Jennifer Chiou

Background: RNA-binding proteins (RBPs) function as master regulators of gene expression. Alterations in RBP expression and function are often observed in cancer and influence critical pathways implicated in tumor initiation and growth. Identification and characterization of oncogenic RBPs and their regulatory networks provide new opportunities for targeted therapy. Results: We identify the RNA-binding protein SERBP1 as a novel regulator of glioblastoma (GBM) development. High SERBP1 expression is prevalent in GBMs and correlates with poor patient survival and poor response to chemo- and radiotherapy. SERBP1 knockdown causes delay in tumor growth and impacts cancer-relevant phenotypes in GBM and glioma stem cell lines. RNAcompete identifies a GC-rich region as SERBP1-binding motif; subsequent genomic and functional analyses establish SERBP1 regulation role in metabolic routes preferentially used by cancer cells. An important consequence of these functions is SERBP1 impact on methionine production. SERBP1 knockdown decreases methionine levels causing a subsequent reduction in histone methylation as shown for H3K27me3 and upregulation of genes associated with neurogenesis, neuronal differentiation, and function. Further analysis demonstrates that several of these genes are downregulated in GBM, potentially through epigenetic silencing as indicated by the presence of H3K27me3 sites. Conclusions: SERBP1 is the first example of an RNA-binding protein functioning as a central regulator of cancer metabolism and indirect modulator of epigenetic regulation in GBM. By bridging these two processes, SERBP1 enhances glioma stem cell phenotypes and contributes to GBM poorly differentiated state.

Leukemia cell and melanoma tumor tissue extracts were studied for small (mostly m/z n analysis powered by a hybrid Quadrupole-Orbitrap. MS data was simultaneously acquired in fast polarity switching mode operating in MS ¹ and MS/MS (All Ion Fragmentation, AIF) full-scan analyses at high mass resolution. Positive assignments were achieved by AIF analysis considering at least two characteristic transitions of metabolites. A targeted metabolite profiling was achieved by the relative quantification of 18 metabolites through spiking their respective deuterated counterparts. Manual data processing of MS ¹ and AIF scans were compared for accurate determination of natural metabolites and their deuterated analogs by chromatographic alignment and peak area integration. Evaluation of manual and automated (MetaboList R package) AIF data processing yielded comparable results. The versatility of AIF analysis also enabled the untargeted metabolite profiling of leukemia and melanoma samples in which 22 and 53 compounds were respectively identified outside those studied by labeling. The main limitation of the method was that low abundance metabolites with scan rates below 8 scans/peak could not be accurately quantified by AIF analysis. Combination of AIF analysis with MetaboList R package represents an opportunity to move towards automated, faster and more global metabolomics approaches supported by an entirely flexible open source automated data processing platform freely available from Comprehensive R Archive Network (CRAN, https://CRAN.R-project.org/package=MetaboList ).