Atim Enyenihi

Atim A. Enyenihi obtained her PhD in analytical chemistry at the University of North Carolina, Chapel Hill, U.S.A.  Her PhD dissertation was on tandem mass spectrometry of proteins and peptides (PI: Prof. Gary L. Glish). She joined the Zubarev lab in August 2010 and is currently doing quantitative proteomics and pathway analysis of cancer cells.

Project description

Objective: to identify drug targets as well as activated elements among signaling pathways and key nodes (bottleneck regulatory molecules) revealed by the Pathway Search Engine (PSE [1]) that converts proteomics data into quantitative status of signaling pathways as well as key nodes.

Background: Proteomics is based on quantitative, high-throughput analysis of the whole proteome of cells, tissues and organs. Proteomics is done today using a nanoflow liquid chromatography (nanoLC) coupled on-line with tandem mass spectrometry (MS/MS). The proteome under study is first digested with trypsin and then the mixture of tryptic peptides is separated by nanoLC, ionized and injected into an MS/MS instrument which determines their identity and abundances.  Subsequently, the MS/MS data is searched against a database for protein identification. The output of this analysis is a list of identified proteins together with their relative abundances.

In traditional proteomics, the above list is the end point. However, the recent trend is to make one step further and interpret proteomics data using pathway analysis and its newest tool, PSE [2]. The protein list serves as an input to PSE, with the output being a list of key nodes (or pathways) with their scores. The score differences DS between the Case and Control form a bell-shaped distribution, with the outliers being the statistically significantly up- and down-regulated key nodes (pathways), which are the end points of pathway analysis.

The goal is now to apply this technology for the analysis of quantitative proteomics data from cancer cell lines treated with different anticancer drugs.

1. Zubarev, R. A.; Nielsen,  M. L.; Savitski, M. M.; Kel-Margoulis, O.; Wingender, E.; Kel, A. Identification of dominant signaling pathways from proteomics expression data, J. Proteomics, 2008, 1, 89-96.
2. Marin-Vicente, C.; Zubarev, R. A. Search engine for proteomics, Fact or Fiction? G.I.T. Lab J, 2009, 11-12, 10-11.