1 Genomic-scale metabolic reconstruction of a human neuron: 2 application to traumatic brain injury 3
4 Puentes Cruz Aura Milena1*, Andrés Pinzón 2, Cynthia Martin1, George E. Barreto1, and 5 Janneth Gonzalez1*
6 1 Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, D.C., 7 Colombia 8 2 Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia Bogotá, D.C., Colombia 9 10 Abstract
11 This work allows deepening the investigation of neuronal metabolic changes under a physiological 12 condition (CTL) integrating gene expression data (information encoded in a gene is used to direct the 13 assembly of a protein molecule and that is widely reported in databases of data) in a model that allows us 14 to represent neuronal metabolism after injury (Durot et al., 2009). Such information would not only 15 provide a better understanding at the systemic level of the neuronal response to an injury, but would also 16 provide potential new therapeutic targets framed in systemic medicine, opening ways to improve the 17 specificity and sensitivity of existing ones (Casamassimi et al. , 2017). Additionally, this research will lay 18 the foundations for future pharmacological research, guided to create more effective therapies, and in 19 the field of control, guided to extend its applications in the biological field. As a result of this study, a 20 model was obtained that is available in SBML format in the BioModels database (MODEL1809090002), 21 which will allow the scientific community to analyze changes in the flows of metabolic pathways under 22 stimuli and / or pathological states of interest.
23 Keywords: fba, genome-scale metabolic reconstruction, glutamate, neuron, neurotoxicity, systems 24 biology, traumatic brain injury
25 *Addres correspondence: Janneth Gonzalez, Departamento de Nutrición y Bioquímica, Facultad de 26 Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra 7 40-62, Bogotá, D.C., 27 Colombia Email: [email protected] 28
29 HIGHLIGHTS
30 A new large genomic scale metabolic reconstruction tissue-specific model for glutamatergic neuron is 31 developed.
32 Is possible identify the flow of metabolites, their reactions, enzymes and genes associated to the interior 33 of the network.
34 The analyses of our model improved research in applied problems and can be used in the discovery of 35 potential therapeutic targets.
36 1. INTRODUCTION
37 Traumatic brain injuries (TBI) constitute an important public health problem because they are the cause 38 of premature mortality or loss of years of healthy life in adults and the cause of different types of disability 39 in children and young adults around the world; being the reason for approximately half of the deaths 40 associated with this type of trauma (Dewan et al., 2018). CTLs are caused by blows to the head, where 41 the impact causes the brain to move within the skull and collide with the meninges or against the interior 42 of it, affecting the nervous tissue and blood vessels inside. 43 44 The disability that causes an injury after an event related to a CTL can have different effects, among 45 which are: mental sequelae with personality changes, memory disorders, reduced reasoning capacity, 46 among others. Additionally, they alter neurons, glial cells, astrocytes, microglia, oligodendrocytes, blood 47 vessel cells that supply brain tissue, and cells that produce and recycle cerebrospinal fluid; causing direct 48 cell damage, cell loss, interruption of blood flow and obstruction in the blood-brain barrier (Ghajar, 49 2000). This produces an increase in Na + ions in the extracellular medium and depolarizes neuronal 50 membranes, with the consequent release of neurotransmitters, which, together with high levels of 51 excitatory amino acids such as glutatamate, can trigger excitotoxic processes that contribute to cell 52 damage, disconnecting the neuronal circuit and causing dysfunction of the entire neurovascular unit, 53 leading to a state of metabolic crisis (Vespa et al., 2005). 54 55 Recent studies have concluded that the concentrations of metabolites, particularly ATP, ADP, AMP, 56 creatine-phosphate, creatine, Pi, O2, glucose, lactate, glucose-6-phosphate, fructose-6-phosphate, 57 glyceraldehyde-3-phosphate, glucono-1,5-lactone-6-phosphate, 6-phospho-D-gluconate, ribulose-5- 58 phosphate, ribose-5-phosphate, D-xylulose-5-phosphate, sedoheptulose-7-phosphate, erythrose-4- 59 phosphate, phosphoenolpyruvate, pyruvate, NADH, NAD +, NADPH, NADP, Na +, and glutamate; 60 They are determined by metabolic reactions that occur and by the concentration of available substrates,
61 which determine the intracellular response to extracellular stimuli with high concentrations of 62 neurotransmitters, O2 and glucose (Arundine & Tymianski, 2004; Dusick et al., 2007). 63 64 The study of the production and demand of metabolites in terms of metabolic fluxes is relevant to 65 advance the understanding of molecular mechanisms involved in active metabolic pathways under CTL 66 conditions. 67 68 Preliminary studies (Arundine & Tymianski, 2004; Dusick et al., 2007; Marcoux et al., 2008; Vespa et al., 69 2005) have provided experimental evidence of concentrations of metabolites such as lactate (22.8 µmol 70 / L), pyruvate ( 24.8 µmol / L), glutamate (3.4 to 4.4 µmol / L) and glucose (7.3 µmol / L) in patients 71 who suffered blows to the head, with techniques such as microdialysis through the induction of 13C- 72 glucose in different areas of the brain: the results show an activation of glycolysis and the pentose 73 phosphate pathway (Jalloh et al., 2015). However, the knowledge about the variation in metabolite 74 concentrations and its relationship with possible metabolic mechanisms involved in CTL is limited. 75 76 Therefore, approaches based on metabolic reconstructions at the genomic scale have allowed the 77 integration of experimental data in cellular metabolic models (Mardinoglu et al., 2013a), becoming a 78 fundamental tool to advance in the understanding of metabolic alterations such as inhibition or activation. 79 of enzymes that are part of a complex network of reactions (Cloutier et al., 2009), generating a great 80 variety of responses to a certain stimulus that, in turn, can trigger the production of favorable or harmful 81 metabolites and provide evidence for the analysis of the effects that a drug exerts on the different classes 82 of cells of the nervous system (Raškevičius et al., 2018). Various genomic-scale metabolic models have 83 been used to simulate cancer cell growth, detect their degree of lethality, and identify new drugs for cancer 84 treatment (Folger et al., 2011; Jerby-Arnon et al., 2014) . Another model used to identify potential new 85 biomarkers was the one proposed by Väremo et al. (2015) for diabetes mellitus or type II, where a 86 metabolic reconstruction was performed using myocyte RNA-seq data. 87 88 Regarding brain cells, in the last two decades various computational approaches have been reported for 89 their study: 90 91 a) Computational model of the interaction between a neuron and its extracellular environment, Aubert 92 & Costalat (2002) developed this model, focusing their research on energy metabolism and exchanges in 93 the concentration of metabolites in the blood-brain barrier. implemented a mathematical model of the 94 coupling between membrane ion currents, energy metabolism (that is, ATP regeneration through
95 phosphocreatine buffering effect, glycolysis, and mitochondrial respiration), blood-brain barrier 96 exchanges, and hemodynamics. By studying the effect of the variation of some physiologically important 97 parameters in the time course of the modeled signal dependent on the level of blood oxygenation 98 (BOLD), they were able to formulate hypotheses about the physiological or biochemical importance of 99 the functional magnetic resonance data, especially the post-stimulus undershoot and baseline drift. 100 101 b) Model focused on the modulation of the methionine cycle (Reed et al., 2004), which is associated with 102 cardiovascular and liver diseases, neuronal deficiencies and various types of cancer. The model consists 103 of four differential equations, based on known reaction kinetics, that can be solved to give the time course 104 of the concentrations of the four main substrates in the cycle under various circumstances. The behavior 105 of the model in response to genetic abnormalities and dietary deficiencies is similar to the changes 106 observed in a wide variety of experimental studies. 107 108 c) Specific models to simulate the phosphorylation of DARPP-32 in a dopaminergic neuron (Fernandez 109 et al. (2006). The models included all the combinations of the three best characterized phosphorylation 110 sites of DARPP-32, its regulation by kinases and phosphatases , and the regulation of these enzymes by 111 cAMP and Ca2þ signals. Dynamic simulations allowed observing the temporal relationships between 112 cAMP and Ca2þ signals. Confirming that the proposed regulation of protein phosphatase-2A (PP2A) by 113 calcium can explain the observed decrease in phosphorylation of threonine 75 after glutamate receptor 114 activation. DARPP-32 is not simply a change between the PP1 inhibitor and PKA inhibitor states. 115 Sensitivity analysis showed that CDK5 activity is an important regulator of response, as suggested above. 116 In contrast, the strength of regulation of PP2A by PKA or calcium had little effect on inhibitory function 117 a of DARPP-32 PP1 under these conditions. 118 119 d) Astrocyte and neuron interaction (Cakir et al., 2007), carried out a stoichiometric model of the energy 120 metabolism of the brain with the main objective of including the main interaction pathways between 121 astrocytes and neurons, the built model includes: central metabolism (glycolysis, pentose phosphate 122 pathway, tricarboxylic acid cycle (TCA), lipid metabolism, reactive oxygen species (ROS) detoxification, 123 amino acid metabolism (synthesis and catabolism), glutamate-glutamine cycle and metabolism of 124 neurotransmitters, modeling the basal physiological behavior and hypoxic behavior of brain cells where 125 astrocytes and neurons are tightly coupled. The predictive power of the model built for key flux 126 distributions, especially carbon metabolism and cycle fluxes glutamate-glutamine, and its application to 127 hypoxia reinforce the power of stoichiometric models in the analysis of cell metabolism. 128
129 e) BST biochemical systems theory (Sass et al., 2009). This approach uses relative values as a reasonable 130 initial estimate for BST and provides a theoretical means of applying numerical solutions to qualitative 131 and semi-quantitative understanding of cellular pathways and mechanisms. The approach allows the 132 simulation of human diseases such as CTL through its ability to organize and integrate existing 133 information about metabolic pathways without having a complete quantitative description of those 134 pathways, so that hypotheses about individual processes can be tested in a systems environment, 135 specifically neurodegenerative diseases, where it is often difficult to obtain quantifiable data for system 136 processes, but it is still necessary and desirable to study the emerging behavior of disease pathways. 137 138 This pragmatic approach to BST makes the critical assumption that as long as the components and flows 139 are represented in adequate and justifiable relative terms, the system will demonstrate behavior similar to 140 a more quantitatively accurate description. 141 142 f) Dynamic systems (Shlomi et al., 2011): Using a genome-scale human metabolic network model that 143 takes into account stoichiometric and enzymatic solvent capacity considerations, they demonstrated that 144 the Warburg effect is a direct consequence of adaptation metabolism of cancer cells to increase the rate 145 of biomass production. The analysis is shown to accurately capture a three-phase metabolic behavior that 146 is experimentally observed during oncogenic progression, as well as a prominent feature of cancer cells 147 involving their preference for glutamine uptake over other amino acids. 148 149 This model takes into account a restriction of solvent capacity assuming a limited protein mass per cell, 150 without considering the effect of the subcellular compartmentalization of enzymes. Incorporation of 151 solvent capacity limitations for different cell compartments may lead to higher prediction accuracy in the 152 future, when additional data on enzyme turnover rates become available. Specifically, the addition of 153 specific membrane constraints may be a promising direction, as many metabolically important proteins 154 are confined to membranes (eg, those of the respiratory chain and membrane biosynthesis). 155 156 g) Analysis of metabolic networks based on restrictions, for the interaction between neurons and glial 157 cells (Wang et al., 2012). They developed a method called Metabolic Context-Specificity Evaluated by 158 Deterministic Reaction Evaluation (mCADRE) that allows predicting a metabolic phenotype under a 159 specific condition, mCADRE which is capable of inferring a specific tissue network based on gene 160 expression data and topology of the metabolic network, together with the evaluation of functional 161 capacities during model construction. The construction of metabolic models at the genomic scale 162 provides a useful resource for studying the metabolic basis of a variety of human diseases in many tissues.
