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Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism.

Jerby L, Shlomi T, Ruppin E - Mol. Syst. Biol. (2010)

Bottom Line: The model is verified using standard cross-validation procedures, and through its ability to carry out hepatic metabolic functions.The model's flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46).The approach presented can be used to construct other human tissue-specific models, and be applied to other organisms.

View Article: PubMed Central - PubMed

Affiliation: The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel. livnatje@post.tau.ac.il

ABSTRACT
The computational study of human metabolism has been advanced with the advent of the first generic (non-tissue specific) stoichiometric model of human metabolism. In this study, we present a new algorithm for rapid reconstruction of tissue-specific genome-scale models of human metabolism. The algorithm generates a tissue-specific model from the generic human model by integrating a variety of tissue-specific molecular data sources, including literature-based knowledge, transcriptomic, proteomic, metabolomic and phenotypic data. Applying the algorithm, we constructed the first genome-scale stoichiometric model of hepatic metabolism. The model is verified using standard cross-validation procedures, and through its ability to carry out hepatic metabolic functions. The model's flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46). Finally, the model better predicts biomarker changes in genetic metabolic disorders than the generic human model (accuracy of 0.67 versus 0.59). The approach presented can be used to construct other human tissue-specific models, and be applied to other organisms.

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The mean urea/glutamine ratio and the standard error in (A) the liver and (B) the generic model simulations of the healthy (i.e., normal homozygote), partial (i.e., heterozygote) and full knockout cases.
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f3: The mean urea/glutamine ratio and the standard error in (A) the liver and (B) the generic model simulations of the healthy (i.e., normal homozygote), partial (i.e., heterozygote) and full knockout cases.

Mentions: One of the main functions that take place in the liver is the conversion of ammonia to urea. Urea cycle deficiencies are inborn errors of hepatic metabolism that may cause severe hyperammonemia and hyperglutaminemia. In the study by Lee et al (2000), metabolic fluxes of urea secretion and glutamine uptake were measured in vivo in subjects with urea disorders, including argininosuccinate synthetase (ASS) deficiency, argininosuccinate lyase (ASL) deficiency, and ornithine transcarbamylase (OTC) deficiency, as well as in control healthy subjects. As the secretion and synthesis of urea are correlated with the consumption of dietary amino acids, the ratio of urea secretion versus glutamine uptake can better depict the functionality of the urea cycle than the rate of each of the fluxes by itself, as depicted in the results obtained by Lee et al (2000). For each of the three disorders, we simulated the metabolic phenotype of the healthy homozygote genotype (enforcing a non-zero flux through the corresponding reaction), the mutated heterozygote genotype (limiting the flux through the corresponding reaction to 50% of its maximal rate found by flux variability analysis (FVA; Mahadevan and Schilling, 2003), and the full knockout genotype (i.e., enforcing a zero flux through the reaction), both in the generic human model and in the liver model. Specifically, in each case, we computed the urea:glutamine ratio by sampling the space of feasible flux distributions, satisfying stoichiometric mass-balance and reaction directionality constraints (Price et al, 2004), deriving a mean and s.d. of the ratio across 1000 sampled solutions. We find that the results obtained by the liver model better represent the experimentally observed metabolic profiles than the generic model. Primarily, in the liver model, the urea:glutamine ratio decreases monotonically with the severity of the disease, with the exception of the full knockout in the OTC simulation (Figure 3; Supplementary Table I). In the liver model, the simulation of the healthy case obtains a significantly higher urea:glutamine ratio in comparison to the ratio obtained in the full knockout of ASS and ASL simulations (t-test P-values of 6 × 10−2 and 1 × 10−4, respectively), unlike the generic model (t-test P-values of 0.8469 and 0.2235, respectively). In addition, the variance of the ratio across the solution spaces that are defined by the liver model is fairly smaller than the variance of the one defined by the generic model (Supplementary Table I). These latter results are in line with the phenomenon described by Lee et al (2000), that the ratio is kept rather stable for each of the genotypes.


Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism.

