Model Of Human Metabolism Research Articles

Validation of an inexpensive and accurate mathematical method to measure long-term changes in free-living energy intake

Accurate measurement of free-living energy intake (EI) over long periods is imperative for understanding obesity and its treatment. Unfortunately, traditional methods rely on self-report and are notoriously inaccurate. Although EI can be indirectly estimated by the intake-balance method, this technique is prohibitively labor-intensive and expensive, requiring repeated measures of energy expenditure via doubly labeled water (DLW) along with multiple dual-energy X-ray absorptiometry (DXA) scans to measure changes in body energy stores. Our objective was to validate a mathematical method to measure long-term changes in free-living energy intake. We measured body weight and EI changes (ΔEI) over 4 time intervals by using the intake-balance method in 140 individuals who underwent 2 y of caloric restriction as part of the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy study. We compared the ΔEI values calculated by using DLW/DXA with those obtained by using a mathematical model of human metabolism whose only inputs were the initial demographic information and repeated body weight data. The mean ΔEI values calculated by the model were within 40 kcal/d of the DLW/DXA method throughout the 2-y study. For individual subjects, the overall root mean square deviation between the model and DLW/DXA method was 215 kcal/d, and most of the model-calculated ΔEI values were within 132 kcal/d of the DLW/DXA method. Accurate and inexpensive estimates of ΔEI that are comparable to the DLW/DXA method can be obtained by using a mathematical model and repeated body weight measurements.

Energy Balance After Sodium-Glucose Cotransporter 2 Inhibition.

OBJECTIVESodium–glucose cotransporter 2 (SGLT2) inhibitors cause substantially less weight loss than expected from the energy excreted via glycosuria. Our aim was to analyze this phenomenon quantitatively.RESEARCH DESIGN AND METHODSEighty-six patients with type 2 diabetes (HbA1c 7.8 ± 0.8% [62 ± 9 mmol/mol], estimated glomerular filtration rate [eGFR] 89 ± 19 mL ⋅ min−1 ⋅ 1.73 m−2) received empagliflozin (25 mg/day) for 90 weeks with frequent (n = 11) assessments of body weight, eGFR, and fasting plasma glucose (FPG). Time-dependent glucose filtration was calculated as the product of eGFR and FPG; time-dependent glycosuria was estimated from previous direct measurements. The relation of calorie-to-weight changes was estimated using a mathematical model of human energy metabolism that simulates the time course of weight change for a given change in calorie balance and calculates the corresponding energy intake changes.RESULTSAt week 90, weight loss averaged −3.2 ± 4.2 kg (corresponding to a median calorie deficit of 51 kcal/day [interquartile range (IQR) 112]). However, the observed calorie loss through glycosuria (206 kcal/day [IQR 90]) was predicted to result in a weight loss of –11.3 ± 3.1 kg, assuming no compensatory changes in energy intake. Thus, patients lost only 29 ± 41% of the weight loss predicted by their glycosuria; the model indicated that this difference was accounted for by a 13% (IQR 12) increase in calorie intake (269 kcal/day [IQR 258]) coupled with a 2% (IQR 5) increase in daily energy expenditure (due to diet-induced thermogenesis). This increased calorie intake was inversely related to baseline BMI (partial r = −0.34, P < 0.01) and positively to baseline eGFR (partial r = 0.29, P < 0.01).CONCLUSIONSChronic glycosuria elicits an adaptive increase in energy intake. Combining SGLT2 inhibition with caloric restriction is expected to be associated with major weight loss.

