An operational view of intercellular signaling pathways

Abstract

Animal cells use a conserved repertoire of intercellular signaling pathways to communicate with one another. These pathways are well-studied from a molecular point of view. However, we often lack an "operational" understanding that would allow us to use these pathways to rationally control cellular behaviors. This requires knowing what dynamic input features each pathway perceives and how it processes those inputs to control downstream processes. To address these questions, researchers have begun to reconstitute signaling pathways in living cells, analyzing their dynamic responses to stimuli, and developing new functional representations of their behavior. Here we review important insights obtained through these new approaches, and discuss challenges and opportunities in understanding signaling pathways from an operational point of view.

Figure 1

What signal processing capabilities do core signaling pathways provide? Animal cells utilize several core intercellular signaling pathways that share a similar overall structure (left), in which ligands (red) bind to receptors (blue) and activate transcription factors (green) through intermediate messengers (orange). Despite their similarity, each pathway uses a distinct molecular architecture of protein interactions (left). This representation highlights the pathway architecture but typically provides little information about its operational capabilities. A complementary representation could focus on the signal processing functions of each pathway, indicating how it processes and represents extracellular signals. More research is needed in order to reveal the map between each pathway architecture and the corresponding signal processing function, and to determine how specific interaction parameters quantitatively affect the signal processing functionality.

Figure 2

Microbial two component systems provide an ideal example in which the relationship between molecular architecture and signal processing functions has been mapped. (A) In two component systems a receptor histidine kinase (blue) phosphorylates a response regulator (green) inducing a response. In some cases (left), the kinase additionally dephosphorylates the response regulator giving rise to an apparent futile cycle. In contrast, the bacterial chemotaxis two component system has a distinct architecture (right). In this case, additional components methylate and demethylate the receptor to adjust its activity (M indicates methylation), and there is indirect negative feedback on kinase activity (dashed line). (B) The two different architectures produce distinct signal processing capabilities. Bifunctionality of the kinase can give rise to an approximately linear amplifier, in which outputs (green) are proportional to inputs (red intensities). By contrast, feedback (dashed line) within the bacterial chemotaxis architecture generate an adaptive response, allowing the system to sense temporal derivatives in its inputs.

Figure 3

Examples of input and output signal encoding in metazoan signaling pathways. (A) Signaling pathways possess different signal transduction architectures, which influence their signaling processing abilities, shown in B. (B) From left to right, signal processing in the Wnt, TGF-β, and EGF pathways, respectively. In the Wnt pathway, extracellular ligand concentration is encoded in the fold-change in β-catenin response. An increase in extracellular ligand concentration (red line, top) leads to increases in absolute β-catenin levels that vary between cells (green lines, middle), but the fold-change in β-catenin levels is uniform (green lines, bottom). The TGF-β pathway is rate-responsive. An increase in extracellular ligand concentration (red lines, top) leads to an adaptive response in Smad4 nuclear localization (green lines, bottom). The amplitude of the response depends on the rate of increase of ligand concentration (compare light and dark lines in top and bottom plots). The EGF signaling pathway encodes ligand concentration in the frequency of ERK activity pulses. Step increases in ligand concentration (red lines, top) result in sustained, stochastic, pulses in ERK activity (green lines, bottom). The concentration of ligand is reflected in the average frequency of ERK pulses (compare light and dark lines).

Figure 4

Expression of different combinations of Notch pathway components controls the specificity of signaling. (A) With one ligand and one receptor, when cis receptor ligand interactions (symmetrical inhibitory arrow) are strong, a cell of interest (lower cell) can predominantly send to Notch in a neighboring cell (as pictured) when Delta ligand (red) exceeds Notch receptor levels (blue), or receive when Delta levels are low (not shown). (B) This behavior can be represented more abstractly as a sending state (upper cell) and a receiving state (lower cell), with a connecting red arrow indicating the capability of sending from a cell in one state to a cell in the other. (C) More complex configurations of Notch components are possible and occur frequently. In this example one possibility is illustrated involving one type of Notch receptor, two ligands (Delta and Jagged in red and green, respectively), and Lunatic Fringe. In this configuration, Fringe suppresses cis and trans interactions between Notch and Jagged, but not between Notch and Delta. As a result, there is no inhibition between Jagged and Notch. This allows the cell to send signals using Jagged while receiving signals from trans Delta ligands using Notch. However, Fringe blocks the ability to receive signals from Jagged (inhibitory arrow, right). (D) A diagram of multiple signaling states possible from other component configurations. The cell states shown in C are highlighted in corresponding outline colors. Red and green arrows indicate communication channels, showing which states are capable of sending and receiving to and from other states. Note that this diagram forms an acyclic directed graph.