Strategies to discover regulatory circuits of the mammalian immune system

Abstract

Recent advances in technologies for genome- and proteome-scale measurements and perturbations promise to accelerate discovery in every aspect of biology and medicine. Although such rapid technological progress provides a tremendous opportunity, it also demands that we learn how to use these tools effectively. One application with great potential to enhance our understanding of biological systems is the unbiased reconstruction of genetic and molecular networks. Cells of the immune system provide a particularly useful model for developing and applying such approaches. Here, we review approaches for the reconstruction of signalling and transcriptional networks, with a focus on applications in the mammalian innate immune system.

Figure 1. Immune cell activation, cell states and network reconstruction

A. We first define the goal of network reconstruction with an abstract example of a simple circuit. When resting immune cells encounter a specific ligand (such as LPS, which signals through TLR4, or peptide–MHC, which signals through TCR), they make a transition from a resting to an activated state through a series of intermediate states (where a ‘state’ is defined by specific values of measurable parameters, such as transcript levels or phosphorylated proteins, that are reproducibly observed under certain experimental conditions at specific timepoints). Such state transitions occur as a result of a temporal cascade of signalling and transcriptional events. In this simplified example, protein A is initially modified (for example, by phosphorylation) in response to a ligand and induces transcription of gene B by binding to its promoter. Proteins B and A then form a complex that stimulates transcription of C, a key protein in the final activated state. The challenge of network reconstruction is to identify components A, B and C, their direct and indirect interactions and their roles in regulating the parameters that define each cellular state. This can be carried out using an experimental strategy as described in our studies of dendritic cells (DCs). B–D, Physiological examples of immune phenomena occurring at three timescales (from rapid to slow) that can be studied by network reconstruction: innate immune responses of DCs to pathogens (B), T cell responses to antigen (C) and haematopoeisis (D). B, Dendritic cell gene expression profiles can be measured in response to distinct ligands (such as lipopolysaccharide (LPS), double-stranded RNA (dsRNA), beta-glucan and phosphatidyl serine (PtdSer)). Each of the resulting four states of DCs has distinct effects on an individual’s innate and adaptive immune responses. Many additional states can be studied given the number of ligands affecting DCs. C, T cell polarization into specific states is a crucial decision point in the mammalian immune response, and is controlled by cytokines and other molecules that are present when T cells are activated by peptide–MHC complexes. D, Haematopoeisis is a well-studied developmental process in which haematopoietic stem cells (HSCs) develop into precursors (such as common lymphoid progenitors (CLPs) and common myeloid progenitors (CMPs)) that further develop into mature immune cells. Note that only a few cell types are shown for illustration.

Figure 2. Overview of the proposed network reconstruction strategy

Top, Four major steps in network reconstruction. Bottom, examples of technologies that could be used at each step.

Figure 3. Future clinical applications

Immune cells are isolated from patient blood or other tissues and studied ex vivo using global and focused profiling approaches (for example, as described in Figs.1 and 2 for studying the resting state or the responses to ligands and perturbations) to derive network models that help explain observed cellular states. If alterations in network behavior can be associated with particular disease phenotypes, then individual-specific network models will be helpful in the diagnosis, prognosis and selection of therapeutic interventions for immune disorders.