期刊论文详细信息
BMC Genomics
Exploring mechanisms of diet-colon cancer associations through candidate molecular interaction networks
Gianni Panagiotou2  Irene Kouskoumvekaki1  Kasper Jensen1  Jun Li2  David Westergaard2 
[1] Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kemitorvet, Building 208, Lyngby DK-2800, Denmark;School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China
Others  :  1217221
DOI  :  10.1186/1471-2164-15-380
 received in 2014-01-20, accepted in 2014-05-13,  发布年份 2014
PDF
【 摘 要 】

Background

Epidemiological studies in the recent years have investigated the relationship between dietary habits and disease risk demonstrating that diet has a direct effect on public health. Especially plant-based diets -fruits, vegetables and herbs- are known as a source of molecules with pharmacological properties for treatment of several malignancies. Unquestionably, for developing specific intervention strategies to reduce cancer risk there is a need for a more extensive and holistic examination of the dietary components for exploring the mechanisms of action and understanding the nutrient-nutrient interactions. Here, we used colon cancer as a proof-of-concept for understanding key regulatory sites of diet on the disease pathway.

Results

We started from a unique vantage point by having a database of 158 plants positively associated to colon cancer reduction and their molecular composition (~3,500 unique compounds). We generated a comprehensive picture of the interaction profile of these edible and non-edible plants with a predefined candidate colon cancer target space consisting of ~1,900 proteins. This knowledge allowed us to study systematically the key components in colon cancer that are targeted synergistically by phytochemicals and identify statistically significant and highly correlated protein networks that could be perturbed by dietary habits.

Conclusion

We propose here a framework for interrogating the critical targets in colon cancer processes and identifying plant-based dietary interventions as important modifiers using a systems chemical biology approach. Our methodology for better delineating prevention of colon cancer by nutritional interventions relies heavily on the availability of information about the small molecule constituents of our diet and it can be expanded to any other disease class that previous evidence has linked to lifestyle.

【 授权许可】

   
2014 Westergaard et al.; licensee BioMed Central Ltd.

【 预 览 】
附件列表
Files Size Format View
20150705122516522.pdf 2465KB PDF download
Figure 5. 66KB Image download
Figure 4. 52KB Image download
Figure 3. 60KB Image download
Figure 2. 199KB Image download
Figure 1. 74KB Image download
【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

【 参考文献 】
  • [1]Kussmann M, Fay LB: Nutrigenomics and personalized nutrition: science and concept. Pers Med 2008, 5:447-455.
  • [2]Kaput I: Nutrigenomics research for personalized nutrition and medicine. Curr Opin Biotechnol 2008, 19:110-120.
  • [3]Hutter CM, Chang-Claude J, Slattery ML, Pflugeisen BM, Lin Y, Duggan D, Nan H, Lemire M, Rangrej J, Figueiredo JC, Jiao S, Harrison TA, Liu Y, Chen LS, Stelling DL, Warnick GS, Hoffmeister M, Küry S, Fuchs CS, Giovannucci E, Hazra A, Kraft P, Hunter DJ, Gallinger S, Zanke BW, Brenner H, Frank B, Ma J, Ulrich CM, White E, et al.: Characterization of gene-environment interactions for colorectal cancer susceptibility loci. Cancer Res 2012, 72:2036-2044.
  • [4]Shiels PG, McGlynn LM, MacIntyre A, Johnson PC, Batty GD, Burns H, Cavanagh J, Deans KA, Ford I, McConnachie A, McGinty A, McLean JS, Millar K, Sattar N, Tannahill C, Velupillai YN, Packard CJ: Accelerated telomere attrition is associated with relative household income, diet, and inflammation of the pSoBid cohort. PLoS One 2011, 6:e22521.
  • [5]Nanri H, Nakamura K, Hara M, Higaki Y, Imaizumi T, Taguchi N, Sakamoto T, Horita M, Shinchi K, Tanaka K: Association between dietary pattern and serum C-reactive protein in Japanese men and women. J Epidemiol 2011, 21:122-131.
  • [6]Manson MM: Cancer prevention- the potential for diet to modulate molecular signaling. Trends Mol Med 2003, 9:11-18.
  • [7]Katada S, Imhof A, Sassone-Corsi PL: Connecting threads: epigenetics and metabolism. Cell 2012, 148:24-28.
  • [8]Fu WJ, Stroberg AJ, Viele K, Carroll RJ, Wu G: Statistics and bioinformatics in nutritional sciences: analysis of complex data in the era of systems biology. J Nutr Biochem 2010, 21:561-572.
  • [9]Tammariello A, Milner J: Mouse models for unraveling the importance of diet in colon cancer prevention. J Nutr Biochem 2010, 21:77-88.
  • [10]Hambly R, Saunders M, Rijken PJ, Rowland IR: Influence of dietary components associated with high or low risk of colon cancer on apoptosis in the rat colon. Food Chem Toxicol 2002, 40:801-808.
  • [11]Jensen K, Panagiotou G, Kouskoumvekaki I: Integrated text mining and chemoinformatics analysis associates diet to health benefit at molecular level. PLoS Comput Biol 2013, 10:e1003432.
  • [12]Agren R, Bordel S, Mardinoglu A, Pomputtapong N, Nookaew I, Nielsen J: Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput Biol 2012, 8:e1002518.
  • [13]Oh SC, Park Y-Y, Park ES, Lim JY, Kim SM, Kim S-B, Kim J, Kim SC, Chu I-S, Smith JJ, Beauchamp RD, Yeatman TJ, Kopetz S, Lee J-S: Prognostic gene expression signature associated with two molecularly distinct subtypes of colorectal cancer. Gut 2012, 61:1291-1298.
  • [14]Uhlen M, Oksvold P, Fagerberg L, Lundberg E, Jonasson K, Forsberg M, Zwahlen M, Kampf C, Wester K, Hober S, Wernerus H, Bjorling L, Ponten F: Towards a Knowledge-Based Human Protein Atlas. Nat Biotech 2010, 28:1248-1250.
  • [15]da Huang W, Sherman BT, Lempicki RA: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucelic Acids Res 2009, 37:1-13.
  • [16]Hunter S, Jones P, Mitchell A, Apweiler R, Attwood TK, Bateman A, Bernard T, Binns D, Bork P, Burge S, de Castro E, Coggill P, Corbett M, Das U, Daugherty L, Duquenne L, Finn RD, Fraser M, Gough J, Haft D, Hulo N, Kahn D, Kelly E, Letunic I, Lonsdale D, Lopez R, Madera M, Maslen J, McAnulla C, McDowall J, et al.: InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res 2012, 40:D306-D312.
  • [17]Hsu PP, Sabatini DM: Cancer cell metabolism: warburg and beyond. Cell 2008, 134:703-707.
  • [18]Hu J, Locasale JW, Bielas JH, O’Sullivan JO, Sheahan K, Cantley LC, Vander Heiden MG, Vitkup D: Heterogeneity of tumor-induced gene expression changes in the human metabolic network. Nat Biotech 2013, 31:522-529.
  • [19]Wild CP: Future research perspectives on environment and health: the requirement for a more expansive concept of translational cancer research. Environ Health 2011, 10:S1-S15. BioMed Central Full Text
  • [20]Heinrich J: Influence of indoor factors in dwellings on the development of childhood asthma. Int J Hyg Environ Health 2011, 214:1-25.
  • [21]Brook RD, Rajagopalan S, Pope CA 3rd, Brook JR, Bhatnagar A, Diez-Roux AV, Holguin F, Hong Y, Luepker RV, Mittleman MA, Peters A, Siscovick D, Smith SC Jr, Whitsel L, Kaufman JD, American Heart Association Council on Epidemiology and Prevention, Council on the Kidney in Cardiovascular Disease, and Council on Nutrition, Physical Activity and Metabolism: Particulate matter air pollution and cardiovascular disease: an update to the scientific statement from the American Heart Association. Circulation 2010, 121:2331-2378.
  • [22]Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, Wishart DS: DrugBank 3.0: a Comprehensive Resource for ‘Omics’ Research on Drugs. Nucleic Acids Res 2011, 39:D1035-D1041.
  • [23]Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M: KEGG for Integration and Interpretation of Large-Scale Molecular Data Sets. Nucleic Acids Res 2012, 40:D109-D114.
  • [24]Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C, Jensen LJ: STRING V9.1: Protein-Protein Interaction Networks, with Increased Coverage and Integration. Nucleic Acids Res 2013, 41:D808-D815.
  • [25]Bento AP, Gaulton A, Hersey A, Bellis J, Chambers J, Davies M, Kruger FA, Light Y, Mak L, McGlinchey S, Nowotka M, Papadatos G, Santos R, Overington JP: The ChEMBL bioactivity database: an update. Nucleic Acids Res 2013, 42:D1083-D1090.
  • [26]Kramer C, Kalliokoski T, Gedeck P, Vulpetti A: The Experimental Uncertainty of Heterogeneous Public K(i) Data. J Med Chem 2012, 55:5165-5173.
  • [27]Ertl P, Rohde B, Selzer P: Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties. J Med Chem 2000, 43:3714-3717.
  • [28]Berthold MR, Cebron N, Dill F, Gabriel TR, Kötter T, Meinl T, Ohl P, Sieb C, Thiel K, Wiswedel B: KNIME: The Konstanz Information Miner. In Studies in Classification, Data Analysis, and Knowledge Organization (GfKL 2007). Heidelberg-Berlin: Springer-Verlag; 2007.
  文献评价指标  
  下载次数:30次 浏览次数:1次