Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network

BMC Genomics

Volumn 16, Issue 1, 2015, Pages

  Pacheco, Maria Pires ^a   John, Elisabeth ^a   Kaoma, Tony ^b   Heinäniemi, Merja ^c   Nicot, Nathalie ^b   Vallar, Laurent ^b   Bueb, Jean Luc ^a   Sinkkonen, Lasse ^a   Sauter, Thomas ^a  

^a UNIVERSITY OF LUXEMBOURG   (Luxembourg)

^b LUXEMBOURG INSTITUTE OF HEALTH   (Luxembourg)

^c UNIVERSITY OF EASTERN FINLAND   (Finland)

Author keywords

Active enhancer; High regulatory load; Macrophage differentiation; Metabolic modelling; Regulation of metabolism

Indexed keywords

HISTONE H3; SHORT HAIRPIN RNA; TRANSCRIPTOME;

ARTICLE; CANCER CELL LINE; CANCER MODEL; CELL DIFFERENTIATION; CELL METABOLISM; CELL SPECIFICITY; COMPUTER MODEL; CONTROLLED STUDY; DATA ANALYSIS SOFTWARE; DNA MICROARRAY; ENHANCER REGION; EPIGENETICS; GENE EXPRESSION PROFILING; GENE IDENTIFICATION; HISTONE ACETYLATION; HUMAN; HUMAN CELL; MACROPHAGE; MALE; MONOCYTE; TRANSCRIPTOMICS; BIOLOGICAL MODEL; CELL LINEAGE; GENETIC EPIGENESIS; GENETICS; METABOLISM; SOFTWARE;

CELL LINEAGE; EPIGENESIS, GENETIC; HUMANS; MACROPHAGES; METABOLIC NETWORKS AND PATHWAYS; MODELS, GENETIC; SOFTWARE; TRANSCRIPTOME;

EID: 84945186280     PISSN: None     EISSN: 14712164     Source Type: Journal    
DOI: 10.1186/s12864-015-1984-4     Document Type: Article

References (74)