163 The functionality of the resulting models and the fast calculation speed of the mCADRE algorithm make 164 it a useful tool for building and updating tissue-specific metabolic models. Importantly, metabolism is 165 subject to extensive transcriptional regulation. Mutations in transcription factors can cause a variety of 166 metabolic diseases, and many tumor suppressor genes and oncogenes are also transcriptional regulators 167 of metabolism. Combined with methods that automatically integrate transcriptional regulatory networks 168 and metabolic networks, mCADRE can help systematically identify the metabolic effects of transcription 169 factor disturbances in various tissues. 170 171 h) Simulations of brain metabolism (Sertbaş et al., 2014), the developed stoichiometric model represents 172 healthy brain metabolism and includes 630 metabolic reactions in and between astrocytes and neurons, 173 which are controlled by 570 genes. They integrated transcriptome data from six neurodegenerative 174 diseases (Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, 175 multiple sclerosis, schizophrenia) with the model to identify specific and common reporter characteristics 176 for these diseases, to identify metabolites and pathways. metabolic where the most significant changes of 177 the comparison occur. The identified metabolites are potential biomarkers for the pathology of related 178 diseases. This model indicated disturbances in oxidative stress, energy metabolism, including the TCA 179 cycle and lipid metabolism, as well as various amino acid-related pathways, consistent with the role of 180 these pathways in the diseases studied. Computational prediction of the transcription factors that 181 normally regulate reporter metabolites was achieved by analysis of the binding site. In essence, the 182 reconstructed brain model makes it possible to elucidate the effects of a disturbance in brain metabolism 183 and to determine possible mechanisms in which a specific metabolite or pathway acts as a regulatory 184 point for cell reorganization. Present a detailed catalog of similarities and distinctive properties of the 185 diseases studied at the metabolic level, and can therefore be used as a basis for developing and testing 186 various hypotheses. 187 188 i) Mathematical models of ordinary differential equations (Sengupta et al., 2015), represented a metabolic 189 model of the human energy reserve network (HEPNet) that occurs within the human cellular 190 organization and focuses on the input and output of energy in the form of ATP, GTP and other 191 molecules associated with energy. The backbone of HEPNet consists of primary biomolecules such as 192 carbohydrates, proteins and fats that ultimately constitute the main source for the synthesis and 193 elimination of energy in a cell. A series of biochemical pathways and reactions that constitute the 194 catabolism and anabolism of various metabolites are described through cellular compartmentalization. 195 The pathways depicted work synchronously toward a general goal of producing ATP and other energy- 196 associated fractions to bring a variety of cellular functions into play. HEPNet is manually selected with
197 raw data from experiments and is also connected to the databases of "Kyoto Encyclopedia of Genes and 198 Genomes" KEGG https://www.genome.jp/kegg/ and "Reactome" https: // reactome. org / (Sengupta 199 et al., 2015). 200 201 In terms of an electronic perspective on HEPNet rendering, manual curation is an added bonus. It can 202 undergo a large amount of automated simulation to study the effect of ATP generation / loss in a wide 203 range of metabolic diseases and physiological states. All reactions are compartmentalized and therefore 204 the model will be useful in metabolic and metabolomic diagnosis. Its use is wide and is not limited to 205 blood glucose or uremia, but even opens dimensions to work on metabolic biomarkers where ATP plays 206 an important role. 207 208 j) Synthesis of nucleotides and neurotransmitters (Özca & Çakır, 2016). By mapping glioblastoma 209 multiforme (GBM) gene expression data into the brain-specific genome-wide metabolic network, GBM- 210 specific models were generated. The models were used to calculate the metabolic flux distributions in the 211 tumor cells. The calculated flow profiles quantitatively predict that the main sources of the set of acetyl- 212 CoA and oxaloacetic acid used in the TCA cycle are pyruvate dehydrogenase from glycolysis and 213 anaplerotic flow from glutaminolysis, respectively, this model predicts a contribution from 214 phosphorylation oxidative to the ATP pool through a mildly active TCA cycle, in addition to the major 215 contributor to aerobic glycolysis. 216 217 k) Evaluation of neural networks (Winter et al., 2017), presented a model that allows relating the specific 218 changes in Alzheimer's disease (AD) by analyzing functional magnetic resonance images dependent on 219 the level of oxygen in the blood (BOLD-fMRI ) with changes in the underlying energy metabolism. Peak 220 height, peak moment, and full width at half maximum are sensitive to changes in reaction rate of various 221 metabolic reactions. This systems theory approach allows the use of patient-specific clinical data to 222 predict changes in canonical hemodynamic response function (HRF) caused by dementia, they can be 223 used to improve the results of BOLD-fMRI analyzes in patients with AD. The model describes the 224 dynamic sequence from neuronal stimulation to metabolic and vascular response and predicts the effect 225 of changes in enzyme activity, flux distribution, or metabolite concentration in the BOLD-fMRI form. 226 This systems theory approach allows the use of patient-specific clinical data to predict dementia-driven 227 changes in hemodynamic response function (HRF). This model is used to calculate the sensitivities of 228 peak height, peak time, and full width at half the maximum for all metabolic reactions explicitly 229 considered in the model, It allows simulating different scenarios, including increased expression of key
230 glycolytic enzymes, increased expression of enzymes that regulate flux through the pentose-phosphate 231 pathway, and astrocyte hypertrophy (Winter et al., 2017). 232 233 j) Different specific cellular metabolic studies have developed models depending on the tissue or disease 234 under study, they have been related to Alzheimer's disease and have allowed the development of models 235 that focus their representation on the aggregation of beta-amyloid and Tau proteins to a neuron (Proctor 236 et al., 2013); and in the modulation of neuronal metabolism, looking for stimuli and hemodynamic 237 responses between neurons and astrocytes (Martin et al., 2016, Winter et al., 2017) to computational 238 models that simulate cell metabolism by integrating information from tissue-specific omic data, such as 239 the case of the liver model (Jerby et al., 2010; Wang et al., 2012), the kidney model (Chang et al., 2010), 240 the cancer cell model (Shlomi et al., 2011), the erythrocyte model (Bordbar et al., 2011), the adipocyte, 241 hepatocyte and myocyte model (Bordbar et al., 2011; Mardinoglu et al., 2013 and 2014) and the liver, 242 kidney and brain model (Thiele et al. al., 2013). 243 244 The aforementioned models show the wide spectrum of mathematical and computational approaches 245 used for the study of cell metabolism and the interactions between neurons and their extracellular 246 environment. This type of approach, hand in hand with experimental research, plays a critical role in 247 understanding the complexity of the functioning of brain cells and the changes induced by a physiological 248 condition. 249 250 It is important to point out that the aforementioned models have been limited to specific metabolic 251 functions, providing information on a single process and, due to the complexity of the effects of a CTL; 252 It has not been possible to advance in a model that allows exploring the variations in concentration in 253 the flow of metabolites at the cellular level and their impact on the possible biochemical pathways, whose 254 functioning is altered under a condition of CTL. 255 256 In this context, the objective of this research was to reconstruct and analyze a genomic-scale metabolic 257 model of a glutamatergic neuron, integrating omic data obtained from gene expression profiles of samples 258 of healthy human nervous tissue and human nervous tissue affected by a condition of LTC, in order to 259 predict distributions of metabolic fluxes of the metabolites of interest. The result obtained was 260 information related to the metabolic reconstruction at the genomic level of a glutamatergic neuron, which 261 includes 741 enzymes, 2,393 genes, 4,130 reactions and 5,398 metabolites, distributed in different cell 262 compartments (extracellular, peroxisome, mitochondrion, cytoplasm, lysosome, endoplasmic reticulum , 263 Golgi apparatus and nucleus).
264 265 The presented model describes the flow of metabolites in the active metabolic pathways under a CTL 266 condition, integrating the complete metabolism of a human neuron in terms of the number of 267 biochemical reactions, influencing genes and specific enzymes from a computational perspective. The 268 model is available in the BioModels database (MODEL1809090002). 269 270 2. MATERIALS AND METHODS
271 2.1 Reconstruction of a genomic-scale metabolic model
272 The reconstruction of the metabolic model object of the present study consisted of following a process 273 of four (4) stages: 1. Reconstruction of the metabolic network of a glutamatergic neuron from the 274 annotations of the neuronal genome obtained from neuronal tissue samples of individuals humans 275 affected by CTL, using the list of biomolecular components and related literature; 2. Identification of 276 specific reactions in the tissue of interest; 3. Compartmentalization of the model in cell organelles using 277 a literature-based manual healing, and 4. Simulation and analysis (Thiele & Palsson, 2010). 278 279 For the reconstruction of the metabolic model, an exhaustive bibliographic search was carried out in 280 order to obtain the greatest amount of information available on the cellular tissue under study and on the 281 type of traumatic injury chosen; while, for the manual healing phase of the metabolic model, the protocol 282 proposed by Thiele and Palson (2011) was followed. In addition, several resources were used such as 283 specialized literature found in PubMed (Canese & Weis, 2013) and databases such as KEGG (Kanehisa 284 & Goto, 2000), HumanCyc (Romero et al., 2005), MetaCyc (Karp & Caspi, 2011 ; Caspi et al., 2013; Caspi 285 et al., 2016), GeneCards (Stelzer et al., 2016) and Ensembl (Zerbino et al., 2018). The reconstruction was 286 performed using the Human Metabolic Atlas (HMR) (Mardinoglu et al., 2013a) as a template (obtained 287 from the Metabolicatlas website http://www.metabolicatlas.org/). 288 289 The reactions were identified using all the genes encoding enzymes expressed in the transcriptomic 290 expression profiles for human neurons indexed in the Omnibus gene expression database (GEO) (Edgar 291 et al., 2002; Barrett et al., 2013) with the code GSE104687 (Miller et al., 2017). This data set includes 292 samples of a certain size obtained from four brain regions for each donor: hippocampus, temporal cortex, 293 parietal cortex, and white matter. Each identifier, such as the EC codes for the enzymes involved in these 294 metabolic processes, was obtained through direct queries in PostgreSQL (Jung et al., 2016) to establish 295 Gen-Reaction-Protein (GPR) associations in the model; which were managed to cure manually. 296
