Prediction of intracellular metabolic states from extracellular metabolomic data.

TitlePrediction of intracellular metabolic states from extracellular metabolomic data.
Publication TypeJournal Article
Year of Publication2015
AuthorsAurich MK, Paglia G, Rolfsson O, Hrafnsdóttir S, Magnúsdóttir M, Stefaniak MM, Palsson BØ, Fleming RMT, Thiele I
JournalMetabolomics
Volume11
Issue3
Pagination603-619
PubMed Date06/2015
ISSN1573-3882
Abstract

Metabolic models can provide a mechanistic framework to analyze information-rich omics data sets, and are increasingly being used to investigate metabolic alternations in human diseases. An expression of the altered metabolic pathway utilization is the selection of metabolites consumed and released by cells. However, methods for the inference of intracellular metabolic states from extracellular measurements in the context of metabolic models remain underdeveloped compared to methods for other omics data. Herein, we describe a workflow for such an integrative analysis emphasizing on extracellular metabolomics data. We demonstrate, using the lymphoblastic leukemia cell lines Molt-4 and CCRF-CEM, how our methods can reveal differences in cell metabolism. Our models explain metabolite uptake and secretion by predicting a more glycolytic phenotype for the CCRF-CEM model and a more oxidative phenotype for the Molt-4 model, which was supported by our experimental data. Gene expression analysis revealed altered expression of gene products at key regulatory steps in those central metabolic pathways, and literature query emphasized the role of these genes in cancer metabolism. Moreover, in silico gene knock-outs identified unique control points for each cell line model, e.g., phosphoglycerate dehydrogenase for the Molt-4 model. Thus, our workflow is well-suited to the characterization of cellular metabolic traits based on extracellular metabolomic data, and it allows the integration of multiple omics data sets into a cohesive picture based on a defined model context.

Alternate JournalMetabolomics
PubMed ID25972769
PubMed Central IDPMC4419158
Cover Image: 

Location

Location

417 Powell-Focht Bioengineering Hall

9500 Gilman Drive La Jolla, CA 92093-0412

Contact Us

Contact Us

In Silico Lab:  858-822-1144

Wet Lab:  858-246-1625

FAX:   858-822-3120

Website Concerns: sbrgit@ucsd.edu

User Login