HPLC and Mass Spectrometry-based Proteomics and Glycoproteomics for Biomarker Discovery.

Abstract

Proteomics, analogous with genomics, is the analysis of the protein complement present in a cell, organ, or organism at any given time. While the genome provides information about the theoretical status of the cellular proteins, the proteome describes the actual content, which ultimately determines the phenotype. The broad application of proteomic technologies in basic science and clinical medicine has the potential to accelerate our understanding of the molecular mechanisms underlying disease and may facilitate the discovery of new drug targets and diagnostic disease markers. In the first and second chapters, bacterial cold adaptation and cryotolerance in Exiguobacterium sibiricum (E. sibiricum) 255-15 have been studied. Proteins are first fractionated according to pI by chromatofocusing (CF), and further separated based on hydrophobicity by nonporous silica RP HPLC which is on-line coupled with an ESI-TOF MS for intact protein M(r) measurement. 2D mass maps are generated for sample-to-sample comparison. The differentially expressed proteins are then digested and identified by MALDI MS-based peptide map fingerprinting and peptide sequencing. A total of over 500 proteins were detected in this study, of which 39 were cold acclimation proteins (Caps) to facilitate and allow cell growth at low temperature. The cells cultured under the growth conditions associated with the improved cryotolerance have revealed a general down-regulation of enzymes involved in major metabolic processes. During the following studies, we have developed methods for glycoproteomics analysis to identify plasma/serum markers with utility to assist in the detection of colorectal cancer and esophageal cancer. The plasma samples were first depleted to remove the top 12 abundance proteins and then N-linked glycoproteins were enriched using lectin affinity chromatography and further separated by non-porous silica RP-HPLC. Lectin glycoarrays were then used for comparative screening of N-glycosylation patterns among samples. Statistic tools including principal component analysis, hierarchical clustering, and Z-statistic analysis were employed to identify distinctive glycosylation. The coupling of this approach with lectin blot and LC MS/MS has permitted the identification of potential glycoprotein biomarkers. The potential markers identified to distinguish colorectal cancer from adenoma and normal include elevated sialylation and fucosylation in complement C3, histidine-rich glycoprotein, and kininogen-1.