Identificación de reacciones controladoras en un modelo computacional multi­-ómico astrocitario de lipotoxicidad inducida por ácido palmítico

Los astrocitos juegan un papel importante en varios procesos en el cerebro, incluidas condiciones patológicas como las enfermedades neurodegenerativas. Estudios recientes han demostrado que el aumento de ácidos grasos saturados como el ácido palmítico (PA) desencadena vías proinflamatorias en el cer...

Full description

Autores:
Angarita Rodríguez, María Andrea
Tipo de recurso:
Fecha de publicación:
2022
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/82354
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/82354
https://repositorio.unal.edu.co/
Palabra clave:
610 - Medicina y salud::616 - Enfermedades
Neuroglia
Células
Cells
Astrocitos
Integración de datos
Ácido palmítico
Modelo computacional
Multi-ómico
Teoría de control
Cavidades farmacológicas
Astrocytes
Data integration
Palmitic acid
Computational model
Multi-omics
Control theory
Drugable cavities
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
id UNACIONAL2_9847b9a365045de0b85730f4c762b795
oai_identifier_str oai:repositorio.unal.edu.co:unal/82354
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Identificación de reacciones controladoras en un modelo computacional multi­-ómico astrocitario de lipotoxicidad inducida por ácido palmítico
dc.title.translated.eng.fl_str_mv Identification of controlling reactions in a astrocytic multi-omics computational model of palmitic acid-induced lipotoxicity
title Identificación de reacciones controladoras en un modelo computacional multi­-ómico astrocitario de lipotoxicidad inducida por ácido palmítico
spellingShingle Identificación de reacciones controladoras en un modelo computacional multi­-ómico astrocitario de lipotoxicidad inducida por ácido palmítico
610 - Medicina y salud::616 - Enfermedades
Neuroglia
Células
Cells
Astrocitos
Integración de datos
Ácido palmítico
Modelo computacional
Multi-ómico
Teoría de control
Cavidades farmacológicas
Astrocytes
Data integration
Palmitic acid
Computational model
Multi-omics
Control theory
Drugable cavities
title_short Identificación de reacciones controladoras en un modelo computacional multi­-ómico astrocitario de lipotoxicidad inducida por ácido palmítico
title_full Identificación de reacciones controladoras en un modelo computacional multi­-ómico astrocitario de lipotoxicidad inducida por ácido palmítico
title_fullStr Identificación de reacciones controladoras en un modelo computacional multi­-ómico astrocitario de lipotoxicidad inducida por ácido palmítico
title_full_unstemmed Identificación de reacciones controladoras en un modelo computacional multi­-ómico astrocitario de lipotoxicidad inducida por ácido palmítico
title_sort Identificación de reacciones controladoras en un modelo computacional multi­-ómico astrocitario de lipotoxicidad inducida por ácido palmítico
dc.creator.fl_str_mv Angarita Rodríguez, María Andrea
dc.contributor.advisor.none.fl_str_mv Pinzón Velasco, Andres Mauricio
dc.contributor.author.none.fl_str_mv Angarita Rodríguez, María Andrea
dc.contributor.other.none.fl_str_mv Janneth González Santos
dc.contributor.researchgroup.spa.fl_str_mv Grupo de Investigación en Bioinformática y Biología de Sistemas - GIBBS
dc.subject.ddc.spa.fl_str_mv 610 - Medicina y salud::616 - Enfermedades
topic 610 - Medicina y salud::616 - Enfermedades
Neuroglia
Células
Cells
Astrocitos
Integración de datos
Ácido palmítico
Modelo computacional
Multi-ómico
Teoría de control
Cavidades farmacológicas
Astrocytes
Data integration
Palmitic acid
Computational model
Multi-omics
Control theory
Drugable cavities
dc.subject.lemb.spa.fl_str_mv Neuroglia
Células
dc.subject.lemb.eng.fl_str_mv Cells
dc.subject.proposal.spa.fl_str_mv Astrocitos
Integración de datos
Ácido palmítico
Modelo computacional
Multi-ómico
Teoría de control
Cavidades farmacológicas
dc.subject.proposal.eng.fl_str_mv Astrocytes
Data integration
Palmitic acid
Computational model
Multi-omics
Control theory
Drugable cavities
description Los astrocitos juegan un papel importante en varios procesos en el cerebro, incluidas condiciones patológicas como las enfermedades neurodegenerativas. Estudios recientes han demostrado que el aumento de ácidos grasos saturados como el ácido palmítico (PA) desencadena vías proinflamatorias en el cerebro. El uso de neuroesteroides sintéticos como la tibolona ha demostrado mecanismos neuroprotectores. Sin embargo, faltan estudios amplios, con un punto de vista sistémico, sobre el papel neurodegenerativo de PA y los mecanismos neuroprotectores de la tibolona. En este estudio, realizamos la integración de datos multiómicos (transcriptoma y proteoma) en un modelo metabólico a escala genómica de astrocitos humanos para estudiar la respuesta astrocitaria durante el tratamiento con palmitato. Evaluamos los flujos metabólicos en tres escenarios (saludable, inflamación inducida por PA y tratamiento con tibolona bajo inflamación por PA). También aplicamos un enfoque de teoría de control para identificar aquellas reacciones que ejercen más control en el sistema astrocítico. Por último, analizamos las cavidades de las enzimas asociadas a estas reacciones para determinar sus potenciales sitios de unión caracterizándolos en función de puntajes de ligandabilidad y capacidad de interacción farmacológica (drogabilidad). Nuestros resultados sugieren que PA genera una modulación del metabolismo central y secundario, mostrando un cambio en el uso de la fuente de energía a través de la inhibición del ciclo del folato, la β-oxidación de ácidos grasos y la regulación positiva de la formación de cuerpos cetónicos. Encontramos 25 interruptores metabólicos bajo regulación celular mediada por PA, 9 de los cuales fueron críticos solo en el escenario inflamatorio pero no en el protector de tibolona. Dentro de estas reacciones, los perfiles de acoplamiento inhibitorio, total y direccional fueron hallazgos clave, que desempeñaron un papel fundamental en la desregulación de las vías metabólicas que pueden aumentar la neurotoxicidad. De los 25 interruptores metabólicos 16 presentaron cavidades potencialmente drogables que, a su vez, contienen el sitio activo de la proteína. En su conjunto, estas 16 enzimas se configuran como potenciales objetivos terapéuticos. Finalmente, el marco general de nuestro enfoque facilitó la comprensión de la regulación metabólica compleja y puede usarse para la exploración in silico de los mecanismos de regulación de las células astrocitarias, y potencialmente de otros tipos celulares, dirigiendo un trabajo experimental futuro más complejo en enfermedades neurodegenerativas. (Texto tomado de la fuente)
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-10-05T23:38:20Z
dc.date.available.none.fl_str_mv 2022-10-05T23:38:20Z
dc.date.issued.none.fl_str_mv 2022-10-04
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/82354
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/82354
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.references.spa.fl_str_mv Ã, G. B. S., & Park, E. (2003). Taurine : new implications for an old amino acid. 226, 195–202. https://doi.org/10.1016/S0378-1097(03)00611-6
Agostinho, P., Cunha, R. a, & Oliveira, C. (2010). Neuroinflammation , Oxidative Stress and the Pathogenesis of Alzheimer ’ s Disease. Current Pharmacutical Design, 16, 2766–2778.
Allen, N. J., Eroglu, C., Development, F., Studies, B., & Jolla, L. (2018). Cell biology of astrocyte-synapse interactions. Neuron., 96(3), 697–708. https://doi.org/10.1016/j.neuron.2017.09.056.Cell
Altenbuchinger, M., Zacharias, H. U., Solbrig, S., Schäfer, A., Büyüközkan, M., Schultheiß, U. T., Kotsis, F., Köttgen, A., Spang, R., Oefner, P. J., Krumsiek, J., & Gronwald, W. (2019). A multi-source data integration approach reveals novel associations between metabolites and renal outcomes in the German Chronic Kidney Disease study. Scientific Reports, 9(1), 1–13. https://doi.org/10.1038/s41598-019-50346-2
Arevalo, M. A., Azcoitia, I., & Garcia-Segura, L. M. (2015). The neuroprotective actions of oestradiol and oestrogen receptors. Nature Reviews Neuroscience, 16(1), 17–29. https://doi.org/10.1038/nrn3856
Arnedo, M., Ramos, M., Puisac, B., Concepcion, M., Teresa, E., Pie, A., Bueno, G., J., F., Gomez-Puertas, P., & Pie, J. (2011). Mitochondrial HMG–CoA Synthase Deficiency. Advances in the Study of Genetic Disorders, February. https://doi.org/10.5772/22151
Asgari, Y., Salehzadeh-Yazdi, A., Schreiber, F., & Masoudi-Nejad, A. (2013). Controllability in cancer metabolic networks according to drug targets as driver nodes. PLoS ONE, 8(11), 1–12. https://doi.org/10.1371/journal.pone.0079397
Ávila, M., Garcia-segura, L. M., Cabezas, R., Torrente, D., Capani, F., Gonzalez, J., & Barreto, G. E. (2014). Journal of Steroid Biochemistry & Molecular Biology Tibolone protects T98G cells from glucose deprivation. Journal of Steroid Biochemistry and Molecular Biology, 144, 294–303. https://doi.org/10.1016/j.jsbmb.2014.07.009
Ayyildiz, M., Celiker, S., Ozhelvaci, F., & Akten, E. D. (2020). Identification of Alternative Allosteric Sites in Glycolytic Enzymes for Potential Use as Species-Specific Drug Targets. 7(May), 1–19. https://doi.org/10.3389/fmolb.2020.00088
Badaut, J. (2010). Aquaglyceroporin 9 in brain pathologies. Neuroscience, 168(4), 1047–1057. https://doi.org/10.1016/j.neuroscience.2009.10.030
Bailey, L. B., & Gregory, J. F. (1999). Recent Advances in Nutritional Science Folate Metabolism and. The Journal of Nutrition, 129, 779–782.
Balog, E. (2014). An Allosteric Signaling Pathway of Human 3- Phosphoglycerate Kinase from Force Distribution Analysis. 10(1). https://doi.org/10.1371/journal.pcbi.1003444
Balsa, E., Perry, E. A., Bennett, C. F., Jedrychowski, M., Gygi, S. P., Doench, J. G., & Puigserver, P. (2020). Defective NADPH production in mitochondrial disease complex I causes in fl ammation and cell. Nature Communications, 1–12. https://doi.org/10.1038/s41467-020-16423-1
Barinova, K., Khomyakova, E., Semenyuk, P., Schmalhausen, E., & Muronetz, V. (2018). SC. Archives of Biochemistry and Biophysics. https://doi.org/10.1016/j.abb.2018.02.002
Basler, G., Grimbs, S., & Ebenho, O. (2012). Evolutionary significance of metabolic network properties. November 2011, 1168–1176.
Basler, G., & Nikoloski, Z. (2011). JMassBalance : mass-balanced randomization and analysis of metabolic networks. 27(19), 2761–2762. https://doi.org/10.1093/bioinformatics/btr448
Basler, G., Nikoloski, Z., Larhlimi, A., Barabási, A. L., & Liu, Y. Y. (2016). Control of fluxes in metabolic networks. Genome Research, 26(7), 956–968. https://doi.org/10.1101/gr.202648.115
Becerra-Calixto, A., & Cardona-Gómez, G. P. (2017). The role of astrocytes in neuroprotection after brain stroke: Potential in cell therapy. Frontiers in Molecular Neuroscience, 10(April), 1–12. https://doi.org/10.3389/FNMOL.2017.00088
Bélanger, M., & Magistretti, P. J. (2009). The role of astroglia in neuroprotection. Dialogues in Clinical Neuroscience, 11(3), 281–296.
Bidkhori, G., Benfeitas, R., Elmas, E., Kararoudi, M. N., Arif, M., Uhlen, M., Nielsen, J., & Mardinoglu, A. (2018). Metabolic network-based identification and prioritization of anticancer targets based on expression data in hepatocellular carcinoma. Frontiers in Physiology, 9(JUL), 1–11. https://doi.org/10.3389/fphys.2018.00916
Bordbar, A, & Palsson, B. O. (2011). Using the reconstructed genome-scale human metabolic network to study physiology and pathology. 131–141. https://doi.org/10.1111/j.1365-2796.2011.02494.x
Bordbar, Aarash, Jamshidi, N., & Palsson, B. O. (2011). IAB-RBC-283: A proteomically derived knowledge-base of erythrocyte metabolism that can be used to simulate its physiological and patho-physiological states. BMC Systems Biology, 5, 1–12. https://doi.org/10.1186/1752-0509-5-110
Bordbar, Aarash, Monk, J. M., King, Z. A., & Palsson, B. O. (2014). Constraint-based models predict metabolic and associated cellular functions. 15(February), 107–120. https://doi.org/10.1038/nrg3643
Bordel, S., Agren, R., & Nielsen, J. (2010). Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes. PLoS Computational Biology, 6(7), 16. https://doi.org/10.1371/journal.pcbi.1000859
Brunk, E., Sahoo, S., Zielinski, D. C., Altunkaya, A., Dräger, A., Mih, N., Gatto, F., Nilsson, A., Preciat Gonzalez, G. A., Aurich, M. K., Prlic, A., Sastry, A., Danielsdottir, A. D., Heinken, A., Noronha, A., Rose, P. W., Burley, S. K., Fleming, R. M. T., Nielsen, J., … Palsson, B. O. (2018). Recon3D enables a three-dimensional view of gene variation in human metabolism. Nature Biotechnology, 36(3), 272–281. https://doi.org/10.1038/nbt.4072
Burgard, A. P., Nikolaev, E. V, Schilling, C. H., & Maranas, C. D. (2004). Flux Coupling Analysis of Genome-Scale Metabolic Network Reconstructions. 4, 301–312. https://doi.org/10.1101/gr.1926504.
Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chen, L., Crichlow, G. V, Christie, C. H., Dalenberg, K., Costanzo, L. Di, Duarte, J. M., Dutta, S., Feng, Z., Ganesan, S., Goodsell, D. S., Ghosh, S., Green, R. K., Guzenko, D., Hudson, B. P., Lawson, C. L., … Zhuravleva, M. (2021). RCSB Protein Data Bank : powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology , biomedicine , biotechnology , bioengineering and energy sciences. 49(November 2020), 437–451. https://doi.org/10.1093/nar/gkaa1038
Buskila, Y., Bellot-Saez, A., & Morley, J. W. (2019). Generating Brain Waves, the Power of Astrocytes. Frontiers in Neuroscience, 13(October), 1–10. https://doi.org/10.3389/fnins.2019.01125
Butland, S. L., Sanders, S. S., Schmidt, M. E., Riechers, S. P., Lin, D. T. S., Martin, D. D. O., Vaid, K., Graham, R. K., Singaraja, R. R., Wanker, E. E., Conibear, E., & Hayden, M. R. (2014). The palmitoyl acyltransferase HIP14 shares a high proportion of interactors with huntingtin: Implications for a role in the pathogenesis of Huntington’s disease. Human Molecular Genetics, 23(15), 4142–4160. https://doi.org/10.1093/hmg/ddu137
Bylicky, M. A., Mueller, G. P., & Day, R. M. (2018). Mechanisms of endogenous neuroprotective effects of astrocytes in brain injury. Oxidative Medicine and Cellular Longevity, 2018. https://doi.org/10.1155/2018/6501031
Cabezas, R., El-Bachá, R. S., González, J., & Barreto, G. E. (2012). Mitochondrial functions in astrocytes: Neuroprotective implications from oxidative damage by rotenone. Neuroscience Research, 74(2), 80–90. https://doi.org/10.1016/j.neures.2012.07.008
Cammisa, M., Correra, A., Andreotti, G., & Cubellis, M. V. (2012). Identification and analysis of conserved pockets on protein surfaces. February 2014. https://doi.org/10.1186/1471-2105-14-S7-S9
Carta, G., Murru, E., Banni, S., & Manca, C. (2017). Palmitic acid: Physiological role, metabolism and nutritional implications. Frontiers in Physiology, 8(NOV), 1–14. https://doi.org/10.3389/fphys.2017.00902
Ceccarelli, S. M., Chomienne, O., Gubler, M., & Arduini, A. (2011). Carnitine Palmitoyltransferase ( CPT ) Modulators : A Medicinal Chemistry Perspective on 35 Years of Research.
