Dataset Information

Characterization of the usage of the serine metabolic network in human cancer.

ABSTRACT: The serine, glycine, one-carbon (SGOC) metabolic network is implicated in cancer pathogenesis, but its general functions are unknown. We carried out a computational reconstruction of the SGOC network and then characterized its expression across thousands of cancer tissues. Pathways including methylation and redox metabolism exhibited heterogeneous expression indicating a strong context dependency of their usage in tumors. From an analysis of coexpression, simultaneous up- or downregulation of nucleotide synthesis, NADPH, and glutathione synthesis was found to be a common occurrence in all cancers. Finally, we developed a method to trace the metabolic fate of serine using stable isotopes, high-resolution mass spectrometry, and a mathematical model. Although the expression of single genes didn't appear indicative of flux, the collective expression of several genes in a given pathway allowed for successful flux prediction. Altogether, these findings identify expansive and heterogeneous functions for the SGOC metabolic network in human cancer.

SUBMITTER: Mehrmohamadi M

PROVIDER: S-EPMC4317399 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Characterization of the usage of the serine metabolic network in human cancer.

Mehrmohamadi Mahya M Liu Xiaojing X Shestov Alexander A AA Locasale Jason W JW

Cell reports 20141106 4

The serine, glycine, one-carbon (SGOC) metabolic network is implicated in cancer pathogenesis, but its general functions are unknown. We carried out a computational reconstruction of the SGOC network and then characterized its expression across thousands of cancer tissues. Pathways including methylation and redox metabolism exhibited heterogeneous expression indicating a strong context dependency of their usage in tumors. From an analysis of coexpression, simultaneous up- or downregulation of nu ...[more]

PMID: 25456139

Similar Datasets

Project description:The human pathogen Coxiella burnetii causes Q-fever and is classified as a category B bio-weapon. Exploiting the development of the axenic growth medium ACCM-2, we have now used 13C-labeling experiments and isotopolog profiling to investigate the highly diverse metabolic network of C. burnetii. To this aim, C. burnetii RSA 439 NMII was cultured in ACCM-2 containing 5 mM of either [U-13C3]serine, [U-13C6]glucose, or [U-13C3]glycerol until the late-logarithmic phase. GC/MS-based isotopolog profiling of protein-derived amino acids, methanol-soluble polar metabolites, fatty acids, and cell wall components (e.g., diaminopimelate and sugars) from the labeled bacteria revealed differential incorporation rates and isotopolog profiles. These data served to decipher the diverse usages of the labeled substrates and the relative carbon fluxes into the core metabolism of the pathogen. Whereas, de novo biosynthesis from any of these substrates could not be found for histidine, isoleucine, leucine, lysine, phenylalanine, proline and valine, the other amino acids and metabolites under study acquired 13C-label at specific rates depending on the nature of the tracer compound. Glucose was directly used for cell wall biosynthesis, but was also converted into pyruvate (and its downstream metabolites) through the glycolytic pathway or into erythrose 4-phosphate (e.g., for the biosynthesis of tyrosine) via the non-oxidative pentose phosphate pathway. Glycerol efficiently served as a gluconeogenetic substrate and could also be used via phosphoenolpyruvate and diaminopimelate as a major carbon source for cell wall biosynthesis. In contrast, exogenous serine was mainly utilized in downstream metabolic processes, e.g., via acetyl-CoA in a complete citrate cycle with fluxes in the oxidative direction and as a carbon feed for fatty acid biosynthesis. In summary, the data reflect multiple and differential substrate usages by C. burnetii in a bipartite-type metabolic network, resembling the overall topology of the related pathogen Legionella pneumophila. These strategies could benefit the metabolic capacities of the pathogens also as a trait to adapt for replication under intracellular conditions.

Project description:BACKGROUND: Direct in vivo investigation of human metabolism is complicated by the distinct metabolic functions of various sub-cellular organelles. Diverse micro-environments in different organelles may lead to distinct functions of the same protein and the use of different enzymes for the same metabolic reaction. To better understand the complexity in the human metabolism, a compartmentalized human metabolic network with integrated sub-cellular location information is required. RESULTS: We extended the previously reconstructed Edinburgh Human Metabolic Network (EHMN) [Ma, et al. Molecular Systems Biology, 3:135, 2007] by integrating the sub-cellular location information for the reactions, adding transport reactions and refining the protein-reaction relationships based on the location information. Firstly, protein location information was obtained from Gene Ontology and complemented by a Swiss-Prot location keywords search. Then all the reactions in EHMN were assigned to a location based on the protein-reaction relationships to get a preliminary compartmentalized network. We investigated the localized sub-networks in each pathway to identify gaps and isolated reactions by connectivity analysis and refined the location information based on information from literature. As a result, location information for hundreds of reactions was revised and hundreds of incorrect protein-reaction relationships were corrected. Over 1400 transport reactions were added to link the location specific metabolic network. To validate the network, we have done pathway analysis to examine the capability of the network to synthesize or degrade certain key metabolites. Compared with a previously published human metabolic network (Human Recon 1), our network contains over 1000 more reactions assigned to clear cellular compartments. CONCLUSIONS: By combining protein location information, network connectivity analysis and manual literature search, we have reconstructed a more complete compartmentalized human metabolic network. The whole network is available at http://www.ehmn.bioinformatics.ed.ac.uk and free for academic use.

Dataset Information

Characterization of the usage of the serine metabolic network in human cancer.

Publications

Characterization of the usage of the serine metabolic network in human cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets