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Democratizing systems immunology with modular transcriptional repertoire analyses


Individual elements that constitute the immune system have been characterized over the few past decades, mostly through reductionist approaches. The introduction of large-scale profiling platforms has more recently facilitated the assessment of these elements on a global scale. However, the analysis and the interpretation of such large-scale datasets remains a challenge and a barrier for the wider adoption of systems approaches in immunological and clinical studies. In this Innovation article, we describe an analytical strategy that relies on the a priori determination of co-dependent gene sets for a given biological system. Such modular transcriptional repertoires can in turn be used to simplify the analysis and the interpretation of large-scale datasets, and to design targeted immune fingerprinting assays and web applications that will further facilitate the dissemination of systems approaches in immunology.

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Figure 1: Modular repertoire identification.
Figure 2: Mapping perturbations of the modular repertoire.
Figure 3: Mapping perturbations of the modular repertoire across individual samples.
Figure 4: Mapping perturbations of the modular repertoire across studies.
Figure 5: Transcriptome fingerprinting assays.


  1. 1

    Schuh, W., Meister, S., Herrmann, K., Bradl, H. & Jack, H. M. Transcriptome analysis in primary B lymphoid precursors following induction of the pre-B cell receptor. Mol. Immunol. 45, 362–375 (2008).

    CAS  Article  Google Scholar 

  2. 2

    Chaussabel, D., Pascual, V. & Banchereau, J. Assessing the human immune system through blood transcriptomics. BMC Biol. 8, 84 (2010).

    Article  Google Scholar 

  3. 3

    Pascual, V., Chaussabel, D. & Banchereau, J. A genomic approach to human autoimmune diseases. Annu. Rev. Immunol. 28, 535–571 (2010).

    CAS  Article  Google Scholar 

  4. 4

    Li, S., Nakaya, H. I., Kazmin, D. A., Oh, J. Z. & Pulendran, B. Systems biological approaches to measure and understand vaccine immunity in humans. Semin. Immunol. 25, 209–218 (2013).

    CAS  Article  Google Scholar 

  5. 5

    Ravindran, R. et al. Vaccine activation of the nutrient sensor GCN2 in dendritic cells enhances antigen presentation. Science 343, 313–317 (2014).

    CAS  Article  Google Scholar 

  6. 6

    Germain, R. N., Meier-Schellersheim, M., Nita-Lazar, A. & Fraser, I. D. Systems biology in immunology: a computational modeling perspective. Annu. Rev. Immunol. 29, 527–585 (2011).

    CAS  Article  Google Scholar 

  7. 7

    Amit, I., Regev, A. & Hacohen, N. Strategies to discover regulatory circuits of the mammalian immune system. Nature Rev. Immunol. 11, 873–880 (2011).

    CAS  Article  Google Scholar 

  8. 8

    Chevrier, N. et al. Systematic discovery of TLR signaling components delineates viral-sensing circuits. Cell 147, 853–867 (2011).

    CAS  Article  Google Scholar 

  9. 9

    Litvak, V. et al. A FOXO3–IRF7 gene regulatory circuit limits inflammatory sequelae of antiviral responses. Nature 490, 421–425 (2012).

    CAS  Article  Google Scholar 

  10. 10

    Shapira, S. D. & Hacohen, N. Systems biology approaches to dissect mammalian innate immunity. Curr. Opin. Immunol. 23, 71–77 (2011).

    CAS  Article  Google Scholar 

  11. 11

    Diercks, A. & Aderem, A. Systems approaches to dissecting immunity. Curr. Top. Microbiol. Immunol. 363, 1–19 (2013).

    CAS  PubMed  Google Scholar 

  12. 12

    Ergun, A. et al. Differential splicing across immune system lineages. Proc. Natl Acad. Sci. USA 110, 14324–14329 (2013).

    CAS  Article  Google Scholar 

  13. 13

    Schutte, J., Moignard, V. & Gottgens, B. Establishing the stem cell state: insights from regulatory network analysis of blood stem cell development. Wiley Interdiscip. Rev. Syst. Biol. Med. 4, 285–295 (2012).

    CAS  Article  Google Scholar 

  14. 14

    Keller, M. A. et al. Transcriptional regulatory network analysis of developing human erythroid progenitors reveals patterns of coregulation and potential transcriptional regulators. Physiol. Genom. 28, 114–128 (2006).

    CAS  Article  Google Scholar 

  15. 15

    Novershtern, N. et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell 144, 296–309 (2011).

    CAS  Article  Google Scholar 

  16. 16

    Jojic, V. et al. Identification of transcriptional regulators in the mouse immune system. Nature Immunol. 14, 633–643 (2013).

    CAS  Article  Google Scholar 

  17. 17

    Allantaz, F. et al. Expression profiling of human immune cell subsets identifies miRNA–mRNA regulatory relationships correlated with cell type specific expression. PLoS ONE 7, e29979 (2012).

    CAS  Article  Google Scholar 

  18. 18

    Nutt, S. L., Taubenheim, N., Hasbold, J., Corcoran, L. M. & Hodgkin, P. D. The genetic network controlling plasma cell differentiation. Semin. Immunol. 23, 341–349 (2011).

    CAS  Article  Google Scholar 

  19. 19

    Murn, J. et al. A Myc-regulated transcriptional network controls B-cell fate in response to BCR triggering. BMC Genom. 10, 323 (2009).

    Article  Google Scholar 

  20. 20

    Holmes, M. L., Pridans, C. & Nutt, S. L. The regulation of the B-cell gene expression programme by Pax5. Immunol. Cell Biol. 86, 47–53 (2008).

    CAS  Article  Google Scholar 

  21. 21

    Sarkar, S. et al. Functional and genomic profiling of effector CD8 T cell subsets with distinct memory fates. J. Exp. Med. 205, 625–640 (2008).

    CAS  Article  Google Scholar 

  22. 22

    Haining, W. N. et al. Identification of an evolutionarily conserved transcriptional signature of CD8 memory differentiation that is shared by T and B cells. J. Immunol. 181, 1859–1868 (2008).

    CAS  Article  Google Scholar 

  23. 23

    Luckey, C. J. et al. Memory T and memory B cells share a transcriptional program of self-renewal with long-term hematopoietic stem cells. Proc. Natl Acad. Sci. USA 103, 3304–3309 (2006).

    CAS  Article  Google Scholar 

  24. 24

    He, F. et al. PLAU inferred from a correlation network is critical for suppressor function of regulatory T cells. Mol. Systems Biol. 8, 624 (2012).

    Article  Google Scholar 

  25. 25

    Doering, T. A. et al. Network analysis reveals centrally connected genes and pathways involved in CD8+ T cell exhaustion versus memory. Immunity 37, 1130–1144 (2012).

    CAS  Article  Google Scholar 

  26. 26

    Quigley, M. et al. Transcriptional analysis of HIV-specific CD8+ T cells shows that PD-1 inhibits T cell function by upregulating BATF. Nature Med. 16, 1147–1151 (2010).

    CAS  Article  Google Scholar 

  27. 27

    Yosef, N. et al. Dynamic regulatory network controlling TH17 cell differentiation. Nature 496, 461–468 (2013).

    CAS  Article  Google Scholar 

  28. 28

    Angelosanto, J. M. & Wherry, E. J. Transcription factor regulation of CD8+ T-cell memory and exhaustion. Immunol. Rev. 236, 167–175 (2010).

    CAS  Article  Google Scholar 

  29. 29

    Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, Article17 (2005).

  30. 30

    Stuart, J. M., Segal, E., Koller, D. & Kim, S. K. A gene-coexpression network for global discovery of conserved genetic modules. Science 302, 249–255 (2003).

    CAS  Article  Google Scholar 

  31. 31

    Novershtern, N., Regev, A. & Friedman, N. Physical Module Networks: an integrative approach for reconstructing transcription regulation. Bioinformatcs 27, i177–i185 (2011).

    CAS  Article  Google Scholar 

  32. 32

    Shmulevich, I., Dougherty, E. R., Kim, S. & Zhang, W. Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18, 261–274 (2002).

    CAS  Article  Google Scholar 

  33. 33

    Berry, M. P. et al. An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature 466, 973–977 (2010).

    CAS  Article  Google Scholar 

  34. 34

    Pascual, V. et al. How the study of children with rheumatic diseases identified interferon-α and interleukin-1 as novel therapeutic targets. Immunol. Rev. 223, 39–59 (2008).

    CAS  Article  Google Scholar 

  35. 35

    Gaucher, D. et al. Yellow fever vaccine induces integrated multilineage and polyfunctional immune responses. J. Exp. Med. 205, 3119–3131 (2008).

    CAS  Article  Google Scholar 

  36. 36

    Obermoser, G. et al. Systems scale interactive exploration reveals quantitative and qualitative differences in response to influenza and pneumococcal vaccines. Immunity 38, 831–844 (2013).

    CAS  Article  Google Scholar 

  37. 37

    Nakaya, H. I. et al. Systems biology of vaccination for seasonal influenza in humans. Nature Immunol. 12, 786–795 (2011).

    CAS  Article  Google Scholar 

  38. 38

    Franco, L. M. et al. Integrative genomic analysis of the human immune response to influenza vaccination. eLife 2, e00299 (2013).

    Article  Google Scholar 

  39. 39

    Querec, T. D. et al. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nature Immunol. 10, 116–125 (2009).

    CAS  Article  Google Scholar 

  40. 40

    Klechevsky, E. et al. Functional specializations of human epidermal Langerhans cells and CD14+ dermal dendritic cells. Immunity 29, 497–510 (2008).

    CAS  Article  Google Scholar 

  41. 41

    Banchereau, R. et al. Host immune transcriptional profiles reflect the variability in clinical disease manifestations in patients with Staphylococcus aureus infections. PloS ONE 7, e34390 (2012).

    CAS  Article  Google Scholar 

  42. 42

    Shen-Orr, S. S. et al. Cell type-specific gene expression differences in complex tissues. Nature Methods 7, 287–289 (2010).

    CAS  Article  Google Scholar 

  43. 43

    Chaussabel, D. et al. A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus. Immunity 29, 150–164 (2008).

    CAS  Article  Google Scholar 

  44. 44

    Ardura, M. I. et al. Enhanced monocyte response and decreased central memory T cells in children with invasive Staphylococcus aureus infections. PLoS ONE 4, e5446 (2009).

    Article  Google Scholar 

  45. 45

    Mejias, A. et al. Whole blood gene expression profiles to assess pathogenesis and disease severity in infants with respiratory syncytial virus infection. PLoS Med. 10, e1001549 (2013).

    Article  Google Scholar 

  46. 46

    Pankla, R. et al. Genomic transcriptional profiling identifies a candidate blood biomarker signature for the diagnosis of septicemic melioidosis. Genome Biol. 10, R127 (2009).

    Article  Google Scholar 

  47. 47

    Caskey, M. et al. Synthetic double-stranded RNA induces innate immune responses similar to a live viral vaccine in humans. J. Exp. Med. 208, 2357–2366 (2011).

    CAS  Article  Google Scholar 

  48. 48

    Li, S. et al. Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nature Immunol. 15, 185–205 (2013).

    Google Scholar 

  49. 49

    Schadt, E. E. Molecular networks as sensors and drivers of common human diseases. Nature 461, 218–223 (2009).

    CAS  Article  Google Scholar 

  50. 50

    Krzywinski, M., Birol, I., Jones, S. J. & Marra, M. A. Hive plots — rational approach to visualizing networks. Brief. Bioinformat. 13, 627–644 (2012).

    Article  Google Scholar 

  51. 51

    Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).

    CAS  Article  Google Scholar 

  52. 52

    Aloy, P. & Russell, R. B. Taking the mystery out of biological networks. EMBO Rep. 5, 349–350 (2004).

    CAS  Article  Google Scholar 

  53. 53

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

    CAS  Article  Google Scholar 

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The authors would like to thank S. Presnell, M. C. Altman and E. Whalen for input and comments, B. Norris for editorial help, and C. Quinn, S. Presnell, K. Domico, E. Whalen, A. Bjork and B. Zeitner for the development of web tools. N.B. and D.C. are supported by US National Institutes of Health grants U01AI082110, U19-AI089987, U19-AI08998 and U19-AI057234. The authors apologize to those in the field whose important work was not cited here because of space limitations.

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Correspondence to Damien Chaussabel.

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The authors are listed as inventors on patent applications and they receive funding from grants to support their research from the National Institute of Allergy and Infectious Diseases, National Institute of Health, USA.

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Online Supplementary Material: Interactive web applications for exploration of modular repertoire analysis results (PDF 85 kb)

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Chaussabel, D., Baldwin, N. Democratizing systems immunology with modular transcriptional repertoire analyses. Nat Rev Immunol 14, 271–280 (2014).

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