Identification of biomarkers for childhood obesity based on expressional correlation and functional similarity
- Authors:
- Published online on: October 27, 2017 https://doi.org/10.3892/mmr.2017.7913
- Pages: 109-116
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Copyright: © Zhu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Childhood obesity is a prevalent disease worldwide (1)and has been recognized as a serious public health concern due to its increasing morbidity in addition to adverse health effects (2). Compared with normal-weight children, obese children have an increased probability of obesity in adulthood (3), which may lead to hypertension, diabetes, dyslipidemia, chronic heart disease and other disorders (4). In addition, the prevalence of depression in obese children is greater than those of normal-weight children (5).
Obesity in children is a multifactorial disease, to which numerous genetic factors contribute (6). Previous genetic studies have identified that the mutation in melanocortin-4 receptor (MC4R) is associated with childhood obesity through binding with α-melanocyte-stimulating hormone (α-MSH). It has been identified that α-MSH could inhibit feeding, thus leading to an increasing risk of obesity (7–10). A missense amino acid substitution (R236G) has been reported to contribute to an inherited susceptibility to obesity. The R236G mutation contributes to an aberrant fusion protein, which is capable of interfering with central melanocortin signaling and appears to increase the risk of early-onset obesity (11–13). The fat mass and obesity associated gene has been recognized as another gene relevant to childhood obesity by influencing appetite and body composition (14,15). However, the effective approaches of preventing obesity development in children remain limited and further studies of the molecular mechanisms are required.
The aim of the present study was to identify potential biomarkers of childhood obesity. Furthermore, the current study aimed to elucidate the molecular mechanism for the therapy of childhood obesity. The significant differentially expressed genes (DEGs) in omental adipose tissues were screened by comparison of samples between obese and normal-weight children with two-way hierarchical clustering, co-expression (CE) analysis and gene network construction. The notable amino acids were identified by measuring of codon usage bias.
Materials and methods
Source of data
The microarray expression profile of GSE9624 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE9624) was extracted from the Gene Expression Omnibus database, based on the Affymetrix Human Genome U133 Plus 2.0 Array GPL570 (HG-U133_Plus_2). The data used was originally from 11 samples of omental adipose tissues, with 5 from obese children and 6 from normal-weight children.
Data preprocessing and DEGs analysis
The probe-level data in the CEL files were converted into expression measures using the Affy package (version 1.48.0) (16) with R software (https://www.r-project.org/). Missing data were inputted and data normalization was performed using quartile normalization (17). A boxplot graph was produced to present the chip data distribution and the median values. When the median values were consistent subsequent to normalization, the chip data were used for DEGs analysis.
The omental adipose tissues of normal-weight children were considered as the controls. DEGs in omental adipose tissues between obese children samples and normal controls were identified by the limma package (version 3.26.8) (https://bioconductor.org/packages/release/bioc/html/limma.html) (18). The P-values and log fold change (logFC) were calculated. P<0.01 and |logFC| >1 were considered as the cut-off criteria.
Hierarchical clustering of DEGs
Two-way hierarchical clustering was applied using the pheatmap package in R language (version 1.0.8) (https://cran.r-project.org/web/packages/pheatmap/index.html) (19). Subsequently, the heat map of the DEGs was generated. The clustering was performed using the Euclidean distance and Ward's method (20).
CE analysis of DEGs
The CE analysis was performed using online CoExpress software (http://www.bioinformatics.lu/CoExpress/). To identify CE DEGs, the correlation coefficient of each gene pair was calculated using the Pearson's correlation coefficient (21). The correlation coefficient ranged from −1 to +1, ‘−’ represented a negative correlation and ‘+ represented a positive correlation. When CE<0.95, the gene pairs were cut off. Subsequently, the CE gene network was constructed by connecting the CE genes with straight lines.
Gene network construction
The functional similarity of DEGs was calculated using GOSim package (version 1.8.0) (https://www.bioconductor.org/packages/release/bioc/html/GOSim.html) (22), which functions based on various information theoretic similarity concepts for gene ontology (GO) terms. P<0.05 was used as the cut-off criterion.
