Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The IBD interactome: an integrated view of aetiology, pathogenesis and therapy

Abstract

Crohn's disease and ulcerative colitis are prototypical complex diseases characterized by chronic and heterogeneous manifestations, induced by interacting environmental, genomic, microbial and immunological factors. These interactions result in an overwhelming complexity that cannot be tackled by studying the totality of each pathological component (an '–ome') in isolation without consideration of the interaction among all relevant –omes that yield an overall 'network effect'. The outcome of this effect is the 'IBD interactome', defined as a disease network in which dysregulation of individual –omes causes intestinal inflammation mediated by dysfunctional molecular modules. To define the IBD interactome, new concepts and tools are needed to implement a systems approach; an unbiased data-driven integration strategy that reveals key players of the system, pinpoints the central drivers of inflammation and enables development of targeted therapies. Powerful bioinformatics tools able to query and integrate multiple –omes are available, enabling the integration of genomic, epigenomic, transcriptomic, proteomic, metabolomic and microbiome information to build a comprehensive molecular map of IBD. This approach will enable identification of IBD molecular subtypes, correlations with clinical phenotypes and elucidation of the central hubs of the IBD interactome that will aid discovery of compounds that can specifically target the hubs that control the disease.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Role of epigenetic modifications in the development of IBD.
Figure 2: Knowledge expansion and building of the IBD interactome.
Figure 3: Building and therapeutic targeting of the IBD interactome.

References

  1. 1

    Busse, R., Blu¨mel, M., Scheller-Kreinsen, D. & Zentner, A. Tackling Chronic Diseases in Europe — Strategies, Interventions and Challenges. (European Observatory on Health Systems and Polices & World Health Organization, 2010).

    Google Scholar 

  2. 2

    Iyengar, R. Complex diseases require complex therapies. EMBO Rep. 14, 1039–1042 (2013). This insightful commentary highlights the molecular complexity of chronic diseases and suggests that current therapeutic approaches may have reached their limits.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 3

    Sivakumaran, S. et al. Abundant pleiotropy in human complex diseases and traits. Am. J. Hum. Genet. 89, 607–618 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 4

    Ghiassian, S. D. et al. Endophenotype network models: common core of complex diseases. Sci. Rep. 6, 27414 (2016). This article presents an alternative view emphasizing that different diseases often have common underlying mechanisms and shared endophenotypes. The authors construct endophenotype network models and explore their relation to different diseases, identifying critical modules within the subnetworks of the human interactome.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 5

    de Souza, H. S. & Fiocchi, C. Immunopathogenesis of IBD: current state of the art. Nat. Rev. Gastroenterol. Hepatol. 13, 13–27 (2016). The authors present the current knowledge of the immunity and expand the concept of IBD immunopathogenesis to include various novel components that affect the intestinal inflammatory process.

    Article  CAS  PubMed  Google Scholar 

  6. 6

    Rogler, G. & Vavricka, S. Exposome in IBD: recent insights in environmental factors that influence the onset and course of IBD. Inflamm. Bowel Dis. 21, 400–408 (2015).

    Article  PubMed  Google Scholar 

  7. 7

    Saidel-Odes, L. & Odes, S. Hygiene hypothesis in inflammatory bowel disease. Ann. Gastroenterol. 27, 189–190 (2014).

    PubMed  PubMed Central  Google Scholar 

  8. 8

    Cleynen, I. & Vermeire, S. The genetic architecture of inflammatory bowel disease: past, present and future. Curr. Opin. Gastroenterol. 31, 456–463 (2015).

    CAS  PubMed  Google Scholar 

  9. 9

    McGovern, D. P., Kugathasan, S. & Cho, J. H. Genetics of inflammatory bowel diseases. Gastroenterology 149, 1163–1176.e2 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    Wang, M. H. et al. A novel approach to detect cumulative genetic effects and genetic interactions in Crohn's disease. Inflamm. Bowel Dis. 19, 1799–1808 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  11. 11

    Stappenbeck, T. S. et al. Crohn disease: a current perspective on genetics, autophagy and immunity. Autophagy 7, 355–374 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Kostic, A. D., Xavier, R. J. & Gevers, D. The microbiome in inflammatory bowel disease: current status and the future ahead. Gastroenterology 146, 1489–1499 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Manichanh, C., Borruel, N., Casellas, F. & Guarner, F. The gut microbiota in IBD. Nat. Rev. Gastroenterol. Hepatol. 9, 599–608 (2012).

    Article  CAS  PubMed  Google Scholar 

  14. 14

    Choung, R. S. et al. Serologic microbial associated markers can predict Crohn's disease behaviour years before disease diagnosis. Aliment. Pharmacol. Ther. 43, 1300–1310 (2016).

    Article  CAS  PubMed  Google Scholar 

  15. 15

    Darfeuille-Michaud, A. et al. High prevalence of adherent-invasive Escherichia coli associated with ileal mucosa in Crohn's disease. Gastroenterology 127, 412–421 (2004).

    Article  PubMed  Google Scholar 

  16. 16

    Machiels, K. et al. A decrease of the butyrate-producing species Roseburia hominis and Faecalibacterium prausnitzii defines dysbiosis in patients with ulcerative colitis. Gut 63, 1275–1283 (2013).

    Article  CAS  PubMed  Google Scholar 

  17. 17

    Goodrich, J. K. et al. Human genetics shape the gut microbiome. Cell 159, 789–799 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    Braunstein, J., Qiao, L., Autschbach, F., Schurmann, G. & Meuer, S. C. T cells of the human intestinal lamina propria are high producers of interleukin 10. Gut 53, 215–220 (1997).

    Article  Google Scholar 

  19. 19

    Monteleone, G. et al. Blocking Smad7 restores TGF-β1 signaling in chronic inflammatory bowel disease. J. Clin. Invest. 108, 601–609 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Neurath, M. F. Cytokines in inflammatory bowel disease. Nat. Rev. Immunol. 14, 329–342 (2014).

    Article  CAS  PubMed  Google Scholar 

  21. 21

    Reinisch, W. et al. A dose escalating, placebo controlled, double blind, single dose and multidose, safety and tolerability study of fontolizumab, a humanised anti-interferon gamma antibody, in patients with moderate to severe Crohn's disease. Gut 55, 1138–1144 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Hueber, W. et al. Secukinumab, a human anti-IL-17A monoclonal antibody, for moderate to severe Crohn's disease: unexpected results of a randomised, double-blind placebo-controlled trial. Gut 61, 1693–1700 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Danese, S. et al. Tralokinumab for moderate-to-severe UC: a randomised, double-blind, placebo-controlled, phase IIa study. Gut 64, 243–249 (2015).

    Article  CAS  PubMed  Google Scholar 

  24. 24

    Spencer, D. M., Veldman, G. M., Banerjee, S., Willis, J. & Levine, A. D. Distinct inflammatory mechanisms mediate early versus late colitis in mice. Gastroenterology 122, 94–105 (2002).

    Article  PubMed  Google Scholar 

  25. 25

    Kugathasan, S. et al. Mucosal T-cell immunoregulation varies in early and late Crohn's disease. Gut 56, 1696–1705 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Wang, P., Han, W. & Ma, D. Electronic sorting of immune cell subpopulations based on highly plastic genes. J. Immunol. 197, 665–673 (2016).

    Article  CAS  PubMed  Google Scholar 

  27. 27

    Berg, J. Genes-environment interplay. Science 354, 15 (2016).

    Article  CAS  PubMed  Google Scholar 

  28. 28

    Fan, S., Hansen, M. E., Lo, Y. & Tishkoff, S. A. Going global by adapting local: A review of recent human adaptation. Science 354, 54–59 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Miska, E. A. & Ferguson-Smith, A. C. Transgenerational inheritance: Models and mechanisms of non-DNA sequence-based inheritance. Science 354, 59–63 (2016).

    Article  CAS  PubMed  Google Scholar 

  30. 30

    Yosef, N. & Regev, A. Writ large: Genomic dissection of the effect of cellular environment on immune response. Science 354, 64–68 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Franks, P. W. & McCarthy, M. I. Exposing the exposures responsible for type 2 diabetes and obesity. Science 354, 69–73 (2016).

    Article  CAS  PubMed  Google Scholar 

  32. 32

    Suez, J. et al. Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature 514, 181–186 (2014).

    Article  CAS  PubMed  Google Scholar 

  33. 33

    Kreitinger, J. M., Beamer, C. A. & Shepherd, D. M. Environmental immunology: lessons learned from exposure to a select panel of immunotoxicants. J. Immunol. 196, 3217–3225 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    Carr, E. J. et al. The cellular composition of the human immune system is shaped by age and cohabitation. Nat. Immunol. 17, 461–468 (2016). Using a systems-level approach, the authors establish a resource of cellular immune profiles of healthy individuals.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 35

    Ye, B. D. & McGovern, D. P. Genetic variation in IBD: progress, clues to pathogenesis and possible clinical utility. Expert Rev. Clin. Immunol. 12, 1091–1107 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Glocker, E. O. et al. Inflammatory bowel disease and mutations affecting the interleukin-10 receptor. N. Engl. J. Med. 361, 2033–2045 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Chakravarti, A. Genomics is not enough. Science 334, 15 (2011).

    Article  CAS  PubMed  Google Scholar 

  38. 38

    Abreu, M. T. et al. Mutations in NOD2 are associated with fibrostenosing disease in patients with Crohn's disease. Gastroenterology 123, 679–688 (2002).

    Article  CAS  PubMed  Google Scholar 

  39. 39

    Sahni, N. et al. Widespread macromolecular interaction perturbations in human genetic disorders. Cell 161, 647–660 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Chen, Y. et al. Variations in DNA elucidate molecular networks that cause disease. Nature 452, 429–435 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

    Benfey, P. N. & Mitchell-Olds, T. From genotype to phenotype: systems biology meets natural variation. Science 320, 495–497 (2008). The authors propose that systems biology approaches can be applied to decipher gene's function and related abnormalities, generating genotype-phenotype maps.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Manolio, T. A. Genomewide association studies and assessment of the risk of disease. N. Engl. J. Med. 363, 166–176 (2010).

    Article  CAS  PubMed  Google Scholar 

  43. 43

    Silvestri, G. A. et al. A bronchial genomic classifier for the diagnostic evaluation of lung cancer. N. Engl. J. Med. 373, 243–251 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Bailey, P. et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature 531, 47–52 (2016).

    Article  CAS  PubMed  Google Scholar 

  45. 45

    Papaemmanuil, E. et al. Genomic classification and prognosis in acute myeloid leukemia. N. Engl. J. Med. 374, 2209–2221 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. 46

    Ellison, D. W. Multiple molecular data sets and the classification of adult diffuse gliomas. N. Engl. J. Med. 372, 2555–2557 (2015).

    Article  CAS  PubMed  Google Scholar 

  47. 47

    Edwards, S. L., Beesley, J., French, J. D. & Dunning, A. M. Beyond GWASs: illuminating the dark road from association to function. Am. J. Hum. Genet. 93, 779–797 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    [No authors listed.] Editorial: beyond the genome. Nature 518, 273 (2015).

  49. 49

    Kasowski, M. et al. Extensive variation in chromatin states across humans. Science 342, 750–752 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. 50

    Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015). In this study, investigators establish global maps of regulatory elements and define regulatory modules, revealing biologically relevant cell types for diverse human traits, and demonstrating the central role of epigenomic events for the development of disease.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 51

    Ventham, N. T., Kennedy, N. A., Nimmo, E. R. & Satsangi, J. Beyond gene discovery in inflammatory bowel disease: the emerging role of epigenetics. Gastroenterology 145, 293–308 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. 52

    Nimmo, E. R. et al. Genome-wide methylation profiling in Crohn's disease identifies altered epigenetic regulation of key host defense mechanisms including the Th17 pathway. Inflamm. Bowel Dis. 18, 889–899 (2012).

    Article  PubMed  Google Scholar 

  53. 53

    Polytarchou, C. et al. MicroRNA214 is associated with progression of ulcerative colitis, and inhibition reduces development of colitis and colitis-associated cancer in mice. Gastroenterology 149, 981–992.e11 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. 54

    Liu, S. et al. The host shapes the gut microbiota via fecal microRNA. Cell Host Microbe 19, 32–43 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 55

    Lee, T. I. & Young, R. A. Transcriptional regulation and its misregulation in disease. Cell 152, 1237–1251 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. 56

    Planell, N. et al. Transcriptional analysis of the intestinal mucosa of patients with ulcerative colitis in remission reveals lasting epithelial cell alterations. Gut 62, 967–976 (2013).

    Article  CAS  PubMed  Google Scholar 

  57. 57

    Li, M. et al. Widespread RNA and DNA sequence differences in the human transcriptome. Science 333, 53–58 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. 58

    Wu, L. et al. Variation and genetic control of protein abundance in humans. Nature 499, 79–82 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. 59

    Starr, A. E. et al. Proteomic analysis of ascending colon biopsies from a paediatric inflammatory bowel disease inception cohort identifies protein biomarkers that differentiate Crohn's disease from UC. Gut http://dx.doi.org/10.1136/gutjnl-2015-310705 (2016).

  60. 60

    Schicho, R. et al. Quantitative metabolomic profiling of serum, plasma, and urine by 1H NMR spectroscopy discriminates between patients with inflammatory bowel disease and healthy individuals. J. Proteome Res. 11, 3344–3357 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. 61

    Patti, G. J., Yanes, O. & Siuzdak, G. Innovation: metabolomics: the apogee of the omics trilogy. Nat. Rev. Mol. Cell Biol. 13, 263–269 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 62

    Lim, E. S. et al. Early life dynamics of the human gut virome and bacterial microbiome in infants. Nat. Med. 21, 1228–1234 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. 63

    Zhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. 64

    Franzosa, E. A. et al. Identifying personal microbiomes using metagenomic codes. Proc. Natl Acad. Sci. USA 112, E2930–E2938 (2015).

    Article  CAS  PubMed  Google Scholar 

  65. 65

    Chu, H. et al. Gene–microbiota interactions contribute to the pathogenesis of inflammatory bowel disease. Science 352, 1116–1120 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. 66

    Imhann, F. et al. Interplay of host genetics and gut microbiota underlying the onset and clinical presentation of inflammatory bowel disease. Gut http://dx.doi.org/10.1136/gutjnl-2016-312135 (2016).

  67. 67

    Van Noorden, R. Global scientific output doubles every nine years. Nature News Blog http://blogs.nature.com/news/2014/05/global-scientific-output-doubles-every-nine-years.html(2014).

  68. 68

    Goldberger, A. L. Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside. Lancet 347, 1312–1314 (1996).

    Article  CAS  PubMed  Google Scholar 

  69. 69

    West, B. J. A mathematics for medicine: the Network Effect. Front. Physiol. 5, 456 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  70. 70

    Sanchez, C. et al. Grasping at molecular interactions and genetic networks in Drosophila melanogaster using FlyNets, an Internet database. Nucleic Acids Res. 27, 89–94 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. 71

    Barabasi, A. L. & Albert, R. Emergence of scaling in random networks. Science 286, 509–512 (1999).

    Article  CAS  PubMed  Google Scholar 

  72. 72

    Ideker, T., Galitski, T. & Hood, L. A new approach to decoding life: systems biology. Annu. Rev. Genomics Hum. Genet. 2, 343–372 (2001). A very comprehensive review of the concept and application of systesm biology in biomedicine.

    Article  CAS  PubMed  Google Scholar 

  73. 73

    Polytarchou, C., Koukos, G. & Iliopoulos, D. Systems biology in inflammatory bowel diseases: ready for prime time. Curr. Opin. Gastroenterol. 30, 339–346 (2014). A systems biology approach, including novel computational methodologies capable of integrating high-throughput molecular data, is proposed for the study of pathogenic mechanisms in IBD.

    Article  CAS  PubMed  Google Scholar 

  74. 74

    Kabakchiev, B. & Silverberg, M. S. Expression quantitative trait loci analysis identifies associations between genotype and gene expression in human intestine. Gastroenterology 144, 1488–1496.e3 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. 75

    Adams, A. T. et al. Two-stage genome-wide methylation profiling in childhood-onset Crohn's Disease implicates epigenetic alterations at the VMP1/MIR21 and HLA loci. Inflamm. Bowel Dis. 20, 1784–1793 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  76. 76

    Saito, S. et al. DNA methylation of colon mucosa in ulcerative colitis patients: correlation with inflammatory status. Inflamm. Bowel Dis. 17, 1955–1965 (2011).

    Article  PubMed  Google Scholar 

  77. 77

    Foran, E. et al. Upregulation of DNA methyltransferase-mediated gene silencing, anchorage-independent growth, and migration of colon cancer cells by interleukin-6. Mol. Cancer Res. 8, 471–481 (2010).

    Article  CAS  PubMed  Google Scholar 

  78. 78

    Jones, B. & Chen, J. Inhibition of IFN-gamma transcription by site-specific methylation during T helper cell development. EMBO J. 25, 2443–2452 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. 79

    Gu, H. et al. Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling. Nat. Protoc. 6, 468–481 (2011).

    Article  CAS  PubMed  Google Scholar 

  80. 80

    Chen, T. & Dent, S. Y. Chromatin modifiers and remodellers: regulators of cellular differentiation. Nat. Rev. Genet. 15, 93–106 (2014).

    Article  CAS  PubMed  Google Scholar 

  81. 81

    Zhang, Q. et al. Tet2 is required to resolve inflammation by recruiting Hdac2 to specifically repress IL-6. Nature 525, 389–393 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. 82

    Meddens, C. A. et al. Systematic analysis of chromatin interactions at disease associated loci links novel candidate genes to inflammatory bowel disease. Genome Biol. 17, 247 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. 83

    Ellinghaus, D. et al. Analysis of five chronic inflammatory diseases identifies 27 new associations and highlights disease-specific patterns at shared loci. Nat. Genet. 48, 510–518 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. 84

    Koukos, G. et al. MicroRNA-124 regulates STAT3 expression and is down-regulated in colon tissues of pediatric patients with ulcerative colitis. Gastroenterology 145, 842–852.e2 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. 85

    Koukos, G. et al. A microRNA signature in pediatric ulcerative colitis: deregulation of the miR-4284/CXCL5 pathway in the intestinal epithelium. Inflamm. Bowel Dis. 21, 996–1005 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  86. 86

    Wu, F. et al. MicroRNAs are differentially expressed in ulcerative colitis and alter expression of macrophage inflammatory peptide-2 alpha. Gastroenterology 135, 1624–1635.e24 (2008).

    Article  CAS  PubMed  Google Scholar 

  87. 87

    Davidovich, C. & Cech, T. R. The recruitment of chromatin modifiers by long noncoding RNAs: lessons from PRC2. RNA 21, 2007–2022 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. 88

    Lasda, E. & Parker, R. Circular RNAs: diversity of form and function. RNA 20, 1829–1842 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. 89

    M'Koma, A. E. et al. Proteomic profiling of mucosal and submucosal colonic tissues yields protein signatures that differentiate the inflammatory colitides. Inflamm. Bowel Dis. 17, 875–883 (2011).

    Article  PubMed  Google Scholar 

  90. 90

    Meuwis, M. A. et al. Proteomics for prediction and characterization of response to infliximab in Crohn's disease: a pilot study. Clin. Biochem. 41, 960–967 (2008).

    Article  CAS  PubMed  Google Scholar 

  91. 91

    Humphrey, S. J., Azimifar, S. B. & Mann, M. High-throughput phosphoproteomics reveals in vivo insulin signaling dynamics. Nat. Biotechnol. 33, 990–995 (2015).

    Article  CAS  PubMed  Google Scholar 

  92. 92

    Le Gall, G. et al. Metabolomics of fecal extracts detects altered metabolic activity of gut microbiota in ulcerative colitis and irritable bowel syndrome. J. Proteome Res. 10, 4208–4218 (2011).

    Article  CAS  PubMed  Google Scholar 

  93. 93

    Stephens, N. S. et al. Urinary NMR metabolomic profiles discriminate inflammatory bowel disease from healthy. J. Crohns Colitis 7, e42–e48 (2013).

    Article  PubMed  Google Scholar 

  94. 94

    Lupp, C. et al. Host-mediated inflammation disrupts the intestinal microbiota and promotes the overgrowth of Enterobacteriaceae. Cell Host Microbe 2, 119–129 (2007).

    Article  CAS  PubMed  Google Scholar 

  95. 95

    Ohkusa, T. et al. Fusobacterium varium localized in the colonic mucosa of patients with ulcerative colitis stimulates species-specific antibody. J. Gastroenterol. Hepatol. 17, 849–853 (2002).

    Article  PubMed  Google Scholar 

  96. 96

    Ogura, Y. et al. A frameshift mutation in Nod2 associated with susceptibility to Crohn's disease. Nature 411, 603–606 (2001).

    Article  CAS  PubMed  Google Scholar 

  97. 97

    Gedela, S. Integration, warehousing, and analysis strategies of Omics data. Methods Mol. Biol. 719, 399–414 (2011).

    Article  CAS  PubMed  Google Scholar 

  98. 98

    Shen, R., Olshen, A. B. & Ladanyi, M. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 25, 2906–2912 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. 99

    Shen, R. et al. Integrative subtype discovery in glioblastoma using iCluster. PLoS ONE 7, e35236 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. 100

    Mo, Q. et al. Pattern discovery and cancer gene identification in integrated cancer genomic data. Proc. Natl Acad. Sci. USA 110, 4245–4250 (2013).

    Article  CAS  PubMed  Google Scholar 

  101. 101

    Vaske, C. J. et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26, i237–245 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. 102

    Paull, E. O. et al. Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE). Bioinformatics 29, 2757–2764 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. 103

    Wang, W. et al. iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data. Bioinformatics 29, 149–159 (2013).

    Article  CAS  PubMed  Google Scholar 

  104. 104

    Calvano, S. E. et al. A network-based analysis of systemic inflammation in humans. Nature 437, 1032–1037 (2005).

    Article  CAS  PubMed  Google Scholar 

  105. 105

    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. 106

    Saito, R. et al. A travel guide to Cytoscape plugins. Nat. Methods 9, 1069–1076 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. 107

    Maere, S., Heymans, K. & Kuiper, M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21, 3448–3449 (2005).

    Article  CAS  PubMed  Google Scholar 

  108. 108

    Vlasblom, J. et al. GenePro: a Cytoscape plug-in for advanced visualization and analysis of interaction networks. Bioinformatics 22, 2178–2179 (2006).

    Article  CAS  PubMed  Google Scholar 

  109. 109

    Xia, T. & Dickerson, J. A. OmicsViz: Cytoscape plug-in for visualizing omics data across species. Bioinformatics 24, 2557–2558 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. 110

    Gao, J. et al. Metscape: a Cytoscape plug-in for visualizing and interpreting metabolomic data in the context of human metabolic networks. Bioinformatics 26, 971–973 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. 111

    Moni, M. A., Xu, H. & Lio, P. CytoCom: a Cytoscape app to visualize, query and analyse disease comorbidity networks. Bioinformatics 31, 969–971 (2015).

    Article  CAS  PubMed  Google Scholar 

  112. 112

    Hu, Z., Mellor, J., Wu, J. & DeLisi, C. VisANT: an online visualization and analysis tool for biological interaction data. BMC Bioinformatics 5, 17 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  113. 113

    Nikitin, A., Egorov, S., Daraselia, N. & Mazo, I. Pathway studio — the analysis and navigation of molecular networks. Bioinformatics 19, 2155–2157 (2003).

    Article  CAS  PubMed  Google Scholar 

  114. 114

    Iragne, F., Nikolski, M., Mathieu, B., Auber, D. & Sherman, D. ProViz: protein interaction visualization and exploration. Bioinformatics 21, 272–274 (2005).

    Article  CAS  PubMed  Google Scholar 

  115. 115

    Agoston, V., Cemazar, M., Kajan, L. & Pongor, S. Graph-representation of oxidative folding pathways. BMC Bioinformatics 6, 19 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. 116

    Pujol, A., Mosca, R., Farres, J. & Aloy, P. Unveiling the role of network and systems biology in drug discovery. Trends Pharmacol. Sci. 31, 115–123 (2010).

    Article  CAS  PubMed  Google Scholar 

  117. 117

    Lamb, J. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).