1. Gluckman PD, Hanson MA, Buklijas T, Low FM, Beedle AS. Epigenetic mechanisms that underpin metabolic and cardiovascular diseases. Nat Rev Endocrinol. 2009;5:401-8.
2. Lin MT, Beal MF. Mitochondrial dysfunction and oxidative stress in neurodegenerative diseases. Nature. 2006;443:787-95.
3. Cairns RA, Harris IS, Mak TW. Regulation of cancer cell metabolism. Nat Rev Cancer. 2011;11:85-95.
4. Lewis NE, Hixson KK, Conrad TM, Lerman JA, Charusanti P, Polpitiya AD, et al. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol Syst Biol. 2010;6:390.
5. Maston GA, Landt SG, Snyder M, Green MR. Characterization of enhancer function from genome-wide analyses. Annu Rev Genomics Hum Genet. 2012;13:29-57.
6. Calo E, Wysocka J. Modification of Enhancer Chromatin: What, How, and Why? Mol Cell. 2013;49:825-37.
7. Rada-Iglesias A, Bajpai R, Swigut T, Brugmann SA, Flynn RA, Wysocka J. A unique chromatin signature uncovers early developmental enhancers in humans. Nature. 2011;470:279-83.
8. Creyghton MP, Cheng AW, Welstead GG, Kooistra T, Carey BW, Steine EJ, et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc Natl Acad Sci U S A. 2010;107:21931-6.
9. Whyte WA, Orlando DA, Hnisz D, Abraham BJ, Lin CY, Kagey MH, et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell. 2013;153:307-19.
10. Parker SCJ, Stitzel ML, Taylor DL, Orozco JM, Erdos MR, Akiyama JA, et al. Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants. Proc Natl Acad Sci U S A. 2013;110:17921-6.
11. Hnisz D, Abraham BJ, Lee TI, Lau A, Saint-André V, Sigova AA, et al. Super-enhancers in the control of cell identity and disease. Cell. 2013;155:934-47.
12. Dowen JM, Fan ZP, Hnisz D, Ren G, Abraham BJ, Zhang LN, et al. Control of Cell Identity Genes Occurs in Insulated Neighborhoods in Mammalian Chromosomes. Cell. 2014;159:374-87.
13. Siersbæk R, Rabiee A, Nielsen R, Sidoli S, Traynor S, Loft A, et al. Transcription Factor Cooperativity in Early Adipogenic Hotspots and Super-Enhancers. Cell Rep. 2014;7:1443-55.
14. Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, Vo TD, et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci. 2007;104:1777-82.
15. Thiele I, Swainston N, Fleming RMT, Hoppe A, Sahoo S, Aurich MK, et al. A community-driven global reconstruction of human metabolism. Nat Biotechnol. 2013;3:419-25.
16. Ma H, Sorokin A, Mazein A, Selkov A, Selkov E, Demin O, et al. The Edinburgh human metabolic network reconstruction and its functional analysis. Mol Syst Biol. 2007;3:135.
17. Agren R, Bordel S, Mardinoglu A, Pornputtapong 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.
18. Agren R, Mardinoglu A, Asplund A, Kampf C, Uhlen M, Nielsen J. Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol Syst Biol. 2014;10:3.
19. Caspi R, Altman T, Dale JM, Dreher K, Fulcher CA, Gilham F, et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 2010;38 suppl 1:D473-9.
20. Romero P, Wagg J, Green ML, Kaiser D, Krummenacker M, Karp PD. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 2004;6:R2.
21. Folger O, Jerby L, Frezza C, Gottlieb E, Ruppin E, Shlomi T. Predicting selective drug targets in cancer through metabolic networks. Mol Syst Biol. 2011;7:501.
22. Mardinoglu A, Agren R, Kampf C, Asplund A, Uhlen M, Nielsen J. Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat Commun. 2014;5:3083.
23. Becker SA, Palsson BØ. Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol. 2008;4:e1000082.
24. Zur H, Ruppin E, Shlomi T. iMAT: an integrative metabolic analysis tool. Bioinformatics. 2010;26:3140-2.
25. Jensen PA, Papin JA. Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics. 2011;27:541-7.
26. Wang Y, Eddy JA, Price ND. Reconstruction of genome-scale metabolic models for 126 human tissues using {mCADRE}. BMC Syst Biol. 2012;6:153.
27. Jerby L, Shlomi T, Ruppin E. Computational Reconstruction of Tissue-specific Metabolic Models: Application to Human Liver Metabolism. Mol Syst Biol. 2010;6:401.
28. Vlassis N, Pacheco MP, Sauter T. Fast reconstruction of compact context-specific metabolic network models. PLoS Comput Biol. 2014;10:e1003424.
29. McCall MN, Uppal K, Jaffee HA, Zilliox MJ, Irizarry RA. The Gene Expression Barcode: leveraging public data repositories to begin cataloging the human and murine transcriptomes. Nucleic Acids Res. 2011;39 suppl 1:D1011-5.
30. Zilliox MJ, Irizarry RA. A gene expression bar code for microarray data. Nat Meth. 2007;4:911-3.
31. Shankavaram UT, Varma S, Kane D, Sunshine M, Chary KK, Reinhold WC, et al. Cell Miner: a relational database and query tool for the NCI-60 cancer cell lines. BMC Genomics. 2009;10:277.
32. Pfister TD, Reinhold WC, Agama K, Gupta S, Khin SA, Kinders RJ, et al. Topoisomerase I levels in the NCI-60 cancer cell line panel determined by validated ELISA and microarray analysis and correlation with indenoisoquinoline sensitivity. Mol Cancer Ther. 2009;8:1878-84.
33. Luo B, Cheung HW, Subramanian A, Sharifnia T, Okamoto M, Yang X, et al. Highly parallel identification of essential genes in cancer cells. Proc Natl Acad Sci. 2008;105:20380-5.
34. Queralt-Rosinach N, Furlong LI. DisGeNET RDF: A Gene-Disease Association Linked Open Data Resource. In: SWAT4LS. 2013.
35. Mabbott NA, Baillie JK, Brown H, Freeman TC, Hume DA. An expression atlas of human primary cells: inference of gene function from coexpression networks. BMC Genomics. 2013;14:632.
36. Huber R, Pietsch D, Günther J, Welz B, Vogt N, Brand K. Regulation of monocyte differentiation by specific signaling modules and associated transcription factor networks. Cell Mol Life Sci. 2014;71:63-92.