Context-Specific Metabolic Model Extraction Based on Regularized Least Squares Optimization

Genome-scale metabolic models have proven highly valuable in investigating cell physiology. Recent advances include the development of methods to extract context-specific models capable of describing metabolism under more specific scenarios (e.g., cell types). Yet, none of the existing computational approaches allows for a fully automated model extraction and determination of a flux distribution independent of user-defined parameters. Here we present RegrEx, a fully automated approach that relies solely on context-specific data and ℓ1-norm regularization to extract a context-specific model and to provide a flux distribution that maximizes its correlation to data. Moreover, the publically available implementation of RegrEx was used to extract 11 context-specific human models using publicly available RNAseq expression profiles, Recon1 and also Recon2, the most recent human metabolic model. The comparison of the performance of RegrEx and its contending alternatives demonstrates that the proposed method extracts models for which both the structure, i.e., reactions included, and the flux distributions are in concordance with the employed data. These findings are supported by validation and comparison of method performance on additional data not used in context-specific model extraction. Therefore, our study sets the ground for applications of other regularization techniques in large-scale metabolic modeling.

Reconstruction and validation of a constraint‐based metabolic network model for bone marrow‐derived mesenchymal stem cells

Over recent years, constraint-based modelling of metabolic networks has become increasingly popular; the models are suitable for system-level modelling of cell physiology. The goal of the present work was to reconstruct a constraint-based metabolic network model of bone marrow-derived mesenchymal stem cells (BMMSCs). To reconstruct a BMMSC-specific metabolic model, transcriptomic data of BMMSCs, and additionally, the human generic metabolic network model (Recon1) were used. Then, using the mCADRE algorithm, a draft metabolic network was reconstructed. Literature and proteomic data were subsequently used to refine and improve the draft. From this, iMSC1255 was derived to be the metabolic network model of BMMSCs. iMSC1255 has 1255 genes, 1850 metabolites and 2288 reactions. After including additional constraints based on previously reported experimental results, our model successfully predicted BMMSC growth rate and metabolic phenotypes. Here, iMSC1255 is introduced to be the metabolic network model of bone marrow-derived mesenchymal stem cells. Based on current knowledge, this is the first report on genome-scale reconstruction and validation of a stem cell metabolic network model.

Effects of Differently Processed Soy Protein Compared to Milk Protein on Gut Microbial Composition: Possible Mechanisms for Cardiometabolic Effects of Soy

Diet is a major factor influencing the composition of the gut microbiota, but studies investigating the effects of different proteins to modulate the microbiome are lacking. Soy protein consumption has several cardiometabolic benefits; however, the exact mechanism(s) whereby soy protein lowers serum lipids is unknown. This study is the first to investigate the effects of different protein sources on gut microbiota composition in a model of human lipid metabolism and to assess whether gut microbiota changes can account for the lipid lowering activity of soy protein. Golden Syrian hamsters (N=8/group) were fed a control semi‐purified diet containing 20% (wt) milk protein isolate (MPI) for 3 weeks, and then randomized to one of 4 treatment groups (MPI (control), soy protein concentrate 1 (SPC 1) or soy protein isolate (SPI 1) or (SPI 2)) for 6 weeks. The diets differed only in protein source. Feces were collected on day ‐1 and weeks 1, 3 and 6. Terminal blood samples were analyzed for serum lipids and intestinal sections were collected for 16S rDNA based microbial profiling. VLDL1+2 and non‐HDL‐C were significantly lower in all soy groups compared to the milk group suggesting that soy protein has its major effect to reduce hepatic lipogenesis. Total and LDL‐C were significantly lower in the SPC 1, and triglycerides were lower in the SPI 1 group vs the milk group respectively (soy groups NS diff from each other). There was greater microbial diversity with observed shifts in composition in hamsters fed soy diets vs MPI. Soy protein results in a different microbiota composition vs milk protein which may contribute to its lipid lowering effect. DuPont Nutrition &amp; Health funded research.

Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework

The gut microbiota is well known to affect host metabolic phenotypes. The systemic effects of the gut microbiota on host metabolism are generally evaluated via the comparison of germfree and conventional mice, which is impossible to perform for humans. Hence, it remains difficult to determine the impact of the gut microbiota on human metabolic phenotypes. We demonstrate that a constraint-based modeling framework that simulates “germfree” and “ex-germfree” human individuals can partially fill this gap and allow for in silico predictions of systemic human-microbial co-metabolism. To this end, we constructed the first constraint-based host-microbial community model, comprising the most comprehensive model of human metabolism and 11 manually curated, validated metabolic models of commensals, probiotics, pathogens, and opportunistic pathogens. We used this host-microbiota model to predict potential metabolic host-microbe interactions under 4 in silico dietary regimes. Our model predicts that gut microbes secrete numerous health-relevant metabolites into the lumen, thereby modulating the molecular composition of the body fluid metabolome. Our key results include the following: 1. Replacing a commensal community with pathogens caused a loss of important host metabolic functions. 2. The gut microbiota can produce important precursors of host hormone synthesis and thus serves as an endocrine organ. 3. The synthesis of important neurotransmitters is elevated in the presence of the gut microbiota. 4. Gut microbes contribute essential precursors for glutathione, taurine, and leukotrienes. This computational modeling framework provides novel insight into complex metabolic host-microbiota interactions and can serve as a powerful tool with which to generate novel, non-obvious hypotheses regarding host-microbe co-metabolism.

Reconstruction of genome-scale human metabolic models using omics data.

The impact of genome-scale human metabolic models on human systems biology and medical sciences is becoming greater, thanks to increasing volumes of model building platforms and publicly available omics data. The genome-scale human metabolic models started with Recon 1 in 2007, and have since been used to describe metabolic phenotypes of healthy and diseased human tissues and cells, and to predict therapeutic targets. Here we review recent trends in genome-scale human metabolic modeling, including various generic and tissue/cell type-specific human metabolic models developed to date, and methods, databases and platforms used to construct them. For generic human metabolic models, we pay attention to Recon 2 and HMR 2.0 with emphasis on data sources used to construct them. Draft and high-quality tissue/cell type-specific human metabolic models have been generated using these generic human metabolic models. Integration of tissue/cell type-specific omics data with the generic human metabolic models is the key step, and we discuss omics data and their integration methods to achieve this task. The initial version of the tissue/cell type-specific human metabolic models can further be computationally refined through gap filling, reaction directionality assignment and the subcellular localization of metabolic reactions. We review relevant tools for this model refinement procedure as well. Finally, we suggest the direction of further studies on reconstructing an improved human metabolic model.

Investigation on metabolism of cisplatin resistant ovarian cancer using a genome scale metabolic model and microarray data.

Many cancer cells show significant resistance to drugs that kill drug sensitive cancer cells and non-tumor cells and such resistance might be a consequence of the difference in metabolism. Therefore, studying the metabolism of drug resistant cancer cells and comparison with drug sensitive and normal cell lines is the objective of this research. Metabolism of cisplatin resistant and sensitive A2780 epithelial ovarian cancer cells and normal ovarian epithelium has been studied using a generic human genome-scale metabolic model and transcription data. The results demonstrate that the most different metabolisms belong to resistant and normal models, and the different reactions are involved in various metabolic pathways. However, large portion of distinct reactions are related to extracellular transport for three cell lines. Capability of metabolic models to secrete lactate was investigated to find the origin of Warburg effect. Computational results introduced SLC25A10 gene, which encodes mitochondrial dicarboxylate transporter involved in exchanging of small metabolites across the mitochondrial membrane that may play key role in high growing capacity of sensitive and resistant cancer cells. The metabolic models were also used to find single and combinatorial targets that reduce the cancer cells growth. Effect of proposed target genes on growth and oxidative phosphorylation of normal cells were determined to estimate drug side-effects. The deletion results showed that although the cisplatin did not cause resistant cancer cells death, but it shifts the cancer cells to a more vulnerable metabolism.

Discovery of substrate cycles in large scale metabolic networks using hierarchical modularity.

