Studying Cellular Signal Transduction with OMIC Technologies

Abstract

In the gulf between genotype and phenotype exists proteins and, in particular, protein signal transduction systems. These systems use a relatively limited parts list to respond to a much longer list of extracellular, environmental, and/or mechanical cues with rapidity and specificity. Most signaling networks function in a highly nonlinear and often contextual manner. Furthermore, these processes occur dynamically across space and time. Because of these complexities, systems and “OMIC” approaches are essential for the study of signal transduction. One challenge in using OMIC-scale approaches to study signaling is that the “signal” can take different forms in different situations. Signals are encoded in diverse ways such as protein-protein interactions, enzyme activities, localizations, or post-translational modifications to proteins. Furthermore, in some cases signals may be encoded only in the dynamics, duration, or rates of change of these features. Accordingly, systems-level analyses of signaling may need to integrate multiple experimental and/or computational approaches. As the field has progressed, the non-triviality of integrating experimental and computational analyses has become apparent. Successful use of OMIC methods to study signaling will require the “right” experiments and the “right” modeling approaches, and it is critical to consider both in the design phase of the project. In this review, we discuss common OMIC and modeling approaches for studying signaling, emphasizing the philosophical and practical considerations for effectively merging these two types of approaches to maximize the probability of obtaining reliable and novel insights into signaling biology.

Integrated Strategies to Gain a Systems-Level View of Dynamic Signaling Networks

“…We and others have recently reviewed this topic in detail(E. K. Day, Sosale, & Lazzara, 2016; Di Palma & Bodenmiller, 2015; Gingras & Wong, 2016; Grecco, Imtiaz, & Zamir, 2016; Landry, Clarke, & Lee, 2015; Newman et al, 2014; Sutandy et al, 2013; Tape, 2016; Terfve & Saez-Rodriguez, 2012); therefore, here, we will only provide a brief overview of select quantitative phosphoproteomics approaches and highlight how they are being used to gain insights into the global changes in the phosphorylation status of cellular proteins under various cellular conditions. We begin by examining several ensemble methods designed to analyze cellular material (e.g., cell lysates) that has been pooled from a population of cells.…”

“…In fact, emerging evidence suggests that several signaling systems are characterized by bistable (or multistable) responses(Albeck et al, 2008; Barr, Heldt, Zhang, Bakal, & Novak, 2016; Fosbrink, Aye-Han, Cheong, Levchenko, & Zhang, 2010; Montero et al, 2015; Spencer, Gaudet, Albeck, Burke, & Sorger, 2009). As a consequence, responses that appear to be graded when measured using ensemble approaches may, in fact, reflect differences in the percentage of cells that are in the “on” state rather than the degree to which a particular pathway is activated(Bagowski & Ferrell, 2001; Landry et al, 2015; Ni et al, 2011). Therefore, to better understand how information is processed within individual phosphorylation networks, researchers have developed a series of analytical tools designed to study phosphorylation dynamics at single cell resolution.…”

“…4C). Importantly, predictions made by computational models can lead to testable hypotheses that, in turn, can be evaluated experimentally using FRET-based reporters(Ganesan & Zhang, 2012; Landry et al, 2015; Ni et al, 2011; Sample et al, 2009; Tobias & Newton, 2016; Yaniv et al, 2015). For instance, to better understand crosstalk between Ca 2+ - and cAMP/PKA-dependent signaling pathways during insulin secretion in pancreatic β cells, Ni et al first used the FRET-based PKA reporter, A-kinase activity reporter 4 (AKAR4), to identify oscillatory changes in PKA activity in individual MIN6 β cells treated with the K + channel inhibitor, tetraethylammonium chloride (TEA)(Mehta et al, 2014; Ni et al, 2011).…”

“…Logic-based models, which use a series of logical operators (e.g., “AND”, “OR”, “NOR”) to describe the relationships between the activities of protein intermediates, have been used successfully to model large, tightly integrated systems like phosphorylation networks. For instance, logic-based models based on Boolean logic, fuzzy logic, and extreme pathways analysis have been useful frameworks for modeling feasible solution spaces, feedback loops, and global network topologies in a variety of cellular systems(Landry et al, 2015; Terfve & Saez-Rodriguez, 2012). The versatility of these models stems from the fact they do not require rate parameters to be measured or estimated.…”

“…However, though this improves their scalability, it can be a double-edged sword when studying highly dynamic systems such as phosphorylation networks. For example, because logic-based models assume that the influences of all protein states in the system are binary and equal in magnitude, they do not generally provide information about dynamic spatiotemporal relationships that underlie the regulation of cellular phosphorylation networks(Landry et al, 2015). Moreover, aside from nuclear versus cytoplasmic localization, these models rarely incorporate spatial information that may be critical to achieving signaling specificity within the system.…”