Following functional classification, the gene network enriched by the screened DEGs was constructed. The Cytoscape package (version 3.2.1) (http://www.cytoscape.org/) was applied for the network visualization (23).
Measures of codon usage bias
It has been previously identified that there are large differences in codon usage bias among genes with different functions (24). The program of codon usage analysis has been previously used for analyzing codon and amino acid usage patterns (25). In the current study, the codon usage bias of the DEGs in each cluster was examined using the codon adaptation index (CAI) value, which was calculated with the CAI program from the European Molecular Biology Open Software Suite (version 6.5.0) (http://emboss.sourceforge.net/index.html) (26). CAI values between 0 and 1 indicated a positive correlation between the codon usage bias and the CAI value.
Results
DEGs analysis
The boxplot graph demonstrated that the chip data had been normalized and were available for DEGs selection (Fig. 1).
Based on the cut-off criteria of P<0.01 and |log FC| >1, 583 DEGs were identified in omental adipose tissues between the samples from obese children and those of normal-weight controls, including 310 upregulated genes and 273 downregulated genes.
Hierarchical clustering of DEGs
The heat map of DEGs (Fig. 2) indicated a marked difference in the DEGs of omental adipose tissues between the samples from obese children and those of normal-weight controls, observed by the clear color difference.
CE analysis of DEGs
A total of 130 DEGs were selected using the cut-off criterion of CE>0.95. The CE network of those DEGs is presented in Fig. 3.
Gene network construction
Functional classification of the 130 DEGs was performed. According to the GO functional nodes of these DEGs, they were classified into 4 clusters (summarized in Table I).
The gene network of the DEGs with similarities of expression and function is presented in Fig. 4. The gene network included 38 nodes, 34 DEGs and 35 edges. There were 3 significantly upregulated genes [microtubule-associated protein tau (MAPT), destrin (actin depolymerizing factor) (DSTN) and spectrin, β, non-erythrocytic 1 (SPTBN1)] and 2 significantly downregulated genes [Rho/Rac guanine nucleotide exchange factor 2 (ARHGEF2) and spindle and kinetochore associated complex subunit 1 (SKA1)].
Codon usage bias
The CAI values of the DEGs in the 4 clusters were 0.724 (cluster 1), 0.687 (cluster 2), 0.712 (cluster 3) and 0.705 (cluster 4), indicating a significant codon usage bias. To confirm the codon usage bias, amino acids translated by the codon in each sequence were summarized in Table II. The top 3 amino acids were glycine, leucine and serine, which exhibited a high bias.
Discussion
Childhood obesity is a multisystem disease with potentially life-threatening consequences (27). In spite of numerous genetic studies aiming to elucidate the pathogenesis of childhood obesity, the molecular mechanisms in the development and progression of this disease remain unclear. In the present study, 3 notably upregulated genes (MAPT, DSTN and SPTBN1) and 2 notable downregulated genes (ARHGEF2 and SKA1) were identified. Several functions, including microtubule polymerization or depolymerization, condensed chromosome kinetochore, regulation of cytoskeleton organization and regulation of microtubule cytoskeleton organization, were observed to be significantly enriched by these DEGs.
SKA1 is a protein-coding gene. The SKA1 complex is part of the conserved kinetochore-microtubule interface and directly associates with microtubules as part of oligomeric assemblies. It has been previously demonstrated to serve a critical role in interacting with dynamic microtubules at the outer kinetochore by depolymerizing microtubule ends (28). As presented in Table I and Fig. 4, SKA1 was significantly enriched in regulation of microtubule polymerization or depolymerization. The depolymerizing microtubules attached by macromolecular kinetochores are necessary for the chromosome movements to ensure regular chromosome segregation (29,30). Notably, SKA1 was observed in the current study to also be enriched in cluster 4, which consisted of biological functions such as condensed chromosome kinetochore. Previous studies have identified that chromosomal (such as those on chromosome 11p14-p12 and chromosome 16p11.2) deletions may result in obesity (31–34). Therefore, it is suggested that SKA1 may be associated with childhood obesity by depolymerizing microtubules and disturbing chromosome segregation.