37. Pham T-H, Benner C, Lichtinger M, Schwarzfischer L, Hu Y, Andreesen R, et al. Dynamic epigenetic enhancer signatures reveal key transcription factors associated with monocytic differentiation states. Blood. 2012;119:e161-71.
38. Najafi-Shoushtari SH, Kristo F, Li Y, Shioda T, Cohen DE, Gerszten RE, et al. MicroRNA-33 and the SREBP Host Genes Cooperate to Control Cholesterol Homeostasis. Science. 2010;328(5985):1566-9.
39. Suzuki E, Williams S, Sato S, Gilkeson G, Watson DK, Zhang XK. The transcription factor Fli-1 regulates monocyte, macrophage and dendritic cell development in mice. Immunology. 2013;139:318-27.
40. Galhardo M, Sinkkonen L, Berninger P, Lin J, Sauter T, Heinäniemi M. Integrated analysis of transcript-level regulation of metabolism reveals disease-relevant nodes of the human metabolic network. Nucleic Acids Res. 2014;42:1474-96.
41. Pasquali L, Gaulton KJ, Rodríguez-Seguí SA, Mularoni L, Miguel-Escalada I, Akerman I, et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet. 2014;46:136-43.
42. Björkhem I. Mechanism of degradation of the steroid side chain in the formation of bile acids. J Lipid Res. 1992;33(4):455-71.
43. Russell DW, Setchell KD. Bile acid biosynthesis. Biochemistry. 1992;31:4737-49.
44. Björkhem I, Andersson O, Diczfalusy U, Sevastik B, Xiu RJ, Duan C, et al. Atherosclerosis and sterol 27-hydroxylase: evidence for a role of this enzyme in elimination of cholesterol from human macrophages. Proc Natl Acad Sci. 1994;91(18):8592-6.
45. Suzuki H, Forrest ARR, van Nimwegen E, Daub CO, Balwierz PJ, Irvine KM, et al. The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line. Nat Genet. 2009;41:553-62.
46. Escher G, Krozowski Z, Croft KD, Sviridov D. Expression of sterol 27-hydroxylase (CYP27A1) enhances cholesterol efflux. J Biol Chem. 2003;278:11015-9.
47. Cali JJ, Hsieh CL, Francke U, Russell DW. Mutations in the bile acid biosynthetic enzyme sterol 27-hydroxylase underlie cerebrotendinous xanthomatosis. J Biol Chem. 1991;266:7779-83.
48. Shanahan CM, Carpenter KLH, Cary NRB. A potential role for sterol 27-hydroxylase in atherogenesis. Atherosclerosis. 2001;154:269-76.
49. Hnisz D, Abraham BJ, Lee TI, Lau A, Saint-André V, Sigova AA, et al. Super-enhancers in the control of cell identity and disease. Cell. 2013;155:934-47.
50. Galhardo M, Berninger P, Nguyen T-P, Sauter T, Sinkkonen L. Cell type-selective disease-association of genes under high regulatory load. Nucleic Acids Res. 2015 Sep 3. pii: gkv863.
51. Klipp E, Herwig R, Kowald A, Wierling C, Lehrach H. Systems Biology in Practice: Concepts, Implementation and Application. Hoboken, New Jersey: John Wiley & Sons; 2008.
52. Schwarcz R, Bruno JP, Muchowski PJ, Wu H-Q. Kynurenines in the mammalian brain: when physiology meets pathology. Nat Rev Neurosci. 2012;13:465-77.
53. Ishibashi Y, Kohyama-Koganeya A, Hirabayashi Y. New insights on glucosylated lipids: Metabolism and functions. Biochim Biophys Acta. 2013;1831:1475-85.
54. Puryear WB, Yu X, Ramirez NP, Reinhard BM, Gummuluru S. HIV-1 incorporation of host-cell-derived glycosphingolipid GM3 allows for capture by mature dendritic cells. Proc Natl Acad Sci. 2012;109:7475-80.
55. Brown MS, Goldstein JL. A receptor-mediated pathway for cholesterol homeostasis. 1985.
56. Ross R. Atherosclerosis--an inflammatory disease. N Engl J Med. 1999;340:115-26.
57. Halaris A. Inflammation, heart disease, and depression. Curr Psychiatry Rep. 2013:400.
58. Norris PC, Dennis EA. A lipidomic perspective on inflammatory macrophage eicosanoid signaling. Adv Biol Regul. 2014;54:99-110.
59. Gras G, Porcheray F, Samah B, Leone C. The glutamate-glutamine cycle as an inducible, protective face of macrophage activation. J Leukoc Biol. 2006;80:1067-75.
60. Tan L, Yu JT, Tan L. The kynurenine pathway in neurodegenerative diseases: Mechanistic and therapeutic considerations. J Neurol Sci. 2012;323:1-8.
61. Machado D, Herrgård M. Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput Biol. 2014;10:e1003580.
62. McCall MN, Bolstad BM, Irizarry RA. Frozen robust multiarray analysis (fRMA). Biostatistics. 2010;11:242-53.
63. Bordbar A, Lewis NE, Schellenberger J, Palsson BØ, Jamshidi N. Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol Syst Biol. 2010;6:422.
64. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003;4:249-64.
65. Smyth G. limma: Linear Models for Microarray Data. In: Gentleman R, Carey V, Huber W, Irizarry R, Dudoit S, Editors. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Berlin, Heidelberg: Springer-Verlag; 2005:397-420.
66. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc Ser B. 1995;57:289-300.
67. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.
68. Valouev A, Johnson DS, Sundquist A, Medina C, Anton E, Batzoglou S, et al. Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nat Methods. 2008;5:829-34.
69. Thorvaldsdóttir H, Robinson JT, Mesirov JP. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Briefings Bioinforma. 2013;14(2):178-92.
70. McLean CY, Bristor D, Hiller M, Clarke SL, Schaar BT, Lowe CB, et al. GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol. 2010;28:495-501.
71. Machanick P, Bailey TL. MEME-ChIP: Motif analysis of large DNA datasets. Bioinformatics. 2011;27:1696-7.
72. Jolma A, Yan J, Whitington T, Toivonen J, Nitta KR, Rastas P, et al. DNA-binding specificities of human transcription factors. Cell. 2013;152:327-39.
73. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44-57.
74. Baumuratova T, Dobre S, Bastogne T, Sauter T. Switch of Sensitivity Dynamics Revealed with DyGloSA Toolbox for Dynamical Global Sensitivity Analysis as an Early Warning for System's Critical Transition. PLoS One. 2013;8:e82973.