BioMed Research International

BioMed Research International / 2020 / Article

Research Article | Open Access

Volume 2020 |Article ID 9267475 | https://doi.org/10.1155/2020/9267475

Jianqing Li, Xue Yin, Bingyu Zhang, Chen Li, Peirong Lu, "Bioinformatical Analysis of miRNA-mRNA Interaction Network Underlying Macrophage Aging and Cholesterol-Responsive Difference between Young and Aged Macrophages", BioMed Research International, vol. 2020, Article ID 9267475, 11 pages, 2020. https://doi.org/10.1155/2020/9267475

Bioinformatical Analysis of miRNA-mRNA Interaction Network Underlying Macrophage Aging and Cholesterol-Responsive Difference between Young and Aged Macrophages

Academic Editor: Maurizio Battaglia Parodi
Received10 Feb 2020
Revised28 Apr 2020
Accepted18 May 2020
Published13 Jun 2020

Abstract

Purpose. Macrophage aging is involved with the occurrence and progression of age-related macular degeneration (AMD). The purpose of this study was to identify the specific microRNAs (miRNA), mRNAs, and their interactions underlying macrophage aging and response to cholesterol through bioinformatical analysis in order to get a better understanding of the mechanism of AMD. Methods. The microarray data were obtained from Gene Expression Omnibus (accession GSE111304 and GSE111382). The age-related differentially expressed genes in macrophages were identified using R software. Further miRNA-mRNA interactions were analyzed through miRWalk, mirTarBase, starBase, and then produced by Cytoscape. The functional annotations including Gene Ontology and KEGG pathways of the miRNA target genes were performed by the DAVID and the STRING database. In addition, protein-protein interaction network was constructed to identify the key genes in response to exogenous cholesterol. Results. When comparing aged and young macrophages, a total of 14 miRNAs and 101 mRNAs were detected as differentially expressed. Besides, 19 validated and 544 predicted miRNA-mRNA interactions were detected. Lipid metabolic process was found to be associated with macrophage aging through functional annotations of the miRNA targets. After being treated with oxidized and acetylated low-density lipoprotein, miR-714 and 16 mRNAs differentially expressed in response to both kinds of cholesterol between aged and young macrophages. Among them, 6 miRNA-mRNA predicted pairs were detected. The functional annotations were mainly related to lipid metabolism process and farnesyl diphosphate farnesyl transferase 1 (FDFT1) was identified to be the key gene in the difference of response to cholesterol between aged and young macrophages. Conclusions. Lipid metabolic process was critical in both macrophage aging and response to cholesterol thus was regarded to be associated with the occurrence and progression of AMD. Moreover, miR-714-FDFT1 may modulate cholesterol homeostasis in aged macrophages and have the potential to be a novel therapeutic target for AMD.

1. Introduction

Macrophages, being critical cells of the innate immune system, play significant roles in development, homeostasis, immunity, and tissue repair [1]. Nevertheless, aged macrophages have been generally reported to exhibit functional changes such as reduced phagocytosis [2], increased angiogenesis [3], and impaired cholesterol metabolism [4]. Impairment in cholesterol homeostatic mechanism has been regarded to be associated with some diseases of the elderly, such as atherosclerosis [5] and age-related macular degeneration (AMD) [6].

AMD is a progressive disease of the central retina and a leading cause of vision loss worldwide [7]. AMD is initially characterized by accumulation of lipid-rich deposits known as drusen, which is a risk factor of the disease progression into late AMD [8]. However, the role of macrophages in cholesterol homeostasis in the pathogenesis of AMD remains elusive. With the development of anti-VEGF therapies [9], treatments for wet AMD have been largely evolved. However, because anti-VEGF agents have some adverse events [10] and do not address early AMD and the process of progression to late AMD [11], there is an urgent need for new therapeutic options for AMD. Therefore, a better understanding of the pathological mechanism of the disease development and progression is required for the development of new treatments.