BackgroundA substrate cycle is a set of metabolic reactions, arranged in a loop, which does not result in net consumption or production of the metabolites. The cycle operates by transforming a cofactor, e.g. oxidizing a reducing equivalent. Substrate cycles have been found experimentally in many parts of metabolism; however, their physiological roles remain unclear. As genome-scale metabolic models become increasingly available, there is now the opportunity to comprehensively catalogue substrate cycles, and gain additional insight into this potentially important motif of metabolic networks.ResultsWe present a method to identify substrate cycles in the context of metabolic modules, which facilitates functional analysis. This method utilizes elementary flux mode (EFM) analysis to find potential substrate cycles in the form of cyclical EFMs, and combines this analysis with network partition based on retroactive (cyclical) interactions between reactions. In addition to providing functional context, partitioning the network into modules allowed exhaustive EFM calculations on smaller, tractable subnetworks that are enriched in metabolic cycles. Applied to a large-scale model of human liver metabolism (HepatoNet1), our method found not only well-known substrate cycles involving ATP hydrolysis, but also potentially novel substrate cycles involving the transformation of other cofactors. A key characteristic of the substrate cycles identified in this study is that the lengths are relatively short (2–13 reactions), comparable to many experimentally observed substrate cycles.ConclusionsEFM computation for large scale networks remains computationally intractable for exhaustive substrate cycle enumeration. Our algorithm utilizes a ‘divide and conquer’ strategy where EFM analysis is performed on systematically identified network modules that are designed to be enriched in cyclical interactions. We find that several substrate cycles uncovered using our approach are not identified when the network is partitioned in a more generic manner based solely on connectivity rather than cycles, demonstrating the value of targeting motif searches to sub-networks replete with a topological feature that resembles the desired motif itself.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0146-2) contains supplementary material, which is available to authorized users.

Bone-remodeling transcript levels are independent of perching in end-of-lay white leghorn chickens.

Osteoporosis is a bone disease that commonly results in a 30% incidence of fracture in hens used to produce eggs for human consumption. One of the causes of osteoporosis is the lack of mechanical strain placed on weight-bearing bones. In conventionally-caged hens, there is inadequate space for chickens to exercise and induce mechanical strain on their bones. One approach is to encourage mechanical stress on bones by the addition of perches to conventional cages. Our study focuses on the molecular mechanism of bone remodeling in end-of-lay hens (71 weeks) with access to perches. We examined bone-specific transcripts that are actively involved during development and remodeling. Using real-time quantitative PCR, we examined seven transcripts (COL2A1 (collagen, type II, alpha 1), RANKL (receptor activator of nuclear factor kappa-B ligand), OPG (osteoprotegerin), PTHLH (PTH-like hormone), PTH1R (PTH/PTHLH type-1 receptor), PTH3R (PTH/PTHLH type-3 receptor), and SOX9 (Sry-related high mobility group box)) in phalange, tibia and femur. Our results indicate that the only significant effect was a difference among bones for COL2A1 (femur > phalange). Therefore, we conclude that access to a perch did not alter transcript expression. Furthermore, because hens have been used as a model for human bone metabolism and osteoporosis, the results indicate that bone remodeling due to mechanical loading in chickens may be a product of different pathways than those involved in the mammalian model.

Allometric Scaling and Cell Ratios in Multi-Organ in vitro Models of Human Metabolism.

Intelligent in vitro models able to recapitulate the physiological interactions between tissues in the body have enormous potential as they enable detailed studies on specific two-way or higher order tissue communication. These models are the first step toward building an integrated picture of systemic metabolism and signaling in physiological or pathological conditions. However, the rational design of in vitro models of cell–cell or cell–tissue interaction is difficult as quite often cell culture experiments are driven by the device used, rather than by design considerations. Indeed, very little research has been carried out on in vitro models of metabolism connecting different cell or tissue types in a physiologically and metabolically relevant manner. Here, we analyze the physiological relationship between cells, cell metabolism, and exchange in the human body using allometric rules, downscaling them to an organ-on-a-plate device. In particular, in order to establish appropriate cell ratios in the system in a rational manner, two different allometric scaling models (cell number scaling model and metabolic and surface scaling model) are proposed and applied to a two compartment model of hepatic-vascular metabolic cross-talk. The theoretical scaling studies illustrate that the design and hence relevance of multi-organ models is principally determined by experimental constraints. Two experimentally feasible model configurations are then implemented in a multi-compartment organ-on-a-plate device. An analysis of the metabolic response of the two configurations demonstrates that their glucose and lipid balance is quite different, with only one of the two models recapitulating physiological-like homeostasis. In conclusion, not only do cross-talk and physical stimuli play an important role in in vitro models, but the numeric relationship between cells is also crucial to recreate in vitro interactions, which can be extrapolated to the in vivo reality.