Chang, R. L., Xie, L., Xie, L., Bourne, P. E., & Palsson, B. (2010). Drug off-target effects predicted using structural analysis in the context of a metabolic network model. PLoS Computational Biology, 6(9). https://doi.org/10.1371/journal.pcbi.1000938
Chaudhry, F. A., Krizaj, D., Larsson, P., Reimer, R. J., Wreden, C., Storm-Mathisen, J., Copenhagen, D., Kavanaugh, M., & Edwards, R. H. (2001). Coupled and uncoupled proton movement by amino acid transport system N. EMBO Journal, 20(24), 7041–7051. https://doi.org/10.1093/emboj/20.24.7041
Chaudhry, F. A., Reimer, R. J., Krizaj, D., Barber, D., Storm-Mathisen, J., Copenhagen, D. R., & Edwards, R. H. (1999). Molecular analysis of system N suggests novel physiological roles in nitrogen metabolism and synaptic transmission. Cell, 99(7), 769–780. https://doi.org/10.1016/S0092-8674(00)81674-8
Chen, K., Wu, S., Ye, S., Huang, H., Zhou, Y., & Zhou, H. (2021). Dimethyl Fumarate Induces Metabolic Crisie to Suppress Pancreatic Carcinoma. 12(February), 1–14. https://doi.org/10.3389/fphar.2021.617714
Chen, P., Cheng, S., Lin, H., Lee, C., & Chou, C. (2018). Risk Factors for the Progression of Mild Cognitive Impairment in Different Types of Neurodegenerative Disorders. 2018. https://doi.org/10.1155/2018/6929732
Coppedè, F. (2021). One-carbon epigenetics and redox biology of neurodegeneration. Free Radical Biology and Medicine, 170(October), 19–33. https://doi.org/10.1016/j.freeradbiomed.2020.12.002
Coppedè, F., Mancuso, M., Siciliano, G., Migliore, L., & Murri, L. (2006). Genes and the environment in neurodegeneration. Bioscience Reports, 26(5), 341–367. https://doi.org/10.1007/s10540-006-9028-6
Crespo-Castrillo, A., & Arevalo, M. A. (2020). Microglial and astrocytic function in physiological and pathological conditions: Estrogenic modulation. International Journal of Molecular Sciences, 21(9). https://doi.org/10.3390/ijms21093219
Crespo-Castrillo, A., Yanguas-Casás, N., Arevalo, M. A., Azcoitia, I., Barreto, G. E., & Garcia-Segura, L. M. (2018). The Synthetic Steroid Tibolone Decreases Reactive Gliosis and Neuronal Death in the Cerebral Cortex of Female Mice After a Stab Wound Injury. Molecular Neurobiology, 55(11), 8651–8667. https://doi.org/10.1007/s12035-018-1008-x
Cummings, J. L., Morstorf, T., & Zhong, K. (2014). Alzrt269. 1–7.
Currais, A., Goldberg, J., Farrokhi, C., Chang, M., Prior, M., Dargusch, R., Daugherty, D., Armando, A., Quehenberger, O., Maher, P., & Schubert, D. (2015). A comprehensive multiomics approach toward understanding the relationship between aging and dementia. Aging, 7(11), 937–955. https://doi.org/10.18632/aging.100838
Das, A., Banik, N. L., & Ray, S. K. (2010). Flavonoids Activated Caspases for Apoptosis in Human Glioblastoma T98G and U87MG Cells But Not in Human Normal Astrocytes. 164–176. https://doi.org/10.1002/cncr.24699
David, L., Marashi, S. A., Larhlimi, A., Mieth, B., & Bockmayr, A. (2011). FFCA: A feasibility-based method for flux coupling analysis of metabolic networks. BMC Bioinformatics, 12(1), 236. https://doi.org/10.1186/1471-2105-12-236
De Carvalho, C. C. C. R., & Caramujo, M. J. (2018). The various roles of fatty acids. Molecules, 23(10). https://doi.org/10.3390/molecules23102583
De Young, G. W., & Keizer, J. (1992). A single-pool inositol 1,4,5-trisphosphate-receptor-based model for agonist-stimulated oscillations in Ca2+ concentration. Proceedings of the National Academy of Sciences of the United States of America, 89(20), 9895–9899. https://doi.org/10.1073/pnas.89.20.9895
Devkota, P., & Wuchty, S. (2020). Controllability analysis of molecular pathways points to proteins that control the entire interaction network. Scientific Reports, 10(1), 1–9. https://doi.org/10.1038/s41598-020-59717-6
Dhandapani, K. M., Wade, F. M., Mahesh, V. B., & Brann, D. W. (2005). Astrocyte-derived transforming growth factor-β mediates the neuroprotective effects of 17β-estradiol: Involvement of nonclassical genomic signaling pathways. Endocrinology, 146(6), 2749–2759. https://doi.org/10.1210/en.2005-0014
Dhote, V., Mandloi, A. S., Singour, P. K., Kawadkar, M., Ganeshpurkar, A., & Jadhav, M. P. (2022). Neuroprotective Effects of Combined Trimetazidine and Progesterone on Cerebral Reperfusion Injury. Current Research in Pharmacology and Drug Discovery, 100108. https://doi.org/10.1016/j.crphar.2022.100108
Dilcan, G., Doruker, P., & Demet, E. (2019). binding affinity of alternative conformers of human β 2 - ­ adrenergic receptor in the presence of intracellular loop 3 ( ICL3 ) and their potential use in virtual screening studies. June 2018, 883–899. https://doi.org/10.1111/cbdd.13478
Doengi, M., Hirnet, D., Coulon, P., Pape, H., Deitmer, J. W., & Lohr, C. (2009). GABA uptake-dependent Ca 2 ؉ signaling in developing olfactory bulb astrocytes. 1–6.
Duarte, N. C., Becker, S. A., Jamshidi, N., Thiele, I., Mo, M. L., Vo, T. D., Srivas, R., & Palsson, B. Ø. (2007). Global reconstruction of the human metabolic network based on genomic and bibliomic data. 104(6).
Dupuis, J. R., Ruiz-Arce, R., Barr, N. B., Thomas, D. B., & Geib, S. M. (2019). Range-wide population genomics of the Mexican fruit fly: Toward development of pathway analysis tools. Evolutionary Applications, 12(8), 1641–1660. https://doi.org/10.1111/eva.12824
Durkee, C. A., & Araque, A. (2019). Diversity and Specificity of Astrocyte–neuron Communication. Neuroscience, 396(November), 73–78. https://doi.org/10.1016/j.neuroscience.2018.11.010
Farfa, E. D., & Gallardo, J. M. (2014). Tibolone Prevents Oxidation and Ameliorates Cholinergic Deficit Induced by Ozone Exposure in the Male Rat Hippocampus. 1776–1786. https://doi.org/10.1007/s11064-014-1385-0
Farmer, B. C., Walsh, A. E., Kluemper, J. C., & Johnson, L. A. (2020). Lipid Droplets in Neurodegenerative Disorders. Frontiers in Neuroscience, 14(July), 1–14. https://doi.org/10.3389/fnins.2020.00742
Fatima, S., Hu, X., Gong, R. H., Huang, C., Chen, M., Wong, H. L. X., Bian, Z., & Kwan, H. Y. (2019). Palmitic acid is an intracellular signaling molecule involved in disease development. Cellular and Molecular Life Sciences, 76(13), 2547–2557. https://doi.org/10.1007/s00018-019-03092-7
Fell, D. A. (2005). Enzymes, metabolites and fluxes. Journal of Experimental Botany, 56(410), 267–272. https://doi.org/10.1093/jxb/eri011
Fellner, L., Irschick, R., Schanda, K., Reindl, M., Klimaschewski, L., Poewe, W., Wenning, G. K., & Stefanova, N. (2013). Toll-like receptor 4 is required for α-synuclein dependent activation of microglia and astroglia. Glia, 61(3), 349–360. https://doi.org/10.1002/glia.22437
Field, M. S., Kamynina, E., Agunloye, O. C., Liebenthal, R. P., Lamarre, S. G., Brosnan, M. E., Brosnan, J. T., & Stover, P. J. (2014). Nuclear enrichment of folate cofactors and methylenetetrahydrofolate dehydrogenase 1 (MTHFD1) protect de novo thymidylate biosynthesis during folate deficiency. Journal of Biological Chemistry, 289(43), 29642–29650. https://doi.org/10.1074/jbc.M114.599589
Flott, B., & Seifert, W. (1991). Characterization of glutamate uptake systems in astrocyte primary cultures from rat brain. Glia, 4(3), 293–304. https://doi.org/10.1002/glia.440040307
Frago, L. M., Canelles, S., Freire-Regatillo, A., Argente-Arizón, P., Barrios, V., Argente, J., Garcia-Segura, L. M., & Chowen, J. A. (2017). Estradiol uses different mechanisms in astrocytes from the hippocampus of male and female rats to protect against damage induced by palmitic acid. Frontiers in Molecular Neuroscience, 10(October), 1–17. https://doi.org/10.3389/fnmol.2017.00330
Fumagalli, M., Lecca, D., Abbracchio, M. P., & Ceruti, S. (2017). Pathophysiological role of purines and pyrimidines in neurodevelopment: Unveiling new pharmacological approaches to congenital brain diseases. Frontiers in Pharmacology, 8(DEC), 1–18. https://doi.org/10.3389/fphar.2017.00941
Gelius-Dietrich, G., Desouki, A. A., Fritzemeier, C. J., & Lercher, M. J. (2013). Sybil - Efficient constraint-based modelling in R. BMC Systems Biology, 7(November). https://doi.org/10.1186/1752-0509-7-125
Gianchandani, E. P., Chavali, A. K., & Papin, J. A. (2010). The application of flux balance analysis in systems biology. https://doi.org/10.1002/wsbm.60
Gille, C., Bölling, C., Hoppe, A., Bulik, S., Hoffmann, S., Hübner, K., Karlstädt, A., Ganeshan, R., König, M., Rother, K., Weidlich, M., Behre, J., & Holzhütter, H. G. (2010). HepatoNet1: A comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology. Molecular Systems Biology, 6(411). https://doi.org/10.1038/msb.2010.62
González-giraldo, Y., Forero, D. A., Echeverria, V., Garcia-segura, L. M., & Barreto, G. E. (2019). Molecular and Cellular Endocrinology Tibolone attenuates in fl ammatory response by palmitic acid and preserves mitochondrial membrane potential in astrocytic cells through estrogen receptor beta. Molecular and Cellular Endocrinology, 486(February), 65–78. https://doi.org/10.1016/j.mce.2019.02.017
González, J., Pinzón, A., Angarita-Rodríguez, A., Aristizabal, A. F., Barreto, G. E., & Martín-Jiménez, C. (2020). Advances in Astrocyte Computational Models: From Metabolic Reconstructions to Multi-omic Approaches. Frontiers in Neuroinformatics, 14(August), 1–13. https://doi.org/10.3389/fninf.2020.00035
Greener, J. G., & Sternberg, M. J. E. (2015). AlloPred : prediction of allosteric pockets on proteins using normal mode perturbation analysis. 1–7. https://doi.org/10.1186/s12859-015-0771-1
Gu, C., Kim, G. B., Kim, W. J., Kim, H. U., & Lee, S. Y. (2019). Current status and applications of genome-scale metabolic models. Genome Biology, 20(1), 1–18. https://doi.org/10.1186/s13059-019-1730-3
Guilloux, V. Le, Schmidtke, P., & Tuffery, P. (2009). Fpocket : An open source platform for ligand pocket detection. February. https://doi.org/10.1186/1471-2105-10-168
Gulsen, M., Yesilova, Z., Bagci, S., Uygun, A., Ozcan, A., Ercin, C. N., Erdil, A., Sanisoglu, S. Y., Ates, Y., Erbil, M. K., Karaeren, N., & Dagalp, K. (2005). Elevated plasma homocysteine concentrations as a predictor of steatohepatitis in patients with non-alcoholic fatty liver disease. October 2004, 1448–1455. https://doi.org/10.1111/j.1440-1746.2005.03891.x
Guo, W. F., Zhang, S. W., Shi, Q. Q., Zhang, C. M., Zeng, T., & Chen, L. (2018). A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification. BMC Genomics, 19(Suppl 1). https://doi.org/10.1186/s12864-017-4332-z
Gupta, M., Sharma, R., & Kumar, A. (2018). Docking techniques in pharmacology: How much promising? Computational Biology and Chemistry, 76, 210–217. https://doi.org/10.1016/j.compbiolchem.2018.06.005
Han, X., Zhang, T., Liu, H., Mi, Y., & Gou, X. (2020). Astrocyte Senescence and Alzheimer’s Disease: A Review. Frontiers in Aging Neuroscience, 12(June), 1–13. https://doi.org/10.3389/fnagi.2020.00148
Haroon, E., Miller, A. H., & Sanacora, G. (2017). Inflammation, Glutamate, and Glia: A Trio of Trouble in Mood Disorders. Neuropsychopharmacology, 42(1), 193–215. https://doi.org/10.1038/npp.2016.199
Hashimoto, M., & Hossain, S. (2018). Fatty Acids: From Membrane Ingredients to Signaling Molecules. Biochemistry and Health Benefits of Fatty Acids. https://doi.org/10.5772/intechopen.80430
Herculano-Houzel, S., & Dos Santos, S. (2018). You Do Not Mess with the Glia. Neuroglia, 1(1), 193–219. https://doi.org/10.3390/neuroglia1010014
Hidalgo-lanussa, O., Ávila-rodriguez, M., Baez-jurado, E., Zamudio, J., Echeverria, V., Garcia-segura, L. M., Barreto, G. E., & Garcia-segura, L. M. (2017). Tibolone Reduces Oxidative Damage and Inflammation in Microglia Stimulated with Palmitic Acid through Mechanisms Involving Estrogen Receptor Beta. https://doi.org/10.1007/s12035-017-0777-y
Hidalgo-Lanussa, O., Baez-Jurado, E., Echeverria, V., Ashraf, G. M., Sahebkar, A., Garcia-Segura, L. M., Melcangi, R. C., & Barreto, G. E. (2020). Lipotoxicity, neuroinflammation, glial cells and oestrogenic compounds. Journal of Neuroendocrinology, 32(1), 1–15. https://doi.org/10.1111/jne.12776
Hilton, B. J., Lang, B. T., & Cregg, J. M. (2012). Keratan Sulfate Proteoglycans in Plasticity and Recovery after Spinal Cord Injury. 32(13), 4331–4333. https://doi.org/10.1523/JNEUROSCI.0333-12.2012
Höfer, T., Venance, L., & Giaume, C. (2002). Control and Plasticity of Intercellular Calcium Waves in Astrocytes: A Modeling Approach. Journal of Neuroscience, 22(12), 4850–4859. https://doi.org/10.1523/jneurosci.22-12-04850.2002
Hood, L., & Friend, S. H. (2011). Predictive, personalized, preventive, participatory (P4) cancer medicine. Nature Reviews Clinical Oncology, 8(3), 184–187. https://doi.org/10.1038/nrclinonc.2010.227
Hood, L., Heath, J. R., Phelps, M. E., & Lin, B. (2004). Systems biology and new technologies enable predictive and preventative medicine. Science, 306(5696), 640–643. https://doi.org/10.1126/science.1104635
Hornak, V., Okur, A., Rizzo, R. C., & Simmerling, C. (2006). HIV-1 Protease Flaps Spontaneously Close to the Correct Structure in Simulations Following Manual Placement of an Inhibitor into the Open State. 2812–2813.
Hu, X., Zhu, X., Yu, W., Zhang, Y., Yang, K., & Liu, Z. (2022). European Journal of Medicinal Chemistry Reports A mini review of small-molecule inhibitors targeting palmitoyltransferases. 5(August 2021).
Huang, J., Hou, J., Li, L., & Wang, Y. (2020). Flux balance analysis of glucose degradation by anaerobic digestion in negative pressure. International Journal of Hydrogen Energy, 45(51), 26822–26830. https://doi.org/10.1016/j.ijhydene.2020.07.053
Huang, Y. N., Lai, C. C., Chiu, C. T., Lin, J. J., & Wang, J. Y. (2014). L-ascorbate attenuates the endotoxin-induced production of inflammatory mediators by inhibiting MAPK activation and NF- κB translocation in cortical neurons/glia cocultures. PLoS ONE, 9(7), 1–12. https://doi.org/10.1371/journal.pone.0097276
Hyduke, D., Hyduke, D., Schellenberger, J., Que, R., Fleming, R., Thiele, I., Orth, J., Feist, A., Zielinski, D., Bordbar, A., Lewis, N., Rahmanian, S., Kang, J., & Palsson, B. (2011). COBRA Toolbox 2.0. Protocol Exchange, May, 0–1. https://doi.org/10.1038/protex.2011.234
Ipata, P. L., & Tozzi, M. G. (2006). Recent advances in structure and function of cytosolic IMP-GMP specific 5′-nucleotidase II (cN-II). Purinergic Signalling, 2(4), 669–675. https://doi.org/10.1007/s11302-006-9009-
Ito, Z., Sakamoto, K., Imagama, S., Matsuyama, Y., Zhang, H., Hirano, K., Ando, K., Yamashita, T., Ishiguro, N., & Kadomatsu, K. (2010). N -Acetylglucosamine 6- O -Sulfotransferase-1-Deficient Mice Show Better Functional Recovery after Spinal Cord Injury. 30(17), 5937–5947. https://doi.org/10.1523/JNEUROSCI.2570-09.2010
Jacobs, A. H., & Tavitian, B. (2012). Noninvasive molecular imaging of neuroinflammation. Journal of Cerebral Blood Flow and Metabolism, 32(7), 1393–1415. https://doi.org/10.1038/jcbfm.2012.53
Jarugumilli, G., Chen, B., & Wu, X. (n.d.). Chemical Probes to Directly Profile Palmitoleoylation of Proteins.
Jendoubi, T. (2021). Approaches to integrating metabolomics and multi-omics data: A primer. Metabolites, 11(3). https://doi.org/10.3390/metabo11030184
Jiang, P., Wang, H., Li, W., Zang, C., Li, B., Wong, Y. J., Meyer, C., Liu, J. S., Aster, J. C., & Liu, X. S. (2015). Network analysis of gene essentiality in functional genomics experiments. Genome Biology, 16(1), 1–10. https://doi.org/10.1186/s13059-015-0808-9
Jones, L. L., & Tuszynski, M. H. (2002). Spinal Cord Injury Elicits Expression of Keratan Sulfate Proteoglycans by Macrophages, Reactive Microglia, and Oligodendrocyte Progenitors. Journal of Neuroscience, 22(11), 4611–4624. https://doi.org/10.1523/jneurosci.22-11-04611.2002
Kanhaiya, K. (2020). Target Controllability of Cancer Networks. Åbo Akademi University, 1, 1–68.