Jerby L, Shlomi T, Ruppin E - Mol. Syst. Biol. (2010)

The mean urea/glutamine ratio and the standard error in (A) the liver and (B) the generic model simulations of the healthy (i.e., normal homozygote), partial (i.e., heterozygote) and full knockout cases.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2964116&req=5

f3: The mean urea/glutamine ratio and the standard error in (A) the liver and (B) the generic model simulations of the healthy (i.e., normal homozygote), partial (i.e., heterozygote) and full knockout cases.
Mentions: One of the main functions that take place in the liver is the conversion of ammonia to urea. Urea cycle deficiencies are inborn errors of hepatic metabolism that may cause severe hyperammonemia and hyperglutaminemia. In the study by Lee et al (2000), metabolic fluxes of urea secretion and glutamine uptake were measured in vivo in subjects with urea disorders, including argininosuccinate synthetase (ASS) deficiency, argininosuccinate lyase (ASL) deficiency, and ornithine transcarbamylase (OTC) deficiency, as well as in control healthy subjects. As the secretion and synthesis of urea are correlated with the consumption of dietary amino acids, the ratio of urea secretion versus glutamine uptake can better depict the functionality of the urea cycle than the rate of each of the fluxes by itself, as depicted in the results obtained by Lee et al (2000). For each of the three disorders, we simulated the metabolic phenotype of the healthy homozygote genotype (enforcing a non-zero flux through the corresponding reaction), the mutated heterozygote genotype (limiting the flux through the corresponding reaction to 50% of its maximal rate found by flux variability analysis (FVA; Mahadevan and Schilling, 2003), and the full knockout genotype (i.e., enforcing a zero flux through the reaction), both in the generic human model and in the liver model. Specifically, in each case, we computed the urea:glutamine ratio by sampling the space of feasible flux distributions, satisfying stoichiometric mass-balance and reaction directionality constraints (Price et al, 2004), deriving a mean and s.d. of the ratio across 1000 sampled solutions. We find that the results obtained by the liver model better represent the experimentally observed metabolic profiles than the generic model. Primarily, in the liver model, the urea:glutamine ratio decreases monotonically with the severity of the disease, with the exception of the full knockout in the OTC simulation (Figure 3; Supplementary Table I). In the liver model, the simulation of the healthy case obtains a significantly higher urea:glutamine ratio in comparison to the ratio obtained in the full knockout of ASS and ASL simulations (t-test P-values of 6 × 10−2 and 1 × 10−4, respectively), unlike the generic model (t-test P-values of 0.8469 and 0.2235, respectively). In addition, the variance of the ratio across the solution spaces that are defined by the liver model is fairly smaller than the variance of the one defined by the generic model (Supplementary Table I). These latter results are in line with the phenomenon described by Lee et al (2000), that the ratio is kept rather stable for each of the genotypes.

Bottom Line: The model is verified using standard cross-validation procedures, and through its ability to carry out hepatic metabolic functions.The model's flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46).The approach presented can be used to construct other human tissue-specific models, and be applied to other organisms.

View Article: PubMed Central - PubMed

Affiliation: The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel. livnatje@post.tau.ac.il

ABSTRACT
The computational study of human metabolism has been advanced with the advent of the first generic (non-tissue specific) stoichiometric model of human metabolism. In this study, we present a new algorithm for rapid reconstruction of tissue-specific genome-scale models of human metabolism. The algorithm generates a tissue-specific model from the generic human model by integrating a variety of tissue-specific molecular data sources, including literature-based knowledge, transcriptomic, proteomic, metabolomic and phenotypic data. Applying the algorithm, we constructed the first genome-scale stoichiometric model of hepatic metabolism. The model is verified using standard cross-validation procedures, and through its ability to carry out hepatic metabolic functions. The model's flux predictions correlate with flux measurements across a variety of hormonal and dietary conditions, and improve upon the predictive performance obtained using the original, generic human model (prediction accuracy of 0.67 versus 0.46). Finally, the model better predicts biomarker changes in genetic metabolic disorders than the generic human model (accuracy of 0.67 versus 0.59). The approach presented can be used to construct other human tissue-specific models, and be applied to other organisms.

Show MeSH
Related in: MedlinePlus