Dataset Information

Nijhout2006_Hepatic_Folate_Metab

ABSTRACT: This is the model described in the article: In silico experimentation with a model of hepatic mitochondrial folate metabolism. H. Frederik Nijhout, Michael C Reed, Shi-Ling Lam, Barry Shane, Jesse F Gregory and Cornelia M Ulrich, Theor Biol Med Model 2006,3:40; PubmedID: 17150100 ; DOI: 10.1186/1742-4682-3-40 ; Abstract: BACKGROUND: In eukaryotes, folate metabolism is compartmentalized and occurs in both the cytosol and the mitochondria. The function of this compartmentalization and the great changes that occur in the mitochondrial compartment during embryonic development and in rapidly growing cancer cells are gradually becoming understood, though many aspects remain puzzling and controversial. APPROACH: We explore the properties of cytosolic and mitochondrial folate metabolism by experimenting with a mathematical model of hepatic one-carbon metabolism. The model is based on known biochemical properties of mitochondrial and cytosolic enzymes. We use the model to study questions about the relative roles of the cytosolic and mitochondrial folate cycles posed in the experimental literature. We investigate: the control of the direction of the mitochondrial and cytosolic serine hydroxymethyltransferase (SHMT) reactions, the role of the mitochondrial bifunctional enzyme, the role of the glycine cleavage system, the effects of variations in serine and glycine inputs, and the effects of methionine and protein loading. CONCLUSION: The model reproduces many experimental findings and gives new insights into the underlying properties of mitochondrial folate metabolism. Particularly interesting is the remarkable stability of formate production in the mitochondria in the face of large changes in serine and glycine input. The model shows that in the presence of the bifunctional enzyme (as in embryonic tissues and cancer cells), the mitochondria primarily support cytosolic purine and pyrimidine synthesis via the export of formate, while in adult tissues the mitochondria produce serine for gluconeogenesis. This model does not reproduce the results in the article completely, but most steady state concentrations are in a range of 10% around the published values. Also parameterscans give nearly identical results as shown in the article. In the SBML version model the volumes of the mitochondrion, the cytoplasm and the cell were all set to one to obtain the same equations as described in the supplemental materials of the article. The total folate is equally split between the cytosol and the mitochondrion and divided by 3/4 for the cytosol and 1/4 for the mitochondrion, respectively. To obtain an SBML model in which the volumes of the compartments, cytosol and mito , are used, the model needs to be altered as follows: for the initial distribution of folate the terms 3/4 and 1/4 have to be replaced by volumes of cytosol and mitochondria respectively in the transport reactions between mitochondrion and cytosol the stoichiometry of the mitochondrial reactants has to be set from 3 to 1 and in the first part of the according rate laws the factor mito/3 should simply be replaced with mito . the stoichiometries of src and dmg have to be changed to cell/mito for mitchondrial and cell/cytosol for cytosolic reactions involving these two species. While the concentrations stay the same after these alteration, the reaction fluxes change by a factor of cytosol and mito for cytosolic and mitchondrial reactions, respectively. Originally created by libAntimony v1.3 (using libSBML 3.4.1) This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

SUBMITTER: Lukas Endler

PROVIDER: MODEL1007200000 | BioModels | 2005-01-01

REPOSITORIES: BioModels

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Publications

In silico experimentation with a model of hepatic mitochondrial folate metabolism.

Nijhout H Frederik HF Reed Michael C MC Lam Shi-Ling SL Shane Barry B Gregory Jesse F JF Ulrich Cornelia M CM

Theoretical biology & medical modelling 20061206

<h4>Background</h4>In eukaryotes, folate metabolism is compartmentalized and occurs in both the cytosol and the mitochondria. The function of this compartmentalization and the great changes that occur in the mitochondrial compartment during embryonic development and in rapidly growing cancer cells are gradually becoming understood, though many aspects remain puzzling and controversial.<h4>Approach</h4>We explore the properties of cytosolic and mitochondrial folate metabolism by experimenting wit ...[more]