Karahalil, B. (2017). Overview of Systems Biology and Omics Technologies Overview of Systems Biology and Omics Technologies. September 2016. https://doi.org/10.2174/0929867323666160926
Kawabata, T. (2009). Detection of multiscale pockets on protein surfaces using mathematical morphology. 1195–1211. https://doi.org/10.1002/prot.22639
Kim, M., Rai, N., Zorraquino, V., & Tagkopoulos, I. (2016). state in unexplored conditions for Escherichia coli. Nature Communications, 7, 1–12. https://doi.org/10.1038/ncomms13090
Kim, Y., Park, J., & Choi, Y. K. (2019). The role of astrocytes in the central nervous system focused on BK channel and heme oxygenase metabolites: A review. Antioxidants, 8(5), 7–13. https://doi.org/10.3390/antiox8050121
Kohl, P., Crampin, E. J., Quinn, T. A., & Noble, D. (2010). Systems biology: An approach. Clinical Pharmacology and Therapeutics, 88(1), 25–33. https://doi.org/10.1038/clpt.2010.92
Larhlimi, A., David, L., Selbig, J., & Bockmayr, A. (2012). F2C2 : a fast tool for the computation of flux coupling in genome-scale metabolic networks.
Le Foll, C., & Levin, B. E. (2016). Fatty acid-induced astrocyte ketone production and the control of food intake. American Journal of Physiology - Regulatory Integrative and Comparative Physiology, 310(11), R1186–R1192. https://doi.org/10.1152/ajpregu.00113.2016
Leanza, L., Ferraro, P., Reichard, P., & Bianchi, V. (2008). Metabolic interrelations within guanine deoxynucleotide pools for mitochondrial and nuclear DNA maintenance. Journal of Biological Chemistry, 283(24), 16437–16445. https://doi.org/10.1074/jbc.M801572200
Lee, W., Reyes, R. C., Gottipati, M. K., Lewis, K., Lesort, M., Parpura, V., & Gray, M. (2013). Enhanced Ca2+-dependent glutamate release from astrocytes of the BACHD Huntington’file:///D:/Escritorio/MAestria/profundización1/artículos/10.1016@j.neuint.2018.08.010.pdfs disease mouse model. Neurobiology of Disease, 58, 192–199. https://doi.org/10.1016/j.nbd.2013.06.002
Lewis, N. E., & Abdel-Haleem, A. M. (2013). The evolution of genome-scale models of cancer metabolism. Frontiers in Physiology, 4 SEP(September), 1–7. https://doi.org/10.3389/fphys.2013.00237
Lewis, N. E., Nagarajan, H., & Palsson, B. O. (2012). Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nature Reviews Microbiology, 10(4), 291–305. https://doi.org/10.1038/nrmicro2737
Li, J., Wei, Z., Zheng, M., Gu, X., Deng, Y., Qiu, R., Chen, F., Ji, C., Gong, W., Xie, Y., & Mao, Y. (2006). Crystal Structure of Human Guanosine Monophosphate Reductase 2 ( GMPR2 ) in Complex with GMP. 2, 980–988. https://doi.org/10.1016/j.jmb.2005.11.047
Li, K., Li, J., Zheng, J., & Qin, S. (2019). Reactive Astrocytes in Neurodegenerative Diseases. Aging and Disease, 10(3), 664. https://doi.org/10.14336/ad.2018.0720
Li, X., Li, M., Tian, L., Chen, J., Liu, R., & Ning, B. (2020). Review Article Reactive Astrogliosis : Implications in Spinal Cord Injury Progression and Therapy. 2020.
Li, Y. X., & Rinzel, J. (1994). Equations for InsP3 receptor-mediated [Ca2+](i) oscillations derived from a detailed kinetic model: A hodgkin-huxley like formalism. In Journal of Theoretical Biology (Vol. 166, Issue 4, pp. 461–473). https://doi.org/10.1006/jtbi.1994.1041
Liu, H., Luo, K., & Luo, D. (2018). Guanosine monophosphate reductase 1 is a potential therapeutic target for Alzheimer ’ s disease. Scientific Reports, November 2017, 1–10. https://doi.org/10.1038/s41598-018-21256-6
Liu, X., & Pan, L. (2014). Detection of driver metabolites in the human liver metabolic network using structural controllability analysis. BMC Systems Biology, 8(1), 1–17. https://doi.org/10.1186/1752-0509-8-51
Liu, Y. Y., Slotine, J. J., & Barabási, A. L. (2011). Controllability of complex networks. Nature, 473(7346), 167–173. https://doi.org/10.1038/nature10011
Luterman, J. D., Haroutunian, V., Yemul, S., Ho, L., Purohit, D., Aisen, P. S., Mohs, R., & Pasinetti, G. M. (2000). Cytokine gene expression as a function of the clinical progression of Alzheimer disease dementia. Archives of Neurology, 57(8), 1153–1160. https://doi.org/10.1001/archneur.57.8.1153
Ma, H., & Zhao, H. (2013). Drug target inference through pathway analysis of genomics data. Advanced Drug Delivery Reviews, 65(7), 966–972. https://doi.org/10.1016/j.addr.2012.12.004
Maarleveld, T. R., Khandelwal, R. A., Olivier, B. G., Teusink, B., & Bruggeman, F. J. (2013). Basic concepts and principles of stoichiometric modeling of metabolic networks. Biotechnology Journal, 8(9), 997–1008. https://doi.org/10.1002/biot.201200291
Mahmoud, S., Gharagozloo, M., Simard, C., & Gris, D. (2019). Astrocytes Maintain Glutamate Homeostasis in the CNS by Controlling the Balance between Glutamate Uptake and Release. 1–27. https://doi.org/10.3390/cells8020184
Manninen, T., Havela, R., & Linne, M.-L. (2019). Computational Models of Astrocytes and Astrocyte–Neuron Interactions: Characterization, Reproducibility, and Future Perspectives (pp. 423–454). https://doi.org/10.1007/978-3-030-00817-8_16
Mardinoglu, A., Agren, R., Kampf, C., Asplund, A., Nookaew, I., Jacobson, P., Walley, A. J., Froguel, P., Carlsson, L. M., Uhlen, M., & Nielsen, J. (2013). Integration of clinical data with a genome-scale metabolic model of the human adipocyte. Molecular Systems Biology, 9(649), 1–16. https://doi.org/10.1038/msb.2013.5
Mardinoglu, A., Agren, R., Kampf, C., Asplund, A., Uhlen, M., & Nielsen, J. (2014). non-alcoholic fatty liver disease. Nature Communications, 5(May 2013), 1–11. https://doi.org/10.1038/ncomms4083
Martín-Jiménez, C. A., Salazar-Barreto, D., Barreto, G. E., & González, J. (2017). Genome-scale reconstruction of the human astrocyte metabolic network. Frontiers in Aging Neuroscience, 9(FEB), 1–17. https://doi.org/10.3389/fnagi.2017.00023
Martin-jiménez, C., & González, J. (2020). Tibolone Ameliorates the Lipotoxic Effect of Palmitic Acid in Normal Human Astrocytes.
Marttinen, M., Paananen, J., Neme, A., Mitra, V., Takalo, M., Natunen, T., Paldanius, K. M. A., Mäkinen, P., Bremang, M., Kurki, M. I., Rauramaa, T., Leinonen, V., Soininen, H., Haapasalo, A., Pike, I., & Hiltunen, M. (2019). A multiomic approach to characterize the temporal sequence in Alzheimer’s disease-related pathology. Neurobiology of Disease, 124, 454–468. https://doi.org/10.1016/j.nbd.2018.12.009
Masid, M., Ataman, M., & Hatzimanikatis, V. (2020). Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN. Nature Communications, 11(1), 1–12. https://doi.org/10.1038/s41467-020-16549-2
Matias, I., Morgado, J., & Gomes, F. C. A. (2019). Astrocyte Heterogeneity: Impact to Brain Aging and Disease. Frontiers in Aging Neuroscience, 11(March), 1–18. https://doi.org/10.3389/fnagi.2019.00059
Matyash, V., & Kettenmann, H. (2009). Heterogeneity in astrocyte morphology and physiology. Brain Research Reviews, 63(1–2), 2–10. https://doi.org/10.1016/j.brainresrev.2009.12.001
McCloskey, D., Palsson, B., & Feist, A. M. (2013). Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli. Molecular Systems Biology, 9(1), 1–15. https://doi.org/10.1038/msb.2013.18
Melo, H. M., Santos, L. E., & Ferreira, S. T. (2019). Diet-Derived Fatty Acids, Brain Inflammation, and Mental Health. Frontiers in Neuroscience, 13(March), 1–12. https://doi.org/10.3389/fnins.2019.00265
Menara, T., Bianchin, G., Innocenti, M., & Pasqualetti, F. (2017). On the number of strongly structurally controllable networks. Proceedings of the American Control Conference, 340–345. https://doi.org/10.23919/ACC.2017.7962976
Michael Hay, David W Thomas, John L Craighead, C. E. & J. R. (2009). Clinical development success rates for investigational drugs. Gastrointestinal Cancer Research, 3(1), 20–28.
Modelska, K., & Cummings, S. (2015). CLINICAL REVIEW 140 Tibolone for Postmenopausal Women : Systematic Review of Randomized Trials. 87(November), 16–23.
Moncada, S. & Higgs, A. (1993). The L-arginine-nitric oxide pathway. N. Engl. J. Med., 329, 2002–2012.
Nagelhus, E. A., & Ottersen, O. P. (2013). Physiological roles of Aquaporin-4 in brain. Physiological Reviews, 93(4), 1543–1562. https://doi.org/10.1152/physrev.00011.2013
Nielsen, J. (2017a). Systems Biology of Metabolism: A Driver for Developing Personalized and Precision Medicine. Cell Metabolism, 25(3), 572–579. https://doi.org/10.1016/j.cmet.2017.02.002
Nielsen, J. (2017b). Systems Biology of Metabolism: A Driver for Developing Personalized and Precision Medicine. Cell Metabolism, 25(3), 572–579. https://doi.org/10.1016/j.cmet.2017.02.002
Nielsen, J. (2017c). Systems Biology of Metabolism. Annual Review of Biochemistry, 86(1), 245–275. https://doi.org/10.1146/annurev-biochem-061516-044757
Niu, Y. C., Feng, R. N., Hou, Y., Li, K., Kang, Z., Wang, J., Sun, C. H., & Li, Y. (2012). Histidine and arginine are associated with inflammation and oxidative stress in obese women. British Journal of Nutrition, 108(1), 57–61. https://doi.org/10.1017/S0007114511005289
Nurse, P., & Hayles, J. (2011). The cell in an era of systems biology. Cell, 144(6), 850–854. https://doi.org/10.1016/j.cell.2011.02.045
Nussinov, R., & Tsai, C. (2013). Review Allostery in Disease and in Drug Discovery. Cell, 153(2), 293–305. https://doi.org/10.1016/j.cell.2013.03.034
Oliveira, A. de A. B., Melo, N. de F. M., Vieira, É. dos S., Nogueira, P. A. S., Coope, A., Velloso, L. A., Dezonne, R. S., Ueira-Vieira, C., Botelho, F. V., Gomes, J. de A. S., & Zanon, R. G. (2018). Palmitate treated-astrocyte conditioned medium contains increased glutathione and interferes in hypothalamic synaptic network in vitro. Neurochemistry International, 120, 140–148. https://doi.org/10.1016/j.neuint.2018.08.010
Orth, J. D., Conrad, T. M., Na, J., Lerman, J. A., Nam, H., Feist, A. M., & Palsson, B. (2011). A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Molecular Systems Biology, 7(535), 1–9. https://doi.org/10.1038/msb.2011.65
Orth, J. D., Thiele, I., & Palsson, B. O. (2010). What is flux balance analysis? Nature Biotechnology, 28(3), 245–248. https://doi.org/10.1038/nbt.1614
Ortiz-Rodriguez, A., Acaz-Fonseca, E., Boya, P., Arevalo, M. A., & Garcia-Segura, L. M. (2019). Lipotoxic Effects of Palmitic Acid on Astrocytes Are Associated with Autophagy Impairment. Molecular Neurobiology, 56(3), 1665–1680. https://doi.org/10.1007/s12035-018-1183-9
Ortiz-Rodriguez, A., & Arevalo, M. A. (2020). The contribution of astrocyte autophagy to systemic metabolism. International Journal of Molecular Sciences, 21(7). https://doi.org/10.3390/ijms21072479
Osorio, D., Botero, K., Gonzalez, J., and Pinzon, A. (2016). “exp2flux” Convierte datos de Gene EXPression a FBA FLUXes. Package Version 0.1. https://doi.org/10.13140/RG.2.2.14401.56168
Osorio, D., Pinzón, A., Martín-Jiménez, C., Barreto, G. E., & González, J. (2020a). Multiple Pathways Involved in Palmitic Acid-Induced Toxicity: A System Biology Approach. Frontiers in Neuroscience, 13(January), 1–14. https://doi.org/10.3389/fnins.2019.01410
Palsson, B. (2009). Metabolic systems biology. FEBS Letters, 583(24), 3900–3904. https://doi.org/10.1016/j.febslet.2009.09.031
Pandey, V., Gardiol, D. H., & Chiappino-pepe, A. (2019). Running head : TEX-FBA TEX-FBA : A constraint-based method for integrating gene expression , thermodynamics , and metabolomics data into genome-scale metabolic models 1 Laboratory of Computational Systems Biotechnology , École Polytechnique Fédérale de La. 1–30.
