From functional genomics to functional immunomics: new challenges, old problems, big rewards

Abstract

The development of DNA microarray technology a decade ago led to the establishment of functional genomics as one of the most active and successful scientific disciplines today. With the ongoing development of immunomic microarray technology-a spatially addressable, large-scale technology for measurement of specific immunological response-the new challenge of functional immunomics is emerging, which bears similarities to but is also significantly different from functional genomics. Immunonic data has been successfully used to identify biological markers involved in autoimmune diseases, allergies, viral infections such as human immunodeficiency virus (HIV), influenza, diabetes, and responses to cancer vaccines. This review intends to provide a coherent vision of this nascent scientific field, and speculate on future research directions. We discuss at some length issues such as epitope prediction, immunomic microarray technology and its applications, and computation and statistical challenges related to functional immunomics. Based on the recent discovery of regulation mechanisms in T cell responses, we envision the use of immunomic microarrays as a tool for advances in systems biology of cellular immune responses, by means of immunomic regulatory network models.

Figure 1. Estimation of Growth Curve for Immunomics Based on a PubMed Search See text for the search criteria used. (Illustration: Russell Howson)

Figure 2. Peptide–MHC Immunomic Microarray Technology

(a) Diagram of a peptide–MHC microarray, with an inset displaying a peptide–MHC spot, which includes co-stimulatory antibody needed to enhance T cell activation, as well as capture antibody to bind secreted cytokine. (b) Binding and activation of T cells on a specific peptide–MHC spot, which acts as an artificial antigen–presenting cell. (c) After washing, captured cytokine is revealed by the use of fluorescent antibodies, leading to a measurement of specific immunological response to the peptide–MHC complex. (Illusrtation: Russell Howson)

Figure 3. Genomic Profiling with DNA Microarrays Consists of One Signal (mRNA) Per Sample, while Immunomic Profiling with Peptide–MHC Microarrays Can Involve Multiple Signals per Sample

For a given T cell population, one could measure for each epitope on the microarray the associated IFN-γ and IL-10 secretion, which corresponds respectively to inflammatory and anti-inflammatory activity, producing multispectral profiles. (Illustration: Russell Howson)

Figure 4. Spectral Signatures for Different Epitopes Associated with a Given T Cell Population Sample

These are simply the measured secretion of different cytokines on microarray spots associated with the same specified epitope. (Illustration: Russell Howson)

Figure 5. Example of a Linear Classifier

The response to epitopes X and Y discriminates the patients protected by immunization from the control patients. (Illustration: Russell Howson)

Figure 6. Example of a Simple Immunomic Network, Consisting of Three Epitopes

Epitope A is a promoter (A is specific to CD4⁺ helper T cells), epitope B is a suppressor (B is specific to CD4⁺ regulatory T cells), while epitope C produces the effector response (C is specific to CD8⁺ cytotoxic T cells), while also promoting the suppressing response of epitope B (negative feedback). (a) Network wiring diagram and transition rules. (b) State transition table. (c) Basins of attraction in state-space, with attractors indicated by dashed rectangles. (Illustration: Russell Howson)