297 Once this procedure was finished and the metabolic model was completed, the dead-end metabolites 298 were identified to proceed to fill the gaps, using a script developed compatible with the MATLAB R2015a 299 interface. These gaps in the model were written manually by mapping them, as well as the metabolic 300 pathways related to each set of reactions achieved in HumanCyc, MetaCyc and KEGG, inserting the 301 necessary reactions to ensure connectivity in the model. This connectivity of metabolic pathways was 302 attested by the addition of transport reactions. 303 304 The simulations were performed in MATLAB R2015a with RAVEN Toolbox (Agren et al., 2013) using 305 the flow balance analysis (FBA) procedure (Orth et al., 2010) and the flow variability analysis (FVA) 306 (Gudmundsson & Thiele, 2010). To simulate the complete model, the basal BrainPhys + supplements 307 (Bardy et al., 2015) (Table 4) were used as simulation means, which allowed the capture of the equivalent 308 metabolites present in the HMR. Direct queries in PostgreSQL were used to integrate the transcriptional 309 data into the metabolic reconstruction. This was done by mapping genes expressed in enzymes that 310 catalyze each GPR or metabolic reaction. 311 312 Table 1. BrainPhys metabolites + supplements (Bardy et al., 2015). All metabolites were converted to 313 HMR compatible reactions. The metabolite of the culture medium and the identification of the reaction 314 in the model and its concentration are related. Basal metabolites in BrainPhys ID HMR Concentration mM Sodium chloride (NaCl) HMR_6524 121,000000 Potassium Chloride (KCl) HMR_5990 4,200000
Calcium chloride (CaCl2) HMR_10278 1,100000
Magnesium sulphate (MgSO4) HMR_10279 1,000000
Magnesium chloride (MgCl2) HMR_10280 0,000000
Ferric nitrate (Fe(NO3)3"9H2O) HMR_10281 0,000124
Zinc sulfate (ZnSO4-7H2O) HMR_10282 0,001500
Cupric sulfate (CuSO4-5H2O) HMR_10283 0,000000
Ferric sulfate (FeSO4-7H2O) HMR_10284 0,000000
Sodium bicarbonate (NaHCO3) HMR_10285 29,000000
Dibasic sodium phosphate (Na2HPO4) HMR_10286 0,500000
Monobasic sodium phosphate (NaH2PO4-H2O) HMR_10301 0,450000 Glycine HMR_10287 0,002000 L-Alanine HMR_10253 0,002000
L- Aspartic acid HMR_10288 0,000000 L- Glutamic acid HMR_10289 0,000000 L- Serine HMR_10290 0,002000 L-Alanil-L-Glutamine HMR_10291 0,500000 L-Arginine Hydrochloride HMR_10292 0,300000
L- Asparagine-H2O HMR_10293 0,050000
L-Cysteine Hydrochloride-H2O HMR_10294 0,100000 L- Cystine 2HCl HMR_10295 0,000000
L-Histidine Hydrochloride-H2O HMR_10296 0,150000 L- Isoleucine HMR_10165 0,416000 L-Leucine HMR_10175 0,451000 L-Lysine Hydrochloride HMR_10297 0,499000 L-Methionine HMR_10298 0,116000 L-Phenylalanine HMR_5082 0,215000 L-Proline HMR_10299 0,060000 L-Threonine HMR_5091 0,449000 L-Tryptophan HMR_10300 0,044100 L-tyrosine disodium salt dihydrate HMR_10302 0,214000 L-Valine HMR_10230 0,452000 D-Glucose (Dextrose) HMR_5029 2,500000 Sodium pyruvate HMR_10303 0,500000 Choline Chloride HMR_10304 0,064100 Calcium D-pantothenate (B5) HMR_10305 0,004700 Folic acid (B9) HMR_10306 0,006010 i-Inositol HMR_10307 0,070000 Niacinamide (B3) HMR_4969 0,016600 Pyridoxine hydrochloride HMR_10308 0,009860 Thiamine hydrochloride HMR_10309 0,006440 Vitamin B12 (cyanocobalamin) HMR_10310 0,000502 Riboflavin (B2) HMR_10311 0,000582 315 316 The analysis of the robustness of the network was performed under different concentrations of oxygen 317 and glucose, listed in table 7. The sensitivity of the FBA solution indicates through the external exchange
318 flow for all metabolites, apart from negative values, that show the metabolites that are demanded in each 319 reaction. On the other hand, positive values identify the metabolites that are excreted. These analyzes 320 were calculated using the RAVEN Toolbox method with MATLAB R2015a. Metabolic reconstruction 321 has been converted to the Systems Biology Markup Language or SBML format, which is used to represent 322 biological process models (Hucka et al., 2003) using MATLAB R2015a and is available from the 323 BioModels database (ID 1809090002) (Le Novère et al., 2006; Li et al., 2010; Chelliah et al., 2015). 324 325 2.2 Description of flows in altered metabolic pathways under a condition of CTL 326 327 2.2.1 Expression data selection 328 For the search for genomic data related to this study, the Gene Expression Omnibus (GEO) database 329 was chosen because it is a public repository of files, microarrays, state-of-the-art scientific sequencing 330 data, and other forms of functional genomic data. , represent the result of previous research deposited 331 by the scientific community (Barrett et al., 2013) and the gene expression data obtained by Miller et al., 332 (2017) identified with the code GSE104687 were selected. In this study, the authors proceeded to take 333 samples of human brain tissue from the regions of the hippocampus, temporal cortex, parietal cortex and 334 white matter, in order to study the long-term effects of their exposure to the effects caused for an TBI. 335 336 2.2.2 Integration of expression data in the metabolic model 337 To the gene expression data to the associated genes, a particular identifier was added according to the 338 platform for obtaining expression data. To differentiate the genes that encode enzymes with metabolic 339 functions, each ID from the GPL16791 Illumina HiSeq 2500 platform (Homo sapiens) was taken and 340 changed to the corresponding identifier, creating a filter for the EC (Enzyme Commission number). 341 Gene expression data from several healthy patients and others who suffered TBI before the study were 342 incorporated into the model, each with its corresponding identifier. 343 344 The gene expression data were used to describe the intracellular behavior that a neuronal cell can display 345 in a certain physiological state, in order to conclude the activity of the encoded enzymes; The reference 346 was different levels of gene expression in prediction and establishment of metabolic flux patterns (Kim 347 & Lun, 2014; Lee et al., 2012). The study of the flow of metabolites through the metabolic network was 348 carried out by means of flow balance analysis, or FBA for its acronym in English. The FBA is used for 349 the study of metabolic reconstructions on a genomic scale, allowing to calculate the flow of metabolites 350 in the metabolic network, making possible the prediction of the growth rate of an organism or the 351 production rate of a metabolite; which is used to define a biological objective represented mathematically
352 by an associated function, which indicates how much each reaction contributes to the phenotype it 353 expresses and which, in general, takes as a reference a reaction that simulates biomass production; 354 predicting growth rates in metabolic reconstruction (Orth et al., 2010). This is possible due to the 355 mathematical representation of metabolic reactions in the form of a numerical matrix, where the 356 stoichiometric coefficients of each reaction are found, which apply restrictions to the flow of metabolites 357 through the network (Durot et al., 2009). 358 359 The stoichiometric matrix includes flow equilibrium restrictions in the system, confirming that the total 360 amount of any chemical compound that is produced must be equal to the total amount that is consumed 361 in a given state (Lee et al., 2012), making it possible to give to each reaction upper and lower limits to 362 define the maximum and minimum allowable fluxes in the reactions, establishing the rates at which each 363 metabolite is consumed or produced by each reaction (Lovatt et al., 2014). 364 365 The FBA allows defining a biological objective represented mathematically by a main white function, 366 which indicates how much each reaction contributes to the expression of the phenotype; taking as 367 reference a reaction that simulates biomass production, in order to determine growth rates in metabolic 368 reconstruction (Thiele et al., 2013). The results obtained from this analysis make it possible to identify 369 the changes that have occurred and describe the behavior of the reconstructed metabolic network from 370 a specific state; in this case, traumatic brain injury (TBI). 371 372 2.2.3 Objective function 373 Two objective functions were established, represented in Table 5. The first objective function was ATP 374 production, identified by the HMR_6916 reaction. For the second objective function, glutamate synthesis 375 was established from glutamine (HMR_3892 reaction) and 2-oxoglutarate (AKG) (HMR_3802 and 376 HMR_3804 reactions) respectively. 377 378 Table 2. Objective function reactions in the metabolic model. Cellular compartment where each 379 reaction occurs: [s] extracellular, [c] cytoplasm and [m] mitochondria. ID Reaction
HMR_6916 ADP[m] + 4 H+[c] + Pi[m] => ATP[m] + 4 H+[m] + H2O[m] HMR_3892 H2O[m] + glutamine[m] => NH4+[m] + glutamate[m]
HMR_3802 AKG[m] + H+[m] + NADH[m] + NH3[m] <=> H2O[m] + NAD+[m] + glutamate[m]
+ + HMR_3804 AKG[m] + H [m] + NADPH[m] + NH3[m] <=> H2O[m] + NADP [m] + glutamate[m] 380 381 2.2.4 Glutamate synthesis 382 To simulate the synthesis of the neurotransmitter glutamate in the model, its precursors were taken as 383 reference: glutamine and 2-oxoglutarate (AKG). Glutamate is the most important neurotransmitter in 384 normal brain function, because it is estimated that 60% of synapses are glutamatergic (REGS). Glutamate 385 is a non-essential amino acid that does not cross the blood-brain barrier and therefore must be 386 synthesized in the cell bodies of neurons from its precursor, the amino acid glutamine, which is produced 387 by glial cells (Murphy-Royal et al. , 2017). Once released, it is taken up by presynaptic terminals and 388 metabolized to glutamate by the mitochondrial enzyme glutaminase. Glutamate can also be synthesized 389 by transamination of 2-oxoglutarate, an intermediate of the tricarboxylic acid cycle. Some of the glucose 390 metabolized by neurons can also be used for glutamate synthesis (Takeda & Ueda, 2017). 391 392 Table 3 presents the reactions associated with glutamate synthesis in a glutamatergic neuron in the 393 metabolic model. The reactions HMR_10079 and HMR_6323 are arginine transport reactions from the 394 extracellular space to the cytoplasm and from the cytoplasm to the mitochondria. Once in the 395 mitochondria, arginine is metabolized to the bibasic amino acid ornithine and urea (reaction HMR_8426), 396 processes where the enzyme arginase (EC: 3.5.3.1) and the ARG1 and ARG2 genes intervene. Ornithine, 397 together with 2-oxoglutarate (AKG), is converted into glutamate and L-glutamate 5-semialdehyde 398 (HMR_3807 reaction) by the action of the enzyme ornithine aminotransferase (EC: 2.6.1.13) and the 399 OAT gene. L-glutamate 5-semialdehyde together with nicotinamide adenine dinucleotide in its oxidized 400 form (NAD +) can also synthesize glutamate and nicotinamide adenine dinucleotide in its reduced form 401 (NADH) (HMR_3806) with the help of the enzyme oxidoreductase (EC: 1.5.1.12 ) and the ALDH4A1 402 gene. 403 404 The HMR_3802 and HMR_3804 t reactions participate in the synthesis of glutamate from 2-oxoglutarate 405 (AKG), with the action of the enzyme glutamate dehydrogenase (EC: 1.4.1.3) and the GLUD1 and 406 GLUD2 genes. In reactions HMR_3827, HMR_3829, HMR_3865, HMR_4109, HMR_6780, 407 HMR_4786, HMR_3747, HMR_3777, HMR_6923, HMR_10043, HMR_10058, HMR_10115, 408 HMR_10163, HMR_10172, HMR_10173, HMR_10196, HMR_10228 and HMR_10252, 2-oxoglutarate 409 (AKG) attached to an amino acid to synthesize glutamate. 410