Papin, J. A., Hunter, T., Palsson, B. O., & Subramaniam, S. (2005). Reconstruction of cellular signalling networks and analysis of their properties. Nature Reviews Molecular Cell Biology, 6(2), 99–111. https://doi.org/10.1038/nrm1570
Paraiso, W. K. D., Garcia-chica, J., Ariza, X., Zagmutt, S., Fukushima, S., Garcia, J., Mochida, Y., Serra, D., Herrero, L., Kinoh, H., Casals, N., Kataoka, K., Rodríguez-rodríguez, R., & Quader, S. (2021). Biomaterials Science conjugated CPT1A inhibitors to modulate lipid metabolism in brain cells †. https://doi.org/10.1039/d1bm00689d
Pardo, B., Contreras, L., & Satrústegui, J. (2013). De novo Synthesis of Glial Glutamate and Glutamine in Young Mice Requires Aspartate Provided by the Neuronal Mitochondrial Aspartate-Glutamate Carrier Aralar/AGC1. Frontiers in Endocrinology, 4(October), 15–18. https://doi.org/10.3389/fendo.2013.00149
Patil, S., Melrose, J., & Chan, C. (2007). Involvement of astroglial ceramide in palmitic acid-induced Alzheimer-like changes in primary neurons. European Journal of Neuroscience, 26(8), 2131–2141. https://doi.org/10.1111/j.1460-9568.2007.05797.x
Patil, S., Sheng, L., Masserang, A., & Chan, C. (2006). Palmitic acid-treated astrocytes induce BACE1 upregulation and accumulation of C-terminal fragment of APP in primary cortical neurons. Neuroscience Letters, 406(1–2), 55–59. https://doi.org/10.1016/j.neulet.2006.07.015
Peracchi, A., & Mozzarelli, A. (2011). Biochimica et Biophysica Acta Exploring and exploiting allostery : Models , evolution , and drug targeting ☆. BBA - Proteins and Proteomics, 1814(8), 922–933. https://doi.org/10.1016/j.bbapap.2010.10.008
Piccolis, M., Bond, L. M., Kampmann, M., Pulimeno, P., Chitraju, C., Jayson, C. B. K., Vaites, L. P., Boland, S., Lai, Z. W., Gabriel, K. R., Elliott, S. D., Paulo, J. A., Harper, J. W., Weissman, J. S., Walther, T. C., & Farese, R. V. (2019). Probing the Global Cellular Responses to Lipotoxicity Caused by Saturated Fatty Acids. Molecular Cell, 74(1), 32-44.e8. https://doi.org/10.1016/j.molcel.2019.01.036
Pietzke, M., Meiser, J., & Vazquez, A. (2020). Formate metabolism in health and disease. Molecular Metabolism, 33(xxxx), 23–37. https://doi.org/10.1016/j.molmet.2019.05.012
Pinu, F. R., Beale, D. J., Paten, A. M., Kouremenos, K., Swarup, S., Schirra, H. J., & Wishart, D. (2019). Systems biology and multi-omics integration: Viewpoints from the metabolomics research community. Metabolites, 9(4), 1–31. https://doi.org/10.3390/metabo9040076
Price, N. D., Reed, J. L., & Palsson, B. (2004). Genome-scale models of microbial cells: Evaluating the consequences of constraints. Nature Reviews Microbiology, 2(11), 886–897. https://doi.org/10.1038/nrmicro1023
Ramon, C., Gollub, M. G., & Stelling, J. (2018). Integrating -omics data into genome-scale metabolic network models: Principles and challenges. Essays in Biochemistry, 62(4), 563–574. https://doi.org/10.1042/EBC20180011
Ravindran, V., Nacher, J. C., Akutsu, T., Ishitsuka, M., Osadcenco, A., Sunitha, V., Bagler, G., Schwartz, J. M., & Robertson, D. L. (2019). Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems. Scientific Reports, 9(1), 1–11. https://doi.org/10.1038/s41598-018-38224-9
Rezola, A., Pey, J., Tobalina, L., Rubio, Á., Beasley, J. E., & Planes, F. J. (2015). Advances in network-basedmetabolic pathway analysis and gene expression data integration. Briefings in Bioinformatics, 16(2), 1–15. https://doi.org/10.1093/bib/bbu009
Robertson, J. M. (2018). The gliocentric brain. International Journal of Molecular Sciences, 19(10). https://doi.org/10.3390/ijms19103033
Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Ballard, A. J., Cowie, A., Romera-paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., … Kavukcuoglu, K. (2021). Highly accurate protein structure prediction with AlphaFold. May, 1–12. https://doi.org/10.1038/s41586-021-03819-2
Rose, J., Brian, C., Pappa, A., Panayiotidis, M. I., & Franco, R. (2020). Mitochondrial Metabolism in Astrocytes Regulates Brain Bioenergetics, Neurotransmission and Redox Balance. Frontiers in Neuroscience, 14(November), 1–20. https://doi.org/10.3389/fnins.2020.536682
Sajitz-Hermstein, M., & Nikoloski, Z. (2013). Structural Control of Metabolic Flux. PLoS Computational Biology, 9(12). https://doi.org/10.1371/journal.pcbi.1003368
Salvatore, D., Bartha, T., & Larsen, P. R. (1998). The Guanosine Monophosphate Reductase Gene Is Conserved in Rats and Its Expression Increases Rapidly in Brown Adipose Tissue during Cold Exposure *. Journal of Biological Chemistry, 273(47), 31092–31096. https://doi.org/10.1074/jbc.273.47.31092
Schafer, J. R. A., Fell, D. A., Rothman, D., & Shulman, R. G. (2004). Protein phosphorylation can regulate metabolite concentrations rather than control flux: The example of glycogen synthase. Proceedings of the National Academy of Sciences of the United States of America, 101(6), 1485–1490. https://doi.org/10.1073/pnas.0307299101
Schousboe, A., Bak, L. K., & Waagepetersen, H. S. (2013). Astrocytic control of biosynthesis and turnover of the neurotransmitters glutamate and GABA. Frontiers in Endocrinology, 4(AUG), 1–11. https://doi.org/10.3389/fendo.2013.00102
Schrödinger, L. (2015). The {PyMOL} Molecular Graphics System, Version~2.4. February. https://doi.org/10.13140/RG.2.2.33676.64641
Schuetz, R., Kuepfer, L., & Sauer, U. (2007). Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Molecular Systems Biology, 3(119). https://doi.org/10.1038/msb4100162
Schwartz, J. M., Otokuni, H., Akutsu, T., & Nacher, J. C. (2019). Probabilistic controllability approach to metabolic fluxes in normal and cancer tissues. Nature Communications, 10(1), 1–10. https://doi.org/10.1038/s41467-019-10616-z
Segrè, D., Vitkup, D., & Church, G. M. (2002). Analysis of optimality in natural and perturbed metabolic networks. Proceedings of the National Academy of Sciences of the United States of America, 99(23), 15112–15117. https://doi.org/10.1073/pnas.232349399
Sertbaş, M., Ülgen, K., & Çakir, T. (2014). Systematic analysis of transcription-level effects of neurodegenerative diseases on human brain metabolism by a newly reconstructed brain-specific metabolic network. FEBS Open Bio, 4, 542–553. https://doi.org/10.1016/j.fob.2014.05.006
Shi, L. F., Zhang, Q., Shou, X. Y., & Niu, H. J. (2021). Expression and prognostic value identification of methylenetetrahydrofolate dehydrogenase 2 (Mthfd2) in brain low-grade glioma. International Journal of General Medicine, 14, 4517–4527. https://doi.org/10.2147/IJGM.S323858
Shlomi, T., Berkman, O., & Ruppin, E. (2005). Regulatory on ͞ off minimization of metabolic flux. Pnas, 102(21), 7695–7700. https://doi.org/10.1073/pnas.0406346102
Singh, A., Kukreti, R., Saso, L., & Kukreti, S. (2019). Oxidative stress: A key modulator in neurodegenerative diseases. Molecules, 24(8), 1–20. https://doi.org/10.3390/molecules24081583
Singh, D., & Lercher, M. J. (2020). Network reduction methods for genome-scale metabolic models. Cellular and Molecular Life Sciences, 77(3), 481–488. https://doi.org/10.1007/s00018-019-03383-z
Siracusa, R., Fusco, R., & Cuzzocrea, S. (2019). Astrocytes: Role and functions in brain pathologies. Frontiers in Pharmacology, 10(SEP), 1–10. https://doi.org/10.3389/fphar.2019.01114
Sofroniew M. V. (2009). Molecular dissection of reactive astrogliosis and glial scar formation. Trends in Neuroscience, 32(12), 638–647. https://doi.org/10.1016/j.tins.2009.08.002.Molecular
Sofroniew, J. E. B. and M. V. (2015). Reactive gliosis and the multicellular response to CNS damage and disease. Neuron, 81(2), 229–248. https://doi.org/10.1016/j.neuron.2013.12.034.Reactive
Sofroniew, M. V. (2014). Multiple roles for astrocytes as effectors of cytokines and inflammatory mediators. Neuroscientist, 20(2), 160–172. https://doi.org/10.1177/1073858413504466
Son, D. O., Satsu, H., & Shimizu, M. (2005). Histidine inhibits oxidative stress- and TNF- a -induced interleukin-8 secretion in intestinal epithelial cells. 579, 4671–4677. https://doi.org/10.1016/j.febslet.2005.07.038
Sonnewald, U., Akiho, H., Koshiya, K., & Iwai, A. (1998). Effect of orotic acid on the metabolism of cerebral cortical astrocytes during hypoxia and reoxygenation: An NMR spectroscopy study. Journal of Neuroscience Research, 51(1), 103–108. https://doi.org/10.1002/(SICI)1097-4547(19980101)51:1<103::AID-JNR11>3.0.CO;2-C
Souders, C. L., Zubcevic, J., & Martyniuk, C. J. (2021). Tumor Necrosis Factor Alpha and the Gastrointestinal Epithelium: Implications for the Gut-Brain Axis and Hypertension. Cellular and Molecular Neurobiology, 0123456789. https://doi.org/10.1007/s10571-021-01044-z
Souza, D. G., Almeida, R. F., Souza, D. O., & Zimmer, E. R. (2019). The astrocyte biochemistry. Seminars in Cell and Developmental Biology, 95(April), 142–150. https://doi.org/10.1016/j.semcdb.2019.04.002
Stank, A., Kokh, D. B., Fuller, J. C., & Wade, R. C. (2016). Protein Binding Pocket Dynamics. https://doi.org/10.1021/acs.accounts.5b00516
Suthers, P. F., Zomorrodi, A., & Maranas, C. D. (2009). Genome-scale gene/reaction essentiality and synthetic lethality analysis. Molecular Systems Biology, 5(301), 1–17. https://doi.org/10.1038/msb.2009.56
Sweetlove, L. J., & George Ratcliffe, R. (2011). Flux-balance modeling of plant metabolism. Frontiers in Plant Science, 2(AUG), 1–10. https://doi.org/10.3389/fpls.2011.00038
Terzer, M., & Stelling, J. (2008). Large-scale computation of elementary flux modes with bit pattern trees. Bioinformatics, 24(19), 2229–2235. https://doi.org/10.1093/bioinformatics/btn401
Thiele, I., Swainston, N., Fleming, R. M. T., Hoppe, A., Sahoo, S., Aurich, M. K., Haraldsdottir, H., Mo, M. L., Rolfsson, O., Stobbe, M. D., Thorleifsson, S. G., Agren, R., Bölling, C., Bordel, S., Chavali, A. K., Dobson, P., Dunn, W. B., Endler, L., Hala, D., … Palsson, B. O. (2013a). A community-driven global reconstruction of human metabolism. Nature Biotechnology, 31(5), 419–425. https://doi.org/10.1038/nbt.2488
Thiele, I., Swainston, N., Fleming, R. M. T., Hoppe, A., Sahoo, S., Aurich, M. K., Haraldsdottir, H., Mo, M. L., Rolfsson, O., Stobbe, M. D., Thorleifsson, S. G., Agren, R., Bölling, C., Bordel, S., Chavali, A. K., Dobson, P., Dunn, W. B., Endler, L., Hala, D., … Palsson, B. O. (2013b). A community-driven global reconstruction of human metabolism. Nature Biotechnology, 31(5), 419–425. https://doi.org/10.1038/nbt.2488
Tong, X., Ao, Y., Faas, G. C., Nwaobi, S. E., Xu, J., Haustein, M. D., Anderson, M. A., Mody, I., Olsen, M. L., Sofroniew, M. V, & Khakh, B. S. (2014). Astrocyte Kir4.1 ion channel deficits contribute to neuronal dysfunction in Huntington’s disease model mice. Nature Neuroscience, 17(5), 694–703. https://doi.org/10.1038/nn.3691
Trott, O. and Olson, A. J. (2011). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. 31(2), 455–461. https://doi.org/10.1002/jcc.21334.AutoDock
Ussher, J. R., Keung, W., Fillmore, N., Koves, T. R., Mori, J., Zhang, L., Lopaschuk, D. G., Ilkayeva, O. R., Wagg, C. S., Jaswal, J. S., Muoio, D. M., & Lopaschuk, G. D. (2014). Treatment with the 3-Ketoacyl-CoA Thiolase Inhibitor Trimetazidine Does Not Exacerbate Whole-Body Insulin Resistance in Obese Mice. June, 487–496.
Valenza, G., Pioggia, G., Armato, A., Ferro, M., Scilingo, E. P., & De Rossi, D. (2011). A neuron-astrocyte transistor-like model for neuromorphic dressed neurons. Neural Networks, 24(7), 679–685. https://doi.org/10.1016/j.neunet.2011.03.013
Verkhratsky, A., and Nedergaard, M. (2018). PHYSIOLOGY OF ASTROGLIA. Physiol. Rev, 98, 239–389. https://doi.org/10.1152/physrev.00042.2016
Verkhratsky, A., & Butt, A. (2018). The History of the Decline and Fall of the Glial Numbers Legend. Neuroglia, 1(1), 188–192. https://doi.org/10.3390/neuroglia1010013
Vicente-Gutierrez, C., Bonora, N., Bobo-Jimenez, V., Jimenez-Blasco, D., Lopez-Fabuel, I., Fernandez, E., Josephine, C., Bonvento, G., Enriquez, J. A., Almeida, A., & Bolaños, J. P. (2019). Astrocytic mitochondrial ROS modulate brain metabolism and mouse behaviour. Nature Metabolism, 1(2), 201–211. https://doi.org/10.1038/s42255-018-0031-6
Voillet, V., Besse, P., Liaubet, L., Cristobal, M. S., & González, I. (2016). Handling missing rows in multi-omics data integration : multiple imputation in multiple factor analysis framework. BMC Bioinformatics, 1–17. https://doi.org/10.1186/s12859-016-1273-5
Volkamer, A., Kuhn, D., Rippmann, F., & Rarey, M. (2012). DoGSiteScorer : a web server for automatic binding site prediction , analysis and druggability assessment. 28(15), 2074–2075. https://doi.org/10.1093/bioinformatics/bts310
Volterra, A., & Meldolesi, J. (2005). Astrocytes, from brain glue to communication elements: The revolution continues. Nature Reviews Neuroscience, 6(8), 626–640. https://doi.org/10.1038/nrn1722
Wang, W., Jiang, Z., Hu, C., Chen, C., Hu, Z., Wang, A., Wang, L., & Liu, J. (2020). Pharmacologically inhibiting phosphoglycerate kinase 1 for glioma with NG52. Acta Pharmacologica Sinica, July. https://doi.org/10.1038/s41401-020-0465-8
Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics in Western Equatoria State. Nature Reviews Genetics, 10(1), 57.
Waterhouse, A., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F. T., Beer, T. A. P. De, Rempfer, C., Bordoli, L., Lepore, R., & Schwede, T. (2018). SWISS-MODEL : homology modelling of protein structures and complexes. May, 1–8. https://doi.org/10.1093/nar/gky427
Wong, K. L., Wu, Y. R., Cheng, K. S., Chan, P., Cheung, C. W., Lu, D. Y., Su, T. H., Liu, Z. M., & Leung, Y. M. (2014a). Palmitic acid-induced lipotoxicity and protection by (+)-catechin in rat cortical astrocytes. Pharmacological Reports, 66(6), 1106–1113. https://doi.org/10.1016/j.pharep.2014.07.009
Wong, K. L., Wu, Y. R., Cheng, K. S., Chan, P., Cheung, C. W., Lu, D. Y., Su, T. H., Liu, Z. M., & Leung, Y. M. (2014b). Palmitic acid-induced lipotoxicity and protection by (+)-catechin in rat cortical astrocytes. Pharmacological Reports, 66(6), 1106–1113. https://doi.org/10.1016/j.pharep.2014.07.009
Wörheide, M. A., Krumsiek, J., Kastenmüller, G., & Arnold, M. (2021). Multi-omics integration in biomedical research – A metabolomics-centric review. Analytica Chimica Acta, 1141, 144–162. https://doi.org/10.1016/j.aca.2020.10.038
Wu, Z., Li, W., Liu, G., & Tang, Y. (2018). Network-Based Methods for Prediction of Drug-Target Interactions. 9(October), 1–14. https://doi.org/10.3389/fphar.2018.01134
Wuchty, S. (2019). Controllability of molecular pathways. BioRxiv, 560375. https://doi.org/10.1101/560375
Xia, C., Fu, Z., Battaile, K. P., & Kim, J. P. (2019). Crystal structure of human mitochondrial trifunctional protein , a fatty acid β -oxidation metabolon. 116(13), 6069–6074. https://doi.org/10.1073/pnas.1816317116
Xiao, Q., Yan, P., Ma, X., Liu, H., Perez, R., Zhu, A., Gonzales, E., Burchett, J. M., Schuler, D. R., Cirrito, J. R., Diwan, A., & Lee, J. M. (2014). Enhancing astrocytic lysosome biogenesis facilitates Aβ clearance and attenuates amyloid plaque pathogenesis. Journal of Neuroscience, 34(29), 9607–9620. https://doi.org/10.1523/JNEUROSCI.3788-13.2014
Xu, Y., Wang, S., Hu, Q., Gao, S., Ma, X., Zhang, W., Shen, Y., Chen, F., Lai, L., Pei, J., & Cavpharmer, C. (2018). CavityPlus : a web server for protein cavity detection with pharmacophore modelling , allosteric site identification and covalent ligand binding ability prediction. 46(May), 374–379. https://doi.org/10.1093/nar/gky380
Yang, M., & Vousden, K. H. (2016). Serine and one-carbon metabolism in cancer. Nature Reviews Cancer, 16(10), 650–662. https://doi.org/10.1038/nrc.2016.81
Yang, S. Y., He, X. Y., & Schulz, H. (1987). Fatty acid oxidation in rat brain is limited by the low activity of 3-ketoacyl-coenzyme A thiolase. The Journal of Biological Chemistry, 262(27), 13027–13032. https://doi.org/10.1016/s0021-9258(18)45161-7
Yin, K. (2015). Positive correlation between expression level of mitochondrial serine hydroxymethyltransferase and breast cancer grade. OncoTargets and Therapy, 8, 1069–1074. https://doi.org/10.2147/OTT.S82433