MicroRNAs (miRNAs) are small noncoding RNAs that can regulate the expression of multiple mRNAs [12]. Identification of miRNA-mRNA interactions can be performed through computational methods [13, 14] and is beneficial to the understanding of the gene-regulatory role of miRNAs in the therapeutic role of mRNAs.

In this study, we identified the impact of senescence on macrophages as well as the difference in cholesterol response between aged and young macrophages regarding the differential expression of miRNAs, mRNAs. Further analysis of miRNA-mRNA interactions and functional annotation of the miRNA target genes were performed to understand the molecular basis and the related pathways. At last, protein-protein interaction (PPI) network was analyzed to identify the key genes in response to exogenous cholesterol. We sought to study the roles of macrophages in cholesterol modulation in order to find a potential therapeutic method for AMD.

2. Methods

2.1. Datasets

The miRNA expression dataset GSE111304 [15] and the mRNA expression dataset GSE11382 [16] were obtained from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/). The profile of GSE111304 was based on the platform of GPL16384 [miRNA-3] Affymetrix Multispecies miRNA-3 Array, and the platform of GSE111382 was GPL6246 [MoGene-1_0-st] Affymetrix Mouse Gene 1.0 ST Array [transcript (gene) version]. The miRNA and mRNA expressions were profiled on aged (18-month-old) and young (2- to 3-month-old) peritoneal macrophages, which were obtained from wild type C57BL/6J mice and then left untreated, treated with 25 μg/ml oxidized low-density lipoprotein (ox-LDL) for 24 hours or treated with 25 μg/ml acetylated low-density lipoprotein (ac-LDL) for 24 hours.

2.2. Identify Differentially Expressed miRNAs and mRNAs

The raw data of miRNA and mRNA microarray were interpreted by limma package (limma, http://www.bioconductor.org/packages/release/bioc/html/limma.html) of R software (version 3.5.1) [17] to identify the differentially expressed miRNAs and mRNAs. Expression comparison was conducted by Student’s -test and the thresholds were and value <0.05.

2.3. miRNA-mRNA Interaction Analysis

We applied miRWalk (http://mirwalk.umm.uni-heidelberg.de/) [18], miRTarBase (http://miRTarBase.mbc.nctu.edu.tw/) [19] and starBase (http://starbase.sysu.edu.cn/starbase2/) [20, 21] to conduct in silico prediction of miRNA targets and visualize the interaction data through Cytoscape [22].

The first step was to identify miRNA targets that have previously been validated by experimental approaches through these three data resources.

Next, predicted miRNA-mRNA targets were detected by miRWalk and the other tools available in that website, including TargetScan [23], miRanda [24], and RNA22 [25]. mRNAs that could be predicted in all four databases were defined as highly predicted miRNA targets.

2.4. Functional Annotations of miRNA Target Genes

For those mRNA targets, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/) [26, 27].

2.5. PPI Network Construction

For cholesterol-responsive miRNA targets, PPI analysis was performed through the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (http://www.string-db.org) and produced by Cytoscape [22].

3. Results

3.1. Differentially Expressed miRNAs and mRNAs in Macrophage Aging

To determine the differentially expressed miRNAs and mRNAs in aged macrophage, we compared the profiles of aged and young macrophages that were remained untreated. A total of 14 miRNAs and 101 mRNAs were detected as differentially expressed. The volcano plots and heat maps were displayed in Figure 1.

3.2. miRNA-mRNA Interactions Underlying Macrophage Aging

Among these differentially expressed miRNAs and mRNAs, a total of 19 validated miRNA-mRNA interactions were identified (Figure 2(a)). In addition, 544 predicted interactions were detected, involving 13 miRNAs and 84 mRNAs (Figure 2(b)). When it comes to the highly predicted miRNA targets, 83 miRNA-mRNA interactions were obtained (Figure 2(c)), which involves 12 miRNAs and 37 mRNAs.

