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. 2010 Oct 19;4:140.
doi: 10.1186/1752-0509-4-140.

A Detailed Genome-Wide Reconstruction of Mouse Metabolism Based on Human Recon 1

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Free PMC article

A Detailed Genome-Wide Reconstruction of Mouse Metabolism Based on Human Recon 1

Martin I Sigurdsson et al. BMC Syst Biol. .
Free PMC article

Abstract

Background: Well-curated and validated network reconstructions are extremely valuable tools in systems biology. Detailed metabolic reconstructions of mammals have recently emerged, including human reconstructions. They raise the question if the various successful applications of microbial reconstructions can be replicated in complex organisms.

Results: We mapped the published, detailed reconstruction of human metabolism (Recon 1) to other mammals. By searching for genes homologous to Recon 1 genes within mammalian genomes, we were able to create draft metabolic reconstructions of five mammals, including the mouse. Each draft reconstruction was created in compartmentalized and non-compartmentalized version via two different approaches. Using gap-filling algorithms, we were able to produce all cellular components with three out of four versions of the mouse metabolic reconstruction. We finalized a functional model by iterative testing until it passed a predefined set of 260 validation tests. The reconstruction is the largest, most comprehensive mouse reconstruction to-date, accounting for 1,415 genes coding for 2,212 gene-associated reactions and 1,514 non-gene-associated reactions.We tested the mouse model for phenotype prediction capabilities. The majority of predicted essential genes were also essential in vivo. However, our non-tissue specific model was unable to predict gene essentiality for many of the metabolic genes shown to be essential in vivo. Our knockout simulation of the lipoprotein lipase gene correlated well with experimental results, suggesting that softer phenotypes can also be simulated.

Conclusions: We have created a high-quality mouse genome-scale metabolic reconstruction, iMM1415 (Mus Musculus, 1415 genes). We demonstrate that the mouse model can be used to perform phenotype simulations, similar to models of microbe metabolism. Since the mouse is an important experimental organism, this model should become an essential tool for studying metabolic phenotypes in mice, including outcomes from drug screening.

Figures

Figure 1
Figure 1
Creation of draft mammalian reconstructions. a) A schematic figure showing the two approaches used to generate draft mammalian reconstructions using Recon 1. Approach A removes all gene-associated reactions from Recon 1 without a homologous gene in the reconstructed animal, while keeping all non-gene-associated reactions. Approach B removes all gene-associated reactions from Recon 1 without a homologous gene in the reconstructed as well as non-gene-associated reactions (excluding transporters and demand reactions). GAR - gene-associated-reactions; nGAR - non-gene-associated reaction; transp/dem - transporters and demand reactions. b) Ratio of reactions (black bar) and genes (gray bar) that were successfully mapped from Recon 1 to the indicated mammalian draft reconstruction. c) A phylogenetic tree based on all transcripts of protein domain sequences from the SuperFamily database [64] for all reconstructed mammals. d) A phylogenetic tree based on flux variability analysis (FVA) of all reactions in all mammals reconstructed via approach A. e) A phylogenetic tree based on flux variability analysis (FVA) of all reactions in all mammals reconstructed via approach B.
Figure 2
Figure 2
Results from comparison of the flux variability analysis (FVA) for a LPL knockout model with a wild type model. Results from comparison of the flux variability analysis (FVA) for a LPL knockout model with a wild type model. a) A part of triacylglycerol metabolism is shown. Reactions with increased flux capacity in the knockout model are shown in red and reactions with decreased flux capacity are shown in green. b) The distribution of fluxes in FVA of a wild type model (dark gray) compared to knockout model (black) indicates increased flux through the diacylglycerol acetyltransferase (DGAT) and monoacylglycerol acetyltransferase (MOGAT). These results suggest that hypertriglyceridemia can result from knockout of the LPL gene. Reactions: AGPAT1 - 1-acylglycerol-3-phosphate O-acyltransferase 1; DGAT - diacylglycerol acyltransferase; GPAM - glycerol-3-phosphate acyltransferase; LPS - lipoprotein lipase LPS2 - lipoprotein lipase 2; LPS3 - lipase; MOGAT - monoacylglycerol acyltransferase; PPAP - phosphatidic acid phosphatase. Metabolites: a-lysophosphatidic acid; DAG - diacylglycerol (Homo sapiens); G - glycerol; G3p - Glycerol 3-phosphate; MAG - monoacylglycerol 2; PA - phosphatidic acid; R - R groups (total); TAG - triacylglycerol.

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