Ying, L., Tippetts, T. S., & Chaurasia, B. (2019). Ceramide dependent lipotoxicity in metabolic diseases. Nutrition and Healthy Aging, 5(1), 1–12. https://doi.org/10.3233/NHA-170032
Young, F. B., Butland, S. L., Sanders, S. S., Sutton, L. M., & Hayden, M. R. (2012). Putting proteins in their place: Palmitoylation in Huntington disease and other neuropsychiatric diseases. Progress in Neurobiology, 97(2), 220–238. https://doi.org/10.1016/j.pneurobio.2011.11.002
Yousofshahi, M., Ullah, E., Stern, R., & Hassoun, S. (2013). MC3: A steady-state model and constraint consistency checker for biochemical networks. BMC Systems Biology, 7. https://doi.org/10.1186/1752-0509-7-129
Yu, J., Zhou, Y., Tanaka, I., & Yao, M. (2010). Roll : a new algorithm for the detection of protein pockets and cavities with a rolling probe sphere. 26(1), 46–52. https://doi.org/10.1093/bioinformatics/btp599
Yuan, Z., Zhao, C., Di, Z., Wang, W. X., & Lai, Y. C. (2013). Exact controllability of complex networks. Nature Communications, 4. https://doi.org/10.1038/ncomms3447
Zahra, W., Rai, S. N., Birla, H., Singh, S. Sen, Rathore, A. S., Dilnashin, H., Keswani, C., & Singh, S. P. (2019). Economic importance of medicinal plants in Asian countries. In Bioeconomy for Sustainable Development. https://doi.org/10.1007/978-981-13-9431-7_19
Zhang, H., Muramatsu, T., Murase, A., Yuasa, S., Uchimura, K., & Kadomatsu, K. (2006). N-Acetylglucosamine 6-O-sulfotransferase-1 is required for brain keratan sulfate biosynthesis and glial scar formation after brain injury. Glycobiology, 16(8), 702–710. https://doi.org/10.1093/glycob/cwj115
Zhang, H., Uchimura, K., & Kadomatsu, K. (2006). Brain keratan sulfate and glial scar formation. Annals of the New York Academy of Sciences, 1086, 81–90. https://doi.org/10.1196/annals.1377.014
Zhang, N., Qi, M., Gao, X., Zhao, L., Liu, J., Gu, C., Song, W., Steven, C., Sun, L., & Qi, D. (2016). Response of the hepatic transcriptome to a fl atoxin B 1 in ducklings. 111, 69–76. https://doi.org/10.1016/j.toxicon.2015.12.022
Zierer, J., Pallister, T., Tsai, P. C., Krumsiek, J., Bell, J. T., Lauc, G., Spector, T. D., Menni, C., & Kastenmüller, G. (2016). Exploring the molecular basis of age-related disease comorbidities using a multi-omics graphical model. Scientific Reports, 6(October), 1–10. https://doi.org/10.1038/srep37646
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
http://creativecommons.org/licenses/by-nc/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.spa.fl_str_mv 96 páginas + anexos
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv Bogotá - Ingeniería - Maestría en Bioinformática
dc.publisher.department.spa.fl_str_mv Departamento de Ingeniería de Sistemas e Industrial
dc.publisher.faculty.spa.fl_str_mv Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv Universidad Nacional de Colombia - Sede Bogotá
institution Universidad Nacional de Colombia
bitstream.url.fl_str_mv https://repositorio.unal.edu.co/bitstream/unal/82354/1/license.txt
https://repositorio.unal.edu.co/bitstream/unal/82354/2/1075676303.2022.pdf
https://repositorio.unal.edu.co/bitstream/unal/82354/3/1075676303.2022.pdf.jpg
bitstream.checksum.fl_str_mv eb34b1cf90b7e1103fc9dfd26be24b4a
9c49711a1e8ac3c097ff9e558582f33b
4459a8f2e4a70577c2a698db2e33eeda
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv repositorio_nal@unal.edu.co
_version_ 1814089841691852800
spelling Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Pinzón Velasco, Andres Mauricio5470e24dc3b68e2116a743d851a290f7600Angarita Rodríguez, María Andrea10ca9bd4b9b47048e0cec74bac63525cJanneth González SantosGrupo de Investigación en Bioinformática y Biología de Sistemas - GIBBS2022-10-05T23:38:20Z2022-10-05T23:38:20Z2022-10-04https://repositorio.unal.edu.co/handle/unal/82354Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Los astrocitos juegan un papel importante en varios procesos en el cerebro, incluidas condiciones patológicas como las enfermedades neurodegenerativas. Estudios recientes han demostrado que el aumento de ácidos grasos saturados como el ácido palmítico (PA) desencadena vías proinflamatorias en el cerebro. El uso de neuroesteroides sintéticos como la tibolona ha demostrado mecanismos neuroprotectores. Sin embargo, faltan estudios amplios, con un punto de vista sistémico, sobre el papel neurodegenerativo de PA y los mecanismos neuroprotectores de la tibolona. En este estudio, realizamos la integración de datos multiómicos (transcriptoma y proteoma) en un modelo metabólico a escala genómica de astrocitos humanos para estudiar la respuesta astrocitaria durante el tratamiento con palmitato. Evaluamos los flujos metabólicos en tres escenarios (saludable, inflamación inducida por PA y tratamiento con tibolona bajo inflamación por PA). También aplicamos un enfoque de teoría de control para identificar aquellas reacciones que ejercen más control en el sistema astrocítico. Por último, analizamos las cavidades de las enzimas asociadas a estas reacciones para determinar sus potenciales sitios de unión caracterizándolos en función de puntajes de ligandabilidad y capacidad de interacción farmacológica (drogabilidad). Nuestros resultados sugieren que PA genera una modulación del metabolismo central y secundario, mostrando un cambio en el uso de la fuente de energía a través de la inhibición del ciclo del folato, la β-oxidación de ácidos grasos y la regulación positiva de la formación de cuerpos cetónicos. Encontramos 25 interruptores metabólicos bajo regulación celular mediada por PA, 9 de los cuales fueron críticos solo en el escenario inflamatorio pero no en el protector de tibolona. Dentro de estas reacciones, los perfiles de acoplamiento inhibitorio, total y direccional fueron hallazgos clave, que desempeñaron un papel fundamental en la desregulación de las vías metabólicas que pueden aumentar la neurotoxicidad. De los 25 interruptores metabólicos 16 presentaron cavidades potencialmente drogables que, a su vez, contienen el sitio activo de la proteína. En su conjunto, estas 16 enzimas se configuran como potenciales objetivos terapéuticos. Finalmente, el marco general de nuestro enfoque facilitó la comprensión de la regulación metabólica compleja y puede usarse para la exploración in silico de los mecanismos de regulación de las células astrocitarias, y potencialmente de otros tipos celulares, dirigiendo un trabajo experimental futuro más complejo en enfermedades neurodegenerativas. (Texto tomado de la fuente)Our results suggest that PA generates a modulation of central and secondary metabolism, showing a change in the use of the energy source through the inhibition of the folate cycle, the β-oxidation of fatty acids and the positive regulation of the formation of fatty acids. ketone bodies. We found 25 metabolic switches under PA-mediated cellular regulation, 9 of which were critical only in the inflammatory but not in the protective tibolone scenario. Within these reactions, inhibitory, total, and directional coupling profiles were key findings, playing a critical role in the dysregulation of metabolic pathways that can increase neurotoxicity. Of the 25 metabolic switches, 16 presented potentially drugable cavities that, in turn, contain the active site of the protein. As a whole, these 16 enzymes are configured as potential therapeutic targets. Finally, the general framework of our approach facilitated the understanding of complex metabolic regulation and can be used for in silico exploration of regulatory mechanisms of astrocytic cells, and potentially other cell types, directing future more complex experimental work in diseases. neurodegenerative Our results suggest that PA generates a modulation of central and secondary metabolism, showing a switch in energy source use through inhibition of folate cycle and fatty acid β-oxidation and upregulation of ketone bodies formation. We found 25 metabolic switches under PA-mediated cellular regulation, 9 of which were critical only in the inflammatory but not in the protective tibolone scenario. Within these reactions, inhibitory, total, and directional coupling profiles were key findings, playing a critical role in the dysregulation of metabolic pathways that can increase neurotoxicity. Of the 25 metabolic switches, 16 presented potentially druggable cavities that, in turn, contain the protein's active site. As a whole, these 16 enzymes are configured as potential therapeutic targets. Finally, the general framework of our approach facilitated the understanding of complex metabolic regulation. It can be used for in silico exploration of regulatory mechanisms of astrocytic cells, and potentially other cell types, directing future more complex experimental work in neurodegenerative diseases.La Pontificia Universidad Javeriana- Sede Bogotá y Minciencias - convocatoria 874 de 2020 “Convocatoria para el Fortalecimiento de Proyectos en Ejecución de CTeI en Ciencias de la Salud con Talento Joven e Impacto Regional” - financiaron el proyecto número 9307, dentro del cual se encuentra enmarcado este trabajo.MaestríaMagíster en BioinformáticaBiología de Sistemas96 páginas + anexosapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en BioinformáticaDepartamento de Ingeniería de Sistemas e IndustrialFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá610 - Medicina y salud::616 - EnfermedadesNeurogliaCélulasCellsAstrocitosIntegración de datosÁcido palmíticoModelo computacionalMulti-ómicoTeoría de controlCavidades farmacológicasAstrocytesData integrationPalmitic acidComputational modelMulti-omicsControl theoryDrugable cavitiesIdentificación de reacciones controladoras en un modelo computacional multi­-ómico astrocitario de lipotoxicidad inducida por ácido palmíticoIdentification of controlling reactions in a astrocytic multi-omics computational model of palmitic acid-induced lipotoxicityTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMÃ, G. B. S., & Park, E. (2003). Taurine : new implications for an old amino acid. 226, 195–202. https://doi.org/10.1016/S0378-1097(03)00611-6Agostinho, P., Cunha, R. a, & Oliveira, C. (2010). Neuroinflammation , Oxidative Stress and the Pathogenesis of Alzheimer ’ s Disease. Current Pharmacutical Design, 16, 2766–2778.Allen, N. J., Eroglu, C., Development, F., Studies, B., & Jolla, L. (2018). Cell biology of astrocyte-synapse interactions. Neuron., 96(3), 697–708. https://doi.org/10.1016/j.neuron.2017.09.056.CellAltenbuchinger, M., Zacharias, H. U., Solbrig, S., Schäfer, A., Büyüközkan, M., Schultheiß, U. T., Kotsis, F., Köttgen, A., Spang, R., Oefner, P. J., Krumsiek, J., & Gronwald, W. (2019). A multi-source data integration approach reveals novel associations between metabolites and renal outcomes in the German Chronic Kidney Disease study. Scientific Reports, 9(1), 1–13. https://doi.org/10.1038/s41598-019-50346-2Arevalo, M. A., Azcoitia, I., & Garcia-Segura, L. M. (2015). The neuroprotective actions of oestradiol and oestrogen receptors. Nature Reviews Neuroscience, 16(1), 17–29. https://doi.org/10.1038/nrn3856Arnedo, M., Ramos, M., Puisac, B., Concepcion, M., Teresa, E., Pie, A., Bueno, G., J., F., Gomez-Puertas, P., & Pie, J. (2011). Mitochondrial HMG–CoA Synthase Deficiency. Advances in the Study of Genetic Disorders, February. https://doi.org/10.5772/22151Asgari, Y., Salehzadeh-Yazdi, A., Schreiber, F., & Masoudi-Nejad, A. (2013). Controllability in cancer metabolic networks according to drug targets as driver nodes. PLoS ONE, 8(11), 1–12. https://doi.org/10.1371/journal.pone.0079397Ávila, M., Garcia-segura, L. M., Cabezas, R., Torrente, D., Capani, F., Gonzalez, J., & Barreto, G. E. (2014). Journal of Steroid Biochemistry & Molecular Biology Tibolone protects T98G cells from glucose deprivation. Journal of Steroid Biochemistry and Molecular Biology, 144, 294–303. https://doi.org/10.1016/j.jsbmb.2014.07.009Ayyildiz, M., Celiker, S., Ozhelvaci, F., & Akten, E. D. (2020). Identification of Alternative Allosteric Sites in Glycolytic Enzymes for Potential Use as Species-Specific Drug Targets. 7(May), 1–19. https://doi.org/10.3389/fmolb.2020.00088Badaut, J. (2010). Aquaglyceroporin 9 in brain pathologies. Neuroscience, 168(4), 1047–1057. https://doi.org/10.1016/j.neuroscience.2009.10.030Bailey, L. B., & Gregory, J. F. (1999). Recent Advances in Nutritional Science Folate Metabolism and. The Journal of Nutrition, 129, 779–782.Balog, E. (2014). An Allosteric Signaling Pathway of Human 3- Phosphoglycerate Kinase from Force Distribution Analysis. 10(1). https://doi.org/10.1371/journal.pcbi.1003444Balsa, E., Perry, E. A., Bennett, C. F., Jedrychowski, M., Gygi, S. P., Doench, J. G., & Puigserver, P. (2020). Defective NADPH production in mitochondrial disease complex I causes in fl ammation and cell. Nature Communications, 1–12. https://doi.org/10.1038/s41467-020-16423-1Barinova, K., Khomyakova, E., Semenyuk, P., Schmalhausen, E., & Muronetz, V. (2018). SC. Archives of Biochemistry and Biophysics. https://doi.org/10.1016/j.abb.2018.02.002Basler, G., Grimbs, S., & Ebenho, O. (2012). Evolutionary significance of metabolic network properties. November 2011, 1168–1176.Basler, G., & Nikoloski, Z. (2011). JMassBalance : mass-balanced randomization and analysis of metabolic networks. 27(19), 2761–2762. https://doi.org/10.1093/bioinformatics/btr448Basler, G., Nikoloski, Z., Larhlimi, A., Barabási, A. L., & Liu, Y. Y. (2016). Control of fluxes in metabolic networks. Genome Research, 26(7), 956–968. https://doi.org/10.1101/gr.202648.115Becerra-Calixto, A., & Cardona-Gómez, G. P. (2017). The role of astrocytes in neuroprotection after brain stroke: Potential in cell therapy. Frontiers in Molecular Neuroscience, 10(April), 1–12. https://doi.org/10.3389/FNMOL.2017.00088Bélanger, M., & Magistretti, P. J. (2009). The role of astroglia in neuroprotection. Dialogues in Clinical Neuroscience, 11(3), 281–296.Bidkhori, G., Benfeitas, R., Elmas, E., Kararoudi, M. N., Arif, M., Uhlen, M., Nielsen, J., & Mardinoglu, A. (2018). Metabolic network-based identification and prioritization of anticancer targets based on expression data in hepatocellular carcinoma. Frontiers in Physiology, 9(JUL), 1–11. https://doi.org/10.3389/fphys.2018.00916Bordbar, A, & Palsson, B. O. (2011). Using the reconstructed genome-scale human metabolic network to study physiology and pathology. 131–141. https://doi.org/10.1111/j.1365-2796.2011.02494.xBordbar, Aarash, Jamshidi, N., & Palsson, B. O. (2011). IAB-RBC-283: A proteomically derived knowledge-base of erythrocyte metabolism that can be used to simulate its physiological and patho-physiological states. BMC Systems Biology, 5, 1–12. https://doi.org/10.1186/1752-0509-5-110Bordbar, Aarash, Monk, J. M., King, Z. A., & Palsson, B. O. (2014). Constraint-based models predict metabolic and associated cellular functions. 15(February), 107–120. https://doi.org/10.1038/nrg3643Bordel, S., Agren, R., & Nielsen, J. (2010). Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes. PLoS Computational Biology, 6(7), 16. https://doi.org/10.1371/journal.pcbi.1000859Brunk, E., Sahoo, S., Zielinski, D. C., Altunkaya, A., Dräger, A., Mih, N., Gatto, F., Nilsson, A., Preciat Gonzalez, G. A., Aurich, M. K., Prlic, A., Sastry, A., Danielsdottir, A. D., Heinken, A., Noronha, A., Rose, P. W., Burley, S. K., Fleming, R. M. T., Nielsen, J., … Palsson, B. O. (2018). Recon3D enables a three-dimensional view of gene variation in human metabolism. Nature Biotechnology, 36(3), 272–281. https://doi.org/10.1038/nbt.4072Burgard, A. P., Nikolaev, E. V, Schilling, C. H., & Maranas, C. D. (2004). Flux Coupling Analysis of Genome-Scale Metabolic Network Reconstructions. 4, 301–312. https://doi.org/10.1101/gr.1926504.Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chen, L., Crichlow, G. V, Christie, C. H., Dalenberg, K., Costanzo, L. Di, Duarte, J. M., Dutta, S., Feng, Z., Ganesan, S., Goodsell, D. S., Ghosh, S., Green, R. K., Guzenko, D., Hudson, B. P., Lawson, C. L., … Zhuravleva, M. (2021). RCSB Protein Data Bank : powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology , biomedicine , biotechnology , bioengineering and energy sciences. 