In order to provide timely treatment for organ damage initiated by therapeutic drugs or exposure to environmental toxicants, we first need to identify markers that provide an early diagnosis of potential adverse effects before permanent damage occurs. Specifically, the liver, as a primary organ prone to toxicants-induced injuries, lacks diagnostic markers that are specific and sensitive to the early onset of injury. Here, to identify plasma metabolites as markers of early toxicant-induced injury, we used a constraint-based modeling approach with a genome-scale network reconstruction of rat liver metabolism to incorporate perturbations of gene expression induced by acetaminophen, a known hepatotoxicant. A comparison of the model results against the global metabolic profiling data revealed that our approach satisfactorily predicted altered plasma metabolite levels as early as 5 h after exposure to 2 g/kg of acetaminophen, and that 10 h after treatment the predictions significantly improved when we integrated measured central carbon fluxes. Our approach is solely driven by gene expression and physiological boundary conditions, and does not rely on any toxicant-specific model component. As such, it provides a mechanistic model that serves as a first step in identifying a list of putative plasma metabolites that could change due to toxicant-induced perturbations. According to the 2012 U.S. Military Health System report2, the prevalence of APAP overdose is higher among military personnel than among the general populace, and higher among those aged below 45 years. Furthermore, drug-induced liver injury is a major challenge for developing new drugs because it is often the primary reason that a drug is withdrawn from the market3. Identification of injury-specific markers would allow for an early diagnosis and, potentially, the deployment of a more effective early treatment to mitigate injury progression.Adverse effects associated with acute or chronic exposure to therapeutic drugs and toxic environmental chemicals pose serious human health concerns, including long-term debilitation, permanent organ damage, and even death. Among internal organs, the liver is the first to encounter ingested or absorbed chemicals because of its active role in the metabolism and clearance of toxicants. For example, overexposure to acetaminophen , a widely used over-the-counter analgesic and antipyretic drug, is a leading cause of acute liver failure and one of the major reasons for liver transplantation in the United States (U.S.)6. Furthermore, because ALT and AST are secreted after cell death and, thus, reflect events that occur late in the cell-injury process, these enzymes may not be specific for identifying early-stage injuries that can more readily be treated.For most toxicants, the biological mechanisms that underlie the occurrence of liver injury and its transition to severe liver damage remain unknown. This limits our ability to detect early toxicant-induced changes, which, if not countered in a timely manner, can lead to irreversible alterations and functional degradation of the liver. Currently used markers for diagnosing liver injury, such as serum levels of alanine transaminase (ALT) and aspartate aminotransferase (AST), are not ideal for several reasons. For example, because these enzymes are ubiquitously expressed at similar levels in multiple organs, interpreting any change in their serum levels in response to toxicant exposure can be complicated9. Here, we leverage metabolomic, fluxomic, and transcriptomic measurements from in vivo studies as a means to generate mechanism-based predictions of metabolite profiles, using a metabolic network modeling approach that is biologically constrained. Our study examines canonical cellular metabolism, i.e., endogenous metabolism excluding xenobiotic metabolism, to identify a sensitive and specific response to perturbations in cell physiology induced by exposure to a chemical toxicant. The goal of these studies is to provide a computational/experimental framework that can identify molecular processes specifically perturbed in a disease state and, hence, proteins and metabolites whose circulating levels in biofluids systematically differ between a disease group and a control group, and to propose these as diagnostic and prognostic markers15.The development of methods to screen for robust diagnostic markers characteristic of the early injury process remains a challenge. The emergence of multiple genome-scale and high-throughput technologies over the past decade has made it possible to measure multiple whole-cell physiological readouts of mRNA, proteins, microRNA, metabolites, protein-DNA binding, protein-protein interactions, and other molecules or molecular processes. However, it remains a daunting task to integrate these data into a comprehensive, mechanistic framework that clarifies how to characterize the state of a cell, tissue, or organism in terms of underlying biological processes. Metabolomics is a rapidly evolving field that can provide novel insights into toxicant-induced hepatotoxicity, mechanisms of disease pathogenesis, and markers for diagnosis and prognosis17, and provide a framework for elucidating genotype-to-phenotype relationships22. Several studies have used GENREs to identify metabolites as markers for different disease states. For example, a human GENRE was used to predict metabolite markers for inborn errors of metabolism23, and a multi-tissue mouse GENRE was used to identify high levels of branched-chain amino acids and free fatty acids in the plasma of type 2 diabetes subjects by integrating gene expression data24. Recently, a reconciled rat and human GENRE was developed and used to study comparative toxicogenomics and metabolite marker predictions for the effect of different drugs on liver cells in culture16.Cellular metabolism can be modeled by genome-scale network reconstructions (GENREs), which are mathematical representations of interconnected metabolic pathways that consider stoichiometric and thermodynamic constraints. These models capture metabolic phenotypes under diverse physiological and genetic conditions25. These studies implement different algorithmic approaches to link the different data types26. In particular, GENREs that incorporate genotype-to-phenotype relationships through gene-protein-reaction (GPR) rules provide one means for integrating transcriptomic data to identify differences in plasma metabolite levels between control and toxicant-treated conditions.In studies of GENREs that integrate transcriptomic data to discover drug targets and identify metabolite markers, the primary data used are mRNA expression profiles, as measured by microarrays or RNA sequencing (RNA-seq), and levels of small metabolites, as quantified by chromatographic separation methods in combination with mass spectrometryin vivo transcriptomic and metabolomic data, as well as 2H/13C metabolic flux analysis to measure central carbon metabolism. We integrated these data into the recently developed rat GENRE (iRno) using a novel algorithm called Transcriptionally Inferred Metabolic Biomarker Response (TIMBR)16, and investigated the metabolic differences in plasma between control and APAP-treated conditions. We then characterized the efficacy of the rat GENRE in predicting the metabolites as markers of APAP-induced liver injury and identified the underlying pathways perturbed by APAP treatment based on changes in gene expression. Using these highly perturbed pathways and the metabolites within them, we identified a criterion to further classify the predicted metabolites as the most likely to be associated with the gene expression changes, and hence, selected as list of potential metabolites for further investigation. These predictions of the model may be used for further validation by targeted metabolomic analysis. More generally, the developed modeling strategy has the potential to identify a set of robustly changing metabolites as injury markers, using publicly available transcriptomic data from liver toxicity databases, and may be used to identify canonical metabolites as early markers of injury progression for other toxicant-induced organ injuries. Overall, the model developed here provides a framework for inter-relating multiple sources of omics-level data and generating a list of putative metabolites associated with toxicant-induced organ injuries based on observed changes of gene expression in vivo in specific tissues.In the current study, we used male Sprague Dawley rats and acetaminophen (APAP) as the exemplar toxicant to generate To identify an optimal APAP dose that initially causes non-symptomatic perturbations in liver physiology before inducing extensive liver injury, we varied the APAP dose and examined the time course of injury evolution by measuring plasma levels of ALT Fig.  and AST 2H/13C tracer studies under the same single high dose (2 g/kg) treatment, and collected samples at the 10-h time point to identify the major fluxes within the glucose production pathway of central carbon metabolism under fasting conditions  treatment and monitored one group of rats for 5 h and the other for 10 h, before sacrificing them to collect samples to investigate changes of global gene expression in liver tissue and blood metabolite profiles induced by APAP under fasting conditions Fig. . AdditioWe used an RNA-seq analysis platform  to analyze the raw RNA sequencing reads, and identified differentially expressed genes (DEGs) by comparing transcript abundance levels between control animals and two groups of rats treated with a single dose of APAP (2 g/kg) and observed for 5 Fig.  or 10 h iRno model using the gene-protein-reaction