China
*Correspondence: litong3zx@sina.com (Tong Li)
†These authors contributed equally.
Academic Editor: Wayne L. Miller
Rev. Cardiovasc. Med. 2022, 23(3), 109; https://doi.org/10.31083/j.rcm2303109
Submitted: 25 October 2021 | Revised: 19 December 2021 | Accepted: 29 December 2021 | Published: 18 March 2022
Copyright: © 2022 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.
Download PDF
Brower Figures
Cite
Abstract

Background: Elevated left ventricular mass index contributes to morbidity and mortality induced by heart failure and M2 macrophages play a critical role in left ventricular remodeling. Here, our aim was to investigate the roles of M2 macrophage-related genes in heart failure. Methods: GSE10161 was downloaded and the abundance of immune cells were estimated utilizing the CIBERSORT algorithm. Using the limma test and correlation analysis, differentially expressed plasm B cells and M2 macrophages-related genes (DEBRGs and DEMRGs) were documented. Functional pathways and the protein-protein interaction network were analyzed and the hub DEMRGs were obtained. The hub DEMRGs and their interactions were analyzed using NetworkAnalyst 3.0 and for validation, the hub DEMRGs expressions were analyzed using the GSE135055, GSE116250 and GSE74144 datasets. Results: 103 differentially expressed genes were correlated with the abundance of M2 Macrophages and were identified as DEMRGs (PCC >0.4), which were mainly enriched in extracellular matrix organization, cell adhesion molecule binding and postsynaptic membrane. After screening out, 5 hub DEMRGs were obtained, including FN1 (degree = 21), COL3A1 (degree = 13), COL1A2 (degree = 13), FBN1 (degree = 12), and MMP2 (degree = 11). However, no hub DEBRGs were obtained in the network. The expression patterns of the screened DEMRGs were further validated in the patients with heart failure, dilated cardiomyopathy, ischemic cardiomyopathy or hypertension. Conclusions: The results can improve our understanding of the macrophages-associated molecular mechanisms in heart failure induced by dilated cardiomyopathy, ischemic cardiomyopathy or hypertension and 5 hub DEMRGs may help prevent the adverse left ventricular remodeling to decrease mortality and morbidity.

Keywords
macrophages
plasm B cells
PPI
differentially expressed genes
left ventricular remodeling
CIBERSORT
GO/KEGG pathways analysis
1. Introduction

Currently, coronary artery disease (CAD) is still one of the leading causes of death in patients [1]. In addition, acute myocardial infarction (MI) mortality has increased 5.6-fold in the past 30 years [2]. Young patients with type 2 diabetes and MI have higher long-term cardiovascular and all-cause mortality and more than one-third of patients die within 10 years, which may emphasize more aggressive secondary prevention for those patients [3]. Acute heart failure (HF) contributes to the mortality above mentioned, which is characterized by an acute or subacute deterioration in cardiac function due to the underlying heart diseases and precipitating factors.

Isabelle et al. [4] reported that 23,291 patients with HF from 40 countries in 8 different world regions were investigated and there were 4460 (19%) deaths, 3885 (17%) HF hospitalizations, and 6949 (30%) instances of either adverse event, suggesting that HF has become the leading problem in CAD. Previous researches have confirmed the importance of neurohormonal systems, such as the renin-angiotensin-aldosterone axis in the pathogenesis of heart failure phenotypes [5, 6]. However, a large number of drug trials have failed, highlighting the limitations of animal models in replicating complex diseases and investigating critical regulators in HF [7]. The underlying critical genes and interactions that control the transformation of heart failure remain largely unknown.

Elevated left ventricular mass (LVM) contributes to morbidity and mortality induced by heart failure, which is partially regulated by hemodynamic indicators [8]. However, only a small portion of LVM variation is investigated by hemodynamic effects, and it has been suggested that genetic influences may also be important.

In this study, GSE10161 was downloaded and immune cells analysis was carried out to screen out the hub genes associated with immune cells. Then the expression levels of the hub genes in heart failure, dilated cardiomyopathy, ischemic cardiomyopathy and hypertension patients were demonstrated to validate the correlation to left ventricular remodeling, which will help to diagnose the adverse left ventricular remodeling at an early stage and reduce the patients’ mortality.

2. Methods
2.1 Microarray Data

Using the keywords “left ventricular” in “Homo sapiens”, GSE10161 from the Gene Expression Omnibus (GEO) database was downloaded and analyzed [9]. There were 7 left ventricular biopsies from control patients and 20 cardiac biopsies from aortic stenosis (AS) patients based on the Affymetrix Human Genome U133A Array. Firstly, the raw transcriptomic data were processed with log2 transformation for normalization utilizing a robust multichip average algorithm [10].

2.2 Evaluation of Immune Cells in Left Ventricle

Based on their gene expression profiles CIBERSORT is a deconvolution approach to characterize the cell compositions in bulk tissues [11]. To obtain the abundance of immune cells in the left ventricle, the CIBERSORT algorithm with 100 permutations was applied in the GSE10161 dataset, utilizing the LM22 matrix as reference. CIBERSORT outputs a deconvolution p-value for each sample to determine the reliability of the results. In this study, we retained the samples with p < 0.05 to analyze the fractions of immune cells.

2.3 Identification of DEMRGs and DEBRGs

The differentially expressed genes (DEGs) between left ventricle samples from control patients and aortic stenosis patients were investigated utilizing the “limma” package. The genes with |fold change|>1.5 or p < 0.05 were considered as differentially expressed genes (DEGs). Then, Pearson correlation analysis was applied to obtain the genes associated with the abundance of M2 Macrophages and B cells. The DEGs with Pearson correlation coefficients (PCC) >0.4 were regarded as DEMRGs and DEBRGs, respectively.

