Biological Network Analysis of PPAR and Related Signaling PathwaysView this Special Issue
Research Article | Open Access
Rongyuan Cao, Yan Dong, Kamil Can Kural, "Integrating Literature-Based Knowledge Database and Expression Data to Explore Molecular Pathways Connecting PPARG and Myocardial Infarction", PPAR Research, vol. 2020, Article ID 1892375, 6 pages, 2020. https://doi.org/10.1155/2020/1892375
Integrating Literature-Based Knowledge Database and Expression Data to Explore Molecular Pathways Connecting PPARG and Myocardial Infarction
Peroxisome proliferator-activated receptor γ (PPARG) might play a protective role in the development of myocardial infarction (MI) with limited mechanisms identified. Genes associated with both PPARG and MI were extracted from Elsevier Pathway Studio to construct the initial network. The gene expression activity within the network was estimated through a mega-analysis with eight independent expression datasets derived from Gene Expression Omnibus (GEO) to build PPARG and MI connecting pathways. After that, gene set enrichment analysis (GSEA) was conducted to explore the functional profile of the genes involved in the PPARG-driven network. PPARG demonstrated a significantly low expression in MI patients (; ). Consequently, PPARG could indicatively be promoting three MI inhibitors (e.g., SOD1, CAV1, and POU5F1) and three MI-downregulated markers (e.g., ALB, ACADM, and ADIPOR2), which were deactivated in MI cases (), and inhibit two MI-upregulated markers (RELA and MYD88), which showed increased expression levels in MI cases ( and 0.047, respectively). These eight genes were mainly enriched in nutrient- and cell metabolic-related pathways and functionally linked by GSEA and PPCN. Our results suggest that PPARG could protect the heart against both the development and progress of MI through the regulation of nutrient- and metabolic-related pathways.
Myocardial infarction (MI) and afterward heart failure are the significant causes of death and disability in the developed countries, which is characterized by acute myocardial ischemia derived from coronary artery occlusion, myocardial injury, and even necrosis [1–3]. Atherosclerotic plaque rupture with thrombus formation is determined to be the most dominant cause of myocardial infarction, which will result in an acute reduction of blood supply and imbalance in oxygen supply and demand. The prolonged ischemia will cause irreversible myocardial necrosis and heart failure [4–6]. To negate the life-threatening condition, rapid diagnoses and the proper therapy to restore the perfusion are urgent to salvage the jeopardized myocardium.
The members of the peroxisome proliferator-activated receptor (PPAR) family involve PPARα, PPARβ/δ, and PPARγ (PPARG), which might play vital roles in glucose and lipid metabolism. Among these members, PPARG is enriched in the adipose tissue and widely expressed in extra-adipose tissues, such as the heart, the vascular wall, and the skeletal muscle. PPARG can control the balance between glucose utilization and fatty acid oxidation, which is essential in the energy homeostasis in human myocardia physiology demand and postischemic remodeling [7–9].
As the nuclear hormone receptor superfamily of ligand-activated transcription factors, PPARG could recruit transcription coactivators that are necessary for the initiation of target gene transcription and may also inhibit the development and progress of myocardial infarction [10, 11]. Although some simulators of PPARG have been testified to show a protective effect on the development of myocardial infarction, a systemic literature text mining investigation has been performed to screen the genes and relevant molecular pathways connecting PPARG to myocardial infarction. In this study, literature-based Elsevier Pathway Studio information and expression data retrieved from Gene Expression Omnibus (GEO) were integrated to explore the specific molecules and pathways connecting PPARG and myocardial infarction.
2. Materials and Methods
To explore meaningful genetic networks through which PPARG could influence the development and progress of MI, we set up the following rules for the identification of the networks: (1) For each edge (relationship) within a network, there were one or more scientific studies supporting the relationship. (2) A node (gene) demonstrated significant expression changes in the patients of MI.