annotations and identified the metabolic genes in the RNA-seq data  was approximately 15% for both time points (Supplementary Table Ldlr), tryptophan 2, 3-dioxygenase (Tdo2), and members of the solute carrier family (Slc25a15 and Slc34a2) as early as 5 h post-treatment  and thioredoxin reductase 1 (Txnrd1), members of the solute carrier family (Slc1a4 and Slco2a1), and genes in the cytochrome p450 subfamily (Cyp8b1)  for the 5-h and 10-h groups, respectively. We used Entrez gene ID annotations and mapped all DEGs from the RNA-seq analysis onto the ata Fig. . The proent Fig. . Similarb1) Fig. . A summa27. We selected highly significant DEGs from among the metabolic genes mapped onto the iRno model based on a strict FDR cut-off of less than 0.05. Using this criterion, 5 h after APAP treatment, there was enrichment of mostly global pathways, such as, metabolic pathways and the glycerophospholipid pathway in lipid metabolism, based on an enrichment cut-off of FDR < 0.10. In contrast, 10 h post-treatment, there was significant enrichment of several other pathways, including, among others, pathways in nucleotide metabolism (pyrimidine and purine), lipid metabolism , amino acid–related metabolism  and carbohydrate metabolism . These results indicate that the APAP-induced perturbations in pathways were moderate at 5 h post-treatment. However, the majority of pathway perturbations observed at 10 h post-treatment indicated progression towards liver injury. We provide a complete summary of the KEGG-enriched pathways, along with the number of genes (and their Entrez gene IDs) mapping to each pathway and the associated significance tests in Supplementary Table To identify APAP-induced DEGs that lead to metabolic pathway alterations in the liver, we performed KEGG pathway enrichment analysis using the DAVID functional annotation tooliRno model, we performed 2H/13C- tracer labeling to assess the major metabolic fluxes in the liver glucose production pathway 10 h after administration of a single dose of APAP (2 mg/kg) under fasting conditions. Using a minimal network . However, APAP-induced alterations in the reaction fluxes of the glucose production pathways under such conditions are unknown. To identify these changes and thereby constrain the ork Fig. . The APA33. Briefly, the fractional contributions of glycerol, lactate and amino acids, and glycogenolysis to liver glucose production reported in the literature were used to compute the central carbon fluxes corresponding to a twenty percent higher glucose output flux at 5 h when compared to that at 10 h34. We then compared the fluxes calculated at 5 h . Of the 569 metabolites detected, we were able to map 226 to the model based on KEGG ID annotation and name matching. Using the FDR criterion of <0.10, 103 and 67 of these 226 metabolites were significantly altered at 5  and secondary bile acid metabolism (hyocholate and hyodeoxycholate) were ranked high among the other significantly altered metabolites for the 5 h group  observed in the plasma metabolite data  one in which flux measurements obtained from MFA were not used as constraints (No MFA) and, 2) another in which they were (MFA).The 2 fold changes of metabolites identified from the global metabolic profiling analysis . Using the input and output boundary constraints under fasting conditions, we predicted TIMBR production scores, which represent altered plasma levels of metabolites that were either secreted or consumed as a result of APAP treatment. We then compared the logsis Fig.  with theent Fig. . Of the iRno model showed better correspondence with the measured metabolite changes (65%) when gene expression data at 10 h after APAP treatment were incorporated together with some of the measured fluxes as constraints. However, when the model was provided with gene expression data alone (without MFA constraints), its prediction capability decreased (57%), indicating the importance of integrating multiple data sources to obtain better predictions.Interestingly, with the same input and output constraints under the fasting state, when we used the flux measurements from MFA as constraints along with the gene expression changes, our model correctly predicted the direction of change for 65% of the significantly altered metabolites FDR < 0.10) 10 h after APAP treatment Fig. , MFA. Th0 10 h afTo further differentiate the metabolites that could be changing in the plasma because of changes in gene expression, we combined the highly enriched pathways obtained from KEGG pathway enrichment analysis  was the most significantly altered gene (>23 fold change) 10 h after APAP treatment , another key gene upregulated 10 h after APAP treatment, plays an active role in redox reactions involving hydrogen peroxide detoxification36 along with glutathione, which is perturbed because of acetaminophen toxicity37. Observations of the top three genes at both time points . Consistent with this view, our analysis suggests that APAP exposure leads to early perturbations of specific metabolic pathways in the liver , which compensate for glutathione depletion by upregulating the majority of genes involved in these pathways.Interestingly, heme-oxygenase 1  for 65% of the metabolites 10 h after APAP treatment  compared to that for unaltered gene expression data  were used, suggesting that APAP-induced gene expression changes were indeed the driving force for the observed metabolite changes. Similarly, local and global perturbations in the boundary constraints of uptake rates reduced model correspondence with the data, showing the importance of physiological boundary conditions in improving predictions. We also investigated methods other than the TIMBR algorithm, such as a constraint-based modeling (CBM) approach based on flux variability analysis23, and an expression-guided flux minimization (E-Fmin) approach using the flux minimization principle41 for integrating gene expression changes into the iRno model to predict metabolite alterations. However, none of these methods yielded better results (data not shown).To ascertain the robustness of the iRno model and could be driven by changes in gene expression  and extended (10 h) exposures, with the latter yielding more gene perturbations in greater concordance with metabolite alterations. Furthermore, we identified metabolite alterations—driven by changes in gene expression consistent with the model predictions—which can be further evaluated to assess their ability to serve as markers of APAP-induced liver injury. Our results suggest that the platform developed here, could serve as a tool in the initial step of identifying putative plasma metabolites as markers of toxicant-induced organ injuries, and has the potential to be applied broadly to other studies in drug development and metabolite marker discovery.ad libitum in an environmentally controlled room, set at 23 °C and on a 12:12-h light-dark cycle. All experiments were conducted in accordance with the Guide for the Care and Use of Laboratory Animals of the United States Department of Agriculture and the National Institutes of Health, and all protocols were approved by the Vanderbilt University Institutional Animal Care and Use Committee, and the U.S. Army Medical Research and Materiel Command Animal Care and Use Review Office. The investigators adhered to the Animal Welfare Act Regulations and other Federal statutes relating to animals and experiments involving animals.Male Sprague-Dawley rats, 10 weeks of age, were purchased from Charles River Laboratories . The rats were fed with Formulab Diet 5001  and were given water 45. Rats were anesthetized with isoflurane. For studies to determine the appropriate APAP dose and exposure time and those to measure changes in gene expression and plasma metabolite profiles, the right external jugular vein was cannulated with sterile silicone catheters (0.51 mm inner diameter [ID]/0.94 mm outer diameter [OD]). For studies to measure metabolic flux, the carotid artery and the right external jugular vein were cannulated with sterile silicone catheters (0.51 mm ID/0.94 mm OD). The free end of the catheter was passed subcutaneously to the back of the neck where it was fixed. The catheter was occluded with a metal plug following a flush of heparinized saline (200 U heparin/ml). After surgery, rats were housed individually.Surgery for implanting the catheters was performed 7 days before each experiment as previously describedTwo days before each study, the rats were moved from their regular housing cages to metabolic cages . To determine the appropriate dose and exposure time of APAP, they were treated with either vehicle  or either 1 g/kg (n = 6) or 2 g/kg (n = 7) of APAP at 7 a.m. by gavage. Blood and accumulated urine were collected at 7 a.m. and 5 p.m. daily for 3 days.ad libitum but were not given food. At 12 p.m. (5-h group) or 5 p.m. (10-h group), after blood was collected from each group, animals were anesthetized by an intravenous injection of sodium pentobarbital through the jugular vein catheter and a laparotomy was performed immediately. The liver was dissected and frozen using Wollenberger tongs precooled in liquid nitrogen. The collected plasma was kept in a −80 °C freezer prior to analyses.We chose 2 g/kg as the appropriate APAP dose and two exposure times, one short  and the other long , based on the results of the dose-response study Fig. . Followi2H]2water (99.9%) was delivered subcutaneously to enrich total body water to 4.5%. At 1 p.m. , after they had recovered from anesthesia, the rats were placed in bedded containers without food or water and connected to sampling and infusion lines. A prime-constant infusion of glucose (80 mg/kg prime + 