2.4 Enrichment Analysis

To understand the functions of DEMRGs and DEBRGs, GO and KEGG pathway analysis, as well as Gene Set Enrichment Analysis (GSEA) analysis, were applied utilizing the “clusterProfiler” package in R [12]. A p < 0.05 was set as the significance threshold for enrichment analyses.

2.5 Protein-Protein Interaction (PPI) Network Analysis

STRING (https://string-db.org) is a biological resource that provides systematic screens of human protein interactions [13]. To investigate the hub genes, DEMRGs and DEBRGs were uploaded to STRING to investigate the protein network interaction diagram and significant PPIs were identified with a combined score >0.4. Then the result was imported into Cytoscape v.3.7.1 (https://cytoscape.org/) and the hub genes were investigated by applying the Cytoscape plug-in (MCODE and Cytohubba). Nodes with a connectivity degree ≥10 and ranked by MCC in the network were selected as hub DEMRGs or DEBRGs.

2.6 The Hub DEMRGs and Their Interactions

NetworkAnalyst 3.0 (https://www.networkanalyst.ca/) is a comprehensive network visual analyzed platform for gene expression analysis [14]. The hub DEMRGs and their interactions were analyzed using NetworkAnalyst 3.0. Specifically, transcription factors (TFs)-miRNA coregulatory interactions with 5 screened hub DEMRGs were shown using the RegNetwork repository where the literature curated regulatory interaction information was collected (Applicable for human and mouse data only). Left ventricle tissue-specific PPI were shown using the DifferentialNet database (Filter is 15), which shows the differential protein-protein interactions across human tissues. The hub DEMRGs-chemical interactions were shown using the Comparative Toxicogenomics Database (CTD). Left ventricle tissue-specific co-expression with 5 screened hub DEMRGs were shown using the TCSBN database. The hub DEMRGs-drugs interactions were shown using the DrugBank database (Version 5.0) (https://go.drugbank.com/releases/5-0-11). MMP2 and its edges were validated in THP-1 cells from the Immuno-Navigator database.

2.7 Validation of Hub Genes in Left Ventricular Remodeling Induced by Heart Failure

To view the correlation between the hub DEMRGs and left ventricular remodeling, we performed unsupervised hierarchical clustering in the GSE135055 dataset utilizing the “Pheatmap” package [15]. Then, the expression patterns of the screened DEMRGs in left ventricular remodeling process induced by heart failure were validated in three independent datasets, including GSE135055 (n = 30) [15], GSE116250 (n = 64) [16, 17] and GSE74144 (n = 28). The detailed information for the datasets was shown in Supplementary Table 1.

Receiver operation characteristic (ROC) curve and joint ROC curve analysis were conducted to investigate the diagnostic performance of the DEMRGs and the area under the curve (AUC) was determined using the “pROC” package.

3. Results
3.1 DEMRGs and DEBRGs in Afterload Overload

The CIBERSORT algorithm was conducted to obtain the immune cell compositions in the GSE10161 dataset and Fig. 1A summarized the results investigated from the 7 left ventricular biopsies from control patients and 20 cardiac biopsies from AS patients. Compared with control biopsies, the biopsies from AS patients exhibited a higher infiltration of M2 Macrophages and a lower infiltration of plasm B cells (Fig. 1B; Supplementary Fig. 1). Utilizing the “limma” package, a total of 860 DEGs were obtained and of these DEGs, 103 were correlated (PCC >0.4) with the abundance of M2 Macrophages and were identified as DEMRGs (Fig. 1C), while 43 were correlated with the abundance of plasm B cells and were considered as DEBRGs (Supplementary Fig. 2). Compare to control biopsies, 69 DEMRGs were demonstrated to be high-expressed and 34 DEMRGs were demonstrated to be low-expressed in AS biopsies. Compare to control biopsies, 31 DEBRGs were demonstrated to be high-expressed and only 12 DEBRGs were demonstrated to be low-expressed in AS biopsies.

Fig. 1.

Identification of DEMRGs in left ventricular biopsies from control patients and aortic stenosis patients. (A) Abundance of 22 immune cells across 27 samples estimated by the CIBERSORT algorithm. (B) Fraction of plasm B cells and M2 Macrophages between left ventricular biopsies from control patients and aortic stenosis patients. (C) Volcano plot for DEMRGs between left ventricular biopsies from control patients and aortic stenosis patients. DEMRGs, differentially expressed M2 Macrophages-related genes.

3.2 Functional Enrichment for DEGs, DEMRGs and DEBRGs

GO and KEGG pathway analysis, as well as GSEA analysis, were performed to analyze the functions of DEGs, DEMRGs and DEBRGs. These DEGs were mainly involved in extracellular matrix organization, extracellular structure organization, collagen-containing extracellular matrix, calcium-dependent protein binding and ECM-receptor interaction. Moreover, GSEA analysis showed that NABA_CORE_MATRISOME and NABA_ECM_GLYCOPROTEINS were top2 gene sets associated with DEGs (Fig. 2A,B; Table 1).

Fig. 2.

Enrichment pathway analysis and protein-protein interaction network for the DEMRGs. (A) The significant GO and KEGG pathways enriched by DEGs. (B) The top2 gene sets associated with DEGs using GSEA analysis. (C) The significant GO and KEGG pathways enriched by up-regulated DEMRGs compared left ventricular biopsies from aortic stenosis patients to control patients. (D) The significant GO-Cellular Component enriched by down-regulated DEMRGs. (E) Protein-protein interaction network for the DEMRGs using Cytohubba. The nodes were ranked by MCC, which were labeled successively as red, yellow and blue. (F) 3 modules were screened out using MCODE in Cytoscape.

