Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network.
Mucopolysaccharidosis (MPS) is a group of lysosomal storage diseases (LSD), characterized by the deficiency of a lysosomal enzyme responsible for the degradation of glycosaminoglycans (GAG). This deficiency leads to the lysosomal accumulation of partially degraded GAG. Nevertheless, deficiency of a single lysosomal enzyme has been associated with impairment in other cell mechanism, such as apoptosis and redox balance. Although GAG analysis represents the main biomarker for MPS diagnosis, it has several limitations that can lead to a misdiagnosis, whereby the identification of new biomarkers represents an important issue for MPS. In this study, we used a system biology approach, through the use of a genome-scale human metabolic reconstruction to understand the effect of metabolism alterations in cell homeostasis and to identify potential new biomarkers in MPS. In-silico MPS models were generated by silencing of MPS-related enzymes, and were analyzed through a flux balance and variability analysis. We found that MPS models used approximately 2286 reactions to satisfy the objective function. Impaired reactions were mainly involved in cellular respiration, mitochondrial process, amino acid and lipid metabolism, and ion exchange. Metabolic changes were similar for MPS I and II, and MPS III A to C; while the remaining MPS showed unique metabolic profiles. Eight and thirteen potential high-confidence biomarkers were identified for MPS IVB and VII, respectively, which were associated with the secondary pathologic process of LSD. In vivo evaluation of predicted intermediate confidence biomarkers (β-hexosaminidase and β-glucoronidase) for MPS IVA and VI correlated with the in-silico prediction. These results show the potential of a computational human metabolic reconstruction to understand the molecular mechanisms this group of diseases, which can be used to identify new biomarkers for MPS.
Grupo Bioquímica Computacional y Bioinformática, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá D.C., Colombia.
Diego A
DA
Salazar
Institute for the Study of Inborn Errors of Metabolism, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá D.C., Colombia; Chemistry Department, School of Science, Pontificia Universidad Javeriana, Bogotá, Colombia.
Alexander
A
Rodríguez-López
Institute for the Study of Inborn Errors of Metabolism, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá D.C., Colombia.
Angélica
A
Herreño
Institute for the Study of Inborn Errors of Metabolism, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá D.C., Colombia.
Hector
H
Barbosa
Institute for the Study of Inborn Errors of Metabolism, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá D.C., Colombia.
Juliana
J
Herrera
Institute for the Study of Inborn Errors of Metabolism, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá D.C., Colombia; Hospital Universitario San Ignacio, Bogotá D.C., Colombia.
Andrea
A
Ardila
Grupo Bioquímica Computacional y Bioinformática, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá D.C., Colombia.
George E
GE
Barreto
Grupo Bioquímica Computacional y Bioinformática, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá D.C., Colombia. Electronic address: janneth.gonzalez@javeriana.edu.co.
Janneth
J
González
Institute for the Study of Inborn Errors of Metabolism, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá D.C., Colombia. Electronic address: cjalmeciga@javeriana.edu.co.
Carlos J
CJ
Alméciga-Díaz
D015415:Biomarkers / Q000378:metabolism; D003198:Computer Simulation; D057809:HEK293 Cells; D006801:Humans; D007963:Leukocytes, Mononuclear / Q000201:enzymology; D064688:Metabolic Flux Analysis; D053858:Metabolic Networks and Pathways; D009083:Mucopolysaccharidoses / Q000378:metabolism*; D049490:Systems Biology; D001619:beta-N-Acetylhexosaminidases / Q000378:metabolism
D015415:Biomarkers; D001619:beta-N-Acetylhexosaminidases
Diagnostic; Disease biomarkers; Flux balance analysis; Lysosomal storage disease; Systems biology