2.1. Identifying PPARG-MI Connection Network
We conducted large-scale literature data mining to identify common genes that were downstream targets of PPARG and also linked to MI. The data was extracted from Elsevier Pathway Studio (http://www.pathwaystudio.com; version 12.3), the database of which is a network of interactions between molecules, processes, and diseases. Each relationship/edge is build based on the fact extracted from the literature by natural language processing (NLP) technology. A manual quality control process was enforced to remove unreliable relationships and relationships with nonspecific polarities by reading the sentences where a relationship was identified. Here, unreliable relationships refer to these with unmatched sentences, which were false positives by the NLP technique. After that, all the entities within the remaining network were tested using a mega-analysis with eight independent MI RNA expression datasets. The process is described as follows.
2.2. Selection of Gene Expression Datasets for Mega-analysis
The MI expression datasets were identified within the GEO database (https://www.ncbi.nlm.nih.gov/geo/) .The search was conducted using the keyword ‘myocardial infarction,’ where 678 studies with series data were identified and downloaded. We made an outline of the metadata of the identified datasets and selected a subset for the mega-analysis with the following criteria applied: (1) The dataset was array expression data. (2) The organism of the dataset was Homo sapiens. (3) The study design was MI case vs. healthy control. (4) The original data and the corresponding format file were downloadable. (5) The sample size was bigger than 10. Eight datasets satisfied the above criteria and were included for the mega-analysis, as shown in Table 1.
2.3. Mega-analysis Models
For each gene, the mega-analysis estimated the effect size in terms of gene expression log2 fold-change (LFC). Results from using both the random effects model and fixed effects model were compared . To determine the heterogeneity of the datasets, between- and within-study variance was calculated and compared. When the total variance was no bigger than the expected value of the between-study variances (df), the model sets the ISq (percentage of the within-study over between-study variance) to zero. In this case, the fixed effects model, instead of the random effects model, will be selected for the mega-analysis. All analyses were performed using MATLAB (R2017a version).
2.4. Analysis of Influential Factors
To estimate the possible influence of several factors (e.g., study date, country of origin, and sample size) on the gene expression in MI patients, we conducted a multiple linear regression (MLR) analysis and reported the values for each of these factors.
2.5. GSEA and Protein-Protein Connection
To test the functional profile of the genes involved in the PPARG-MI regulation, we conducted a Gene Set Enrichment Analysis (GSEA)  against the Pathway Studio pathways and Gene Ontology (GO; http://geneontology.org) terms . The purpose of GSEA was to identify GO terms and Pathway Studio collected pathways enriched with the genes identified within the PPARG-MI network. Additionally, we explored the connections between the genes involved in the PPARG-MI regulation network by using Pathway Studio and constructed the protein-protein connection network (PPCN). Each relationship (edge) within the network was supported by one or more references, which were presented in the Supplementary Material (available here): Ref4PPCN. The PPCN was used to explore the potential functional linkage among the proteins identified within the PPARG-MI network.
3.1. PPARG-MI Regulating Pathway and Mega-analysis Results
Pathway Studio literature text mining identified 30 genes that were promoted by PPARG and also upstream MI regulators (see Supplementary Material: 30 Genes). To identify these genes, we first explored all genes promoted by PPARG; then, we mined all the genes that inhibit MI; after that, we took the overlap and identified these 30 genes. Mega-analysis identified three out of these 30 genes demonstrating a significant decrease in expression levels, including SOD1, CAV1, and POU5F1 (Table 2). These genes were appended in the network connecting PPARG-MI, as shown in Figure 1 (highlighted in yellow).
Following the similar literature text mining approach, we identified 125 genes that were contradirectionally influenced by PPARG and MI (see Supplementary Material: 125 Genes). Out of these 125 genes, three demonstrated significantly increased expression levels in MI patients, including ALB, ACADM, and ADIPOR2 (Table 2). These genes were inhibited in MI while stipulated by PPARG. On the contrary, two genes (e.g., RELA and MYD88) were upregulated in MI patients, which could be suppressed by PPARG (Table 2). These pathways may partially explain the protective role of PPARG in the contradevelopment of MI. Please note that one or more previous studies supported each of these relationships presented in Figure 1. For the details of the supporting references, including relation type, polarity, reference PMID, title, and the sentences where the relation has been described, please refer to Supplementary Material: PPARG_MI_Network.