0.8 mg/kg/min infusion) was administered into the systemic circulation through the jugular vein catheter for the duration of the study. Sodium [13C3]propionate (99%) was delivered as a prime-constant infusion (110 mg/kg + 5.5 mg/kg/min infusion) starting 120 min after the [2H]2water bolus. All infusates were prepared in a 4.5% [2H]2water-saline solution unless otherwise specified. Stable isotopes were obtained from Cambridge Isotope Laboratories . Blood glucose was monitored  and donor erythrocytes were infused to maintain hematocrit throughout the study. Three blood samples (300 μl each) were collected over a 20-min period following 100 min of [13C3] propionate infusion. Arterial blood samples were centrifuged in EDTA-coated tubes for plasma isolation, and the three 100 μl plasma samples were stored at −20 °C prior to glucose derivatization and gas chromatography-mass spectrometry (GC-MS) analysis. Rats were rapidly euthanized through the carotid artery catheter immediately after the final steady-state sample.For flux measurements, at 7 a.m. on the day of the study, rats were administered either APAP  or vehicle  by oral gavage, and food and water were subsequently removed. At 12:50 p.m., they were anesthetized with isoflurane, and following collection of 200 µl of arterial blood through the carotid artery catheter to determine the natural isotopic abundance of circulating glucose, a bolus of . Relative fluxes were converted to absolute values using the known glucose infusion rate and rat weights. Flux estimates for the steady-state samples were averaged to obtain a representative set of values for each rat.The 51. Briefly, individual samples were subjected to methanol extraction and then split into aliquots for analysis by ultrahigh performance liquid chromatography/MS (UHPLC/MS). The global biochemical profiling analysis comprised four unique arms, consisting of reverse-phase chromatography positive ionization methods optimized for hydrophilic compounds (LC/MS Pos Polar) and hydrophobic compounds (LC/MS Pos Lipid), reverse-phase chromatography with negative ionization conditions (LC/MS Neg), as well as a hydrophilic interaction liquid chromatography (HILIC) method coupled to negative ionization (LC/MS Polar)52. All methods alternated between full scan MS and data-dependent MSn scans. The scan range varied slightly between methods but generally covered 70–1000 m/z.Sample preparation was carried out at Metabolon Inc. , in a manner similar to a previous studym/z), preferred adducts, and in-source fragments as well as associated MS spectra, and curated by visual inspection for quality control using software developed at Metabolon. Identification of known chemical entities was based on comparison to metabolomic library entries of purified standards53.Metabolites were identified by automated comparison of the ion features in the experimental samples to a reference library of chemical standard entries that included retention time, molecular weight (1) significance tests and 2) classification analysis. Standard statistical analyses were performed in ArrayStudio on log‐transformed data. The R program (http://cran.r‐project.org) was used for non-standard analyses. Following log transformation and imputation of missing values, if any, with the minimum observed value for each compound, Welch’s two-sample t-test was used to identify biochemicals that differed significantly (p < 0.05) between experimental groups. An estimate of the FDR  was calculated to take into account the multiple comparisons that normally occur in metabolomics‐based studies.Two types of statistical analyses were performed: Frozen whole liver was powdered in liquid nitrogen. Total RNA was isolated from the liver using TRIzol Reagent  and the direct-zol RNA MiniPrep kit . The isolated RNA samples were then submitted to the Vanderbilt University Medical Center VANTAGE Core  for RNA quality determination and sequencing. Total RNA quality was assessed using a 2100 Bioanalyzer . At least 200 ng of DNase-treated total RNA with high RNA integrity was used to generate poly-A-enriched mRNA libraries, using KAPA Stranded mRNA sample kits with indexed adaptors . Library quality was assessed using the 2100 Bioanalyzer (Agilent), and libraries were quantitated using KAPA library Quantification kits (Roche). Pooled libraries were subjected to 75-bp single-end sequencing according to the manufacturer’s protocol . Bcl2fastq2 Conversion Software (Illumina) was used to generate de-multiplexed Fastq files.54. In this study, we pseudo-aligned the reads to the rat transcriptome downloaded from the Kallisto web-site . Kallisto achieves a level of accuracy similar to that of other methods but is orders of magnitude faster; this allows calculation of the uncertainty of transcript abundance estimates, via the bootstrap technique of repeating analyses after resampling with replacement from the data. Here we employed bootstrapping by repeating analyses 100 times with resampling for each data set. Considering that the average number of reads per data set is 35 million (25 to 51 million single-end reads), using other software tools to perform the same bootstrap analysis becomes prohibitively expensive.We analyzed RNA-seq data with Kallisto, a recently developed RNA-seq data analysis tool for read alignment and quantification. Kallisto pseudo-aligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases55. Many software tools for differential gene expression analysis of RNA-seq experiments assume that the technical variance of gene counts follows a Poisson distribution, in which the variance equals the mean56. However, for many genes, the technical variance can be much higher than the expected Poisson variance57. A distinct advantage of Sleuth is that it models biological and technical variances explicitly with a response error model.To identify DEGs from transcript abundance data quantified by Kallisto, we used the companion tool Sleuth, which uses the results of the bootstrap analysis during transcript quantitation to estimate the technical variance directly for each sample27 to perform this task.To understand the biological significance of the lists of genes whose expression levels were altered by APAP exposure, we used the DEGs derived from Kallisto-Sleuth analyses and identified significantly altered DEGs that were mapped to the rat GENRE, and used KEGG pathways to identify molecular pathways that were significantly enriched. We used the online tool Database for Annotation, Visualization, and Integrated Discovery (DAVID)iRno), using orthology annotations from genes in the Human Metabolic Reaction 2 (HMR2) database58, and manually reconciled several reactions by referring to the experimental literature and annotation databases16. The developed model, which contains 2,324 genes and 5,620 metabolites in 8,268 reactions connected by GPR rules, was validated for simulating 327 liver-specific metabolite functions successfully representing liver metabolism16. In this work, we further updated the iRno by incorporating new reactions or modifying some of the existing reactions based on experimental evidence , we corrected the substrates and stoichiometry of the reaction components for consistency60. Using the metabolite evidence from the global metabolic profiling of plasma samples in the current study, we added 90 transport and 105 exchange reactions to the original model to increase the number of metabolites mapping to the data. We provide the updated iRno model with these modifications in Supplementary Table We reconstructed a functional rat GENRE (1) AAs, 2) lactate, and 3) FAs and glycerol. The output fluxes (secretion rates) are those of 1) glucose derived from glycogenolysis and gluconeogenesis, as well as 2) urea and ketone bodies.Our experimental design involved subjecting rats to APAP treatment under fasting conditions to maintain similar weight loss in the control and treatment groups. During fasting, the liver takes up gluconeogenic substrates, such as amino acids (AAs), lactate, and glycerol, to produce blood glucose, urea, and ketone bodies and takes up free fatty acids (FAs) for energy maintenance. Thus, the input fluxes (uptake rates) to our model are those of in situ MFA66. In these studies, sham-treated control animals subjected to fasting conditions showed significant uptake rates of AAs and lactate with subsequent production of glucose, urea, and ketone bodies62. Furthermore, most of these studies measured the uptake/secretion rates from rats fasted for about 24 h and evaluated the metabolic state of the liver under ex vivo perfusion conditions66. We noted considerable inconsistency in the uptake rates reported in these studies  and output (urea and ketone bodies) fluxes, using liver perfusion and 16. Briefly, it converts log2 fold changes of all DEGs into weights (W) for each of the GPRs in the GENRE. These reaction weights are then transformed into larger  weights to represent relative levels of expression between the control and toxicant-treated conditions. TIMBR then calculates the global network demand required for producing a metabolite (Xmet) by minimizing the weighted sum of fluxes across all reactions for each condition and metabolite, so as to satisfy the associated mass balance and an optimal fraction of maximum network capability (vopt) to produce a metabolite as follows16 (see ref.16 for details):W denotes the vector representing the reaction weights, v is a vector of reaction fluxes, and S is the stoichiometric matrix. We integrated the aforementioned boundary conditions for uptake and secretion rates into the algorithm by fixing the respective lower (vlb) and upper bounds (vub) of the exchangeable reactions (vex) in the model  (Eq. TIMBR is a novel method used for predicting toxicant-induced perturbations in metabolites by integrating gene expression changes into GENREsXraw) from control (Xcontrol) and toxicant-treated (Xtreatment) conditions (Eq. Xs) as the z-transformed scores across all exchangeable metabolites  plasma metabolites from the global metabolic profiling data (Supplementary Table iRno model predictions under no MFA and MFA conditions 5 or 10 h after APAP treatment (Supplementary Tables We used the experimental logiRno model are provided in Supplementary Table iRno model are provided in Supplementary Table iRno model are provided in Supplementary Table iRno model is provided in Supplementary Table https://github.com/BHSAI/APAP_toxicity_liver. Detailed explanations for TIMBR algorithm are available as part of the original TIMBR publication16 at www.github.com/csbl/ratcon1.Normalized gene expression data from the RNA-seq analysis and genes mapped to the Supplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4Supplementary Table S5Supplementary Table S6Supplementary Table S7Supplementary Table S8Supplementary Table S9Supplementary Information