Table 1.The significant GO and KEGG pathways enriched by DEGs.
Gene ONTOLOGY ID Description GeneRatio BgRatio p value p.adjust q value
BP GO:0030198 extracellular matrix organization 31/555 368/18670 2.07e-07 9.52e-04 8.77e-04
BP GO:0043062 extracellular structure organization 32/555 422/18670 1.36e-06 0.003 0.003
BP GO:0001505 regulation of neurotransmitter levels 26/555 354/18670 2.31e-05 0.017 0.015
BP GO:0007596 blood coagulation 25/555 336/18670 2.66e-05 0.017 0.015
BP GO:0050849 negative regulation of calcium-mediated signaling 7/555 31/18670 2.79e-05 0.017 0.015
CC GO:0062023 collagen-containing extracellular matrix 48/568 406/19717 8.82e-17 4.82e-14 4.19e-14
CC GO:0005604 basement membrane 13/568 95/19717 3.30e-06 9.02e-04 7.85e-04
CC GO:0031091 platelet alpha granule 12/568 91/19717 1.16e-05 0.002 0.002
CC GO:0005925 focal adhesion 27/568 405/19717 5.13e-05 0.006 0.005
CC GO:0005924 cell-substrate adherens junction 27/568 408/19717 5.82e-05 0.006 0.005
MF GO:0005201 extracellular matrix structural constituent 22/552 163/17697 7.63e-09 5.99e-06 5.43e-06
MF GO:0048306 calcium-dependent protein binding 11/552 61/17697 2.50e-06 9.82e-04 8.90e-04
MF GO:0008201 heparin binding 17/552 169/17697 2.23e-05 0.006 0.005
MF GO:0005539 glycosaminoglycan binding 20/552 229/17697 3.48e-05 0.007 0.006
MF GO:0005178 integrin binding 14/552 132/17697 6.55e-05 0.010 0.009
KEGG hsa04512 ECM-receptor interaction 11/283 88/8076 2.34e-04 0.064 0.059
KEGG hsa04510 Focal adhesion 17/283 201/8076 6.68e-04 0.091 0.084
DEGs, Different Expressed Genes; GO, Gene ONTOLOGY; BP, Biological Process; CC, cellular component; MF, Molecular Function; KEGG, Kyoto Encyclopedia of Genes and Genomes.

The up-regulated DEMRGs were mainly involved in extracellular matrix organization, collagen-containing extracellular matrix, cell adhesion molecule binding and AGE-RAGE signaling pathway in diabetic complications, while the down-regulated DEMRGs were mainly involved in GO-cellular components, such as postsynaptic membrane and postsynaptic membrane (Fig. 2C,D; Tables 2,3). The up-regulated DEBRGs were mainly involved in regulation of cell growth, cell growth, external side of plasma membrane and Glycine, serine and threonine metabolism, while the down-regulated DEBRGs were mainly involved in Retrograde endocannabinoid signaling and Chemokine signaling pathway (Supplementary Fig. 3; Supplementary Tables 2,3).

Table 2.The significant GO and KEGG pathways enriched by up-regulated DEMRGs.
Gene ONTOLOGY ID Description GeneRatio BgRatio p value p.adjust q value
BP GO:0030198 extracellular matrix organization 10/57 368/18670 1.48e-07 2.36e-04 2.01e-04
BP GO:0043062 extracellular structure organization 10/57 422/18670 5.23e-07 4.16e-04 3.54e-04
BP GO:0071230 cellular response to amino acid stimulus 4/57 68/18670 5.50e-05 0.029 0.025
BP GO:0001101 response to acid chemical 7/57 343/18670 7.98e-05 0.032 0.027
BP GO:0046651 lymphocyte proliferation 6/57 272/18670 1.76e-04 0.046 0.039
CC GO:0062023 collagen-containing extracellular matrix 14/58 406/19717 8.78e-12 1.65e-09 1.28e-09
CC GO:0005788 endoplasmic reticulum lumen 10/58 309/19717 2.07e-08 1.95e-06 1.51e-06
CC GO:0044420 extracellular matrix component 5/58 51/19717 3.91e-07 2.45e-05 1.89e-05
CC GO:0005604 basement membrane 5/58 95/19717 8.74e-06 4.11e-04 3.18e-04
CC GO:0098644 complex of collagen trimers 3/58 19/19717 2.26e-05 5.45e-04 4.21e-04
MF GO:0050839 cell adhesion molecule binding 12/56 499/17697 4.00e-08 4.53e-06 3.56e-06
MF GO:0005201 extracellular matrix structural constituent 8/56 163/17697 4.25e-08 4.53e-06 3.56e-06
MF GO:0019838 growth factor binding 6/56 137/17697 4.56e-06 2.59e-04 2.04e-04
MF GO:0048407 platelet-derived growth factor binding 3/56 11/17697 4.86e-06 2.59e-04 2.04e-04
MF GO:0098641 cadherin binding involved in cell-cell adhesion 3/56 19/17697 2.81e-05 0.001 9.39e-04
KEGG hsa04933 AGE-RAGE signaling pathway in diabetic complications 5/24 100/8076 9.28e-06 6.19e-04 5.22e-04
KEGG hsa05146 Amoebiasis 5/24 102/8076 1.02e-05 6.19e-04 5.22e-04
KEGG hsa05205 Proteoglycans in cancer 6/24 205/8076 2.28e-05 9.21e-04 7.77e-04
KEGG hsa04926 Relaxin signaling pathway 5/24 129/8076 3.20e-05 9.69e-04 8.18e-04
KEGG hsa04510 Focal adhesion 5/24 201/8076 2.63e-04 0.006 0.005
DEGs, Different Expressed Macrophage(M2)-Related Genes; GO, Gene ONTOLOGY; BP, Biological Process; CC, cellular component; MF, Molecular Function; KEGG, Kyoto Encyclopedia of Genes and Genomes.
Table 3.The significant GO and KEGG pathways enriched by down-regulated DEMRGs.
Gene ONTOLOGY ID Description GeneRatio BgRatio p value p.adjust q value
CC GO:0045211 postsynaptic membrane 4/27 323/19717 9.21e-04 0.066 0.055
CC GO:0009898 cytoplasmic side of plasma membrane 3/27 154/19717 0.001 0.066 0.055
CC GO:0098562 cytoplasmic side of membrane 3/27 178/19717 0.002 0.067 0.056
CC GO:0097060 synaptic membrane 4/27 432/19717 0.003 0.074 0.062
DEGs, Different Expressed Macrophage(M2)-Related Genes; GO, Gene ONTOLOGY; CC, cellular component.
3.3 PPI Network Analysis

