Alejandro Wolf-Yadlin

Time-resolved Mass Spectrometry of Tyrosine Phosphorylation Sites in the Epidermal Growth Factor Receptor Signaling Network Reveals Dynamic Modules

Ligand binding to cell surface receptors initiates a cascade of signaling events regulated by dynamic phosphorylation events on a multitude of pathway proteins. Quantitative features, including intensity, timing, and duration of phosphorylation of particular residues, may play a role in determining cellular response, but experimental data required for analysis of these features have not previously been available. To understand the dynamic operation of signaling cascades, we have developed a method enabling the simultaneous quantification of tyrosine phosphorylation of specific residues on dozens of key proteins in a time-resolved manner, downstream of epidermal growth factor receptor (EGFR) activation. Tryptic peptides from four different EGFR stimulation time points were labeled with four isoforms of the iTRAQ reagent to enable downstream quantification. After mixing of the labeled samples, tyrosine-phosphorylated peptides were immunoprecipitated with an anti-phosphotyrosine antibody and further enriched by IMAC before LC/MS/MS analysis. Database searching and manual confirmation of peptide phosphorylation site assignments led to the identification of 78 tyrosine phosphorylation sites on 58 proteins from a single analysis. Replicate analyses of a separate biological sample provided both validation of this first data set and identification of 26 additional tyrosine phosphorylation sites and 18 additional proteins. iTRAQ fragment ion ratios provided time course phosphorylation profiles for each site. The data set of quantitative temporal phosphorylation profiles was further characterized by self-organizing maps, which resulted in identification of several cohorts of tyrosine residues exhibiting self-similar temporal phosphorylation profiles, operationally defining dynamic modules in the EGFR signaling network consistent with particular cellular processes. The presence of novel proteins and associated tyrosine phosphorylation sites within these modules indicates additional components of this network and potentially localizes the topological action of these proteins. Additional analysis and modeling of the data generated in this study are likely to yield more sophisticated models of receptor tyrosine kinase-initiated signal transduction, trafficking, and regulation.

Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks

Although recent developments in MS have enabled the identification and quantification of hundreds of phosphorylation sites from a given biological sample, phosphoproteome analysis by MS has been plagued by inconsistent reproducibility arising from automated selection of precursor ions for fragmentation, identification, and quantification. To address this challenge, we have developed a new MS-based strategy, based on multiple reaction monitoring of stable isotope-labeled peptides, that enables highly reproducible quantification of hundreds of nodes (phosphorylation sites) within a signaling network and across multiple conditions simultaneously. We have applied this strategy to quantify temporal phosphorylation profiles of 222 tyrosine phosphorylated peptides across seven time points following EGF treatment, including 31 tyrosine phosphorylation sites not previously known to be regulated by EGF stimulation. With this approach, 88% of the signaling nodes were reproducibly quantified in four analyses, as compared with only 34% by typical information-dependent analysis. As a result of the improved reproducibility, full temporal phosphorylation profiles were generated for an additional 104 signaling nodes with the multiple reaction monitoring strategy, an 88% increase in our coverage of the signaling network. This method is broadly applicable to multiple signaling networks and to a variety of samples, including quantitative analysis of signaling networks in clinical samples. Using this approach, it should now be possible to routinely monitor the phosphorylation status of hundreds of nodes across multiple biological conditions.

Effects of HER2 overexpression on cell signaling networks governing proliferation and migration

Although human epidermal growth factor receptor 2 (HER2) overexpression is implicated in tumor progression for a variety of cancer types, how it dysregulates signaling networks governing cell behavioral functions is poorly understood. To address this problem, we use quantitative mass spectrometry to analyze dynamic effects of HER2 overexpression on phosphotyrosine signaling in human mammary epithelial cells stimulated by epidermal growth factor (EGF) or heregulin (HRG). Data generated from this analysis reveal that EGF stimulation of HER2‐overexpressing cells activates multiple signaling pathways to stimulate migration, whereas HRG stimulation of these cells results in amplification of a specific subset of the migration signaling network. Self‐organizing map analysis of the phosphoproteomic data set permitted elucidation of network modules differentially regulated in HER2‐overexpressing cells in comparison with parental cells for EGF and HRG treatment. Partial least‐squares regression analysis of the same data set identified quantitative combinations of signals within the networks that strongly correlate with cell proliferation and migration measured under the same battery of conditions. Combining these modeling approaches enabled association of epidermal growth factor receptor family dimerization to activation of specific phosphorylation sites, which appear to most critically regulate proliferation and/or migration.

