Fabp4 is central to eight obesity associated genes: A functional gene network-based polymorphic study

Journal of Theoretical Biology

Volumn 364, Issue , 2015, Pages 344-354

  Bag, Susmita ^a   Ramaiah, Sudha ^a   Anbarasu, Anand ^a  

^a VIT UNIVERSITY   (India)

Author keywords

Adipogenesis; Fatty acid binding protein 4; Gene ontology; Lipogenic; Metabolic disorders

Indexed keywords

FATTY ACID BINDING PROTEIN 4; CYTOKINE; FABP4 PROTEIN, HUMAN; FATTY ACID BINDING PROTEIN; LIPID; PROTEIN BINDING;

BIOCHEMISTRY; FATTY ACID; GENETIC ANALYSIS; METABOLISM; OBESITY; POLYMORPHISM; PROTEIN;

ACP1 GENE; ARTICLE; ENZYME BINDING; EXT2 GENE; FUNCTIONAL GENE; GENE; GENE FUNCTION; GENE INTERACTION; GENE NETWORK; GENE ONTOLOGY; GENETIC VARIABILITY; HUMAN; INSR GENE; LIPE GENE; LIPID METABOLISM; NONHUMAN; OBESITY; OSTF1 GENE; PROTEIN BINDING; PROTEIN EXPRESSION; PROTEIN FUNCTION; SINGLE NUCLEOTIDE POLYMORPHISM; SNCG GENE; USP15 GENE; VIM GENE; ADIPOCYTE; ADIPOGENESIS; ALLELE; BIOLOGICAL MODEL; BIOLOGY; CHEMISTRY; COMPUTER PROGRAM; CYTOLOGY; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORK; GENETIC POLYMORPHISM; GENETICS; INTERNET; LIPOGENESIS; METABOLISM;

ADIPOCYTES; ADIPOGENESIS; ALLELES; COMPUTATIONAL BIOLOGY; CYTOKINES; FATTY ACID-BINDING PROTEINS; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORKS; GENETIC VARIATION; HUMANS; INTERNET; LIPIDS; LIPOGENESIS; MODELS, GENETIC; OBESITY; POLYMORPHISM, GENETIC; POLYMORPHISM, SINGLE NUCLEOTIDE; PROTEIN BINDING; SOFTWARE;

EID: 84908317461     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2014.09.034     Document Type: Article

References (108)

1. Akhmetshina A., et al. Activation of canonical Wnt signalling is required for TGF-β-mediated fibrosis. Nat. Commun. 2012, 3:735.
2. Alfarano C., et al. The biomolecular interaction network database and related tools 2005 update. Nucleic Acids Res. 2005, 33 (Database issue):D418-424.
3. Althaus I.W., et al. Kinetic studies with the nonnucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry 1993, 32:6548-6554.
4. Anbarasu A., Kundu A. Influence of SMAD1 gene in osteoporosis: a bioinformatics approach. Open Access Bioinform. 2010, 2:79-87.
5. Ashburner M., et al. Gene ontology: tool for the unification of biology. the gene ontology consortium. Nat. Genet. 2000, 25:25-29.
6. Bhushan B., et al. Fatty-acid binding protein 4 gene polymorphisms and plasma levels in children with obstructive sleep apnea. Sleep Med. 2011, 6:666-671.
7. Buelt M.K., et al. In vitro phosphorylation of the adipocyte lipid-binding protein (p15) by the insulin receptor. Effects of fatty acid on receptor kinase and substrate phosphorylation. J. Biol. Chem. 1991, 266:12266-12271.
8. Calabrese R., et al. Functional annotations improve the predictive score of human disease-related mutations in proteins. Hum. Mutat. 2009, 30:1237-1244.
9. Camon E., et al. The Gene Ontology Annotation (GOA) database: sharing knowledge in uniprot with gene ontology. Nucleic Acids Res. 2004, 32 (Database issue):D262-D266.
10. Capriotti E., et al. A three-state prediction of single point mutations on protein stability changes. BMC Bioinform. 2008, 9:S6.
11. Capriotti E., et al. Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. BMC Bioinform. 2006, 22:2729-2734.
12. Carl F.S., et al. PID: the Pathway Interaction Database. Nucleic Acids Res. 2009, 37:D674-D679.
13. Cerami E.G., et al. Pathway commons, a web resource for biological pathway data. Nucleic Acids Res. 2011, 39 (Database issue):D685-D690.
14. Chaiboonchoe A., et al. Integrated analysis of gene network in childhood leukemia from microarray and pathway databases. BioMed Res. Int. 2014, 2014:278748.
15. Chatr Aryamontri A., et al. The BioGRID interaction database: 2013 update. Nucleic Acids Res. 2013, 41 (Database issue):816-823.
16. Chen W., et al. iNuc-PhysChem: a sequence-based predictor for identifying nucleosomes via physicochemical properties. PLoS One 2012, 7:e47843.
17. Chen W., et al. iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res. 2013, 41:e68.
18. Chen W., et al. iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Anal. Biochem. 2014, S0003-2697:281-284.
19. Chen W., et al. iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. BioMed Res. Int. 2014, 2014:623149.
20. Chen W., et al. PseKNC: a flexible web-server for generating pseudo K-tuple nucleotide composition. Anal. Biochem. 2014, 456:53-60.
21. Chou K.C. Prediction of protein cellular attributes using pseudo amino acid composition. Proteins 2001, 43:246-255.
22. Chou K.C. Some remarks on predicting multi-label attributes in molecular biosystems. Mol. BioSyst. 2013, 9:1092-1100.
23. Chou K.C. Graphic rules in steady and non-steady enzyme kinetics. J. Biol. Chem. 1989, 264:12074-12079.
24. Chou K.C. Graphic rule for drug metabolism systems. Curr. Drug Metab. 2010, 11:369-378.
25. Chou K.C., Forsen S. Graphical rules for enzyme-catalyzed rate laws. Biochem. J. 1980, 187:829-835.
26. Collins F.S., et al. A vision for the future of genomics research. Nature 2003, 422:835-847.
27. Croft D., et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 2011, 39 (Database issue):691-697.
28. Du P., et al. PseAAC-General: fast building various modes of general form of Chou[U+05F3]s pseudo-amino acid composition for large-scale protein datasets. Int. J. Mol. Sci. 2014, 15:3495-3506.
29. Ewing R.M., et al. Large-scale mapping of human protein-protein interactions by mass spectrometry. Mol. Syst. Biol. 2007, 3:89.
30. Franke L., et al. Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am. J. Hum. Genet. 2006, 78:1011-1025.
31. Fuchs K.R., et al. Analysis of the ACP1 gene product: classification as an FMN phosphatase. Biochem. Biophys. Res. Commun. 1992, 189:1598-1605.
32. Furuhashi M., Hotamisligil G.S. Fatty acid-binding proteins: role in metabolic diseases and potential as drug targets. Nat. Rev. Drug Discov. 2008, 7:489-503.
33. Furuhashi M., et al. Lipid chaperones and metabolic inflammation. Int. J. Inflamm. 2011, 2011:642612.
34. Guo S.H., et al. iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics 2014, 2014(30):1522-1529.
35. Hayasaka S., et al. A network of genes, genetic disorders, and brain areas. PLoS One 2011, 6:e20907.
36. Henning H., et al. IntAct: an open source molecular interaction database. Nucleic Acids Res. 2004, 32:D452-D455.
37. Hermjakob H., et al. IntAct: an open source molecular interaction database. Nucleic Acids Res. 2004, 32 (Database issue):D452-D455.
38. Hu L.L., et al. Predicting biological functions of compounds based on chemical-chemical interactions. PLoS One 2011, 6:e29491.