The PPI network of DEMRGs and DEBRGs constructed utilizing the STRING database was a scale-free network and the genes with connective degrees ≥10 and ranked by MCC were regarded as hub DEMRGs or DEBRGs in the network. After screening out, 5 hub DEMRGs were obtained, including FN1 (degree = 21), COL3A1 (degree = 13), COL1A2 (degree = 13), FBN1 (degree = 12), and MMP2 (degree = 11) (Fig. 2E,F; Supplementary Fig. 4). All the hub genes were up-regulated in the AS biopsies. However, no hub DEBRGs were obtained in the network (Supplementary Fig. 5).

3.4 The Hub DEMRGs and Their Interactions

The hub DEMRGs and their interactions were demonstrated (Fig. 3). TFs-miRNA- hub DEMRGs coregulatory network, left ventricle tissue-specific co-expression and left ventricle tissue-specific PPI were constructed. Besides, the hub DEMRGs-chemical interactions were shown and 7 chemicals, including 4-(5-benzo(1,3)dioxol-5-yl-4-pyridin-2-yl-1H-imidazol-2-yl)benzamide, (6-(4-(2-piperidin-1-ylethoxy)phenyl))-3-pyridin-4-ylpyrazolo(1,5-a)pyrimidine, Copper, (+)-JQ1 compound, Paclitaxel, Phenylmercuric Acetate, Tretinoin and Valproic Acid, can all be interacted with 5 screened hub DEMRGs. The hub DEMRGs-drugs interactions were also shown utilizing the DrugBank database and 6 drugs seemed to have antagonistic effects on adverse left ventricular remodeling. Besides, MMP2 and its edges were validated in THP-1 cells, which can provide further evidence that MMP2 was high-expressed in M2 Macrophages for left ventricular remodeling.

Fig. 3.

The hub DEMRGs and their interactions. (A) TF-miRNA coregulatory interactions with 5 screened hub DEMRGs. (B) Left ventricle tissue-specific PPI, including 5 screened hub DEMRGs and their edges. (C) The hub DEMRGs-chemical interactions. (D) Left ventricle tissue-specific co-expression with 5 screened hub DEMRGs. (E) The hub DEMRGs-drugs interactions. (F) MMP2 and its edges validated in THP-1 cells.

3.5 Validation of Hub Genes in Left Ventricular Remodeling Induced by Heart Failure

Unsupervised hierarchical clustering was shown based on the expressions of hub DEMRGs in the GSE135055 dataset (Fig. 4A). Next, the DEMRGs expression patterns were validated in three independent datasets (GSE135055 (n = 30), GSE116250 (n = 64) and GSE74144 (n = 28)). In the GSE135055 dataset, MMP2, FN1, COL1A2 and COL3A1 were highly expressed and the AUC of 4 DEMRGs were 0.905 (Fig. 4B–D). Interestingly, although the AUC of FBN1 expression was 0.556, the AUC of all 5 DEMRGs was 1 (Supplementary Fig. 6A). In the GSE116250 dataset, MMP2, COL1A2 and COL3A1 were highly expressed in patients with dilated cardiomyopathy and MMP2, FN1, COL1A2 and COL3A1 were highly expressed in patients with ischemic cardiomyopathy. The ROC curves and joint ROC curves demonstrated the 5 hub DEMRGs can be diagnostic biomarkers for heart failure patients with dilated and ischemic cardiomyopathy (Fig. 4E–I; Supplementary Fig. 6B,C). In the GSE74144 dataset, FN1, FBN1, COL1A2 and COL3A1 were highly expressed in patients with hypertension and left ventricular remodeling and the AUC of the above 4 hub DEMRGs was 0.964 (Fig. 4J–L; Supplementary Fig. 6D). Taken together, MMP2, FN1, FBN1, COL1A2 and COL3A1 can be the prognostic biomarkers for the patients with left ventricular remodeling induced by heart failure and the kit of the 5 hub DEMRGs may test all the conditions of left ventricular remodeling events, which can help prevent the adverse left ventricular remodeling to decrease the mortality and morbidity.

Fig. 4.

Expression patterns of hub DEMRGs in left ventricular remodeling induced by heart failure. (A) The heatmap for unsupervised hierarchical clustering of the five hub DEMRGs in GSE135055 dataset. (B) Expression levels of the hub DEMRGs in GSE135055 dataset. (C–D) ROC curve of 5 hub DEMRGs (C) and joint ROC curve of MMP2, FN1, COL1A2 and COL3A1 (D) in GSE135055 dataset. (E) Expression levels of the hub DEMRGs in GSE116250 dataset. (F–G) ROC curve of 5 hub DEMRGs (F) and joint ROC curve of MMP2, COL1A2 and COL3A1 (G) in DCM. (H–I) ROC curve of 5 hub DEMRGs (H) and joint ROC curve of MMP2, FN1, COL1A2 and COL3A1 (I) in ICM. (J) Expression levels of the hub DEMRGs in GSE74144 dataset. (K–L) ROC curve of 5 hub DEMRGs (K) and joint ROC curve of FN1, FBN1, COL1A2 and COL3A1 (L) in GSE74144 dataset. *p < 0.05; **p < 0.01; ***p < 0.001; ns, not significant.