411 Another reaction involved in the synthesis of glutamate is HMR_4693, where 4-aminobutyrate and 2- 412 oxoglutarate (AKG) are taken as precursor by the action of the enzyme 4-aminobutyrate-2-oxoglutarate 413 transaminase (EC: 2.6.1.19) and the ABAT gene. Glutamate can also be synthesized from 2-methyl-3- 414 oxopropanoate (reaction HMR_6923) and 3-oxopropanoate (reaction HMR_4330). In addition to the 415 above, the glutamine gliotransmitter can participate as a substrate, being released by astrocytes to the 416 extracellular space (reaction HMR_10246), and subsequently entering the cytoplasm where it is 417 transported to the mitochondria (reaction HMR_5101) to be synthesized to glutamate by the intervention 418 of the glutaminase enzyme (EC: 3.5.1.2), which is regulated by the GLS and GLS2 genes (reactions 419 HMR_5101 and HMR_3892). 420 421 By functioning as cation channels that open upon binding to glutamate, postsynaptic iGluRs mediate 422 rapid excitatory transmission. IGluRs can be classified into N-methyl-d-aspartate (NMDA) receptors, α- 423 amino-3-hydroxy-5-methylisoxazole-4-propionic acid receptors (AMPA), and Kainate receptors (KA). 424 AMPA receptors are the first target of glutamate released from presynaptic terminals, leading to 425 depolarization of the postsynaptic membrane through sodium entry, and thus play a critical role in 426 synapse maturation and plasticity. . Furthermore, AMPA receptors can be regulated by helper proteins 427 involved in trafficking, such as TAPC1. AMPA receptors are heterotetramers made up of two dimers 428 with different combinations of the four subunits of AMPAR GluA1-4, and usually GluA1 / GluA2 and 429 GluA2 / GluA3 in the mammalian CNS. When GluA2 is missing from the tetramer, AMPA receptors 430 allow low permeability to calcium ions. After initial AMPAR-mediated depolarization, NMDA receptors 431 are permeable to sodium and calcium ions to mediate excitatory transmission upon glutamate binding. 432 Especially the NMDA receptor is a coincidence detector, the opening of the channel requires that the 433 postsynaptic cell be depolarized to remove the physical occlusion by magnesium when glutamate binds 434 to the receptor. NMDARs are heterotetramers, there are two GluN1 subunits and two GluN2A or 435 GluN2B subunits. The switch between GluN2A and GluN2B subunits plays a crucial role in modulating 436 receptor function. Kainate receptors also mediate a postsynaptic current through the entry of sodium 437 and calcium (to a lesser degree), resulting in a lesser contribution to neuronal depolarization than AMPA 438 receptors. They are also heterotetramers, which are made up of KA1, KA2, GluR5, GluR6, and GluR7. 439 Generally, AMPA receptors mediate fast synaptic transmission (<10 ms), whereas NMDA and Kainato 440 receptors mediate slower synaptic transmission (10-100 ms), see figure 1. (Korte & Schmitz 2016). 441 442 Furthermore, glutamate can act on mGluRs to modulate neuronal excitability and synaptic transmission. 443 MGluRs act more slowly, since they exert their effects indirectly through the recruitment of second 444 messenger systems, which involves the process of gene expression and protein synthesis. Acting as
445 GPCRs, the mGluRs assemble into dimers, that there are eight subtypes of mGluR (mGluR1-8) that are 446 differentially expressed in specific neuronal populations in the CNS. And they are divided into three 447 subgroups based on sequence homology, G-protein coupling, and ligand selectivity. Group I mGluRs 448 (mGluR1 and 5) are highly expressed in the postsynaptic membrane and are associated primarily with Gq 449 / G. After glutamate binding, Group I mGluRs trigger phospholipase C (PLC) activation, which results 450 in the generation of inositol IP3 and the hydrolysis of phosphoinositides, which leads to the mobilization 451 of calcium from the endoplasmic reticulum and the activation of protein kinase C (PKC). This signaling 452 pathway is responsible for increased neuronal excitability. Group II (mGluR2 and 3) and Group III 453 (mGluR4, 6, 7, and 8) mGluRs are located not only postsynaptically, but also presynaptically, where they 454 function to suppress excess glutamate transmission. Following its action on group II and III mGluRs 455 receptors, glutamate can be transferred from the synaptic cleft by EAATs expressed at the presynaptic 456 terminal or neighboring glial cells. In glial cells, glutamate is converted to glutamine, which is then 457 transported back to the presynaptic terminal and converted back to glutamate. see figure 6 (Altevogt, 458 Davis, & Pankevich 2011). 459 460 Glutamate receptors are found on both neurons and glial cells throughout the CNS. Glutamatergic 461 synapse pathways, which are linked to many other neurotransmitter pathways, play a crucial role in a wide 462 variety of normal physiological functions. Glutamate dysfunction stands out as a key factor in both 463 neurodevelopmental diseases and injuries (Moretto et al., 2018). 464
465 466 Figure 1. Glutamatergic synapse pathway (adapted from https://www.creative- 467 diagnostics.com/glutamatergic-synapse-pathway.htm) 468 469 Table 3. Glutamate synthesis reactions in the metabolic model and the cell compartment where each 470 reaction occurs: [s] extracellular, [c] cytoplasm, [m] mitochondria. ID Reaction HMR_10079 arginine[s] => arginine [c] HMR_6323 H+[c] + arginine[c] => H+[m] + arginine[m]
HMR_8426 H2O[m] + arginine[m] => ornithine[m] + urea[m] HMR_3807 AKG[m] + ornithine[m] <=> L-glutamate 5- semialdehyde[m] + glutamate[m]
HMR_3806 H+[m] + NADH[m] + glutamate[m] <=> H2O[m] + L-glutamate 5- semialdehyde [m] + NAD+[m]
HMR_3802 AKG[m] + H+[m] + NADH[m] + NH3[m] <=> H2O[m] + NAD+[m] + glutamate[m]
+ + HMR_3804 AKG[m] + H [m] + NADPH[m] + NH3[m] <=> H2O[m] + NADP [m] + glutamate[m] HMR_3827 AKG[m] + aspartate[m] <=> OAA[m] + glutamate[m] HMR_3829 AKG[c] + aspartate[c] <=> OAA[c] + glutamate[c] HMR_3865 H+[m] + 3- sulfinoalanine[m] + AKG[m] => 3- sulfinylpyruvic acid[m] + glutamate[m] HMR_4109 AKG[m] + alanine[m] <=> glutamate[m] + piruvate[m] HMR_4693 4-aminobutirate[m] + AKG[m] <=> glutamate[m] + semialdehyde succinate[m] HMR_6780 AKG[m] + tyrosine[m] <=> 4-hydroxyphenylpyruvate[m] + glutamate[m] HMR_4786 AKG[m] + L-erito-4-hydroxyglutamate[m] => 4-hydroxy-2-oxoglutarate[m] + glutamate[m] HMR_3747 AKG[c] + valine[c] <=> 3-methyl-2-oxobutarate[c] + glutamate[c] HMR_3777 AKG[c] + isoleucine[c] <=> 2-oxo-3-methylvalerate[c] + glutamate[c] HMR_6923 AKG[c] + leucine[c] => 4-methyl-2-oxopentanoate[c] + glutamate[c] HMR_8088 2-methyl-3-oxopropanoate[m] + glutamate[m] <=> AKG[m] + L-3-amino- isobutanoate[m] HMR_4330 3-oxopropanoate[m] + glutamate[m] <=> AKG[m] + beta-alanine[m] HMR_10043 H+[m] + AKG[m] + 3-sulfinoalanine[m] => glutamate[m] + 3- sulfinylpyruvic acid[m] HMR_10058 L-2-aminoadipate[c] + AKG[c] => 2-Oxoadipate[c] + glutamate[c] HMR_10115 AKG[m] + cysteine[m] <=> glutamate[m] + mercaptopyruvate[m] HMR_10163 AKG[m] + isoleucine[m] <=> glutamate[m] + 2-oxo-3-methylvalerate[m] HMR_10172 AKG[m] + L-cysteate[m] <=> glutamate[m] + 3-phosphopyruvate[m] HMR_10173 AKG[m] + leucine[m] <=> glutamate[m] + 4-methyl-2-oxopentanoate[m] HMR_10196 AKG[m] + phenylalanine[m] <=> glutamate[m] + phenylpyruvate[m] HMR_10228 AKG[m] + valine[m] <=> glutamate[m] + 3-methyl-2-oxobutarate[m] HMR_10252 AKG[c] + alanine[c] <=> glutamate[c] + pyruvate[c] HMR_10246 Na+[s] + glutamine[s] + H+[c] <=> H+[s] + Na+[c] + glutamate[c] HMR_5101 H+[c] + glutamine[c] => H+[m] + glutamine[m]
HMR_3892 H2O[m] + glutamine[m] => NH4+[m] + glutamate[m] 471
472 To perform the model simulations, the study carried out by Zaaroor et al., (2007) was taken as a reference, 473 which monitored data related to cerebrospinal fluid and brain metabolism for seven consecutive days, 474 using brain tissue from patients who suffered a CTL recently; evaluating the concentrations of oxygen 475 and glucose (table 4). Table 5 shows the metabolites and their concentration in neurons directly affected 476 by injury from TBI, for which intracellular metabolic flux was established under this condition. 477 478 Table 4. metabolic effects and concentration of oxygen and glucose in a glutamatergic neuron in 479 patients who suffered a TBI. Concentration (umol/g/min) Day 1 Day 2 Day 3 Day 4 Metabolite Healthy TBI Healthy TBI Healthy TBI Healthy TBI CMR O2 1.88 2.01 1.79 1.48 1.67 1.31 1.77 1.24 CMR Glucose 5.2 3.79 5.15 4 5.7 4.19 6.5 3.76
Concentration (umol/g/min) Day 5 Day 6 Day 7 Metabolite Healthy TBI Healthy TBI Healthy TBI CMR O2 1.78 1.53 1.7 1.5 1.74 1.33 CMR Glucose 4.6 3.5 4.93 3.38 6.66 5 480 481 Table 5. Simulations to evaluate possible metabolic effects in a TBI. Flow of metabolites from the 482 neuronal metabolic network. The restrictions are set under the units µmol / g / min. Metabolite Healthy Authors Concentration on µmol/g/min ATP 2.2; Aubert & Costalat, 2002; 2.25; Cloutier et al., 2009; 2.26; Winter et al., 2017; 8.198857 Lai et al., 2007 ADP 0.001142; Lai et al., 2007; 0.11 - 0.13; Cloutier et al., 2009; 0.12 Winter et al., 2017 AMP 0.0062; Cloutier et al., 2009; 0.006 Winter et al., 2017
Creatine-phosphate (PCr) 1.5; Winter et al., 2017; 5; Aubert & Costalat, 2002; 2.5 – 5.0; Cloutier et al., 2009; 40.98942 Lai et al., 2007 Creatine (Cr) 0.7471; Cloutier et al., 2009; 1.01056; Lai et al., 2007; 3.5 Winter et al., 2017 Pi 0.5 Lai et al., 2007 O2 0.0262; Aubert & Costalat, 2002; 0.03; Winter et al., 2017; 0.09 – 0.102; Cloutier et al., 2009; 0.03969 – 5.281527 Lai et al., 2007 Glucose 0.26 - 0.47; Cloutier et al., 2009; 1.14; Winter et al., 2017; 1.2 Aubert & Costalat, 2002 Lactate 0.28 - 0.38; Cloutier et al., 2009; 1; Aubert & Costalat, 2002; 1.36 Winter et al., 2017 Glucose-6-phosphate 0.11; Winter et al., 2017; 0.72 - 0.75 Cloutier et al., 2009 Fructose-6-phosphate 0.10 - 0.2; Cloutier et al., 2009; 0.45 Winter et al., 2017 Glyceraldehyde-3-P (GAP) 0.001; Winter et al., 2017; 0.0057; Aubert & Costalat, 2002; 0.04 - 0.05 Cloutier et al., 2009 Glucono-1,5-lactone-6- 0.000003 Winter et al., 2017 phosphate (G6L) 6-phospho-D-gluconate 0.0029 Winter et al., 2017 (P6G) ribulose-5-phosphate (Ru5P) 0.00067 Winter et al., 2017 ribose-5-phosphate (R5P) 0.000027 Winter et al., 2017 D-xylulose-5-phosphate 0.02 Winter et al., 2017 (X5P)
sedoheptulose-7-phosphate 0.2 Winter et al., 2017 (S7P) erythrose-4-phosphate (E4P) 0.006 Winter et al., 2017 Phosphoenolpyruvate (PEP) 0.003; Winter et al., 2017; 0.0037 - 0.025; Cloutier et al., 2009; 0.02 Aubert & Costalat, 2002 Pyruvate (PYR) 0.0388 - 0.12; Cloutier et al., 2009; 0.13; Winter et al., 2017; 0.16 Aubert & Costalat, 2002 NADH 0.016; Winter et al., 2017; 0.03 – 0.22; Cloutier et al., 2009; 0.026 Aubert & Costalat, 2002 NAD+ 0.1881; Cloutier et al., 2009; 0.204 Winter et al., 2017 NADPH 0.29123 Winter et al., 2017 NADP 0.0000000022 Winter et al., 2017 Na+ 15; Aubert & Costalat, 2002; 15.53; Winter et al., 2017; 15 – 150 Cloutier et al., 2009 Glutamate 3 Cloutier et al., 2009; Winter et al., 2017 483 484 3. RESULTS AND DISCUSSION
485 3.1 Characterization of metabolic reconstruction
486 The metabolic reconstruction of a glutamatergic neuron has 741 enzymes, 2,393 genes, 4,130 reactions, 487 and 5,138 metabolites. Table 9 shows the key components in the reconstruction, by cell compartment.