Following the subcellular compartmentalization and curation to ensure full network connectivity and reflect the complexity of eukaryotic cells, cultivar specific data on sucrose uptake and biomass synthesis were input, and rMeCBM model was used to simulate storage root growth in KU50 and HN. Results showed that rMeCBM-KU50 and rMeCBM-HN models well imitated the storage root growth. The flux-sum analysis revealed that both cultivars utilized different metabolic precursors to produce energy in plastid. More carbon flux was invested in the syntheses of carbohydrates and amino acids in KU50 than in HN. Also, KU50 utilized less flux for respiration and less energy to synthesize one gram of dry storage root. These results may disclose metabolic potential of KU50 underlying its higher storage root and starch yield over HN. Moreover, sensitivity analysis indicated the robustness of rMeCBM model. The knowledge gained might be useful for identifying engineering targets for cassava yield improvement.Analysis of metabolic flux was used for system level assessment of carbon partitioning in Kasetsart 50 (KU50) and Hanatee (HN) cassava cultivars to understand the metabolic routes for their distinct phenotypes. First, the  Manihot esculenta Crantz) is a promising staple crop for attaining food sufficiency in the 21st century1 for several reasons. First, cassava is a primary source of calories for more than 800 million people every year especially in Africa2, and its roots contain high amount of starch that usually range from 70 to 90% on dry matter basis depending on variety3. Next, cassava is a resilient crop and grows in marginal environments, typically deficient in rainfall and soil nutrients, where most food crops cannot survive5. Besides its utilization as food, cassava starch serves as raw material for a wide range of industries, for example, pharmaceuticals, textiles, plywood, paper, and bioethanol production1. Despite its resilience, cassava production is affected by unpredictable weather events that are associated with global climate change6. In 2015, atypically low level of rainfall together with high temperature resulted in cassava yield loss in Tanzania7. Cassava productivity in sub-Saharan Africa declined at an annual rate of 0.024 tons ha−1 from 2004 to 20148. To ensure adequate supply of carbohydrate for the rapidly growing global population, estimated to reach 11.2 billion people by year 21009, research is needed to develop climate resilient cultivars with improved productivity, while minimizing resource use .Cassava , a Thai cultivar, was bred to enhance yield and starch content12, while the MH97/296, a Ugandan cultivar, was bred to improve resistance to cassava mosaic disease13. Crop phenotype is determined not only by heredity, but also by the interaction between underlying genetics (G) and environment (E), i.e. P = G × E. Srihawong et al.14 showed that root yield of KU50 and Hanatee (HN) cassava cultivars vary with age of plant and soil moisture. Hence, understanding the combinatorial effect of both factors on phenotype is crucial for cassava trait improvement.Conventional and molecular plant breeding techniques have enabled the development of cultivars with superior plant phenotypes such as high yields, resistance to diseases, and tolerance to abiotic stressin silico metabolic modeling has been adopted as a strategy to investigate the metabolic regulation behind the expressed phenotype under the constraints of genetic background and exposed environment. Flux balance analysis (FBA), a well-established approach for constraint-based modeling, enables the quantitative study of metabolic behavior, i.e. net flux distribution, in the genome-scale metabolic pathway of cells composed of all cellular biochemical reactions according to the genetic information of a given organism. An optimal flux distribution is proposed by optimizing the objective function such as maximum biomass yield or minimum ATP production15. Flux balance analysis has been applied to study the metabolic fluxes underlying growth and development of microorganisms, animals, and plants as well as physiological responses to prevailing environment18. The first plant metabolic model was developed for Arabidopsis thaliana, and was employed to synthesize the main biomass components in non-compartmentalized heterotrophic cell suspension culture. First, the active reaction fluxes were minimized and then the model was applied to identify the total ATP demand for cell growth and maintenance19. FBA has since been used to study the growth and development of barley seed endosperm21, oilseed rape embryos24, rice leaf cells25, and tomato fruit26, and to investigate the metabolic adaptation of rice to flood and drought stresses27. The ultimate aim of these comprehensive studies was to explore its rationale towards sustainable improvement of crop yield. However, there are no metabolic models for starchy root crops to date.In the post-genomic era, 28. El-Sharkawy and De Tafur29 found that photosynthetic efficiency is likely to have a limited effect on cassava yield as similar photosynthetic rate was found in cultivars with quite distinct phenotypes, i.e. different leaf canopy size and root yield. On the other hand, cassava has already high photosynthetic efficiency among C3 plants30, and the gap of improvement for this factor may be little. Hence, breeding for photosynthetic efficiency is not only challenging but also may be futile.Cassava roots are the largest carbohydrate sink of all starchy staple crops, but there is limited knowledge of the underlying carbon assimilation mechanism; thus, carbon assimilation is of huge research interest. It has been suggested that yield potential of cassava is related to photosynthetic efficiency and carbon partitioning31. It contained 393 metabolites which correspond to 468 reactions. These reactions were scoped to cover the primary metabolism involved in conversion of sucrose to biomass compounds of cassava roots. The model was compartmentalized into cytosol, plastid, and mitochondria to imitate subcellular metabolism in cassava root cells. The constraint-based metabolic model of cassava storage roots (rMeCBM) was simulated to fit the specific root growth of KU50  and HN  based on measurements that were taken in field conditions. Specificity of the model to cultivar was verified by simulation of root growth rate for CMC9 cassava cultivar grown under similar condition32. Regarding the simulation of metabolic fluxes, the results showed that half of all reactions in the carbon metabolism were required for growth in both KU50 and HN cultivars. By comparing carbon flux partitioning based on flux-sum analysis34, differences in the metabolic phenotypes of KU50 and HN including (1) carbon flux channeling towards carbohydrate biomass production, (2) carbon substrates supplied for biomass biosynthesis in plastid, and (3) metabolic balance for biomass production via the pentose phosphate pathway and the non-cyclic TCA were identified. These variations in metabolic states likely explain the difference in yield between KU50 and HN cultivars. The proposed genome-scale metabolic model of carbon assimilation is first of its kind. It is expected to be a gateway to assess the metabolism of cassava storage roots and unravel the underlying metabolic complexity of starch synthesis and accumulation in starchy roots.This work was therefore conducted to investigate the carbon flux partition in cassava via constraint-based metabolic modeling of carbon assimilation in storage roots of two distinct cassava cultivars, KU50—with high root and starch yield, and HN—with low root and starch yield. We hypothesized that the distinct starch yield in these cultivars is a result of differences in carbon flux partitioning in cellular metabolism of roots that refers to dissimilarity of carbon utilization in root biomass production. KU50 may preferably utilize photosynthates towards starch biosynthesis and other root biomass, whereas HN may rather expend carbon substrates for other cellular activities, such as substrate consumption (e.g. respiration) to support plant growth. The compartmentalized carbon assimilation metabolic model of cassava roots for KU50 and HN was developed from the MeRecon network35.To investigate the growth and development of cassava storage roots, KU50 and HN were propagated from stem cuttings in the field under a rainfed condition. Growth was assessed by measuring biomass accumulation in whole plant, shoot, and root during the