4. Discussion

Heart failure contributes to increased morbidity and mortality and affects >24 million patients worldwide [18]. Near half of the patients with heart failure die of cardiac sudden death due to adverse left ventricular remodeling and ventricular arrhythmia. So, prevention of adverse left ventricular remodeling and cardiac dysfunction can improve the prognosis of patients with heart failure [19, 20]. Previous studies have reported that MI promotes the release of progenitor cells and hematopoietic stem cells from the bone marrow niche. These progenitor cells subsequently colonize the spleen, thereby increasing the number of monocytes in the blood and macrophage in the myocardium and arteries, which further promotes atherosclerosis and thus myocardial infarction progression [21, 22].

In the event of acute inflammation, a large number of immune cells, especially monocytes, are recruited to the site of inflammation to repair the tissue to eventually restore the tissue to homeostasis [23, 24]. The monocytes differentiated into M1-type macrophages and combined with resident M1 macrophages to remove foreign bodies or necrotic tissues. In the fiber repair stage of the disease course, M1-type macrophages have been gradually replaced by M2-type macrophages, which play a role in promoting tissue repair [25, 26]. Besides, a network of macrophages can actively take up material, including mitochondria, derived from cardiomyocytes and support mitochondrial homeostasis in the heart [27]. Macrophages can also facilitate electrical conduction in the physiological and pathological heart through gap junction [28, 29, 30]. However, the specific mechanism of the differentiation of M1-type macrophages to M2-type macrophages and the role of M2-type macrophages has not been fully clarified and needs further studies. In this study, GSE10161 was downloaded and immune cells analysis was carried out to screen out the hub genes associated with immune cells. DEMRGs and DEBRGs were screened out and after further GO/KEGG enrichment analysis and PPI analysis, 5 hub DEMRGs was investigated, which may be the critical hub genes to promote left ventricular remodeling induced by heart failure. Then the expression levels of the hub genes in heart failure, dilated cardiomyopathy, ischemic cardiomyopathy and hypertension patients were demonstrated to validate the correlation to left ventricular remodeling, which will help to diagnose the adverse left ventricular remodeling at an early stage and reduce the patients’ mortality.

5 hub DEMRGs, including MMP2, FN1, FBN1, COL3A1 and COL1A2, were investigated to be associated with M2 Macrophages phenotype. Matrix metalloproteinase 2 (MMP2), as a fibrosis-related gene, affected extracellular matrix remodeling after an ischemic myocardial injury and can be the detection of gelatinase expression after MI [31, 32]. Treatment with verapamil can decline calpain-1 and MMP-2 activities and ameliorate cardiac hypertrophy [33]. MMP-2/JNK apoptotic pathway was activated in the border zone after MI in adult sheep [34]. Transverse aortic constriction in mice model induced cardiac hypertrophy, collagen deposition, and the expression of transforming growth factor (TGF)-β and MMP-2 in the left ventricle [35]. Besides, MMP2 was highly expressed in the left and right ventricles of the patients with hypoplastic left heart through single-cell RNA sequencing [36]. Fibronectin 1 (FN1) is a left ventricular extracellular matrix remodeling-related gene, and FN1, COL1A1 (Collagen Type I Alpha 1 Chain) and COL3A1 (Collagen Type III Alpha 1 Chain) can be inhibited by transient angiotensin-converting enzyme (ACE) utilizing single-cell RNA sequencing to determine the hypothesis that transient ACE inhibitor treatment can induce a persistent shift in cardiac fibroblast subpopulations [37]. Interestingly, FN1 and COL3A1 expressions differed from sex but were not regulated by angiotensin II receptor blocker [38]. Besides, Hydrogen-containing saline treatment can decrease the phosphorylation of p38 MAPK and Smad2/3, and Col I and FN1 mRNA levels, which can alleviate pressure overload-induced cardiac dysfunction [39]. Fibrillin-1 (FBN1) is the critical gene in the pathogenesis of Marfan syndrome (MFS) and the [Fbn1C1039G/+] mouse model replicates a large number of the anomalies of MFS patients, for instance, central aortic stiffness, systolic and diastolic dysfunction [40, 41, 42]. Besides, Fbn1, Itga8, Itga11, Itgb5 and Thbs2 were the hub genes applying weighted gene co-expression network analysis (WGCNA) and PPI analysis in the GSE77798 dataset, a dataset of patients with hypoplastic left heart syndrome (HLHS) [43]. COL1A2 (Collagen Type I Alpha 2 Chain) and COL3A1 are the expressions of fibrosis indicators and the expressions increased in overexpressing a constitutively active form of the calcium-dependent phosphatase calcineurin A mice model while connexin43 expression decreased at the same time, which led to abnormal conduction and arrhythmias, similar to the results in cardiac remodeling in the patients with heart failure [44, 45]. The crosstalk between macrophage and the fibroblast-like cell was investigated in both left and right atriums through the ligand-receptor interactions of COL1A1/COL1A2-CD36 [46]. Besides, COL1A1 and COL1A2 were the diagnostic biomarkers of Osteogenesis imperfecta, which may also be involved in the crosstalk between bone marrow niche and left ventricle after MI [47]. The expression of COL3A1, an extracellular matrix protein (ECM) gene, increased during the postpartum period [48]. COL3A1 as well as COL1A1, MMP9, elastin and TIMP1 expressions also increased after treatment with TGF-β1 to fetal cardiac fibroblasts [49]. Besides, COL3A1 expression can be elevated through the angiotensin II-ROS-HuR-TGFβ pathway in hypertensive heart disease and be inhibited by miR-29a and miR-101a after MI [50, 51]. In this study, MMP2, FN1, COL1A2 and COL3A1 were highly expressed in patients with heart failure in the GSE135055 dataset. MMP2, COL1A2 and COL3A1 were highly expressed in patients with dilated cardiomyopathy and MMP2, FN1, COL1A2 and COL3A1 were highly expressed in patients with ischemic cardiomyopathy in the GSE116250 dataset. Besides, FN1, FBN1, COL1A2 and COL3A1 were highly expressed in patients with hypertension and left ventricular remodeling in the GSE74144 dataset. Taken together, MMP2, FN1, FBN1, COL1A2 and COL3A1 can be the prognostic biomarkers for the patients with left ventricular remodeling induced by heart failure and the kit of the 5 hub DEMRGs may test left ventricular remodeling events and help prevent the adverse left ventricular remodeling to decrease the mortality and morbidity.