3.3. Functional Annotations of Age-Related miRNA Target Genes

GO analysis of the validated and predicted miRNA targets was conducted, and a total of 65 biological processes (BP), 14 molecular functionings (MF), and 9 cellular components (CC) were identified in DAVID. In addition, 7 KEGG pathways were detected. The top 9 GO and the KEGG pathways were displayed in Table 1. Lipid metabolic process is one of the top 9 BP, and the rest were immune response, inflammatory response, chemotaxis, positive regulation of angiogenesis, oxidation-reduction process, chemokine-mediated signaling pathway, cellular response to interleukin-1, and positive regulation of cell proliferation.


CategoryGO termDescriptionCountGenes

BPGO:0006955Immune response16CCL24, CCL2, CXCR5, CXCL5, ENPP2, PRG4, CXCL13, H2-OB, MCPT4, CCL8, CMA1, TGTP2, CCL5, LTB, CCL7, BMPR1A
GO:0006954Inflammatory response10CCL24, SELP, CCL2, CXCL5, CXCL13, EPHX2, CCL8, CD5L, CCL5, CCL7
GO:0006935Chemotaxis9CCL24, CCL2, CXCR5, CXCL5, CXCL13, ENPP2, CCL8, CCL5, CCL7
GO:0045766Positive regulation of angiogenesis8CCL24, PTGIS, CYP1B1, LRG1, SFRP2, HSPB1, CMA1, CCL5
GO:0055114Oxidation-reduction process8CYP7B1, PTGIS, CYP1B1, SCD2, MAOA, CH25H, CP, DHCR24
GO:0070098Chemokine-mediated signaling pathway7CCL24, CCL2, CXCL5, CXCL13, CCL8, CCL5, CCL7
GO:0071347Cellular response to interleukin-17LCN2, CCL24, CCL2, PTGIS, CCL8, CCL5, CCL7
GO:0006629Lipid metabolic process7CYP7B1, PTGIS, SCD2, ENPP2, CH25H, EPHX2, DHCR24
GO:0008284Positive regulation of cell proliferation7PRL2C3, CCND2, ENPP2, SFRP2, MZB1, PLAC8, TIMP1

MFGO:0005125Cytokine activity10CCL24, CCL2, CXCR5, CXCL5, ENPP2, PRG4, CXCL13, H2-OB, MCPT4, CCL8, CMA1, TGTP2, CCL5, LTB, CCL7, BMPR1A
GO:0008009Chemokine activity7CCL24, SELP, CCL2, CXCL5, CXCL13, EPHX2, CCL8, CD5L, CCL5, CCL7
GO:0005525GTP binding7CCL24, CCL2, CXCR5, CXCL5, CXCL13, ENPP2, CCL8, CCL5, CCL7
GO:0016491Oxidoreductase activity7CCL24, PTGIS, CYP1B1, LRG1, SFRP2, HSPB1, CMA1, CCL5
GO:0042803Protein homodimerization activity7CYP7B1, PTGIS, CYP1B1, SCD2, MAOA, CH25H, CP, DHCR24
GO:0008201Heparin binding6CCL24, CCL2, CXCL5, CXCL13, CCL8, CCL5, CCL7
GO:0005506Iron ion binding6LCN2, CCL24, CCL2, PTGIS, CCL8, CCL5, CCL7
GO:0004497Monooxygenase activity4CYP7B1, PTGIS, SCD2, ENPP2, CH25H, EPHX2, DHCR24
GO:0030414Peptidase inhibitor activity4PRL2C3, CCND2, ENPP2, SFRP2, MZB1, PLAC8, TIMP1