Mega-analysis showed that the expression levels of PPARG were significantly downregulated in MI patients (; value = 1.84-9), which was calculated by using a fixed effects model. This was due to the fact that there was no significant between-study variance () according to the heterogeneity analysis.
Moreover, MLR analysis showed that two factors (country and study age) could significantly influence the expression of PPARG among different studies. For a more detailed description of the mega-analysis results of the nine genes involved in the network presented in Figure 1, please refer to Supplementary Material: Mega-analysis.
3.2. GSEA Results and PPI Network
To investigate the biological functions of the nine genes (including PPARG) within the PPARG-MI functional network (Figure 1), a GSEA was executed by using Pathway Studio. A total of eight significantly enriched GO terms ( value < 0.005, for FDR) were identified and presented in Table 3, with details made available in Supplementary Material: GSEA. Notably, a majority of the shared GO terms highlighted by the GSEA approach were related to cell metabolic process, nutrient levels, and response to the metal ion, which were implicated with MI [16–18].
A literature-based PPI network has been constructed and presented in Figure 2. The relation between a pair of genes was identified through literature data mining. For each relationship/edge within Figure 2, there was at least one supporting reference. For the details of these references, please refer to Supplementary Material: Ref4PPCN. The PPCN showed that there were direct physical or indirect functional connection among PPARG and eight of its driven genes.
This study confirmed the downregulation of PPARG in the case of myocardial infarction and revealed multiple pathways through which PPARG could regulate the development of myocardial infarction. Our results shed light on the understanding of the PPARG-MI association, suggesting PPARG as a potential therapeutic target for the treatment of myocardial infarction.
Among the eight genes identified to be driven by PPARG, ALB could be utilized as a monitor biomarker, as a low level of serum ALB is associated with increased risk of coronary artery disease and myocardial infarction . ACADM could be a rate-limiting factor for the initial step of the mitochondrial fatty acid beta-oxidation catalyzation, which plays a vital role in myocardial infarction and diabetic cardiomyopathy .
The other six genes may be involved in the functional recovery and cellular protection involved in myocardial infarction. SOD1 overexpression, RELA blockade, and diminished MyD88-mediated inflammation can enhance functional and metabolic recovery and greatly decreased myocardial infarction [21–23], while ADIPOR2 is required for revascularization . On the other hand, HIF-2α and POU5F1 (OCT4) could collaboratively promote the survival and differentiation of embryonic-like mesenchymal stem cells in myocardial infarction to repair the damaged myocardia . It is worth to note that downregulated pulmonary CAV-1 expression subjected to myocardial infarction may lead to STAT3/Cyclin pathway activation, pulmonary hypertension, and lung structural remodeling development . All of this evidence indicates that PPARG not only works in the progression of myocardial infarction but also plays a role in the functional recovery and cellular protection of myocardial infarction.
In addition to the exogenous activators, PPARs can also be activated by endogenous secreted ligands, such as free fatty acids or prostaglandins. It is not surprising to find that a majority of the shared pathways highlighted by the GSEA approach are related to cell metabolic process and nutrient levels, which are also implicated in the development of myocardial infarction. It is worth noting that, although detailed information should be deciphered, mitochondrial fatty acid beta-oxidation catalyzation rate limited by ACADM might be the vital energy pathway mediated by PPARG.
The PPCN showed that, besides the relation between PPARG and its eight driven genes (Figure 1), the majority of the eight genes (6 out of 8) were physically or functionally linked to each other (Figure 2). Especially, five out of the rest seven genes were connected to SOD1; the overexpression of which enhances functional and metabolic recovery and significantly decreases MI . These functional connections (Figure 2) suggested that the genes connecting PPARG and MI may be also functionally linked to each other.
In this study, we propose an integrated analysis employing both literature-based knowledge database and expression data to explore the functional connection between PPARG and MI. This approach could help the exploration of the crucial genes and pathways further to decipher the association of factors in interest with a particular disease. Both the metabolic-and nutrient-associated pathways involved in the development and progress of myocardial infarction can be regulated by PPARG, which indicates that PPARG might be utilized as an essential target in myocardial infarction treatment.
