Abstract
Objective
Type 2 diabetes (T2D) is the most common metabolic disorder that is associated with insulin resistance. The aim of the present study is to discover details of the molecular mechanism of exercise on control or progress of diabetic condition in patients via network analysis.
Methods
Gene expression profiles of patients with T2D before and after doing exercise are retrieved from Gene Expression Omnibus (GEO) and are pre-evaluated by the GEO2R program. Data are studied based on expression values, regulatory relationships between the differentially expressed genes (DEGs), gene ontology analyses, and protein-protein interaction PPI network analysis.
Results
A number of 118 significant DEGs were identified and classified based on fold change (FC) values as most dysregulated genes and dysregulated individuals. Action map analysis revealed that 18 DEGs appeared as the critical genes. Gene ontology analysis showed that 24 DEGs are connected to at least four pathways. JUN, IL6, IL1B, PTGS2, FOS, MYC, ATF3, CXCL8, EGR1, EGR2, NR4A1, PLK3, TTN, and UCP3 were identified as central DEGs.
Conclusion
Finally; JUN, IL6, IL1B, PTGS2, FOS, ATF3, CXCL8, EGR1, and EGR2 were introduced as the critical targeted genes by exercise. Since the critical genes after exercise are upregulated and mostly are known as the risk factors of T2D, it can be concluded that unsuitable exercise can develop diabetic conditions in patients. Acute exercise-induced inflammation and immune disturbances seem to be associated with the development of T2D in patients.
Keywords: Exercise, Type 2 diabetes, Gene ontology, Network analysis, Action map
Introduction
T2D is known as a metabolic disease which largely branded by decreased insulin secretion and action. This disease is the most public and clinically significant metabolic disorder that has become a worldwide pandemic recently. It is expected that up to 590 million patients will be diagnosed with T2D by 2035 [1]. It is suggested that a combination of dietary including calorie restriction and weight loss plus physical activity and fitness are important to prevent T2D and improve this condition in patients [2].
Exercise is introduced as a first-line therapy for patients with type 2 diabetes. It is pointed out that exercise is typically one of the first management strategies counseled for patients who newly are diagnosed with T2D [3, 4]. Metformin is a well-known medicine that is applied against T2D by physicians [5]. There are several documents about the positive role of exercise in controlling blood glucose and the improvement of T2D disorder [2, 6]. It is reported that type of exercise has different effects on the body; diabetic patients with active feet lesions or ulcers should restrict exercise to non-weight-bearing activities such as bicycle exercise, rowing, swimming and other water activities, exercises in chairs and exercises with upper limbs [7–9]. Aerobic and high-impact exercises are considered developers of diabetic retinopathy conditions [8, 10].
Network analysis as a useful method is applied to detect details of molecular mechanisms of different types of diseases. Relationships between large numbers of dysregulated genes or proteins are studied via network analysis to detect the crucial individuals [11, 12]. In the present study, genomic data related to the effect of exercise on diabetic adipose tissue of T2D patients is retrieved from GEO and analyzed via network analysis to find details of the molecular mechanism of events.
Methods
Data collection
Gene expression information of adipose tissue biopsy of 26 people with T2D after doing acute exercise versus 24 gene expression profiles of individuals with T2D (post-exercise versus pre-exercise) is recorded in the GEO database under the title of GSE198922 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse198922). The gene expression profiles of this dataset were selected for more analysis. Library strategy; RNA-Seq, library resource; transcriptomics, library selection; cDNA and instrument model; Illumina NextSeq 500 are applied.
Pre-evaluation of data
The samples were grouped in pre-exercise and post-exercise and analyzed via the GEO2R program. A volcano plot was used to visualize the distribution of up and down-regulated genes. Box plot analysis was applied to compare the gene expression profiles.– The variance trend was assessed to determine the relationship between the normalized average of gene expression values and the related dispersion parameters.
Expression analysis
The significant DEGs were determined based on adjusted p-value (padj ≤ 0.05). And the uncharacterized data were ignored. LogFC ≥ 1 was considered as the lower base of analyses. To identify the crucial significant DEGs, based on|LogFC|≥3 the most dysregulated genes were determined.