SteatoNet: the first integrated human metabolic model with multi-layered regulation to investigate liver-associated pathologies.

Current state-of-the-art mathematical models to investigate complex biological processes, in particular liver-associated pathologies, have limited expansiveness, flexibility, representation of integrated regulation and rely on the availability of detailed kinetic data. We generated the SteatoNet, a multi-pathway, multi-tissue model and in silico platform to investigate hepatic metabolism and its associated deregulations. SteatoNet is based on object-oriented modelling, an approach most commonly applied in automotive and process industries, whereby individual objects correspond to functional entities. Objects were compiled to feature two novel hepatic modelling aspects: the interaction of hepatic metabolic pathways with extra-hepatic tissues and the inclusion of transcriptional and post-transcriptional regulation. SteatoNet identification at normalised steady state circumvents the need for constraining kinetic parameters. Validation and identification of flux disturbances that have been proven experimentally in liver patients and animal models highlights the ability of SteatoNet to effectively describe biological behaviour. SteatoNet identifies crucial pathway branches (transport of glucose, lipids and ketone bodies) where changes in flux distribution drive the healthy liver towards hepatic steatosis, the primary stage of non-alcoholic fatty liver disease. Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors. SteatoNet thus serves as an intuitive in silico platform to identify systemic changes associated with complex hepatic metabolic disorders.

Computer Modeling of Obesity Links Theoretical Energetic Measures with Experimental Measures of Fuel Use for Lean and Obese Men

The goal of this research was to use a computational model of human metabolism to predict energy metabolism for lean and obese men. The model is composed of 6 state variables representing amino acids, muscle protein, visceral protein, glucose, triglycerides, and fatty acids (FAs). Differential equations represent carbohydrate, amino acid, and FA uptake and output by tissues based on ATP creation and use for both lean and obese men. Model parameterization is based on data from previous studies. Results from sensitivity analyses indicate that model predictions of resting energy expenditure (REE) and respiratory quotient (RQ) are dependent on FA and glucose oxidation rates with the highest sensitivity coefficients (0.6, 0.8 and 0.43, 0.15, respectively, for lean and obese models). Metabolizable energy (ME) is influenced by ingested energy intake with a sensitivity coefficient of 0.98, and a phosphate-to-oxygen ratio by FA oxidation rate and amino acid oxidation rate (0.32, 0.24 and 0.55, 0.65 for lean and obese models, respectively). Simulations of previously published studies showed that the model is able to predict ME ranging from 6.6 to 9.3 with 0% differences between published and model values, and RQ ranging from 0.79 to 0.86 with 1% differences between published and model values. REEs >7 MJ/d are predicted with 6% differences between published and model values. Glucose oxidation increases by ∼0.59 mol/d, RQ increases by 0.03, REE increases by 2 MJ/d, and heat production increases by 1.8 MJ/d in the obese model compared with lean model simulations. Increased FA oxidation results in higher changes in RQ and lower relative changes in REE. These results suggest that because fat mass is directly related to REE and rate of FA oxidation, body fat content could be used as a predictor of RQ.

Integrating transcriptomics with metabolic modeling predicts biomarkers and drug targets for Alzheimer's disease.