488 Most of the reactions are carried out within the cytosol (2449), followed by the mitochondria (991), which 489 is intrinsically related to the number of genes and metabolites, as well as their relationship with oxidative 490 phosphorylation and the transport chain of electrons.
491 Table 6. Metabolic reconstruction of a human glutamatergic neuron. Number of genes, reactions and 492 metabolites per cell compartment.
Cell compartment Genes Reactions Metabolites Extracellular 153 486 481 Peroxisome 175 280 345 Mitochondria 732 991 722 Cytosol 2072 2449 2260 Lysosome 99 347 368 Endoplasmic 308 506 550 reticulum Golgi apparatus 178 181 284 Nucleus 171 88 91 493
494 This model focuses on both intracellular metabolism and the synthesis of the neurotransmitter glutamate. 495 The metabolic reactions that occur within the cell can be classified into sixty-one subsystems (Figures 496 S1-S10, annex 5), of which sixteen stand out; that they are those that contain a greater number of genes, 497 enzymes and reactions; such as those corresponding to the metabolism of arachidonic acid, estrogen, 498 metabolism of arginine and proline, purine, hydrolysis of acyl-CoA, phenylalanine, tyrosine and 499 tryptophan; glycerolipid, Gaican N-O, glycosphingolipids, omega 3-6, leukotriene, fatty acid, cholesterol, 500 beta oxidation, carnitine shuttle and transport (Figure 2).
501
502 Figure 2. Distribution of genes, enzymes and reactions in the main metabolic subsystems: metabolism 503 of arachidonic acid, estrogen, metabolism of arginine and proline, purine, hydrolysis of acial-CoA, 504 phenylalanine, tyrosine and tryptophan; glycerolipids, gallican N-O, glycosphingolipids, omega 3-6, 505 leukotriene, fatty acid, cholesterol, beta oxidation, carnitine shuttle and transport.
506
507 The results showed that the subsystems with the highest number of reactions are: transport (11.8%), 508 carnitine shuttle (10.1%), beta oxidation (8.9%), cholesterol (7.7%), fatty acids (6.6%), leukotriene (3.6 509 %), omega 3-6 (3.3%), glycosphingolipid and gaican NO (2.6%), glycerolipid (2.5%), phenylalanine, 510 tyrosine and tryptophan (2.4%); hydrolysis of acal-CoA and purines (2.1%), metabolism of arginine and 511 proline (2.0%), metabolism of estrogen and arachidonic acid (1.9%), valine, leucine and isoleucine (1.6%); 512 sphingolipids (1.5%), P450 xenobiotics and nucleotides (1.4%), tricarboxylic acid cycle and vitamins B6, 513 B12 D and E (1.3%); glycolysis / gluconeogenesis and pyrimidines (1.2%); chondroitin and alanine, 514 aspartate and glutamate metabolism (1.1%); folate (1.0%), lysine, pyruvate, glycine, serine and threonine 515 (0.9%); bile acid and protein biosynthesis (0.7%), nicotinate and nicotinamide, cysteine, methionine and 516 prostaglandins (0.6%); pentose phosphate pathway, steroids, tyrosine, acylglyceride metabolism, 517 terpenoids, fructose, mannose, and propanoate (0.5%); butanoate, glutathione, starch and sucrose (0.4%);
518 galactose, retinol, beta-alanine, histidine, oxidative phosphorylation, porphyrin and tryptophan (0.3%); 519 serotonin and melatonin, sulfur, ROS detoxification, biopterin and thiamine (0.2%); metabolism of 520 ascorbate and aldarate, biotin, riboflavin and linoleate (0.1%), and lipoic acid (0.03%).
521 To describe the tissue-specific metabolic model, the reactions were classified based on the enzymatic 522 activity required to catalyze each reaction according to their EC numbers (Enzyme Commission 523 numbers) (Figure 3) and with the subcellular location according to the metabolites, compartments and 524 assigned metabolic pathways; taking into account the database as KEGG (Figure 3 and 4). According to 525 the enzymes associated with each biochemical reaction, 35% of these belong to the oxidoreductases class 526 (EC: 1.1.1.1 to EC: 1.9.3.1), 30% to the transferase class (EC: 2.1.1.1 to EC : 2.8.3.5), 19% to hydrolases 527 class (EC: 3.1.1.1 to EC: 3.7.1.3), 6% to lyase class (EC: 4.1.1.1 to EC: 4.99.1.1), 2 % to the class of 528 isomerases (EC: 5.1.3.1 to EC: 5.4.99.7) and, finally, 7% to the class of ligases (EC: 6.1.1.1 to EC: 6.4.1.4). 529 (Figure 2).
530
531 Figure 3. Percentage of enzymes present in tissue-specific metabolic reconstruction and their 532 classification by class according to their EC number.
533
534 According to the predictive condition of the model, the results showed that the enzymes mentioned 535 above are key pieces for the proper functioning of the metabolism of a neuron, since they are found in
536 the blood plasma in concentrations lower than those detected in the cells of the neurons. other tissues of 537 the human body; When a TBI occurs and due to the deterioration suffered by the cell membrane function, 538 these enzymes can be observed in excessive concentration according to the normal parameters that had 539 been recorded in the literature and loaded into the model. This damage could be caused by the reduction 540 of the oxygen level in the medium, by a bacterial infection or by the presence of toxic chemical substances, 541 which produced changes in the permeability of the membrane.
542 With cellular involvement, the release of intracellular enzymes was altered. These changes resulted from 543 a decrease in the intracellular ATP concentration due to enzymatic deficiencies in its synthesis, to the 544 limitation of available glucose or to hypoxia (Bhagavan & Ha, 2011). The distribution of reactions in the 545 metabolic pathways is shown in Figures S1-S10 (Annex 5). Complete information about the model, in 546 terms of genes, enzymes, reactions and metabolites, is available in the SBML format, as part of the 547 BioModels database (ID1809090002).
548
549 3.2 Identification of metabolic fluxes of metabolites
550 The variations in metabolic fluxes of the metabolites treated in GEM are presented below, as well as the 551 differences found as a consequence of the changes in oxygen and glucose concentrations in glutamatergic 552 neurons subjected to mechanical damage by TBI.
553 3.2.1 Healthy scenario: glutamate synthesis
554 In the process of rebuilding the tissue-specific model of a glutamatergic neuron, two previously 555 established metabolic states were calculated and compared: the healthy stage and the neuronal damage 556 caused by TBI. Our model accurately predicted the synthesis of glutamate from glutamine and 2- 557 oxoglutarate, which was released into the extracellular space by the neuron within a normal concentration 558 range (Mark et al., 2001) (Figures 4 and 5). Glutamate is the most important neurotransmitter in normal 559 brain function and it is because almost all excitatory neurons that are part of the central nervous system 560 (CNS) are glutamatergic, so it is estimated that more than half of all brain synapses they release this 561 neurotransmitter. Glutamate is also characterized as a non-essential amino acid that fails to cross the 562 blood-brain barrier; it must be synthesized in neurons from its amino acid precursor glutamine, which is 563 released by glial cells, in close relationship with neuronal cells (Murphy-Royal et al., 2017). Once released, 564 glutamine was absorbed by the presynaptic terminals and metabolized to glutamate by the mitochondrial 565 enzyme glutaminase. This compound could be synthesized through the transamination of 2-oxoglutarate,
566 an intermediate of the tricarboxylic acid cycle (TCA) where part of the glucose metabolized by neurons 567 was used for the synthesis of glutamate (Takeda & Ueda, 2017).
568
569 Figure 4. Healthy state. Variation of metabolic fluxes of the ATP metabolite and synthesis of 570 glutamate from glutamine and 2-oxoglutarate at normal oxygen concentrations for seven consecutive 571 days.
572
573 Figure 5. Healthy state. Variation of metabolic fluxes of the ATP metabolite and synthesis of 574 glutamate from glutamine and 2-oxoglutarate in normal glucose concentrations for seven consecutive 575 days.
576 Thanks to the elaboration of this model, it was possible to identify the reactions involved in the synthesis 577 of glutamate; in the same way as the genes and specific enzymes for each reaction; data summarized in 578 table 7.
579 In each of the glutamate synthesis reactions, precursor substances, specific enzymes, coenzymes, isomeric 580 compounds and specific genes are involved for each of the reactions that give way to this amino acid 581 neurotransmitter; as described in section 5.2.4. Glutamate synthesis, and in Tables 6 and 10, which show 582 the healthy glutamate synthesis scenario proposed in this in silico metabolic model.
583 For this scenario, glutamate synthesis also took place in the HMR_4693 reactions, where 4-aminobutyrate 584 and 2-oxoglutarate (AKG) were taken as precursors by the action of the enzyme 4-aminobutyrate-2- 585 oxoglutarate transaminase (EC: 2.6 .1.19) and the ABAT gene; HMR_6923, where 2-methyl-3- 586 oxopropanate participated, and HMR_4330, with the participation of 3-oxopropanoate.
587 Table 7. Glutamate synthesis reactions in the metabolic model. Cellular compartment where each 588 reaction occurs: [s] extracellular, [c] cytoplasm, [m] mitochondria.