There are some limitations which should be mentioned. Firstly, only hub DEMRGs were validated in patients with heart failure, dilated cardiomyopathy, ischemic cardiomyopathy or hypertension and left ventricular remodeling. There may be some false negatives because of the enrichment methods and validation methods. More researches are still needed to proceed with other DEGs, such as DEBRGs and neighbor DEMRGs. Secondly, the sample sizes of included datasets were not too large, however, after validation, the results are highly reliable. Lastly, further researches are still needed to confirm the functional effects of the screened hub genes in human being to improve the prognosis and decrease the adverse left ventricular remodeling mortality.

5. Conclusions

Based on our current study, our research provided a bioinformatics analysis for the patients with AS. The screened hub DEMRGs, MMP2, FN1, FBN1, COL1A2 and COL3A1, may be therapeutic targets for treatment in patients with heart failure and prevention the heart failure induced dilated cardiomyopathy, ischemic cardiomyopathy or hypertension. MMP2, FN1, FBN1, COL1A2 and COL3A1 expressions increased due to the adverse left ventricular remodeling and the kit of the 5 hub DEMRGs may test left ventricular remodeling events and help prevent the adverse left ventricular remodeling to decrease the mortality and morbidity.

Abbreviations

CAD, Coronary artery disease; MI, Myocardial infarction; HF, Heart failure; LVM, Left ventricular mass index; GEO, Gene Expression Omnibus; AS, aortic stenosis; PCC, Pearson correlation coefficients; DEGs, Differentially expressed genes; DEMRGs, Differentially expressed M2 Macrophages-related genes; DEBRGs, ifferentially expressed B cells-related genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSEA, Gene Set Enrichment Analysis; PPI, Protein-protein internet; TF, Transcription factor; CTD, Comparative Toxicogenomics Database; ROC, Receiver operation characteristic; AUC, Area under curve; WGCNA, Weighted gene co-expression network analysis; ECM, Extracellular matrix protein; MMP2, Matrix metallopeptidase 2; TGF-β, Transforming growth factor-β; FN1, Fibronectin 1; FBN1, Fibrillin 1; COL1A1, Collagen type III alpha 1 chain; COL3A1, Collagen type III alpha 1 chain; COL1A2, Collagen type I alpha 2 chain; ACE, Angiotensin-converting enzyme; MFS, Marfan syndrome.

Author Contributions

Conceptualization, YZ; Funding acquisition, WQG, YHL and TL; Investigation, YZ, WQG, BCQ, ZCQ and XMH; Visualization, YZ and YHL; Writing – original draft, YZ; Writing – review & editing, YZ, XMH and TL.

Ethics Approval and Consent to Participate

Not applicable.

Acknowledgment

We would like to express our gratitude to all those who helped us during the writing of this manuscript. Thanks to all the peer reviewers for their opinions and suggestions.

Funding

This work was supported by the Tianjin “Project + Team” Key Training Special Project, China (no. XC202040), the Tianjin “131” Innovative Talent Team Project (no. 201939), the Tianjin Science and Technology Support Key Project (no. 18YFZCSY01080), the Tianjin Municipal Health and Health Committee Science and Technology Project (no. ZD20001), Tianjin Health Committee traditional Chinese medicine and integrated traditional Chinese and Western medicine project (no. 2021139), and the Tianjin Municipal Health and Health Committee Science and Technology Talent Cultivation Project (no. KJ20008).

Conflict of Interest

The authors declare no conflict of interest.