nine-months period of study. Cultivar specific differences in whole plant and storage root growth patterns were found  Fig. . Overallconstraint-based metabolic model of cassava storage roots, rMeCBM, was constructed to simulate the intracellular carbon partitioning underlying storage root growth in KU50 and HN. The rMeCBM model covered the primary metabolism for converting sucrose (Suc) to biomass  of cassava roots. The model was basically derived from the well-defined carbon assimilation pathway of cassava, MeRecon, proposed by Siriwat31. MeRecon was reconstructed based on genome and biochemical information of cassava, using comparative genomics approach. It contains 259 metabolites corresponding to 259 reactions  to α-D-Glc and β-D-Fru or metabolized by sucrose synthase (EC 2.4.1.13) to β-D-Fru and UDP-Glc. The UDP-Glc is then used to synthesize fibers, in the form of cellulose(n + 1) and 1,4-beta-D-xylan(n + 1), in the CEL pathway. Additionally, the UDP-Glc is converted to α-D-Glc-1-P, a 6-carbon compound, which is subsequently converted to D-ribulose-5-P, a 5-carbon compound, utilized for the biosynthesis of purine in the NUC pathway. Moreover, α-D-Glc-1-P is also utilized to synthesize starch in the SSP pathway. In the PPP pathway, β-D-Fru is converted to β-D-Fru-6-P from which pyruvate, a 3-carbon compound, is synthesized in the RES pathway. In the AMI pathway, the amino acids: L-Ala, L-Arg, L-Asp, L-Asn, L-Cys, L-Glu, L-Gln, Gly, L-Ile, L-Leu, L-Lys, L-Met, L-Pro, L-Ser, L-Thr, and L-Val are synthesized from pyruvate; whereas L-His, L-Phe, L-Trp, and L-Tyr are synthesized from a 5-carbon compound. Moreover, pyruvate is utilized in the tricarboxylic acid (TCA) cycle and is also a precursor of acetyl-CoA which is utilized to synthesize hexadecanoic acid, in the FAT pathway . Suc, a product of photosynthesis, is transported via phloem in the shoot cells to storage rootsway Fig. .37 and the prediction from web-based tools43. Additionally, the transport and exchange reactions that are involved in the translocation of metabolites across compartments were added to the model based upon their occurrence in other published metabolic models of Arabidopsis44, rice27, maize45, barley20, and rapeseed46. Subsequently, the compartmentalized model was curated by refining the atomic mass balance of each reaction and enhancing the network connectivity. Gap metabolites, which are the missing reactions in the metabolic model construction, were identified and manually filled using the data obtained from biochemical databases and literature. Following curation, the distribution of reactions in each subcellular compartment was examined  of the rMeCBM model was 9.8 mmolATP gDW−1storage roots for KU50 and 14.7 mmolATP gDW−1storage roots for HN. These values lie within the 5–42 mmolATP gDW−1 range reported in literature of plant metabolic models . The SGAM of rMeCBM-KU50 was lower than that of rMeCBM-HN, probably denoting inferior utilization of carbon substrate to produce biomass in HN relative to KU50. To assure that the model solutions were in optimal range, rMeCBM models underwent flux variability analysis (FVA) for each reaction in three compartments , and exchange and transport reactions as summarized in Supplementary Figs The rMeCBM-KU50 and rMeCBM-HN were employed to simulate the storage root growth rate at Suc uptake rates of 0.0548 and 0.0680 mmol49. The shadow prices indicate how much does growth rate increase as a given metabolite is increasing. The results of shadow prices show that biomass biosynthesis in both cultivars was limited by similar set of metabolites, with the exception of cytosolic 6-phospho-D-gluconate which was found to activate biomass only in KU50 , which allowed deviations from predicted optimal values for KU50 and HN by ±20%. The sensitivity results for both rMeCBM-KU50 and rMeCBM-HN were comparable, and the coefficient values were 7.84–12.74 mmolATP gDW−1storage roots and 11.76–19.11 mmolATP gDW−1storage roots, respectively .In sink organs, carbohydrates are used as the main carbon source for plant growth and development. The storage root of cassava is composed of 92% carbohydrates, on dry weight basis, which are typically stored in the form of sugars and starch  was closer to that of KU50 (0.45) than HN (0.34). Model specificity was critical to identifying flux patterns responsible for the storage root yield differences in KU50 and HN cultivars. Variations in flux patterns were observed in both cultivars: during Suc import to storage root cells in cytosol, more Suc was channeled into carbohydrate synthesis in KU50 than HN; KU50 utilizes β-D-Glc as metabolic precursor of biomass production in the plastid, whereas HN utilizes α-D-Glc-1-P. KU50 uses the bypass reaction in PPP to produce energy in plastid, while HN uses the same bypass reaction to produce energy, but in cytosol.The 55. The modeling revealed that more carbon flux was channeled towards the syntheses of carbohydrates, amino acids, and fibers in KU50 than in HN, which explains the yield gap between both cultivars. Soluble sugars and starch play a central role in carbon metabolism and may have a significant influence on the rate of plant biomass accumulation56. Besides, results also showed respiration was higher in HN than KU50. Maintenance respiration can represent a significant carbon cost to plants57. Carbon lost to respiration, as sink, further constitutes resource drawdown, which may also explain the lower storage root yield in HN.Generally, carbon is required by cells to fuel metabolic processes and for respiration, and it is exchanged between source (mature leaves) and sink tissues  as simple sugars, primarily Suc58. Based on the integration of transcriptomics data into the carbon metabolic network of cassava, α-D-Glc-1-P is mainly used as carbon source for starch biosynthesis, rather than in RES pathway, during root development of TMS60444 cassava cultivar59. Likewise, in KU50, α-D-Glc-1-P imported from cytosol into plastid was directly utilized for the synthesis of starch in SSP. The phenomenon appeared to be different in HN, which a majority of imported α-D-Glc-1-P was utilized for energy production in RES and to a limited extent for starch synthesis in SSP. Therefore, the cultivar-based differences in metabolite utilization reflect underlying variations in carbon flux partitioning.The plastid is an important compartment for the synthesis and storage of many compounds such as starch, amino acids, fatty acids, and pigments; and these metabolic activities affect plant growth and developmentet al.60 reported that the plastidial phosphoglucomutase activity plays a role in the control of starch and sucrose accumualtion in potato tubers. The reduced activity of plastidial phosphoglucomutase is correlated with decreased starch content, whereas sucrose content increases. Similarly, Yu et al.61 found that plastidial glucose-6-phosphate isomerase activity is correlated with starch content in leaves of Arabidopsis mutant. Glucose-6-phosphate isomerase is likely an enzyme coupling gluconeogenesis and glycolysis pathways that would help adjust the balance of anabolic and catabolic processes in cells. However, various studies demonstrate a close relationship of glucose-6-phosphate isomerase and phosphoglucomutase with other enzymes in RES and oxidative PPP in regulating metabolism in response to energy and carbon skeletons requirement of cells62. Up to the current knowledge, rationale by which the organisms selectively use such enzymes in sucrose metabolism is yet not fully unraveled.Interestingly, KU50 and HN selectively utilized different precursor metabolites to generate α-D-Glc-6-P, which is able to support fatty acid biosynthesis and uses as the intermediate metabolite in PPP. Whereas α-D-Glc-6-P produced in the plastid of KU50 was derived through glucose-6-phosphate isomerase (EC 5.3.1.9) from β-D-Glc-6-P imported, that in HN was generated from α-D-Glc-1-P via phosphoglucomutase (EC 5.4.2.2). Activity of both enzymes has been proven necessitated to retain a normal starch biosynthesis in many plant species. Fernie Overall, crop performance depends so much on source-sink relations with respect to photosynthetic carbon fixation and partitioning. Efficient nutrient uptake and mobilization for growth as well as the ability to cope with external stressors are critical. This research focused on linking carbon partitioning in cassava to storage root yield with a view to understanding how variations in flux patterns affect biomass synthesis. The constraint-based modeling revealed that carbon flux invested in the syntheses of carbohydrates and amino acids was higher in KU50 than HN, and both cultivars utilized different metabolic precursors to produce energy in plastid. The carbon flux utilized for respiration was higher in HN than in KU50. Moreover, KU50 had higher energy use efficiency; it utilized less energy to synthesize one gram of dry storage root. These results possibly explain the predominant storage root yield of KU50 and demonstrate the robustness of the rMeCBM model. The knowledge gained might be useful for identifying engineering targets for cassava yield improvement.63. For the growth assessment, fresh weight of biomass, partitioned into above-ground (shoot) and below-ground (roots) biomass were measured. Roots emerging at the base of stem cuttings were harvested and separated into three classes: fibrous (diameter <0.5 cm), intermediate (0.5 cm ≤ diameter ≤ 1.0 cm), and storage roots (diameter > 1.0 cm) based on the stage of development before weighing from Sojikul et al.64. The root samples were subsequently freeze-dried at −50 °C until stable weight was reached, and then the dry weight