Genomics & proteomics: reduce, rebuild, reveal
Article first published online: 28 MAR 2006
Volume 210, Issue 1, pages 5–7, April 2006
How to Cite
Shaffer, A. L. and Staudt, L. M. (2006), Genomics & proteomics: reduce, rebuild, reveal. Immunological Reviews, 210: 5–7. doi: 10.1111/j.0105-2896.2006.00376.x
- Issue published online: 28 MAR 2006
- Article first published online: 28 MAR 2006
Scientists now have the capacity to reduce a cell to its component parts using technologies that comprehensively survey and measure the genome, the transcriptome, and the proteome. A brief ‘gee-whiz’ era existed when the generation of vast data sets was an end in itself, but this issue of Immunological Reviews makes it clear that high-throughput technologies are most valuable when brought to bear on an interesting biological or medical problem. A key creative challenge going forward is to devise ways to ‘rebuild’ a cell from the ground up using a catalog of its component parts and their interactions. Indeed, one of the most pleasing revelations brought by the genomics revolution is that complex biological phenomena are mathematically tractable. Signaling circuits in cells can be modeled in ways that predict the outcome of experiments yet to be performed. In complex diseases such as cancer, clinical phenotypes such as the length of survival following diagnosis or the response to therapy can be modeled based on quantitative measurements of gene expression in the tumor at diagnosis. Such an integrative and mathematical point of view is the essence of systems biology. The excitement surrounding systems biology is spawning new departments at many universities and drawing a new generation of graduate students toward these quantitative and comprehensive methods that can reveal biology in its glorious complexity.
In this issue of Immunological Reviews, we have brought together researchers whose efforts highlight the latest ‘omic’ perspectives on the function and derangements of the immune system. Somewhat arbitrarily, we have chosen to organize these articles to parallel the information flow in a cell, from genes to mRNA to protein.
This issue begins with a review by Hsu et al. (1) that discusses the influence of evolution on the structure and function of immunoglobulin genes. There are striking similarities among vertebrate immune systems, such as the utilization of somatic recombination to assemble receptors, but intriguing and profound differences exist, such as in the evolution of immunoglobulin isotypes. By analyzing these experiments of nature, we can derive a deeper understanding of the forces that have shaped the functional organization of the immune system.
In a tour de force of mouse genetics, Hoyne and Goodnow (2) describe genetic screens based on the random introduction of mutations into the mouse genome. They demonstrate the power of this approach to identify genes that cause immunological diseases and describe sensitized genetic screens aimed at genes that affect the development of autoimmune diabetes.
Plenge and Rioux (3) lay out a careful framework for researchers to consider when using human allelic variation to understand the relationship between genes and immunological diseases. Such studies must have sufficient power (number of samples) to identify an association between a gene and a disease trait and must be replicated in independent patient cohorts.
Several reviews use gene expression profiling and other functional genomics methods to reveal the functional capacities of normal immune cells and to dissect the derangements of the immune system in cancer and autoimmunity. To begin, Yamagata et al. (4) present a new analysis of gene expression patterns in innate and adaptive immune cells. These authors find that innate immune cells share a core gene expression profile that distinguishes them from adaptive immune cells, implying that innate immune cells have a shared functional capacity that needs to be explored. Next, Shaffer et al. (5) detail the use of gene expression profiling to understand the germinal center B cell to plasma cell transition, and they describe the subversion of this differentiation process during lymphomagenesis. This work also presents a new tool for the organization and interpretation of large gene expression data sets, the Signatures database. A gene expression signature is a set of genes with coordinate expression that can reflect a cellular differentiation state, the action of a transcription factor, or the activity of a signaling pathway. Shaffer et al. (5) also detail a new type of functional genomics approach, dubbed the Achilles' heel screen, which employs an RNA interference library to identify genes that are responsible for the abnormal proliferation and survival of malignant lymphoid cells. De Vos et al. (6) describe the use of gene expression profiling to investigate the biology of normal plasma cells and to explore the derangements in these cells that result in multiple myeloma.
Three reviews describe the insights provided by gene expression profiling into the mechanisms of immune tolerance and the circumvention of these checkpoints in autoimmune diseases. Borde et al. (7) describe investigations into the mechanisms of T-cell tolerance, and they focus particularly on T-cell anergy, a process that makes these cells refractory to antigen stimulation. The authors review the critical influence of the transcription factor NFAT (nuclear factor for activation of T cells) in T-cell anergy and the involvement of this same factor in B-cell anergy. Baechler et al. (8) provide a comprehensive overview of how gene expression profiling has been applied to the study of human autoimmunity and the perils and pitfalls of such analysis. They propose a set of guidelines by which the immunology community, especially those studying diseases such as rheumatoid arthritis and lupus, might proceed rationally with the generation and analysis of commonly useful gene expression data. Finally, Sarwal (9) describes how gene expression profiling has provided new insights into transplant rejection, beyond what can be monitored by pathology alone.
The power of high-throughput protein–protein interaction screens is shown in the review by Crawford et al. (10), which focuses on peptide/major histocompatibility complex (MHC) interactions. Using a baculovirus library expressing various peptide ligands bound to MHC molecules, both class I and class II, Crawford et al. (10) describe how one may use tagged, soluble T-cell receptors (TCRs) to screen for antigen mimotopes. They show that this kind of large-scale receptor/ligand screen can be used to rationally manipulate amino acids in order to change MHC/TCR interactions. Potentially, these concepts can be applied to clinically relevant antigens expressed by bacteria or tumors.
The complex dynamics of intracellular signaling networks is the focus of much systems biology research. Hoffman and Baltimore (11) provide an in-depth review of the NF-κB pathway, perhaps the most studied signaling network in biology. This pathway is required not only for the development of lymphocytes but also is essential for their survival and proliferation. This review explores how the transcriptional consequences of the NF-κB signaling pathway are modulated by various cellular stimuli in different cell types. Importantly, mathematical models of the NF-κB pathway reveal how feed-back and feed-forward loops result in the temporal behavior and transcriptional output of this pathway. Bauch and Superti-Furga (12) discuss the use of new methods to quantitatively dissect protein–protein interactions and, in particular, dissect the changes in protein networks that occur upon tumor necrosis factor (TNF) activation of the NF-κB pathway. These powerful methods may eventually annotate all protein–protein interactions in a cell, which can be exploited experimentally and pharmacologically. Perez and Nolan (13) also focus on proteomics, but they particularly address the dynamic changes in protein phosphorylation that occur during signaling. With advances in both multiparameter flow cytometry and phospho-specific antibodies, it has become possible to assess the phosphorylation state of multiple proteins within single cells in a high-throughput and functionally dynamic manner.
This issue concludes in a unique manner for this journal, with a commentary by three outstanding immunologists (14). As noted by these authors, the immunology community has not wholly embraced the power of ‘omic’ studies. If substantial advances are to be made in science, particularly in immunology, scientists and publishers will have to change the way in which experiments are performed and communicated. It is our hope that the immunological community may act as leaders in showing how high-throughput data generation married with new concepts in data analysis may further our knowledge of normal biology and disease pathogenesis.