49(November 2020), 437–451. https://doi.org/10.1093/nar/gkaa1038Buskila, Y., Bellot-Saez, A., & Morley, J. W. (2019). Generating Brain Waves, the Power of Astrocytes. Frontiers in Neuroscience, 13(October), 1–10. https://doi.org/10.3389/fnins.2019.01125Butland, S. L., Sanders, S. S., Schmidt, M. E., Riechers, S. P., Lin, D. T. S., Martin, D. D. O., Vaid, K., Graham, R. K., Singaraja, R. R., Wanker, E. E., Conibear, E., & Hayden, M. R. (2014). The palmitoyl acyltransferase HIP14 shares a high proportion of interactors with huntingtin: Implications for a role in the pathogenesis of Huntington’s disease. Human Molecular Genetics, 23(15), 4142–4160. https://doi.org/10.1093/hmg/ddu137Bylicky, M. A., Mueller, G. P., & Day, R. M. (2018). Mechanisms of endogenous neuroprotective effects of astrocytes in brain injury. Oxidative Medicine and Cellular Longevity, 2018. https://doi.org/10.1155/2018/6501031Cabezas, R., El-Bachá, R. S., González, J., & Barreto, G. E. (2012). Mitochondrial functions in astrocytes: Neuroprotective implications from oxidative damage by rotenone. Neuroscience Research, 74(2), 80–90. https://doi.org/10.1016/j.neures.2012.07.008Cammisa, M., Correra, A., Andreotti, G., & Cubellis, M. V. (2012). Identification and analysis of conserved pockets on protein surfaces. February 2014. https://doi.org/10.1186/1471-2105-14-S7-S9Carta, G., Murru, E., Banni, S., & Manca, C. (2017). Palmitic acid: Physiological role, metabolism and nutritional implications. Frontiers in Physiology, 8(NOV), 1–14. https://doi.org/10.3389/fphys.2017.00902Ceccarelli, S. M., Chomienne, O., Gubler, M., & Arduini, A. (2011). Carnitine Palmitoyltransferase ( CPT ) Modulators : A Medicinal Chemistry Perspective on 35 Years of Research.Chang, R. L., Xie, L., Xie, L., Bourne, P. E., & Palsson, B. (2010). Drug off-target effects predicted using structural analysis in the context of a metabolic network model. PLoS Computational Biology, 6(9). https://doi.org/10.1371/journal.pcbi.1000938Chaudhry, F. A., Krizaj, D., Larsson, P., Reimer, R. J., Wreden, C., Storm-Mathisen, J., Copenhagen, D., Kavanaugh, M., & Edwards, R. H. (2001). Coupled and uncoupled proton movement by amino acid transport system N. EMBO Journal, 20(24), 7041–7051. https://doi.org/10.1093/emboj/20.24.7041Chaudhry, F. A., Reimer, R. J., Krizaj, D., Barber, D., Storm-Mathisen, J., Copenhagen, D. R., & Edwards, R. H. (1999). Molecular analysis of system N suggests novel physiological roles in nitrogen metabolism and synaptic transmission. Cell, 99(7), 769–780. https://doi.org/10.1016/S0092-8674(00)81674-8Chen, K., Wu, S., Ye, S., Huang, H., Zhou, Y., & Zhou, H. (2021). Dimethyl Fumarate Induces Metabolic Crisie to Suppress Pancreatic Carcinoma. 12(February), 1–14. https://doi.org/10.3389/fphar.2021.617714Chen, P., Cheng, S., Lin, H., Lee, C., & Chou, C. (2018). Risk Factors for the Progression of Mild Cognitive Impairment in Different Types of Neurodegenerative Disorders. 2018. https://doi.org/10.1155/2018/6929732Coppedè, F. (2021). One-carbon epigenetics and redox biology of neurodegeneration. Free Radical Biology and Medicine, 170(October), 19–33. https://doi.org/10.1016/j.freeradbiomed.2020.12.002Coppedè, F., Mancuso, M., Siciliano, G., Migliore, L., & Murri, L. (2006). Genes and the environment in neurodegeneration. Bioscience Reports, 26(5), 341–367. https://doi.org/10.1007/s10540-006-9028-6Crespo-Castrillo, A., & Arevalo, M. A. (2020). Microglial and astrocytic function in physiological and pathological conditions: Estrogenic modulation. International Journal of Molecular Sciences, 21(9). https://doi.org/10.3390/ijms21093219Crespo-Castrillo, A., Yanguas-Casás, N., Arevalo, M. A., Azcoitia, I., Barreto, G. E., & Garcia-Segura, L. M. (2018). The Synthetic Steroid Tibolone Decreases Reactive Gliosis and Neuronal Death in the Cerebral Cortex of Female Mice After a Stab Wound Injury. Molecular Neurobiology, 55(11), 8651–8667. https://doi.org/10.1007/s12035-018-1008-xCummings, J. L., Morstorf, T., & Zhong, K. (2014). Alzrt269. 1–7.Currais, A., Goldberg, J., Farrokhi, C., Chang, M., Prior, M., Dargusch, R., Daugherty, D., Armando, A., Quehenberger, O., Maher, P., & Schubert, D. (2015). A comprehensive multiomics approach toward understanding the relationship between aging and dementia. Aging, 7(11), 937–955. https://doi.org/10.18632/aging.100838Das, A., Banik, N. L., & Ray, S. K. (2010). Flavonoids Activated Caspases for Apoptosis in Human Glioblastoma T98G and U87MG Cells But Not in Human Normal Astrocytes. 164–176. https://doi.org/10.1002/cncr.24699David, L., Marashi, S. A., Larhlimi, A., Mieth, B., & Bockmayr, A. (2011). FFCA: A feasibility-based method for flux coupling analysis of metabolic networks. BMC Bioinformatics, 12(1), 236. https://doi.org/10.1186/1471-2105-12-236De Carvalho, C. C. C. R., & Caramujo, M. J. (2018). The various roles of fatty acids. Molecules, 23(10). https://doi.org/10.3390/molecules23102583De Young, G. W., & Keizer, J. (1992). A single-pool inositol 1,4,5-trisphosphate-receptor-based model for agonist-stimulated oscillations in Ca2+ concentration. Proceedings of the National Academy of Sciences of the United States of America, 89(20), 9895–9899. https://doi.org/10.1073/pnas.89.20.9895Devkota, P., & Wuchty, S. (2020). Controllability analysis of molecular pathways points to proteins that control the entire interaction network. Scientific Reports, 10(1), 1–9. https://doi.org/10.1038/s41598-020-59717-6Dhandapani, K. M., Wade, F. M., Mahesh, V. B., & Brann, D. W. (2005). Astrocyte-derived transforming growth factor-β mediates the neuroprotective effects of 17β-estradiol: Involvement of nonclassical genomic signaling pathways. Endocrinology, 146(6), 2749–2759. https://doi.org/10.1210/en.2005-0014Dhote, V., Mandloi, A. S., Singour, P. K., Kawadkar, M., Ganeshpurkar, A., & Jadhav, M. P. (2022). Neuroprotective Effects of Combined Trimetazidine and Progesterone on Cerebral Reperfusion Injury. Current Research in Pharmacology and Drug Discovery, 100108. https://doi.org/10.1016/j.crphar.2022.100108Dilcan, G., Doruker, P., & Demet, E. (2019). binding affinity of alternative conformers of human β 2 - ­ adrenergic receptor in the presence of intracellular loop 3 ( ICL3 ) and their potential use in virtual screening studies. June 2018, 883–899. https://doi.org/10.1111/cbdd.13478Doengi, M., Hirnet, D., Coulon, P., Pape, H., Deitmer, J. W., & Lohr, C. (2009). GABA uptake-dependent Ca 2 ؉ signaling in developing olfactory bulb astrocytes. 1–6.Duarte, N. C., Becker, S. A., Jamshidi, N., Thiele, I., Mo, M. L., Vo, T. D., Srivas, R., & Palsson, B. Ø. (2007). Global reconstruction of the human metabolic network based on genomic and bibliomic data. 104(6).Dupuis, J. R., Ruiz-Arce, R., Barr, N. B., Thomas, D. B., & Geib, S. M. (2019). Range-wide population genomics of the Mexican fruit fly: Toward development of pathway analysis tools. Evolutionary Applications, 12(8), 1641–1660. https://doi.org/10.1111/eva.12824Durkee, C. A., & Araque, A. (2019). Diversity and Specificity of Astrocyte–neuron Communication. Neuroscience, 396(November), 73–78. https://doi.org/10.1016/j.neuroscience.2018.11.010Farfa, E. D., & Gallardo, J. M. (2014). Tibolone Prevents Oxidation and Ameliorates Cholinergic Deficit Induced by Ozone Exposure in the Male Rat Hippocampus. 1776–1786. https://doi.org/10.1007/s11064-014-1385-0Farmer, B. C., Walsh, A. E., Kluemper, J. C., & Johnson, L. A. (2020). Lipid Droplets in Neurodegenerative Disorders. Frontiers in Neuroscience, 14(July), 1–14. https://doi.org/10.3389/fnins.2020.00742Fatima, S., Hu, X., Gong, R. H., Huang, C., Chen, M., Wong, H. L. X., Bian, Z., & Kwan, H. Y. (2019). Palmitic acid is an intracellular signaling molecule involved in disease development. Cellular and Molecular Life Sciences, 76(13), 2547–2557. https://doi.org/10.1007/s00018-019-03092-7Fell, D. A. (2005). Enzymes, metabolites and fluxes. Journal of Experimental Botany, 56(410), 267–272. https://doi.org/10.1093/jxb/eri011Fellner, L., Irschick, R., Schanda, K., Reindl, M., Klimaschewski, L., Poewe, W., Wenning, G. K., & Stefanova, N. (2013). Toll-like receptor 4 is required for α-synuclein dependent activation of microglia and astroglia. Glia, 61(3), 349–360. https://doi.org/10.1002/glia.22437Field, M. S., Kamynina, E., Agunloye, O. C., Liebenthal, R. P., Lamarre, S. G., Brosnan, M. E., Brosnan, J. T., & Stover, P. J. (2014). Nuclear enrichment of folate cofactors and methylenetetrahydrofolate dehydrogenase 1 (MTHFD1) protect de novo thymidylate biosynthesis during folate deficiency. Journal of Biological Chemistry, 289(43), 29642–29650. https://doi.org/10.1074/jbc.M114.599589Flott, B., & Seifert, W. (1991). Characterization of glutamate uptake systems in astrocyte primary cultures from rat brain. Glia, 4(3), 293–304. https://doi.org/10.1002/glia.440040307Frago, L. M., Canelles, S., Freire-Regatillo, A., Argente-Arizón, P., Barrios, V., Argente, J., Garcia-Segura, L. M., & Chowen, J. A. (2017). Estradiol uses different mechanisms in astrocytes from the hippocampus of male and female rats to protect against damage induced by palmitic acid. Frontiers in Molecular Neuroscience, 10(October), 1–17. https://doi.org/10.3389/fnmol.2017.00330Fumagalli, M., Lecca, D., Abbracchio, M. P., & Ceruti, S. (2017). Pathophysiological role of purines and pyrimidines in neurodevelopment: Unveiling new pharmacological approaches to congenital brain diseases. Frontiers in Pharmacology, 8(DEC), 1–18. https://doi.org/10.3389/fphar.2017.00941Gelius-Dietrich, G., Desouki, A. A., Fritzemeier, C. J., & Lercher, M. J. (2013). Sybil - Efficient constraint-based modelling in R. BMC Systems Biology, 7(November). https://doi.org/10.1186/1752-0509-7-125Gianchandani, E. P., Chavali, A. K., & Papin, J. A. (2010). The application of flux balance analysis in systems biology. https://doi.org/10.1002/wsbm.60Gille, C., Bölling, C., Hoppe, A., Bulik, S., Hoffmann, S., Hübner, K., Karlstädt, A., Ganeshan, R., König, M., Rother, K., Weidlich, M., Behre, J., & Holzhütter, H. G. (2010). HepatoNet1: A comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology. Molecular Systems Biology, 6(411). https://doi.org/10.1038/msb.2010.62González-giraldo, Y., Forero, D. A., Echeverria, V., Garcia-segura, L. M., & Barreto, G. E. (2019). Molecular and Cellular Endocrinology Tibolone attenuates in fl ammatory response by palmitic acid and preserves mitochondrial membrane potential in astrocytic cells through estrogen receptor beta. Molecular and Cellular Endocrinology, 486(February), 65–78. https://doi.org/10.1016/j.mce.2019.02.017González, J., Pinzón, A., Angarita-Rodríguez, A., Aristizabal, A. F., Barreto, G. E., & Martín-Jiménez, C. (2020). Advances in Astrocyte Computational Models: From Metabolic Reconstructions to Multi-omic Approaches. Frontiers in Neuroinformatics, 14(August), 1–13. https://doi.org/10.3389/fninf.2020.00035Greener, J. G., & Sternberg, M. J. E. (2015). AlloPred : prediction of allosteric pockets on proteins using normal mode perturbation analysis. 1–7. https://doi.org/10.1186/s12859-015-0771-1Gu, C., Kim, G. B., Kim, W. J., Kim, H. U., & Lee, S. Y. (2019). Current status and applications of genome-scale metabolic models. Genome Biology, 20(1), 1–18. https://doi.org/10.1186/s13059-019-1730-3Guilloux, V. Le, Schmidtke, P., & Tuffery, P. (2009). Fpocket : An open source platform for ligand pocket detection. February. https://doi.org/10.1186/1471-2105-10-168Gulsen, M., Yesilova, Z., Bagci, S., Uygun, A., Ozcan, A., Ercin, C. N., Erdil, A., Sanisoglu, S. Y., Ates, Y., Erbil, M. K., Karaeren, N., & Dagalp, K. (2005). Elevated plasma homocysteine concentrations as a predictor of steatohepatitis in patients with non-alcoholic fatty liver disease. October 2004, 1448–1455. https://doi.org/10.1111/j.1440-1746.2005.03891.xGuo, W. F., Zhang, S. W., Shi, Q. Q., Zhang, C. M., Zeng, T., & Chen, L. (2018). A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification. BMC Genomics, 19(Suppl 1). https://doi.org/10.1186/s12864-017-4332-zGupta, M., Sharma, R., & Kumar, A. (2018). Docking techniques in pharmacology: How much promising? Computational Biology and Chemistry, 76, 210–217. https://doi.org/10.1016/j.compbiolchem.2018.06.005Han, X., Zhang, T., Liu, H., Mi, Y., & Gou, X. (2020). Astrocyte Senescence and Alzheimer’s Disease: A Review. Frontiers in Aging Neuroscience, 12(June), 1–13. https://doi.org/10.3389/fnagi.2020.00148Haroon, E., Miller, A. H., & Sanacora, G. (2017). Inflammation, Glutamate, and Glia: A Trio of Trouble in Mood Disorders. Neuropsychopharmacology, 42(1), 193–215. https://doi.org/10.1038/npp.2016.199Hashimoto, M., & Hossain, S. (2018). Fatty Acids: From Membrane Ingredients to Signaling Molecules. Biochemistry and Health Benefits of Fatty Acids. https://doi.org/10.5772/intechopen.80430Herculano-Houzel, S., & Dos Santos, S. (2018). You Do Not Mess with the Glia. Neuroglia, 1(1), 193–219. https://doi.org/10.3390/neuroglia1010014Hidalgo-lanussa, O., Ávila-rodriguez, M., Baez-jurado, E., Zamudio, J., Echeverria, V., Garcia-segura, L. M., Barreto, G. E., & Garcia-segura, L. M. (2017). Tibolone Reduces Oxidative Damage and Inflammation in Microglia Stimulated with Palmitic Acid through Mechanisms Involving Estrogen Receptor Beta. https://doi.org/10.1007/s12035-017-0777-yHidalgo-Lanussa, O., Baez-Jurado, E., Echeverria, V., Ashraf, G. M., Sahebkar, A., Garcia-Segura, L. M., Melcangi, R. C., & Barreto, G. E. (2020). Lipotoxicity, neuroinflammation, glial cells and oestrogenic compounds. Journal of Neuroendocrinology, 32(1), 1–15. https://doi.org/10.1111/jne.12776Hilton, B. J., Lang, B. T., & Cregg, J. M. (2012). Keratan Sulfate Proteoglycans in Plasticity and Recovery after Spinal Cord Injury. 32(13), 4331–4333. https://doi.org/10.1523/JNEUROSCI.0333-12.2012Höfer, T., Venance, L., & Giaume, C. (2002). Control and Plasticity of Intercellular Calcium Waves in Astrocytes: A Modeling Approach. Journal of Neuroscience, 22(12), 4850–4859. https://doi.org/10.1523/jneurosci.22-12-04850.2002Hood, L., & Friend, S. H. (2011). Predictive, personalized, preventive, participatory (P4) cancer medicine. Nature Reviews Clinical Oncology, 8(3), 184–187. https://doi.org/10.1038/nrclinonc.2010.227Hood, L., Heath, J. R., Phelps, M. E., & Lin, B. (2004). Systems biology and new technologies enable predictive and preventative medicine. Science, 306(5696), 640–643. https://doi.org/10.1126/science.1104635Hornak, V., Okur, A., Rizzo, R. C., & Simmerling, C. (2006). HIV-1 Protease Flaps Spontaneously Close to the Correct Structure in Simulations Following Manual Placement of an Inhibitor into the Open State. 2812–2813.Hu, X., Zhu, X., Yu, W., Zhang, Y., Yang, K., & Liu, Z. (2022). European Journal of Medicinal Chemistry Reports A mini review of small-molecule inhibitors targeting palmitoyltransferases. 5(August 2021).Huang, J., Hou, J., Li, L., & Wang, Y. (2020). Flux balance analysis of glucose degradation by anaerobic digestion in negative pressure. International Journal of Hydrogen Energy, 45(51), 26822–26830. https://doi.org/10.1016/j.ijhydene.2020.07.053Huang, Y. N., Lai, C. C., Chiu, C. T., Lin, J. J., & Wang, J. Y. (2014). L-ascorbate attenuates the endotoxin-induced production of inflammatory mediators by inhibiting MAPK activation and NF- κB translocation in cortical neurons/glia cocultures. PLoS ONE, 9(7), 1–12. https://doi.org/10.1371/journal.pone.0097276Hyduke, D., Hyduke, D., Schellenberger, J., Que, R., Fleming, R., Thiele, I., Orth, J., Feist, A., Zielinski, D., Bordbar, A., Lewis, N., Rahmanian, S., Kang, J., & Palsson, B. (2011). COBRA Toolbox 2.0. Protocol Exchange, May, 0–1. https://doi.org/10.1038/protex.2011.234Ipata, P. L., & Tozzi, M. G. (2006). Recent advances in structure and function of cytosolic IMP-GMP specific 5′-nucleotidase II (cN-II). Purinergic Signalling, 2(4), 669–675. https://doi.org/10.1007/s11302-006-9009-Ito, Z., Sakamoto, K., Imagama, S., Matsuyama, Y., Zhang, H., Hirano, K., Ando, K., Yamashita, T., Ishiguro, N., & Kadomatsu, K. (2010). N -Acetylglucosamine 6- O -Sulfotransferase-1-Deficient Mice Show Better Functional Recovery after Spinal Cord Injury. 