CCGO:0005615Extracellular space29GDF3, CCL2, CXCL5, ENPP2, LUM, IGFBP7, SERPINB1A, CCL8, CCL5, MMP3, CCL7, TIMP1, PRL2C3, CCL24, PTGIS, LRG1, MS4A1, CPA3, LTB, SELP, ACTA2, PRG4, SERPING1, LCN2, CXCL13, SFRP2, SERPINB2, HSPB1, CP
GO:0005576Extracellular region25GDF3, CCL2, CXCL5, ENPP2, LUM, IGFBP7, CCL8, CCL5, MMP3, CCL7, TIMP1, CCL24, PRL2C3, PRG4, MZB1, SERPING1, CD5L, LCN2, BGN, PENK, CXCL13, SFRP2, SERPINB2, CMA1, CP
GO:0070062Extracellular exosome21CPNE8, ACTA2, LUM, IGFBP7, EPHX2, SERPINB1A, SERPING1, CD5L, TIMP1, LCN2, CD38, CD55, ASPA, CD19, BGN, LRG1, MS4A1, HSPB1, CD79B, CP, VSIG4
GO:0009897External side of plasma membrane11LY6A, FCER1A, LY6C1, SELP, CD55, CD19, CXCR5, MS4A1, CD79B, CD79A, BMPR1A
GO:0005789Endoplasmic reticulum membrane8CYP7B1, PTGIS, CYP1B1, SCD2, CH25H, TGTP2, DHCR24, GIMAP1
GO:0031012Extracellular matrix7BGN, LUM, IGFBP7, HSPB1, CMA1, MMP3, TIMP1
GO:0031225Anchored component of membrane4LY6A, LY6C1, CD55, LY6D
GO:0031090Organelle membrane3CYP7B1, CYP1B1, SCD2
GO:0019815B cell receptor complex2CD79B, CD79A

KEGG pathwaysmmu04060Cytokine-cytokine receptor interaction10CCL24, CCL2, CXCR5, CXCL5, CXCL13, CCL8, CCL5, LTB, CCL7, BMPR1A
mmu04062Chemokine signaling pathway8CCL24, CCL2, CXCR5, CXCL5, CXCL13, CCL8, CCL5, CCL7
mmu05323Rheumatoid arthritis6CCL2, CXCL5, H2-OB, CCL5, MMP3, LTB
mmu04640Hematopoietic cell lineage4CD38, CD55, CD19, MS4A1
mmu00380Tryptophan metabolism3KYNU, CYP1B1, MAOA
mmu04662B cell receptor signaling pathway3CD19, CD79B, CD79A
mmu00120Primary bile acid biosynthesis2CYP7B1, CH25H

Abbreviations: GO: gene ontology; BP: biological process; MF: molecular functioning; CC: cellular component; KEGG pathways: Kyoto Encyclopedia of Genes and Genomes pathways; GTP: guanosine triphosphate.
3.4. Cholesterol-Responsive Differentially Expressed miRNAs and mRNAs

We separately analyzed differentially expressed miRNAs and mRNAs in young and aged macrophages when treated with oxLDL or acLDL to study the different response of these cells to exogenous cholesterol.

In young macrophages, only miR-714 was downregulated in response to both acLDL and oxLDL, though 6 and 8 miRNAs were differentially expressed in response to oxLDL (Figure 3(a)) and acLDL (Figure 3(b)), respectively. In aged macrophages, no differentially expressed miRNA was identified in response to oxLDL, and miR-5129 was the only differentially upregulated miRNA in response to acLDL (Figure 3(c)). Hence, the differentially expressed miRNAs between young and aged macrophage’s response to exogenous cholesterol were miR-714.

47 differentially expressed mRNAs were detected in response to exogenous oxLDL in young macrophages (Figure 3(d)), and 39 were found differentially expressed in response to acLDL (Figure 3(e)). Among them, 25 mRNAs were identified differentially expressed in response to both oxLDL and acLDL, with 21 mRNAs downregulated and 4 upregulated (Figure 3(f)). In aged macrophages, 30 mRNAs expressed differentially in response to oxLDL (Figure 3(g)), and 16 mRNAs expressed differentially in response to acLDL (Figure 3(h)). A total of 13 mRNAs were identified differentially expressed in response to both kinds of exogenous cholesterol, 9 and 4 being down- and upexpressed, respectively (Figure 3(i)). By comparing the 25 cholesterol-responsive mRNAs in young macrophages and the 13 mRNAs in aged ones, a total of 16 mRNAs were found to differentially expressed between young and aged macrophages in response to exogenous cholesterol.

