Integrating quantitative proteomics and metabolomics in a cellular model of diabetic retinopathy

Curs 2013-2014Diabetic retinopathy is the leading cause of visual loss in individuals under the age of 55. Most investigations into the pathogenesis of diabetic retinopathy have been concentrated on the neural retina since this is where clinical lesions are manifested. Recently, however, various abnormalities in the structural and secretory functions of retinal pigment epithelium that are essential for neuroretina survival, have been found in diabetic retinopathy. In this context, here we study the effect of hyperglycemic and hypoxic conditions on the metabolism of a human retinal pigment epithelial cell line (ARPE-19) by integrating quantitative proteomics using tandem mass tagging (TMT), untargeted metabolomics using MS and NMR, and 13C-glucose isotopic labeling for metabolic tracking. We observed a remarkable metabolic diversification under our simulated in vitro hyperglycemic conditions of diabetes, characterized increased flux through polyol pathways and inhibition of the Krebs cycle and oxidative phosphorylation. Importantly, under low oxygen supply RPE cells seem to consume rapidly glycogen storages and stimulate anaerobic glycolysis. Our results therefore pave the way to future scenarios involving new therapeutic strategies addressed to modulating RPE metabolic impairment, with the aim of regulating structural and secretory alterations of RPE. Finally, this study shows the importance of tackling biomedical problems by integrating metabolomic and proteomics results.Director/a: Oscar Yanes Torrado, Jordi Planas Cuch

From spectrometric data to metabolic networks: an integrated view of cell metabolism

La biologia molecular ha avançat considerablement gràcies a importants progressos com la seqüenciació del ADN o la seva modificació per CRISPR. Tot i això, per entendre el metabolisme requerim estudiar els perfils metabòlics i les seves reaccions metabòliques. L™objectiu d™aquesta tesi és contribuir en aquest estudi del metabolism, el qual unifica dels camps de la proteòmica i la metabolòmica. Tradicionalment, l™anàlisi de dades òmiques es basa en el tractament independent de les diferents variables encara que està profundament establert que els mecanismes moleculars són controlats per la interacció de diferents molècules, i per tant seria més correcte tractar les dades de la mateixa manera. Avui dia, s™han descrit una gran quantitat de vies metabòliques, incluint els enzims responsables de les transformacions dels metabòlits que les formen, aquesta informació s™ha recopilat en bases de dades, que a la vegada poden ser utilitzades per a construir xarxes metabòliques. En aquesta tesi, s™han utilitzat xarxes metabòliques per a desenvolupar un algoritme que prediu metabòlits desregulats basant-se en el perfil d™expressió d™enzims gràcies a proteòmica quantitativa. Per a validar tals prediccions, és possible mesurar l™abundància d™aquests metabòlits, o el seu flux, o sigui la velocitat a la que s™han transformat, utilitzant experiments de marcatge amb isòtops estables, mesures completades mitjançant metabolòmica. Aqui, mostrem els productes del desenvolupament de dos mètodes per a l™anàlisi de dades de metabolòmica per a experiments amb isòtops estables: el primer per a la quantificació dirigida del flux en metabòlits del metabolisme central; i un segon, per la detecció no-dirigida de metabòlits marcats amb isòtops en altres vies metabòliques. Aquests mètodes han sigut provats en diferents estudis on han aportat resultats remarcables, revelant nous mecanismes moleculars en una complicació de la diabetes o en relació al metabolisme del càncer.La biología molecular ha avanzado considerablemente gracias a progresos como la secuenciación de ADN o su modificación por CRISPR. Sin embargo, para entender el metabolismo es indispensable estudiar los perfiles metabólicos y sus reacciones metabólicas. El objetivo de esta tesis es contribuir en el estudio del metabolismo, el cual implica los campos de la proteómica y la metabolómica. Tradicionalmente, el análisis de datos ómicas se basa en el tratamiento independiente de las diferentes variables aunque está profundamente aceptado que los mecanismos moleculares son controlados por la interacción de diferentes moléculas, y por lo tanto sería más correcto tratar los datos de esa manera. Hoy día, se han descrito una gran cantidad de vías metabólicas, incluyendo las enzimas responsables de las transformaciones de los metabolitos que las forman, esta información se ha recopilado en bases de datos, que a su vez pueden ser utilizadas para construir redes metabólicas . En esta tesis, se han utilizado redes metabólicas para desarrollar un algoritmo que predice metabolitos desregulados basándose en el perfil de expresión de enzimas por proteómica cuantitativa. Para validar tales predicciones, es posible medir la abundancia de estos metabolitos, o su flujo, o sea la velocidad a la que se han transformado, utilizando experimentos de marcado con isótopos estables, estas medidas se obtienen por metabolómica. Aquí, mostramos los productos del desarrollo de dos métodos para el análisis de datos de metabolómica para experimentos con isótopos estables: el primero para la cuantificación dirigida del flujo en metabolitos del metabolismo central; y un segundo, para la detección no-dirigida de metabolitos marcados con isótopos en otras vías metabólicas. Estos métodos han sido probados en diferentes estudios donde han aportado resultados interesantes, revelando nuevos mecanismos moleculares en una complicación de la diabetes o en relación al metabolismo del cáncer.Understanding the molecular basis of life has been in the spotlight of biochemistry research for more than a century already. Molecular biology has taken medicine forward thanks to technological breakthroughs like DNA sequencing and CRISPR editing. However, in order to understand metabolism we must rely on the study of metabolite profiles and metabolic reactions. The purpose of this thesis to contribute to this area, which unites the fields of proteomics and metabolomics. Traditionally, omics data analysis treats variables independently even if it is strongly settled that molecular mechanisms involve the interaction of diverse pathways, therefore data should be analyzed correspondingly. A vast amount of metabolic pathways have been described, together with enzymes that are responsible for metabolite transformations, this information has been assembled in databases that, in turn, can be used to build metabolic networks. In here, we use metabolic networks to predict metabolite dysregulation based on quantitative proteomics profiles. To validate the predictions, it is possible to measure the abundance of metabolites or their flux, namely the rate at which they are transformed, using stable isotope labelling experiments, both measurements can be performed by metabolomics. In this thesis, two different metabolomics-based stable isotope labelling approaches have been developed, one for the study of central carbon metabolites and one for the unbiased detection of deregulated fluxes in other metabolic pathways. These approaches have been tested on different datasets and have proven valuable to obtain remarkable results, unraveling molecular mechanisms in diabetes complications or novel metabolic hallmarks of cancer

