Open Access

Identification of biomarkers for childhood obesity based on expressional correlation and functional similarity

  • Authors:
    • Zheng‑Lun Zhu
    • Qiu‑Meng Yang
    • Chen Li
    • Jun Chen
    • Min Xiang
    • Ming‑Min Chen
    • Min Yan
    • Zheng‑Gang Zhu
  • View Affiliations

  • Published online on: October 27, 2017     https://doi.org/10.3892/mmr.2017.7913
  • Pages:109-116
  • Copyright: © Zhu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: HTML 0 views | PDF 0 views     Cited By (CrossRef): 0 citations

Abstract

The aim of the current study was to identify potential biomarkers of childhood obesity, and investigate molecular mechanisms and candidate agents in order to improve therapeutic strategies for childhood obesity. The GSE9624 gene expression profile was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) in omental adipose tissues were analyzed with limma package by comparing samples from obese and normal control children. Two‑way hierarchical clustering was applied using the pheatmap package. The co‑expression (CE) analysis was performed using online CoExpress software. Subsequent to functional classification via the GOSim package, the gene network enriched by DEGs was visualized using the Cytoscape package. The codon usage bias of the DEGs was then examined using the CAI program from the European Molecular Biology Open Software Suite. In total, 583 DEGs (273 upregulated genes and 310 downregulated genes) were observed in the omental adipose tissues between samples from obese and normal control children. Hierarchical clustering identified a significant difference between samples from obese and normal control children. Subsequent to CE analysis, 130 DEGs, which were classified into 4 clusters, were selected. The following 3 upregulated and 2 downregulated genes were identified to be significant: Upregulated genes, microtubule‑associated protein tau (MAPT), destrin (actin depolymerizing factor) (DSTN) and spectrin, β, non‑erythrocytic 1 (SPTBN1); downregulated genes, Rho/Rac guanine nucleotide exchange factor 2 (ARHGEF2) and spindle and kinetochore associated complex subunit 1 (SKA1). The top 3 amino acids were identified to be glycine, leucine and serine with a high bias. The DEGs MAPT, DSTN, SPTBN1, ARHGEF2 and SKA1 are suggested to be candidate biomarkers for childhood obesity.

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 (710). 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 (1113). 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).

Table I.

Functional classification of correlated differentially expressed genes.

Table I.

Functional classification of correlated differentially expressed genes.

Cluster nameGenesEnrichment P-valueBiological function
C1 (BP)ARHGEF2, MAPT, SPTBN1, SKA1, DSTN0.0154 GO:0051493~regulation of cytoskeleton organization
ARHGEF2, MAPT, SKA10.0199 GO:0031110~regulation of microtubule polymerization or depolymerization
ARHGEF2, MAPT, SKA10.0350 GO:0070507~regulation of microtubule cytoskeleton organization
ARHGEF2, MAPT, SKA10.0464 GO:0032886~regulation of microtubule-based process
C2 (BP)ARHGEF2, MAPT, EIF5A, SPTBN1, DSTN0.0004 GO:0043244~regulation of protein complex disassembly
ARHGEF2, MAPT, SPTBN1, SKA1, DSTN0.0154 GO:0051493~regulation of cytoskeleton organization
ARHGEF2, MAPT, SPTBN10.0335GO:0043242~negative regulation of protein complex disassembly
C3 (BP)NRP2, LEP, SLITRK3, IRX5, EFNA2, EPHB3, SNAP25 NRP2, SLITRK3, EFNA20.0320GO:0048666~neuron development
EPHB3, SNAP250.0469 GO:0007409~axonogenesis
C4 (CC)CENPN, CENPM, SKA10.0064 GO:0000777~condensed chromosome kinetochore
CENPN, CENPM, SKA10.0080 GO:0000779~condensed chromosome, centromeric region
CENPN, CENPM, SKA10.0104 GO:0000776~kinetochore
CENPN, CENPM, SKA10.0221 GO:0000775~chromosome, centromeric region
CENPN, CENPM, SKA10.0234 GO:0000793~condensed chromosome