An additional significantly downregulated gene identified in the present study is ARHGEF2. ARHGEF2 encodes guanine nucleotide exchange factor H1, which is a microtubule-associated exchange factor (35,36). ARHGEF2 activity is downregulated by interaction of its C-terminus with microtubules (37). Rho GEF is associated with microtubules and becomes active upon microtubule depolymerization (38). Through functional analysis, ARHGEF2 was observed to be significantly enriched in the regulation of microtubule polymerization or depolymerization. Hence, similar to that of SKA1, it is suggested that ARHGEF2 may be a candidate gene in childhood obesity, acting via targeting the regulation of microtubule polymerization or depolymerization.
In the present study, MAPT was observed as a significantly upregulated gene. MAPT encodes the microtubule-associated protein (MAP) tau. MAP has been observed to serve an important role in the promotion of microtubule assembly in vitro (39). The results of the current study indicated that MAPT targeted regulation of microtubule cytoskeleton organization. Previous studies have demonstrated that disruption of microtubule assembly inhibited the translocation of the insulin responsive glucose transporter isoform (GLUT4) (40,41). GLUT4 translocation promotes insulin to stimulate glucose uptake in adipose tissue, ultimately resulting in obesity (42). Thus, MAPT may serve a stimulative role in the development and progression of childhood obesity.
SPTBN1 is a member of the β-spectrin gene family (43). Spectrin is a protein functioning in actin cross-linking and the molecular scaffold. It connects the plasma membrane to the actin cytoskeleton and determines cytoskeleton organization including cell shape, arrangement of transmembrane proteins and organization of organelles (44). SPTBN1 was identified in the current study as a significantly upregulated gene, enriched in pathway of regulation of cytoskeleton organization. Cytoskeleton organization is a biological process involving various cellular components in adipose tissues. Fat deposition in mature fat cells and cell proliferation have been suggested to accelerate pre-adipocytes to differentiate into adipocytes, serving a role in the regulation of body weight (45). Thus, it is suggested that SPTBN1 may be a candidate gene for childhood obesity, acting via the regulation of cytoskeleton organization function.
DSTN encodes for the protein destrin. Destrin is a pH-independent protein, with the capacity to sever actin filaments (F-actin) (46). Destrin has been previously reported to serve a role in the reorganization of the actin cytoskeleton in response to stress and cell stimuli (47). In addition, destrin belongs to the actin-depolymerizing factor family, which mediates a pH-sensitive destruction of actin filaments (48–51). Filament networks, such as peripheral actin filaments, have been identified to have a mechanical connection to various cytoskeletal structures (52). By reviewing the functional classifications and the gene network in the current study, it is suggested that DSTN participates in the regulation of cytoskeletal organization. Previously, organization of the actin cytoskeleton was suggested to be necessary for early adipocyte differentiation, resulting in hyperplasia of adipose tissues which is a critical event for the development of obesity (53,54). This led to it being hypothesized that DSTN may be a biomarkers of childhood obesity.
However, there were limitations in the present study. The results obtained were web-based and were not verified by any biological experiments. In addition, the data downloaded were from a European database, it remains unclear whether these results would be consistent for children from other continents. Thus, further experimental studies based on the observations of the current study are required.
In conclusion, MAPT, DSTN, SPTBN1, ARHGEF2 and SKA1 may be candidate biomarkers of childhood obesity. Microtubule polymerization or depolymerization, condensed chromosome kinetochore, regulation of cytoskeleton organization and regulation of microtubule cytoskeleton organization may be important biological pathways in the progression of childhood obesity. The observations of the current study may have implications for the understanding of the molecular mechanisms, and identification of candidate therapeutic agents, for childhood obesity. However, further studies are required to confirm the preliminary observations outlined in the current study, with the aim of this information being used in a clinical setting.
Acknowledgements
The current study was supported by the Shanghai Universities Young Teachers Training Scheme (grant no. Zzjdyx12021).
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