    Article  CAS  PubMed  Google Scholar 

  118. 118

    Kutalik, Z., Beckmann, J. S. & Bergmann, S. A modular approach for integrative analysis of large-scale gene-expression and drug-response data. Nat. Biotechnol. 26, 531–539 (2008).

    Article  CAS  PubMed  Google Scholar 

  119. 119

    Ma, H. & Zhao, H. iFad: an integrative factor analysis model for drug-pathway association inference. Bioinformatics 28, 1911–1918 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. 120

    Shankavaram, U. T. et al. CellMiner: a relational database and query tool for the NCI-60 cancer cell lines. BMC Genomics 10, 277 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. 121

    Woo, J. H. et al. Elucidating compound mechanism of action by network perturbation analysis. Cell 162, 441–451 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. 122

    Hu, Z. et al. VisANT 4.0: integrative network platform to connect genes, drugs, diseases and therapies. Nucleic Acids Res. 41, W225–W231 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  123. 123

    Hirsch, H. A., Iliopoulos, D., Tsichlis, P. N. & Struhl, K. Metformin selectively targets cancer stem cells, and acts together with chemotherapy to block tumor growth and prolong remission. Cancer Res. 69, 7507–7511 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. 124

    Iliopoulos, D., Hirsch, H. A. & Struhl, K. Metformin decreases the dose of chemotherapy for prolonging tumor remission in mouse xenografts involving multiple cancer cell types. Cancer Res. 71, 3196–3201 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. 125

    Niraula, S. et al. Metformin in early breast cancer: a prospective window of opportunity neoadjuvant study. Breast Cancer Res. Treat. 135, 821–830 (2012).

    Article  CAS  PubMed  Google Scholar 

  126. 126

    Dudley, J. T. et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci. Transl Med. 3, 96ra76 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. 127

    Loscalzo, J., Barabasi, A. L. & Silverman, E. K. Network Medicine: Complex Systems in Human Disease and Therapeutics. (Harvard Univ. Press, 2017). This book introduces and explains the concept of network medicine as the correct approach to tackle the molecular underpinnings of most complex diseases. Various chapters address molecular, translational and clinical aspects of network medicine in a fashion accessible to non-experts.

    Book  Google Scholar 

  128. 128

    Li-Pook-Than, J. & Snyder, M. iPOP goes the world: integrated personalized Omics profiling and the road toward improved health care. Chem. Biol. 20, 660–666 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. 129

    Banchereau, R. et al. Personalized immunomonitoring uncovers molecular networks that stratify lupus patients. Cell 165, 551–565 (2016). In this study, the molecular heterogeneity of systemic lupus erythematosus, a complex autoimmune disease, is revealed through a systems biology approach, offering an explanation for the failure of current clinical trials.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. 130

    Jameson, J. L. & Longo, D. L. Precision medicine — personalized, problematic, and promising. N. Engl. J. Med. 372, 2229–2234 (2015). This article discusses the overall scope and potential applications of precision medicine, and analyses questions regarding its implementation in the practice of medicine.

    Article  CAS  PubMed  Google Scholar 

  131. 131

    Chan, A. C. & Behrens, T. W. Personalizing medicine for autoimmune and inflammatory diseases. Nat. Immunol. 14, 106–109 (2013).

    Article  CAS  PubMed  Google Scholar 

  132. 132

    Cox, D. B., Platt, R. J. & Zhang, F. Therapeutic genome editing: prospects and challenges. Nat. Med. 21, 121–131 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Affiliations

Authors

Contributions

All authors contributed equally to all aspects of this manuscript.

Corresponding author

Correspondence to Claudio Fiocchi.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

PowerPoint slides

Supplementary information

Supplementary information S1 (figure)

Screenshot of CellFateScout result. (PDF 613 kb)

Supplementary information S2 (figure)

Flow diagram for utilization of human IBD clinical biosamples for integrated –omics analyses. (PDF 439 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

de Souza, H., Fiocchi, C. & Iliopoulos, D. The IBD interactome: an integrated view of aetiology, pathogenesis and therapy. Nat Rev Gastroenterol Hepatol 14, 739–749 (2017). https://doi.org/10.1038/nrgastro.2017.110

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing