Uncovering the molecular context of dysregulated metabolites is crucial to understand pathogenic pathways. However, their system-level analysis has been limited owing to challenges in global metabolite identification. Most metabolite features detected by untargeted metabolomics carried out by liquid-chromatography-mass spectrometry cannot be uniquely identified without additional, time-consuming experiments. We report a network-based approach, prize-collecting Steiner forest algorithm13 for integrative analysis of untargeted metabolomics (PIUMet), that infers molecular pathways and components via integrative analysis of metabolite features, without requiring their identification. We demonstrated PIUMet by analyzing changes in metabolism of sphingolipids, fatty acids and steroids in a Huntington’s disease model. Additionally, PIUMet enabled us to elucidate putative identities of altered metabolite features in diseased cells, and infer experimentally undetected, disease-associated metabolites and dysregulated proteins. Finally, we established PIUMet’s ability for integrative analysis of untargeted metabolomics data with proteomics data, demonstrating that this approach elicits disease-associated metabolites and proteins that cannot be inferred by individual analysis of these data.

Despite extensive study of the EGF receptor (EGFR) signaling network, the immediate posttranslational changes that occur in response to growth factor stimulation remain poorly characterized; as a result, the biological mechanisms underlying signaling initiation remain obscured. To address this deficiency, we have used a mass spectrometry-based approach to measure system-wide phosphorylation changes throughout the network with 10-s resolution in the 80 s after stimulation in response to a range of eight growth factor concentrations. Significant changes were observed on proteins far downstream in the network as early as 10 s after stimulation, indicating a system capable of transmitting information quickly. Meanwhile, canonical members of the EGFR signaling network fall into clusters with distinct activation patterns. Src homology 2 domain containing transforming protein (Shc) and phosphoinositol 3-kinase (PI3K) phosphorylation levels increase rapidly, but equilibrate within 20 s, whereas proteins such as Grb2-associated binder-1 (Gab1) and SH2-containing tyrosine phosphatase (SHP2) show slower, sustained increases. Proximity ligation assays reveal that Shc and Gab1 phosphorylation patterns are representative of separate timescales for physical association with the receptor. Inhibition of phosphatases with vanadate reveals site-specific regulatory mechanisms and also uncovers primed activating components in the network, including Src family kinases, whose inhibition affects only a subset of proteins within the network. The results presented highlight the complexity of signaling initiation and provide a window into exploring mechanistic hypotheses about receptor tyrosine kinase (RTK) biology.signal transduction | tyrosine phosphorylation | epidermal growth factor receptor | mass spectrometry T he EGF receptor (EGFR) sits atop a complex signaling network that controls cell behavior in response to environmental cues. Cascades of posttranslational modifications initiated from EGFR, notably phosphorylation on tyrosine, serine, and threonine, influence protein-protein interactions and enzymatic activity to activate transcriptional programs that regulate proliferation, differentiation, and apoptosis (1). Mutation or overexpression of EGFR has been identified as an oncogenic driver for many tumor types, making it an attractive target for anticancer therapies (2).Building on decades of specific characterization using traditional biochemistry techniques, platforms such as protein microarrays and mass spectrometry have allowed systems biology to provide a comprehensive picture of intact and aberrant network behavior, which can be used to establish design criteria for therapeutic interventions (3). Manipulating components within the network experimentally and computationally has uncovered many features of the system that influence behaviors such as proliferation and survival (4-6). With many phenotypic responses occurring on the order of hours to days, most phosphorylation measurements have been on the timescale of minutes to...

Phosphotyrosine (pTyr)-dependent signaling is critical for many cellular processes. It is highly dynamic, as signal output depends not only on phosphorylation and dephosphorylation rates but also on the rates of binding and dissociation of effectors containing phosphotyrosine-dependent binding modules such as Src homology 2 (SH2) and phosphotyrosine-binding (PTB) domains. Previous studies suggested that binding of SH2 and PTB domains can enhance protein phosphorylation by protecting the sites bound by these domains from phosphatase-mediated dephosphorylation. To test whether this occurs, we used the binding of growth factor receptor bound 2 (GRB2) to phosphorylated epidermal growth factor receptor (EGFR) as a model system. We analyzed the effects of SH2 domain overexpression on protein tyrosine phosphorylation by quantitative Western and far-Western blotting, mass spectrometry, and computational modeling. We found that SH2 overexpression results in a significant, dose-dependent increase in EGFR tyrosine phosphorylation, particularly of sites corresponding to the binding specificity of the overexpressed SH2 domain. Computational models using experimentally determined EGFR phosphorylation and dephosphorylation rates, and pTyr-EGFR and GRB2 concentrations, recapitulated the experimental findings. Surprisingly, both modeling and biochemical analyses suggested that SH2 domain overexpression does not result in a major decrease in the number of unbound phosphorylated SH2 domain-binding sites. Our results suggest that signaling via SH2 domain binding is buffered over a relatively wide range of effector concentrations and that SH2 domain proteins with overlapping binding specificities are unlikely to compete with one another for phosphosites .

Absolute quantification of protein expression and post-translational modifications by mass spectrometry has been challenging due to a variety of factors, including the potentially large dynamic range of phosphorylation response. To address these issues, we have developed MARQUIS — Multiplex Absolute Regressed Quantification with Internal Standards — a novel mass spectrometry-based approach using a combination of isobaric tags and heavy-labeled standard peptides to construct internal standard curves for peptides derived from key nodes in signal transduction networks. We applied MARQUIS to quantify phosphorylation dynamics within the EGFR network at multiple time points following stimulation with several ligands, enabling a quantitative comparison of EGFR phosphorylation sites and demonstrating that receptor phosphorylation is qualitatively similar but quantitatively distinct for each EGFR ligand tested. MARQUIS was also applied to quantify the effect of EGFR kinase inhibition on glioblastoma patient derived xenografts. MARQUIS is a versatile method, broadly applicable and extendable to multiple mass spectrometric platforms.

Advances in mass spectrometry-based proteomic technologies have increased the speed of analysis and the depth provided by a single analysis. Computational tools to evaluate the accuracy of peptide identifications from these high-throughput analyses have not kept pace with technological advances; currently the most common quality evaluation methods are based on statistical analysis of the likelihood of false positive identifications in large-scale data sets. While helpful, these calculations do not consider the accuracy of each identification, thus creating a precarious situation for biologists relying on the data to inform experimental design. Manual validation is the gold standard approach to confirm accuracy of database identifications, but is extremely time-intensive. To palliate the increasing time required to manually validate large proteomic datasets, we provide computer aided manual validation software (CAMV) to expedite the process. Relevant spectra are collected, catalogued, and pre-labeled, allowing users to efficiently judge the quality of each identification and summarize applicable quantitative information. CAMV significantly reduces the burden associated with manual validation and will hopefully encourage broader adoption of manual validation in mass spectrometry-based proteomics.