Accumulating evidence links numerous abnormalities in cerebral metabolism with the progression of Alzheimer's disease (AD), beginning in its early stages. Here, we integrate transcriptomic data from AD patients with a genome-scale computational human metabolic model to characterize the altered metabolism in AD, and employ state-of-the-art metabolic modelling methods to predict metabolic biomarkers and drug targets in AD. The metabolic descriptions derived are first tested and validated on a large scale versus existing AD proteomics and metabolomics data. Our analysis shows a significant decrease in the activity of several key metabolic pathways, including the carnitine shuttle, folate metabolism and mitochondrial transport. We predict several metabolic biomarkers of AD progression in the blood and the CSF, including succinate and prostaglandin D2. Vitamin D and steroid metabolism pathways are enriched with predicted drug targets that could mitigate the metabolic alterations observed. Taken together, this study provides the first network wide view of the metabolic alterations associated with AD progression. Most importantly, it offers a cohort of new metabolic leads for the diagnosis of AD and its treatment.

Correction: Mice with Chimeric Livers Are an Improved Model for Human Lipoprotein Metabolism

Correction: Mice with Chimeric Livers Are an Improved Model for Human Lipoprotein Metabolism

Network Thermodynamic Curation of Human and Yeast Genome-Scale Metabolic Models

Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties.

High-resolution mass spectrometric investigation of the phase I and II metabolites of finasteride in pig plasma, urine and bile

1. The metabolite profile of the 5α-reductase type II inhibitor finasteride has been studied in pig plasma, urine and bile using high-resolution mass spectrometry. The porcine biotransformation products were compared to those formed by human liver microsomes and to literature data of recently identified human in vivo metabolites. The objective of this study was to gain further evidence for the validity of using pigs for advanced, invasive drug–drug interaction studies that are not possible to perform in humans.2. The use of high-resolution mass spectrometry with accurate mass measurements enabled identification of the metabolites by calculation of their elemental compositions as well as their fragmentation patterns.3. There was an excellent match between the porcine and human metabolic profiles, corroborating the pig as a model of human drug metabolism. The glucuronides of the two recently described human hydroxylated metabolites MX and MY and the carboxylated metabolite M3 were identified as the major biotransformation products of finasteride in pig urine and bile.4. Furthermore, the CYP enzymes involved in the formation of the hydroxylated metabolites were characterized. Human recombinant CYP3A4 could produce the two major hydroxylated metabolites MX and MY, whereas human recombinant CYP2D6 formed MY only.

A Computational Model of Liver Iron Metabolism

Iron is essential for all known life due to its redox properties; however, these same properties can also lead to its toxicity in overload through the production of reactive oxygen species. Robust systemic and cellular control are required to maintain safe levels of iron, and the liver seems to be where this regulation is mainly located. Iron misregulation is implicated in many diseases, and as our understanding of iron metabolism improves, the list of iron-related disorders grows. Recent developments have resulted in greater knowledge of the fate of iron in the body and have led to a detailed map of its metabolism; however, a quantitative understanding at the systems level of how its components interact to produce tight regulation remains elusive. A mechanistic computational model of human liver iron metabolism, which includes the core regulatory components, is presented here. It was constructed based on known mechanisms of regulation and on their kinetic properties, obtained from several publications. The model was then quantitatively validated by comparing its results with previously published physiological data, and it is able to reproduce multiple experimental findings. A time course simulation following an oral dose of iron was compared to a clinical time course study and the simulation was found to recreate the dynamics and time scale of the systems response to iron challenge. A disease state simulation of haemochromatosis was created by altering a single reaction parameter that mimics a human haemochromatosis gene (HFE) mutation. The simulation provides a quantitative understanding of the liver iron overload that arises in this disease. This model supports and supplements understanding of the role of the liver as an iron sensor and provides a framework for further modelling, including simulations to identify valuable drug targets and design of experiments to improve further our knowledge of this system.

Mice with chimeric livers are an improved model for human lipoprotein metabolism.

ObjectiveRodents are poor model for human hyperlipidemias because total cholesterol and low density lipoprotein levels are very low on a normal diet. Lipoprotein metabolism is primarily regulated by hepatocytes and we therefore assessed whether chimeric mice extensively repopulated with human cells can model human lipid and bile acid metabolism.DesignFRG [ F ah(−/−) R ag2(−/−)Il2r g (−/−)]) mice were repopulated with primary human hepatocytes. Serum lipoprotein lipid composition and distribution (VLDL, LDL, and HDL) was analyzed by size exclusion chromatography. Bile was analyzed by LC-MS or by GC-MS. RNA expression levels were measured by quantitative RT-PCR.ResultsChimeric mice displayed increased LDL and VLDL fractions and a lower HDL fraction compared to wild type, thus significantly shifting the ratio of LDL/HDL towards a human profile. Bile acid analysis revealed a human-like pattern with high amounts of cholic acid and deoxycholic acid (DCA). Control mice had only taurine-conjugated bile acids as expcted, but highly repopulated mice had glycine-conjugated cholic acid as found in human bile. RNA levels of human genes involved in bile acid synthesis including CYP7A1, and CYP27A1 were significantly upregulated as compared to human control liver. However, administration of recombinant hFGF19 restored human CYP7A1 levels to normal.ConclusionHumanized-liver mice showed a typical human lipoprotein profile with LDL as the predominant lipoprotein fraction even on a normal diet. The bile acid profile confirmed presence of an intact enterohepatic circulation. Although bile acid synthesis was deregulated in this model, this could be fully normalized by FGF19 administration. Taken together these data indicate that chimeric FRG-mice are a useful new model for human lipoprotein and bile-acid metabolism.

Abstract C151: Contribution of serine, folate, and glycine metabolism to the ATP, NADPH, and purine requirements of cancer cells.

Abstract Background: Recent observations on cancer cell metabolism indicate increased serine synthesis from glucose as a marker of poor prognosis. Expression of genes in this pathway also correlate with sensitivity to the antifolate methotrexate (Vazquez A, et al, Cancer Res. 2013 Jan 15;73(2):478-82). Using a large-scale model of human cell metabolism we have predicted that serine synthesis can be routed to a pathway for ATP production. The pathway is composed by reactions from the serine synthesis, one carbon (folate) metabolism and the glycine cleavage system (SOG pathway) and its flux is predicted to increase at high proliferation rates. Results: Here we show that the SOG pathway is upregulated at the level of gene expression in a subset of human tumors and its level of expression correlates with gene signatures of cell proliferation and Myc targets activation. To investigate the activity of the SOG pathway at the level of metabolic fluxes we estimated the metabolic fluxes of the NCI60 panel of tumor derived cell lines, using previously reported exchange fluxes and a flux balance model of cell metabolism. We show that the estimated rates of reactions in the SOG pathway are highly correlated with the proliferation rates of these cell lines. We also find that the SOG pathway contributes significantly to the energy requirements of biosynthesis, the NADPH requirements of fatty acid synthesis and to the synthesis of purines. Finally, when the PC-3 prostate cancer cell line is subject to treatment with the antifolate methotrexate, we observe a decrease in the ATP levels, an inhibition of the proliferation rate and a decrease in the ribonucleotides and fatty acids synthesized from glucose. Conclusions: Taken together our results indicate that the SOG pathway activity increases with the rate of cell proliferation and it contributes to the biosynthetic requirements of purines, ATP and NADPH. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):C151. Citation Format: Philip M. Tedeschi, Elke K. Markert, Murugesan Gounder, HongXia Lin, Sonia C. Dolfi, Leo Li-Ying Chan, Jean Qiu, Kim M. Hirshfield, Laszlo G. Boros, Joseph R. Bertino, Zoltan N. Oltvai, Alexei Vazquez. Contribution of serine, folate, and glycine metabolism to the ATP, NADPH, and purine requirements of cancer cells. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr C151.