Network analysis
The characterized significant DEGs were included in CluePedia v 1.5.7 via Cytoscape software v 3.7.2 to find the regulatory relationships between the queried genes. Activation, inhibition, and expression relationships between the studied DEGs were identified. The queried DEGs were enriched via gene ontology. Genes were included in ClueGO v 2.5.7 and the related biochemical pathways were extracted from KEGG_08.05.2020. To screen the queried DEGs, the number of related pathways for each gene were determined. To find the central nodes, the queried DEGs were assessed via the STRING database by using Cytoscape software v 3.7.2 [11]. The top 10 nodes based on degree value and betweenness centrality were introduced as hubs and bottlenecks respectively. The common hubs and bottlenecks were identified as hub-bottlenecks.
Statistical analysis
The significant DEGs were determined based on p-adjusted (padj) ≤ 0.05. Kappa score threshold = 0.4 and medium network specificity were considered for ClueGO analysis. Term p-value and term p-value correction test of Bonferroni step-down ≤ 0.05 were applied. A confidence score = 0.2 was considered for construction of the PPI network.
Results
The volcano plot for the analyzed gene expression profiles is presented in Fig. 1. As shown in Fig. 1, many significant DEGs differentiated the post-exercise profiles from pre-exercise individuals. Results indicate that among 19,258 changed expression genes, there are 915 significant DEGs. The box plot is shown in Fig. 2. The studied samples are median-centric and can be compared. The results of the box plot analysis are depicted in Fig. 2. The studied gene expression profiles are median-centric in the box plot and can be compared statistically. The relationship between normalized average of gene expression values and the related dispersion parameters for the analyzed gene expression profiles is shown in Fig. 3. As presented in Fig. 3a constant fitted amount appears for the higher values of the gene expression averages.
Fig. 1.
Volcano plot for the analyzed gene expression profiles of post-exercise samples versus the pre-exercise individuals. The colored spots refer to DEGs
Fig. 2.
Box plot results of the analyzed gene expression profiles of post-exercise samples versus the pre-exercise individuals
Fig. 3.
The mean-variance trend of the analyzed gene expression profiles of post-exercise samples versus the pre-exercise individuals
Among the 915 queried DEGs 118 individuals (including 18 downregulated and 100 upregulated genes) were identified as characterized and significant DEGs. A number of 22 DEGs (FOSB, FOS, EGR1, NR4A2, EGR3, NR4A1, C2CD4B, SELE, CCN1, OSM, IL6, ATF3, NR4A3, CXCL2, PTGS2, DUSP2, EGR2, AREG, SOCS3, RGS1, MYH1, and ATP2A1) including 2 downregulated genes and 20 upregulated individuals were considered as the most dysregulated DGs (see Table 1).
Table 1.
List of the most dysregulated genes
| No. | Symbol | Description | log2FC |
|---|---|---|---|
| 1 | FOSB | FosB proto-oncogene, AP-1 transcription factor subunit | 5.11 |
| 2 | FOS | Fos proto-oncogene, AP-1 transcription factor subunit | 4.98 |
| 3 | EGR1 | Early growth response 1 | 4.30 |
| 4 | NR4A2 | Nuclear receptor subfamily 4 group A member 2 | 4.29 |
| 5 | EGR3 | Early growth response 3 | 4.25 |
| 6 | NR4A1 | Nuclear receptor subfamily 4 group A member 1 | 3.95 |
| 7 | C2CD4B | C2 calcium dependent domain containing 4B | 3.84 |
| 8 | SELE | Selectin E | 3.71 |
| 9 | CCN1 | Cellular communication network factor 1 | 3.52 |
| 10 | OSM | Oncostatin M | 3.47 |
| 11 | IL6 | Interleukin 6 | 3.45 |
| 12 | ATF3 | Activating transcription factor 3 | 3.37 |
| 13 | NR4A3 | Nuclear receptor subfamily 4 group A member 3 | 3.30 |
| 14 | CXCL2 | C-X-C motif chemokine ligand 2 | 3.26 |
| 15 | PTGS2 | Prostaglandin-endoperoxide synthase 2 | 3.21 |
| 16 | DUSP2 | Dual specificity phosphatase 2 | 3.16 |
| 17 | EGR2 | Early growth response 2 | 3.16 |
| 18 | AREG | Amphiregulin | 3.12 |
| 19 | SOCS3 | Suppressor of cytokine signaling 3 | 3.08 |
| 20 | RGS1 | Regulator of G protein signaling 1 | 3.01 |
| 21 | MYH1 | Myosin heavy chain 1 | -3.90 |
| 22 | ATP2A1 | ATPase sarcoplasmic/endoplasmic reticulum Ca2 + transporting 1 | -5.46 |
The characterized significant DEGs were assessed via CluPedia. The main connected component including 60 DEGs is shown in Fig. 4. Analysis indicates that there are 16 common genes (FOSB, FOS, EGR1, NR4A2, NR4A1, SELE, CCN1, OSM, IL6, ATF3, CXCL2, PTGS2, DUSP2, EGR2, AREG, and SOCS3) between the most dysregulated DEGs and the elements of the main connected component of the regulatory network.
Fig. 4.
A main connected component of the regulatory network of the 118 queried DEGs. The 60 DEGs are connected by activation (green), inhibition (red), and expression (yellow) directed edges
A number of 62 biochemical pathways were identified via gene ontology enrichment of the DEGs. Analysis indicates that 43 DEGs are involved in the 62 determined pathways. A list of these 43 DEGs is presented in Table 2. Among the 118 queried DEGs 102 individuals were recognized by the STRING database. The PPI network including 2 isolated genes and a main connected component of 100 nodes and 1473 edges was created (see Fig. 5). A list of hubs, bottlenecks, and hub-bottlenecks are shown in Table 3.
Table 2.
List of the 43 DEGs which are related to the 62 introduced pathways
| Group | Gene name | No. of genes | No. of related pathways |
|---|---|---|---|
| 1 | IL6 | 1 | 41 |
| 2 | JUN | 1 | 39 |
| 3 | CXCL8 | 1 | 36 |
| 4 | FOS | 1 | 32 |
| 5 | IL1B | 1 | 30 |
| 6 | CDKN1A | 1 | 26 |
| 7 | MYC | 1 | 23 |
| 8 | CXCL2 | 1 | 17 |
| 9 | IL10 | 1 | 16 |
| 10 | CCL2 | 1 | 14 |
| 11 | GADD45B, PTGS2 | 2 | 11 |
| 12 | HBEGF, CCL3 | 2 | 8 |
| 13 | EGR2 | 1 | 7 |
| 14 | CCL4 | 1 | 6 |
| 15 | EGR1, IL1R2, ZFP36, FOSL1, EGR3, SELE | 6 | 5 |
| 16 | SOCS3, FOSB | 2 | 4 |
| 17 | PRKAG3, CXCR4, AREG, PMAIP1, SERPINE1 | 5 | 3 |
| 18 | JUNB, DLL4, HES1, PLK3 | 4 | 2 |
| 19 | TTN, MCL, OSM, CKS2, DUSP1, NFKBIZ1, ATP2A1, SGK1, PDE4B, NR4A2 | 10 | 1 |
Fig. 5.
A main connected component of the PPI network. Nodes are sorted by degree value. Green nodes and larger nodes have higher degree values
Table 3.
List of hubs, bottlenecks, and hub-bottlenecks. Betweenness centrality is normalized
| No. | display name | Degree | Betweenness centrality |
Central node | Log(fold change) |
|---|---|---|---|---|---|
| 1 | JUN | 69 | 0.47 | Hub-bottleneck | 2.92 |
| 2 | IL6 | 68 | 0.77 | Hub-bottleneck | 3.45 |
| 3 | IL1B | 65 | 0.89 | Hub-bottleneck | 1.70 |
| 4 | PTGS2 | 64 | 0.46 | Hub-bottleneck | 3.21 |
| 5 | FOS | 63 | 0.45 | Hub-bottleneck | 4.98 |
| 6 | ATF3 | 62 | 0.15 | Hub | 3.37 |
| 7 | CXCL8 | 62 | 0.12 | Hub | 2.75 |
| 8 | EGR1 | 62 | 0.12 | Hub | 4.30 |
| 9 | MYC | 62 | 0.84 | Hub-bottleneck | 1.35 |
| 10 | EGR2 | 55 | 0 | Hub | 3.16 |
| 11 | NR4A1 | 54 | 0.27 | Bottleneck | 3.95 |
| 12 | PLK3 | 28 | 0.27 | Bottleneck | 1.02 |
| 13 | TTN | 12 | 1 | Bottleneck | -2.59 |
| 14 | UCP3 | 9 | 0.45 | Bottleneck | -2.41 |
Discussion
The positive role of exercise in improvement of blood glucose control in type 2 diabetes is reported by researchers [13]. The exercise-related biomarkers in diabetic samples are studied via bioinformatics [14]. In the present study role of exercise on gene expression change of adipose tissue is assessed via a system biology approach. Volcano plot analysis indicates that large numbers of genes are expressed differentially and significantly before and after exercise in the studied tissue of diabetic patients. Box plot assessment showed the gene expression profiles are comparable. Based on Mean-variance trend analysis the gene expression amounts in higher values have a constant fitted quantity. Since gene set analysis based on gene expression analysis is a common way to find the critical genes [15]. The studied DEGs were grouped into dysregulated and super-dysregulated DEGs. Corresponding to the gene expression analysis 20 super up-regulated and 2 down-regulated genes were introduced as the critical genes (see Table 1).
