Correlation-Based Network Generation, Visualization, and Analysis As a Powerful Tool in Biological Studies: A Case Study in Cancer Cell Metabolism

Overview

Journal Biomed Res Int

Publisher Wiley

Specialties Biomedical Engineering
Biotechnology
General Medicine

Date 2016 Nov 15

PMID 27840831

Citations 33

Authors

Albert Batushansky

David Toubiana

Aaron Fait

Affiliations

Soon will be listed here.

Abstract

In the last decade vast data sets are being generated in biological and medical studies. The challenge lies in their summary, complexity reduction, and interpretation. Correlation-based networks and graph-theory based properties of this type of networks can be successfully used during this process. However, the procedure has its pitfalls and requires specific knowledge that often lays beyond classical biology and includes many computational tools and software. Here we introduce one of a series of methods for correlation-based network generation and analysis using freely available software. The pipeline allows the user to control each step of the network generation and provides flexibility in selection of correlation methods and thresholds. The pipeline was implemented on published metabolomics data of a population of human breast carcinoma cell lines MDA-MB-231 under two conditions: normal and hypoxia. The analysis revealed significant differences between the metabolic networks in response to the tested conditions. The network under hypoxia had 1.7 times more significant correlations between metabolites, compared to normal conditions. Unique metabolic interactions were identified which could lead to the identification of improved markers or aid in elucidating the mechanism of regulation between distantly related metabolites induced by the cancer growth.

Citing Articles

Network analysis combined with genome-wide association study helps identification of genes related to amino acid contents in soybean.

Van K, Lee S, Mian M, McHale L BMC Genomics. 2025; 26(1):21.

PMID: 39780068 PMC: 11715193. DOI: 10.1186/s12864-024-11163-8.

The metabolic role of vitamin D in children's neurodevelopment: a network study.

De Marzio M, Lasky-Su J, Chu S, Prince N, Litonjua A, Weiss S Sci Rep. 2024; 14(1):16929.

PMID: 39043876 PMC: 11266698. DOI: 10.1038/s41598-024-67835-8.

A pilot study suggests the correspondence between SAR202 bacteria and dissolved organic matter in the late stage of a year-long microcosm incubation.

Jia Y, He C, Lahm M, Chen Q, Powers L, Gonsior M Front Microbiol. 2024; 15:1357822.

PMID: 38633701 PMC: 11021592. DOI: 10.3389/fmicb.2024.1357822.

Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases.

Meng W, Pan H, Sha Y, Zhai X, Xing A, Lingampelly S Metabolites. 2024; 14(2).

PMID: 38392985 PMC: 10890086. DOI: 10.3390/metabo14020093.

Large airway T cells in adults with former bronchopulmonary dysplasia.

Gao J, Um-Bergstrom P, Pourbazargan M, Berggren-Brostrom E, Li C, Merikallio H Respir Res. 2024; 25(1):86.

PMID: 38336805 PMC: 10858477. DOI: 10.1186/s12931-024-02717-1.

References

Pan X, Yuan D, Xiang D, Wang X, Sookhai-Mahadeo S, Bader J . A robust toolkit for functional profiling of the yeast genome. Mol Cell. 2004; 16(3):487-96. DOI: 10.1016/j.molcel.2004.09.035. View

Warburg O . On the origin of cancer cells. Science. 1956; 123(3191):309-14. DOI: 10.1126/science.123.3191.309. View

Vaupel P, Mayer A . Hypoxia in cancer: significance and impact on clinical outcome. Cancer Metastasis Rev. 2007; 26(2):225-39. DOI: 10.1007/s10555-007-9055-1. View

Madl J, Royer S . Glutamate dependence of GABA levels in neurons of hypoxic and hypoglycemic rat hippocampal slices. Neuroscience. 2000; 96(4):657-64. DOI: 10.1016/s0306-4522(99)00548-5. View

Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y . A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci U S A. 2001; 98(8):4569-74. PMC: 31875. DOI: 10.1073/pnas.061034498. View