References
[1]
Naghavi M, Abajobir AA, Abbafati C, Abbas KM, Abd-Allah F, Abera SF, et al. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017; 390: 1151–1210.
[2]
Chang J, Liu X, Sun Y. Mortality due to acute myocardial infarction in China from 1987 to 2014: Secular trends and age-period-cohort effects. International Journal of Cardiology. 2017; 227: 229–238.
[3]
Singh A, Gupta A, DeFilippis EM, Qamar A, Biery DW, Almarzooq Z, et al. Cardiovascular Mortality after Type 1 and Type 2 Myocardial Infarction in Young Adults. Journal of the American College of Cardiology. 2020; 75: 1003–1013.
[4]
Johansson I, Joseph P, Balasubramanian K, McMurray JJV, Lund LH, Ezekowitz JA, et al. Health-Related Quality of Life and Mortality in Heart Failure: the Global Congestive Heart Failure Study of 23 000 Patients from 40 Countries. Circulation. 2021; 143: 2129–2142.
[5]
Packer M. The neurohormonal hypothesis: a theory to explain the mechanism of disease progression in heart failure. Journal of the American College of Cardiology. 1992; 20: 248–254.
[6]
Swynghedauw B. Molecular Mechanisms of Myocardial Remodeling. Physiological Reviews. 1999; 79: 215–262.
[7]
Rouleau JL, Pfeffer MA, Stewart DJ, Isaac D, Sestier F, Kerut EK, et al. Comparison of vasopeptidase inhibitor, omapatrilat, and lisinopril on exercise tolerance and morbidity in patients with heart failure: IMPRESS randomised trial. Lancet. 2000; 356: 615–620.
[8]
Lorell BH, Carabello BA. Left ventricular hypertrophy: pathogenesis, detection, and prognosis. Circulation. 2000; 102: 470–479.
[9]
Petretto E, Sarwar R, Grieve I, Lu H, Kumaran MK, Muckett PJ, et al. Integrated genomic approaches implicate osteoglycin (Ogn) in the regulation of left ventricular mass. Nature Genetics. 2008; 40: 546–552.
[10]
Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003; 4: 249–264.
[11]
Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nature Methods. 2015; 12: 453–457.
[12]
Yu G, Wang L, Han Y, He Q. ClusterProfiler: An R package for comparing biological themes among gene clusters. Omics. 2012; 16: 284–287.
[13]
Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, Goehler H, et al. A human protein-protein interaction network: a resource for annotating the proteome. Cell. 2005; 122: 957–968.
[14]
Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Research. 2019; 47: W234–W241.
[15]
Hua X, Wang Y, Jia P, Xiong Q, Hu Y, Chang Y, et al. Multi-level transcriptome sequencing identifies COL1a1 as a candidate marker in human heart failure progression. BMC Medicine. 2020; 18: 2.
[16]
Sweet ME, Cocciolo A, Slavov D, Jones KL, Sweet JR, Graw SL, et al. Transcriptome analysis of human heart failure reveals dysregulated cell adhesion in dilated cardiomyopathy and activated immune pathways in ischemic heart failure. BMC Genomics. 2018; 19: 812.
[17]
Yamaguchi T, Sumida TS, Nomura S, Satoh M, Higo T, Ito M, et al. Cardiac dopamine D1 receptor triggers ventricular arrhythmia in chronic heart failure. Nature Communications. 2020; 11: 4364.
[18]
Bui AL, Horwich TB, Fonarow GC. Epidemiology and risk profile of heart failure. Nature Reviews. Cardiology. 2011; 8: 30–41.
[19]
Engelstein ED. Prevention and management of chronic heart failure with electrical therapy. The American Journal of Cardiology. 2003; 91: 62–73.
[20]
Doval HC, Nul DR, Grancelli HO, Varini SD, Soifer S, Corrado G, et al. Nonsustained ventricular tachycardia in severe heart failure. Independent marker of increased mortality due to sudden death. GESICA-GEMA Investigators. Circulation. 1996; 94: 3198–3203.
[21]
Dutta P, Courties G, Wei Y, Leuschner F, Gorbatov R, Robbins CS, et al. Myocardial infarction accelerates atherosclerosis. Nature. 2012; 487: 325–329.
[22]
Marino A, Zhang Y, Rubinelli L, Riemma MA, Ip JE, Di Lorenzo A. Pressure overload leads to coronary plaque formation, progression, and myocardial events in ApoE–/– mice. JCI Insight. 2019; 4: e128220.
[23]
Dal-Secco D, Wang J, Zeng Z, Kolaczkowska E, Wong CHY, Petri B, et al. A dynamic spectrum of monocytes arising from the in situ reprogramming of CCR2+ monocytes at a site of sterile injury. The Journal of Experimental Medicine. 2015; 212: 447–456.
[24]
Amit I, Winter DR, Jung S. The role of the local environment and epigenetics in shaping macrophage identity and their effect on tissue homeostasis. Nature Immunology. 2016; 17: 18–25.
[25]
Varga T, Mounier R, Horvath A, Cuvellier S, Dumont F, Poliska S, et al. Highly Dynamic Transcriptional Signature of Distinct Macrophage Subsets during Sterile Inflammation, Resolution, and Tissue Repair. Journal of Immunology. 2016; 196: 4771–4782.
[26]
Das A, Yang CS, Arifuzzaman S, Kim S, Kim SY, Jung KH, et al. High-Resolution Mapping and Dynamics of the Transcriptome, Transcription Factors, and Transcription Co-Factor Networks in Classically and Alternatively Activated Macrophages. Frontiers in Immunology. 2018; 9: 22.
[27]
Nicolás-Ávila JA, Lechuga-Vieco AV, Esteban-Martínez L, Sánchez-Díaz M, Díaz-García E, Santiago DJ, et al. A Network of Macrophages Supports Mitochondrial Homeostasis in the Heart. Cell. 2020; 183: 94–109.e23.
[28]
Hulsmans M, Clauss S, Xiao L, Aguirre AD, King KR, Hanley A, et al. Macrophages Facilitate Electrical Conduction in the Heart. Cell. 2017; 169: 510–522.e20.
[29]
Fei Y, Wang Q, Hou J, Li W, Cai X, Yang Y, et al. Macrophages facilitate post myocardial infarction arrhythmias: roles of gap junction and KCa3.1. Theranostics. 2019; 9: 6396–6411.
[30]