Experiences have shown that interaction mapping is a suitable method to screen and study the sets of genes [16]. Action map analysis showed that 60 genes are connected via different types of actions. As shown in Fig. 4, the map includes 18 genes (CDKN1A, SNAI1, CCN2, CCL2, DUSP1, MCL1, PTGS2, IL6, SOCS3, IL10, JUN, EGR1, CXCL8, IL1B, CXCR4, FOS, CXCL4, and ATF3) that play critical roles in the regulation of the neighbors.
Pathway analysis is a well-known method to investigate diseases [17, 18]. Pathway evaluation revealed that the queried DEGs are related to 62 biochemical pathways. Further analysis showed that the identified pathways are related to the 43 DEGs. A number of 19 DEGs among the 43 genes which were connected to the pathways are dependent to the maximum 3 pathways. Each one of the remaining 24 genes are connected to at least 4 pathways.
PPI network analysis is a common method to introduce the central genes as the critical DEGs [19]. As depicted in Tables 3 and 14 central genes including 6 hub-bottlenecks (JUN, IL6, IL1B, PTGS2, FOS, and MYC), 4 hubs (ATF3, CXCL8, EGR1, and EGR2), and 4 bottlenecks (NR4A1, PLK3, TTN, and UCP3) are introduced as the critical DEGs.
To find the final crucial genes, the central DEGs were searched in the three lists; expression analysis (Table 1), 18 critical DEGs of regulatory network, and 24 genes that were connected to at least 4 pathways. If a central node was included in at least two lists was considered as final crucial gene. Five hub-bottlenecks (JUN, IL6, IL1B, PTGS2, and FOS) and four hubs (ATF3, CXCL8, EGR1, and EGR2) were identified as the final crucial genes which are dysregulated by exercise in T2D cells. Results indicate that IL6, PTGS2, Fos, and EGR1 are common between the discussed lists, and JUN, IL1B, ATF3, CXCL8, and EGR2 are included in two lists.
As shown in Table 1, all final crucial genes belong to the super up-regulated set of DEGs. Computational analysis showed FOS gene plays a critical role in causing T2D. It also is related to the progression of the associated disorders of diabetes such as neuropathy, nephropathy, rheumatoid arthritis, and cancer [20]. Investigations indicate that EGR1 and EGR2 induce insulin resistance in adipocytes [21, 22]. It is reported that IL6 is up-regulated in the liver and adipose tissue of insulin resistance people [23]. IL6 is highlighted as a common critical gene in celiac disease and diabetes [24]. There is no significant data about the role of PTGS2 and diabetes [25]. It is reported that c-Jun N-terminal kinases as signaling molecules are associated with inflammation and insulin resistance [26]. Investigation indicates that IL1B is upregulated in adipose tissue of insulin-resistant mouse models and it can be concluded that it plays possible important role in the progression of insulin resistance in human adipose cells [27]. The role of ATF3 in promoting β-cell dysfunction and the development of type-2 diabetes is confirmed by researchers. It is suggested that ATF3 is a suitable therapeutic target to treat type-2 diabetes [28]. There are evidence that the level of CXCL8 is increased in the serum and urine of patients with diabetic nephropathy [29].
Conclusion
In conclusion, exercise delivered complex effects on diabetic tissue. JUN, IL6, IL1B, PTGS2, FOS, ATF3, CXCL8, EGR1, and EGR2 which are mostly risk factors of diabetes were up-regulated in the post-condition of exercise in T2D samples. The finding indicates that control of the inflammatory genes such as IL6 is a possible effective way to prevent the progress of T2D. Perhaps, regulation of the immune system is a more essential method than blood glucose control to prevent the progression of T2D. Lack of experimental validation is a limitation of this study. The future study can explore the expression change of the introduced critical genes in a suitable sample size of patients.
Acknowledgements
This project is supported by Shahid Beheshti University of Medical Sciences.
Declarations
Conflict of interest
There is no conflict of interest.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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