was measured. All measurements were reported as mean ± SE of 16 plants. The statistical significance analysis was performed based on student’s t-test with 95% confidence (α ≤ 0.05).Kasetsart 50 (KU50: high starch and root yield) and Hanatee (HN: low starch and root yield) cassava cultivars were propagated from stem cuttings in the field at the Rayong Field Crops Research Center, Rayong, Thailand . Plants were grown under rainfed condition (no irrigation) from September 2013 to June 2014 and no chemical fertilizer was applied during the experiment. Sixteen plants of each cassava cultivar were randomly harvested at 40, 66, 101, 186, and 269 days after planting (DAP). At the time of harvest, leaf area per plant was estimated by photography method whereby fully expanded leaves were scanned and images were analyzed using ImageJ freeware65. All values were reported as mean ± SE of three plant replicates.The freeze-dried samples of storage roots were ground to powder in liquid nitrogen; 20 mg of ground sample was used to determine the soluble sugar content  via HPLC, and 100 mg was used for measuring soluble starch content according to Jeong’s methodconstraint-based metabolic model of cassava storage roots (rMeCBM) was derived from the carbon assimilation pathway modified from MeRecon31, which was reconstructed using comparative genomic approach based on genome and biochemical information available for cassava. The model included 468 reactions involved in the conversion of Suc, as carbon substrate, to cellular biomass of cassava roots . It covered seven pathways including starch and sucrose biosynthesis (SSP), respiration (RES), pentose phosphate (PPP), cell wall biosynthesis (CEL), amino acid biosynthesis (AMI), fatty acid biosynthesis (FAT), and nucleotide biosynthesis (NUC) pathways. Subcellular compartments were assigned to these metabolic reactions based on existing data in the SWISS-PROT database37 and the prediction from six web-based tools: WoLF PSORT41, Predotar39, MultiLoc242, CELLO40, TargetP38, and iLoc-Plant43. The transport and exchange reactions that are involved in the translocation of metabolites across compartments were added to the model based upon their occurrence in other plant metabolic models, including Arabidopsis44, barley seed20, maize45, rapeseed46, and rice27. Finally, the subcellular metabolic model was refined to ensure the balance of mass for all reactions and to attain a gap-free metabolic network. All metabolic gaps in the model were manually filled based upon reactions in biochemical databases, literature, and published plant models. The model curation and analyses were performed using COBRA Toolbox 2.0.566 in MATLAB .The 67. Therefore, the rMeCBM model was designed to describe a parenchyma root cell by assumedly representing the cassava root cells. The rMeCBM model was built to simulate growth of storage roots in the two cassava cultivars, KU50 and HN, and also to predict carbon flux partitioning in their respective metabolism. The biomass function of the rMeCBM model was developed based on measured biomass composition including carbohydrates, proteins, lipid, and fibers in storage roots of KU50 and HN , were comparable and the differences were not statistically significant. Hence, the reported average value of 16.08 µmolCO2 m−2 s−1 was used for the rMeCBM model. All captured CO2 was assumed to be fully converted to Suc in the cellular tissues of leaves.The rate of CO68.Approximately 37% of the sucrose synthesized in shoot part was assumed to be allocated to underground roots for biomass production. This percentage is based on the proportion of soluble Suc to total soluble carbohydrates in shoot and root parts on dry weight basisSuc was used as carbon substrate and was fed to the rMeCBM model. Suc uptake rate was estimated from the photosynthetic capacity of plants based upon the following assumptions:Suc gDW−1storage roots day−1 for KU50 and HN, respectively were used for the rMECBM model. The model was optimized through FBA as shown in Equation  and upper bound . The FBA was performed using COBRA Toolbox 2.0.566. The details of the rMeCBM model of KU50 and HN are provided in the Supplementary Data Subject to32 that was grown in similar condition as our experiment. Mahon et al.32 showed that CMC9 is physiologically closer to KU50 than HN in terms of harvest index. The rMeCBM models of KU50 and HN were employed to model the growth of CMC9 storage roots. The specificity of the rMeCBM model to the studied cultivars was deduced from the deviation of the CMC9 growth rate simulated by the KU50 and HN models.The rMeCBM models of KU50 and HN were verified for specificity to the respective cultivars by testing the models with independent data on Suc uptake rate and storage root growth rate of CMC9 cassava cultivar69 was performed for both KU50 and HN rMeCBM models. The FVA, Equation Equation  below, pcj is a vector that specifies which flux is being optimized and Zobj is the value of the objective calculated from FBA. In this study, FVA was performed using COBRA Toolbox 2.0.566.Subject to49, respectively. The shadow prices indicate how much the corresponding metabolite would increase or decrease biomass production. Analogous to the shadow prices, the reduced costs indicate how much the corresponding reaction would increase or decrease biomass production. Both the shadow prices and the reduced costs were performed using COBRA Toolbox 2.0.566.The sensitivity of biomass production to changes in metabolites and reactions in the model was analyzed by shadow prices and the reduced costsSGAM was analyzed. The SGAM is the energy requirement for root growth, and it is located in the biomass biosynthesis reaction (reaction no.: v351) in the form of a stoichiometric coefficient of ATP, ADP, and P. Changes in predicted growth rate, i.e. biomass flux, relative to optimal point in response to alteration of SGAM were investigated in Equation (ε is the percentage error of model simulation to measured cassava storage root growth rate. vp and vo are the storage roots growth rates predicted from perturbed and optimal conditions, respectively.Moreover, the sensitivity of model to 34. Flux-sum analysis is a metabolite-centric approach which sums up all influxes or effluxes around the metabolite. It explains the phenotypic changes at each metabolite level but is unable to represent the direction and quantity of change. To overcome these limitations, flux-sum analysis was modified as demonstrated in Equation (i in reaction j, which consumes metabolite i to produce its derivatives. i at reaction k, what generates metabolite i from its substrate metabolites. j, which consumes metabolite i to produce its derivatives. k, which generates metabolite i from its substrate metabolites.Carbon flux partitioning was calculated from the predicted metabolic flux distribution based on flux-sum approachSupplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Supplementary Information


Parkinson’s disease is a widespread neurodegenerative disorder which affects brain metabolism. Although changes in gene expression during disease are often measured, it is difficult to predict metabolic fluxes from gene expression data. Here we explore the hypothesis that changes in gene expression for enzymes tend to parallel flux changes in biochemical reaction pathways in the brain metabolic network. This hypothesis is the basis of a computational method to predict metabolic flux changes from post-mortem gene expression measurements in Parkinson’s disease (PD) brain.We use a network model of central metabolism and optimize the correspondence between relative changes in fluxes and in gene expression. To this end we apply the Least-squares with Equalities and Inequalities algorithm integrated with Flux Balance Analysis (Lsei-FBA). We predict for PD (1) decreases in glycolytic rate and oxygen consumption and an increase in lactate production in brain cortex that correspond with measurements (2) relative flux decreases in ATP synthesis, in the malate-aspartate shuttle and midway in the TCA cycle that are substantially larger than relative changes in glucose uptake in the substantia nigra, dopaminergic neurons and most other brain regions (3) shifts in redox shuttles between cytosol and mitochondria (4) in contrast to Alzheimer’s disease: little activation of the gamma-aminobutyric acid shunt pathway in compensation for decreased alpha-ketoglutarate dehydrogenase activity (5) in the globus pallidus internus, metabolic fluxes are increased, reflecting increased functional activity.Our method predicts metabolic changes from gene expression data that correspond in direction and order of magnitude with presently available experimental observations during Parkinson’s disease, indicating that the hypothesis may be useful for some biochemical pathways. Lsei-FBA generates predictions of flux distributions in neurons and small brain regions for which accurate metabolic flux measurements are not yet possible. Many human diseases are associated with changes in metabolism at the cellular level. Metabolic fluxes are hard to measure in patients, but changes in expression of metabolic genes during disease are often measured, with spatial resolution down to the level of small anatomical regions and even specific cell types. It would therefore be of interest to predict changes in metabolic fluxes from gene expression measurements during disease.13C labeling data, but the algorithmic predictions turned out to be of low quality and were in several cases worse than predictions by parsimonious Flux Balance Analysis which does not even take gene expression into account [It has long been considered difficult to predict