Role of moesin in hyaluronan induced cell migration in glioblastoma multiforme.

BACKGROUND: A major barrier to effective treatment of glioblastoma multiforme (GBM) is the invasion of glioma cells into the brain parenchyma rendering local therapies such as surgery and radiation therapy ineffective. GBM patients with such highly invasive and infiltrative tumors have poor prognosis with a median survival time of only about a year. However, the mechanisms leading to increased cell migration, invasion and diffused behavior of glioma cells are still poorly understood. METHODS: In the current study, we applied quantitative proteomics for the identification of differentially expressed proteins in GBMs as compared to non-malignant brain tissues. RESULTS: Our study led to the identification of 23 proteins showing overexpression in GBM; these include membrane proteins, moesin and CD44. The results were verified using Western blotting and immunohistochemistry in independent set of GBM and non-malignant brain tissues. Both GBM tissues and glioma cell lines (U87 / U373) demonstrated membranous expression of moesin and CD44, as revealed by immunohistochemistry and immunofluorescence, respectively. Notably, glioma cells transfected with moesin siRNA displayed reduced migration and invasion on treatment with hyaluronan (HA), an important component of the extracellular matrix in GBM. CD44, a transmembrane glycoprotein, acts as a major receptor for hyaluronan (HA). Using co-immunoprecipitation assays, we further demonstrated that moesin interacts with CD44 in glioma cells only after treatment with HA; this implicates a novel role of moesin in HA-CD44 signaling in gliomas. CONCLUSIONS: Our results suggest that development of inhibitors which interfere with CD44-moesin interactions may open a new avenue in the future to mitigate cellular migration in gliomas.

GBM secretome induces transient transformation of human neural precursor cells.

Glioblastoma (GBM) is the most aggressive primary brain tumor in humans, with a uniformly poor prognosis. The tumor microenvironment is composed of both supportive cellular substrates and exogenous factors. We hypothesize that exogenous factors secreted by brain tumor initiating cells (BTICs) could predispose normal neural precursor cells (NPCs) to transformation. When NPCs are grown in GBM-conditioned media, and designated as "tumor-conditioned NPCs" (tcNPCs), they become highly proliferative and exhibit increased stem cell self-renewal, or the unique ability of stem cells to asymmetrically generate another stem cell and a daughter cell. tcNPCs also show an increased transcript level of stem cell markers such as CD133 and ALDH and growth factor receptors such as VEGFR1, VEGFR2, EGFR and PDGFRα. Media analysis by ELISA of GBM-conditioned media reveals an elevated secretion of growth factors such as EGF, VEGF and PDGF-AA when compared to normal neural stem cell-conditioned media. We also demonstrate that tcNPCs require prolonged or continuous exposure to the GBM secretome in vitro to retain GBM BTIC characteristics. Our in vivo studies reveal that tcNPCs are unable to form tumors, confirming that irreversible transformation events may require sustained or prolonged presence of the GBM secretome. Analysis of GBM-conditioned media by mass spectrometry reveals the presence of secreted proteins Chitinase-3-like 1 (CHI3L1) and H2A histone family member H2AX. Collectively, our data suggest that GBM-secreted factors are capable of transiently altering normal NPCs, although for retention of the transformed phenotype, sustained or prolonged secretome exposure or additional transformation events are likely necessary.

Secretome-based identification and characterization of potential biomarkers in thyroid cancer.

In search of thyroid cancer biomarkers, proteins secreted by thyroid cancer cell lines, papillary-derived TPC-1 and anaplastic-derived CAL62, were analyzed using liquid chromatography-tandem mass spectrometry. Of 46 high-confidence identifications, 6 proteins were considered for verification in thyroid cancer patients' tissue and blood. The localization of two proteins, nucleolin and prothymosin-α (PTMA), was confirmed in TPC-1 and CAL62 cells by confocal microscopy and immunohistochemically in xenografts of TPC-1 cells in NOD/SCID/γ mice and human thyroid cancers (48 tissues). Increased nuclear and cytoplasmic expression of PTMA was observed in anaplastic compared to papillary and poorly differentiated carcinomas. Nuclear expression of nucleolin was observed in all subtypes of thyroid carcinomas, along with faint cytoplasmic expression in anaplastic cancers. Importantly, PTMA, nucleolin, clusterin, cysteine-rich angiogenic inducer 61, enolase 1, and biotinidase were detected in thyroid cancer patients' sera, warranting future analysis to confirm their potential as blood-based thyroid cancer markers. In conclusion, we demonstrated the potential of secretome analysis of thyroid cancer cell lines to identify novel proteins that can be independently verified in cell lines, xenografts, tumor tissues, and blood samples of thyroid cancer patients. These observations support their potential utility as minimally invasive biomarkers for thyroid carcinomas and their application in management of these diseases upon future validation.

The extent and effects of peptide sequence scrambling via formation of macrocyclic B ions in model proteins.

The extent and effects of sequence scrambling in peptide ions during tandem mass spectrometry (MS/MS) have been examined using tryptic peptides from model proteins. Sequence-scrambled b ions appeared in about 35% of 43 tryptic peptides examined under MS/MS conditions. In general, these ions had relatively low abundances with averages of 8% and 16%, depending on the instrumentation used. A few tryptic peptides gave abundant scrambled b ions in MS/MS. However, peptide and protein identifications under proteomic conditions with Mascot were not affected, even for these peptides wherein scrambling was prominent. From the 43 tryptic peptides that have been investigated, the conclusion is that sequence scrambling is unlikely to impact negatively on the accuracy of automated peptide and protein identifications in proteomics.