39. Huang T., et al. Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networks. PLoS One 2010, 5:e10972.
40. Huang T., et al. Classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional property. PLoS One 2011, 6:e25297.
41. Huntley R.P., et al. Understanding how and why the gene ontology and its annotations evolve: the GO within UniProt. Gigascience 2014, 3:4.
42. Jensen L.J., et al. STRING 8-a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res 2009, 37 (Database issue):D412-D416.
43. Jia P., et al. A comprehensive network and pathway analysis of candidate genes in major depressive disorder. BMC Syst. Biol. 2011, 5:S12.
44. Johnson M.M., et al. Screening for deleterious non synonymous single-nucleotide polymorphisms in genes involved in steroid hormone metabolism and response. Cancer Epidemiol. Biomark. Prev. 2005, 4:1326-1329.
45. Kanehisa M., et al. The KEGG resource for deciphering the genome). Nucleic Acids Res. 2004, 32 (Database issue):D277-D280.
46. Karakas S.E., et al. Serum fatty acid binding protein 4, free fatty acids, and metabolic risk markers. Metabolism 2009, 58:1002-1007.
47. Kerrien S., et al. The IntAct molecular interaction database in 2012. Nucleic Acids Res. 2012, 40 (Database issue):D841-D846.
48. Keshava P.T.S., et al. Human protein reference database-2009 update. Nucleic Acids Res. 2008, 37 (Database issue):D767-D772.
49. Koh J.H., et al. Serum adipocyte fatty acid-binding protein levels are associated with nonalcoholic fatty liver disease in type 2 diabetic patients. Diabetes Care 2009, 32:147-152.
50. Kundu A., Anbarasu A. Analysis of single nuceleotide polymorphisms of collagen type I alpha1gene associated with osteoporosis: an in-silico study. J. OMICS Res. 2011, 1:32-40.
51. Kundu A., Anbarasu A. Computational study of ADD1 gene polymorphism associated with hypertension. Cell Biochem. Biophys. 2013, 65:13-19.
52. Kundu A., et al. Mutation in Angiotensin II type 1 receptor disrupts sits binding to Angiotensin II leading to hypotension: an insight into hydrogen bonding patterns. Front. Biol. 2012, 7:447-484.
53. Kundu A., et al. Leucine to proline substitution by SNP at position197 in Caspase 9 gene expression leads to neuroblastoma: a bioinformatics analysis. Interdiscip. Sci. 2013, 4:225-234.
54. Li B.Q., et al. Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network. PLoS One 2012, 7:e33393.
55. Li J., et al. Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts. PLoS Comput. Biol. 2009, 5:1-15.
56. Li X., et al. Bio-informatics analysis of a gene co-expression module in adipose tissue containing the diet-responsive gene Nnat. BMC Syst. Biol. 2010, 4:175.
57. Lin A., et al. A genome-wide map of human genetic interactions inferred from radiation hybrid genotypes. Genome Res. 2010, 20:1122-1132.
58. Lin W.Z., Xiao X. Wenxiang: a web-server for drawing wenxiang diagrams. Nat. Sci. 2011, 3:862-865.
59. Maeda K., et al. Adipocyte/macrophage fatty acid binding proteins control integrated metabolic responses in obesity and diabetes. Cell Metab. 2005, 1:107-119.
60. Makowski L., Hotamisligil G.S. Fatty acid binding proteins-the evolutionary crossroads of inflammatory and metabolic responses. J. Nutr. 2004, 134:2464S-2468S.
61. Mallon B.S., et al. StemCellDB: the human pluripotent stem cell database at the National Institutes of Health. Stem Cell Res. 2013, 10:57-66.
62. Marr E., et al. Expression, purification, crystallization and structure of human adipocyte lipid-binding protein (aP2). Acta Crystallogr. Sect. F Struct. Biol. Cryst. Commun. 2006, 62:1058-1060.
63. Ng P.C., Henikoff S. Predicting deleterious amino acid substitutions. Genome Res. 2001, 11:863-874.
64. Ng P.C., Henikoff S. Accounting for human polymorphisms predicted to affect protein function. Genome Res. 2002, 12:436-446.
65. Ng P.C., Henikoff S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003, 31:3812-3814.
66. Nguyen D.M., El-Serag H.B. The epidemiology of obesity. Gastroenterol. Clin. N. Am. 2010, 39:1-7.
67. Qi L., Cho Y.A. Gene-environment interaction and obesity. Nutr. Rev. 2008, 66:684-694.
68. Qiu W.R., et al. iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components. Int. J. Mol. Sci. 2014, 15:1746-1766.
69. Queipo-Ortuño, et al. FABP4 dynamics in obesity: discrepancies in adipose tissue and liver expression regarding circulating plasma levels. PLoS One 2012, 7:e48605.
70. Ramensky V., et al. Human non-synonymous SNPs: server and survey. Nucleic Acids Res. 2002, 30:3894-3900.
71. Rebhan M., et al. GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support. Bioinformatics 1998, 14:656-664.
72. Reinehr T., et al. Retinol-binding protein 4 and its relation to insulin resistance in obese children before and after weight loss. J. Clin. Endocrinol. Metab. 2008, 93:2287-2293.
73. Serebriiskii I., et al. Yeast two-hybrid system for studying protein-protein interactions-stage 1: construction and characterization of a bait protein. Cold Spring Harb. Protoc. 2010, 5.