[i] BP, biological process; GO, gene ontology; C1-4, cluster 1–4; CC, cellular component; ARHGEF, 2 Rho/Rac guanine nucleotide exchange factor 2; MAPT, microtubule-associated protein tau; SPTBN1, spectrin, β, non-erythrocytic 1; SKA1, spindle and kinetochore associated complex subunit 1; DSTN, destrin (actin depolymerizing factor); EIF5A, eukaryotic translation initiation factor 5A-1; NRP2, neuropilin 2; LEP, leptin; SLITRK3, SLIT and NTRK-like family member 3; IRX5, iroquois homeobox 5; EFNA2, ephrin-A2; EPHB3, ephrin type-B receptor 3; SNAP25, synaptosomal-associated protein 25; CENPN, centromere protein N; CENPM, centromere protein M.

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.

Table II.

Codon usage of differential expression genes sequence in the 4 clusters.

Table II.

Codon usage of differential expression genes sequence in the 4 clusters.

AA Sum

CodonAACluster 1Cluster 2Cluster 3Cluster 4Cluster 1Cluster 2Cluster 3Cluster 4
GCAA194266951907121482147666
GCCA25340641210
GCGA  4548163  58
GCTA22034648208
TGCC21224771198376  511579427
TGTC16427808229
GACD12820469117270  401058255
GATD14220589138
GAAE21218880187573  601731436
GAGE36142851249
TTCF203468572165311082242616
TTTF328621385400
GGAG2814684219811461852907765
GGCG25543658203
GGGG38762825180
GGTG22334582184
CACH17922693200341  431519355
CATH16221826155
ATAI11411752171406  522302528
ATCI12719605153
ATTI16522945204
AAAK294181478406491  442328609
AAGK19726850203
CTAL13418470136132221246251282
CTCL27549804249
CTGL37242958288
CTTL22045851230
TTAL13521778173
TTGL18637764206
ATGM16522776183165  22776183
AACN10910617163244  221550384
AATN13512933221
CCAP2733588021410291572750682
CCCP33957831170
CCGP  8617213  69
CCTP33148826229
CAAQ17824832177495  601815448
CAGQ31736983271
AGAR2893010202288461372503663
AGGR33741843233
CGAR  35  9128  48
CGCR  6416143  58
CGGR  8028233  51
CGTR  4113136  45
AGCS26430662194123518239301118
AGTS20723666188
TCAS19131795227
TCCS25244771202
TCGS  4712126  40
TCTS27442910267
ACAT16412845206538  722187587
ACCT19725564134
ACGT  3310131  54
ACTT14425647193
GTAV124134351366421122144634
GTCV13624433126
GTGV24639680212
GTTV13636596160
TGGW33142945233331  42945233
TACY  9511397132179  241090305
TATY  8413693173
TAA*108  9819214
TAG*13417526149
TGA*20822827206

[i] Columns 3–6 indicate the number of codons of the differentially expressed genes in each cluster. The last column shows the number of AA encoded by different codons. *No corresponding amino acid. AA, amino acid; A, alanine; C, cysteine; D, aspartate; E, glutamate; F, phenylalanine; G, glycine; H, histidine; I, isoleucine; K, lysine; L, leucine; M, methionine; N, asparagine; P, proline; Q, glutamine; R, arginine; S, serine; T, threonine; V, valine; W, tryptophan; Y, tyrosine.

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 (3134). 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 (4851). 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).