PMID: 17150100

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Project description:This is the model described in the article: A mathematical model of glutathione metabolism. Michael C Reed, Rachel L Thomas, Jovana Pavisic, S. Jill James, Cornelia M Ulrich and H. Frederik Nijhout, Theor Biol Med Model 2008,5:8; PubmedID:18442411 ; DOI:10.1186/1742-4682-5-8; Abstract: BACKGROUND: Glutathione (GSH) plays an important role in anti-oxidant defense and detoxification reactions. It is primarily synthesized in the liver by the transsulfuration pathway and exported to provide precursors for in situ GSH synthesis by other tissues. Deficits in glutathione have been implicated in aging and a host of diseases including Alzheimer's disease, Parkinson's disease, cardiovascular disease, cancer, Down syndrome and autism. APPROACH: We explore the properties of glutathione metabolism in the liver by experimenting with a mathematical model of one-carbon metabolism, the transsulfuration pathway, and glutathione synthesis, transport, and breakdown. The model is based on known properties of the enzymes and the regulation of those enzymes by oxidative stress. We explore the half-life of glutathione, the regulation of glutathione synthesis, and its sensitivity to fluctuations in amino acid input. We use the model to simulate the metabolic profiles previously observed in Down syndrome and autism and compare the model results to clinical data. CONCLUSION: We show that the glutathione pools in hepatic cells and in the blood are quite insensitive to fluctuations in amino acid input and offer an explanation based on model predictions. In contrast, we show that hepatic glutathione pools are highly sensitive to the level of oxidative stress. The model shows that overexpression of genes on chromosome 21 and an increase in oxidative stress can explain the metabolic profile of Down syndrome. The model also correctly simulates the metabolic profile of autism when oxidative stress is substantially increased and the adenosine concentration is raised. Finally, we discuss how individual variation arises and its consequences for one-carbon and glutathione metabolism. parameter orig. article this model Vm_CBS 700000 420000 Vm_GNMT 245 260 K_sam_GNMT 32 63 Vr_MTD(mito) 600000 595000 V_CBS kinetic law rearranged V_bmetc 913 913.4 Vm_GR 8925 892.5 This version of the model contains a feeding rhythm as used in figure 5 of the original article. Four parameters, breakfast, lunch dinner and fasting, describe the relative level of amino acids, described by the parameter aa_input or Aminoacid_input, in the blood. To remove the daily feeding rhythm, either set the parameters for meals and fasting to 1 (or for figure 3 to 0.333), or remove the assignment rule for the Aminoacid_input. For the steady state evaluations for figure 6, the mealtime parameters were set to one, which, while making Copasi complain about explicit time dependency, still gives valid results. This version of the model differs slightly from the version described in the supplement, in which contains some typos. It was corrected using the version of JWS-online, created using the original matlab files, thankfully provided by the articles authors. Many thanks to Jacky Snoep for his help and support. In the SBML version of the model the volumes of the mitochondrion, the cytoplasm and the cell were all set to one to obtain the same equations as described in the supplemental materials of the article. The total folate is equally split between the cytosol and the mitochondrion and divided by 3/4 for the cytosol and 1/4 for the mitochondrion, respectively. To obtain an SBML model in which the volumes of the compartments, cytosol and mito, are used, the model needs to be altered as follows: for the initial distribution of folate the terms 3/4 and 1/4 have to be replaced by volumes of cytosol and mitochondria respectively in the transport reactions between mitochondrion and cytosol the stoichiometry of the mitochondrial reactants has to be set from 3 to 1 and in the first part of the according rate laws the factor mito/3 should simply be replaced with mito. the stoichiometries of src and dmg have to be changed to cell/mito for mitchondrial and cell/cytosol for cytosolic reactions involving these two species (for the relative volumes used in the article this would be 4 for mitochondrial reactions and 1.33333 for cytosolic ones). While the concentrations stay the same after these alteration, the reaction fluxes change by a factor of cytosol and mito for cytosolic and mitchondrial reactions, respectively. Originally created by libAntimony v1.3 (using libSBML 3.4.1)

Project description:MicroRNAs (miRNAs) are small non-coding RNAs that associate with Argonaute 2 protein to regulate gene expression at the post-transcriptional level in the cytoplasm. However, recent studies have reported that some miRNAs localize to and function in other cellular compartments. Mitochondria harbour their own genetic system that may be a potential site for miRNA-mediated post-transcriptional regulation. We aimed at investigating whether nuclear-encoded miRNAs can localize to and function in human mitochondria. To enable identification of mitochondrial-enriched miRNAs, we profiled the mitochondrial and cytosolic RNA fractions from the same HeLa cells by miRNA microarray analysis. Mitochondria were purified using a combination of cell fractionation and immunoisolation, and assessed for the lack of protein and RNA contaminants. We found 57 miRNAs differentially expressed in HeLa mitochondria and cytosol. Of these 57, a signature of 13 nuclear-encoded miRNAs was reproducibly enriched in mitochondrial RNA and validated by RT-PCR for hsa-miR-494, hsa-miR-1275 and hsa-miR-1974. This study provides the first comprehensive view of the localization of RNA interference components to the mitochondria. Our data outline the molecular bases for a novel layer of crosstalk between nucleus and mitochondria through a specific subset of human miRNAs that we termed ‘mitomiRs’. To assess whether nuclear-encoded miRNA are detectable in human mitochondria, we performed the following four steps approach. First, cultured HeLa cells were allowed to reach 80-100% confluence and subjected to fractionation in order to isolate the cytosolic fraction. From the same HeLa cells, mitochondria were isolated by immunomagnetic Anti-TOM22 MicroBeads from the Mitochondria Isolation Kit (Miltenyi Biotec). In total, six mitochondria preparations were perfromed, three of these were additionally treated with RNase A. Second, total RNA was extracted from the mitochondrial and cytosolic fractions. Third, mitochondrial and cytosolic RNA were respectively profiled by microRNA microarray analysis. Last, data were analyzed and normalized.Three independent assays were performed.

Project description:Fatty acid synthesis is closely linked to nutrient availability and cellular energetic status. The committed step in fatty acid synthesis is the acetyl CoA carboxylase. Eukaryotes have two genes encoding acetyl CoA carboxylases, one encoding a cytosolic enzyme and another coding for a mitochondrial enzyme. They catalyze the synthesis of malonyl CoA in the cytosol and the mitochondria, respectively. While cytosolic malonyl CoA is the precursor for fatty acid synthesis, mitochondrial malonyl CoA controls the transfer of fatty acyl group into the mitochondria by inhibiting carnitine/palmitoyl transferase activity and thus, regulates Î²-oxidation. In Saccharomyces cerevisiae, Î²-oxidation is restricted to the peroxisomes, raising the question of the function of the mitochondrial isoform (HFA1). In this study, we replaced the cytosolic Acc1 with Hfa1 expressed in the cytosol by removing the mitochondrial leader peptide, under control of the HFA1 promoter. We studied fatty acid synthesis and transcription profiles in this strain during starvation for carbon or nitrogen, using glucose or ethanol as the carbon source. Under all the conditions studied, the key sensor of energetic status, Snf1, was activated, indicating active inhibition of fatty acid synthesis. The pool size of fatty acids was smaller when Acc1 was replaced with truncated Hfa1 for fatty acid synthesis. Yet, the transcription profiles were similar in both the cases. These results point towards the conclusion that Hfa1 is either catalytically less efficient or it is more sensitive to inhibition by Snf1. Gene expression from a strain of Saccharomyces cerevisiae where cytosolic fatty acid synthesis occurs by mitochondrial acetyl CoA carboxylase (without its mitochondrial leader peptide) is compared with that in a reference strain while growing in chemostats on carbon or nitrogen starvation using glucose or ethanol as the carbon source. There are two strains (reference or mutant), two carbon sources (glucose or ethanol) and two limitations (carbon or nitrogen), resulting in 8 comparisons. Each array was performed in duplicate, resulting in 16 CEL files. Growth was limited by either carbon or nitrogen. When carbon was the limited nutrient, we tested growth on either glucose or ethanol (both using ammonium sulfate as the nitrogen source). When ammonium sulfate was limiting, we used either glucose or ethanol as the carbon source.

Dataset Information

Nijhout2006_Hepatic_Folate_Metab

Publications

In silico experimentation with a model of hepatic mitochondrial folate metabolism.

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