This study has several limitations that need to be addressed in further work. First, the PPARG-MI connecting network was constructed using Pathway Studio only. More data sources should be employed to explore more potential relationships. Second, we used array data to study the expression variation of PPARG and its driven genes. Expression by RNA sequencing may provide higher resolution in studying the expression profile.
Literature-based knowledge database and expression data integration may significantly promote the illustration of the relevant mechanism involved in PPARG-mediated myocardial infarction protection.
The data in our study are available from the corresponding author upon reasonable request.
Conflicts of Interest
All the authors declare no conflict of interest.
This work was supported by the 2018 Jiangsu University Medical/Clinical Science and Technology Development Fund Project (Natural Science) (JLY20180021) and Lianyungang Second People’s Hospital Middle-Aged and Young People’s Fund Project (TQ201902).
Supplementary materials were provided for this study (an excel file), including the following worksheets: PPARG_MI_Pathway, 30 Genes, 125 Genes, Mega-analysis, GSEA, and Ref4PPCN. (Supplementary Materials)
- L. Lu, M. Liu, R. Sun, Y. Zheng, and P. Zhang, “Myocardial infarction: symptoms and treatments,” Cell Biochemistry and Biophysics, vol. 72, no. 3, pp. 865–867, 2015.
- A. Sakaguchi, C. Nishiyama, and W. Kimura, “Cardiac regeneration as an environmental adaptation,” Biochimica et Biophysica Acta (BBA) - Molecular Cell Research, vol. 1867, no. 4, article 118623, 2020.
- K. Thygesen, J. S. Alpert, and H. D. White, “Universal definition of myocardial infarction,” Journal of the American College of Cardiology, vol. 50, no. 22, pp. 2173–2195, 2007.
- A. O. Jackson, H. Tang, and K. Yin, “HiPS-cardiac trilineage cell generation and transplantation: a novel therapy for myocardial infarction,” Journal of Cardiovascular Translational Research, vol. 13, no. 1, pp. 110–119, 2020.
- J. Frampton, J. T. Devries, T. D. Welch, and B. J. Gersh, “Modern management of ST-segment elevation myocardial infarction,” Current Problems in Cardiology, vol. 45, no. 3, article 100393, 2020.
- D. Q. Chen, X. S. Kong, X. B. Shen et al., “Identification of differentially expressed genes and signaling pathways in acute myocardial infarction based on integrated bioinformatics analysis,” Cardiovascular Therapeutics, vol. 2019, Article ID 8490707, 13 pages, 2019.
- H. H. Liao, X. H. Jia, H. J. Liu, Z. Yang, and Q. Z. Tang, “The role of PPARs in pathological cardiac hypertrophy and heart failure,” Current Pharmaceutical Design, vol. 23, no. 11, pp. 1677–1686, 2017.
- B. N. Finck, “The PPAR regulatory system in cardiac physiology and disease,” Cardiovascular Research, vol. 73, no. 2, pp. 269–277, 2007.
- Y. F. Zhang, H. M. Xu, F. Yu et al., “Crosstalk between MicroRNAs and Peroxisome Proliferator-Activated Receptors and Their Emerging Regulatory Roles in Cardiovascular Pathophysiology,” PPAR Research, vol. 2018, Article ID 8530371, 11 pages, 2018.
- E. Legchenko, P. Chouvarine, P. Borchert et al., “PPARγ agonist pioglitazone reverses pulmonary hypertension and prevents right heart failure via fatty acid oxidation,” Science Translational Medicine, vol. 10, no. 438, article eaao0303, 2018.
- X. Niu, J. Zhang, L. Zhang et al., “Weighted gene co-expression network analysis identifies critical genes in the development of heart failure after acute myocardial infarction,” Frontiers in Genetics, vol. 10, article 1214, 2019.
- E. Clough and T. Barrett, “The gene expression omnibus database,” Methods in Molecular Biology, vol. 1418, pp. 93–110, 2016.
- M. Borenstein, L. V. Hedges, J. P. T. Higgins, and H. R. Rothstein, “A basic introduction to fixed-effect and random-effects models for meta-analysis,” Research Synthesis Methods, vol. 1, no. 2, pp. 97–111, 2010.
- A. Subramanian, P. Tamayo, V. K. Mootha et al., “Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 43, pp. 15545–15550, 2005.
- A. W. Vesztrocy and C. Dessimoz, “A Gene Ontology tutorial in Python,” Methods in Molecular Biology, vol. 1446, pp. 221–229, 2017.
- P. Pierucci, G. Misciagna, M. T. Ventura et al., “Diet and myocardial infarction: a nested case-control study in a cohort of elderly subjects in a Mediterranean area of southern Italy,” Nutrition, Metabolism, and Cardiovascular Diseases, vol. 22, no. 9, pp. 727–733, 2012.
- S. Shen, H. Jiang, Y. Bei et al., “Qiliqiangxin attenuates adverse cardiac remodeling after myocardial infarction in ovariectomized mice via activation of PPARγ,” Cellular Physiology and Biochemistry, vol. 42, no. 3, pp. 876–888, 2017.
- B. N. Plummer, H. Liu, X. Wan, I. Deschenes, and K. R. Laurita, “Targeted antioxidant treatment decreases cardiac alternans associated with chronic myocardial infarction,” Circulation Arrhythmia and Electrophysiology, vol. 8, no. 1, pp. 165–173, 2015.
- L. Djousse, K. J. Rothman, L. A. Cupples, D. Levy, and R. C. Ellison, “Serum albumin and risk of myocardial infarction and all-cause mortality in the Framingham Offspring Study,” Circulation, vol. 106, no. 23, pp. 2919–2924, 2002.
- A. Mitra, T. Basak, S. Ahmad et al., “Comparative Proteome Profiling during Cardiac Hypertrophy and Myocardial Infarction Reveals Altered Glucose Oxidation by Differential Activation of Pyruvate Dehydrogenase E1 Component Subunit β,” Journal of Molecular Biology, vol. 427, no. 11, pp. 2104–2120, 2015.
- P. Wang, H. Chen, H. Qin et al., “Overexpression of human copper, zinc-superoxide dismutase (SOD1) prevents postischemic injuryOverexpression of human copper,zinc-superoxide dismutase (SOD1) prevents postischemic injury,” Proceedings of the National Academy of Sciences of the United States of America, vol. 95, no. 8, pp. 4556–4560, 1998.
- S. Kawano, T. Kubota, Y. Monden et al., “Blockade of NF-κB improves cardiac function and survival after myocardial infarction,” American Journal of Physiology-Heart and Circulatory Physiology, vol. 291, no. 3, pp. H1337–H1344, 2006.
- M. V. Singh, P. D. Swaminathan, E. D. Luczak, W. Kutschke, R. M. Weiss, and M. E. Anderson, “MyD88 mediated inflammatory signaling leads to CaMKII oxidation, cardiac hypertrophy and death after myocardial infarction,” Journal of Molecular and Cellular Cardiology, vol. 52, no. 5, pp. 1135–1144, 2012.
- Y. Wang, X. L. Ma, and W. B. Lau, “Cardiovascular adiponectin resistance: the critical role of adiponectin receptor modification,” Trends in Endocrinology and Metabolism, vol. 28, no. 7, pp. 519–530, 2017.
- S. Zhang, L. Zhao, J. Wang, N. Chen, J. Yan, and X. Pan, “HIF-2 α and Oct4 have synergistic effects on survival and myocardial repair of very small embryonic-like mesenchymal stem cells in infarcted hearts,” Cell Death & Disease, vol. 8, no. 1, article e2548, 2018.
- J. F. Jasmin, I. Mercier, R. Hnasko et al., “Lung remodeling and pulmonary hypertension after myocardial infarction: pathogenic role of reduced caveolin expression,” Cardiovascular Research, vol. 63, no. 4, pp. 747–755, 2004.
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