Xue J, Yan X, Yang Y, Chen M, Wu L, Gou Z, et al. Connexin 43 dephosphorylation contributes to arrhythmias and cardiomyocyte apoptosis in ischemia/reperfusion hearts. Basic Research in Cardiology. 2019; 114: 40.
[31]
Kiugel M, Kytö V, Saanijoki T, Liljenbäck H, Metsälä O, Ståhle M, et al. Evaluation of 68Ga-labeled peptide tracer for detection of gelatinase expression after myocardial infarction in rat. Journal of Nuclear Cardiology. 2018; 25: 1114–1123.
[32]
Ru N, Cui L, Jiao B, Zhang L, Jiang S, Yu Z. Glycosylated CD147 reduces myocardial collagen cross-linking in cardiac hypertrophy. Journal of Cellular Biochemistry. 2018; 119: 8022–8034.
[33]
Mendes AS, Blascke de Mello MM, Parente JM, Omoto ACM, Neto-Neves EM, Fazan R, et al. Verapamil decreases calpain-1 and matrix metalloproteinase-2 activities and improves hypertension-induced hypertrophic cardiac remodeling in rats. Life Sciences. 2020; 244: 117153.
[34]
Li T, Wei X, Evans CF, Sanchez PG, Li S, Wu ZJ, et al. Left Ventricular Unloading after Acute Myocardial Infarction Reduces MMP/JNK Associated Apoptosis and Promotes FAK Cell-Survival Signaling. The Annals of Thoracic Surgery. 2016; 102: 1919–1924.
[35]
Silva MM, de Souza-Neto FP, Jesus ICGD, Gonçalves GK, Santuchi MDC, Sanches BDL, et al. Alamandine improves cardiac remodeling induced by transverse aortic constriction in mice. American Journal of Physiology-Heart and Circulatory Physiology. 2021; 320: H352–H363.
[36]
Ma L, Zhou N, Zou R, Shi W, Luo Y, Du N, et al. Single-Cell RNA Sequencing and Quantitative Proteomics Analysis Elucidate Marker Genes and Molecular Mechanisms in Hypoplastic Left Heart Patients With Heart Failure. Frontiers in Cell and Developmental Biology. 2021; 9: 617853.
[37]
Garvin AM, De Both MD, Talboom JS, Lindsey ML, Huentelman MJ, Hale TM. Transient ACE (Angiotensin-Converting Enzyme) Inhibition Suppresses Future Fibrogenic Capacity and Heterogeneity of Cardiac Fibroblast Subpopulations. Hypertension. 2021; 77: 904–918.
[38]
Walsh-Wilkinson É, Drolet M, Le Houillier C, Roy È, Arsenault M, Couet J. Sex differences in the response to angiotensin II receptor blockade in a rat model of eccentric cardiac hypertrophy. PeerJ. 2019; 7: e7461.
[39]
Yang J, Wu S, Zhu L, Cai J, Fu L. Hydrogen-containing saline alleviates pressure overload-induced interstitial fibrosis and cardiac dysfunction in rats. Molecular Medicine Reports. 2017; 16: 1771–1778.
[40]
Wang Y, Li X, Li R, Yang Y, Du J. Identification of Novel Causal FBN1 Mutations in Pedigrees of Marfan Syndrome. International Journal of Genomics. 2018; 2018: 1246516.
[41]
Cavanaugh NB, Qian L, Westergaard NM, Kutschke WJ, Born EJ, Turek JW. A Novel Murine Model of Marfan Syndrome Accelerates Aortopathy and Cardiomyopathy. The Annals of Thoracic Surgery. 2017; 104: 657–665.
[42]
Lee L, Cui JZ, Cua M, Esfandiarei M, Sheng X, Chui WA, et al. Aortic and Cardiac Structure and Function Using High-Resolution Echocardiography and Optical Coherence Tomography in a Mouse Model of Marfan Syndrome. PLoS ONE. 2016; 11: e0164778.
[43]
Liu X, Shang H, Li B, Zhao L, Hua Y, Wu K, et al. Exploration and validation of hub genes and pathways in the progression of hypoplastic left heart syndrome via weighted gene co-expression network analysis. BMC Cardiovascular Disorders. 2021; 21: 300.
[44]
Li Y, Liu M, Yi J, Song X, Zheng X, Liu D, et al. Exogenous hydrogen sulfide inhibits apoptosis by regulating endoplasmic reticulum stress–autophagy axis and improves myocardial reconstruction after acute myocardial infarction. Acta Biochimica Et Biophysica Sinica. 2020; 52: 1325–1336.
[45]
Fontes MSC, Raaijmakers AJA, van Doorn T, Kok B, Nieuwenhuis S, van der Nagel R, et al. Changes in Cx43 and NaV1.5 expression precede the occurrence of substantial fibrosis in calcineurin-induced murine cardiac hypertrophy. PLoS ONE. 2014; 9: e87226.
[46]
Zhang L, Shi X, Gu C, Chen B, Wang M, Yu Y, et al. Identification of cell-to-cell interactions by ligand-receptor pairs in human fetal heart. Biochimica Et Biophysica Acta (BBA) - Molecular Basis of Disease. 2020; 1866: 165917.
[47]
Vandersteen AM, Lund AM, Ferguson DJP, Sawle P, Pollitt RC, Holder SE, et al. Four patients with Sillence type i osteogenesis imperfecta and mild bone fragility, complicated by left ventricular cardiac valvular disease and cardiac tissue fragility caused by type i collagen mutations. American Journal of Medical Genetics. Part a. 2014; 164A: 386–391.
[48]
Parrott ME, Aljrbi E, Biederman DL, Montalvo RN, Barth JL, LaVoie HA. Maternal cardiac messenger RNA expression of extracellular matrix proteins in mice during pregnancy and the postpartum period. Experimental Biology and Medicine. 2018; 243: 1220–1232.
[49]
Hodges MM, Zgheib C, Xu J, Hu J, Dewberry LC, Hilton SA, et al. Differential Expression of Transforming Growth Factor-β1 is Associated with Fetal Regeneration after Myocardial Infarction. The Annals of Thoracic Surgery. 2019; 108: 59–66.
[50]
Bai D, Ge L, Gao Y, Lu X, Wang H, Yang G. Cytoplasmic translocation of HuR contributes to angiotensin II induced cardiac fibrosis. Biochemical and Biophysical Research Communications. 2015; 463: 1273–1277.
[51]
Xiao L, He H, Ma L, Da M, Cheng S, Duan Y, et al. Effects of miR-29a and miR-101a Expression on Myocardial Interstitial Collagen Generation after Aerobic Exercise in Myocardial-infarcted Rats. Archives of Medical Research. 2017; 48: 27–34.
Share
Back to top
Rev. Cardiovasc. Med. Print ISSN 1530-6550 Electronic ISSN 2153-8174
Disclaimer
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept