Naïve CD4 T cells have multiple fates that they may acquire when stimulated; the fate decision is regulated by the network of cytokines and transcription factors epressed in the activated cells. We are beginning to define the complexity of the cytokine and transcription factor networks as well as the diversity among Th cell subsets. There is still incertainty as to whether more Th cell subsets exist in vivo and, if so, how many. Reliable tools are needed to identify all the CD4 T cell subsets in vivo. Generation of multi-color indicator mice, in which the expression of all known lineage-specific master regulators are reported by different fluoresent proteins, would greatly benefit research on CD4 T cells.
The combination of cytokine signaling at different stages of T cell activation and the combination of transcription factors governing the differentiation processes are complex. A particular cytokine may have different function when it is combined with other cytokines. Since there are many cytokines being made at different levels during each immune response, fully understanding the differentiation process in vivo is difficult. Therefore, reconstitution of an in vivo differentiation in an in vitro culture remains a useful way to learn about cytokine networks. As our knowledge of the players grows, the conditions for in vitro differentiation cultures can be modified in such a way that it better represents an in vivo situation. Nonetheless, it must be recognized that many of the in vitro “networks” may be used rarely in vivo so that in vivo tests will be essential for a true understanding of the physiology and pathophysiology of CD4 T cell differentiation.
Due to the complexity of CD4 T cell population and their differentiation, the classical methods of studying individual factors one at a time, may be inefficient and sometimes misleading. This is because the function of any particular factor is always influenced by additional factors whose activation may vary under different conditions, and many biological responses are determined by quantitative changes in the expression of key factors. Therefore, a comparative analysis of different immune responses at a systemic level, in a quantitative way, is essential to yield a better understanding of the immune system.
Rapid advances in technology and genome informatics allow the performance of genome-wide studies. ChIPseq provide genome-wide maps of DNA binding sites of key transcription factors and transcription factor complexes. Profiling gene expression and mapping epigenetic modifications including DNA methylation, different histone modifications and appearance of DNase I hypersensitivity sites at a global level are essential to efficiently identify large numbers of cirtical cis-regulatory elements.
Quantitative measurements are also necessary to gain a complete picture of the transcriptional regulatory network and to understand how this network affects gene expression. The functions of many transcription factors depend on the amount of their expression relative to expression of other factors. Perturbation experiments, either knocking-down or enforced-expression in a titrated way, will yield valuable quantitative information on gene regulation.
Finally, mathematical models may be built to simulate the immune responses when the central pieces of the network including key transcription factors and crucial cis-regulatory elements of the target genes have been identified through genome-wide studies, and quantitative data sets are obtained through titrated pertubation of the key components.
Our ultimate goal of studying immune responses in mice is to help understand and treat human diseases. Indeed, some immune-related human diseases have been attributed to genetic mutations in key transcription factors, such as mutantions in FOXP3
that result in IPEX syndrome (87
) and STAT3
mutations that result in hyper-IgE syndrome associated with failure in Th17 responses to extracellular bacterial and fungal infections (79
). Many other immune-related diseases could also result from the mutations in particular cis-regulatory elements that are critical for the expression of key factors. Thus, understanding the molecular mechanisms, through which the network of transcription factors precisely control Th cell differentiation and Th cell heterogeneity, plasticity and stability, has great implications for understanding and treating a broad range of immune-related human diseases, including chronic viral, fungal, bacterial and parasitic infections, autoimmune diseases, allergic diseases and tumors.