3.5. miRNA-mRNA Interactions of Cholesterol-Responsive Difference between Young and Aged Macrophages

Identification of miRNA-mRNA interactions was conducted on the differentially expressed miRNA and mRNAs between young and aged macrophage’s response to exogenous cholesterol. No validated interaction was found; nevertheless, 6 miRNA-mRNA predicted pairs were detected, and they were all predicted by one or two databases (Figure 4).

3.6. Functional Annotations of Age-Related miRNA Target Genes in Response to Cholesterol

GO analysis of the cholesterol-responsive miRNA targets was conducted. In all, 12 BP and 2 MF were found through the String online database and were mainly lipid metabolism associate, including lipid metabolic process, cellular lipid metabolic process, small molecule metabolic process, steroid metabolic process, lipid biosynthetic process, small molecule biosynthetic process, oxidation-reduction process, cellular lipid biosynthetic process, cholesterol biosynthetic process, cholesterol metabolic process, lipid modification, fatty acid metabolic process, acetyltransferase activity, oxidoreductase activity, and acting on the CH-OH group of donors. In addition, the detected 3 KEGG pathways were all about lipid metabolism, including metabolic pathways, fatty acid metabolism, and steroid biosynthesis (shown in Table 2).


CategoryGO termDescriptionCountGenes

BPGO:0006629Lipid metabolic process5Stard4, Fdft1, Hsd17b7, Fasn, Acat2
GO:0044255Cellular lipid metabolic process4Stard4, Fdft1, Fasn, Acat2
GO:0044281Small molecule metabolic process4Fdft1, Hsd17b7, Fasn, Acat2
GO:0008202Steroid metabolic process3Stard4, Fdft1, Hsd17b7
GO:0008610Lipid biosynthetic process3Fdft1, Hsd17b7, Fasn
GO:0044283Small molecule biosynthetic process3Fdft1, Hsd17b7, Fasn
GO:0055114Oxidation-reduction process3Hsd17b7, Fasn, Acat2
GO:0097384Cellular lipid biosynthetic process2Fdft1, Fasn
GO:0006695Cholesterol biosynthetic process2Stard4, Fdft1
GO:0008203Cholesterol metabolic process2Fdft1, Hsd17b7
GO:0030258Lipid modification2Stard4, Acat2
GO:0006631Fatty acid metabolic process2Fasn, Acat2

MFGO:0016407Acetyltransferase activity2Fasn, Acat2
GO:0016616Oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor2Hsd17b7, Fasn

KEGG pathwaysmmu01100Metabolic pathways4Fdft1, Hsd17b7, Fasn, Acat2
mmu01212Fatty acid metabolism2Fasn, Acat2
mmu00100Steroid biosynthesis2Fdft1, Hsd17b7

Abbreviations: GO: gene ontology; BP: biological process; MF: molecular functioning; KEGG pathways: Kyoto Encyclopedia of Genes and Genomes pathways; NAD: nicotinamide adenine dinucleotide; NADP: nicotinamide adenine dinucleotide phosphate.
3.7. PPI Analysis of Age-Related miRNA Target Genes in Response to Cholesterol

PPI analysis was performed on the 6 miRNA targets which included farnesyl diphosphate farnesyl transferase 1 (FDFT1), hydroxysteroid 17-beta dehydrogenase 7 (HSD17B7), steroidogenic acute regulatory protein-related lipid transfer domain-4 (STARD4), acetyl-CoA acetyltransferase 2 (ACAT2), fatty acid synthase (FASN), and CD5 antigen-like (CD5L). The interactions were visualized by the Cytoscape software, and the style of the figure was generated from statistics; to be specific, the size and color were influenced by the degree and the combined score dictated the edge size. It was designed so that low value led to small sizes and light colors. As is displayed in Figure 5, FDFT1 was identified as the key mRNA in the difference of response to cholesterol between aged and young macrophages.