ID Reaction HMR_10079 arginine[s] => arginine[c] HMR_6323 H+[c] + arginine[c] => H+[m] + arginine[m]
HMR_8426 H2O[m] + arginine[m] => ornithine[m] + urea[m] HMR_3807 AKG[m] + ornithine[m] L-glutamate 5-semialdehyde[m] + glutamate[m]
+ HMR_3806 H [m] + NADH[m] + glutamate[m] H2O[m] + L-glutamate 5-
semialdehyde[m] + NAD+[m]
+ + HMR_3802 AKG[m] + H [m] + NADH[m] + NH3[m] H2O[m] + NAD [m] + glutamate[m]
+ + HMR_3804 AKG[m] + H [m] + NADPH[m] + NH3[m] H2O[m] + NADP [m] + glutamate[m] HMR_3827 AKG[m] + aspartate[m] OAA[m] + glutamate[m] HMR_3829 AKG[c] + aspartate[c] OAA[c] + glutamate[c] HMR_3865 H+[m] + 3-sulfinoalanine[m] + AKG[m] => 3-sulfinylpyruvic acid[m] + glutamate[m] HMR_4109 AKG[m] + alanine[m] glutamate[m] + pyruvate[m] HMR_4693 4-aminobutyrate[m] + AKG[m] glutamate[m] + semialdehyde succinate[m] HMR_6780 AKG[m] + tyrosine[m] 4-hydroxyphenylbutyrate[m] + glutamate[m] HMR_4786 AKG[m] + L-eritro-4-hidroxiglutamate[m] => 4-hydroxy-2- oxoglutarate[m] + glutamate[m] HMR_3747 AKG[c] + valine[c] 3-methyl-2-oxobutyrate[c] + glutamate[c] HMR_3777 AKG[c] + isoleucine[c] 2-oxo-3-methylvalerate[c] + glutamate[c] HMR_6923 AKG[c] + leucine[c] => 4-methyl-2-oxopentanoate[c] + glutamate[c] HMR_8088 2-methyl-3-oxopropanoate[m] + glutamate[m] AKG[m] + L-3- amino-isobutanoate[m] HMR_4330 3-oxopropanoate[m] + glutamate[m] AKG[m] + beta-alanine[m] HMR_10043 H+[m] + AKG[m] + 3-sulfinoalanine[m] => glutamate[m] + 3- sufinylpyruvic acid[m] HMR_10058 L-2-aminoadipate[c] + AKG[c] => 2-Oxoadipate[c] + glutamate[c]
HMR_10115 AKG[m] + cysteine[m] glutamate[m] + mercaptopyruvate[m] HMR_10163 AKG[m] + isoleucine[m] glutamate[m] + 2-oxo-3- methylvalerate[m] HMR_10172 AKG[m] + L-cysteate[m] glutamate[m] + 3-sulfopyruvate[m] HMR_10173 AKG[m] + leucine[m] glutamate[m] + 4-methyl-2- oxopentanoate[m] HMR_10196 AKG[m] + fenilalanine[m] glutamate[m] + fenilpyruvate[m] HMR_10228 AKG[m] + valine[m] glutamate[m] + 3-methyl-2-oxobutyrate[m] HMR_10252 AKG[c] + alanine[c] glutamate[c] + pyruvate[c] HMR_10246 Na+[s] + glutamine[s] + H+[c] H+[s] + Na+[c] + glutamine[c] HMR_5101 H+[c] + glutamine[c] => H+[m] + glutamine[m]
+ HMR_3892 H2O[m] + glutamine[m] => NH4 [m] + glutamate[m] 589
590 3.2.2 In silico simulation of TBI
591 In our metabolic model, the neurons that suffered disturbance, and using as a reference the study carried 592 out by Zaaroor et al., (2007), where the level and state of cerebrospinal fluid are monitored, in addition 593 to brain metabolism for seven consecutive days in patients who suffered a CTL, taking as reference the 594 concentrations of oxygen and glucose (table 7); they are characterized by producing molecular and 595 anatomical changes that follow a complex course after the injury by blow or direct impact on the head; 596 being able to evolve positively or negatively with the passage of time in minutes, days or weeks, after the 597 traumatic event (Lavoie et al., 2017).
598 Some physical symptoms associated with a TBI include mood swings, epilepsies or tonic-clonic 599 movements, muscle spasms, brain injuries, cognitive problems and; in severe cases, neurodegeneration, 600 limited arm or leg function, speech abnormalities, and axonal disconnection may occur (Casanueva et al., 601 2004). On the other hand, an excess of glutamate in the extracellular space causes hyperexcitability that, 602 in turn, leads to excitotoxicity, oxidative stress and possible apoptosis, contributing to the loss of cells 603 and damage to the integrity of brain tissue (Carron et al., 2016). Therefore, we proceeded to analyze the 604 results obtained, in order to identify changes in glutamate synthesis and ATP production.
605 As a result, a consistent sample of the known metabolic changes in a TBI was obtained and, given the 606 predictive capacity of our model; it was possible to show that some metabolic enzymes such as adenosine 607 triphosphatase (EC: 3.6.1.3) and apyrase (EC: 3.6.1.5), which participate in the production of ATP,
608 exhibit altered activity under the effects of injury; which represented a reduction in the synthesis of this 609 metabolite.
610 Other enzymes that were altered with a TBI were glutaminase (EC: 3.5.1.2) and glutamic dehydrogenase 611 (EC: 1.4.1.3), which are linked to the synthesis of glutamate from glutamine and 2-oxoglutarate. At 612 different concentrations of oxygen and glucose in the intracellular medium, ATP production decreased, 613 while the synthesis of glutamate from glutamine and 2-oxoglutarate (Figures 11 and 12) increased; which 614 caused the mentioned neuronal damage (Kaneko, 2000). The metabolic pathway affected in ATP 615 synthesis was oxidative phosphorylation; while, in the metabolism of alanine, the levels of aspartate and 616 glutamate increased; evidence of an increase in glutamate synthesis.
617
618 Figure 6. Alteration caused by TBI. Variation of metabolic fluxes of the ATP metabolite and glutamate 619 synthesis from glutamine and 2-oxoglutarate in oxygen concentrations under TBI conditions for seven 620 consecutive days.
621
622 Figure 7. Alteration caused by TBI. Variation of metabolic fluxes of the ATP metabolite and synthesis 623 of glutamate from glutamine and 2-oxoglutarate in glucose concentrations under TBI conditions for 624 seven consecutive days.
625 3.2.3 Identification of metabolic fluxes of metabolites
626 The predictive analysis of our model was consistent with the metabolic changes related to enzymatic and 627 / or genetic disturbances experienced by the glutamatergic neurons that were used for this study, as a 628 consequence of TBI; indicating the beginning of cellular processes that evolve towards critical conditions 629 with the passage of hours, days, and even weeks, after the injury occurs.
630 At different concentrations of oxygen and glucose, the metabolites exhibited altered activity, as described 631 in Figures 13 and 14 for the metabolites ATP, ADP and AMP. These reactions affected the functioning 632 of homeostatic cellular mechanisms by not producing the required energy required in the form of glucose, 633 as a consequence of a failure of the enzymatic activity in the oxidative phosphorylation cycle. A deficiency 634 in the supply of this form of energy that can be used by neurons was able to deteriorate the mechanisms 635 for maintaining the balance of the environment and biochemical functions, resulting in mitochondrial 636 dysfunction, increased intracellular calcium concentration and apoptosis in the last case ( Verweij et al., 637 2000).
638 The creatine phosphate (PCr) and creatine (Cr) metabolites were also affected as a consequence of TBI
639 due to the change suffered by the enzyme creatine kinase (EC: 2.7.3.2) and its related genes (CKM, 640 CKMT2, CKB, CKMT1A and CKMT1B); as well as inorganic phosphate (Pi), affected by the inorganic 641 diphosphatase enzyme (EC: 3.6.1.1) and its genes (LHPP, PPA2, PRUNE and PPA) (figures 10 and 11). 642 The changes mentioned above, managed to modify the concentrations of metabolites and; consequently, 643 cellular energy expenditure was altered (Signoretti et al., 2010; Lindsey et al., 2010).
644
645 Figure 8. Variation of metabolic fluxes of the metabolites ATP, ADP and AMP in response to 646 different oxygen concentrations under TBI conditions for seven consecutive days.
647
648 Figure 9. Variation of metabolic fluxes of the ATP, ADP and AMP metabolites in response to 649 different glucose concentrations under TBI conditions for seven consecutive days.
650
651 Figure 10. Variation of metabolic fluxes of the creatine phosphate (PCr), creatine (Cr) and phosphate 652 (Pi) metabolites in response to different oxygen concentrations under TBI conditions for seven
653 consecutive days.
654
655 Figure 11. Variation of metabolic fluxes of the creatine phosphate (PCr), creatine (Cr) and phosphate 656 (Pi) metabolites in response to different glucose concentrations under TBI conditions for seven 657 consecutive days.
658 The results of the analysis of our model showed that the metabolites present in glycolysis and in the 659 pentose phosphate pathway (PPP): glucose-6-phosphate, fructose-6-phosphate, glyceraldehyde 3-P 660 (GAP), phosphoenolpyruvate (PEP), pyruvate (PYR), glucono-1,5 lactone-6-phosphate (G6L), 6- 661 phospho-D-gluconate (P6G), ribulose-5-phosphate (Ru5P), ribose-5-phosphate (R5P), D-xylulose-5- 662 phosphate (X5P), sedoheptulose-7-phosphate (S7P), erythrose-4-phosphate (E4P), lactate, NADH, 663 NAD +, NADPH and NADP, post-injury; compromised the glycolytic pathway that gives rise to lactate 664 from a glucose molecule, which behaved differently (Figures 17 to 25). The synthesis of these metabolites 665 was reduced, affecting different enzymes and their associated genes: hexokinase (EC: 2.7.1.1; GCK, 666 HKDC1, HK1, ADPGK, HK2 and HK3), glucose-6-phosphate isomerase (EC: 5.3. 1.9; GPI), 6- 667 phosphofructokinase (EC: 2.7.1.11; PFKP, PFKL, PFKM and C21orf2), aldolase (EC: 4.1.2.13; ALDOC, 668 ALDOB and ALDOA), phosphoglyceraldehyde dehydrogenase (EC: 1.2.1.12 ; GAPDHS, GAPDHS) 669 and GAPDHP29), phosphoglycerate kinase (EC: 2.7.2.3; CRISP3, PGK1, MIA3 and PGK2), 670 phosphoglycerate mutase (EC: 5.4.2.11; PGAM2, PGAM1, BPGM and PGAM4), enolase ( EC: 4.2.1.1;
671 ENO1, ENO3 and ENO2), pyruvate kinase (EC: 2.7.1.40; PKM, TMEM54, PKLR and HCN3), glucose- 672 6-phosphate dehydrogenase (EC: 1.1.1.49; G6PD and H6PD) , 6-phosphogluconolactonase (EC: 673 3.1.1.31; H6PD and PGLS), phosphogluconate dehydrogenase (EC: 1.1.1.44; PGD), ribose-5-phosphate 674 isomerase (EC: 5.3.1.6; RPIA), transketolase ( EC: 2.2.1.1; TKTL1, TKTL2 and TKT), transaldolase (EC: 675 2.2.1.2; TALDO1) and ibose-phosphate 3-epimerase (EC: 5.1.3 .one; RPE); due to the alterations 676 produced in the metabolic pathways mentioned above.
677
678 Figure 12. Variation of metabolic fluxes of glucose-6-phosphate, fructose-6-phosphate, glyceraldehyde 679 3-P (GAP), phosphoenolpyruvate (PEP) and pyruvate (PYR) metabolites in response to different 680 oxygen concentrations under TBI conditions during seven consecutive days.
681
682 Figure 13. Variation of metabolic fluxes of glucose-6-phosphate, fructose-6-phosphate, glyceraldehyde 683 3-P (GAP), phosphoenolpyruvate (PEP) and pyruvate (PYR) metabolites in response to different 684 glucose concentrations under TBI conditions during seven consecutive days.
685
686 Figure 14. Variation of metabolic fluxes of glucono-1,5-lactone-6-phosphate (G6L), 6-phospho-D- 687 gluconate (P6G), ribulose-5-phosphate (Ru5P), ribose-5-phosphate (R5P) metabolites ), D-xylulose-5- 688 phosphate (X5P), sedoheptulose-7-phosphate (S7P) and erythrose-4-phosphate (E4P) in response to 689 different oxygen concentrations under TBI conditions for seven consecutive days.
690
691 Figure 15. Variation of metabolic fluxes of glucono-1,5 lactone-6-phosphate (G6L), 6-phospho-D- 692 gluconate (P6G), ribulose-5-phosphate (Ru5P), ribose-5-phosphate (R5P) metabolites ), D-xylulose-5- 693 phosphate (X5P), sedoheptulose-7-phosphate (S7P) and erythrose-4-phosphate (E4P) in response to 694 different glucose concentrations under TBI conditions for seven consecutive days.
