Recombinant strains with well defined genetic backgrounds are often found to exhibit small functional differences despite specific changes at the genetic level while in other cases, single gene alterations result in profound phenotypic variations. Although a first step in explaining such macroscopic differences is to probe the full detail of the expression phenotype by genome-wide expression measurements, transcription data alone are insufficient to elucidate the actual metabolic state of a cell and its functions. The latter require information about intracellular metabolic fluxes, which constitute fundamental determinants of cell physiology and excellent metrics of cell function. """"""""Metabolic phenotyping"""""""" is the process and methods of determining intracellular fluxes as determinants of the cellular metabolic state. Combined with transcription data, the investigators provide a complete framework for analyzing the effect of drugs and studying disease. This application integrates the expertise of three participating laboratories for the purpose of combining metabolic and expression phenotyping to elucidate central carbon and lipid metabolism in model mouse hepatoma and hepatocyte cultures. Determination of intracellular fluxes will follow a systems approach termed metabolic reconstruction whereby the entire metabolic network is configured such as to best represent macroscopic rate and isotopic label distribution measurements made by GC-MS. Of particular attention are issues of observability, redundancy, and solution stability to ensure method feasibility and accuracy of the results. Differential transcription data will be obtained by DNA microarrays for mouse genes involved in central carbon metabolic, gluconeogenic and lipid biosynthetic pathways, as well as for other genes with particular expression variability that will be identified in the course of the research. Bioinformatics methods and programs, developed over the past 12 years will be deployed for this purpose. The general goal of the research is to identify relationships between the metabolic phenotype as defined above and the transcriptional state as defined by expression data of consequence in pathways important to diabetes.
Specific aims will focus on flux quantification in mouse hepatoma and hepatocyte cultures to elucidate glutamine metabolism and lipogenesis, other central metabolic pathways and cholesterol synthesis, the effect of nutrients, hormones and drugs like Metformin and finally, pleiotrophic effects generated by altering the normal expression of a single gene, such as over-expressing the truncated verion of sterol-regulatory element binding protein-1a in transgenic mice. The broader contribution of this research is to extend the paradigm of holistic transcriptional investigation introduced by DNA microarray technologies to the study of metabolic level processes by metabolic phenotyping. As such, it holds the promise of identifying most, if not all points in metabolism affected by the action of drugs or genetic modifications thus guiding future programs of drug development and gene therapy.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
3R01DK058533-02S1
Application #
6664792
Study Section
Special Emphasis Panel (ZRG1 (02))
Program Officer
Smith, Philip F
Project Start
2000-09-30
Project End
2004-08-31
Budget Start
2001-09-01
Budget End
2004-08-31
Support Year
2
Fiscal Year
2002
Total Cost
$198,600
Indirect Cost
Name
Massachusetts Institute of Technology
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
City
Cambridge
State
MA
Country
United States
Zip Code
02139
Noguchi, Yasushi; Nishikata, Natsumi; Shikata, Nahoko et al. (2010) Ketogenic essential amino acids modulate lipid synthetic pathways and prevent hepatic steatosis in mice. PLoS One 5:e12057
Antoniewicz, Maciek R; Stephanopoulos, Gregory; Kelleher, Joanne K (2006) Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway. Metabolomics 2:41-52
Wong, Matthew S; Raab, R Michael; Rigoutsos, Isidore et al. (2004) Metabolic and transcriptional patterns accompanying glutamine depletion and repletion in mouse hepatoma cells: a model for physiological regulatory networks. Physiol Genomics 16:247-55
Schmitt Jr, William A; Raab, R Michael; Stephanopoulos, Gregory (2004) Elucidation of gene interaction networks through time-lagged correlation analysis of transcriptional data. Genome Res 14:1654-63
Raab, R Michael; Stephanopoulos, Gregory (2004) Dynamics of gene silencing by RNA interference. Biotechnol Bioeng 88:121-32
Yoo, Hyuntae; Stephanopoulos, Gregory; Kelleher, Joanne K (2004) Quantifying carbon sources for de novo lipogenesis in wild-type and IRS-1 knockout brown adipocytes. J Lipid Res 45:1324-32
Bederman, Ilya R; Kasumov, Takhar; Reszko, Aneta E et al. (2004) In vitro modeling of fatty acid synthesis under conditions simulating the zonation of lipogenic [13C]acetyl-CoA enrichment in the liver. J Biol Chem 279:43217-26
Hwang, Daehee; Alevizos, Ilias; Schmitt, William A et al. (2003) Genomic dissection for characterization of cancerous oral epithelium tissues using transcription profiling. Oral Oncol 39:259-68
Schmitt Jr, William A; Stephanopoulos, Gregory (2003) Prediction of transcriptional profiles of Synechocystis PCC6803 by dynamic autoregressive modeling of DNA microarray data. Biotechnol Bioeng 84:855-63
Hwang, Daehee; Schmitt, William A; Stephanopoulos, George et al. (2002) Determination of minimum sample size and discriminatory expression patterns in microarray data. Bioinformatics 18:1184-93

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