Circulating metabolomic and lipidomic changes in subjects with new-onset type 1 diabetes after optimization of glycemic control

AIMS: To uncover novel candidate metabolomic and lipidomic biomarkers in newly-diagnosed type 1 diabetes (T1DM) after achieving optimal glucose control. METHODS: Comprehensive lipidomic and metabolomic analysis was performed in serum of 12 adults with T1DM at onset and after achieving optimal glycemic control (HbA1c < 7 %) (after 2–6 months). RESULTS: After intensive therapy, subjects (mean age 25.2 years, 58.3 % men) showed decreases in blood glucose (p < 0.001), HbA1c [11.5 % (9.2–13.4) to 6.2 % (5.2 – 6.7); p < 0.001] and changes in 51 identified lipids. Among these changes, we found that triglycerides (TG) containing medium chain fatty acids (TG45:0, TG47:1), sphingomyelins (SM) (SM(d18:2/20:0), SM42:4)), and phosphatidylcholines (PC) (PC(O-26:2), PC(O-30:0), PC(O-32:0), PC(O-42:6), PC(O-44:5), PC(O-38:3), PC(O-33:0), PC(O-46:8), PC(O-44:6), PC(O-40:3), PC(O-42:4), PC(O-46:7), PC(O-46:6), PC(O-44:5), PC(O-42:3), PC(O-44:4)) decreased; whereas PC(35:1), PC(37:1) and TG containing longer chain fatty acids (TG(52:1), TG(55:7), TG(51:2), TG(53:3), TG52:2), TG(53:2), TG(57:3), TG(61:3), TG(61:2) increased. Further, dihydro O-acylceramide (18:1/18:0/16:0), diacylglycerophosphoethanolamine (PE(34:1)), diacylglycerophosphoinositol (PI(38:6), and dihydrosphingomyelins (dihydroSM(36:0), dihydroSM(40:0), dihydroSM(41:0), dihydroSM(42:0)) increased. Uric acid, mannitol, and mannitol-1-acetate levels also increased. CONCLUSIONS: Our data uncovered potential favorable changes in the metabolism of glycerophospholipids, glycerolipids, and sphingolipids in new-onset T1DM after achieving optimal glycemic control. Further research on their potential role in developing diabetes-related complications is needed.This work was funded by Ministerio de Sanidad y Consumo, Instituto de Salud Carlos III (Madrid, Spain) grant PI15/0625 (to DM and EC), PI17/01362 (to NA), and PI17/00232 (to JJ), FEDER “Una manera de hacer Europa”, and by Fundació La Marató de TV3 2016 (303/C/2016) (201602.30.31) (to NA and JJ). This research was supported by CIBER-Consorcio Centro de Investigación Biomédica en Red-CIBERDEM (CB15/00071), Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación. Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau is accredited by the Generalitat de Catalunya as Centre de Recerca de Catalunya (CERCA).Peer ReviewedPostprint (author's final draft