695
696 Figure 16. Variation of metabolic fluxes of lactate and glutamate metabolites in response to different 697 oxygen concentrations under TBI conditions for seven consecutive days.
698
699 Figure 17. Variation of metabolic fluxes of lactate and glutamate metabolites in response to different 700 glucose concentrations under TBI conditions for seven consecutive days.
701
702 Figure 18. Variation of metabolic fluxes of metabolites NADH, NAD +, NADPH and NADP in 703 response to different oxygen concentrations under TBI conditions for seven consecutive days.
704
705 Figure 19. Variation of metabolic fluxes of metabolites NADH, NAD +, NADPH and NADP in 706 response to different glucose concentrations under TBI conditions for seven consecutive days.
707
708 3.3 Identification of metabolic flows in metabolic pathways
709 Figures 25 and 26 show the variation in the flows of the metabolic routes involved in the energy 710 production processes under different concentrations of oxygen and glucose, respectively, after the 711 occurrence of a TBI. The percentage of metabolic pathway fluxes under TBI conditions at different 712 oxygen (O2) concentrations was reduced by 83%, while, at different glucose concentrations, the flux was 713 reduced by 71%; which affected both different metabolic pathways, as well as the concentration of 714 metabolites, enzymes and associated genes.
715 In the hydrolysis of Acyl-CoA, the enzyme palmitoyl-CoA hydrolase (EC: 3.1.2.2; ACOT2) present in 716 98% of the subsystem reactions was affected. In the metabolism of arachidonic acid, the enzymes that 717 reduced its activity were microsome monooxygenase (EC: 1.14.14.1; CYP3A7), 3-hydroxyacyl-CoA 718 dehydrogenase (EC: 1.1.1.35; EHHADH), arachidonate 12-lipoxygenase ( EC: 1.13.11.31; ALOX12), 719 epoxide hydrase (EC: 3.3.2.9; EPHX1) and 15-hydroxycosatetraenoate dehydrogenase (EC: 1.1.1.232), 720 associated with subsystem reactions in 45, 30, 10, 7 and 3% respectively. In the metabolism of arginine 721 and proline, the affected enzymes were N1-acetylpolyamine oxidase (EC: 1.5.3.13), diamine N- 722 acetyltransferase (EC: 2.3.1.57), primary amine oxidase (EC: 1.4.3.21 ), nitric oxide synthase (NADPH) 723 (EC: 1.14.13.39), deoxyhipusine synthase (EC: 2.5.1.46), arginase (EC: 3.5.3.1), L-glutamate gamma- 724 semialdehyde dehydrogenase (EC: 1.5 .1.12) and ornithine aminotransferase (EC: 2.6.1.13); related to 725 subsystem reactions by 15, 13, 12, 11, 9, 8, 7 and 6%, respectively.
726 In beta oxidation, the enzymes that decreased its function were enoyl-CoA hydratase (EC: 4.2.1.17), 3- 727 hydroxyacyl-CoA dehydrogenase (EC: 1.1.1.35), acyl coenzyme A dehydrogenase (EC: 1.3. 8.7), acyl- 728 CoA oxidase (EC: 1.3.3.6) and acetyl-CoA C-acyltransferase (EC: 2.3.1.16), associated with subsystem 729 reactions by 29, 22, 9, 8 and 6%, respectively. In the carnitine shuttle, the enzyme that reduced its activity 730 was carnitine O-palmitoyltransferase (EC: 2.3.1.21) present in 45% of the subsystem reactions.
731 In cholesterol metabolism, the altered enzymes were cholesterol esterase (EC: 3.1.1.13) and cholesterol 732 acyltransferase (EC: 2.3.1.26), associated with subsystem reactions in 51 and 25%, respectively; while, in 733 estrogen metabolism, the enzymes that reduced its activity were glutathione transferase (EC: 2.5.1.18), 734 peroxidase (EC: 1.11.1.7), xenobiotic monooxygenase (EC: 1.14.14.1), catechol O-methyltransferase (EC: 735 2.1.1.6) and quinine 3-monooxygenase (EC: 1.14.13.67), related to subsystem reactions by 43, 19, 18, 13 736 and 5%, respectively. In the metabolism of fatty acids, on the other hand, the results showed that the 737 affected enzymes were acyl-CoA synthetase (EC: 6.2.1.3), 3-oxoacyl-CoA reductase (EC: 1.1.1.330), beta
738 -ketoacyl-CoA synthase (EC: 2.3.1.199), (3R) -3-hydroxyacyl-CoA dehydratase (EC: 4.2.1.134) and 739 stearoyl-CoA 9-desaturase (EC: 1.14.19.1), associated with subsystem reactions by 59, 9, 8, 7 and 6%, 740 respectively.
741 In the metabolism of glycerolipids, in the same way, it could be evidenced that the enzymes that decreased 742 their activity were acylglycerol kinase (EC: 2.7.1.94), aldehyde reductase (EC: 1.1.1.1), aldehyde 743 dehydrogenase (NAD +) ( EC: 1.2.1.3) and phospholipase A2 (EC: 3.1.1.4), present in the subsystem 744 reactions in 38, 14, 12 and 11%, respectively. In the metabolism of glycosphingolipids, for its part; the 745 affected enzymes were 4-galactosyl-N-acetylglucosaminide, 3-alpha-L-fucosyltransferase (EC: 2.4.1.152), 746 ganglioside galactosyltransferase (EC: 2.4.1.62), GM2 synthase (EC: 2.4.1.92) , lactosylceramide alpha- 747 2,3-sialyltransferase (EC: 2.4.99.9), beta-galactoside alpha-2,3-sialyltransferase (EC: 2.4.99.4) and 748 globoside synthetase (EC: 2.4.1.79), related to subsystem reactions by 14, 10, 8, 7, 6 and 4%, respectively. 749 In the metabolism of leukotrienes, the analyzes showed that the altered enzymes were acyl-CoA 750 synthetase (EC: 6.2.1.3), beta-hydroxyacyl dehydrogenase (EC: 1.1.1.35), acyl-CoA oxidase (EC: 1.3 .3.6), 751 enoyl-CoA hydratase (EC: 4.2.1.17), carbonyl reductase (NADPH) (EC: 1.1.1.184), xenobiotic 752 monooxygenase (EC: 1.14.14.1), aldehyde dehydrogenase (NAD +) (EC : 1.2.1.3) and glutathione 753 transferase (EC: 2.5.1.18), associated with subsystem reactions by 14, 12, 11, 10, 9, 7, 5 and 4%, 754 respectively.
755 In the metabolism of NO glycans, the results showed that the enzymes that reduced their activity were 756 1,2-alpha-mannosidase (EC: 3.2.1.113) and acetylglucosaminyltransferase (EC: 2.4.1.102), associated with 757 the reactions of subsystem by 27 and 3%, respectively. In the metabolism of omega 3-6, it was possible 758 to show that the altered enzymes were 3-oxoacyl-CoA synthase (EC: 2.3.1.199; EC: 1.1.1.330), 3- 759 hydroxyacyl-CoA dehydrogenase (EC: 1.1. 1.35), enoyl-CoA hydratase (EC: 4.2.1.17), acyl-CoA 760 dehydrogenase (EC: 1.3.8.7) and acetyl-CoA C-acyltransferase (EC: 2.3.1.16), linked to the reactions of 761 the subsystem by 13, 7, 6, 5 and 4%, respectively. The enzymes (3R) -3-hydroxyacyl-CoA dehydratase 762 (EC: 4.2.1.134) and enoyl-CoA reductase (EC: 1.3.1.93) were altered by 12% in this subsystem.
763 In the metabolism of phenylalanine, tyrosine and tryptophan, the results showed that the enzymes that 764 reduced their activity were catechol O-methyltransferase (EC: 2.1.1.6), aldehyde dehydrogenase [NAD 765 (P) +] (EC: 1.2.1.5) and glutathione transferase (EC: 2.5.1.18); associated with subsystem reactions in 18, 766 13 and 11%, respectively; the enzymes kinurenine 2-oxoglutarate transaminase (EC: 2.6.1.7) and L-amino 767 acid decarboxylase (EC: 4.1.1.28), which are found in 5% of the subsystem reactions, while the enzymes 768 aspartate transaminase (EC: 2.6.1.1) and phenylpyruvate tautomerase (EC: 5.3.2.1) are linked to 3% of 769 the reactions of this subsystem as a whole.
770 In purine metabolism, it was possible to show that the altered enzymes were ribonucleoside-diphosphate 771 reductase (EC: 1.17.4.1) and nucleoside-diphosphate kinase (EC: 2.7.4.6), present in 13% of the reactions 772 of the subsystem; AMP phosphatase (EC: 3.1.3.5) and pyruvate kinase (EC: 2.7.1.40), associated with 5% 773 of the reactions; while the enzymes present in 3% of the reactions were adenylate cyclase (EC: 4.6.1.1), 774 adenylate kinase (EC: 2.7.4.3), GMP kinase (EC: 2.7.4.8) and adenylosuccinate lyase ( EC: 4.3.2.2). The 775 enzyme hexokinase (EC: 2.7.1.1) was evidenced in 10% of the subsystem reactions.
776 This work presents the complete tissue-specific reconstruction of a glutamatergic neuron that includes 777 gene expression data from healthy human brain tissue samples and from brain tissue samples taken from 778 people who suffered from TBI. In this reconstruction, the gene-protein-reaction relationships were 779 included in detail, taking 741 enzymes, 2,393 genes, 4,130 reactions and 5,398 metabolites, distributed in 780 eight cellular compartments (extracellular, peroxisome, mitochondrion, cytoplasm, lysosome, 781 endoplasmic reticulum, Golgi apparatus and nucleus ). For this metabolic reconstruction, different 782 sources of information and specialized databases were used, such as PubMed, KEGG, HumanCyc, 783 MetaCyc, GeneCards and Ensembl.
784 In accordance with the research carried out prior to the present study and thanks to the experimental 785 phase, we conclude that our model represents the most detailed metabolic reconstruction of a 786 glutamatergic neuron reported to date. The validity of the consulted models can be demonstrated through 787 comparisons with physiological data; in contrast to our model that predicts internal flow measurements 788 consistent with previously experimentally measured values (Jalloh et al., 2015).
789 Reconstruction includes neuronal metabolism pathways: lipoic acid metabolism and fatty acid 790 metabolism, beta oxidation, beta-alanine, biopterin, bile acid biosynthesis, biotin and butanoate; 791 cholesterol, chondroitin, ROS detoxification, sphingolipids, steroids and estrogens; of the tricarboxylic 792 acid cycle, such as the metabolism of phenylalanine, tyrosine, tryptophan and folate; of oxidative 793 phosphorylation, of fructose and mannose; from galactose, from N-O glycans, from glycerolipids, from 794 glycosphingolipids, from glycolysis / gluconeogenesis and glutathione; the metabolism of glycine, serine 795 and threonine; the hydrolysis of Acyl-CoA, the metabolism of histidine, the carnitine shuttle, leukotrienes, 796 linoleate and lysine; arachidonic acid metabolism, alanine, aspartate and glutamate metabolism; 797 metabolism of arginine and proline, metabolism of ascorbate and aldarate, metabolism of sulfur, 798 metabolism of cysteine and methionine, metabolism of valine, leucine and isoleucine; metabolism of 799 Vitamins B6, B12, D and E; the metabolism of acylglycerides, nicotinate and nicotinamide, of nucleotides, 800 of omega 3-6, of pyrimidines, of pyruvate, of porphyrin, of propanoate, of prostaglandins, of proteins; 801 the metabolism of purines, retinol, riboflavin, the pentose phosphate pathway; the metabolism of
802 serotonin and melatonin; and the metabolism of terpenoids, thiamine, tyrosine, tryptophan and 803 extracellular transport. Therefore, this reconstruction simulates the global metabolic behavior of the 804 glutamatergic neuron, since it encompasses all routes of intracellular metabolism.