30(17), 5937–5947. https://doi.org/10.1523/JNEUROSCI.2570-09.2010Jacobs, A. H., & Tavitian, B. (2012). Noninvasive molecular imaging of neuroinflammation. Journal of Cerebral Blood Flow and Metabolism, 32(7), 1393–1415. https://doi.org/10.1038/jcbfm.2012.53Jarugumilli, G., Chen, B., & Wu, X. (n.d.). Chemical Probes to Directly Profile Palmitoleoylation of Proteins.Jendoubi, T. (2021). Approaches to integrating metabolomics and multi-omics data: A primer. Metabolites, 11(3). https://doi.org/10.3390/metabo11030184Jiang, P., Wang, H., Li, W., Zang, C., Li, B., Wong, Y. J., Meyer, C., Liu, J. S., Aster, J. C., & Liu, X. S. (2015). Network analysis of gene essentiality in functional genomics experiments. Genome Biology, 16(1), 1–10. https://doi.org/10.1186/s13059-015-0808-9Jones, L. L., & Tuszynski, M. H. (2002). Spinal Cord Injury Elicits Expression of Keratan Sulfate Proteoglycans by Macrophages, Reactive Microglia, and Oligodendrocyte Progenitors. Journal of Neuroscience, 22(11), 4611–4624. https://doi.org/10.1523/jneurosci.22-11-04611.2002Kanhaiya, K. (2020). Target Controllability of Cancer Networks. Åbo Akademi University, 1, 1–68.Karahalil, B. (2017). Overview of Systems Biology and Omics Technologies Overview of Systems Biology and Omics Technologies. September 2016. https://doi.org/10.2174/0929867323666160926Kawabata, T. (2009). Detection of multiscale pockets on protein surfaces using mathematical morphology. 1195–1211. https://doi.org/10.1002/prot.22639Kim, M., Rai, N., Zorraquino, V., & Tagkopoulos, I. (2016). state in unexplored conditions for Escherichia coli. Nature Communications, 7, 1–12. https://doi.org/10.1038/ncomms13090Kim, Y., Park, J., & Choi, Y. K. (2019). The role of astrocytes in the central nervous system focused on BK channel and heme oxygenase metabolites: A review. Antioxidants, 8(5), 7–13. https://doi.org/10.3390/antiox8050121Kohl, P., Crampin, E. J., Quinn, T. A., & Noble, D. (2010). Systems biology: An approach. Clinical Pharmacology and Therapeutics, 88(1), 25–33. https://doi.org/10.1038/clpt.2010.92Larhlimi, A., David, L., Selbig, J., & Bockmayr, A. (2012). F2C2 : a fast tool for the computation of flux coupling in genome-scale metabolic networks.Le Foll, C., & Levin, B. E. (2016). Fatty acid-induced astrocyte ketone production and the control of food intake. American Journal of Physiology - Regulatory Integrative and Comparative Physiology, 310(11), R1186–R1192. https://doi.org/10.1152/ajpregu.00113.2016Leanza, L., Ferraro, P., Reichard, P., & Bianchi, V. (2008). Metabolic interrelations within guanine deoxynucleotide pools for mitochondrial and nuclear DNA maintenance. Journal of Biological Chemistry, 283(24), 16437–16445. https://doi.org/10.1074/jbc.M801572200Lee, W., Reyes, R. C., Gottipati, M. K., Lewis, K., Lesort, M., Parpura, V., & Gray, M. (2013). Enhanced Ca2+-dependent glutamate release from astrocytes of the BACHD Huntington’file:///D:/Escritorio/MAestria/profundización1/artículos/10.1016@j.neuint.2018.08.010.pdfs disease mouse model. Neurobiology of Disease, 58, 192–199. https://doi.org/10.1016/j.nbd.2013.06.002Lewis, N. E., & Abdel-Haleem, A. M. (2013). The evolution of genome-scale models of cancer metabolism. Frontiers in Physiology, 4 SEP(September), 1–7. https://doi.org/10.3389/fphys.2013.00237Lewis, N. E., Nagarajan, H., & Palsson, B. O. (2012). Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nature Reviews Microbiology, 10(4), 291–305. https://doi.org/10.1038/nrmicro2737Li, J., Wei, Z., Zheng, M., Gu, X., Deng, Y., Qiu, R., Chen, F., Ji, C., Gong, W., Xie, Y., & Mao, Y. (2006). Crystal Structure of Human Guanosine Monophosphate Reductase 2 ( GMPR2 ) in Complex with GMP. 2, 980–988. https://doi.org/10.1016/j.jmb.2005.11.047Li, K., Li, J., Zheng, J., & Qin, S. (2019). Reactive Astrocytes in Neurodegenerative Diseases. Aging and Disease, 10(3), 664. https://doi.org/10.14336/ad.2018.0720Li, X., Li, M., Tian, L., Chen, J., Liu, R., & Ning, B. (2020). Review Article Reactive Astrogliosis : Implications in Spinal Cord Injury Progression and Therapy. 2020.Li, Y. X., & Rinzel, J. (1994). Equations for InsP3 receptor-mediated [Ca2+](i) oscillations derived from a detailed kinetic model: A hodgkin-huxley like formalism. In Journal of Theoretical Biology (Vol. 166, Issue 4, pp. 461–473). https://doi.org/10.1006/jtbi.1994.1041Liu, H., Luo, K., & Luo, D. (2018). Guanosine monophosphate reductase 1 is a potential therapeutic target for Alzheimer ’ s disease. Scientific Reports, November 2017, 1–10. https://doi.org/10.1038/s41598-018-21256-6Liu, X., & Pan, L. (2014). Detection of driver metabolites in the human liver metabolic network using structural controllability analysis. BMC Systems Biology, 8(1), 1–17. https://doi.org/10.1186/1752-0509-8-51Liu, Y. Y., Slotine, J. J., & Barabási, A. L. (2011). Controllability of complex networks. Nature, 473(7346), 167–173. https://doi.org/10.1038/nature10011Luterman, J. D., Haroutunian, V., Yemul, S., Ho, L., Purohit, D., Aisen, P. S., Mohs, R., & Pasinetti, G. M. (2000). Cytokine gene expression as a function of the clinical progression of Alzheimer disease dementia. Archives of Neurology, 57(8), 1153–1160. https://doi.org/10.1001/archneur.57.8.1153Ma, H., & Zhao, H. (2013). Drug target inference through pathway analysis of genomics data. Advanced Drug Delivery Reviews, 65(7), 966–972. https://doi.org/10.1016/j.addr.2012.12.004Maarleveld, T. R., Khandelwal, R. A., Olivier, B. G., Teusink, B., & Bruggeman, F. J. (2013). Basic concepts and principles of stoichiometric modeling of metabolic networks. Biotechnology Journal, 8(9), 997–1008. https://doi.org/10.1002/biot.201200291Mahmoud, S., Gharagozloo, M., Simard, C., & Gris, D. (2019). Astrocytes Maintain Glutamate Homeostasis in the CNS by Controlling the Balance between Glutamate Uptake and Release. 1–27. https://doi.org/10.3390/cells8020184Manninen, T., Havela, R., & Linne, M.-L. (2019). Computational Models of Astrocytes and Astrocyte–Neuron Interactions: Characterization, Reproducibility, and Future Perspectives (pp. 423–454). https://doi.org/10.1007/978-3-030-00817-8_16Mardinoglu, A., Agren, R., Kampf, C., Asplund, A., Nookaew, I., Jacobson, P., Walley, A. J., Froguel, P., Carlsson, L. M., Uhlen, M., & Nielsen, J. (2013). Integration of clinical data with a genome-scale metabolic model of the human adipocyte. Molecular Systems Biology, 9(649), 1–16. https://doi.org/10.1038/msb.2013.5Mardinoglu, A., Agren, R., Kampf, C., Asplund, A., Uhlen, M., & Nielsen, J. (2014). non-alcoholic fatty liver disease. Nature Communications, 5(May 2013), 1–11. https://doi.org/10.1038/ncomms4083Martín-Jiménez, C. A., Salazar-Barreto, D., Barreto, G. E., & González, J. (2017). Genome-scale reconstruction of the human astrocyte metabolic network. Frontiers in Aging Neuroscience, 9(FEB), 1–17. https://doi.org/10.3389/fnagi.2017.00023Martin-jiménez, C., & González, J. (2020). Tibolone Ameliorates the Lipotoxic Effect of Palmitic Acid in Normal Human Astrocytes.Marttinen, M., Paananen, J., Neme, A., Mitra, V., Takalo, M., Natunen, T., Paldanius, K. M. A., Mäkinen, P., Bremang, M., Kurki, M. I., Rauramaa, T., Leinonen, V., Soininen, H., Haapasalo, A., Pike, I., & Hiltunen, M. (2019). A multiomic approach to characterize the temporal sequence in Alzheimer’s disease-related pathology. Neurobiology of Disease, 124, 454–468. https://doi.org/10.1016/j.nbd.2018.12.009Masid, M., Ataman, M., & Hatzimanikatis, V. (2020). Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN. Nature Communications, 11(1), 1–12. https://doi.org/10.1038/s41467-020-16549-2Matias, I., Morgado, J., & Gomes, F. C. A. (2019). Astrocyte Heterogeneity: Impact to Brain Aging and Disease. Frontiers in Aging Neuroscience, 11(March), 1–18. https://doi.org/10.3389/fnagi.2019.00059Matyash, V., & Kettenmann, H. (2009). Heterogeneity in astrocyte morphology and physiology. Brain Research Reviews, 63(1–2), 2–10. https://doi.org/10.1016/j.brainresrev.2009.12.001McCloskey, D., Palsson, B., & Feist, A. M. (2013). Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli. Molecular Systems Biology, 9(1), 1–15. https://doi.org/10.1038/msb.2013.18Melo, H. M., Santos, L. E., & Ferreira, S. T. (2019). Diet-Derived Fatty Acids, Brain Inflammation, and Mental Health. Frontiers in Neuroscience, 13(March), 1–12. https://doi.org/10.3389/fnins.2019.00265Menara, T., Bianchin, G., Innocenti, M., & Pasqualetti, F. (2017). On the number of strongly structurally controllable networks. Proceedings of the American Control Conference, 340–345. https://doi.org/10.23919/ACC.2017.7962976Michael Hay, David W Thomas, John L Craighead, C. E. & J. R. (2009). Clinical development success rates for investigational drugs. Gastrointestinal Cancer Research, 3(1), 20–28.Modelska, K., & Cummings, S. (2015). CLINICAL REVIEW 140 Tibolone for Postmenopausal Women : Systematic Review of Randomized Trials. 87(November), 16–23.Moncada, S. & Higgs, A. (1993). The L-arginine-nitric oxide pathway. N. Engl. J. Med., 329, 2002–2012.Nagelhus, E. A., & Ottersen, O. P. (2013). Physiological roles of Aquaporin-4 in brain. Physiological Reviews, 93(4), 1543–1562. https://doi.org/10.1152/physrev.00011.2013Nielsen, J. (2017a). Systems Biology of Metabolism: A Driver for Developing Personalized and Precision Medicine. Cell Metabolism, 25(3), 572–579. https://doi.org/10.1016/j.cmet.2017.02.002Nielsen, J. (2017b). Systems Biology of Metabolism: A Driver for Developing Personalized and Precision Medicine. Cell Metabolism, 25(3), 572–579. https://doi.org/10.1016/j.cmet.2017.02.002Nielsen, J. (2017c). Systems Biology of Metabolism. Annual Review of Biochemistry, 86(1), 245–275. https://doi.org/10.1146/annurev-biochem-061516-044757Niu, Y. C., Feng, R. N., Hou, Y., Li, K., Kang, Z., Wang, J., Sun, C. H., & Li, Y. (2012). Histidine and arginine are associated with inflammation and oxidative stress in obese women. British Journal of Nutrition, 108(1), 57–61. https://doi.org/10.1017/S0007114511005289Nurse, P., & Hayles, J. (2011). The cell in an era of systems biology. Cell, 144(6), 850–854. https://doi.org/10.1016/j.cell.2011.02.045Nussinov, R., & Tsai, C. (2013). Review Allostery in Disease and in Drug Discovery. Cell, 153(2), 293–305. https://doi.org/10.1016/j.cell.2013.03.034Oliveira, A. de A. B., Melo, N. de F. M., Vieira, É. dos S., Nogueira, P. A. S., Coope, A., Velloso, L. A., Dezonne, R. S., Ueira-Vieira, C., Botelho, F. V., Gomes, J. de A. S., & Zanon, R. G. (2018). Palmitate treated-astrocyte conditioned medium contains increased glutathione and interferes in hypothalamic synaptic network in vitro. Neurochemistry International, 120, 140–148. https://doi.org/10.1016/j.neuint.2018.08.010Orth, J. D., Conrad, T. M., Na, J., Lerman, J. A., Nam, H., Feist, A. M., & Palsson, B. (2011). A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Molecular Systems Biology, 7(535), 1–9. https://doi.org/10.1038/msb.2011.65Orth, J. D., Thiele, I., & Palsson, B. O. (2010). What is flux balance analysis? Nature Biotechnology, 28(3), 245–248. https://doi.org/10.1038/nbt.1614Ortiz-Rodriguez, A., Acaz-Fonseca, E., Boya, P., Arevalo, M. A., & Garcia-Segura, L. M. (2019). Lipotoxic Effects of Palmitic Acid on Astrocytes Are Associated with Autophagy Impairment. Molecular Neurobiology, 56(3), 1665–1680. https://doi.org/10.1007/s12035-018-1183-9Ortiz-Rodriguez, A., & Arevalo, M. A. (2020). The contribution of astrocyte autophagy to systemic metabolism. International Journal of Molecular Sciences, 21(7). https://doi.org/10.3390/ijms21072479Osorio, D., Botero, K., Gonzalez, J., and Pinzon, A. (2016). “exp2flux” Convierte datos de Gene EXPression a FBA FLUXes. Package Version 0.1. https://doi.org/10.13140/RG.2.2.14401.56168Osorio, D., Pinzón, A., Martín-Jiménez, C., Barreto, G. E., & González, J. (2020a). Multiple Pathways Involved in Palmitic Acid-Induced Toxicity: A System Biology Approach. Frontiers in Neuroscience, 13(January), 1–14. https://doi.org/10.3389/fnins.2019.01410Palsson, B. (2009). Metabolic systems biology. FEBS Letters, 583(24), 3900–3904. https://doi.org/10.1016/j.febslet.2009.09.031Pandey, V., Gardiol, D. H., & Chiappino-pepe, A. (2019). Running head : TEX-FBA TEX-FBA : A constraint-based method for integrating gene expression , thermodynamics , and metabolomics data into genome-scale metabolic models 1 Laboratory of Computational Systems Biotechnology , École Polytechnique Fédérale de La. 1–30.Papin, J. A., Hunter, T., Palsson, B. O., & Subramaniam, S. (2005). Reconstruction of cellular signalling networks and analysis of their properties. Nature Reviews Molecular Cell Biology, 6(2), 99–111. https://doi.org/10.1038/nrm1570Paraiso, W. K. D., Garcia-chica, J., Ariza, X., Zagmutt, S., Fukushima, S., Garcia, J., Mochida, Y., Serra, D., Herrero, L., Kinoh, H., Casals, N., Kataoka, K., Rodríguez-rodríguez, R., & Quader, S. (2021). Biomaterials Science conjugated CPT1A inhibitors to modulate lipid metabolism in brain cells †. https://doi.org/10.1039/d1bm00689dPardo, B., Contreras, L., & Satrústegui, J. (2013). De novo Synthesis of Glial Glutamate and Glutamine in Young Mice Requires Aspartate Provided by the Neuronal Mitochondrial Aspartate-Glutamate Carrier Aralar/AGC1. Frontiers in Endocrinology, 4(October), 15–18. https://doi.org/10.3389/fendo.2013.00149Patil, S., Melrose, J., & Chan, C. (2007). Involvement of astroglial ceramide in palmitic acid-induced Alzheimer-like changes in primary neurons. European Journal of Neuroscience, 26(8), 2131–2141. https://doi.org/10.1111/j.1460-9568.2007.05797.xPatil, S., Sheng, L., Masserang, A., & Chan, C. (2006). Palmitic acid-treated astrocytes induce BACE1 upregulation and accumulation of C-terminal fragment of APP in primary cortical neurons. Neuroscience Letters, 406(1–2), 55–59. https://doi.org/10.1016/j.neulet.2006.07.015Peracchi, A., & Mozzarelli, A. (2011). Biochimica et Biophysica Acta Exploring and exploiting allostery : Models , evolution , and drug targeting ☆. BBA - Proteins and Proteomics, 1814(8), 922–933. https://doi.org/10.1016/j.bbapap.2010.10.008Piccolis, M., Bond, L. M., Kampmann, M., Pulimeno, P., Chitraju, C., Jayson, C. B. K., Vaites, L. P., Boland, S., Lai, Z. W., Gabriel, K. R., Elliott, S. D., Paulo, J. A., Harper, J. W., Weissman, J. S., Walther, T. C., & Farese, R. V. (2019). Probing the Global Cellular Responses to Lipotoxicity Caused by Saturated Fatty Acids. Molecular Cell, 74(1), 32-44.e8. https://doi.org/10.1016/j.molcel.2019.01.036Pietzke, M., Meiser, J., & Vazquez, A. (2020). Formate metabolism in health and disease. Molecular Metabolism, 33(xxxx), 23–37. https://doi.org/10.1016/j.molmet.2019.05.012Pinu, F. R., Beale, D. J., Paten, A. M., Kouremenos, K., Swarup, S., Schirra, H. J., & Wishart, D. (2019). Systems biology and multi-omics integration: Viewpoints from the metabolomics research community. Metabolites, 9(4), 1–31. https://doi.org/10.3390/metabo9040076Price, N. D., Reed, J. L., & Palsson, B. (2004). Genome-scale models of microbial cells: Evaluating the consequences of constraints. Nature Reviews Microbiology, 2(11), 886–897. https://doi.org/10.1038/nrmicro1023Ramon, C., Gollub, M. G., & Stelling, J. (2018). Integrating -omics data into genome-scale metabolic network models: Principles and challenges. Essays in Biochemistry, 62(4), 563–574. https://doi.org/10.1042/EBC20180011Ravindran, V., Nacher, J. C., Akutsu, T., Ishitsuka, M., Osadcenco, A., Sunitha, V., Bagler, G., Schwartz, J. M., & Robertson, D. L. (2019). Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems. Scientific Reports, 9(1), 1–11. https://doi.org/10.1038/s41598-018-38224-9Rezola, A., Pey, J., Tobalina, L., Rubio, Á., Beasley, J. E., & Planes, F. J. (2015). Advances in network-basedmetabolic pathway analysis and gene expression data integration. Briefings in Bioinformatics, 16(2), 1–15. https://doi.org/10.1093/bib/bbu009Robertson, J. M. (2018). The gliocentric brain. International Journal of Molecular Sciences, 19(10). https://doi.org/10.3390/ijms19103033Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Ballard, A. J., Cowie, A., Romera-paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., … Kavukcuoglu, K. (2021). Highly accurate protein structure prediction with AlphaFold. May, 1–12. https://doi.org/10.1038/s41586-021-03819-2Rose, J., Brian, C., Pappa, A., Panayiotidis, M. I., & Franco, R. (2020). Mitochondrial Metabolism in Astrocytes Regulates Brain Bioenergetics, Neurotransmission and Redox Balance. Frontiers in Neuroscience, 14(November), 1–20. https://doi.org/10.3389/fnins.2020.536682Sajitz-Hermstein, M., & Nikoloski, Z. (2013). Structural Control of Metabolic Flux. PLoS Computational Biology, 9(12). https://doi.org/10.1371/journal.pcbi.1003368Salvatore, D., Bartha, T., & Larsen, P. R. (1998). The Guanosine Monophosphate Reductase Gene Is Conserved in Rats and Its Expression Increases Rapidly in Brown Adipose Tissue during Cold Exposure *. Journal of Biological Chemistry, 273(47), 31092–31096. https://doi.org/10.1074/jbc.273.47.31092Schafer, J. R. A., Fell, D. A., Rothman, D., & Shulman, R. G. (2004). Protein phosphorylation can regulate metabolite concentrations rather than control flux: The example of glycogen synthase. Proceedings of the National Academy of Sciences of the United States of America, 101(6), 1485–1490. https://doi.org/10.1073/pnas.0307299101Schousboe, A., Bak, L. K., & Waagepetersen, H. S. (2013). Astrocytic control of biosynthesis and turnover of the neurotransmitters glutamate and GABA. Frontiers in Endocrinology, 4(AUG), 1–11. https://doi.org/10.3389/fendo.2013.00102Schrödinger, L. (2015). The {PyMOL} Molecular Graphics System, Version~2.4. February. https://doi.org/10.13140/RG.2.2.33676.64641Schuetz, R., Kuepfer, L., & Sauer, U. (2007). Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Molecular Systems Biology, 3(119). https://doi.org/10.1038/msb4100162Schwartz, J. M., Otokuni, H., Akutsu, T., & Nacher, J. C. (2019). Probabilistic controllability approach to metabolic fluxes in normal and cancer tissues. Nature Communications, 10(1), 1–10. https://doi.org/10.1038/s41467-019-10616-zSegrè, D., Vitkup, D., & Church, G. M. (2002). Analysis of optimality in natural and perturbed metabolic networks. Proceedings of the National Academy of Sciences of the United States of America, 99(23), 15112–15117. https://doi.org/10.1073/pnas.232349399Sertbaş, M., Ülgen, K., & Çakir, T. (2014). Systematic analysis of transcription-level effects of neurodegenerative diseases on human brain metabolism by a newly reconstructed brain-specific metabolic network. FEBS Open Bio, 4, 542–553. https://doi.org/10.1016/j.fob.2014.05.006Shi, L. F., Zhang, Q., Shou, X. Y., & Niu, H. J. (2021). Expression and prognostic value identification of methylenetetrahydrofolate dehydrogenase 2 (Mthfd2) in brain low-grade glioma. International Journal of General Medicine, 14, 4517–4527. https://doi.org/10.2147/IJGM.S323858Shlomi, T., Berkman, O., & Ruppin, E. (2005). Regulatory on ͞ off minimization of metabolic flux. Pnas, 102(21), 7695–7700. https://doi.org/10.1073/pnas.0406346102Singh, A., Kukreti, R., Saso, L., & Kukreti, S. (2019). Oxidative stress: A key modulator in neurodegenerative diseases. Molecules, 24(8), 1–20. https://doi.org/10.3390/molecules24081583Singh, D., & Lercher, M. J. (2020). Network reduction methods for genome-scale metabolic models. Cellular and Molecular Life Sciences, 77(3), 481–488. https://doi.org/10.1007/s00018-019-03383-zSiracusa, R., Fusco, R., & Cuzzocrea, S. (2019). Astrocytes: Role and functions in brain pathologies. Frontiers in Pharmacology, 10(SEP), 1–10. https://doi.org/10.3389/fphar.2019.01114Sofroniew M. V. (2009). Molecular dissection of reactive astrogliosis and glial scar formation. Trends in Neuroscience, 32(12), 638–647. https://doi.org/10.1016/j.tins.2009.08.002.MolecularSofroniew, J. E. B. and M. V. (2015). Reactive gliosis and the multicellular response to CNS damage and disease. Neuron, 81(2), 229–248. https://doi.org/10.1016/j.neuron.2013.12.034.ReactiveSofroniew, M. V. (2014). Multiple roles for astrocytes as effectors of cytokines and inflammatory mediators. Neuroscientist, 20(2), 160–172. https://doi.org/10.1177/1073858413504466Son, D. O., Satsu, H., & Shimizu, M. (2005). Histidine inhibits oxidative stress- and TNF- a -induced interleukin-8 secretion in intestinal epithelial cells. 579, 4671–4677. https://doi.org/10.1016/j.febslet.2005.07.038Sonnewald, U., Akiho, H., Koshiya, K., & Iwai, A. (1998). Effect of orotic acid on the metabolism of cerebral cortical astrocytes during hypoxia and reoxygenation: An NMR spectroscopy study. Journal of Neuroscience Research, 51(1), 103–108. https://doi.org/10.1002/(SICI)1097-4547(19980101)51:1<103::AID-JNR11>3.0.CO;2-CSouders, C. L., Zubcevic, J., & Martyniuk, C. J. (2021). Tumor Necrosis Factor Alpha and the Gastrointestinal Epithelium: Implications for the Gut-Brain Axis and Hypertension. Cellular and Molecular Neurobiology, 0123456789. https://doi.org/10.1007/s10571-021-01044-zSouza, D. G., Almeida, R. F., Souza, D. O., & Zimmer, E. R. (2019). The astrocyte biochemistry. Seminars in Cell and Developmental Biology, 95(April), 142–150. https://doi.org/10.1016/j.semcdb.2019.04.002Stank, A., Kokh, D. B., Fuller, J. C., & Wade, R. C. (2016). Protein Binding Pocket Dynamics. https://doi.org/10.1021/acs.accounts.5b00516Suthers, P. F., Zomorrodi, A., & Maranas, C. D. (2009). Genome-scale gene/reaction essentiality and synthetic lethality analysis. Molecular Systems Biology, 5(301), 1–17. https://doi.org/10.1038/msb.2009.56Sweetlove, L. J., & George Ratcliffe, R. (2011). Flux-balance modeling of plant metabolism. Frontiers in Plant Science, 2(AUG), 1–10. https://doi.org/10.3389/fpls.2011.00038Terzer, M., & Stelling, J. (2008). Large-scale computation of elementary flux modes with bit pattern trees. Bioinformatics, 24(19), 2229–2235. https://doi.org/10.1093/bioinformatics/btn401Thiele, I., Swainston, N., Fleming, R. M. T., Hoppe, A., Sahoo, S., Aurich, M. K., Haraldsdottir, H., Mo, M. L., Rolfsson, O., Stobbe, M. D., Thorleifsson, S. G., Agren, R., Bölling, C., Bordel, S., Chavali, A. K., Dobson, P., Dunn, W. B., Endler, L., Hala, D., … Palsson, B. O. (2013a). A community-driven global reconstruction of human metabolism. Nature Biotechnology, 31(5), 419–425. https://doi.org/10.1038/nbt.2488Thiele, I., Swainston, N., Fleming, R. M. T., Hoppe, A., Sahoo, S., Aurich, M. K., Haraldsdottir, H., Mo, M. L., Rolfsson, O., Stobbe, M. D., Thorleifsson, S. G., Agren, R., Bölling, C., Bordel, S., Chavali, A. K., Dobson, P., Dunn, W. B., Endler, L., Hala, D., … Palsson, B. O. (2013b). A community-driven global reconstruction of human metabolism. Nature Biotechnology, 31(5), 419–425. https://doi.org/10.1038/nbt.2488Tong, X., Ao, Y., Faas, G. C., Nwaobi, S. E., Xu, J., Haustein, M. D., Anderson, M. A., Mody, I., Olsen, M. L., Sofroniew, M. V, & Khakh, B. S. (2014). Astrocyte Kir4.1 ion channel deficits contribute to neuronal dysfunction in Huntington’s disease model mice. Nature Neuroscience, 17(5), 694–703. https://doi.org/10.1038/nn.3691Trott, O. and Olson, A. J. (2011). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. 31(2), 455–461. https://doi.org/10.1002/jcc.21334.AutoDockUssher, J. R., Keung, W., Fillmore, N., Koves, T. R., Mori, J., Zhang, L., Lopaschuk, D. G., Ilkayeva, O. R., Wagg, C. S., Jaswal, J. S., Muoio, D. M., & Lopaschuk, G. D. (2014). Treatment with the 3-Ketoacyl-CoA Thiolase Inhibitor Trimetazidine Does Not Exacerbate Whole-Body Insulin Resistance in Obese Mice. June, 487–496.Valenza, G., Pioggia, G., Armato, A., Ferro, M., Scilingo, E. P., & De Rossi, D. (2011). A neuron-astrocyte transistor-like model for neuromorphic dressed neurons. Neural Networks, 24(7), 679–685. https://doi.org/10.1016/j.neunet.2011.03.013Verkhratsky, A., and Nedergaard, M. (2018). PHYSIOLOGY OF ASTROGLIA. Physiol. Rev, 98, 239–389. https://doi.org/10.1152/physrev.00042.2016Verkhratsky, A., & Butt, A. (2018). The History of the Decline and Fall of the Glial Numbers Legend. Neuroglia, 1(1), 188–192. https://doi.org/10.3390/neuroglia1010013Vicente-Gutierrez, C., Bonora, N., Bobo-Jimenez, V., Jimenez-Blasco, D., Lopez-Fabuel, I., Fernandez, E., Josephine, C., Bonvento, G., Enriquez, J. A., Almeida, A., & Bolaños, J. P. (2019). Astrocytic mitochondrial ROS modulate brain metabolism and mouse behaviour. Nature Metabolism, 1(2), 201–211. https://doi.org/10.1038/s42255-018-0031-6Voillet, V., Besse, P., Liaubet, L., Cristobal, M. S., & González, I. (2016). Handling missing rows in multi-omics data integration : multiple imputation in multiple factor analysis framework. BMC Bioinformatics, 1–17. https://doi.org/10.1186/s12859-016-1273-5Volkamer, A., Kuhn, D., Rippmann, F., & Rarey, M. (2012). DoGSiteScorer : a web server for automatic binding site prediction , analysis and druggability assessment. 28(15), 2074–2075. https://doi.org/10.1093/bioinformatics/bts310Volterra, A., & Meldolesi, J. (2005). Astrocytes, from brain glue to communication elements: The revolution continues. Nature Reviews Neuroscience, 6(8), 626–640. https://doi.org/10.1038/nrn1722Wang, W., Jiang, Z., Hu, C., Chen, C., Hu, Z., Wang, A., Wang, L., & Liu, J. (2020). Pharmacologically inhibiting phosphoglycerate kinase 1 for glioma with NG52. Acta Pharmacologica Sinica, July. https://doi.org/10.1038/s41401-020-0465-8Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics in Western Equatoria State. Nature Reviews Genetics, 10(1), 57.Waterhouse, A., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F. T., Beer, T. A. P. De, Rempfer, C., Bordoli, L., Lepore, R., & Schwede, T. (2018). SWISS-MODEL : homology modelling of protein structures and complexes. May, 1–8. https://doi.org/10.1093/nar/gky427Wong, K. L., Wu, Y. R., Cheng, K. S., Chan, P., Cheung, C. W., Lu, D. Y., Su, T. H., Liu, Z. M., & Leung, Y. M. (2014a). Palmitic acid-induced lipotoxicity and protection by (+)-catechin in rat cortical astrocytes. Pharmacological Reports, 66(6), 1106–1113. https://doi.org/10.1016/j.pharep.2014.07.009Wong, K. L., Wu, Y. R., Cheng, K. S., Chan, P., Cheung, C. W., Lu, D. Y., Su, T. H., Liu, Z. M., & Leung, Y. M. (2014b). Palmitic acid-induced lipotoxicity and protection by (+)-catechin in rat cortical astrocytes. Pharmacological Reports, 66(6), 1106–1113. https://doi.org/10.1016/j.pharep.2014.07.009Wörheide, M. A., Krumsiek, J., Kastenmüller, G., & Arnold, M. (2021). Multi-omics integration in biomedical research – A metabolomics-centric review. Analytica Chimica Acta, 1141, 144–162. https://doi.org/10.1016/j.aca.2020.10.038Wu, Z., Li, W., Liu, G., & Tang, Y. (2018). Network-Based Methods for Prediction of Drug-Target Interactions. 9(October), 1–14. https://doi.org/10.3389/fphar.2018.01134Wuchty, S. (2019). Controllability of molecular pathways. BioRxiv, 560375. https://doi.org/10.1101/560375Xia, C., Fu, Z., Battaile, K. P., & Kim, J. P. (2019). Crystal structure of human mitochondrial trifunctional protein , a fatty acid β -oxidation metabolon. 116(13), 6069–6074. https://doi.org/10.1073/pnas.1816317116Xiao, Q., Yan, P., Ma, X., Liu, H., Perez, R., Zhu, A., Gonzales, E., Burchett, J. M., Schuler, D. R., Cirrito, J. R., Diwan, A., & Lee, J. M. (2014). Enhancing astrocytic lysosome biogenesis facilitates Aβ clearance and attenuates amyloid plaque pathogenesis. Journal of Neuroscience, 34(29), 9607–9620. https://doi.org/10.1523/JNEUROSCI.3788-13.2014Xu, Y., Wang, S., Hu, Q., Gao, S., Ma, X., Zhang, W., Shen, Y., Chen, F., Lai, L., Pei, J., & Cavpharmer, C. (2018). CavityPlus : a web server for protein cavity detection with pharmacophore modelling , allosteric site identification and covalent ligand binding ability prediction. 46(May), 374–379. https://doi.org/10.1093/nar/gky380Yang, M., & Vousden, K. H. (2016). Serine and one-carbon metabolism in cancer. Nature Reviews Cancer, 16(10), 650–662. https://doi.org/10.1038/nrc.2016.81Yang, S. Y., He, X. Y., & Schulz, H. (1987). Fatty acid oxidation in rat brain is limited by the low activity of 3-ketoacyl-coenzyme A thiolase. The Journal of Biological Chemistry, 262(27), 13027–13032. https://doi.org/10.1016/s0021-9258(18)45161-7Yin, K. (2015). Positive correlation between expression level of mitochondrial serine hydroxymethyltransferase and breast cancer grade. OncoTargets and Therapy, 8, 1069–1074. https://doi.org/10.2147/OTT.S82433Ying, L., Tippetts, T. S., & Chaurasia, B. (2019). Ceramide dependent lipotoxicity in metabolic diseases. Nutrition and Healthy Aging, 5(1), 1–12. https://doi.org/10.3233/NHA-170032Young, F. B., Butland, S. L., Sanders, S. S., Sutton, L. M., & Hayden, M. R. (2012). Putting proteins in their place: Palmitoylation in Huntington disease and other neuropsychiatric diseases. Progress in Neurobiology, 97(2), 220–238. https://doi.org/10.1016/j.pneurobio.2011.11.002Yousofshahi, M., Ullah, E., Stern, R., & Hassoun, S. (2013). MC3: A steady-state model and constraint consistency checker for biochemical networks. BMC Systems Biology, 7. https://doi.org/10.1186/1752-0509-7-129Yu, J., Zhou, Y., Tanaka, I., & Yao, M. (2010). Roll : a new algorithm for the detection of protein pockets and cavities with a rolling probe sphere. 26(1), 46–52. https://doi.org/10.1093/bioinformatics/btp599Yuan, Z., Zhao, C., Di, Z., Wang, W. X., & Lai, Y. C. (2013). Exact controllability of complex networks. Nature Communications, 4. https://doi.org/10.1038/ncomms3447Zahra, W., Rai, S. N., Birla, H., Singh, S. Sen, Rathore, A. S., Dilnashin, H., Keswani, C., & Singh, S. P. (2019). Economic importance of medicinal plants in Asian countries. In Bioeconomy for Sustainable Development. https://doi.org/10.1007/978-981-13-9431-7_19Zhang, H., Muramatsu, T., Murase, A., Yuasa, S., Uchimura, K., & Kadomatsu, K. (2006). N-Acetylglucosamine 6-O-sulfotransferase-1 is required for brain keratan sulfate biosynthesis and glial scar formation after brain injury. Glycobiology, 16(8), 702–710. https://doi.org/10.1093/glycob/cwj115Zhang, H., Uchimura, K., & Kadomatsu, K. (2006). Brain keratan sulfate and glial scar formation. Annals of the New York Academy of Sciences, 1086, 81–90. https://doi.org/10.1196/annals.1377.014Zhang, N., Qi, M., Gao, X., Zhao, L., Liu, J., Gu, C., Song, W., Steven, C., Sun, L., & Qi, D. (2016). Response of the hepatic transcriptome to a fl atoxin B 1 in ducklings. 111, 69–76. https://doi.org/10.1016/j.toxicon.2015.12.022Zierer, J., Pallister, T., Tsai, P. C., Krumsiek, J., Bell, J. T., Lauc, G., Spector, T. D., Menni, C., & Kastenmüller, G. (2016). Exploring the molecular basis of age-related disease comorbidities using a multi-omics graphical model. Scientific Reports, 6(October), 1–10. https://doi.org/10.1038/srep37646Identificación de reacciones controladoras en un modelo computacional multi­ómico astrocitario de lipotoxicidad inducida por ácido palmíticoMincienciasPontificia Universidad Javeriana- Sede BogotáInvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/82354/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1075676303.2022.pdf1075676303.2022.pdfTesis de Maestría en Bioinformáticaapplication/pdf3834321https://repositorio.unal.edu.co/bitstream/unal/82354/2/1075676303.2022.pdf9c49711a1e8ac3c097ff9e558582f33bMD52THUMBNAIL1075676303.2022.pdf.jpg1075676303.2022.pdf.jpgGenerated Thumbnailimage/jpeg4620https://repositorio.unal.edu.co/bitstream/unal/82354/3/1075676303.2022.pdf.jpg4459a8f2e4a70577c2a698db2e33eedaMD53unal/82354oai:repositorio.unal.edu.co:unal/823542023-08-09 23:04:30.336Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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