Kotze H, Armitage E, Sharkey K, Allwood J, Dunn W, Williams K . A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions. BMC Syst Biol. 2013; 7:107. PMC: 3874763. DOI: 10.1186/1752-0509-7-107. View

Tong A, Evangelista M, Parsons A, Xu H, Bader G, Page N . Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science. 2001; 294(5550):2364-8. DOI: 10.1126/science.1065810. View

Kose F, Weckwerth W, Linke T, Fiehn O . Visualizing plant metabolomic correlation networks using clique-metabolite matrices. Bioinformatics. 2001; 17(12):1198-208. DOI: 10.1093/bioinformatics/17.12.1198. View

Yang Y, Han L, Yuan Y, Li J, Hei N, Liang H . Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types. Nat Commun. 2014; 5:3231. PMC: 3951205. DOI: 10.1038/ncomms4231. View

10.

Lisec J, Meyer R, Steinfath M, Redestig H, Becher M, Witucka-Wall H . Identification of metabolic and biomass QTL in Arabidopsis thaliana in a parallel analysis of RIL and IL populations. Plant J. 2007; 53(6):960-72. PMC: 2268983. DOI: 10.1111/j.1365-313X.2007.03383.x. View

11.

Ma X, Gao L . Biological network analysis: insights into structure and functions. Brief Funct Genomics. 2012; 11(6):434-42. DOI: 10.1093/bfgp/els045. View

12.

Shannon P, Markiel A, Ozier O, Baliga N, Wang J, Ramage D . Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003; 13(11):2498-504. PMC: 403769. DOI: 10.1101/gr.1239303. View

13.

Barabasi A, Gulbahce N, Loscalzo J . Network medicine: a network-based approach to human disease. Nat Rev Genet. 2010; 12(1):56-68. PMC: 3140052. DOI: 10.1038/nrg2918. View

14.

Griffiths J, McSheehy P, Robinson S, Troy H, Chung Y, Leek R . Metabolic changes detected by in vivo magnetic resonance studies of HEPA-1 wild-type tumors and tumors deficient in hypoxia-inducible factor-1beta (HIF-1beta): evidence of an anabolic role for the HIF-1 pathway. Cancer Res. 2002; 62(3):688-95. View

15.

Bekaert M, Edger P, Pires J, Conant G . Two-phase resolution of polyploidy in the Arabidopsis metabolic network gives rise to relative and absolute dosage constraints. Plant Cell. 2011; 23(5):1719-28. PMC: 3123947. DOI: 10.1105/tpc.110.081281. View

16.

Toubiana D, Batushansky A, Tzfadia O, Scossa F, Khan A, Barak S . Combined correlation-based network and mQTL analyses efficiently identified loci for branched-chain amino acid, serine to threonine, and proline metabolism in tomato seeds. Plant J. 2014; 81(1):121-33. DOI: 10.1111/tpj.12717. View

17.

Wise D, Ward P, Shay J, Cross J, Gruber J, Sachdeva U . Hypoxia promotes isocitrate dehydrogenase-dependent carboxylation of α-ketoglutarate to citrate to support cell growth and viability. Proc Natl Acad Sci U S A. 2011; 108(49):19611-6. PMC: 3241793. DOI: 10.1073/pnas.1117773108. View

18.

Toubiana D, Semel Y, Tohge T, Beleggia R, Cattivelli L, Rosental L . Metabolic profiling of a mapping population exposes new insights in the regulation of seed metabolism and seed, fruit, and plant relations. PLoS Genet. 2012; 8(3):e1002612. PMC: 3315483. DOI: 10.1371/journal.pgen.1002612. View

19.

Mitra K, Carvunis A, Ramesh S, Ideker T . Integrative approaches for finding modular structure in biological networks. Nat Rev Genet. 2013; 14(10):719-32. PMC: 3940161. DOI: 10.1038/nrg3552. View

20.

Kubota A, Meguid M, Hitch D . Amino acid profiles correlate diagnostically with organ site in three kinds of malignant tumors. Cancer. 1992; 69(9):2343-8. DOI: 10.1002/1097-0142(19920501)69:9<2343::aid-cncr2820690924>3.0.co;2-s. View