Xuemei Yang

A positive/negative ion–switching, targeted mass spectrometry–based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue

The revival of interest in cancer cell metabolism in recent years has prompted the need for quantitative analytical platforms for studying metabolites from in vivo sources. We implemented a quantitative polar metabolomics profiling platform using selected reaction monitoring with a 5500 QTRAP hybrid triple quadrupole mass spectrometer that covers all major metabolic pathways. The platform uses hydrophilic interaction liquid chromatography with positive/negative ion switching to analyze 258 metabolites (289 Q1/Q3 transitions) from a single 15-min liquid chromatography–mass spectrometry acquisition with a 3-ms dwell time and a 1.55-s duty cycle time. Previous platforms use more than one experiment to profile this number of metabolites from different ionization modes. The platform is compatible with polar metabolites from any biological source, including fresh tissues, cancer cells, bodily fluids and formalin-fixed paraffin-embedded tumor tissue. Relative quantification can be achieved without using internal standards, and integrated peak areas based on total ion current can be used for statistical analyses and pathway analyses across biological sample conditions. The procedure takes ∼12 h from metabolite extraction to peak integration for a data set containing 15 total samples (∼6 h for a single sample).

Regulation of Oxysterol-binding Protein Golgi Localization through Protein Kinase D–mediated Phosphorylation

Protein kinase D (PKD) is a critical regulator of Golgi structure and function. Biochemical evidence is presented that demonstrates the oxysterol-binding protein OSBP as a novel PKD substrate. Phosphorylation inhibits OSBP Golgi localization, impairs CERT Golgi localization, and promotes Golgi fragmentation.

Metabolomics of Human Cerebrospinal Fluid Identifies Signatures of Malignant Glioma

Cerebrospinal fluid is routinely collected for the diagnosis and monitoring of patients with neurological malignancies. However, little is known as to how its constituents may change in a patient when presented with a malignant glioma. Here, we used a targeted mass-spectrometry based metabolomics platform using selected reaction monitoring with positive/negative switching and profiled the relative levels of over 124 polar metabolites present in patient cerebrospinal fluid. We analyzed the metabolic profiles from 10 patients presenting malignant gliomas and seven control patients that did not present malignancy to test whether a small sample size could provide statistically significant signatures. We carried out multiple unbiased forms of classification using a series of unsupervised techniques and identified metabolic signatures that distinguish malignant glioma patients from the control patients. One subtype identified contained metabolites enriched in citric acid cycle components. Newly diagnosed patients segregated into a different subtype and exhibited low levels of metabolites involved in tryptophan metabolism, which may indicate the absence of an inflammatory signature. Together our results provide the first global assessment of the polar metabolic composition in cerebrospinal fluid that accompanies malignancy, and demonstrate that data obtained from high throughput mass spectrometry technology may have suitable predictive capabilities for the identification of biomarkers and classification of neurological diseases.

Using Tandem Mass Spectrometry in Targeted Mode to Identify Activators of Class IA PI3K in Cancer

Phosphatiditylinositide-3-kinase (PI3K) is activated in some cancers by direct mutation, but it is activated more commonly in cancer by mutation of upstream acting receptor tyrosine kinases (TK). At present, there is no systematic method to determine which TK signaling cascades activate PI3K in certain cancers, despite the likely utility of such information to help guide selection of tyrosine kinase inhibitor (TKI) drug strategies for personalized therapy. Here, we present a quantitative liquid chromatography tandem mass spectrometry approach that identifies upstream activators of PI3K both in vitro and in vivo. Using non-small cell lung carcinoma to illustrate this approach, we show a correct identification of the mechanism of PI3K activation in several models, thereby identifying the most appropriate TKI to downregulate PI3K signaling. This approach also determined the molecular mechanisms and adaptors required for PI3K activation following inhibition of the mTOR kinase TORC1. We further validated the approach in breast cancer cells with mutational activation of PIK3CA, where tandem mass spectrometry detected and quantitatively measured the abundance of a helical domain mutant (E545K) of PIK3CA connected to PI3K activation. Overall, our findings establish a mass spectrometric approach to identify functional interactions that govern PI3K regulation in cancer cells. Using this technique to define the pathways that activate PI3K signaling in a given tumor could help inform clinical decision making by helping guide personalized therapeutic strategies for different patients.

An Integrative Analysis of the InR/PI3K/Akt Network Identifies the Dynamic Response to Insulin Signaling

Insulin regulates an essential conserved signaling pathway affecting growth, proliferation, and metabolism. To expand our understanding of the insulin pathway, we combine biochemical, genetic, and computational approaches to build a comprehensive Drosophila InR/PI3K/Akt network. First, we map the dynamic protein-protein interaction network surrounding the insulin core pathway using bait-prey interactions connecting 566 proteins. Combining RNAi screening and phospho-specific antibodies, we find that 47% of interacting proteins affect pathway activity, and, using quantitative phosphoproteomics, we demonstrate that ∼10% of interacting proteins are regulated by insulin stimulation at the level of phosphorylation. Next, we integrate these orthogonal datasets to characterize the structure and dynamics of the insulin network at the level of protein complexes and validate our method by identifying regulatory roles for the Protein Phosphatase 2A (PP2A) and Reptin-Pontin chromatin-remodeling complexes as negative and positive regulators of ribosome biogenesis, respectively. Altogether, our study represents a comprehensive resource for the study of the evolutionary conserved insulin network.

A Cross-Species Study of PI3K Protein-Protein Interactions Reveals the Direct Interaction of P85 and SHP2.

Using a series of immunoprecipitation (IP) – tandem mass spectrometry (LC-MS/MS) experiments and reciprocal BLAST, we conducted a fly-human cross-species comparison of the phosphoinositide-3-kinase (PI3K) interactome in a drosophila S2R+ cell line and several NSCLC and human multiple myeloma cell lines to identify conserved interacting proteins to PI3K, a critical signaling regulator of the AKT pathway. Using H929 human cancer cells and drosophila S2R+ cells, our data revealed an unexpected direct binding of Corkscrew, the drosophila ortholog of the non-receptor protein tyrosine phosphatase type II (SHP2) to the Pi3k21B (p60) regulatory subunit of PI3K (p50/p85 human ortholog) but no association with Pi3k92e, the human ortholog of the p110 catalytic subunit. The p85-SHP2 association was validated in human cell lines, and formed a ternary regulatory complex with GRB2-associated-binding protein 2 (GAB2). Validation experiments with knockdown of GAB2 and Far-Western blots proved the direct interaction of SHP2 with p85, independent of adaptor proteins and transfected FLAG-p85 provided evidence that SHP2 binding on p85 occurred on the SH2 domains. A disruption of the SHP2-p85 complex took place after insulin/IGF1 stimulation or imatinib treatment, suggesting that the direct SHP2-p85 interaction was both independent of AKT activation and positively regulates the ERK signaling pathway.

Abstract LB-255: A mass spectrometry platform to quantitatively profile cancer cell metabolism from cells, tumors, and fixed tissue

The metabolic requirements of cancer and proliferating cells are different from that of normal differential tissue and may have diverse applications in the treatment of cancers. However, many of the molecular mechanisms that reorganize metabolism to support cell proliferation are unknown. To study cancer cell metabolism, we implemented a mass spectrometry based platform to quantitatively profile endogenous metabolites from proliferating cell lines, tumor tissues and formalin fixed paraffin embedded (FFPE) tissue. Cell lines are derived from several cancers including lung, multiple myeloma, prostate, and gliobastoma (GBM). In some cases, these were compared to a drosophila reference cell line. Patients were also profiled for disease classification from their cerebrospinal fluid (CSF). We also were successful in extracting polar metabolites from FFPE tissue more than five years old stored at room temperature. For FFPE tissue samples, we show that we can observe differences in disease states involving PI3K-TSC-TOR pathway and we compared different extraction methods for acquiring metabolomics data from FFPE blocks. We show that we can cluster GBM versus normal patients from analyzing their CSF. We target more than 255 unique metabolites using selected reaction monitoring (SRM) based analyses with an AB/Sciex 5500 QTRAP mass spectrometer coupled to a Shimadzu UFLC using normal phase chromatography. For a single 18 min run, our platform allows for unprecedented sensitivity, quantitation and coverage of metabolites that comprise of diverse metabolic pathways from as little as a single 6 cm tissue culture dish of cells or approximately 2–3 million cells from tissue samples. We find that amide XBridge columns (Waters) at 275 uL/min perform well in both negative and positive ion switching mode and that the sampling rate of the instrument is sufficiently fast (cycle time of 1.6 sec with 3 msec dwell times) to effectively capture up to 300 metabolite targets without scheduled SRM runs. Peak areas of metabolites are integrated using MultiQuant 1.1 software (Applied Biosystems). Peak areas from triplicate runs are hierarchically clustered and statistical analyses are applied to generate P values for metabolite changes over different biological conditions. We also probed metabolic flux in pathways by targeting a set of 13C glucose labeled metabolites. In addition, we have also analyzed a model cell line after stimulation with Insulin and EGF to examine if growth factor induced metabolic changes are evolutionarily conserved. Using metabolic inhibitors, such as Iodoacetic acid, KCN, etc., we have been able to characterize the consequences of inhibiting glycolysis and oxidative phosphorylation, respectively. Finally, we considered kinase inhibitors and measured their effects on metabolism in proliferating cancer cell lines. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr LB-255. doi:10.1158/1538-7445.AM2011-LB-255