References

1 

Etelson D, Brand DA, Patrick PA and Shirali A: Childhood obesity: Do parents recognize this health risk? Obes Res. 11:1362–1368. 2003. View Article : Google Scholar : PubMed/NCBI

2 

Dietz WH: Health consequences of obesity in youth: Childhood predictors of adult disease. Pediatrics. 101:518–525. 1998.PubMed/NCBI

3 

Dehghan M, Akhtar-Danesh N and Merchant AT: Childhood obesity, prevalence and prevention. Nutr J. 4:242005. View Article : Google Scholar : PubMed/NCBI

4 

Daniels SR: The consequences of childhood overweight and obesity. Future Child. 16:47–67. 2006. View Article : Google Scholar : PubMed/NCBI

5 

French SA, Story M and Perry CL: Self-esteem and obesity in children and adolescents: A literature review. Obes Res. 3:479–490. 1995. View Article : Google Scholar : PubMed/NCBI

6 

Han JC, Lawlor DA and Kimm SY: Childhood obesity. Lancet. 375:1737–1748. 2010. View Article : Google Scholar : PubMed/NCBI

7 

Govaerts C, Srinivasan S, Shapiro A, Zhang S, Picard F, Clement K, Lubrano-Berthelier C and Vaisse C: Obesity-associated mutations in the melanocortin 4 receptor provide novel insights into its function. Peptides. 26:1909–1919. 2005. View Article : Google Scholar : PubMed/NCBI

8 

Tao YX and Segaloff DL: Functional characterization of melanocortin-4 receptor mutations associated with childhood obesity. Endocrinology. 144:4544–4551. 2003. View Article : Google Scholar : PubMed/NCBI

9 

Yeo GS, Farooqi IS, Aminian S, Halsall DJ, Stanhope RG and O'Rahilly S: A frameshift mutation in MC4R associated with dominantly inherited human obesity. Nat Gene. 20:111–112. 1998. View Article : Google Scholar

10 

Del Giudice Miraglia E, Cirillo G, Nigro V, Santoro N, D'urso L, Raimondo P, Cozzolino D, Scafato D and Perrone L: Low frequency of melanocortin-4 receptor (MC4R) mutations in a Mediterranean population with early-onset obesity. Int J Obes Relat Metab Disord. 26:647–651. 2002. View Article : Google Scholar : PubMed/NCBI

11 

Challis BG, Pritchard LE, Creemers JW, Delplanque J, Keogh JM, Luan J, Wareham NJ, Yeo GS, Bhattacharyya S, Froguel P, et al: A missense mutation disrupting a dibasic prohormone processing site in pro-opiomelanocortin (POMC) increases susceptibility to early-onset obesity through a novel molecular mechanism. Hum Mol Genet. 11:1997–2004. 2002. View Article : Google Scholar : PubMed/NCBI

12 

Santoro N, Perrone L, Cirillo G, Raimondo P, Amato A, Coppola F, Santarpia M, D'Aniello A and Del Giudice Miraglia E: Weight loss in obese children carrying the proopiomelanocortin R236G variant. J Endocrinol Invest. 29:226–230. 2006. View Article : Google Scholar : PubMed/NCBI

13 

Hung CN, Poon WT, Lee CY, Law CY and Chan AY: A case of early-onset obesity, hypocortisolism, and skin pigmentation problem due to a novel homozygous mutation in the proopiomelanocortin (POMC) gene in an Indian boy. J Pediatr Endocrinol Metab. 25:175–179. 2012. View Article : Google Scholar : PubMed/NCBI

14 

Wardle J, Carnell S, Haworth CM, Farooqi IS, O'Rahilly S and Plomin R: Obesity associated genetic variation in FTO is associated with diminished satiety. J Clin Endocrinol Metab. 93:3640–3643. 2008. View Article : Google Scholar : PubMed/NCBI

15 

Gulati P, Cheung MK, Antrobus R, Church CD, Harding HP, Tung YC, Rimmington D, Ma M, Ron D, Lehner PJ, et al: Role for the obesity-related FTO gene in the cellular sensing of amino acids. Proc Natl Acad Sci USA. 110:2557–2562. 2013. View Article : Google Scholar : PubMed/NCBI

16 

Liu WM, Mei R, Di X, Ryder TB, Hubbell E, Dee S, Webster TA, Harrington CA, Ho MH, Baid J, et al: Analysis of high density expression microarrays with signed-rank call algorithms. Bioinformatics. 18:1593–1599. 2002. View Article : Google Scholar : PubMed/NCBI

