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.

  • Review Article
  • Published:

Network biology concepts in complex disease comorbidities

Nature Reviews Genetics volume 17, pages 615–629 (2016)Cite this article

Subjects

Key Points

  • Disease progression patterns of patients with more than one disease have recently received increasing attention, as disease co-occurrences can help to elucidate the interaction between the molecular level and external exposures such as diet, lifestyle and patient care. Additionally, they can provide information about the underlying network biology of shared and multifunctional genes and pathways.

  • The concepts of pleiotropy, robustness and rewiring are central to the investigation of comorbidity and network dynamics and should be viewed together, as they all relate to the disease trajectory of an individual.

  • The temporal disease progression of the non-idealized patient can be described in terms of trajectories in a multimorbidity space, in which each dimension corresponds to a quantitative phenotype.

  • Dynamic network models can be constructed to study complex disease progression and are increasingly becoming feasible with the advances in high-throughput omics, single-cell technologies and sophisticated analysis tools.

  • The utility of network concepts has been hampered by confusion and inconsistent terminology. This can be mediated by the clear delineation of the concepts, especially in regards to context, including the clear specification of timeframe, phenotype and organizational level.

  • The increased collection of health transaction data combined with advances in omics technologies require a further concerted view on how robustness, rewiring and pleiotropy come together in frameworks that can rationalize comorbidities and their relationships at the molecular level, knowledge that can also facilitate drug repositioning and the development of targeted therapeutic strategies.

Abstract

The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being collected electronically, disease co-occurrences are starting to be quantitatively characterized. Linking network dynamics to the real-life, non-ideal patient in whom diseases co-occur and interact provides a valuable basis for generating hypotheses on molecular disease mechanisms, and provides knowledge that can facilitate drug repurposing and the development of targeted therapeutic strategies.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: The pervasiveness of dynamic concepts.
Figure 2: Multimorbidity space and dynamic disease progression.
Figure 3: Five models of multifunctionality and pleiotropy.
Figure 4: Rewiring in differential versus dynamic networks.

Similar content being viewed by others

References

  1. Tyson, J. J., Chen, K. & Novak, B. Network dynamics and cell physiology. Nat. Rev. Mol. Cell. Biol. 2, 908–916 (2001).

    Article  CAS  PubMed  Google Scholar 

  2. Paaby, A. B. & Rockman, M. V. The many faces of pleiotropy. Trends Genet. 29, 66–73 (2013).

    Article  CAS  PubMed  Google Scholar 

  3. Solovieff, N., Cotsapas, C., Lee, P. H., Purcell, S. M. & Smoller, J. W. Pleiotropy in complex traits: challenges and strategies. Nat. Rev. Genet. 14, 483–495 (2013). A review presenting the concept of pleiotropy and its controversies in the light of GWAS for complex traits.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Hodgkin, J. Seven types of pleiotropy. Int. J. Dev. Biol. 42, 501–505 (1998).

    CAS  PubMed  Google Scholar 

  5. Pyeritz, R. E. Pleiotropy revisited: molecular explanations of a classic concept. Am. J. Med. Genet. 34, 124–134 (1989).

    Article  CAS  PubMed  Google Scholar 

  6. Wagner, G. P. & Zhang, J. The pleiotropic structure of the genotype–phenotype map: the evolvability of complex organisms. Nat. Rev. Genet. 12, 204–213 (2011).

    Article  CAS  PubMed  Google Scholar 

  7. Félix, M.-A. & Barkoulas, M. Pervasive robustness in biological systems. Nat. Rev. Genet. 16, 483–496 (2015). A comprehensive review of the concept of robustness.

    Article  CAS  PubMed  Google Scholar 

  8. Kitano, H. Towards a theory of biological robustness. Mol. Syst. Biol. 3, 137 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Diss, G. et al. Integrative avenues for exploring the dynamics and evolution of protein interaction networks. Curr. Opin. Biotechnol. 24, 775–783 (2013).

    Article  CAS  PubMed  Google Scholar 

  10. Ideker, T. & Krogan, N. J. Differential network biology. Mol. Syst. Biol. 8, 565 (2012). A review presenting the main approaches in differential network biology.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Lambert, J.-P. et al. Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition. Nat. Methods 10, 1239–1245 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Greene, C. S. et al. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47, 569–576 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Bindea, G. et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39, 782–795 (2013).

    Article  CAS  PubMed  Google Scholar 

  14. Sun, S., Liu, Z., Zeng, T., Wang, Y. & Chen, L. Spatio-temporal analysis of type 2 diabetes mellitus based on differential expression networks. Sci. Rep. 3, 2268 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Bandyopadhyay, S. et al. Rewiring of genetic networks in response to DNA damage. Science 330, 1385–1389 (2010). The first study that uses differential epistasis mapping to systematically map out massive rewiring of genetic interaction networks in yeast.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Bensimon, A., Heck, A. J. & Aebersold, R. Mass spectrometry-based proteomics and network biology. Annu. Rev. Biochem. 81, 379–405 (2012).

    Article  CAS  PubMed  Google Scholar 

  17. Bisson, N. et al. Selected reaction monitoring mass spectrometry reveals the dynamics of signaling through the GRB2 adaptor. Nat. Biotechnol. 29, 653–658 (2011).

    Article  CAS  PubMed  Google Scholar 

  18. Lee, M. J. et al. Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell 149, 780–794 (2012). This study monitors the rewiring of breast cancer cells to time- and order-dependent combinations of therapeutic agents and detects the optimal combination that can push cancer cells into a more vulnerable state.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Creixell, P. et al. Kinome-wide decoding of network attacking mutations driving cancer signaling. Cell 163, 202–217 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Gijsen, R. et al. Causes and consequences of comorbidity: a review. J. Clin. Epidemiol. 54, 661–674 (2001).

    Article  CAS  PubMed  Google Scholar 

  21. Von Lueder, T. G. & Atar, D. Comorbidities and polypharmacy. Heart Fail. Clin. 10, 367–372 (2014).

    Article  PubMed  Google Scholar 

  22. Feinstein, A. R. The pre-therapeutic classification co-morbidity in chronic disease. J. Chronic Dis. 23, 455–468 (1970).

    Article  CAS  PubMed  Google Scholar 

  23. Valderas, J. M., Sibbald, B. & Salisbury, C. Defining comorbidity: implications for understanding health and health services. Ann. Fam. Med. 7, 357–363 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Meghani, S. H. et al. The conceptualization and measurement of comorbidity: a review of the interprofessional discourse. Nurs. Res. Pract. 2013, 192782 (2013).

    PubMed  PubMed Central  Google Scholar 

  25. Loscalzo, J., Kohane, I. & Barabási, A.-L. Human disease classification in the postgenomic era: a complex systems approach to human pathobiology. Mol. Syst. Biol. 3, 124 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Maj, M. 'Psychiatric comorbidity': an artefact of current diagnostic systems? Br. J. Psychiatry 186, 182–184 (2005).

    Article  PubMed  Google Scholar 

  27. Radner, H., Yoshida, K., Smolen, J. S. & Solomon, D. H. Multimorbidity and rheumatic conditions-enhancing the concept of comorbidity. Nat. Rev. Rheumatol. 10, 252–256 (2014).

    Article  PubMed  Google Scholar 

  28. van den Akker, M., Buntinx, F. & Knottnerus, J. A. Comorbidity or multimorbidity. Eur. J. Gen. Pract. 2, 65–70 (2009).

    Article  Google Scholar 

  29. Scanlon, P. H. Diabetic retinopathy. Medicine 38, 656–660 (2010).

    Article  Google Scholar 

  30. Thomas, M. C. et al. Diabetic kidney disease. Nat. Rev. Dis. Prim. 1, 15038 (2015).

    Article  Google Scholar 

  31. Barnes, P. J. et al. Chronic obstructive pulmonary disease. Nat. Rev. Dis. Prim. 362, 15076 (2015).

  32. Mannino, D. & Kiri, V. Changing the burden of COPD mortality. Int. J. Chron. Obstruct. Pulmon. Dis. 1, 219–233 (2006).

    PubMed  PubMed Central  Google Scholar 

  33. Agustí, A. & Vestbo, J. Current controversies and future perspectives in chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 184, 507–513 (2012).

    Article  Google Scholar 

  34. Hidalgo, C. A, Blumm, N., Barabási, A.-L. & Christakis, N. A. A dynamic network approach for the study of human phenotypes. PLoS Comput. Biol. 5, e1000353 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Roque, F. S. et al. Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput. Biol. 7, e1002141 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Jensen, A. B. et al. Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nat. Commun. 5, 4022 (2014). This paper uses registry data on 6.2 million patients from the Danish population to create temporal disease trajectories.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Oh, W. et al. Type 2 diabetes mellitus trajectories and associated risks. Big Data 4, 25–30 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Capobianco, E. & Liò, P. Comorbidity: a multidimensional approach. Trends Mol. Med. 19, 515–521 (2013).

    Article  PubMed  Google Scholar 

  39. Capobianco, E. & Liò, P. Comorbidity networks: beyond disease correlations. J. Complex. Networks 3, 319–332 (2015).

    Article  Google Scholar 

  40. Gibson, G. Decanalization and the origin of complex disease. Nat. Rev. Genet. 10, 134–140 (2009).

    Article  CAS  PubMed  Google Scholar 

  41. Faner, R. et al. Molecular and clinical diseasome of comorbidities in exacerbated COPD patients. Eur. Respir. J. 46, 1001–1010 (2015).

    Article  CAS  PubMed  Google Scholar 

  42. Ibáñez, K., Boullosa, C., Tabarés-Seisdedos, R., Baudot, A. & Valencia, A. Molecular evidence for the inverse comorbidity between central nervous system disorders and cancers detected by transcriptomic meta-analyses. PLoS Genet. 10, e1004173 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 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). GWAS on five chronic inflammatory diseases detecting shared disease variants that could not have been found using a single disease approach.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Park, J., Lee, D.-S., Christakis, N. a & Barabási, A.-L. The impact of cellular networks on disease comorbidity. Mol. Syst. Biol. 5, 262 (2009). This paper combines Medicare clinical data and cellular OMIM data to assess the impact of cellular networks on comorbidity.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Catalá-López, F. et al. Inverse and direct cancer comorbidity in people with central nervous system disorders: a meta-analysis of cancer incidence in 577,013 participants of 50 observational studies. Psychother. Psychosom. 83, 89–105 (2014).

    Article  PubMed  Google Scholar 

  46. Driver, J. a et al. Inverse association between cancer and Alzheimer's disease: results from the Framingham Heart Study. BMJ 344, e1442 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Tabarés-Seisdedos, R. et al. No paradox, no progress: inverse cancer comorbidity in people with other complex diseases. Lancet. Oncol. 12, 604–608 (2011).

    Article  PubMed  Google Scholar 

  48. Pernicova, I. & Korbonits, M. Metformin—mode of action and clinical implications for diabetes and cancer. Nat. Rev. Endocrinol. 10, 143–156 (2014).

    Article  CAS  PubMed  Google Scholar 

  49. Kohane, I. S. Deeper, longer phenotyping to accelerate the discovery of the genetic architectures of diseases. Genome Biol. 15, 115 (2014).

    Article  PubMed Central  Google Scholar 

  50. Robinson, P. N. Deep phenotyping for precision medicine. Hum. Mutat. 33, 777–780 (2012).

    Article  PubMed  Google Scholar 

  51. Miotto, R., Li, L., Kidd, B. A. & Dudley, J. T. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci. Rep. 6, 26094 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Zheng, X. et al. Genome-wide copy-number variation study of psychosis in Alzheimer's disease. Transl. Psychiatry 5, e574 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Plate, L. in Festschrift zum sechzigsten Geburtstag Richard Hertwigs. 536–610 (in German) (Fischer, 1910).

    Google Scholar 

  54. 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 

  55. Rzhetsky, A., Wajngurt, D., Park, N. & Zheng, T. Probing genetic overlap among complex human phenotypes. Proc. Natl Acad. Sci. USA 104, 11694–11699 (2007).

    Article  CAS  PubMed  Google Scholar 

  56. Cotsapas, C. et al. Pervasive sharing of genetic effects in autoimmune disease. PLoS Genet. 7, e1002254 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Parkes, M., Cortes, A., van Heel, D. A. & Brown, M. A. Genetic insights into common pathways and complex relationships among immune-mediated diseases. Nat. Rev. Genet. 14, 661–673 (2013).

    Article  CAS  PubMed  Google Scholar 

  58. Ellis, J. D. et al. Tissue-specific alternative splicing remodels protein-protein interaction networks. Mol. Cell 46, 884–892 (2012). This paper mines 110 million electronic medical records and detects thousands of associations between Mendelian and complex diseases.

    Article  CAS  PubMed  Google Scholar 

  59. Drivas, T. G., Wojno, A. P., Tucker, B. A., Stone, E. M. & Bennett, J. Basal exon skipping and genetic pleiotropy: A predictive model of disease pathogenesis. Sci. Transl. Med. 10, 291ra97 (2015).

    Article  CAS  Google Scholar 

  60. Dickey, T. H., Altschuler, S. E. & Wuttke, D. S. Single-stranded DNA-binding proteins: multiple domains for multiple functions. Structure 21, 1074–1084 (2013).

    Article  CAS  PubMed  Google Scholar 

  61. Bossi, A. & Lehner, B. Tissue specificity and the human protein interaction network. Mol. Syst. Biol. 5, 260 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Verstraeten, A., Alaerts, M., Van Laer, L. & Loeys, B. Marfan syndrome and related disorders: 25 years of gene discovery. Hum. Mutat. 37, 524–531 (2016).

    Article  CAS  PubMed  Google Scholar 

  63. Lomas, D. A. Does protease-antiprotease imbalance explain chronic obstructive pulmonary disease? Ann. Am. Thorac. Soc. 13, S130–S137 (2016).

    Article  PubMed  Google Scholar 

  64. Wong, T. Y., Cheung, C. M. G., Larsen, M., Sharma, S. & Simó, R. Diabetic retinopathy. Nat. Rev. Dis. Prim. 2, 16012 (2016).

    Article  PubMed  Google Scholar 

  65. Denny, J. C. et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 1102–1111 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Bush W. S., Oetjens M. T., & Crawford, D. C. Unravelling the human genome-phenome relationship using phenome-wide association studies. Nat. Rev. Genet. 17, 129–145 (2016).

    Article  CAS  PubMed  Google Scholar 

  67. Rastegar-Mojarad, M., Ye, Z., Kolesar, J. M., Hebbring, S. J. & Lin, S. M. Opportunities for drug repositioning from phenome-wide association studies. Nat. Biotechnol. 33, 342–345 (2015).

    Article  CAS  PubMed  Google Scholar 

  68. Blair, D. R. et al. A nondegenerate code of deleterious variants in Mendelian loci contributes to complex disease risk. Cell 155, 70–80 (2013).

    Article  CAS  PubMed  Google Scholar 

  69. Han, B. et al. A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases. Nat. Genet. 48, 803–810 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Queitsch, C., Carlson, K. D. & Girirajan, S. Lessons from model organisms: phenotypic robustness and missing heritability in complex disease. PLoS Genet. 8, e1003041 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Kitano, H. Biological robustness. Nat. Rev. Genet. 5, 826–837 (2004).

    Article  CAS  PubMed  Google Scholar 

  72. Hsiao, T. L. & Vitkup, D. Role of duplicate genes in robustness against deleterious human mutations. PLoS Genet. 4, e1000014 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Queitsch, C., Sangster, T. A. & Lindquist, S. Hsp90 as a capacitor of phenotypic variation. Nature 417, 618–624 (2002).

    Article  CAS  PubMed  Google Scholar 

  74. Barabasi, A.-L., Oltvai, Z. N. Z. N. & Barabási, A.-L. Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5, 101–113 (2004).

    Article  CAS  PubMed  Google Scholar 

  75. Kitano, H. et al. Metabolic syndrome and robustness tradeoffs. Diabetes 53, S6–S15 (2004).

    Article  CAS  PubMed  Google Scholar 

  76. Eichler, E. E. et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nat. Rev. Genet. 11, 446–450 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Shao, H. et al. Genetic architecture of complex traits: large phenotypic effects and pervasive epistasis. Proc. Natl Acad. Sci. USA 105, 19910–19914 (2008).

    Article  CAS  PubMed  Google Scholar 

  78. Zuk, O., Hechter, E., Sunyaev, S. R. & Lander, E. S. The mystery of missing heritability: genetic interactions create phantom heritability. Proc. Natl Acad. Sci. USA 109, 1193–1198 (2012).

    Article  CAS  PubMed  Google Scholar 

  79. Bloom, J. S., Ehrenreich, I. M., Loo, W. T., Lite, T.-L. V. & Kruglyak, L. Finding the sources of missing heritability in a yeast cross. Nature 494, 234–237 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Wang, Y. et al. Parameters in dynamic models of complex traits are containers of missing heritability. PLoS Comput. Biol. 8, e1002459 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Wagner, A. Causal drift, robust signaling, and complex disease. PLoS ONE 10, e0118413 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Paaby, A. B. & Rockman, M. V. Cryptic genetic variation: evolution's hidden substrate. Nat. Rev. Genet. 15, 247–258 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Chen, R. et al. Analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases. Nat. Biotechnol. 34, 531–538 (2016).

    Article  CAS  PubMed  Google Scholar 

  84. Narasimhan, V. M. et al. Health and population effects of rare gene knockouts in adult humans with related parents. Science 352, 474–477 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Harper, A. R., Nayee, S. & Topol, E. J. Protective alleles and modifier variants in human health and disease. Nat. Rev. Genet. 16, 689–701 (2015).

    Article  CAS  PubMed  Google Scholar 

  86. Shtir, C. et al. Exome-based case-control association study using extreme phenotype design reveals novel candidates with protective effect in diabetic retinopathy. Hum. Genet. 135, 193–200 (2016).

    Article  CAS  PubMed  Google Scholar 

  87. Zhuang, L. et al. The Leu72Met polymorphism of the GHRL gene prevents the development of diabetic nephropathy in Chinese patients with type 2 diabetes mellitus. Mol. Cell. Biochem. 387, 19–25 (2014).

    Article  CAS  PubMed  Google Scholar 

  88. Lapice, E. et al. The PPARγ2 Pro12Ala variant is protective against progression of nephropathy in people with type 2 diabetes. J. Transl. Med. 13, 85 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Heng, H. H. Q. Missing heritability and stochastic genome alterations. Nat. Rev. Genet. 11, 813 (2010).

    Article  CAS  PubMed  Google Scholar 

  90. Zhang, F., Gu, W., Hurles, M. E. & Lupski, J. R. Copy number variation in human health, disease, and evolution. Annu. Rev. Genom. Hum. Genet. 10, 451–481 (2009).

    Article  CAS  Google Scholar 

  91. Girirajan, S., Campbell, C. D. & Eichler, E. E. Human copy number variation and complex genetic disease. Annu. Rev. Genet. 45, 203–226 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Zarrei, M., MacDonald, J. R., Merico, D. & Scherer, S. W. A copy number variation map of the human genome. Nat. Rev. Genet. 16, 172–183 (2015).

    Article  CAS  PubMed  Google Scholar 

  93. Mittelman, D., Sykoudis, K., Hersh, M., Lin, Y. & Wilson, J. H. Hsp90 modulates CAG repeat instability in human cells. Cell Stress Chaperones 15, 753–759 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Chen, G., Bradford, W. D., Seidel, C. W. & Li, R. Hsp90 stress potentiates rapid cellular adaptation through induction of aneuploidy. Nature 482, 246–250 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Shameer, K. et al. Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams. Brief. Bioinform. http://dx.doi.org/10.1093/bib/bbv118 (2016).

  96. Biotechnology, N. The coming era of human phenotyping. Nat. Biotechnol. 33, 567–567 (2015).

    Article  CAS  Google Scholar 

  97. Schork, N. J. Personalized medicine: time for one-person trials. Nature 520, 609–611 (2015).

    Article  CAS  PubMed  Google Scholar 

  98. Muoio, D. M. & Newgard, C. B. Mechanisms of disease: molecular and metabolic mechanisms of insulin resistance and β-cell failure in type 2 diabetes. Nat. Rev. Mol. Cell. Biol. 9, 193–205 (2008).

    Article  CAS  PubMed  Google Scholar 

  99. Kaushik, A., Bhatia, Y., Ali, S. & Gupta, D. Gene network rewiring to study melanoma stage progression and elements essential for driving melanoma. PLoS ONE 10, e0142443 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Hou, L., Chen, M., Zhang, C. K., Cho, J. & Zhao, H. Guilt by rewiring: gene prioritization through network rewiring in genome wide association studies. Hum. Mol. Genet. 23, 2780–2790 (2014).

    Article  CAS  PubMed  Google Scholar 

  101. Zeng, T., Wang, D. C., Wang, X., Xu, F. & Chen, L. Prediction of dynamical drug sensitivity and resistance by module network rewiring-analysis based on transcriptional profiling. Drug Resist. Updat. 17, 64–76 (2014).

    Article  PubMed  Google Scholar 

  102. Shou, C. et al. Measuring the evolutionary rewiring of biological networks. PLoS Comput. Biol. 7, e1001050 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Holme, P. & Saramäki, J. Temporal networks. Phys. Rep. 519, 97–125 (2013).

    Article  Google Scholar 

  104. Kim, J., Kim, I., Han, S. K., Bowie, J. U. & Kim, S. Network rewiring is an important mechanism of gene essentiality change. Sci. Rep. 2, 900 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Hein, M. Y. et al. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell 163, 712–723 (2015).

    Article  CAS  PubMed  Google Scholar 

  106. Kastritis, P. L. & Bonvin, A. M. J. J. On the binding affinity of macromolecular interactions: daring to ask why proteins interact. J. R. Soc. Interface 10, 20120835 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Kiel, C., Verschueren, E., Yang, J.-S. & Serrano, L. Integration of protein abundance and structure data reveals competition in the ErbB signaling network. Sci. Signal. 6, ra109 (2013).

    Article  CAS  PubMed  Google Scholar 

  108. Stingele, S. et al. Global analysis of genome, transcriptome and proteome reveals the response to aneuploidy in human cells. Mol. Syst. Biol. 8, 608 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Purvis, J. E. et al. p53 dynamics control cell fate. Science 336, 1440–1444 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Moni, M. A. & Liò, P. Network-based analysis of comorbidities risk during an infection: SARS & HIV case studies. BMC Bioinformatics 15, 333 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  111. Albert, R., Jeong, H. & Barabasi, A. Error and attack tolerance of complex networks. Nature 406, 378–382 (2000).

    Article  CAS  PubMed  Google Scholar 

  112. Yu, H., Greenbaum, D., Xin Lu, H., Zhu, X. & Gerstein, M. Genomic analysis of essentiality within protein networks. Trends Genet. 20, 227–231 (2004).

    Article  CAS  PubMed  Google Scholar 

  113. Yu, H., Kim, P. M., Sprecher, E., Trifonov, V. & Gerstein, M. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput. Biol. 3, e59 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Joy, M. P., Brock, A., Ingber, D. E. & Huang, S. High-betweenness proteins in the yeast protein interaction network. J. Biomed. Biotechnol. 2005, 96–103 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Hahn, M. W. & Kern, A. D. Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mol. Biol. Evol. 22, 803–806 (2005).

    Article  CAS  PubMed  Google Scholar 

  116. Cai, J. J., Borenstein, E. & Petrov, D. A. Broker genes in human disease. 2, 815–825 (2010).

  117. Kolch, W., Halasz, M., Granovskaya, M. & Kholodenko, B. N. The dynamic control of signal transduction networks in cancer cells. Nat. Rev. Cancer 15, 515–527 (2015).

    Article  CAS  PubMed  Google Scholar 

  118. Promislow, D. E. L. Protein networks, pleiotropy and the evolution of senescence. Proc. Biol. Sci. 271, 1225–1234 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Levy, S. F. & Siegal, M. L. Network hubs buffer environmental variation in Saccharomyces cerevisiae. PLoS Biol. 6, 2588–2604 (2008).

    Article  CAS  Google Scholar 

  121. Nguyen, T.-P., Liu, W. & Jordán, F. Inferring pleiotropy by network analysis: linked diseases in the human PPI network. BMC Syst. Biol. 5, 179 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  122. He, X. & Zhang, J. Toward a molecular understanding of pleiotropy. Genetics 173, 1885–1891 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Garcia-alonso, L. et al. The role of the interactome in the maintenance of deleterious variability in human populations. Mol. Syst. Biol. 10, 752 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Azevedo, H. & Moreira-Filho, C. A. Topological robustness analysis of protein interaction networks reveals key targets for overcoming chemotherapy resistance in glioma. Sci. Rep. 5, 16830 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Gutierrez-Arcelus, M., Rich, S. S. & Raychaudhuri, S. Autoimmune diseases — connecting risk alleles with molecular traits of the immune system. Nat. Rev. Genet. 17, 160–174 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  126. Sollid, L. M. & Jabri, B. Triggers and drivers of autoimmunity: lessons from coeliac disease. Nat. Rev. Immunol. 13, 294–302 (2013).

    Article  CAS  PubMed  Google Scholar 

  127. Costenbader, K. H., Feskanich, D., Mandl, L. A. & Karlson, E. W. Smoking intensity, duration, and cessation, and the risk of rheumatoid arthritis in women. Am. J. Med. 119, 503.e1–503.e9 (2006).

    Article  Google Scholar 

  128. Klareskog, L. et al. A new model for an etiology of rheumatoid arthritis: smoking may trigger HLA-DR (shared epitope)-restricted immune reactions to autoantigens modified by citrullination. Arthritis Rheum. 54, 38–46 (2006).

    Article  CAS  PubMed  Google Scholar 

  129. James, J. A. et al. Systemic lupus erythematosus in adults is associated with previous Epstein-Barr virus exposure. Arthritis Rheum. 44, 1122–1126 (2001).

    Article  CAS  PubMed  Google Scholar 

  130. Criswell, L. A. et al. Analysis of families in the Multiple Autoimmune Disease Genetics Consortium (MADGC) collection: the PTPN22 620W allele associates with multiple autoimmune phenotypes. Am. J. Hum. Genet. 76, 561–571 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Park, J.-H. et al. Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat. Genet. 42, 570–575 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Graham, R. R. et al. Three functional variants of IFN regulatory factor 5 (IRF5) define risk and protective haplotypes for human lupus. Proc. Natl Acad. Sci. USA 104, 6758–6763 (2007).

    Article  CAS  PubMed  Google Scholar 

  133. Dendrou, C. A. et al. Cell-specific protein phenotypes for the autoimmune locus IL2RA using a genotype-selectable human bioresource. Nat. Genet. 41, 1011–1015 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Kofler, D. M., Severson, C. a, Mousissian, N. & De Jager, P. L. and Hafler, D. A. The CD6 multiple sclerosis susceptibility allele is associated with alterations in CD4+ T cell proliferation. J. Immunol. 187, 3286–3291 (2011).

    Article  CAS  PubMed  Google Scholar 

  135. Lewis, M. J. et al. UBE2L3 polymorphism amplifies NF-κB activation and promotes plasma cell development, linking linear ubiquitination to multiple autoimmune diseases. Am. J. Hum. Genet. 96, 221–234 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Fairfax, B. P. et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Shi, J. et al. Emerging role and therapeutic implication of wnt signaling pathways in autoimmune diseases. J. Immunol. Res. 2016, 9392132 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Ding, Y., Shen, S., Lino, A. C., Curotto de Lafaille, M. A. & Lafaille, J. J. β-catenin stabilization extends regulatory T cell survival and induces anergy in nonregulatory T cells. Nat. Med. 14, 162–169 (2008).

    Article  CAS  PubMed  Google Scholar 

  139. Suryawanshi, A. et al. Canonical wnt signaling in dendritic cells regulates Th1/Th17 responses and suppresses autoimmune neuroinflammation. J. Immunol. 194, 3295–3304 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Rabelo, F. d S. et al. The Wnt signaling pathway and rheumatoid arthritis. Autoimmun. Rev. 9, 207–210 (2010).

    Article  CAS  Google Scholar 

  141. Biros, I. & Forrest, S. Spinal muscular atrophy: untangling the knot? J. Med. Genet. 36, 1–8 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. Feldkötter, M., Schwarzer, V., Wirth, R., Wienker, T. F. & Wirth, B. Quantitative analyses of SMN1 and SMN2 based on real-time lightCycler PCR: fast and highly reliable carrier testing and prediction of severity of spinal muscular atrophy. Am. J. Hum. Genet. 70, 358–368 (2002).

    Article  PubMed  Google Scholar 

  143. Mailman, M. D. et al. Molecular analysis of spinal muscular atrophy and modification of the phenotype by SMN2. Genet. Med. 4, 20–26 (2002).

    Article  CAS  PubMed  Google Scholar 

  144. Lillie, E. O. et al. The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Per. Med. 8, 161–173 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  145. Liu, J. et al. Comorbidity analysis according to sex and age in hypertension patients in china. Int. J. Med. Sci. 13, 99–107 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  146. Vogel, F. A preliminary estimate of the number of human genes. Nature 201, 847 (1964).

    Article  CAS  PubMed  Google Scholar 

  147. US Department of Health and Human Services. Understanding our genetic inheritance, The U.S. Human Genome Project: The first five years: fiscal years 1991–1995. (US Dept. of Energy,1990).

  148. Pertea, M. & Salzberg, S. L. Between a chicken and a grape: estimating the number of human genes. Genome Biol. 11, 206 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Menche, J. Uncovering disease-disease relationships through the incomplete interactome. Science 347, 1257601 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Gstaiger, M. & Aebersold, R. Applying mass spectrometry-based proteomics to genetics, genomics and network biology. Nat. Rev. Genet. 10, 617–627 (2009).

    Article  CAS  PubMed  Google Scholar 

  151. Collins, B. C. et al. Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system. Nat. Methods 10, 1246–1253 (2013).

    Article  CAS  PubMed  Google Scholar 

  152. 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 

  153. Daub, H. et al. Kinase-selective enrichment enables quantitative phosphoproteomics of the kinome across the cell cycle. Mol. Cell 31, 438–448 (2008).

    Article  CAS  PubMed  Google Scholar 

  154. Chattopadhyay, P. K., Gierahn, T. M., Roederer, M. & Love, J. C. Single-cell technologies for monitoring immune systems. Nat. Immunol. 15, 128–135 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  156. Mitra, K., Carvunis, A.-R., Ramesh, S. K. & Ideker, T. Integrative approaches for finding modular structure in biological networks. Nat. Rev. Genet. 14, 719–732 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. Kitano, H. A robustness-based approach to systems-oriented drug design. Nat. Rev. Drug Discov. 6, 202–210 (2007).

    Article  CAS  PubMed  Google Scholar 

  158. Baffy, G. & Loscalzo, J. Complexity and network dynamics in physiological adaptation: an integrated view. Physiol. Behav. 131, 49–56 (2014).

    Article  CAS  PubMed  Google Scholar 

  159. de Lichtenberg, U. Dynamic complex formation during the yeast cell cycle. Science 307, 724–727 (2005).

    Article  CAS  PubMed  Google Scholar 

  160. Faisal, F. E. & Milenkovi, T. Dynamic networks reveal key players in aging. Bioinformatics 30, 1721–1729 (2014).

    Article  CAS  PubMed  Google Scholar 

  161. Luscombe, N. M. et al. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431, 308–312 (2004).

    Article  CAS  PubMed  Google Scholar 

  162. Roguev, A. et al. Conservation and rewiring of functional modules revealed by an epistasis map in fission yeast. Science 322, 405–410 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Altelaar, a F. M., Munoz, J. & Heck, A. J. R. Next-generation proteomics: towards an integrative view of proteome dynamics. Nat. Rev. Genet. 14, 35–48 (2013).

    Article  CAS  PubMed  Google Scholar 

  164. Barrios-Rodiles, M. et al. High-throughput mapping of a dynamic signaling network in mammalian cells. Science 307, 1621–1625 (2005).

    Article  CAS  PubMed  Google Scholar 

  165. Dalianis, H., Hassel, M. & Velupillai, S. The Stockholm EPR corpus: characteristics and some initial findings. 14th Int. Symp. Health Inf. Manag. Res. 219, 243–249 (2009).

    Google Scholar 

  166. McGeachie, M. J. et al. Joint GWAS analysis: comparing similar GWAS at different genomic resolutions identifies novel pathway associations with six complex diseases. Genom. Data 2, 202–211 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  167. Pendergrass, S. a et al. Phenome-wide association study (PheWAS) for detection of pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network. PLoS Genet. 9, e1003087 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  168. Glymour, M. M., Tchetgen Tchetgen, E. J. & Robins, J. M. Credible Mendelian randomization studies: approaches for evaluating the instrumental variable assumptions. Am. J. Epidemiol. 175, 332–339 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  169. Lawlor, D. A., Harbord, R. M., Sterne, J. A. C., Timpson, N. & Davey Smith, G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat. Med. 27, 1133–1163 (2008).

    Article  PubMed  Google Scholar 

  170. Assenov, Y., Ramírez, F., Schelhorn, S.-E., Lengauer, T. & Albrecht, M. Computing topological parameters of biological networks. Bioinformatics 24, 282–284 (2008).

    Article  CAS  PubMed  Google Scholar 

  171. Li, F. et al. PerturbationAnalyzer: a tool for investigating the effects of concentration perturbation on protein interaction networks. Bioinformatics 26, 275–277 (2010).

    Article  CAS  PubMed  Google Scholar 

  172. Zhang, B. et al. DDN: a caBIG® analytical tool for differential network analysis. Bioinformatics 27, 1036–1038 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank L. J. Jensen, C. Workman and H. V. Cook for comments on the manuscript, and D. Westergaard, J. M. Gonzalez-Izarzugaza and K. Banasik for useful discussions and suggestions. The work was supported by the Novo Nordisk Foundation (grant agreement NNF14CC0001), as well as the Innovation Fund Denmark.

Author information

Author notes

  1. Jessica Xin Hu and Cecilia Engel Thomas: These authors contributed equally to this work.

Authors and Affiliations

  1. Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, DK-2200, Denmark

    Jessica Xin Hu, Cecilia Engel Thomas & Søren Brunak

  2. Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, Copenhagen, DK-2100, Denmark

    Søren Brunak

Authors
  1. Jessica Xin Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cecilia Engel Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Søren Brunak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Søren Brunak.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

FURTHER INFORMATION

BUHMBOX

Comorbidity-View

GIANT

PowerPoint slides

Glossary

Pleiotropy

The property of a genetic locus that affects more than one trait.

Robustness

The property that allows a system to maintain its functions against internal and external perturbations.

Rewiring

Restructuring of interactions between biological components due to conditional changes.

Complex disease

A disease that is a result of complex interactions between genetics and environment that is hard to explain by a few factors.

Multifunctionalities

Properties of a biological component that have multiple distinct roles.

Comorbidities

Diseases that co-occur on top of a primary disease of interest in an individual.

Genetic interaction networks

Networks in which nodes are genes and edges are their epistatic interactions.

Physical interaction networks

Networks in which nodes physically interact. In biology interactions may be between and among, for example, proteins, DNA and RNA.

Differential networks

Analytical approaches to identify edge changes between two static network states.

Hub

A hub node in a network has a high degree of edges, meaning that it interacts with many other nodes in the network.

Organizational levels

Levels in the hierarchy of biological structures and systems such as protein, cell, tissue, organ or organism.

Dynamic network

A network that continuously changes topology over time.

Multimorbidity

The coexistence of two or more diseases in the same individual without disease prioritization.

Health transaction data

Data describing patients' contacts with the health care system. Data accumulates in electronic patient records and registries.

Inversely comorbid

Diseases that co-occur less often in an individual than expected given their individual frequencies in the population.

Drug repurposing

The application of a known drug to new indications. Synonymous with the term drug repositioning.

Scale-free

A network structure that has a degree distribution following a power law.

Bow tie

A multi-layered network structure where intermediate layers have far fewer components than input and output layers.

Modularity

A network structure with dense connections between clusters of nodes and sparse connections between nodes in different clusters.

Homeostasis

The ability to sustain various physiological parameters in a steady state.

Plasticity

Variation of a phenotype as a response to a given environmental exposure.

Epistasis

A phenomenon in which the function of one gene affects the function of another gene in a non-additive manner.

Penetrance

From a genome-wide association study perspective, penetrance describes the proportion of individuals for which a genetic variant results in a changed phenotype.

Network topology

The layout of nodes and edges in a network.

Microsatellites

Polymorphic DNA loci containing repeated nucleotide sequences of typically 2–7 nucleotides per unit.

Cryptic variation

Genetic variation that has little or no effect on phenotypic variation under normal conditions, but can generate heritable phenotypic variation when circumstances change.

Edge

An edge represents the interaction between nodes in a network. In biological systems an edge can represent a physical interaction between two proteins or the co-occurrence of two diseases.

Wearables

Personal portable devices that monitor the state of an individual.

Nodes

In biological networks nodes are connection points, for example, of proteins, genes or diseases. They may or may not directly interact.

Bottleneck

A bottleneck node in a network has a high degree of intersections (high betweenness), meaning that it will often be a linker between different subnetworks.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, J., Thomas, C. & Brunak, S. Network biology concepts in complex disease comorbidities. Nat Rev Genet 17, 615–629 (2016). https://doi.org/10.1038/nrg.2016.87

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrg.2016.87

This article is cited by

Search

Advanced 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