Gordana Ivosev

Selected reaction monitoring mass spectrometry reveals the dynamics of signaling through the GRB2 adaptor

Signaling pathways are commonly organized through inducible protein-protein interactions, mediated by adaptor proteins that link activated receptors to cytoplasmic effectors. However, we have little quantitative data regarding the kinetics with which such networks assemble and dissolve to generate specific cellular responses. To address this deficiency, we designed a mass spectrometry method, affinity purification–selected reaction monitoring (AP-SRM), which we used to comprehensively and quantitatively investigate changes in protein interactions with GRB2, an adaptor protein that participates in a remarkably diverse set of protein complexes involved in multiple aspects of cellular function. Our data reliably define context-specific and time-dependent networks that form around GRB2 after stimulation, and reveal core and growth factor–selective complexes comprising 90 proteins identified as interacting with GRB2 in HEK293T cells. Capturing a key hub protein and dissecting its interactions by SRM should be equally applicable to quantifying signaling dynamics for a range of hubs in protein interaction networks.

Mapping differential interactomes by affinity purification coupled with data independent mass spectrometry acquisition

Characterizing changes in protein-protein interactions associated with sequence variants (e.g., disease-associated mutations or splice forms) or following exposure to drugs, growth factors or hormones is critical to understanding how protein complexes are built, localized and regulated. Affinity purification (AP) coupled with mass spectrometry permits the analysis of protein interactions under near-physiological conditions, yet monitoring interaction changes requires the development of a robust and sensitive quantitative approach, especially for large-scale studies in which cost and time are major considerations. We have coupled AP to data-independent mass spectrometric acquisition (sequential window acquisition of all theoretical spectra, SWATH) and implemented an automated data extraction and statistical analysis pipeline to score modulated interactions. We used AP-SWATH to characterize changes in protein-protein interactions imparted by the HSP90 inhibitor NVP-AUY922 or melanoma-associated mutations in the human kinase CDK4. We show that AP-SWATH is a robust label-free approach to characterize such changes and propose a scalable pipeline for systems biology studies.

Dimensionality Reduction and Visualization in Principal Component Analysis

Many modern applications of analytical chemistry involve the collection of large megavariate data sets and subsequent processing with multivariate analysis techniques (MVA), two of the more common goals being data analysis (also known as data mining and exploratory data analysis) and classification. Classification attempts to determine variables that can distinguish known classes allowing unknown samples to be correctly assigned, whereas data analysis seeks to uncover and understand or confirm relationships between the samples and the variables. An important part of analysis is visualization which allows analysts to apply their expertise and knowledge and is often easier for the samples than the variables since there are frequently far more of the latter. Here we describe principal component variable grouping (PCVG), an unsupervised, intuitive method that assigns a large number of variables to a smaller number of groups that can be more readily visualized and understood. Knowledge of the source or nature of the variables in a group allows them all to be appropriately treated, for example, removed if they result from uninteresting effects or replaced by a single representative for further processing.

Instrumental and experimental effects in LC-MS-based metabolomics.

The experimental complexity of a metabolomics study can cause uncontrolled variance that is not related to the biological effect being studied and may distort or obscure the data analysis. While some sources can be controlled with good experimental techniques and careful sample handling, others are inherent in the analytical technique used and cannot easily be avoided. We discuss the sources and appearance of some of these artifacts and show ways in which they can be detected using visualization and statistical tools, allowing appropriate treatment prior to multivariate analysis (MVA).

Comprehensive analytical strategy for biomarker identification based on liquid chromatography coupled to mass spectrometry and new candidate confirmation tools

A comprehensive analytical LC-MS(/MS) platform for low weight biomarkers molecule in biological fluids is described. Two complementary retention mechanisms were used in HPLC by optimizing the chromatographic conditions for a reversed-phase column and a hydrophilic interaction chromatography column. LC separation was coupled to mass spectrometry by using an electrospray ionization operating in positive polarity mode. This strategy enables us to correctly retain and separate hydrophobic as well as polar analytes. For that purpose artificial model study samples were generated with a mixture of 38 well characterized compounds likely to be present in biofluids. The set of compounds was used as a standard aqueous mixture or was spiked into urine at different concentration levels to investigate the capability of the LC-MS(/MS) platform to detect variations across biological samples. Unsupervised data analysis by principal component analysis was performed and followed by principal component variable grouping to find correlated variables. This tool allows us to distinguish three main groups whose variables belong to (a) background ions (found in all type of samples), (b) ions distinguishing urine samples from aqueous standard and blank samples, (c) ions related to the spiked compounds. Interpretation of these groups allows us to identify and eliminate isotopes, adducts, fragments, etc. and to generate a reduced list of m/z candidates. This list is then submitted to the prototype MZSearcher software tool which simultaneously searches several lists of potential metabolites extracted from metabolomics databases (e.g., KEGG, HMDB, etc) to propose biomarker candidates. Structural confirmation of these candidates was done off-line by fraction collection followed by nanoelectrospray infusion to provide high quality MS/MS data for spectral database queries.

Development of a highly automated and multiplexed targeted proteome pipeline and assay for 112 rat brain synaptic proteins

We present a comprehensive workflow for large scale (>1000 transitions/run) label‐free LC‐MRM proteome assays. Innovations include automated MRM transition selection, intelligent retention time scheduling that improves S/N by twofold, and automatic peak modeling. Improvements to data analysis include a novel Q/C metric, normalized group area ratio, MLR normalization, weighted regression analysis, and data dissemination through the Yale protein expression database. As a proof of principle we developed a robust 90 min LC‐MRM assay for mouse/rat postsynaptic density fractions which resulted in the routine quantification of 337 peptides from 112 proteins based on 15 observations per protein. Parallel analyses with stable isotope dilution peptide standards (SIS), demonstrate very high correlation in retention time (1.0) and protein fold change (0.94) between the label‐free and SIS analyses. Overall, our method achieved a technical CV of 11.4% with >97.5% of the 1697 transitions being quantified without user intervention, resulting in a highly efficient, robust, and single injection LC‐MRM assay.

Abstract IA19: Regulation of signaling interactomes in cancer

With the advent of next generation DNA sequencing technologies, the pace of discovery of cancer-associated sequence variants has greatly accelerated, leading to the realization that tumors (and especially solid tumors) harbor hundreds of mutations. Ongoing statistical analysis across multiple laboratories worldwide is progressing rapidly, helping to identify which of these mutations are likely drivers of the cancer phenotype. However, in spite of rapid progress in mapping of cancer related signaling and interaction networks, there has been an increasing disconnect between the identification of a cancer variant, and the mechanistic understanding of transformation induced by the mutation. Such molecular understanding is crucial for developing therapeutic interventions. In particular, frequent consequences of cancer-related mutations are specific alterations of protein-protein interactions affecting downstream signaling networks: an analysis of these altered interactions offers novel therapeutic avenues. At the same time, we still need to gain more knowledge regarding the protein-protein interactions targeted by anticancer drugs, in order to validate on-target effects and identify potential off-target modulation. For both of these objectives, we require the development of approaches that can quantify both gain and loss of interactions, in a sensitive manner, and as much as possible, under conditions that recapitulate the cellular context. Our research team has coupled affinity purification of a protein to quantitative mass spectrometry approaches. Key to our success has been the expression at near endogenous levels of a recombinant bait protein (as wild type or sequence variants) in human cells, followed by affinity purification using an antibody directed against the epitope tag and simultaneous identification and quantification by mass spectrometry. In particular, we are reporting here on our efforts to apply the novel data-independent mass spectrometric acquisition (DIA) method known as SWATH to these questions (see Lambert et al., Nature Methods, 2013; Tsou et al., Nature Methods, 2015). We are demonstrating how the approach can be used to profile the differential interactomes of cancer-associated mutants of kinase and phosphatase proteins (CDK4, PPP2R1A, PPP6C), and identify potentially actionable interactions. We are further demonstrating, using JQ1, an inhibitor of the interaction between acetyl-lysine modified histones and the bromodomain and extra-terminal (BET) protein family, (BRD2, BRD3, BRD4 and BRDT) that our approach permits to globally study the modulation of interactions following exposure to an anticancer agent. We reveal that JQ1 induces a rapid rewiring of the interactome of each BET protein, both decreasing interactions with acetylated histones as expected, but also modulating BET association with numerous interaction partners. Furthermore, multiple new interactions were induced upon JQ1 treatment that may alter the compound9s potency. Taken together, our results demonstrate that the AP-SWATH-MS allows for the characterization of dynamically modulated interactomes for a wide array of cancer-associated proteins. Citation Format: Jean-Philippe Lambert, Yiwang Zhou, Amber Couzens, Chih-Chiang Tsou, Sarah Picaud, Gordana Ivosev, Stephen Tate, Alexey Nesvizhskii, Panagis Filippakopoulos, Anne-Claude Gingras. Regulation of signaling interactomes in cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr IA19.