4. Discussion

Impaired cholesterol metabolism has been discovered in senescent macrophages [4]. Although several studies have confirmed the relationship between altered cholesterol homeostasis in aged macrophages and AMD [4, 28], the miRNA-mRNA regulatory network is far from being fully understood. In this study, we sought to identify miRNA-mRNA interactions of macrophage aging and cholesterol-responsive difference between aged and young macrophages and then further analyzed the functional annotation and PPI of the miRNA targets. To the best of our knowledge, this is the first study to explore the miRNA-mRNA interactions aiming to get a better understanding of the pathological mechanism of AMD. Besides, our study is of significance for other lipid-related diseases of the elderly such as type 2 diabetes, cardiovascular disease.

Numerous mechanisms were found to be associated with macrophage aging through functional annotation of the differentially expressed miRNA targets. Among them, some have been reported to be related to AMD, including immunity [29, 30], inflammation [31, 32], chemotaxis [33, 34], angiogenesis [35, 36], oxidative stress [31, 37], and lipid metabolism [4, 28]. We further analyzed the impact of lipid dysregulation on aged macrophages by comparing aged and young macrophages which were treated with oxLDL or acLDL, because exogenous cholesterol plays a pathogenic role in promoting cholesterol dysregulation. In early AMD, lipid-rich drusen is a risk factor of disease progression; thus, our study on the influence of cholesterol on aged macrophages is significant to understand the lipid modulation role of macrophages in AMD progression.

We found that miR-714 was upregulated in aged murine peritoneal macrophages in response to cholesterol, and 6 miRNA-mRNA pairs were detected to play the role of skewing aged macrophages into a disease-promoting phenotype through abnormal lipid metabolism. MiR-714 has been reported to be upregulated in radiation-induced thymic lymphoma [38] and ischemia-reperfusion kidney injury [39] in mice. Besides, it has been reported that miR-714 is involved with vascular smooth muscle cell calcification by disrupting Ca2+ efflux proteins [40], suggesting that miR-714 may have a role in vascular homeostasis. According to miRTarBase [19], which is a database for experimentally validated microRNA-target interactions, it is currently known that miR-714 has less strong evidence pointing to Slc5a3, Wdr26, Ddr2, and Gprc5b through next-generation sequencing method. However, the role of miR-714 in macrophage aging or AMD pathogenesis has never been reported.

Among the 6 miRNA target genes, FDFT1, interacting with the other four genes, was the most significant one. FDFT1 encodes squalene synthase, which catalyzes the first committed step in cholesterol biosynthesis [41]. Biallelic pathogenic variants in FDFT1 will lead to squalene synthase deficiency, which is a rare inborn error of cholesterol biosynthesis with multisystem clinical manifestations including facial dysmorphism, nonspecific structural brain malformations, cortical visual impairment, and optic nerve hypoplasia [42]. FDFT1 has been reported to be related to sterol synthesis, which is expected to increase intracellular cholesterol and is associated with type 2 diabetes and coronary artery calcium [43]. FDFT1 has been found to be enriched in steroid biosynthesis pathway and upregulated in AMD by Zhao et al. [44]. They infer that FDFT1 may induce AMD by elevating the expression of cholesterol, which coincides with our results. Further studies should be conducted on miR-714-FDFT1, since modulation of cholesterol homeostasis may be a novel strategy for treating AMD.

5. Conclusion

Lipid metabolic process was found to play a significant role in both macrophage aging and response to cholesterol thus was regarded to be associated with the occurrence and progression of AMD. In addition, miR-714-FDFT1 may modulate cholesterol homeostasis in aged macrophages and have the potential to be a novel therapeutic target for AMD.

Data Availability

All raw data in this article can be obtained by emailing the corresponding author.

Conflicts of Interest

All authors have no conflicts of interest.

Acknowledgments

The authors report receiving funding from the National Natural Science Foundation of China (NSFC no. 81671641), Jiangsu Provincial Medical Innovation Team (no. CXTDA2017039), Jiangsu Provincial Natural Science Foundation (no. BK20151208), Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX18_2528), and the Soochow Scholar Project of Soochow University (no. R5122001).

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Copyright © 2020 Jianqing Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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