Linear combinations of docking affinities explain quantitative differences in RTK signaling.

Receptor tyrosine kinases (RTKs) process extracellular cues by activating a broad array of signaling proteins. Paradoxically, they often use the same proteins to elicit diverse and even opposing phenotypic responses. Binary, ‘on–off’ wiring diagrams are therefore inadequate to explain their differences. Here, we show that when six diverse RTKs are placed in the same cellular background, they activate many of the same proteins, but to different quantitative degrees. Additionally, we find that the relative phosphorylation levels of upstream signaling proteins can be accurately predicted using linear models that rely on combinations of receptor‐docking affinities and that the docking sites for phosphoinositide 3‐kinase (PI3K) and Shc1 provide much of the predictive information. In contrast, we find that the phosphorylation levels of downstream proteins cannot be predicted using linear models. Taken together, these results show that information processing by RTKs can be segmented into discrete upstream and downstream steps, suggesting that the challenging task of constructing mathematical models of RTK signaling can be parsed into separate and more manageable layers.

Lysate microarrays enable high-throughput, quantitative investigations of cellular signaling

Lysate microarrays (reverse-phase protein arrays) hold great promise as a tool for systems-level investigations of signaling and multiplexed analyses of disease biomarkers. To date, however, widespread use of this technology has been limited by questions concerning data quality and the specificity of detection reagents. To address these concerns, we developed a strategy to identify high-quality reagents for use with lysate microarrays. In total, we tested 383 antibodies for their ability to quantify changes in protein abundance or modification in 20 biological contexts across 17 cell lines. Antibodies yielding significant differences in signal were further evaluated by immunoblotting and 82 passed our rigorous criteria. The large-scale data set from our screen revealed that cell fate decisions are encoded not just by the identities of proteins that are activated, but by differences in their signaling dynamics as well. Overall, our list of validated antibodies and associated protocols establish lysate microarrays as a robust tool for systems biology.

Quantitative Proteomic Analysis of Phosphotyrosine-Mediated Cellular Signaling Networks

Receptor tyrosine kinases receive extracellular cues, such as ligand binding, and transmit this information to the cell through both autophosphorylation and phosphorylation of tyrosine residues on selected substrates, stimulating a variety of signal transduction pathways. Quantitative features, including intensity, timing, and duration of phosphorylation of particular residues, may play a role in determining cellular response, but experimental data required for analysis of these features have not previously been available. We have recently developed a methodology enabling the simultaneous quantification of tyrosine phosphorylation of specific residues on dozens of key proteins in a time-resolved manner, downstream of receptor tyrosine kinase activation. In this chapter, we present a detailed description of this mass spectrometry-based method, including conditions for cell culture and stimulation, sample preparation for stable isotope labeling and peptide immunoprecipitation, immobilized metal affinity chromatography-liquid chromatography-tandem mass spectrometry analysis of affinity-enriched tyrosine phosphorylated peptides, and analysis of the resulting MS data.

Maximum Entropy Reconstructions of Dynamic Signaling Networks from Quantitative Proteomics Data

Advances in mass spectrometry among other technologies have allowed for quantitative, reproducible, proteome-wide measurements of levels of phosphorylation as signals propagate through complex networks in response to external stimuli under different conditions. However, computational approaches to infer elements of the signaling network strictly from the quantitative aspects of proteomics data are not well established. We considered a method using the principle of maximum entropy to infer a network of interacting phosphotyrosine sites from pairwise correlations in a mass spectrometry data set and derive a phosphorylation-dependent interaction network solely from quantitative proteomics data. We first investigated the applicability of this approach by using a simulation of a model biochemical signaling network whose dynamics are governed by a large set of coupled differential equations. We found that in a simulated signaling system, the method detects interactions with significant accuracy. We then analyzed a growth factor mediated signaling network in a human mammary epithelial cell line that we inferred from mass spectrometry data and observe a biologically interpretable, small-world structure of signaling nodes, as well as a catalog of predictions regarding the interactions among previously uncharacterized phosphotyrosine sites. For example, the calculation places a recently identified tumor suppressor pathway through ARHGEF7 and Scribble, in the context of growth factor signaling. Our findings suggest that maximum entropy derived network models are an important tool for interpreting quantitative proteomics data.