Interpretation of the complexity of innate immune responses by functional genomics


Understanding how the immune system is regulated and responds to pathogens will require whole-system approaches, because the study of single immunological parameters has, so far, been unable to unlock immune-system complexity. Global transcription analysis using microarray technologies provides a new approach to the description of complex biological phenomena. Here, we discuss insights into innate immunity that have been provided by genome-wide approaches and their impact on the interpretation of immune-system complexity.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Changes in the macrophage transcriptome after bacterial activation.
Figure 2: Common and pathogen-specific dendritic-cell responses.
Figure 3: Temporal regulation of dendritic-cell functions.
Figure 4: Global visualization of the data set obtained by kinetic gene-expression analysis of maturing dendritic cells.


  1. 1

    Service, R. F. Complex systems. Exploring the systems of life. Science 284, 80–83 (1999).

    CAS  Article  Google Scholar 

  2. 2

    Goldenfeld, N. & Kadanoff, L. P. Simple lessons from complexity. Science 284, 87–89 (1999).

    CAS  Article  Google Scholar 

  3. 3

    Nurse, P. Reductionism. The ends of understanding. Nature 387, 657 (1997).

    CAS  Article  Google Scholar 

  4. 4

    Whitehead, A. N. Science and the Modern World (Macmillan, New York, 1925).

    Google Scholar 

  5. 5

    Keil, D., Luebke, R. W. & Pruett, S. B. Quantifying the relationship between multiple immunological parameters and host resistance: probing the limits of reductionism. J. Immunol. 167, 4543–4552 (2001).

    CAS  Article  Google Scholar 

  6. 6

    Granucci, F. et al. Inducible IL-2 production by dendritic cells revealed by global gene-expression analysis. Nature Immunol. 2, 882–888 (2001).

    CAS  Article  Google Scholar 

  7. 7

    Boldrick, J. C. et al. Stereotyped and specific gene-expression programs in human innate immune responses to bacteria. Proc. Natl Acad. Sci. USA 99, 972–977 (2002).

    CAS  Article  Google Scholar 

  8. 8

    Lockhart, D. J. et al. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nature Biotechnol. 14, 1675–1680 (1996).

    CAS  Article  Google Scholar 

  9. 9

    Wodicka, L. et al. Genome-wide expression monitoring in Saccharomyces cerevisiae. Nature Biotechnol. 15, 1359–1367 (1997).

    CAS  Article  Google Scholar 

  10. 10

    DeRisi, J. L., Iyer, V. R. & Brown, P. O. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680–686 (1997).

    CAS  Article  Google Scholar 

  11. 11

    Zhao, N. et al. High-density cDNA filter analysis: a novel approach for large-scale, quantitative analysis of gene expression. Gene 156, 207–213 (1995).

    CAS  Article  Google Scholar 

  12. 12

    Schena, M., Shalon, D., Davis, R. W. & Brown, P. O. Quantitative monitoring of gene-expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995).

    CAS  Article  Google Scholar 

  13. 13

    Iyer, V. R. et al. The transcriptional program in the response of human fibroblasts to serum. Science 283, 83–87 (1999).

    CAS  Article  Google Scholar 

  14. 14

    Glynne, R. J., Ghandour, G. & Goodnow, C. C. Genomic-scale gene-expression analysis of lymphocyte growth, tolerance and malignancy. Curr. Opin. Immunol. 12, 210–214 (2000).

    CAS  Article  Google Scholar 

  15. 15

    Rogge, L. et al. Transcript imaging of the development of human T helper cells using oligonucleotide arrays. Nature Genet. 25, 96–101 (2000).

    CAS  Article  Google Scholar 

  16. 16

    Lee, C. K., Klopp, R. G., Weindruch, R. & Prolla, T. A. Gene-expression profile of aging and its retardation by caloric restriction. Science 285, 1390–1393 (1999).

    CAS  Article  Google Scholar 

  17. 17

    Winzler, C. et al. Maturation stages of mouse dendritic cells in growth-factor-dependent long-term cultures. J. Exp. Med. 185, 317–328 (1997).

    CAS  Article  Google Scholar 

  18. 18

    Kitano, H. Systems biology: a brief overview. Science 295, 1662–1664 (2002).

    CAS  Article  Google Scholar 

  19. 19

    Ince, T. A. & Weinberg, R. A. Functional genomics and the breast-cancer problem. Cancer Cell 1, 15–17 (2002).

    CAS  Article  Google Scholar 

  20. 20

    Dopazo, J. et al. Methods and approaches in the analysis of gene-expression data. J. Immunol. Methods 250, 92–112 (2001).

    Article  Google Scholar 

  21. 21

    Staudt, L. M. & Brown, P. O. Genomic views of the immune system. Annu. Rev. Immunol. 18, 829–859 (2000).

    CAS  Article  Google Scholar 

  22. 22

    Staudt, L. M. Gene-expression physiology and pathophysiology of the immune system. Trends Immunol. 22, 35–40 (2001).

    CAS  Article  Google Scholar 

  23. 23

    Teague, T. K. et al. Activation changes the spectrum but not the diversity of genes expressed by T cells. Proc. Natl Acad. Sci. USA 96, 12691–12696 (1999).

    CAS  Article  Google Scholar 

  24. 24

    Fahrer, A. M. et al. A genomic view of immunology. Nature 409, 836–838 (2001).

    CAS  Article  Google Scholar 

  25. 25

    Manger, I. D. & Relman, D. A. How the host 'sees' pathogens: global gene-expression responses to infection. Curr. Opin. Immunol. 12, 215–218 (2000).

    CAS  Article  Google Scholar 

  26. 26

    Janeway, C. A. Jr & Medzhitov, R. Innate immune recognition. Annu. Rev. Immunol. 20, 197–216 (2002).

    CAS  Article  Google Scholar 

  27. 27

    Gordon, S., Clarke, S., Greaves, D. & Doyle, A. Molecular immunobiology of macrophages: recent progress. Curr. Opin. Immunol. 7, 24–33 (1995).

    CAS  Article  Google Scholar 

  28. 28

    Morrissette, N., Gold, E. & Aderem, A. The macrophage — a cell for all seasons. Trends Cell. Biol. 9, 199–201 (1999).

    CAS  Article  Google Scholar 

  29. 29

    Laskin, D. L., Weinberger, B. & Laskin, J. D. Functional heterogeneity in liver and lung macrophages. J. Leukocyte Biol. 70, 163–170 (2001).

    CAS  PubMed  Google Scholar 

  30. 30

    Wang, Z. M., Liu, C. & Dziarski, R. Chemokines are the main proinflammatory mediators in human monocytes activated by Staphylococcus aureus, peptidoglycan and endotoxin. J. Biol. Chem. 275, 20260–20267 (2000).

    CAS  Article  Google Scholar 

  31. 31

    Rosenberger, C. M. et al. Salmonella typhimurium infection and lipolysaccharide stimulation induce similar changes in macrophage gene expression. J. Immunol. 164, 5894–5904 (2000).

    CAS  Article  Google Scholar 

  32. 32

    Ehrt, S. et al. Reprogramming of the macrophage transcriptome in response to interferon-γ and Mycobacterium tuberculosis: signaling role of nitric oxide synthase-2 and phagocyte oxidase. J. Exp. Med. 194, 1123–1139 (2001).

    CAS  Article  Google Scholar 

  33. 33

    Nau, G. J. et al. Human macrophage activation programs induced by bacterial pathogens. Proc. Natl Acad. Sci. USA 99, 1503–1508 (2002).

    CAS  Article  Google Scholar 

  34. 34

    Mellman, I. & Steinman, R. M. Dendritic cells: specialized and regulated antigen-processing machines. Cell 106, 255–258 (2001).

    CAS  Article  Google Scholar 

  35. 35

    Banchereau, J. et al. Immunobiology of dendritic cells. Annu. Rev. Immunol. 18, 767–811 (2000).

    CAS  Article  Google Scholar 

  36. 36

    Lanzavecchia, A. & Sallusto, F. The instructive role of dendritic cells on T-cell responses: lineages, plasticity and kinetics. Curr. Opin. Immunol. 13, 291–298 (2001).

    CAS  Article  Google Scholar 

  37. 37

    Langenkamp, A., Messi, M., Lanzavecchia, A. & Sallusto, F. Kinetics of dendritic-cell activation: impact on priming of TH1, TH2 and nonpolarized T cells. Nature Immunol. 1, 311–316 (2000).

    CAS  Article  Google Scholar 

  38. 38

    Sallusto, F. & Lanzavecchia, A. Efficient presentation of soluble antigen by cultured human dendritic cells is maintained by granulocyte–macrophage colony-stimulating factor plus interleukin-4 and downregulated by tumor-necrosis factor-α. J. Exp. Med. 179, 1109–1118 (1994).

    CAS  Article  Google Scholar 

  39. 39

    Reid, C. D., Stackpoole, A., Meager, A. & Tikerpae, J. Interactions of tumor necrosis factor with granulocyte–macrophage colony-stimulating factor and other cytokines in the regulation of dendritic-cell growth in vitro from early bipotent CD34+ progenitors in human bone marrow. J. Immunol. 149, 2681–2688 (1992).

    CAS  PubMed  Google Scholar 

  40. 40

    Huang, Q. et al. The plasticity of dendritic-cell responses to pathogens and their components. Science 294, 870–875 (2001).

    CAS  Article  Google Scholar 

  41. 41

    d'Ostiani, C. F. et al. Dendritic cells discriminate between yeast and hyphae of the fungus Candida albicans. Implications for initiation of T helper cell immunity in vitro and in vivo. J. Exp. Med. 191, 1661–1674 (2000).

    CAS  Article  Google Scholar 

  42. 42

    Borisy, G. G. & Svitkina, T. M. Actin machinery: pushing the envelope. Curr. Opin. Cell Biol. 12, 104–112 (2000).

    CAS  Article  Google Scholar 

  43. 43

    Movilla, N. & Bustelo, X. N. Biological and regulatory properties of Vav-3, a new member of the Vav family of oncoprotein. Mol. Cell. Biol. 19, 7870–7885 (1999).

    CAS  Article  Google Scholar 

  44. 44

    Rescigno, M. et al. Bacteria-induced neo-biosynthesis, stabilization and surface expression of functional class I molecules in mouse dendritic cells. Proc. Natl Acad. Sci. USA 95, 5229–5234 (1998).

    CAS  Article  Google Scholar 

  45. 45

    Hashimoto, S. I. et al. Identification of genes specifically expressed in human activated and mature dendritic cells through serial analysis of gene expression. Blood 96, 2206–2214 (2000).

    CAS  PubMed  Google Scholar 

  46. 46

    Rescigno, M. et al. Dendritic-cell survival and maturation are regulated by different signaling pathways. J. Exp. Med. 188, 2175–2180 (1998).

    CAS  Article  Google Scholar 

  47. 47

    Granucci, F. et al. Transcriptional reprogramming of dendritic cells by differentiation stimuli. Eur. J. Immunol. 31, 2539–2546 (2001).

    CAS  Article  Google Scholar 

  48. 48

    Andrews, D. M. et al. Infection of dendritic cells by murine cytomegalovirus induces functional paralysis. Nature Immunol. 2, 1077–1084 (2001).

    CAS  Article  Google Scholar 

  49. 49

    Zitvogel, L. Dendritic and natural killer cells cooperate in the control/switch of innate immunity. J. Exp. Med. 195, F9–F14 (2002).

    CAS  Article  Google Scholar 

  50. 50

    Csete, M. E. & Doyle, J. C. Reverse engineering of biological complexity. Science 295, 1664–1669 (2002).

    CAS  Article  Google Scholar 

  51. 51

    Chong, L. & Ray, L. B. Whole-istic biology. Science 295, 1661 (2002).

    CAS  Article  Google Scholar 

  52. 52

    Gallager, R. & Appenzeller, T. Beyond reductionism. Science 284, 79 (1999).

    Article  Google Scholar 

  53. 53

    Dawkins, R. The Selfish Gene (Oxford University Press, New York, 1976).

    Google Scholar 

  54. 54

    Noble, D. Modeling the heart — from genes to cells to the whole organ. Science 295, 1678–1682 (2002).

    CAS  Article  Google Scholar 

  55. 55

    Sorlie, T. et al. Gene-expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl Acad. Sci. USA 98, 10869–10874 (2001).

    CAS  Article  Google Scholar 

  56. 56

    Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863–14868 (1998).

    CAS  Article  Google Scholar 

  57. 57

    Chu, S. et al. The transcriptional program of sporulation in budding yeast. Science 282, 699–705 (1998).

    CAS  Article  Google Scholar 

  58. 58

    Tamayo, P. et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl Acad. Sci. USA 96, 2907–2912 (1999).

    CAS  Article  Google Scholar 

  59. 59

    Butte, A. J. et al. Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc. Natl Acad. Sci. USA 97, 12182–12186 (2000).

    CAS  Article  Google Scholar 

  60. 60

    Kao, C. M. Functional genomic technologies: creating new paradigms for fundamental and applied biology. Biotechnol. Prog. 15, 304–311 (1999).

    CAS  Article  Google Scholar 

  61. 61

    Kell, D. B., King, R. D. On the optimization of classes for the assignment of unidentified reading frames in functional genomics programmes: the need for machine learning. Trends Biotechnol. 18, 93–98 (2000).

    CAS  Article  Google Scholar 

  62. 62

    Shaffer, A. L. et al. Signatures of the immune response. Immunity 15, 375–385 (2001).

    CAS  Article  Google Scholar 

  63. 63

    Glynne, R. et al. How self-tolerance and the immunosuppressive drug FK506 prevent B-cell mitogenesis. Nature 403, 672–676 (2000).

    CAS  Article  Google Scholar 

  64. 64

    Crescenzi, M. & Giuliani, A. The main biological determinants of tumor-line taxonomy elucidated by a principal component analysis of microarray data. FEBS Lett. 507, 114–118 (2001).

    CAS  Article  Google Scholar 

  65. 65

    Raychaudhuri, S., Stuart, J. M. & Altman, R. B. Principal-component analysis to summarize microarray experiments: application to sporulation time series. Pac. Symp. Biocomput. 455–466 (2000).

Download references


We thank N. Pavelka for the figures. This work was supported by grants from the Italian Association against Cancer (AIRC), the 5th EC Programs (DC strategies and TAGAPO) and MIUR (Ministero dell'Istruzione dell'Università e della Ricerca).

Author information



Corresponding author

Correspondence to Paola Ricciardi-Castagnoli.

Related links

Related links



Candida albicans


Mycobacterium tuberculosis




















IL-7 receptor



IL-12 p35

IL-12 p40

IL-15 receptor α-chain




















Rights and permissions

Reprints and Permissions

About this article

Cite this article

Ricciardi-Castagnoli, P., Granucci, F. Interpretation of the complexity of innate immune responses by functional genomics. Nat Rev Immunol 2, 881–888 (2002).

Download citation

Further reading


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