805 The in-silico analysis was carried out taking experimental data of different concentrations of O2 and 806 glucose, where the predictive capacity of the model was evidenced, since the results revealed the variation 807 in the concentration of fluxes of metabolites of interest and percentage of change of the routes metabolic. 808 In addition, the model allowed us to understand, in a global way, the properties of the metabolic network 809 in a physiological state, such as those associated with glutamate synthesis, and in a pathological state that 810 produced significant changes in the variation of O2 and glucose under conditions of injury. traumatic 811 brain injury (TBI); in addition to proving that the high concentrations of lactate in the brain are the 812 product of a mitochondrial dysfunction that forces neurons to depend on glycolysis to produce ATP. 813 Without electron transport chains at the mitochondrial level, the conversion of pyruvate to lactate is 814 necessary for the recycling of NADH into NAD +, which allows the continuity of the cycle of anaerobic 815 glycolysis.
816 The adequate presence of O2 is essential for mitochondrial function; however, the mitochondria of the 817 neuronal cells analyzed became dysfunctional in the presence of O2 since the tricarboxylic acid cycle 818 (TCA) was compromised as a result of the cellular effects of TBI. The results show that a high 819 concentration of extracellular lactate accumulates under TBI conditions because this metabolite cannot 820 enter the system and integrate into metabolic pathways.
821 Experimental data from magnetic resonance spectroscopic images and positron emission tomography 822 (PET) of 18F-fluorodeoxy-glucose have suggested that neurons present high levels of flux in glycolysis, 823 indicating a disturbance in energy metabolism that manifests itself specifically as a relative increase in 824 anaerobic over aerobic respiration (Devience et al., 2017).
825 In particular, several studies that analyzed the conversion of pyruvate to lactate to determine the changes 826 resulting from the relative amount of anaerobic versus aerobic respiration (Golman et al., 2006; Serrao 827 & Brindle, 2016) and previous scientific studies, have measured the conversion of pyruvate in bicarbonate 828 to evaluate the changes presented in the mitochondrial aerobic energy metabolism (Park et al., 2017). 829 Evidence indicates that pyruvate dehydrogenase enzyme activity is inhibited within 4 hours after TBI in 830 rats (Sharma et al., 2009), causing a change in the metabolism of pyruvate from bicarbonate to lactate 831 (Robertson, Saraswati, & Fiswkum, 2007). However, the contributions of the metabolites present in 832 glycolysis, the tricarboxylic acid cycle and oxidative phosphorylation to neuronal energy metabolism
833 remain a subject of research.
834 The neuron can present different metabolic states, which change in response to external stimuli, so that, 835 at an experimental level, several measurements of active metabolic pathways can be found in this cell 836 (Brooks & Martin, 2015).
837 The results showed an activation of glycolytic fluxes in response to the different concentrations of O2 838 and glucose, confirming a decrease in the activation of reactions belonging to the metabolic pathways of 839 glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway. and oxidative phosphorylation; in 840 addition to the predictive capacity of our model to describe the altered metabolism of neuronal cells that 841 leads to a decrease in the capacity of glutamatergic neurotransmission.
842 At the flow level, a higher activity was observed in glycolysis and the pentose phosphate pathway and, 843 finally, in oxidative phosphorylation; exposing direct relationships between the different cycles. 844 Therefore, the flux distributions in the simulations could be related to metabolic tasks established in the 845 model, where an excitatory cellular state is simulated due to the synthesis and release of glutamate. In this 846 sense, the model responded with its energy metabolism to variations in the concentration of O2 and 847 glucose, in response to requirements generated by the entry of these metabolites.
848 The production of ATP and the synthesis of glutamate were used as objective functions, requiring the 849 model to respond in the generation of the maximum energy production of the system. During the 850 analysis, it was shown that under TBI conditions, the model responded by reducing metabolic fluxes in 851 ATP generation, increasing glutamate levels; results that agree with the experimental data obtained 852 previously (Jalloh et al., 2015). The results showed that a decrease in the concentration of O2 and glucose 853 produced a cellular energy failure and the levels of ATP decreased, causing the release of glutamate until 854 causing excitotoxicity. The result evidenced the predictive and consistent capacity of the model, 855 compared with the experimental results (Guerriero et al., 2015). This was reflected by decreasing the 856 concentration of metabolites, mainly in the mitochondria. Flux changes were observed in the pathways 857 that produce energy metabolites (glycolysis and the tricarboxylic acid cycle) (Cheng et al., 2012). 858 Considering the exposed results, it was confirmed that the model responded with the synthesis of ATP, 859 with the increase in oxidative activity and with the increase in the consumption of energy substrates.
860 Different studies that support the results obtained (Kann & Kovacs, 2006) have concluded that neurons 861 are cells that need large amounts of ATP for the maintenance of positive ionic gradients across cell 862 membranes, as well as for neurotransmission processes. As most of the neuronal ATP is generated in 863 oxidative metabolism; neurons are primarily dependent on mitochondrial function and oxygen supply.
864 The metabolites NADH and NAD + that are present in glucose and lactate metabolism are controlled 865 by three main processes: glycolysis, the activity of the enzyme lactate dehydrogenase, and the activity of 866 the NADH malate-aspartate shuttle; where cytosolic NADH dehydrogenase reduces its activity in 867 response to a decrease in the activity of glycolysis, which converts NAD + into NADH by the action of 868 the enzyme glyceraldehyde-3-phosphate dehydrogenase; Result observed in the variations of these 869 metabolites in the model (Díaz-García et al., 2017).
870 The experimental studies consulted (Rodriguez-Rodriguez et al., 2013) support the results of the 871 simulation, indicating that neurons activate an oxidative metabolism under different concentrations of 872 O2 and glucose, which does not depend exclusively on glycolysis as its major source of ATP.
873 The results of the present study also demonstrated the predictive capacity of the neuron to react to 874 external stimuli and the sensitivity of the model under certain laboratory conditions; which is supported 875 by experimental reports. In the case of glucose, the activation of glycolysis, the TCA tricarboxylic acid 876 cycle and the electron transport chain could be evidenced (Carpenter et al., 2015). On the other hand, 877 changes in O2 levels caused fluctuations in the fluxes of metabolites such as pyruvate and NADH (Ma 878 et al., 2017). Previous results revealed that when glucose and O2 concentration are reduced, 879 hypermetabolism associated with increased glutamate synthesis is activated, leading to excitotoxicity. In 880 the model, it was observed that this phenomenon reduces the flows of the main metabolic pathways, 881 affecting cell function (Lai et al., 2014).
882 The model developed showed that neuronal metabolism, under TBI conditions and at different 883 concentrations of O2 and glucose, presents flow values of 1.2 µmole / g / min and 3.3 µmole / g / min, 884 respectively; which is consistent with what was reported by Zaaroor et al. (2007). In the model, 885 restrictions were used and glutamate production was maximized. Therefore, metabolic flux values were 886 obtained that are consistent with that reported in the literature (Cloutier et al., 2009). The results also 887 showed the predictive capacity of the concentration of metabolites such as ATP, ADP, NADH, NAD 888 +, NADPH and NADP +, important cofactors in metabolic reactions; which was to be expected, thanks 889 to the degree of connectivity of cellular metabolism; which allowed to perform simulations under 890 pathological conditions in neurodegenerative diseases, mood disorders, among other related conditions; 891 being able to observe how variations in the entry of metabolites into the system (glucose, O2, among 892 others), prediction of metabolic fluxes of metabolites and / or metabolic pathways of interest, studies by 893 cellular compartments, activation / inhibition of genes and / or enzymes associated with reactions of 894 interest, as well as prediction of genes, enzymes and / or metabolites of pharmaceutical interest.
895 Knowing these reactions in detail facilitated the identification of metabolic alterations at the reaction, 896 gene, metabolite and enzyme levels that are involved in the glutamate synthesis process. This knowledge 897 is of vital importance in the investigation of potential therapeutic enzyme inhibitors or enhancers to treat 898 neuronal damage, such as the arginase enzyme (EC: 3.5.3.1), which is involved in stroke (Quirié et al., 899 2013) .
900 The tissue-specific reconstruction presented in this study allows significant progress in: a) understanding 901 the processes that mediate neurodegeneration; particularly those that originate after undergoing a TBI, 902 b) integrate omic data into a previously reconstructed, published and public domain metabolic model for 903 a glutamatergic neuron; available in the BIOMODELS database (MODEL1809090002), and c) simulate 904 possible scenarios focused on the treatment of neurological disabilities in the Colombian population, 905 since, according to the data provided by the Institute of Legal Medicine and Forensic Sciences, TBIs are 906 the main cause of death and disability in the population aged 12 to 45 years.
907 The metabolic reconstruction will be available to the scientific community at a national and international 908 level that, through interactive simulations developed by our research group, will allow to advance in the 909 understanding of intracellular metabolism and the responses to stimuli of a human glutamatergic neuron, 910 as scientific material that contributes to the future study of neurodegenerative diseases and disabilities 911 due to neuronal injuries.
912 This development will serve as the basis for the development of new therapeutic and research approaches; 913 since the reconstruction will generate a point of convergence of collaborative work for the transfer, 914 analysis and processing of omic data obtained from experimentation with the influence of neuronal 915 activity in neurodegenerative diseases, in addition to personalized medicine treatments.
916 To our understanding, it is the most complete reconstruction of a human glutamatergic neuron, which 917 will allow the scientific community to advance holistically in the understanding of neurodegeneration 918 processes and in the development of therapeutic or pharmacological strategies that reduce the impact 919 and the consequences of these diseases on the patient and his family, as well as on all actors external to 920 the patient and his family, involved (hospitals, clinics and laboratories). Additionally, research on 921 neurodegenerative processes would allow to advance and extrapolate the results consulted and obtained 922 to the treatment of other diseases that show similar processes during development and progression in 923 patients suffering from neurodegenerative diseases.
924 4. CONCLUSIONS
925 A tissue-specific model of glutamatergic neuron was reconstructed with a total of 3,888 genes, 5,328 926 reactions and 5,101 metabolites, distributed in 8 cell compartments. This genomic-scale metabolic 927 reconstruction represents the most complete tissue-specific reconstruction of a glutamatergic neuron.
928 Glutamatergic neuron metabolic mechanisms were identified during TBI from the integration of gene 929 expression data from human brains into a metabolic reconstruction. The reconstructed metabolic model 930 allows for joint analysis by cell compartment, subsystem, metabolite, enzyme and genes associated with 931 reactions.
932 The restriction parameters in the concentration of oxygen and glucose showed that the simulations 933 carried out presented flows consistent with the experimental reports, which suggests that in the 934 reconstructed metabolic model it is possible to simulate metabolic changes that occur in glutamatergic 935 neurons in response to different stimuli (Lai et al., 2007).
936 With the integration of transcriptomic data to the model, metabolic effects were evidenced when 937 suffering a TBI, in the variation of metabolite fluxes at different concentrations of oxygen and glucose, 938 analyzes that were carried out successfully. In the reconstructed metabolic model, changes in the 939 metabolic network were evidenced under a TBI condition.
940 ACKNOWLEDGMENTS
941 This work was supported in part by grants Pontificia Universidad Javeriana IDs 7425, 7714 and 942 7740 to Janneth González and Colciencias. 943 944 CONFLICT OF INTEREST STATEMENT
945 The authors declare that the research was conducted in the absence of any commercial or 946 financial relationships that could be construed as a potential conflict of interest.
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