17 

Smyth GK and Speed T: Normalization of cDNA microarray data. Methods. 31:265–273. 2003. View Article : Google Scholar : PubMed/NCBI

18 

Smyth GK: Limma: Linear models for microarray dataBioinformatics and Computational Biology Solutions Using R and Bioconductor. Gentleman R, Carey VJ, Huber W, Irizarry RA and Dudoit S: Springer; New York: pp. 397–420. 2005, View Article : Google Scholar

19 

Team RC: R: A Language and Environment for Statistical ComputingR Foundation for Statistical Computing. Vienna: 2012

20 

Deza MM and Deza E: Encyclopedia of Distances. 1st edition. Springer-Verlag; Berlin: 2009, View Article : Google Scholar

21 

Obayashi T, Kinoshita K, Nakai K, Shibaoka M, Hayashi S, Saeki M, Shibata D, Saito K and Ohta H: ATTED-II: A database of co-expressed genes and cis elements for identifying co-regulated gene groups in Arabidopsis. Nucleic Acids Res. 35:(Database Issue). D863–D869. 2007. View Article : Google Scholar : PubMed/NCBI

22 

Fröhlich H, Speer N, Poustka A and Beissbarth T: GOSim-an R-package for computation of information theoretic GO similarities between terms and gene products. BMC Bioinformatics. 8:1662007. View Article : Google Scholar : PubMed/NCBI

23 

Smoot ME, Ono K, Ruscheinski J, Wang PL and Ideker T: Cytoscape 2.8: New features for data integration and network visualization. Bioinformatics. 27:431–432. 2011. View Article : Google Scholar : PubMed/NCBI

24 

Liu Q, Dou S, Ji Z and Xue Q: Synonymous codon usage and gene function are strongly related in Oryza sativa. Bio Systems. 80:123–131. 2005. View Article : Google Scholar : PubMed/NCBI

25 

McInerney JO: GCUA: General codon usage analysis. Bioinformatics. 14:372–373. 1998. View Article : Google Scholar : PubMed/NCBI

26 

Rice P, Longden I and Bleasby A: EMBOSS: The European molecular biology open software suite. Trends Genet. 16:276–277. 2000. View Article : Google Scholar : PubMed/NCBI

27 

Ebbeling CB, Pawlak DB and Ludwig DS: Childhood obesity: Public-health crisis, common sense cure. Lancet. 360:473–482. 2002. View Article : Google Scholar : PubMed/NCBI

28 

Welburn JP, Grishchuk EL, Backer CB, Wilson-Kubalek EM, Yates JR III and Cheeseman IM: The human kinetochore Ska1 complex facilitates microtubule depolymerization-coupled motility. Dev Cell. 16:374–385. 2009. View Article : Google Scholar : PubMed/NCBI

29 

Welburn JP, Grishchuk EL, Backer CB, Wilson-Kubalek EM, Yates JR III and Cheeseman IM: The human kinetochore Ska1 complex facilitates microtubule depolymerization-coupled motility. Dev Cell. 16:374–385. 2009. View Article : Google Scholar : PubMed/NCBI

30 

Schmidt JC, Arthanari H, Boeszoermenyi A, Dashkevich NM, Wilson-Kubalek EM, Monnier N, Markus M, Oberer M, Milligan RA, Bathe M, et al: The kinetochore-bound Ska1 complex tracks depolymerizing microtubules and binds to curved protofilaments. Dev Cell. 23:968–980. 2012. View Article : Google Scholar : PubMed/NCBI

31 

Bochukova EG, Huang N, Keogh J, Henning E, Purmann C, Blaszczyk K, Saeed S, Hamilton-Shield J, Clayton-Smith J, O'Rahilly S, et al: Large, rare chromosomal deletions associated with severe early-onset obesity. Nature. 463:666–670. 2010. View Article : Google Scholar : PubMed/NCBI

32 

Farooqi IS: Genetic and hereditary aspects of childhood obesity. Best Pract Res Clin Endocrinol Metab. 19:359–374. 2005. View Article : Google Scholar : PubMed/NCBI

33 

Walters RG, Jacquemont S, Valsesia A, de Smith AJ, Martinet D, Andersson J, Falchi M, Chen F, Andrieux J, Lobbens S, et al: A new highly penetrant form of obesity due to deletions on chromosome 16p11.2. Nature. 463:671–675. 2010. View Article : Google Scholar : PubMed/NCBI

34 

Bachmann-Gagescu R, Mefford HC, Cowan C, Glew GM, Hing AV, Wallace S, Bader PI, Hamati A, Reitnauer PJ, Smith R, et al: Recurrent 200-kb deletions of 16p11. 2 that include the SH2B1 gene are associated with developmental delay and obesity. Genet Med. 12:641–647. 2010. View Article : Google Scholar : PubMed/NCBI

35 

Siesser PF, Motolese M, Walker MP, Goldfarb D, Gewain K, Yan F, Kulikauskas RM, Chien AJ, Wordeman L and Major MB: FAM123A binds to microtubules and inhibits the guanine nucleotide exchange factor ARHGEF2 to decrease actomyosin contractility. Sci Signal. 5:ra642012. View Article : Google Scholar : PubMed/NCBI

36 

Brajenovic M, Joberty G, Küster B, Bouwmeester T and Drewes G: Comprehensive proteomic analysis of human Par protein complexes reveals an interconnected protein network. J Biol Chem. 279:12804–12811. 2004. View Article : Google Scholar : PubMed/NCBI

37 

Poroyko V and Birukova A: ARHGEF2 (rho/rac guanine nucleotide exchange factor (GEF) 2). Atlas Genet Cytogenet Oncol Haematol. 4:582007.

38 

Conde C and Cáceres A: Microtubule assembly, organization and dynamics in axons and dendrites. Nat Rev Neurosci. 10:319–332. 2009. View Article : Google Scholar : PubMed/NCBI

39 

Kuznetsov SA, Rodionov VI, Gelfand VI and Rosenblat VA: Microtubule-associated protein MAP1 promotes microtubule assembly in vitro. FEBS Lett. 135:241–244. 1981. View Article : Google Scholar : PubMed/NCBI

40 

Emoto M, Langille SE and Czech MP: A role for kinesin in insulin-stimulated GLUT4 glucose transporter translocation in 3T3-L1 adipocytes. J Biol Chem. 276:10677–10682. 2001. View Article : Google Scholar : PubMed/NCBI

41 

Patki V, Buxton J, Chawla A, Lifshitz L, Fogarty K, Carrington W, Tuft R and Corvera S: Insulin action on GLUT4 traffic visualized in single 3T3-l1 adipocytes by using ultra-fast microscopy. Mol Biol Cell. 12:129–141. 2001. View Article : Google Scholar : PubMed/NCBI

42 

Kanzaki M and Pessin JE: Insulin-stimulated GLUT4 translocation in adipocytes is dependent upon cortical actin remodeling. J Biol Chem. 276:42436–42444. 2001. View Article : Google Scholar : PubMed/NCBI

43 

Hillen N, Mester G, Lemmel C, Weinzierl AO, Müller M, Wernet D, Hennenlotter J, Stenzl A, Rammensee HG and Stevanović S: Essential differences in ligand presentation and T cell epitope recognition among HLA molecules of the HLA-B44 supertype. Eur J Immunol. 38:2993–3003. 2008. View Article : Google Scholar : PubMed/NCBI

44 

Guilherme R, Guimiot F, Tabet AC, Khung-Savatovsky S, Gauthier E, Nouchy M, Benzacken B, Verloes A, Oury JF, Delezoide AL and Aboura A: Abnormal muscle development of the diaphragm in a fetus with 2p14-p16 duplication. Am J Med Genet A. 149A:2892–2897. 2009. View Article : Google Scholar : PubMed/NCBI

