Recent advances in metabolomics technologies, especially those that combine the complementarity of mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR) enables rapid analysis of tens of thousands of peaks representing thousands of metabolites. Yet, the need for metabolite detection, identification, quantitative analysis, and interpretation for reconstructing metabolic networks still represents key barriers to the optimal use of this flood of high quality, interrelated data. The interpretability is substantially enhanced when atom-resolved pathway tracing with the use of stable isotope tracers (e.g. stable isotope resolved metabolomics or SIRM) is part of the experimental design, but this imposes additional demands on the informatics and statistical analyses. The major goals of the Informatics Core are to provide the necessary bioinformatics, biostatistical, and systems biochemical analyses of metabolomics data from the Analytical Core as a set of automated and interactive services for the Clients of the RCMRCCREAM. These informatics capabilities will have broad applications from straightforward profiling for e.g. biomarker discovery to elucidation of metabolic reprogramming in response to disease pathogenesis or therapeutic interventions. We will achieve these goals by coordinating our efforts with the Administrative, Sample, Analytical Cores, and the Clients, via the following Specific Aims:
Specific Aim 1. Support all RCMRC-CREAM's data handling needs via a web-based informatics platform.
Specific Aim 2. Provide tools for raw data analysis and quality control of MS and NMR analytical data.
Specific Aim 3. Provide basic biostatistical analysis of refined data.
Specific Aim 4. Expand metabolic pathway reconstruction, flux modeling tools, and data integration for mechanism-based analysis.
The unprecedented richness of metabolomics data and particularly SIRM data requires sophisticated informatics and statistical analysis to optimize the range and accuracy of metabolite determination for systems biochemical interpretation. The Informatics Core will provide such crucial developments and services for RCMRC-CREAM, thereby facilitating large-scale understanding of disease pathogenesis for the discovery of novel biomarkers and therapeutic targets
|Lane, Andrew N; Tan, Julie; Wang, Yali et al. (2017) Probing the metabolic phenotype of breast cancer cells by multiple tracer stable isotope resolved metabolomics. Metab Eng 43:125-136|
|Wang, Qingding; Zhou, Yuning; Rychahou, Piotr et al. (2017) Ketogenesis contributes to intestinal cell differentiation. Cell Death Differ 24:458-468|
|Yang, Ye; Fan, Teresa W-M; Lane, Andrew N et al. (2017) Chloroformate derivatization for tracing the fate of Amino acids in cells and tissues by multiple stable isotope resolved metabolomics (mSIRM). Anal Chim Acta 976:63-73|
|Yao, Sen; Flight, Robert M; Rouchka, Eric C et al. (2017) Perspectives and expectations in structural bioinformatics of metalloproteins. Proteins 85:938-944|
|Yao, Sen; Flight, Robert M; Rouchka, Eric C et al. (2017) Aberrant coordination geometries discovered in the most abundant metalloproteins. Proteins 85:885-907|
|Lane, Andrew N; Fan, Teresa W-M (2017) NMR-based Stable Isotope Resolved Metabolomics in systems biochemistry. Arch Biochem Biophys 628:123-131|
|Matrka, Marie C; Watanabe, Miki; Muraleedharan, Ranjithmenon et al. (2017) Overexpression of the human DEK oncogene reprograms cellular metabolism and promotes glycolysis. PLoS One 12:e0177952|
|Qi, Zhen; Voit, Eberhard O (2017) Inference of cancer mechanisms through computational systems analysis. Mol Biosyst 13:489-497|
|Zhao, Jiangsha; Li, Jieran; Fan, Teresa W M et al. (2017) Glycolytic reprogramming through PCK2 regulates tumor initiation of prostate cancer cells. Oncotarget 8:83602-83618|
|Sun, Ramon C; Fan, Teresa W-M; Deng, Pan et al. (2017) Noninvasive liquid diet delivery of stable isotopes into mouse models for deep metabolic network tracing. Nat Commun 8:1646|
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