changes in metabolic fluxes from the gene expression changes . Neverth account . We appl account  to deriv account , rely onParkinson’s disease (PD) is one of the most widespread neurodegenerative disorders. PD is characterized among others by movement disorder, rigidity and tremor caused by the loss of dopaminergic neurons in the substantia nigra pars compacta (SNc) of the brain. Although several genes have been identified in familial cases and by genome wide association analysis, the mechanisms for the PD progression are largely unknown. Damage to the mitochondria resulting in failure to generate energy possibly contributes to PD ,6. SeverPD is in particular often associated with disturbed mitochondrial function in the neurons in the SNc which are the most conspicuous target of the disease. Decrease in complex I activity in the electron transport chain (ETC) during PD has been measured in the substantia nigra  and fronIn the present study we report quantitative predictions of the changes during PD in the distribution of fluxes in central energy metabolism in specific small brain regions and in dopaminergic neurons. The first aim of the study is to explore whether the recently developed Lsei-FBA algorithm can predict changes in metabolism from measured changes in gene expression during human disease that agree with experimental data in direction and approximate magnitude of change. In case this works well for the available data, the hypothesis of parallel changes in gene expression and flux in biochemical pathways appears to be useful, at least for some biochemical pathways, and the algorithm’s predictions gain confidence. Consequently, the second aim of the study can then be to predict the complete change in flux distribution in the central metabolic network, containing 69 biochemical reactions, from gene expression measurements in small brain regions during PD.A metabolic reaction network was constructed consisting of the major pathways representing central carbon and energy metabolism in the brain. Rather than relying on a genome-wide metabolic reconstruction of brain metabolism, we chose a manually curated representation of central metabolism. The detailed rationale for this model and an extensive comparison with a larger model of brain metabolism has been described in . Metabolhttp://national_databank.mclean.harvard.edu/brainbank/Main) and are summarized in et al. [Datasets containing the CEL files with gene expression data of individual post-mortem brain samples for neuropathologically confirmed PD patients and normal controls from the same study were downloaded from the Gene Expression Omnibus (GEO) database  and the n et al.   flux balance in the network for metabolites which are not exchanged between brain and blood 2) restriction to forward flux through irreversible reactions 3) maximization of correspondence between relative changes in mRNA expression and changes in fluxes. We include expression datasets from the SNc and from laser captured microdissected (LCM) dopaminergic neurons where neuronal damage usually occurs most prominently during PD. These SNc measurements are compared with other brain regions that show abundant Lewy bodies (LB) in PD without neuronal loss, such as frontal cortex, prefrontal cortex Brodmann area 9 (BA9) and basal ganglia structures. A statistical meta-analysis at the gene-set level of these datasets  was alre-1 min-1, respectively. A small flux is measured in the PPP in the normal brain, which amounts to 6.9% of glycolysis [A list of reaction equations was prepared according to the reaction list in the BiGG database . The metycolysis . Pyruvatycolysis  while thycolysis . These eFlux balance analysis for the normal brain was done assuming a cost function which maximizes ATP synthesis. The rationale for this assumption was discussed extensively in . AssuminThe flux distribution in the PD patients is subsequently estimated using the Lsei-FBA method, based on the changes in gene expression and the flux distribution in normal brain  calculatIn the next step, all flux estimations were refined based on flux balance in the model . Under tThe equations of this problem of least squares with equalities  and inequalities (irreversible reactions) were solved using the least squares with equality and inequality conditions (lsei) method from the limSolve package . This meThe difference in flux was calculated for n = 8 studies of gene expression in the substantia nigra, including two studies on dopaminergic neurons laser-dissected from that region. The significance of the difference in flux between normal controls and the eight predicted averages for whole SNc and dopaminergic neurons was tested using a one-sample t-test (p < 0.05). To control for multiple comparisons, the Family-wise Type 1 error (FWER) was calculated. Because the flux in a sequence of reactions that contains no side-branch is the same and these fluxes are therefore completely dependent, only one t-test was done per set of dependent fluxes, e.g. R_GLCt1r and R_HEX1 form one set, R_PGK, R_PGM, R_ENO and R_PYK form another set, etc.p value = 0.05 was applied resulting in a threshold value of 0.0016.Permutation analysis was performed to analyze the statistical significance of the predicted changes, taking the variability in gene expression in individual samples into account. For each dataset, 100,000 permutations were performed and the two-tailed p-values are calculated. A Fisher’s meta-analysis was performed that produced one p-value over all datasets. There are a total of 31 independent sets of reactions in the SNc and dopaminergic neurons datasets. Multiple testing is controlled by using the Family-wise Type 1 error: a Holm-Bonferroni correction with a Fold changes of mRNA expression of patients with PD against their healthy controls are visualized mapped on the reaction network in the substantia nigra and dopaminergic neurons in The SN data for the expression in the glycolytic pathway shows mostly downregulation except for the hexokinases HK2 and HK3, phosphofructokinase PFKL and aldolase ALDOB genes. The solute carriers for glucose and lactate in the cell membrane tend to show upregulation. The expression changes in the pentose phosphate pathway (PPP) are small and mixed. Pathways in the mitochondria are generally downregulated, including the TCA cycle, oxidative phosphorylation and transfer of reducing equivalents across the mitochondrial membrane. However, the pyruvate dehydrogenase kinase PDK4, which participates in the regulation of pyruvate dehydrogenase activity, tends to show upregulation. Interestingly, the expression of mitochondrially encoded genes (mtDNA) in the electron transport chain (ETC) such as ND1, ND2, ND3, ND4, ND4L, ND5, ND6, CYTB, COX1, COX2, COX3, ATP6 and ATP8 are increased.Outside the substantia nigra, transcription level changes are in general similar as in the SN datasets, with the GPi region (GSE20146) forming a clear exception. The GPi shows upregulation in most glycolytic genes while TCA cycle and oxidative phosphorylation genes are not downregulated and even show a tendency of slight upregulation .In general, changes in gene expression associated with biochemical reactions in the model were modest and tended to be in most cases in the same direction across the metabolic network, which makes the datasets well suited for flux quantification by the Lsei-FBA algorithm.-1 min-1 of glucose is taken up in the normal brain of elderly people, and a small amount of lactate is excreted under baseline conditions [-1 min-1 ATP is produced in the brain mitochondria. The predicted flux distribution is given in Measurements show that 0.203 μmol g brain (wet)nditions . Based oTo investigate if the FBA yields unique values, we performed a Flux Variability Analysis (FVA) , to esti-1 min-1. In PD, on average, the GABA shunt is increased slightly (about 10%), partially compensating for the measured reduction in alpha ketoglutarate dehydrogenase (AKGDH) expression, whose flux is reduced by 20%. It is striking that the modest decrease in glucose uptake leads to an appreciably larger relative decrease in ATP production.We now predict changes in the metabolic flux distribution from the changes in gene expression data between the normal brain and PD. In most cases, fluxes are decreased from control for the substantia nigra and dopaminergic neuron gene expression datasets. A full list of flux predictions for the substantia nigra and its dopaminergic neurons is given in -1 min-1 through oxidative phosphorylation is predicted, utilizing 1.17 μmol g wet brain-1 min-1 of oxygen. In this case the AKGDH flux is not reduced as in other brain regions, but slightly increased while the GABA shunt, AKGDH’s potential bypass, is slightly reduced.Flux changes in the frontal cortex, BA9, putamen and cerebellum during PD follow the same pattern as in the SN although with slightly smaller changes . The cha2 production and glutamine synthetase , and then calculated the minimal sum of squares of these reaction fluxes with this additional constraint. For the same reaction, we separately also added a lower bound to the Lsei which was higher than the flux in the original solution by a very small number, again testing whether an equally low sum of squares was found as in the original solution. In this way we proved that in each reaction in all datasets, the minimal sum of squares is higher than the original calculated sum of squares if any reaction flux is forced to be displaced from the original solution by even a very small amount, thus proving that the solution is unique.For the solution in the diseased brain obtained with the ‘Least-squares with equalities and inequalities’ algorithm, we proved that the solution was unique in the following way: we systematically tested for each reaction whether a different solution with equally low cost function existed if the flux in this reaction was forced to be slightly lower or higher than the original solution. To this end we first added an upper bound for the reaction which was tested to the Lsei problem, setting this to a value that was lower than the flux value in the original solution by a very small number  have recently been meta-analyzed ,36: in 1In the meta-analysis, the decrease in oxygen consumption in three PD study groups measured by PET ranged from 6–34% (average 19% decrease). From the gene expression changes in the two cortical areas , we compThe spatial resolution of PET measurements is characterized by a Full Width at Half Maximum of at least 5 mm, which in practice is often even considerably larger . In contATP synthesis is driven by protons which are pumped by the ETC complexes from mitochondrial matrix to cytosol and flow back through ATP synthase. In our network model, protons in the mitochondrial matrix are balanced. Our computational analysis predicts that the proton fluxes through all ETC complexes and ATP synthase in the SNc during PD are reduced by the same proportion (average 18%) relative to the healthy brain. As a result, the predicted uptake of oxygen in the brain and the ratio of oxygen to glucose uptake are reduced.Our computational analysis predicts that the reduced pyruvate flux into the mitochondria is associated with production of lactate, accounting for about 10% of pyruvate in the GPi region and for about 6% in the substantia nigra and other brain regions. Increase in cerebral lactate in PD has indeed been measured using magnetic resonance spectroscopy in various parts of the brain ,37 Tabl.The pentose phosphate pathway (PPP) is an important branch of glucose metabolism that supplies NADPH, an important cofactor for antioxidant defense of brain cells by glutathione (GSH) redox cycling. PPP flux in the brain of traumatic brain injury (TBI) patients increases by 19.6% relative to glucose uptake . Dunn etIn the GABA shunt pathway, the flux of alpha-ketoglutarate to succinate in the TCA cycle via alpha-ketoglutarate dehydrogenase (AKGDm) and succinate-CoA ligase (SUCOAS1m) is rerouted through decarboxylation of glutamate to GABA via glutamate decarboxylase (GLUDC) in the cytosol, and subsequently to succinate via GABA transaminase (ABTArm) and succinate semialdehyde dehydrogenase (SSALxm) in the mitochondria . The GABIn PD, a marked reduction in alpha-ketoglutarate dehydrogenase (AKGDm) complex by immunostaining has been reported in the substantia nigra of PD patients . Gene exThe present flux analysis predicted total cellular ATP production in the GPi region of the brain to be higher during PD, accompanied by increased fluxes in all pathways . This maBy computational analysis we predicted changes in metabolic fluxes in small regions in the brain, such as the substantia nigra. In relatively large cortical regions the metabolic rates for glucose and oxygen were measured with PET, agreeing with our computational predictions. Metabolic changes in small regions such as the substantia nigra, and in particular in dopaminergic neurons in this region, could not a priori be assumed to be the same as changes determined in larger regions which are accessible to experimental flux measurements with low spatial resolution. However, the present computational analysis predicts changes in the SN that are similar to other brain regions. Also the results for laser-captured dopaminergic neurons are similar to the whole SN and most other brain regions. In contrast, one particular brain region, the GPi, shows different metabolic changes than other brain regions, including the SN which usually is most prominently affected by PD. Our computational prediction therefore suggests that during PD, metabolism is decreased similarly in most brain regions. However, the GPi represents a small region where metabolism is increased in parallel with increased neuronal activity.The gene expression measurements used for predictions in flux changes in the diseased brain in this study are taken from tissue samples from various part of the brain including the substantia nigra, BA9, cerebellum, putamen, frontal cortex, globus pallidus internus as well as from blood and lymphoblastoid, and we could see hints of connectivity between brain regions as discussed above. Connectivity between regions could in principle be investigated further, however this may require gene expression measurements and prediction of metabolism in the regions under a variety of conditions which may not be practical given the invasive nature of the experimental protocols. Non-invasive imaging methods enabling repeated measurements in the same subject are therefore better suited for studying connectivity between brain regions. However, the present methodology is useful in predicting metabolism in many biochemical reactions in brain regions of patients in comparison with healthy controls.The predicted changes in metabolism are averages for the region sampled based on gene expression changes measured for the sample as a whole. There are several distinct cell types inside these regions. The disease may have progressed much more in some of the cells than in others, and damage may even be heterogeneous for cells of the same type. The changes in metabolic fluxes may therefore be larger in a subset of the cells than in the tissue as a whole. Because neurons and glia are lumped in the most of the mRNA expression measurements, we also used a model which lumps metabolism of neurons and glia. Models of brain metabolism with separate compartments for neurons and glial cells exist ,47, but The Lsei approach can be used to compare patients with different disease states to assess how different brain regions change specifically and how sensitive different brain regions are to disease states. However, in the present study on PD, datasets were not available with sufficient specification and differentiation of the disease states. We are presently analysing this method for Alzheimer’s disease datasets, which include expression data in incipient, moderate and severe stage where the expression changes are clearly larger in severe cases. Predicted flux changes in severe cases tend to be larger especially in the fluxes in the glycolytic pathway, ATP synthesis and oxygen uptake. The sensitivity can be affected by the specific region where the gene expression measurement is taken from. For example, changes in excised neurons (dopaminergic neurons) may be higher than in the whole tissue.Among the SN expression datasets included in the study by Zheng et al. , the datThe present prediction is based on gene expression changes. Regulation of translation of mRNAs in proteins and breakdown and posttranslational modification and allosteric regulation of enzymes in the metabolic network may modify the relation between mRNA expression and flux. The relation between changes in gene expression and metabolic fluxes was investigated for glycolysis in yeast . Only a A recent approach presents flux prediction based on absolute gene expression data on a large scale yeast network . The latSeveral other algorithms exist to predict metabolic fluxes from gene expression data. These algorithms, such as iMAT , GIMME , GX-FBA Our approach has a limitation which is specific to brain tissue: a fraction of the enzymes which are formed from the measured messenger RNAs are transported over relatively long distances to catalyze metabolism in axonal terminals. Many dopaminergic neurons in the SNc receive for instance input via GABAergic synapses from relatively distant GABAergic neuronal cell bodies . The preThe comparison of flux changes predicted by the Lsei-FBA algorithm and experimental data see  suggestsPresently, perhaps the most important limitation for the application of the Lsei-FBA algorithm is the limited possibility to compare its predictions with direct metabolic measurements in patients and controls in vivo. Comparison with measured oxygen and glucose uptake, lactate levels, NADPH production in the PPP and with physiological activity in the GPi region was feasible , but othThis paper describes application of a recent method to predict changes in metabolic fluxes based on changes in gene expression in patient material. The hypothesis underlying the Lsei-FBA algorithm. that fluxes in biochemical pathways change in parallel to gene expression if analyzed at the metabolic network level, appears to give useful predictions, at least for some biochemical pathways. From gene expression changes during Parkinson’s disease, metabolic fluxes through central carbon metabolism are predicted to be reduced in the substantia nigra and other brain regions including frontal cortex, cerebellum and putamen. A striking result is that the predicted relative changes in ATP synthesis are larger than the changes in glucose uptake. We also predicted increase of lactate production and shifts in redox shuttles. Reduced metabolism via alpha ketoglutarate dehydrogenase in the middle of the TCA cycle is less compensated via the GABA shunt than is the case in Alzheimer’s disease. In contrast to the decreases in metabolism in substantia nigra and most other brain regions, the globus pallidus internus part of the brain is predicted to show increased metabolic flux compared to normal controls.S1 Table(XLS)Click here for additional data file.S2 Table(XLS)Click here for additional data file.S3 Table(XLS)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLS)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.