74. Shan T., et al. Fatty acid binding protein 4 expression marks a population of adipocyte progenitors in white and brown adipose tissues. FASEB J. 2013, 27:277-287.
75. Shannon P., et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13:2498-2504.
76. Shekels L.L., et al. Identification of the adipocyte acid phosphatase as a PAO-sensitive tyrosyl phosphatase. Protein Sci. 1992, 1(6):710-721.
77. Shen J., et al. Applications of computational algorithm tools to identify functional SNPs in cytokine genes. Cytokine 2006, 35:62-66.
78. Shen W.J., et al. Interaction of rat hormone-sensitive lipase with adipocyte lipid binding protein. Proc. Natl. Acad. Sci. USA 1999, 96:5528-5532.
79. Shen W.J., et al. Characterization of the functional interaction of adipocyte lipid-binding protein with hormone-sensitive lipase. J. Biol. Chem. 2001, 276:49443-49448.
80. Shin J., et al. Comparative analysis of fatty acid-binding protein 4 promoters: conservation of peroxisome proliferator-activated receptor binding sites. J. Anim. Sci. 2009, 87:3923-3934.
81. Snel B., et al. STRING: a web-server to retrieve and display the repeatedly occurring neighbourhood of a gene. Nucleic Acids Res. 2000, 28:3442-3444.
82. Sowa M.E., et al. Defining the human deubiquitinating enzyme interaction landscape. Cell 2009, 138:389-403.
83. Sunyaev S., et al. Prediction of deleterious human alleles. Hum. Mol. Genet. 2001, 10:591-597.
84. Szklarczyk D., et al. The STRING database in 2011: functional interaction networks of proteins globally integrated and scored. Nucleic Acids Res. 2011, 39 (Database issue):D561-D568.
85. Terra X., et al. FABP 4 is associated with inflammatory markers and metabolic syndrome in morbidly obese women. Eur. J. Endocrinol. 2011, 164:539-547.
86. Thomas P.D., et al. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 2003, 13:2129-2141.
87. Thusberg J., et al. Performance of mutation pathogenicity prediction methods on missense variants. Hum. Mutat. 2011, 32:358-368.
88. Vinay Kumar C., et al. Protein aggregation due to nsSNP resulting in P56S VABP protein is associated with amyotrophic lateral sclerosis. J. Theor. Biol. 2014, 354:72-80.
89. Vinayagam A., et al. A directed protein interaction network for investigating intracellular signal transduction. Sci. Signal. 2011, 4:rs8.
90. von Mering C., et al. STRING: a database of predicted functional associations between proteins. Nucleic Acids Res. 2003, 31:258-261.
91. von Mering C., et al. STRING: known and predicted protein-protein associations integrated and transferred across organisms. Nucleic Acids Res 2005, 33 (Database issue):D433-D437.
92. von Mering C., et al. STRING 7-recent developments in the integration and prediction of protein interactions. Nucleic Acids Res. 2007, 35 (Database issue):D358-D362.
93. Wang J., et al. WEB-based gene set analysis toolkit (WebGestalt): update 2013. Nucleic Acids Res. 2013, 41:W77-W83. (Web Server issue).
94. Warde-Farley D., et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010, 38:W214-W220. (Web Server issue).
95. Wei P., et al. Incorporating predicted functions of non synonymous variants into gene-based analysis of exome sequencing data: a comparative study. BMC Proc. 2011, 5:S20.
96. Wheeler D.L., et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2006, 34 (Database issue):D173-D180.
97. WHO, 2000. The problem of overweight and obesity. Obesity: preventing and managing the global epidemic. Report of a WHO Consultation. WHO Technical Report Series, Series 894, 5-3.
98. Wu X., et al. The effect of insulin on expression of genes and biochemical pathways in human skeletal muscle. Endocrine 2007, 31:5-17.
99. Xenarios I., et al. DIP: the database of interacting proteins. Nucleic Acids Res. 2000, 28:289-291.
100. Xu A., et al. Adipocyte fatty acid-binding protein is a plasma biomarker closely associated with obesity and metabolic syndrome. Clin. Chem. 2006, 52:405-413.
101. Yun K.E., et al. Association between adipocyte fatty acid-binding protein levels and childhood obesity in Korean children. Metabolism 2009, 58:798-802.
102. Zhou G.P. The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism. J. Theor. Biol. 2011, 284:142-148.
103. Zhou G.P., Deng M.H. An extension of Chou[U+05F3]s graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways. Biochem. J. 1984, 222:169-176.
104. Zhou G.P., Huang R.B. The pH-triggered conversion of the PrP(c) to PrP(sc.). Curr. Top. Med. Chem. 2013, 13:1152-1163.
105. Zhu Y., et al. Correlating observed odds ratios from lung cancer case-control studies to SNP functional scores predicted by bioinformatic tools. Mutat. Res. 2008, 639:80-88.
106. Zimmerman A.W., Veerkamp J.H. New insights into the structure and function of fatty acid-binding proteins. Cell Mol. Life Sci. 2002, 59:1096-1116.
107. Zimmet P.Z., Alberti K.G. Introduction: globalization and the non-communicable disease epidemic. Obesity (Silver Spring) 2006, 14:1-3.
108. Zuberi K., et al. GeneMANIA prediction server 2013 update. Nucleic Acids Res. 2013, 41:W115-W122. (Web Server issue).