45 

Aksu S, Koczan D, Renne U, Thiesen HJ and Brockmann GA: Differentially expressed genes in adipose tissues of high body weight-selected (obese) and unselected (lean) mouse lines. J Appl Genet. 48:133–143. 2007. View Article : Google Scholar : PubMed/NCBI

46 

Moriyama K, Yonezawa N, Sakai H, Yahara I and Nishida E: Mutational analysis of an actin-binding site of cofilin and characterization of chimeric proteins between cofilin and destrin. J Biol Chem. 267:7240–7244. 1992.PubMed/NCBI

47 

Yahara I, Aizawa H, Moriyama K, Iida K, Yonezawa N, Nishida E, Hatanaka H and Inagaki F: A role of cofilin/destrin in reorganization of actin cytoskeleton in response to stresses and cell stimuli. Cell Struct Funct. 21:421–424. 1996. View Article : Google Scholar : PubMed/NCBI

48 

Agrawal PB, Greenleaf RS, Tomczak KK, Lehtokari VL, Wallgren-Pettersson C, Wallefeld W, Laing NG, Darras BT, Maciver SK, Dormitzer PR and Beggs AH: Nemaline Myopathy with minicores caused by mutation of the CFL2 gene encoding the skeletal muscle actin-binding protein, cofilin-2. Am J Hum Genet. 80:162–167. 2007. View Article : Google Scholar : PubMed/NCBI

49 

Verdoni AM, Aoyama N, Ikeda A and Ikeda S: Effect of destrin mutations on the gene expression profile in vivo. Physiol Genomics. 34:9–21. 2008. View Article : Google Scholar : PubMed/NCBI

50 

Maciver SK and Hussey PJ: The ADF/cofilin family: Actin-remodeling proteins. Genome Biol. 3:30072002. View Article : Google Scholar

51 

Hawkins M, Pope B, Maciver SK and Weeds AG: Human actin depolymerizing factor mediates a pH-sensitive destruction of actin filaments. Biochemistry. 32:9985–9993. 1993. View Article : Google Scholar : PubMed/NCBI

52 

Wiche G: Role of plectin in cytoskeleton organization and dynamics. J Cell Sci. 111:2477–2486. 1998.PubMed/NCBI

53 

Kawaguchi N, Sundberg C, Kveiborg M, Moghadaszadeh B, Asmar M, Dietrich N, Thodeti CK, Nielsen FC, Möller P, Mercurio AM, et al: ADAM12 induces actin cytoskeleton and extracellular matrix reorganization during early adipocyte differentiation by regulating beta1 integrin function. J Cell Sci. 116:3893–3904. 2003. View Article : Google Scholar : PubMed/NCBI

54 

Bost F, Aouadi M, Caron L and Binétruy B: The role of MAPKs in adipocyte differentiation and obesity. Biochimie. 87:51–56. 2005. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

January 2018
Volume 17 Issue 1

Print ISSN: 1791-2997
Online ISSN:1791-3004

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
APA
Zhu, Z., Yang, Q., Li, C., Chen, J., Xiang, M., Chen, M. ... Zhu, Z. (2018). Identification of biomarkers for childhood obesity based on expressional correlation and functional similarity. Molecular Medicine Reports, 17, 109-116. https://doi.org/10.3892/mmr.2017.7913
MLA
Zhu, Z., Yang, Q., Li, C., Chen, J., Xiang, M., Chen, M., Yan, M., Zhu, Z."Identification of biomarkers for childhood obesity based on expressional correlation and functional similarity". Molecular Medicine Reports 17.1 (2018): 109-116.
Chicago
Zhu, Z., Yang, Q., Li, C., Chen, J., Xiang, M., Chen, M., Yan, M., Zhu, Z."Identification of biomarkers for childhood obesity based on expressional correlation and functional similarity". Molecular Medicine Reports 17, no. 1 (2018): 109-116. https://doi.org/10.3892/mmr.2017.7913