European Cytokine Network

, Volume 29, Issue 3, pp 103–111 | Cite as

Cytokine and molecular networks in sepsis cases: a network biology approach

  • Dong Wook Jekarl
  • Kyung Soo Kim
  • Seungok Lee
  • Myungshin Kim
  • Yonggoo KimEmail author
Original Article



Sepsis is a life-threatening condition of organ dysfunction caused by a dysregulated host immune response to infection. We performed network analysis of cytokine molecules and compared network structures between a systematic inflammatory response syndrome (SIRS) or normal control (NC) group and a sepsis group.


We recruited SIRS (n = 33) and sepsis (n = 89) patients from electronic medical records (EMR) according to whether data on PCT, CRP, interleukin (IL)-1β, IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12p70, IL-13, IL-17, IL-22, TNF-α, and IFN-γ levels were available. From the public GEO dataset, GSE66099, GSE9960, GSE95233, GSE57065 were downloaded. Genes corresponding to 15 molecules were extracted from an expression array. A correlation matrix was formed for the 15 molecules and statistically significant molecular pairs were used as pairs for network analysis of coexpression. The number of molecular or gene expression pairs significantly correlated among the SIRS or control and sepsis groups are as follows for datasets: EMR, 15 and 15; GEO66099-1, 13 and 15; GEO9960, 13 and 11; GSE95233, 13 and 8; GSE66099-2, 15 and 14; GSE57065, 14 and 13, respectively. Network analysis revealed that network diameter, number of nodes and shortest path were equal to or lower in the sepsis group.


The coexpression network in sepsis patients was relatively small sized and had lower shortest paths compared with the SIRS group or healthy control group. Cytokines with one degree (k = 1) are increased in sepsis group compared with SIRS or healthy control group. IL-9 and IL-2 were not included in network of sepsis group indicating that these cytokines showed no correlation with other cytokines. These data might imply that cytokines tend to be dysregulated in the sepsis group compared to that of SIRS or normal control groups

Key words

sepsis cytokine molecules network analysis network topology GEO dataset 


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  1. 1.
    Bone RC, Balk RA, Cerra FB, et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Chest 1992; 101: 1644.CrossRefGoogle Scholar
  2. 2.
    Seymour CW, Liu VX, Iwashyna TJ, et al. Assessment of clinical criteria for sepsis for the third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016; 315:762.CrossRefGoogle Scholar
  3. 3.
    Singer M, Deutschman C, Seymour CW, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016; 315:801.CrossRefGoogle Scholar
  4. 4.
    Angus DC, van der Poll T. Severe sepsis and septic shock. N Engl J Med 2013; 369:840.CrossRefGoogle Scholar
  5. 5.
    Hotchkiss RS, Karl IE. The pathophysiology and treatment of sepsis. N Engl J Med 2003; 348:138.CrossRefGoogle Scholar
  6. 6.
    Kaukonen KM, Bailey M, Pilcher D, Cooper DJ, Bellomo R. Systemic inflammatory response syndrome criteria in defining severe sepsis. N Engl J Med 2015; 372: 1629.CrossRefGoogle Scholar
  7. 7.
    Cohen J. The immunopathogenesis of sepsis. Nature 2002; 420:885.CrossRefGoogle Scholar
  8. 8.
    Boomer JS, To K, Chang KC, et al. Immunosuppression in patients who die of sepsis and multiple organ failure. JAMA 2011; 306: 2594.CrossRefGoogle Scholar
  9. 9.
    Bermejo-Martin JF, Andaluz-Ojeda D, Almansa R, et al. Defining immunological dysfunction in sepsis: a requisite tool for precision medicine. J Infect 2016; 72:525.CrossRefGoogle Scholar
  10. 10.
    Cavailon JM, Adib-Conquy M, Cloez-Tayarani I, Fitting C. Immunodepression in sepsis and SIRS assessed by ex vivo cytokine production is not a generalized phenomenon: a review. J Endotoxin Res 2001; 7:85.Google Scholar
  11. 11.
    Zhao H, Li W, Lu Z, Sheng Z, Yao Y. The growing specrtrum of anti-inflammatory interleukins and their potential roles in the development of sepsis. J Interferon Cytokine Res 2015; 35:242.CrossRefGoogle Scholar
  12. 12.
    Trevelin S, Alves-Filho J, Sonego F, et al. Toll-like receptor 9 activation in neutrophils impairs chemotaxis and reduces sepsis outcome. Crit Care Med 2012; 40: 2631.CrossRefGoogle Scholar
  13. 13.
    Jekarl DW, Lee S, Lee J, et al. Procalcitonin as a diagnostic marker and IL-6 as a prognostic marker for sepsis. Diagn Microbiol Infect Dis 2013; 75:342.CrossRefGoogle Scholar
  14. 14.
    Jekarl DW, Kim JY, Lee S, et al. Diagnosis and evaluation of severity of sepsis via the use of biomarkers and profiles of 13 cytokines: a multiplex analysis. Clin Chem Lab Med 2015; 53:575.CrossRefGoogle Scholar
  15. 15.
    Tang BM, Huang S, McLean A. Genome-wide transcription profiling of human sepsis: a systematic review. Crit Care 2010; 14: R237.CrossRefGoogle Scholar
  16. 16.
    Sweeney TE, Shidham A, Wong HR, Khatri P. A comprehensive time-course-based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set. Sci Transl Med 2015; 7: 287ra71.CrossRefGoogle Scholar
  17. 17.
    Sutherland A, Thomas M, Brandon RA, et al. Development and validation of a novel molecular biomarker diagnostic test for the early detection of sepsis. Crit Care 2011; 15: R149.CrossRefGoogle Scholar
  18. 18.
    Barabasi AL, Oltvai Z. Network biology: understanding the cell’s functional organization. Nat Rev Genet 2004; 5:101.CrossRefGoogle Scholar
  19. 19.
    Barabasi AL, Gulbahce N, Loscalzo J. Network medicine: a networkbased approach to human disease. Nat Rev Genet 2011; 12:56.CrossRefGoogle Scholar
  20. 20.
    Tang B, McLean A, Dawes I, Huang S, Lin R. Gene-expression profiling of peripheral blood mononuclear cells in sepsis. Crit Care Med 2009; 37:882.CrossRefGoogle Scholar
  21. 21.
    Venet F, Schilling J, Cazalis MA, et al. Modulation of LILRB2 and mRNA expression in septic shock patients and after ex vivo lipopolysaccharide stimulation. Hum Immunol 2017; 78:441.CrossRefGoogle Scholar
  22. 22.
    Cazalis MA, Lepape A, Venet F, et al. Early and dynamic changes in gene expression in septic shock patients: a genome-wide approach. Intensive Care Med Exp 2014; 2:20.CrossRefGoogle Scholar
  23. 23.
    Tamayo E, Fernandez A, Almansa R, et al. Pro-and antiinflammatory responses are regulated simultaneously from the first moments of septic shock. Eur Cytokine Netw 2011; 22:82.Google Scholar
  24. 24.
    Bozza FA, Salluh JI, Japiassu AM, et al. Cytokine profiles as markers of disease severity in sepsis: a multiplex analysis. Crit Care 2007; 11: R49.CrossRefGoogle Scholar
  25. 25.
    Lvovschi V, Arnaud L, Parizot C, et al. Cytokine profiles in sepsis have limited relevance for stratifying patients in the emergency department: a prospective observational study. PLoS One 2011; 6: e28870.CrossRefGoogle Scholar
  26. 26.
    R-project. (Accessed 2 Feb. 2017).Google Scholar
  27. 27.
    Davis S, Meltzer P. GEOquery: a bridge between the gene expression omnibus (GEO) and BioConductor. Bioinformatics 2007; 23: 1846.CrossRefGoogle Scholar
  28. 28.
    Falcon S, Morgan M, Gentleman R. An introduction to Bioconductor’s expression set class. 2007.∼lcollado/R /resources/Expression-SetIntroduction.pdf (Accessed 2 Feb. 2017).Google Scholar
  29. 29.
    Cho SB, Kim J, Kim JH. Identifying set-wise differential coexpression in gene expression microarray data. BMC Bioinfomatics 2009; 16:109.CrossRefGoogle Scholar
  30. 30.
    Kim J, Kim K, Kim H, Yoon G, Lee K. Characterization of age signatures of DNA methylation in normal and cancer tissues from multiple studies. BMC Bioinfomatics 2014; 15:997.Google Scholar
  31. 31.
    Draghici S. Statistics and data analysis for microarray using R and bioconductor, 2nd ed. Boca Raton: CRC Press, 2011.Google Scholar
  32. 32.
    Pages H, Carlson M, Falcon S, Li N. AnnotationDbi: annotation database interface. R-package version 1.38.0. 2017. (Accessed 2 Feb. 2017).Google Scholar
  33. 33.
    Andrade J, Riyue B. Analysis of microarray data with R and Bioconductor. 2013. http// (Accessed 2 Feb. 2017).Google Scholar
  34. 34.
    Chang W. R graphics cook book, 1st ed. Sebastopol: O’Reilly, 2013.Google Scholar
  35. 35.
    Zhu Z, Gerstein M, Snyder M. Getting connected: analysis and principles of biological networks. Genes Dev 2007; 21: 1010.CrossRefGoogle Scholar
  36. 36.
    Doncheva NT, Assenov Y, Domingues FS, Albrecht M. Topological analysis and interactive visualization of biological networks and protein structures. Nat Protoc 2012; 7:670.CrossRefGoogle Scholar
  37. 37.
    Saito R, Smoot ME, Ono K, et al. Atravel guide to cytoscape plugins. Nat Method 2012; 9: 1069.CrossRefGoogle Scholar
  38. 38.
    Netanalyzer. 15 Apr. 2017).Google Scholar
  39. 39.
    Xu Ke, Bezakova I, Bunimovich L, Yi SV. Path lengths in proteinprotein interaction networks and biological complexity. Proteomics 2011; 11: 1857.CrossRefGoogle Scholar
  40. 40.
    Zhang Z, Zhan J. A big world inside small-world networks. PLoS One 2009; 4: e5686.CrossRefGoogle Scholar
  41. 41.
    Pavlopoulos GA, Secrier M, Moschopoulos CN, et al. Using graph theory to analyze biological networks. BioData Mining 2011; 4: 10.CrossRefGoogle Scholar

Copyright information

© John Libbey Eurotext 2018

Authors and Affiliations

  • Dong Wook Jekarl
    • 1
    • 4
  • Kyung Soo Kim
    • 2
  • Seungok Lee
    • 1
    • 4
  • Myungshin Kim
    • 3
    • 4
  • Yonggoo Kim
    • 3
    • 4
    Email author
  1. 1.Department of Laboratory Medicine, Incheon St. Mary’s Hospital, College of MedicineThe Catholic University of KoreaSeoulRepublic of Korea
  2. 2.Department of Thoracic and Cardiovascular Surgery, Seoul St. Mary’s Hospital, College of MedicineThe Catholic University of KoreaSeoulRepublic of Korea
  3. 3.Department of Laboratory Medicine, Seoul St. Mary’s Hospital, College of MedicineThe Catholic University of KoreaSeoulRepublic of Korea
  4. 4.Laboratory for Development and Evaluation Center, Seoul St. Mary’s Hospital, College of MedicineThe Catholic University of KoreaSeoulRepublic of Korea

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