Metabolomics of mammalian brain reveals regional differences.

BACKGROUND: The mammalian brain is organized into regions with specific biological functions and properties. These regions have distinct transcriptomes, but little is known whether they may also differ in their metabolome. The metabolome, a collection of small molecules or metabolites, is at the intersection of the genetic background of a given cell or tissue and the environmental influences that affect it. Thus, the metabolome directly reflects information about the physiologic state of a biological system under a particular condition. The objective of this study was to investigate whether various brain regions have diverse metabolome profiles, similarly to their genetic diversity. The answer to this question would suggest that not only the genome but also the metabolome may contribute to the functional diversity of brain regions. METHODS: We investigated the metabolome of four regions of the mouse brain that have very distinct functions: frontal cortex, hippocampus, cerebellum, and olfactory bulb. We utilized gas- and liquid- chromatography mass spectrometry platforms and identified 215 metabolites. RESULTS: Principal component analysis, an unsupervised multivariate analysis, clustered each brain region based on its metabolome content, thus providing the unique metabolic profile of each region. A pathway-centric analysis indicated that olfactory bulb and cerebellum had most distinct metabolic profiles, while the cortical parenchyma and hippocampus were more similar in their metabolome content. Among the notable differences were distinct oxidative-anti-oxidative status and region-specific lipid profiles. Finally, a global metabolic connectivity analysis using the weighted correlation network analysis identified five hub metabolites that organized a unique metabolic network architecture within each examined brain region. These data indicate the diversity of global metabolome corresponding to specialized regional brain function and provide a new perspective on the underlying properties of brain regions. CONCLUSION: In summary, we observed many differences in the metabolome among the various brain regions investigated. All four brain regions in our study had a unique metabolic signature, but the metabolites came from all categories and were not pathway-centric.

Analytical Platforms and Techniques to Study Stem Cell Metabolism.

Over the past decade, advances in systems biology or 'omics techniques have enabled unprecedented insights into the biological processes that occur in cells, tissues, and on the organism level. One of these technologies is the metabolomics, which examines the whole content of the metabolites in a given sample. In a biological system, a stem cell for instance, there are thousands of single components, such as genes, RNA, proteins, and metabolites. These multiple molecular species interact with each other and these interactions may change over the life-time of a cell or in response to specific stimuli, adding to the complexity of the system. Using metabolomics, we can obtain an instantaneous snapshot of the biological status of a cell, tissue, or organism and gain insights on the pattern(s) of numerous analytes, both known and unknown, that result because of a given biological condition. Here, we outline the main methods to study the metabolism of stem cells, including a relatively recent technology of mass spectrometry imaging. Given the abundant and increasing interest in stem cell metabolism in both physiological and pathological conditions, we hope that this chapter will provide incentives for more research in these areas to ultimately reach wide network of applications in biomedical, pharmaceutical, and nutritional research and clinical medicine.

Analytical strategies for studying stem cell metabolism.

Owing to their capacity for self-renewal and pluripotency, stem cells possess untold potential for revolutionizing the field of regenerative medicine through the development of novel therapeutic strategies for treating cancer, diabetes, cardiovascular and neurodegenerative diseases. Central to developing these strategies is improving our understanding of biological mechanisms responsible for governing stem cell fate and self-renewal. Increasing attention is being given to the significance of metabolism, through the production of energy and generation of small molecules, as a critical regulator of stem cell functioning. Rapid advances in the field of metabolomics now allow for in-depth profiling of stem cells both in vitro and in vivo, providing a systems perspective on key metabolic and molecular pathways which influence stem cell biology. Understanding the analytical platforms and techniques that are currently used to study stem cell metabolomics, as well as how new insights can be derived from this knowledge, will accelerate new research in the field and improve future efforts to expand our understanding of the interplay between metabolism and stem cell biology.

Combinatorial therapy discovery using mixed integer linear programming.

MOTIVATION: Combinatorial therapies play increasingly important roles in combating complex diseases. Owing to the huge cost associated with experimental methods in identifying optimal drug combinations, computational approaches can provide a guide to limit the search space and reduce cost. However, few computational approaches have been developed for this purpose, and thus there is a great need of new algorithms for drug combination prediction. RESULTS: Here we proposed to formulate the optimal combinatorial therapy problem into two complementary mathematical algorithms, Balanced Target Set Cover (BTSC) and Minimum Off-Target Set Cover (MOTSC). Given a disease gene set, BTSC seeks a balanced solution that maximizes the coverage on the disease genes and minimizes the off-target hits at the same time. MOTSC seeks a full coverage on the disease gene set while minimizing the off-target set. Through simulation, both BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accuracy. When applied to real disease gene sets, our algorithms not only identified known drug combinations, but also predicted novel drug combinations that are worth further testing. In addition, we developed a web-based tool to allow users to iteratively search for optimal drug combinations given a user-defined gene set. AVAILABILITY: Our tool is freely available for noncommercial use at http://www.drug.liuzlab.org/. CONTACT: zhandong.liu@bcm.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

C/EBPbeta suppression by interruption of CUGBP1 resulting from a complex rearrangement of MLL.

Translocations involving the mixed-lineage leukemia gene (MLL) confer a poor prognosis in acute leukemias. In t(1;11)(q21;q23), MLL is fused reciprocally with AF1q. Here we describe a t(1;11)(q21;q23) with a secondary event involving insertion of the telomeric portion of MLL into the p arm of chromosome 11 (11p11). We show that this latter event interrupts the CUG triplet repeat binding protein-1 (CUGBP1) gene, a translational enhancer of C/EBPbeta. We then showed that these cells have reduced expression of CUGBP1 and C/EBPbeta when compared to other AML blasts. This is the first report to describe insertional disruption of the CUGBP1 gene and to suggest a role for the CUGBP1-C/EBPbeta pathway in leukemogenesis.