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Abstract Multiple myeloma (MM) is the second most common hematological malignancy in adults. It is characterized by clonal proliferation of terminally differentiated B lymphocytes and over-production of monoclonal immunoglobulins. Recurrent genomic aberrations have been identified to contribute to the aggressiveness of this cancer. Despite a wealth of knowledge describing the molecular biology of MM as well as significant advances in therapeutics, this disease remains fatal. The identification of biomarkers, especially through the use of mass spectrometry, however, holds great promise to increasing our understanding of this disease. In particular, novel biomarkers will help in the diagnosis, prognosis and therapeutic stratification of MM. To date, results from mass spectrometry studies of MM have provided valuable information with regards to MM diagnosis and response to therapy. In addition, mass spectrometry was employed to study relevant signaling pathways activated in MM. This review will focus on how mass spectrometry has been applied to increase our understanding of MM.

The precise genetic cause remains elusive in nearly 50% of patients with presumed neurogenetic disease, representing a significant barrier for clinical care. This is despite significant advances in clinical genetic diagnostics, including the application of whole-exome sequencing and next-generation sequencing-based gene panels. In this study, we identify a deep intronic mutation in the DMD gene in a patient with muscular dystrophy using both conventional and RNAseq-based transcriptome analyses. The implications of our data are that noncoding mutations likely comprise an important source of unresolved genetic disease and that RNAseq is a powerful platform for detecting such mutations. [ABSTRACT FROM AUTHOR]

The precise genetic cause remains elusive in nearly 50% of patients with presumed neurogenetic disease, representing a significant barrier for clinical care. This is despite significant advances in clinical genetic diagnostics, including the application of whole‐exome sequencing and next‐generation sequencing‐based gene panels. In this study, we identify a deep intronic mutation in the DMD gene in a patient with muscular dystrophy using both conventional and RNAseq‐based transcriptome analyses. The implications of our data are that noncoding mutations likely comprise an important source of unresolved genetic disease and that RNAseq is a powerful platform for detecting such mutations.

Proliferating embryonic and cancer cells preferentially use aerobic glycolysis to support growth, a metabolic alteration commonly referred to as the "Warburg effect." Here, we show that the glycolytic enzyme hexokinase 2 (HK2) is crucial for the Warburg effect in human glioblastoma multiforme (GBM), the most common malignant brain tumor. In contrast to normal brain and low-grade gliomas, which express predominantly HK1, GBMs show increased HK2 expression. HK2 expression correlates with worse overall survival of GBM patients. Depletion of HK2, but neither HK1 nor pyruvate kinase M2, in GBM cells restored oxidative glucose metabolism and increased sensitivity to cell death inducers such as radiation and temozolomide. Intracranial xenografts of HK2-depleted GBM cells showed decreased proliferation and angiogenesis, but increased invasion, as well as diminished expression of hypoxia inducible factor 1α and vascular endothelial growth factor. In contrast, exogenous HK2 expression in GBM cells led to increased proliferation, therapeutic resistance, and intracranial growth. Growth was dependent on both glucose phosphorylation and mitochondrial translocation mediated by AKT signaling, which is often aberrantly activated in GBMs. Collectively, these findings suggest that therapeutic strategies to modulate the Warburg effect, such as targeting of HK2, may interfere with growth and therapeutic sensitivity of some GBMs.

PROTEOMICS, IN ITS broadest mandate, is the study of proteins and their functions. As the "workhorses" of the genome, proteins govern normal cellular structure and function. Protein function is not just a reflection of its expression level; it is also the cumulative result of many post-transcriptional (splicing) and post-translational events that together determine cellular localization, interactions, and longevity. The composition and variability of the proteome is vastly more complex than the corresponding genome. It is this proteome variation that helps define an organism and the unique characteristics that separate one individual from another. Aberrations in protein function, which alter normal cellular structure and function, are the ultimate basis of disease, including cancer. Therefore, an understanding of protein networks through a systems biology approach of proteomics is necessary to understand normal and abnormal cellular function, with the goal of performing rational therapeutic interventions. In this review, we focus on two emerging proteomic technologies: mass spectrometry and bioluminescence resonance energy transfer. In addition to reviewing the principles and potential utilization of these two techniques, we highlight their application in neuro-oncology research.